From a8d386b0511abab94ab38f928356f90c3297e37c Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 27 Feb 2025 14:48:45 +0000 Subject: [PATCH 01/99] update foundation models list in pyproject --- pyproject.toml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/pyproject.toml b/pyproject.toml index 95e5cbdc..2a022a7f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -92,7 +92,7 @@ virchow2 = [ "torch>=2.0.0", ] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2, uni2, chief_ctranspath, conch1_5, gigapath, h_optimus_0, h_optimus_1]"] [project.scripts] "stamp" = "stamp.__main__:main" From 92f921e7b7d497951604536fd1c7ca930f2511fa Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 28 Feb 2025 10:44:28 +0000 Subject: [PATCH 02/99] add slide encoding config --- src/stamp/__main__.py | 2 + src/stamp/cache.py | 15 ++++ src/stamp/config.py | 2 + src/stamp/preprocessing/__init__.py | 19 +---- .../preprocessing/extractor/ctranspath.py | 1 - src/stamp/slide_encoding/__init__.py | 81 +++++++++++++++++++ src/stamp/slide_encoding/config.py | 18 +++++ src/stamp/slide_encoding/encoder/__init__.py | 16 ++++ src/stamp/slide_encoding/encoder/cobra.py | 2 + 9 files changed, 138 insertions(+), 18 deletions(-) create mode 100644 src/stamp/slide_encoding/__init__.py create mode 100644 src/stamp/slide_encoding/config.py create mode 100644 src/stamp/slide_encoding/encoder/__init__.py create mode 100644 src/stamp/slide_encoding/encoder/cobra.py diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 1d9da434..fee770a1 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -75,6 +75,8 @@ def _run_cli(args: argparse.Namespace) -> None: canny_cutoff=config.preprocessing.canny_cutoff, ) + #TODO: Add slide encoding command + case "train": from stamp.modeling.train import train_categorical_model_ diff --git a/src/stamp/cache.py b/src/stamp/cache.py index 2c801d8a..fe4f0e9a 100644 --- a/src/stamp/cache.py +++ b/src/stamp/cache.py @@ -4,6 +4,7 @@ import urllib.request from pathlib import Path from typing import Final +from functools import cache STAMP_CACHE_DIR: Final[Path] = ( Path(os.environ.get("XDG_CACHE_HOME") or (Path.home() / ".cache")) / "stamp" @@ -36,3 +37,17 @@ def download_file(*, url: str, file_name: str, sha256sum: str) -> Path: def file_digest(file: str | Path) -> str: with open(file, "rb") as fp: return hashlib.file_digest(fp, "sha256").hexdigest() + + +@cache +def get_processing_code_hash(file_path) -> str: + """The hash of the entire process codebase. + + It is used to assure that features extracted with different versions of this code base + can be identified as such after the fact. + """ + hasher = hashlib.sha256() + for file_path in sorted(file_path.parent.glob("*.py")): + with open(file_path, "rb") as fp: + hasher.update(fp.read()) + return hasher.hexdigest() diff --git a/src/stamp/config.py b/src/stamp/config.py index 7e154d7a..7627ebaa 100644 --- a/src/stamp/config.py +++ b/src/stamp/config.py @@ -18,3 +18,5 @@ class StampConfig(BaseModel): statistics: StatsConfig | None = None heatmaps: HeatmapConfig | None = None + + # TODO: Add slide encoding config diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 1e5d79eb..972fdb41 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -1,7 +1,5 @@ -import hashlib import logging from collections.abc import Callable, Iterator -from functools import cache from pathlib import Path from random import shuffle from tempfile import NamedTemporaryFile @@ -30,6 +28,7 @@ get_slide_mpp_, tiles_with_cache, ) +from stamp.cache import get_processing_code_hash __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2022-2024 Marko van Treeck" @@ -56,20 +55,6 @@ _logger = logging.getLogger("stamp") -@cache -def _get_preprocessing_code_hash() -> str: - """The hash of the entire preprocessing codebase. - - It is used to assure that features extracted with different versions of this code base - can be identified as such after the fact. - """ - hasher = hashlib.sha256() - for file_path in sorted(Path(__file__).parent.glob("*.py")): - with open(file_path, "rb") as fp: - hasher.update(fp.read()) - return hasher.hexdigest() - - class _TileDataset(IterableDataset): def __init__( self, @@ -215,7 +200,7 @@ def extract_( assert_never(unreachable) model = extractor.model.to(device).eval() - extractor_id = f"{extractor.identifier}-{_get_preprocessing_code_hash()[:8]}" + extractor_id = f"{extractor.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" _logger.info(f"Using extractor {extractor.identifier}") diff --git a/src/stamp/preprocessing/extractor/ctranspath.py b/src/stamp/preprocessing/extractor/ctranspath.py index 7f8ec1c8..7ff20f37 100644 --- a/src/stamp/preprocessing/extractor/ctranspath.py +++ b/src/stamp/preprocessing/extractor/ctranspath.py @@ -1,6 +1,5 @@ from __future__ import annotations -import hashlib import math import warnings from collections.abc import Iterable diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py new file mode 100644 index 00000000..5013fc06 --- /dev/null +++ b/src/stamp/slide_encoding/__init__.py @@ -0,0 +1,81 @@ +from pathlib import Path +import torch +import h5py +import os +import pandas as pd +from tqdm import tqdm +from torch._prims_common import DeviceLikeType +from stamp.cache import get_processing_code_hash +from stamp.slide_encoding.config import EncoderName + + +def get_pat_embs( + encoder: EncoderName, + output_dir: Path, + feat_dir: Path, + slide_table: Path, + device: DeviceLikeType, + dtype: torch.dtype, +): + match encoder: + case EncoderName.COBRA: + from stamp.slide_encoding.encoder.cobra import cobra + + encoder = cobra() + + # TODO: Add other encoders + + slide_table = pd.read_csv(slide_table) + patient_groups = slide_table.groupby("PATIENT") + slide_dict = {} + + output_name = f"{encoder.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + output_file = os.path.join(output_dir, output_name) + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + + for patient_id, group in tqdm(patient_groups, leave=False): + all_feats_list = [] + + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + if not os.path.exists(h5_path): + tqdm.write(f"File {h5_path} does not exist, skipping") + continue + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] + + + feats = torch.tensor(feats).to(device) + all_feats_list.append(feats) + + if all_feats_list: + # Concatenate all features for this patient along the second dimension + all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) + + with torch.inference_mode(): + assert all_feats_cat.ndim == 3, ( + f"Expected 3D tensor, got {all_feats_cat.ndim}" + ) + slide_feats = encoder.model(all_feats_cat.to(dtype)) + slide_dict[patient_id] = { + "feats": slide_feats.to(torch.float32) + .detach() + .squeeze() + .cpu() + .numpy(), + "encoder": encoder.identifier, + } + else: + tqdm.write(f"No features found for patient {patient_id}, skipping") + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for patient_id, data in slide_dict.items(): + f.create_dataset(f"{patient_id}", data=data["feats"]) + f.attrs["encoder"] = data["encoder"] + tqdm.write(f"Finished encoding, saved to {output_file}") diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/slide_encoding/config.py new file mode 100644 index 00000000..9fbeafad --- /dev/null +++ b/src/stamp/slide_encoding/config.py @@ -0,0 +1,18 @@ +from enum import StrEnum +from pydantic import BaseModel, ConfigDict, Field +from pathlib import Path +from torch._prims_common import DeviceLikeType + +class EncoderName(StrEnum): + COBRA = "katherlab-cobra" + +class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): + model_config = ConfigDict(extra="forbid") + + encoder: EncoderName + output_dir: Path + feat_dir: Path + slide_table: Path + device: DeviceLikeType + dtype: torch.dtype, + # TODO: Add this to the config yaml \ No newline at end of file diff --git a/src/stamp/slide_encoding/encoder/__init__.py b/src/stamp/slide_encoding/encoder/__init__.py new file mode 100644 index 00000000..8a1cf1d4 --- /dev/null +++ b/src/stamp/slide_encoding/encoder/__init__.py @@ -0,0 +1,16 @@ +from dataclasses import KW_ONLY, dataclass +from typing import Generic, TypeVar +from torch import nn + +EncoderModel = TypeVar("EncoderModel", bound=nn.Module) + +@dataclass(frozen=True) +class Encoder(Generic[EncoderModel]): + _: KW_ONLY + model: EncoderModel + identifier: str + """An ID _uniquely_ identifying the model and extractor. + + If possible, it should include the digest of the model weights etc. + so that any change in the model also changes its ID. + """ \ No newline at end of file diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py new file mode 100644 index 00000000..f5034397 --- /dev/null +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -0,0 +1,2 @@ +def cobra(): + pass \ No newline at end of file From 26238ec56a6d13210d1478719a5dbbdbc7947ba8 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 28 Feb 2025 10:48:37 +0000 Subject: [PATCH 03/99] fix slide config --- src/stamp/slide_encoding/config.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/slide_encoding/config.py index 9fbeafad..d3a39b85 100644 --- a/src/stamp/slide_encoding/config.py +++ b/src/stamp/slide_encoding/config.py @@ -2,6 +2,7 @@ from pydantic import BaseModel, ConfigDict, Field from pathlib import Path from torch._prims_common import DeviceLikeType +import torch class EncoderName(StrEnum): COBRA = "katherlab-cobra" @@ -14,5 +15,5 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): feat_dir: Path slide_table: Path device: DeviceLikeType - dtype: torch.dtype, + dtype: torch.dtype # TODO: Add this to the config yaml \ No newline at end of file From a9bcdef14a0b0b0cc787d5574770b3803531a4a9 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 3 Mar 2025 12:59:57 +0000 Subject: [PATCH 04/99] add slide encoding --- src/stamp/__main__.py | 19 +++++++++++- src/stamp/config.py | 3 +- src/stamp/slide_encoding/__init__.py | 32 +++++++++++--------- src/stamp/slide_encoding/config.py | 9 ++++-- src/stamp/slide_encoding/encoder/__init__.py | 1 + src/stamp/slide_encoding/encoder/cobra.py | 4 ++- 6 files changed, 48 insertions(+), 20 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index fee770a1..9a05a368 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -75,7 +75,24 @@ def _run_cli(args: argparse.Namespace) -> None: canny_cutoff=config.preprocessing.canny_cutoff, ) - #TODO: Add slide encoding command + case "encode": + from stamp.slide_encoding import get_pat_embs + + if config.slide_encoding is None: + raise ValueError("no slide encoding configuration supplied") + + _add_file_handle_(_logger, output_dir=config.slide_encoding.output_dir) + _logger.info( + "using the following configuration:\n" + f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" + ) + get_pat_embs( + encoder_name=config.slide_encoding.encoder, + output_dir=config.slide_encoding.output_dir, + feat_dir=config.slide_encoding.feat_dir, + slide_table_path=config.slide_encoding.slide_table, + device=config.slide_encoding.device, + ) case "train": from stamp.modeling.train import train_categorical_model_ diff --git a/src/stamp/config.py b/src/stamp/config.py index 7627ebaa..c50b7b07 100644 --- a/src/stamp/config.py +++ b/src/stamp/config.py @@ -4,6 +4,7 @@ from stamp.modeling.config import CrossvalConfig, DeploymentConfig, TrainConfig from stamp.preprocessing.config import PreprocessingConfig from stamp.statistics import StatsConfig +from stamp.slide_encoding.config import SlideEncodingConfig class StampConfig(BaseModel): @@ -19,4 +20,4 @@ class StampConfig(BaseModel): heatmaps: HeatmapConfig | None = None - # TODO: Add slide encoding config + slide_encoding: SlideEncodingConfig | None = None diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 5013fc06..99433279 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -1,31 +1,36 @@ +import os from pathlib import Path -import torch + import h5py -import os import pandas as pd -from tqdm import tqdm +import torch from torch._prims_common import DeviceLikeType +from tqdm import tqdm + from stamp.cache import get_processing_code_hash from stamp.slide_encoding.config import EncoderName +from stamp.slide_encoding.encoder import Encoder def get_pat_embs( - encoder: EncoderName, + encoder_name: EncoderName, output_dir: Path, feat_dir: Path, - slide_table: Path, + slide_table_path: Path, device: DeviceLikeType, - dtype: torch.dtype, -): - match encoder: +) -> None: + """""" + match encoder_name: case EncoderName.COBRA: from stamp.slide_encoding.encoder.cobra import cobra - encoder = cobra() - + encoder: Encoder = cobra() + # TODO: Add other encoders - slide_table = pd.read_csv(slide_table) + dtype = torch.float32 + + slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") slide_dict = {} @@ -36,7 +41,6 @@ def get_pat_embs( tqdm.write(f"Output file {output_file} already exists, skipping") return - for patient_id, group in tqdm(patient_groups, leave=False): all_feats_list = [] @@ -47,8 +51,7 @@ def get_pat_embs( tqdm.write(f"File {h5_path} does not exist, skipping") continue with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] - + feats = f["feats"][:] # type: ignore feats = torch.tensor(feats).to(device) all_feats_list.append(feats) @@ -69,6 +72,7 @@ def get_pat_embs( .cpu() .numpy(), "encoder": encoder.identifier, + "precision": dtype, } else: tqdm.write(f"No features found for patient {patient_id}, skipping") diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/slide_encoding/config.py index d3a39b85..06cd890d 100644 --- a/src/stamp/slide_encoding/config.py +++ b/src/stamp/slide_encoding/config.py @@ -1,12 +1,15 @@ from enum import StrEnum -from pydantic import BaseModel, ConfigDict, Field from pathlib import Path -from torch._prims_common import DeviceLikeType + import torch +from pydantic import BaseModel, ConfigDict +from torch._prims_common import DeviceLikeType + class EncoderName(StrEnum): COBRA = "katherlab-cobra" + class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): model_config = ConfigDict(extra="forbid") @@ -16,4 +19,4 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): slide_table: Path device: DeviceLikeType dtype: torch.dtype - # TODO: Add this to the config yaml \ No newline at end of file + # TODO: Add this to the config yaml diff --git a/src/stamp/slide_encoding/encoder/__init__.py b/src/stamp/slide_encoding/encoder/__init__.py index 8a1cf1d4..5eed2e71 100644 --- a/src/stamp/slide_encoding/encoder/__init__.py +++ b/src/stamp/slide_encoding/encoder/__init__.py @@ -1,5 +1,6 @@ from dataclasses import KW_ONLY, dataclass from typing import Generic, TypeVar + from torch import nn EncoderModel = TypeVar("EncoderModel", bound=nn.Module) diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py index f5034397..8490e85a 100644 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -1,2 +1,4 @@ -def cobra(): +from stamp.slide_encoding.encoder import Encoder + +def cobra() -> Encoder[]: pass \ No newline at end of file From 5bfd4dd0f9830193c459d3e8aaa74c6962e98f57 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 4 Mar 2025 16:10:20 +0000 Subject: [PATCH 05/99] add cobra chief and titan --- pyproject.toml | 4 + src/stamp/__main__.py | 4 + src/stamp/config.py | 2 +- src/stamp/config.yaml | 23 +- src/stamp/preprocessing/__init__.py | 2 +- src/stamp/slide_encoding/__init__.py | 14 +- src/stamp/slide_encoding/config.py | 6 +- src/stamp/slide_encoding/encoder/chief.py | 217 ++++++++++++++++ src/stamp/slide_encoding/encoder/cobra.py | 301 +++++++++++++++++++++- src/stamp/slide_encoding/encoder/titan.py | 13 + 10 files changed, 576 insertions(+), 10 deletions(-) create mode 100644 src/stamp/slide_encoding/encoder/chief.py create mode 100644 src/stamp/slide_encoding/encoder/titan.py diff --git a/pyproject.toml b/pyproject.toml index 2a022a7f..61aaeee6 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -91,6 +91,10 @@ virchow2 = [ "timm>=0.9.11", "torch>=2.0.0", ] +cobra = [ + "mamba-ssm>=2.2.2", + "torch>=2.4.1", +] # Blanket target all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2, uni2, chief_ctranspath, conch1_5, gigapath, h_optimus_0, h_optimus_1]"] diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 9a05a368..857c2f0d 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -266,6 +266,10 @@ def main() -> None: commands.add_parser( "preprocess", help="Preprocess whole-slide images into feature vectors" ) + commands.add_parser( + "encode", + help="Encode slide-level features into patient-level embeddings", + ) commands.add_parser("train", help="Train a Vision Transformer model") commands.add_parser( "crossval", diff --git a/src/stamp/config.py b/src/stamp/config.py index c50b7b07..9efb3d92 100644 --- a/src/stamp/config.py +++ b/src/stamp/config.py @@ -3,8 +3,8 @@ from stamp.heatmaps.config import HeatmapConfig from stamp.modeling.config import CrossvalConfig, DeploymentConfig, TrainConfig from stamp.preprocessing.config import PreprocessingConfig -from stamp.statistics import StatsConfig from stamp.slide_encoding.config import SlideEncodingConfig +from stamp.statistics import StatsConfig class StampConfig(BaseModel): diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 15405ad9..93eb5a3c 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -1,6 +1,6 @@ preprocessing: - output_dir: "/path/to/save/files/to" - wsi_dir: "/path/containing/whole/slide/images/to/extract/features/from" + output_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/features" + wsi_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/wsi_dir" # Extractor to use for feature extractor. Possible options are "ctranspath", # "mahmood-uni", "mahmood-conch", "chief_ctranspath", "mahmood-conchv1_5", # "mahmood-uni2", "dino-bloom", "gigapath", "h-optimus-0", "h-optimus-1", "virchow2" @@ -29,6 +29,25 @@ preprocessing: #brightness_cutoff: 240 +slide_encoding: + # Encoder to use for slide encoding. Possible options are "katherlab-cobra". + encoder: "chief" + + # Directory to save the output files. + output_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE" + + # Directory where the extracted features are stored. + feat_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/features/xiyuewang-ctranspath-7c998680-347671c2" + + # A table (.xlsx or .csv) relating every slide to their feature files. + # The table must contain at least two columns, one titled "SLIDE", + # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. + slide_table: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/slide_table.csv" + + # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) + device: "cuda" + + crossval: output_dir: "/path/to/save/files/to" diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 972fdb41..9dddd754 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -17,6 +17,7 @@ from tqdm import tqdm import stamp +from stamp.cache import get_processing_code_hash from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor from stamp.preprocessing.tiling import ( @@ -28,7 +29,6 @@ get_slide_mpp_, tiles_with_cache, ) -from stamp.cache import get_processing_code_hash __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2022-2024 Marko van Treeck" diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 99433279..151de2c1 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -25,10 +25,22 @@ def get_pat_embs( from stamp.slide_encoding.encoder.cobra import cobra encoder: Encoder = cobra() + case EncoderName.TITAN: + from stamp.slide_encoding.encoder.titan import titan + + encoder: Encoder = titan() + case EncoderName.CHIEF: + from stamp.slide_encoding.encoder.chief import chief + + encoder: Encoder = chief() # TODO: Add other encoders dtype = torch.float32 + model = encoder.model.to(device).eval() + # TODO: dtype depends con CUDA capabilities. Check end of + # extract_feat_patients.py on how to handle this + # TODO: add logger slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") @@ -64,7 +76,7 @@ def get_pat_embs( assert all_feats_cat.ndim == 3, ( f"Expected 3D tensor, got {all_feats_cat.ndim}" ) - slide_feats = encoder.model(all_feats_cat.to(dtype)) + slide_feats = model(all_feats_cat.to(dtype)) slide_dict[patient_id] = { "feats": slide_feats.to(torch.float32) .detach() diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/slide_encoding/config.py index 06cd890d..f2e4cedd 100644 --- a/src/stamp/slide_encoding/config.py +++ b/src/stamp/slide_encoding/config.py @@ -8,6 +8,8 @@ class EncoderName(StrEnum): COBRA = "katherlab-cobra" + CHIEF = "chief" + TITAN = "mahmood-titan" class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): @@ -17,6 +19,4 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): output_dir: Path feat_dir: Path slide_table: Path - device: DeviceLikeType - dtype: torch.dtype - # TODO: Add this to the config yaml + device: DeviceLikeType \ No newline at end of file diff --git a/src/stamp/slide_encoding/encoder/chief.py b/src/stamp/slide_encoding/encoder/chief.py new file mode 100644 index 00000000..23a10e8b --- /dev/null +++ b/src/stamp/slide_encoding/encoder/chief.py @@ -0,0 +1,217 @@ +import gdown +import torch +import torch.nn as nn +import torch.nn.functional as F + +from stamp.cache import STAMP_CACHE_DIR +from stamp.slide_encoding.encoder import Encoder + +"""authors: https://github.com/hms-dbmi/CHIEF""" + + +def chief() -> Encoder: + model = CHIEF(size_arg="small", dropout=True, n_classes=2) + + model_path = STAMP_CACHE_DIR / "CHIEF_pretraining.pth" + if not model_path.is_file(): + model_path.parent.mkdir(parents=True, exist_ok=True) + gdown.download( + "https://drive.google.com/u/0/uc?id=10bJq_ayX97_1w95omN8_mESrYAGIBAPb&export=download", + str(model_path), + ) + + chief = torch.load(model_path) + model.load_state_dict(chief, strict=True) + + return Encoder( + model=model, + identifier="chief", + ) + + +def initialize_weights(module): + for m in module.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + m.bias.data.zero_() + + elif isinstance(m, nn.BatchNorm1d): + nn.init.constant_(m.weight, 1) + nn.init.constant_(m.bias, 0) + + +class Att_Head(nn.Module): + def __init__(self, FEATURE_DIM, ATT_IM_DIM): + super(Att_Head, self).__init__() + + self.fc1 = nn.Linear(FEATURE_DIM, ATT_IM_DIM) + self.relu = nn.ReLU() + self.fc2 = nn.Linear(ATT_IM_DIM, 1) + self.sigmoid = nn.Sigmoid() + + def forward(self, x): + x = self.fc1(x) + x = self.relu(x) + x = self.fc2(x) + x = self.sigmoid(x) + return x + + +class Attn_Net(nn.Module): + def __init__(self, L=1024, D=256, dropout=False, n_classes=1): + super(Attn_Net, self).__init__() + self.module = [nn.Linear(L, D), nn.Tanh()] + + if dropout: + self.module.append(nn.Dropout(0.25)) + + self.module.append(nn.Linear(D, n_classes)) + + self.module = nn.Sequential(*self.module) + + def forward(self, x): + return self.module(x), x # N x 1, N * D + + +class Attn_Net_Gated(nn.Module): + def __init__(self, L=1024, D=256, dropout=False, n_classes=1): + super(Attn_Net_Gated, self).__init__() + self.attention_a = [nn.Linear(L, D), nn.Tanh()] + + self.attention_b = [nn.Linear(L, D), nn.Sigmoid()] + if dropout: + self.attention_a.append(nn.Dropout(0.25)) + self.attention_b.append(nn.Dropout(0.25)) + + self.attention_a = nn.Sequential(*self.attention_a) + self.attention_b = nn.Sequential(*self.attention_b) + + self.attention_c = nn.Linear(D, n_classes) + + def forward(self, x): + a = self.attention_a(x) + b = self.attention_b(x) + A = a.mul(b) + A = self.attention_c(A) # N x n_classes + return A, x + + +# clip_tokenizer = CLIPTokenizer.from_pretrained() +# +# clip_tokenizer = CLIPTokenizer.from_pretrained('./') +# +# text_encoder = CLIPTextModel.from_pretrained( +# './', +# subfolder="text_encoder") + + +class CHIEF(nn.Module): + def __init__( + self, + gate=True, + size_arg="large", + dropout=True, + n_classes=2, + instance_loss_fn=nn.CrossEntropyLoss(), + **kwargs, + ): + super(CHIEF, self).__init__() + self.size_dict = { + "xs": [384, 256, 256], + "small": [768, 512, 256], + "big": [1024, 512, 384], + "large": [2048, 1024, 512], + } + size = self.size_dict[size_arg] + print(size) + fc = [nn.Linear(size[0], size[1]), nn.ReLU()] + if dropout: + fc.append(nn.Dropout(0.25)) + if gate: + attention_net = Attn_Net_Gated( + L=size[1], D=size[2], dropout=dropout, n_classes=1 + ) + else: + attention_net = Attn_Net(L=size[1], D=size[2], dropout=dropout, n_classes=1) + fc.append(attention_net) + self.attention_net = nn.Sequential(*fc) + self.classifiers = nn.Linear(size[1], n_classes) + instance_classifiers = [nn.Linear(size[1], 2) for i in range(n_classes)] + self.instance_classifiers = nn.ModuleList(instance_classifiers) + self.instance_loss_fn = instance_loss_fn + self.n_classes = n_classes + initialize_weights(self) + + self.att_head = Att_Head(size[1], size[2]) + self.text_to_vision = nn.Sequential( + nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) + ) + + self.register_buffer("organ_embedding", torch.randn(19, 768)) + word_emb_path = STAMP_CACHE_DIR / "Text_emdding.pth" + if not word_emb_path.is_file(): + word_emb_path.parent.mkdir(parents=True, exist_ok=True) + gdown.download( + "https://drive.google.com/u/0/uc?id=1sVGmlTdnSJdcCK1vuswsuReKMUjvSEmX&export=download", + str(word_emb_path), + ) + word_embedding = torch.load(word_emb_path) + self.organ_embedding.data = word_embedding.float() + self.text_to_vision = nn.Sequential( + nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) + ) + + def relocate(self): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.attention_net = self.attention_net.to(device) + self.classifiers = self.classifiers.to(device) + self.instance_classifiers = self.instance_classifiers.to(device) + + def forward(self, h, x_anatomic): + batch = x_anatomic + h_ori = h + A, h = self.attention_net(h) + A = torch.transpose(A, 1, 0) + A_raw = A + A = F.softmax(A, dim=1) + + embed_batch = self.organ_embedding[batch] + embed_batch = self.text_to_vision(embed_batch) + WSI_feature = torch.mm(A, h) + slide_embeddings = torch.mm(A, h_ori) + + M = WSI_feature + embed_batch + + logits = self.classifiers(M) + + result = { + "bag_logits": logits, + "attention_raw": A_raw, + "WSI_feature": slide_embeddings, + "WSI_feature_anatomical": M, + } + return result + + def patch_probs(self, h, x_anatomic): + batch = x_anatomic + A, h = self.attention_net(h) + A_raw = A + A = torch.transpose(A, 1, 0) + A = F.softmax(A, dim=1) + + embed_batch = self.organ_embedding[batch] + embed_batch = self.text_to_vision(embed_batch) + M = torch.mm(A, h) + M = M + embed_batch + bag_logits = self.classifiers(M) + bag_prob = torch.softmax(bag_logits.squeeze(), dim=0) + patch_logits = self.classifiers(h + embed_batch) + patch_prob = ( + torch.sigmoid(A_raw.squeeze()) * torch.softmax(patch_logits, dim=1)[:, 1] + ) + + return { + "bag_prob": bag_prob, + "patch_prob": patch_prob, + "attention_raw": A_raw.squeeze(), + } diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py index 8490e85a..cfae21d9 100644 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -1,4 +1,301 @@ +import os +import warnings + +import torch +import torch.nn as nn +import torch.nn.functional as F +from einops import rearrange +from huggingface_hub import hf_hub_download +from mamba_ssm import Mamba2 # TODO: add to requirements and try catch + +from stamp.cache import STAMP_CACHE_DIR, file_digest # TODO: assert hash from stamp.slide_encoding.encoder import Encoder -def cobra() -> Encoder[]: - pass \ No newline at end of file +warnings.simplefilter(action="ignore", category=FutureWarning) + + +def cobra() -> Encoder: + checkpoint_path = os.path.join(STAMP_CACHE_DIR, "pytorch_model.bin") + if not os.path.exists(checkpoint_path): + checkpoint_path = hf_hub_download( + "KatherLab/COBRA", + filename="pytorch_model.bin", + local_dir=STAMP_CACHE_DIR, + force_download=True, + ) + print(f"Saving model to {checkpoint_path}") + state_dict = torch.load(checkpoint_path, map_location="cpu") # TODO: Ask why is cpu + model = Cobra( + input_dims=[768, 1024, 1280, 1536], + ) + model.load_state_dict(state_dict) + print("COBRA model loaded successfully") + return Encoder( + model=model, + identifier="cobra", + ) + + +# Copy from https://github.com/KatherLab/COBRA + + +class Embed(nn.Module): + def __init__(self, dim, embed_dim=1024, dropout=0.25): + super(Embed, self).__init__() + + self.head = nn.Sequential( + nn.LayerNorm(dim), + nn.Linear(dim, embed_dim), + nn.Dropout(dropout) if dropout else nn.Identity(), + nn.SiLU(), + nn.Linear(embed_dim, embed_dim), + ) + + def forward(self, x): + return self.head(x) + + +class Cobra(nn.Module): + def __init__( + self, + embed_dim=768, + input_dims=[384, 512, 1024, 1280, 1536], + num_heads=8, + layers=2, + dropout=0.25, + att_dim=96, + d_state=128, + ): + super().__init__() + + self.embed = nn.ModuleDict({str(d): Embed(d, embed_dim) for d in input_dims}) + + self.norm = nn.LayerNorm(embed_dim) + + self.mamba_enc = Mamba2Enc( + embed_dim, + embed_dim, + n_classes=embed_dim, + layer=layers, + dropout=dropout, + d_state=d_state, + ) + + self.num_heads = num_heads + self.attn = nn.ModuleList( + [ + BatchedABMIL( + input_dim=int(embed_dim / num_heads), + hidden_dim=att_dim, + dropout=dropout, + n_classes=1, + ) + for _ in range(self.num_heads) + ] + ) + + def forward(self, x, multi_fm_mode=False, fm_idx=None, get_attention=False): + if multi_fm_mode: + fm_embs = torch.concat( + [self.embed[str(xi.shape[-1])](xi) for xi in x], dim=0 + ) + assert fm_embs.shape[-1] == self.embed_dim, fm_embs.shape + assert len(fm_embs.shape) == 3, fm_embs.shape + assert fm_embs.shape[0] == len(x), fm_embs.shape + logits = torch.mean(fm_embs, dim=0) + else: + logits = self.embed[str(x.shape[-1])](x) + + h = self.norm(self.mamba_enc(logits)) + + if self.num_heads > 1: + h_ = rearrange(h, "b t (e c) -> b t e c", c=self.num_heads) + + attention = [] + for i, attn_net in enumerate(self.attn): + _, processed_attention = attn_net( + h_[:, :, :, i], return_raw_attention=True + ) + attention.append(processed_attention) + + A = torch.stack(attention, dim=-1) + + A = ( + rearrange(A, "b t e c -> b t (e c)", c=self.num_heads) + .mean(-1) + .unsqueeze(-1) + ) + A = torch.transpose(A, 2, 1) + A = F.softmax(A, dim=-1) + else: + A = self.attn[0](h) + + if multi_fm_mode: + if fm_idx: + feats = torch.bmm(A, x[fm_idx]).squeeze(0).squeeze(0) + else: + feats = [] + for i, xi in enumerate(x): + feats.append(torch.bmm(A, xi).squeeze(0).squeeze(0)) + assert ( + len(feats[i].shape) == 1 and feats[i].shape[0] == xi.shape[-1] + ), feats[i].shape + else: + feats = torch.bmm(A, x).squeeze(1) + + if get_attention: + return feats, A + return feats + + +""" +Adapted from: https://github.com/isyangshu/MambaMIL/blob/main/models/MambaMIL.py +Shu Yang, Yihui Wang, and Hao Chen. MambaMIL: En- +hancing Long Sequence Modeling with Sequence Reorder- +ing in Computational Pathology. In proceedings of Medi- +cal Image Computing and Computer Assisted Intervention – +MICCAI 2024. Springer Nature Switzerland, 2024 +""" + + +def initialize_weights(module): + for m in module.modules(): + if isinstance(m, nn.Linear): + nn.init.xavier_normal_(m.weight) + if m.bias is not None: + m.bias.data.zero_() + if isinstance(m, nn.LayerNorm): + nn.init.constant_(m.bias, 0) + nn.init.constant_(m.weight, 1.0) + + +class Mamba2Enc(nn.Module): + def __init__( + self, + in_dim, + dim, + n_classes, + dropout=0.25, + act="gelu", + layer=2, + rate=10, + d_state=64, + ): + super(Mamba2Enc, self).__init__() + self._fc1_layers: list[nn.Module] = [nn.Linear(in_dim, dim)] + if act.lower() == "relu": + self._fc1_layers += [nn.ReLU()] + elif act.lower() == "gelu": + self._fc1_layers += [nn.GELU()] + if dropout: + self._fc1_layers += [nn.Dropout(dropout)] + + self._fc1 = nn.Sequential(*self._fc1_layers) + self.norm = nn.LayerNorm(dim) + self.layers = nn.ModuleList() + + for _ in range(layer): + self.layers.append( + nn.Sequential( + nn.LayerNorm(dim), + Mamba2( + d_model=dim, + d_state=d_state, + d_conv=4, + expand=2, + ), + ) + ) + + self.n_classes = n_classes + self.classifier = nn.Linear(dim, self.n_classes) + self.rate = rate + self.type = type + + self.apply(initialize_weights) + + def forward(self, x): + if len(x.shape) == 2: + x = x.expand(1, -1, -1) + h = x # .float() + + h = self._fc1(h) + + for layer in self.layers: + h_ = h + h = layer[0](h) # type: ignore + h = layer[1](h) + h = h + h_ + + logits = self.classifier(h) + return logits + + def relocate(self): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self._fc1 = self._fc1.to(device) + self.layers = self.layers.to(device) + + self.attention = self.attention.to(device) + self.norm = self.norm.to(device) + self.classifier = self.classifier.to(device) + + +""" adapted from: https://github.com/mahmoodlab/MADELEINE/blob/main/core/models/abmil.py + Guillaume Jaume, Anurag Jayant Vaidya, Andrew Zhang, + Andrew H Song, Richard J. Chen, Sharifa Sahai, Dandan + Mo, Emilio Madrigal, Long Phi Le, and Mahmood Faisal. + Multistain pretraining for slide representation learning in + pathology. In European Conference on Computer Vision. + Springer, 2024. +""" + + +class BatchedABMIL(nn.Module): + def __init__( + self, + input_dim=1024, + hidden_dim=256, + dropout=False, + n_classes=1, + n_heads=1, + activation="softmax", + ): + super(BatchedABMIL, self).__init__() + + self.activation = activation + self.device = "cuda" if torch.cuda.is_available() else "cpu" + self.attention_a = nn.ModuleList([nn.Linear(input_dim, hidden_dim), nn.Tanh()]) + + self.attention_b = nn.ModuleList( + [nn.Linear(input_dim, hidden_dim), nn.Sigmoid()] + ) + + if dropout: + self.attention_a.append(nn.Dropout(0.25)) + self.attention_b.append(nn.Dropout(0.25)) + + self.attention_a = nn.Sequential(*self.attention_a) + self.attention_b = nn.Sequential(*self.attention_b) + self.attention_c = nn.Linear(hidden_dim, n_classes) + + def forward(self, x, return_raw_attention=False): + assert len(x.shape) == 3, x.shape + a = self.attention_a(x) + b = self.attention_b(x) + A = a.mul(b) + A = self.attention_c(A) + if self.activation == "softmax": + activated_A = F.softmax(A, dim=1) + elif self.activation == "leaky_relu": + activated_A = F.leaky_relu(A) + elif self.activation == "relu": + activated_A = F.relu(A) + elif self.activation == "sigmoid": + activated_A = torch.sigmoid(A) + else: + raise NotImplementedError("Activation not implemented.") + + if return_raw_attention: + return activated_A, A + + return activated_A \ No newline at end of file diff --git a/src/stamp/slide_encoding/encoder/titan.py b/src/stamp/slide_encoding/encoder/titan.py new file mode 100644 index 00000000..b5f485b9 --- /dev/null +++ b/src/stamp/slide_encoding/encoder/titan.py @@ -0,0 +1,13 @@ +from transformers import AutoModel + +from stamp.slide_encoding.encoder import Encoder + + +def titan() -> Encoder: + model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) + # TODO: Check precision + + return Encoder( + model=model, + identifier="mahmood-titan", + ) From 6a4c06c04960448bdb57374fde58d718706efa8a Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 5 Mar 2025 13:02:31 +0000 Subject: [PATCH 06/99] add slide_encoding in config --- src/stamp/config.yaml | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 93eb5a3c..7edcfa20 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -1,8 +1,8 @@ preprocessing: - output_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/features" - wsi_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/wsi_dir" + output_dir: "/path/to/save/files/to" + wsi_dir: "/path/containing/whole/slide/images/to/extract/features/from" # Extractor to use for feature extractor. Possible options are "ctranspath", - # "mahmood-uni", "mahmood-conch", "chief_ctranspath", "mahmood-conchv1_5", + # "mahmood-uni", "mahmood-conch", "chief_ctranspath", "mahmood-conch1_5", # "mahmood-uni2", "dino-bloom", "gigapath", "h-optimus-0", "h-optimus-1", "virchow2" # Some of them require requesting access to the respective authors beforehand. extractor: "ctranspath" @@ -34,15 +34,15 @@ slide_encoding: encoder: "chief" # Directory to save the output files. - output_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE" + output_dir: "/path/to/save/files/to" # Directory where the extracted features are stored. - feat_dir: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/features/xiyuewang-ctranspath-7c998680-347671c2" + feat_dir: "/path/your/extracted/features/are/stored/in" # A table (.xlsx or .csv) relating every slide to their feature files. # The table must contain at least two columns, one titled "SLIDE", # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. - slide_table: "/mnt/bulk-sirius/juan/pap_screening/STAMP/DELETEEE/slide_table.csv" + slide_table: "/path/of/slide.csv" # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) device: "cuda" From 9fdc70f09170d0827e467b479d1529372f4630e7 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 5 Mar 2025 16:29:53 +0000 Subject: [PATCH 07/99] add cobra dependencies --- pyproject.toml | 5 +- src/stamp/slide_encoding/__init__.py | 1 + src/stamp/slide_encoding/encoder/chief.py | 66 +++-------------------- 3 files changed, 10 insertions(+), 62 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 61aaeee6..a20b2385 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -92,11 +92,10 @@ virchow2 = [ "torch>=2.0.0", ] cobra = [ - "mamba-ssm>=2.2.2", - "torch>=2.4.1", + "cobra @ git+https://github.com/KatherLab/COBRA.git", ] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2, uni2, chief_ctranspath, conch1_5, gigapath, h_optimus_0, h_optimus_1]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 151de2c1..2b4d6af5 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -76,6 +76,7 @@ def get_pat_embs( assert all_feats_cat.ndim == 3, ( f"Expected 3D tensor, got {all_feats_cat.ndim}" ) + print("all_feats_cat", all_feats_cat.shape) slide_feats = model(all_feats_cat.to(dtype)) slide_dict[patient_id] = { "feats": slide_feats.to(torch.float32) diff --git a/src/stamp/slide_encoding/encoder/chief.py b/src/stamp/slide_encoding/encoder/chief.py index 23a10e8b..9ef8a480 100644 --- a/src/stamp/slide_encoding/encoder/chief.py +++ b/src/stamp/slide_encoding/encoder/chief.py @@ -21,6 +21,8 @@ def chief() -> Encoder: ) chief = torch.load(model_path) + if "organ_embedding" in chief: + del chief["organ_embedding"] model.load_state_dict(chief, strict=True) return Encoder( @@ -92,19 +94,10 @@ def forward(self, x): a = self.attention_a(x) b = self.attention_b(x) A = a.mul(b) - A = self.attention_c(A) # N x n_classes + A = self.attention_c(A) return A, x -# clip_tokenizer = CLIPTokenizer.from_pretrained() -# -# clip_tokenizer = CLIPTokenizer.from_pretrained('./') -# -# text_encoder = CLIPTextModel.from_pretrained( -# './', -# subfolder="text_encoder") - - class CHIEF(nn.Module): def __init__( self, @@ -147,71 +140,26 @@ def __init__( nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) ) - self.register_buffer("organ_embedding", torch.randn(19, 768)) - word_emb_path = STAMP_CACHE_DIR / "Text_emdding.pth" - if not word_emb_path.is_file(): - word_emb_path.parent.mkdir(parents=True, exist_ok=True) - gdown.download( - "https://drive.google.com/u/0/uc?id=1sVGmlTdnSJdcCK1vuswsuReKMUjvSEmX&export=download", - str(word_emb_path), - ) - word_embedding = torch.load(word_emb_path) - self.organ_embedding.data = word_embedding.float() - self.text_to_vision = nn.Sequential( - nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) - ) - def relocate(self): device = torch.device("cuda" if torch.cuda.is_available() else "cpu") self.attention_net = self.attention_net.to(device) self.classifiers = self.classifiers.to(device) self.instance_classifiers = self.instance_classifiers.to(device) - def forward(self, h, x_anatomic): - batch = x_anatomic + def forward(self, h): h_ori = h A, h = self.attention_net(h) A = torch.transpose(A, 1, 0) A_raw = A A = F.softmax(A, dim=1) - - embed_batch = self.organ_embedding[batch] - embed_batch = self.text_to_vision(embed_batch) + print("A", A.shape) WSI_feature = torch.mm(A, h) slide_embeddings = torch.mm(A, h_ori) - M = WSI_feature + embed_batch - - logits = self.classifiers(M) - result = { - "bag_logits": logits, "attention_raw": A_raw, "WSI_feature": slide_embeddings, - "WSI_feature_anatomical": M, + "WSI_feature_transformed": WSI_feature, + "tile_features_transformed": h, } return result - - def patch_probs(self, h, x_anatomic): - batch = x_anatomic - A, h = self.attention_net(h) - A_raw = A - A = torch.transpose(A, 1, 0) - A = F.softmax(A, dim=1) - - embed_batch = self.organ_embedding[batch] - embed_batch = self.text_to_vision(embed_batch) - M = torch.mm(A, h) - M = M + embed_batch - bag_logits = self.classifiers(M) - bag_prob = torch.softmax(bag_logits.squeeze(), dim=0) - patch_logits = self.classifiers(h + embed_batch) - patch_prob = ( - torch.sigmoid(A_raw.squeeze()) * torch.softmax(patch_logits, dim=1)[:, 1] - ) - - return { - "bag_prob": bag_prob, - "patch_prob": patch_prob, - "attention_raw": A_raw.squeeze(), - } From 40e953a0d96db33087d0ab10607b10ccb7e47e47 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 5 Mar 2025 16:30:46 +0000 Subject: [PATCH 08/99] use cobra package directly --- src/stamp/slide_encoding/encoder/cobra.py | 303 +--------------------- 1 file changed, 9 insertions(+), 294 deletions(-) diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py index cfae21d9..43c680bc 100644 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -1,301 +1,16 @@ -import os -import warnings - -import torch -import torch.nn as nn -import torch.nn.functional as F -from einops import rearrange -from huggingface_hub import hf_hub_download -from mamba_ssm import Mamba2 # TODO: add to requirements and try catch - -from stamp.cache import STAMP_CACHE_DIR, file_digest # TODO: assert hash +try: + from cobra.utils.load_cobra import get_cobraII +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "cobra dependencies not installed." + " Please update your venv using `uv sync --extra cobra`" + ) from e from stamp.slide_encoding.encoder import Encoder -warnings.simplefilter(action="ignore", category=FutureWarning) - def cobra() -> Encoder: - checkpoint_path = os.path.join(STAMP_CACHE_DIR, "pytorch_model.bin") - if not os.path.exists(checkpoint_path): - checkpoint_path = hf_hub_download( - "KatherLab/COBRA", - filename="pytorch_model.bin", - local_dir=STAMP_CACHE_DIR, - force_download=True, - ) - print(f"Saving model to {checkpoint_path}") - state_dict = torch.load(checkpoint_path, map_location="cpu") # TODO: Ask why is cpu - model = Cobra( - input_dims=[768, 1024, 1280, 1536], - ) - model.load_state_dict(state_dict) - print("COBRA model loaded successfully") + cobra = return Encoder( model=model, identifier="cobra", - ) - - -# Copy from https://github.com/KatherLab/COBRA - - -class Embed(nn.Module): - def __init__(self, dim, embed_dim=1024, dropout=0.25): - super(Embed, self).__init__() - - self.head = nn.Sequential( - nn.LayerNorm(dim), - nn.Linear(dim, embed_dim), - nn.Dropout(dropout) if dropout else nn.Identity(), - nn.SiLU(), - nn.Linear(embed_dim, embed_dim), - ) - - def forward(self, x): - return self.head(x) - - -class Cobra(nn.Module): - def __init__( - self, - embed_dim=768, - input_dims=[384, 512, 1024, 1280, 1536], - num_heads=8, - layers=2, - dropout=0.25, - att_dim=96, - d_state=128, - ): - super().__init__() - - self.embed = nn.ModuleDict({str(d): Embed(d, embed_dim) for d in input_dims}) - - self.norm = nn.LayerNorm(embed_dim) - - self.mamba_enc = Mamba2Enc( - embed_dim, - embed_dim, - n_classes=embed_dim, - layer=layers, - dropout=dropout, - d_state=d_state, - ) - - self.num_heads = num_heads - self.attn = nn.ModuleList( - [ - BatchedABMIL( - input_dim=int(embed_dim / num_heads), - hidden_dim=att_dim, - dropout=dropout, - n_classes=1, - ) - for _ in range(self.num_heads) - ] - ) - - def forward(self, x, multi_fm_mode=False, fm_idx=None, get_attention=False): - if multi_fm_mode: - fm_embs = torch.concat( - [self.embed[str(xi.shape[-1])](xi) for xi in x], dim=0 - ) - assert fm_embs.shape[-1] == self.embed_dim, fm_embs.shape - assert len(fm_embs.shape) == 3, fm_embs.shape - assert fm_embs.shape[0] == len(x), fm_embs.shape - logits = torch.mean(fm_embs, dim=0) - else: - logits = self.embed[str(x.shape[-1])](x) - - h = self.norm(self.mamba_enc(logits)) - - if self.num_heads > 1: - h_ = rearrange(h, "b t (e c) -> b t e c", c=self.num_heads) - - attention = [] - for i, attn_net in enumerate(self.attn): - _, processed_attention = attn_net( - h_[:, :, :, i], return_raw_attention=True - ) - attention.append(processed_attention) - - A = torch.stack(attention, dim=-1) - - A = ( - rearrange(A, "b t e c -> b t (e c)", c=self.num_heads) - .mean(-1) - .unsqueeze(-1) - ) - A = torch.transpose(A, 2, 1) - A = F.softmax(A, dim=-1) - else: - A = self.attn[0](h) - - if multi_fm_mode: - if fm_idx: - feats = torch.bmm(A, x[fm_idx]).squeeze(0).squeeze(0) - else: - feats = [] - for i, xi in enumerate(x): - feats.append(torch.bmm(A, xi).squeeze(0).squeeze(0)) - assert ( - len(feats[i].shape) == 1 and feats[i].shape[0] == xi.shape[-1] - ), feats[i].shape - else: - feats = torch.bmm(A, x).squeeze(1) - - if get_attention: - return feats, A - return feats - - -""" -Adapted from: https://github.com/isyangshu/MambaMIL/blob/main/models/MambaMIL.py -Shu Yang, Yihui Wang, and Hao Chen. MambaMIL: En- -hancing Long Sequence Modeling with Sequence Reorder- -ing in Computational Pathology. In proceedings of Medi- -cal Image Computing and Computer Assisted Intervention – -MICCAI 2024. Springer Nature Switzerland, 2024 -""" - - -def initialize_weights(module): - for m in module.modules(): - if isinstance(m, nn.Linear): - nn.init.xavier_normal_(m.weight) - if m.bias is not None: - m.bias.data.zero_() - if isinstance(m, nn.LayerNorm): - nn.init.constant_(m.bias, 0) - nn.init.constant_(m.weight, 1.0) - - -class Mamba2Enc(nn.Module): - def __init__( - self, - in_dim, - dim, - n_classes, - dropout=0.25, - act="gelu", - layer=2, - rate=10, - d_state=64, - ): - super(Mamba2Enc, self).__init__() - self._fc1_layers: list[nn.Module] = [nn.Linear(in_dim, dim)] - if act.lower() == "relu": - self._fc1_layers += [nn.ReLU()] - elif act.lower() == "gelu": - self._fc1_layers += [nn.GELU()] - if dropout: - self._fc1_layers += [nn.Dropout(dropout)] - - self._fc1 = nn.Sequential(*self._fc1_layers) - self.norm = nn.LayerNorm(dim) - self.layers = nn.ModuleList() - - for _ in range(layer): - self.layers.append( - nn.Sequential( - nn.LayerNorm(dim), - Mamba2( - d_model=dim, - d_state=d_state, - d_conv=4, - expand=2, - ), - ) - ) - - self.n_classes = n_classes - self.classifier = nn.Linear(dim, self.n_classes) - self.rate = rate - self.type = type - - self.apply(initialize_weights) - - def forward(self, x): - if len(x.shape) == 2: - x = x.expand(1, -1, -1) - h = x # .float() - - h = self._fc1(h) - - for layer in self.layers: - h_ = h - h = layer[0](h) # type: ignore - h = layer[1](h) - h = h + h_ - - logits = self.classifier(h) - return logits - - def relocate(self): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self._fc1 = self._fc1.to(device) - self.layers = self.layers.to(device) - - self.attention = self.attention.to(device) - self.norm = self.norm.to(device) - self.classifier = self.classifier.to(device) - - -""" adapted from: https://github.com/mahmoodlab/MADELEINE/blob/main/core/models/abmil.py - Guillaume Jaume, Anurag Jayant Vaidya, Andrew Zhang, - Andrew H Song, Richard J. Chen, Sharifa Sahai, Dandan - Mo, Emilio Madrigal, Long Phi Le, and Mahmood Faisal. - Multistain pretraining for slide representation learning in - pathology. In European Conference on Computer Vision. - Springer, 2024. -""" - - -class BatchedABMIL(nn.Module): - def __init__( - self, - input_dim=1024, - hidden_dim=256, - dropout=False, - n_classes=1, - n_heads=1, - activation="softmax", - ): - super(BatchedABMIL, self).__init__() - - self.activation = activation - self.device = "cuda" if torch.cuda.is_available() else "cpu" - self.attention_a = nn.ModuleList([nn.Linear(input_dim, hidden_dim), nn.Tanh()]) - - self.attention_b = nn.ModuleList( - [nn.Linear(input_dim, hidden_dim), nn.Sigmoid()] - ) - - if dropout: - self.attention_a.append(nn.Dropout(0.25)) - self.attention_b.append(nn.Dropout(0.25)) - - self.attention_a = nn.Sequential(*self.attention_a) - self.attention_b = nn.Sequential(*self.attention_b) - self.attention_c = nn.Linear(hidden_dim, n_classes) - - def forward(self, x, return_raw_attention=False): - assert len(x.shape) == 3, x.shape - a = self.attention_a(x) - b = self.attention_b(x) - A = a.mul(b) - A = self.attention_c(A) - if self.activation == "softmax": - activated_A = F.softmax(A, dim=1) - elif self.activation == "leaky_relu": - activated_A = F.leaky_relu(A) - elif self.activation == "relu": - activated_A = F.relu(A) - elif self.activation == "sigmoid": - activated_A = torch.sigmoid(A) - else: - raise NotImplementedError("Activation not implemented.") - - if return_raw_attention: - return activated_A, A - - return activated_A \ No newline at end of file + ) \ No newline at end of file From 396d6d28a0594331fb5e81fae70eb3fa64e0a621 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 11 Mar 2025 10:08:23 +0000 Subject: [PATCH 09/99] add cobra --- src/stamp/slide_encoding/encoder/cobra.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py index 43c680bc..07c75c84 100644 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -1,5 +1,5 @@ try: - from cobra.utils.load_cobra import get_cobraII + from cobra.utils.load_cobra import get_cobra except ModuleNotFoundError as e: raise ModuleNotFoundError( "cobra dependencies not installed." @@ -9,8 +9,8 @@ def cobra() -> Encoder: - cobra = + model = get_cobra(download_weights=True) return Encoder( model=model, - identifier="cobra", + identifier="katherlab-cobra", ) \ No newline at end of file From 777f570743851f4e1ac9420f4f3261b4a807558d Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 17 Mar 2025 08:58:23 +0000 Subject: [PATCH 10/99] fix cobra encoder --- src/stamp/__main__.py | 4 +- src/stamp/cache.py | 4 +- src/stamp/preprocessing/__init__.py | 4 +- .../extractor/chief_ctranspath.py | 13 ++++-- src/stamp/preprocessing/extractor/conch1_5.py | 6 +-- src/stamp/preprocessing/extractor/gigapath.py | 18 ++++---- .../preprocessing/extractor/h_optimus_0.py | 22 ++++++---- .../preprocessing/extractor/h_optimus_1.py | 24 +++++----- src/stamp/preprocessing/extractor/uni2.py | 44 ++++++++++--------- src/stamp/slide_encoding/__init__.py | 34 +++++++++++++- src/stamp/slide_encoding/config.py | 3 +- src/stamp/slide_encoding/encoder/__init__.py | 3 +- src/stamp/slide_encoding/encoder/cobra.py | 2 +- 13 files changed, 117 insertions(+), 64 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 857c2f0d..40847ffc 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -76,7 +76,7 @@ def _run_cli(args: argparse.Namespace) -> None: ) case "encode": - from stamp.slide_encoding import get_pat_embs + from stamp.slide_encoding import get_slide_embs if config.slide_encoding is None: raise ValueError("no slide encoding configuration supplied") @@ -86,7 +86,7 @@ def _run_cli(args: argparse.Namespace) -> None: "using the following configuration:\n" f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" ) - get_pat_embs( + get_slide_embs( encoder_name=config.slide_encoding.encoder, output_dir=config.slide_encoding.output_dir, feat_dir=config.slide_encoding.feat_dir, diff --git a/src/stamp/cache.py b/src/stamp/cache.py index fe4f0e9a..d65c7dcd 100644 --- a/src/stamp/cache.py +++ b/src/stamp/cache.py @@ -2,9 +2,9 @@ import os import shutil import urllib.request +from functools import cache from pathlib import Path from typing import Final -from functools import cache STAMP_CACHE_DIR: Final[Path] = ( Path(os.environ.get("XDG_CACHE_HOME") or (Path.home() / ".cache")) / "stamp" @@ -37,7 +37,7 @@ def download_file(*, url: str, file_name: str, sha256sum: str) -> Path: def file_digest(file: str | Path) -> str: with open(file, "rb") as fp: return hashlib.file_digest(fp, "sha256").hexdigest() - + @cache def get_processing_code_hash(file_path) -> str: diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 9dddd754..ae106dd7 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -200,7 +200,9 @@ def extract_( assert_never(unreachable) model = extractor.model.to(device).eval() - extractor_id = f"{extractor.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + extractor_id = ( + f"{extractor.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + ) _logger.info(f"Using extractor {extractor.identifier}") diff --git a/src/stamp/preprocessing/extractor/chief_ctranspath.py b/src/stamp/preprocessing/extractor/chief_ctranspath.py index 39b268e4..a70fb150 100644 --- a/src/stamp/preprocessing/extractor/chief_ctranspath.py +++ b/src/stamp/preprocessing/extractor/chief_ctranspath.py @@ -1,6 +1,7 @@ -from stamp.cache import STAMP_CACHE_DIR, file_digest from pathlib import Path +from stamp.cache import STAMP_CACHE_DIR, file_digest + try: import gdown import torch @@ -15,7 +16,9 @@ from stamp.preprocessing.extractor import Extractor, ctranspath -def chief_ctranspath(model_path: Path | None = None) -> Extractor[ctranspath._SwinTransformer]: +def chief_ctranspath( + model_path: Path | None = None, +) -> Extractor[ctranspath._SwinTransformer]: model_path = model_path or STAMP_CACHE_DIR / "chief_ctranspath.pth" if not model_path.is_file(): model_path.parent.mkdir(parents=True, exist_ok=True) @@ -31,7 +34,9 @@ def chief_ctranspath(model_path: Path | None = None) -> Extractor[ctranspath._Sw f"The digest of the downloaded checkpoint ({model_path}) did not match the expected value." ) - model = ctranspath._swin_tiny_patch4_window7_224(embed_layer=ctranspath._ConvStem, pretrained=False) + model = ctranspath._swin_tiny_patch4_window7_224( + embed_layer=ctranspath._ConvStem, pretrained=False + ) model.head = nn.Identity() chief_ctranspath = torch.load(model_path, weights_only=False) model.load_state_dict(chief_ctranspath["model"], strict=True) @@ -40,7 +45,7 @@ def chief_ctranspath(model_path: Path | None = None) -> Extractor[ctranspath._Sw [ transforms.Resize(224), transforms.ToTensor(), - transforms.Normalize(mean = (0.485, 0.456, 0.406), std = (0.229, 0.224, 0.225)) + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), ] ) diff --git a/src/stamp/preprocessing/extractor/conch1_5.py b/src/stamp/preprocessing/extractor/conch1_5.py index 92d17a2c..6ee092a8 100644 --- a/src/stamp/preprocessing/extractor/conch1_5.py +++ b/src/stamp/preprocessing/extractor/conch1_5.py @@ -1,5 +1,5 @@ try: - from transformers import AutoModel + from transformers import AutoModel except ModuleNotFoundError as e: raise ModuleNotFoundError( "conchv1_5 dependencies not installed." @@ -11,10 +11,10 @@ def conch1_5(): """Extracts features from slide tiles using GigaPath tile encoder.""" - titan = AutoModel.from_pretrained('MahmoodLab/TITAN', trust_remote_code=True) + titan = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) model, transform = titan.return_conch() return Extractor( model=model, transform=transform, identifier="conch1_5", # type: ignore - ) \ No newline at end of file + ) diff --git a/src/stamp/preprocessing/extractor/gigapath.py b/src/stamp/preprocessing/extractor/gigapath.py index 9b176748..690bceef 100644 --- a/src/stamp/preprocessing/extractor/gigapath.py +++ b/src/stamp/preprocessing/extractor/gigapath.py @@ -17,15 +17,17 @@ def gigapath(): model = timm.create_model("hf_hub:prov-gigapath/prov-gigapath", pretrained=True) # Define the transform - transform = transforms.Compose([ - transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC), - transforms.CenterCrop(224), - transforms.ToTensor(), - transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), - ]) - + transform = transforms.Compose( + [ + transforms.Resize(256, interpolation=transforms.InterpolationMode.BICUBIC), + transforms.CenterCrop(224), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] + ) + return Extractor( model=model, transform=transform, identifier="gigapath", # type: ignore - ) \ No newline at end of file + ) diff --git a/src/stamp/preprocessing/extractor/h_optimus_0.py b/src/stamp/preprocessing/extractor/h_optimus_0.py index 94f20e50..0f1c9330 100644 --- a/src/stamp/preprocessing/extractor/h_optimus_0.py +++ b/src/stamp/preprocessing/extractor/h_optimus_0.py @@ -1,5 +1,5 @@ try: - import timm + import timm from torchvision import transforms except ModuleNotFoundError as e: raise ModuleNotFoundError( @@ -12,16 +12,20 @@ def h_optimus_0(): model = timm.create_model( - "hf-hub:bioptimus/H-optimus-0", pretrained=True, init_values=1e-5,dynamic_img_size=False + "hf-hub:bioptimus/H-optimus-0", + pretrained=True, + init_values=1e-5, + dynamic_img_size=False, ) - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize( - mean=(0.707223, 0.578729, 0.703617), - std=(0.211883, 0.230117, 0.177517) - ), - ]) + transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize( + mean=(0.707223, 0.578729, 0.703617), std=(0.211883, 0.230117, 0.177517) + ), + ] + ) return Extractor( model=model, diff --git a/src/stamp/preprocessing/extractor/h_optimus_1.py b/src/stamp/preprocessing/extractor/h_optimus_1.py index 04ebaf06..f0d4709c 100644 --- a/src/stamp/preprocessing/extractor/h_optimus_1.py +++ b/src/stamp/preprocessing/extractor/h_optimus_1.py @@ -1,5 +1,5 @@ try: - import timm + import timm from torchvision import transforms except ModuleNotFoundError as e: raise ModuleNotFoundError( @@ -12,19 +12,23 @@ def h_optimus_1(): model = timm.create_model( - "hf-hub:bioptimus/H-optimus-1", pretrained=True, init_values=1e-5,dynamic_img_size=False + "hf-hub:bioptimus/H-optimus-1", + pretrained=True, + init_values=1e-5, + dynamic_img_size=False, ) - transform = transforms.Compose([ - transforms.ToTensor(), - transforms.Normalize( - mean=(0.707223, 0.578729, 0.703617), - std=(0.211883, 0.230117, 0.177517) - ), - ]) + transform = transforms.Compose( + [ + transforms.ToTensor(), + transforms.Normalize( + mean=(0.707223, 0.578729, 0.703617), std=(0.211883, 0.230117, 0.177517) + ), + ] + ) return Extractor( model=model, transform=transform, identifier="h_optimus_1", # type: ignore - ) \ No newline at end of file + ) diff --git a/src/stamp/preprocessing/extractor/uni2.py b/src/stamp/preprocessing/extractor/uni2.py index 6f51d35b..b3bca2bc 100644 --- a/src/stamp/preprocessing/extractor/uni2.py +++ b/src/stamp/preprocessing/extractor/uni2.py @@ -15,26 +15,30 @@ def uni2() -> Extractor: # pretrained=True needed to load UNI2-h weights (and download weights for the first time) timm_kwargs = { - 'img_size': 224, - 'patch_size': 14, - 'depth': 24, - 'num_heads': 24, - 'init_values': 1e-5, - 'embed_dim': 1536, - 'mlp_ratio': 2.66667*2, - 'num_classes': 0, - 'no_embed_class': True, - 'mlp_layer': timm.layers.SwiGLUPacked, - 'act_layer': torch.nn.SiLU, - 'reg_tokens': 8, - 'dynamic_img_size': True - } - model = timm.create_model("hf-hub:MahmoodLab/UNI2-h", pretrained=True, **timm_kwargs) - transform = create_transform(**resolve_data_config(model.pretrained_cfg, model=model)) + "img_size": 224, + "patch_size": 14, + "depth": 24, + "num_heads": 24, + "init_values": 1e-5, + "embed_dim": 1536, + "mlp_ratio": 2.66667 * 2, + "num_classes": 0, + "no_embed_class": True, + "mlp_layer": timm.layers.SwiGLUPacked, + "act_layer": torch.nn.SiLU, + "reg_tokens": 8, + "dynamic_img_size": True, + } + model = timm.create_model( + "hf-hub:MahmoodLab/UNI2-h", pretrained=True, **timm_kwargs + ) + transform = create_transform( + **resolve_data_config(model.pretrained_cfg, model=model) + ) model.eval() return Extractor( - model=model, - transform=transform, - identifier="uni2", # type: ignore - ) \ No newline at end of file + model=model, + transform=transform, + identifier="uni2", # type: ignore + ) diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 2b4d6af5..772ddc64 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -1,4 +1,5 @@ import os +import pdb from pathlib import Path import h5py @@ -76,7 +77,6 @@ def get_pat_embs( assert all_feats_cat.ndim == 3, ( f"Expected 3D tensor, got {all_feats_cat.ndim}" ) - print("all_feats_cat", all_feats_cat.shape) slide_feats = model(all_feats_cat.to(dtype)) slide_dict[patient_id] = { "feats": slide_feats.to(torch.float32) @@ -96,3 +96,35 @@ def get_pat_embs( f.create_dataset(f"{patient_id}", data=data["feats"]) f.attrs["encoder"] = data["encoder"] tqdm.write(f"Finished encoding, saved to {output_file}") + + +def get_slide_embs( + encoder_name: EncoderName, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + device: DeviceLikeType, +) -> None: + match encoder_name: + case EncoderName.TITAN: + from stamp.slide_encoding.encoder.titan import titan + + encoder: Encoder = titan() + + for tile_feats_filename in os.listdir(feat_dir): + h5_path = os.path.join(feat_dir, tile_feats_filename) + if not os.path.exists(h5_path): + tqdm.write(f"File {h5_path} does not exist, skipping") + continue + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] # type: ignore + coords = f["coords"][:] # type: ignore + feats = torch.tensor(feats).to(device) + coords = torch.tensor(coords).to(device) + + with torch.autocast(device, torch.float16), torch.inference_mode(): + pdb.set_trace() + slide_embedding = encoder.model.encode_slide_from_patch_features( + feats, coords, 512 + ) + print(slide_embedding.shape) diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/slide_encoding/config.py index f2e4cedd..9cc8bbd7 100644 --- a/src/stamp/slide_encoding/config.py +++ b/src/stamp/slide_encoding/config.py @@ -1,7 +1,6 @@ from enum import StrEnum from pathlib import Path -import torch from pydantic import BaseModel, ConfigDict from torch._prims_common import DeviceLikeType @@ -19,4 +18,4 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): output_dir: Path feat_dir: Path slide_table: Path - device: DeviceLikeType \ No newline at end of file + device: DeviceLikeType diff --git a/src/stamp/slide_encoding/encoder/__init__.py b/src/stamp/slide_encoding/encoder/__init__.py index 5eed2e71..a972b080 100644 --- a/src/stamp/slide_encoding/encoder/__init__.py +++ b/src/stamp/slide_encoding/encoder/__init__.py @@ -5,6 +5,7 @@ EncoderModel = TypeVar("EncoderModel", bound=nn.Module) + @dataclass(frozen=True) class Encoder(Generic[EncoderModel]): _: KW_ONLY @@ -14,4 +15,4 @@ class Encoder(Generic[EncoderModel]): If possible, it should include the digest of the model weights etc. so that any change in the model also changes its ID. - """ \ No newline at end of file + """ diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py index 07c75c84..f6d1f0df 100644 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ b/src/stamp/slide_encoding/encoder/cobra.py @@ -13,4 +13,4 @@ def cobra() -> Encoder: return Encoder( model=model, identifier="katherlab-cobra", - ) \ No newline at end of file + ) From 44b6977e1cdecb436f96d53784f63cfc0468e7ab Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 25 Mar 2025 15:20:04 +0000 Subject: [PATCH 11/99] coords pixel conversion --- src/stamp/slide_encoding/__init__.py | 36 ++++++++++++++++++---------- 1 file changed, 24 insertions(+), 12 deletions(-) diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 772ddc64..6e9a7b12 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -116,15 +116,27 @@ def get_slide_embs( if not os.path.exists(h5_path): tqdm.write(f"File {h5_path} does not exist, skipping") continue - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - coords = f["coords"][:] # type: ignore - feats = torch.tensor(feats).to(device) - coords = torch.tensor(coords).to(device) - - with torch.autocast(device, torch.float16), torch.inference_mode(): - pdb.set_trace() - slide_embedding = encoder.model.encode_slide_from_patch_features( - feats, coords, 512 - ) - print(slide_embedding.shape) + if h5_path.endswith(".h5"): + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] # type: ignore + coords = f["coords"][:] # type: ignore + breakpoint() + + # Convert coordinates from microns to pixels + mpp = 1.14 # microns per pixel + coords = coords / mpp # Convert to pixels + coords = torch.tensor(coords, dtype=torch.float32).to(device) + coords = coords.to(torch.int64).to(device) # Convert to integer + + feats = torch.tensor(feats, dtype=torch.float32).to(device) + + # Ensure model weights and biases are on the same device as the input + model = encoder.model.to(device) + + with torch.autocast(device, torch.float32), torch.inference_mode(): + breakpoint() + slide_embedding = model.encode_slide_from_patch_features(feats, coords, 512) + + # TODO: Get the mpp from the new data function, it will throw an error if there is no tile_size px attribute + # needed for the calculation + # TODO: Calculate the patch_size_lvl0 using the proportion knowing that x20 is 0.5 um/px From c428764c814e7aa0e896f568101af7afedbc8102 Mon Sep 17 00:00:00 2001 From: ezicStar Date: Fri, 28 Mar 2025 09:23:02 +0100 Subject: [PATCH 12/99] patch_lvl_0 calculation --- src/stamp/slide_encoding/__init__.py | 66 ++++++++++++++++++---------- 1 file changed, 43 insertions(+), 23 deletions(-) diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 6e9a7b12..386505c8 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -8,6 +8,7 @@ from torch._prims_common import DeviceLikeType from tqdm import tqdm +import stamp from stamp.cache import get_processing_code_hash from stamp.slide_encoding.config import EncoderName from stamp.slide_encoding.encoder import Encoder @@ -41,7 +42,6 @@ def get_pat_embs( model = encoder.model.to(device).eval() # TODO: dtype depends con CUDA capabilities. Check end of # extract_feat_patients.py on how to handle this - # TODO: add logger slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") @@ -109,34 +109,54 @@ def get_slide_embs( case EncoderName.TITAN: from stamp.slide_encoding.encoder.titan import titan - encoder: Encoder = titan() + encoder: Encoder = titan() + + slide_dict: dict[str, dict[str, torch.Tensor]] = {} + + # Ensure model weights and biases are on the same device as the input + model = encoder.model.to(device).eval() + + output_name = f"{encoder.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + output_file = os.path.join(output_dir, f"{output_name}.h5") + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + with h5py.File(output_file, "w") as h5_file: + h5_file.attrs["encoder"] = encoder.identifier + h5_file.attrs["stamp_version"] = stamp.__version__ + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + tqdm.write( + f"File {h5_path} does not exist or is not an h5 file, skipping" + ) + continue - for tile_feats_filename in os.listdir(feat_dir): - h5_path = os.path.join(feat_dir, tile_feats_filename) - if not os.path.exists(h5_path): - tqdm.write(f"File {h5_path} does not exist, skipping") - continue - if h5_path.endswith(".h5"): with h5py.File(h5_path, "r") as f: feats = f["feats"][:] # type: ignore coords = f["coords"][:] # type: ignore - breakpoint() - # Convert coordinates from microns to pixels - mpp = 1.14 # microns per pixel - coords = coords / mpp # Convert to pixels - coords = torch.tensor(coords, dtype=torch.float32).to(device) - coords = coords.to(torch.int64).to(device) # Convert to integer + # Convert coordinates from microns to pixels + mpp = 1.14 # microns per pixel + patch_size_lvl0 = 256 / mpp # Inferred from TITAN docs + coords = coords / mpp # Convert to pixels + coords = torch.tensor(coords, dtype=torch.float32).to(device) + coords = coords.to(torch.int64).to(device) # Convert to integer - feats = torch.tensor(feats, dtype=torch.float32).to(device) + feats = torch.tensor(feats, dtype=torch.float32).to(device) - # Ensure model weights and biases are on the same device as the input - model = encoder.model.to(device) + with torch.inference_mode(): + slide_embedding = model.encode_slide_from_patch_features( + feats, coords, patch_size_lvl0 + ) + slide_embedding = ( + slide_embedding.to(torch.float32).detach().squeeze().cpu().numpy() + ) - with torch.autocast(device, torch.float32), torch.inference_mode(): - breakpoint() - slide_embedding = model.encode_slide_from_patch_features(feats, coords, 512) + slide_name = Path(tile_feats_filename).stem + h5_file.create_dataset(slide_name, data=slide_embedding) - # TODO: Get the mpp from the new data function, it will throw an error if there is no tile_size px attribute - # needed for the calculation - # TODO: Calculate the patch_size_lvl0 using the proportion knowing that x20 is 0.5 um/px + tqdm.write(f"Finished encoding, saved all slide embeddings to {output_file}") From 797d0caee7a93b783bd5957209277eac27fcde1b Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 28 Mar 2025 09:19:03 +0000 Subject: [PATCH 13/99] make patch size lvl as integer --- src/stamp/slide_encoding/__init__.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/slide_encoding/__init__.py index 386505c8..b419c6dc 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/slide_encoding/__init__.py @@ -1,3 +1,4 @@ +import math import os import pdb from pathlib import Path @@ -141,7 +142,7 @@ def get_slide_embs( # Convert coordinates from microns to pixels mpp = 1.14 # microns per pixel - patch_size_lvl0 = 256 / mpp # Inferred from TITAN docs + patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs coords = coords / mpp # Convert to pixels coords = torch.tensor(coords, dtype=torch.float32).to(device) coords = coords.to(torch.int64).to(device) # Convert to integer From 45dbc5b1493a68ad5c3a9f0d93304a3756caaea5 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 31 Mar 2025 12:55:20 +0100 Subject: [PATCH 14/99] encoders now have their own encode function --- src/stamp/__main__.py | 23 ++++- src/stamp/config.py | 4 +- src/stamp/config.yaml | 21 +++- .../{slide_encoding => encoding}/__init__.py | 75 +------------- .../{slide_encoding => encoding}/config.py | 10 ++ src/stamp/encoding/encoder/__init__.py | 29 ++++++ .../encoder/chief.py | 2 +- src/stamp/encoding/encoder/cobra.py | 97 +++++++++++++++++++ .../encoder/titan.py | 0 src/stamp/slide_encoding/encoder/__init__.py | 18 ---- src/stamp/slide_encoding/encoder/cobra.py | 16 --- 11 files changed, 185 insertions(+), 110 deletions(-) rename src/stamp/{slide_encoding => encoding}/__init__.py (52%) rename src/stamp/{slide_encoding => encoding}/config.py (67%) create mode 100644 src/stamp/encoding/encoder/__init__.py rename src/stamp/{slide_encoding => encoding}/encoder/chief.py (99%) create mode 100644 src/stamp/encoding/encoder/cobra.py rename src/stamp/{slide_encoding => encoding}/encoder/titan.py (100%) delete mode 100644 src/stamp/slide_encoding/encoder/__init__.py delete mode 100644 src/stamp/slide_encoding/encoder/cobra.py diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 40847ffc..7076b02f 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -75,8 +75,8 @@ def _run_cli(args: argparse.Namespace) -> None: canny_cutoff=config.preprocessing.canny_cutoff, ) - case "encode": - from stamp.slide_encoding import get_slide_embs + case "encode_slides": + from stamp.encoding import get_slide_embs if config.slide_encoding is None: raise ValueError("no slide encoding configuration supplied") @@ -94,6 +94,25 @@ def _run_cli(args: argparse.Namespace) -> None: device=config.slide_encoding.device, ) + case "encode_patients": + from stamp.encoding import get_pat_embs + + if config.patient_encoding is None: + raise ValueError("no patient encoding configuration supplied") + + _add_file_handle_(_logger, output_dir=config.patient_encoding.output_dir) + _logger.info( + "using the following configuration:\n" + f"{yaml.dump(config.patient_encoding.model_dump(mode='json'))}" + ) + get_pat_embs( + encoder_name=config.patient_encoding.encoder, + output_dir=config.patient_encoding.output_dir, + feat_dir=config.patient_encoding.feat_dir, + slide_table_path=config.patient_encoding.slide_table, + device=config.patient_encoding.device, + ) + case "train": from stamp.modeling.train import train_categorical_model_ diff --git a/src/stamp/config.py b/src/stamp/config.py index 9efb3d92..ca283dba 100644 --- a/src/stamp/config.py +++ b/src/stamp/config.py @@ -1,9 +1,9 @@ from pydantic import BaseModel, ConfigDict +from stamp.encoding.config import PatientEncodingConfig, SlideEncodingConfig from stamp.heatmaps.config import HeatmapConfig from stamp.modeling.config import CrossvalConfig, DeploymentConfig, TrainConfig from stamp.preprocessing.config import PreprocessingConfig -from stamp.slide_encoding.config import SlideEncodingConfig from stamp.statistics import StatsConfig @@ -21,3 +21,5 @@ class StampConfig(BaseModel): heatmaps: HeatmapConfig | None = None slide_encoding: SlideEncodingConfig | None = None + + patient_encoding: PatientEncodingConfig | None = None diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 7edcfa20..5d92ca3d 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -48,6 +48,25 @@ slide_encoding: device: "cuda" +patient_encoding: + # Encoder to use for patient encoding. Possible options are "katherlab-cobra". + encoder: "chief" + + # Directory to save the output files. + output_dir: "/path/to/save/files/to" + + # Directory where the extracted features are stored. + feat_dir: "/path/your/extracted/features/are/stored/in" + + # A table (.xlsx or .csv) relating every slide to their feature files. + # The table must contain at least two columns, one titled "SLIDE", + # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. + slide_table: "/path/of/slide.csv" + + # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) + device: "cuda" + + crossval: output_dir: "/path/to/save/files/to" @@ -100,7 +119,6 @@ crossval: # use_alibi: true - training: output_dir: "/path/to/save/files/to" @@ -206,6 +224,7 @@ statistics: #- "/path/to/split-3/patient-preds.csv" #- "/path/to/split-4/patient-preds.csv" + heatmaps: output_dir: "/path/to/save/files/to" diff --git a/src/stamp/slide_encoding/__init__.py b/src/stamp/encoding/__init__.py similarity index 52% rename from src/stamp/slide_encoding/__init__.py rename to src/stamp/encoding/__init__.py index b419c6dc..fc67206d 100644 --- a/src/stamp/slide_encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -1,6 +1,5 @@ import math import os -import pdb from pathlib import Path import h5py @@ -25,78 +24,12 @@ def get_pat_embs( """""" match encoder_name: case EncoderName.COBRA: - from stamp.slide_encoding.encoder.cobra import cobra + from stamp.slide_encoding.encoder.cobra import Cobra - encoder: Encoder = cobra() - case EncoderName.TITAN: - from stamp.slide_encoding.encoder.titan import titan - - encoder: Encoder = titan() - case EncoderName.CHIEF: - from stamp.slide_encoding.encoder.chief import chief - - encoder: Encoder = chief() - - # TODO: Add other encoders - - dtype = torch.float32 - model = encoder.model.to(device).eval() - # TODO: dtype depends con CUDA capabilities. Check end of - # extract_feat_patients.py on how to handle this - - slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") - slide_dict = {} - - output_name = f"{encoder.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" - output_file = os.path.join(output_dir, output_name) - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - for patient_id, group in tqdm(patient_groups, leave=False): - all_feats_list = [] - - for _, row in group.iterrows(): - slide_filename = row["FILENAME"] - h5_path = os.path.join(feat_dir, slide_filename) - if not os.path.exists(h5_path): - tqdm.write(f"File {h5_path} does not exist, skipping") - continue - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - - feats = torch.tensor(feats).to(device) - all_feats_list.append(feats) + encoder: Encoder = Cobra() + # TODO: Add other encoders - if all_feats_list: - # Concatenate all features for this patient along the second dimension - all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) - - with torch.inference_mode(): - assert all_feats_cat.ndim == 3, ( - f"Expected 3D tensor, got {all_feats_cat.ndim}" - ) - slide_feats = model(all_feats_cat.to(dtype)) - slide_dict[patient_id] = { - "feats": slide_feats.to(torch.float32) - .detach() - .squeeze() - .cpu() - .numpy(), - "encoder": encoder.identifier, - "precision": dtype, - } - else: - tqdm.write(f"No features found for patient {patient_id}, skipping") - - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for patient_id, data in slide_dict.items(): - f.create_dataset(f"{patient_id}", data=data["feats"]) - f.attrs["encoder"] = data["encoder"] - tqdm.write(f"Finished encoding, saved to {output_file}") + encoder.encode_patients(output_dir, feat_dir, slide_table_path, device) def get_slide_embs( diff --git a/src/stamp/slide_encoding/config.py b/src/stamp/encoding/config.py similarity index 67% rename from src/stamp/slide_encoding/config.py rename to src/stamp/encoding/config.py index 9cc8bbd7..92aed9e3 100644 --- a/src/stamp/slide_encoding/config.py +++ b/src/stamp/encoding/config.py @@ -19,3 +19,13 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): feat_dir: Path slide_table: Path device: DeviceLikeType + + +class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): + model_config = ConfigDict(extra="forbid") + + encoder: EncoderName + output_dir: Path + feat_dir: Path + slide_table: Path + device: DeviceLikeType diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py new file mode 100644 index 00000000..e8849991 --- /dev/null +++ b/src/stamp/encoding/encoder/__init__.py @@ -0,0 +1,29 @@ +from abc import ABC, abstractmethod +from dataclasses import KW_ONLY, dataclass +from typing import Generic, TypeVar + +from torch import nn + +EncoderModel = TypeVar("EncoderModel", bound=nn.Module) + + +@dataclass() +class Encoder(ABC, Generic[EncoderModel]): + _: KW_ONLY + model: EncoderModel + identifier: str + """An ID _uniquely_ identifying the model and extractor. + + If possible, it should include the digest of the model weights etc. + so that any change in the model also changes its ID. + """ + + @abstractmethod + def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs): + """Abstract method to encode slide from patch features.""" + pass + + @abstractmethod + def encode_patients(self, output_dir, feat_dir, slide_table_path, device): + """Abstract method to encode patient from slide features.""" + pass diff --git a/src/stamp/slide_encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py similarity index 99% rename from src/stamp/slide_encoding/encoder/chief.py rename to src/stamp/encoding/encoder/chief.py index 9ef8a480..16053a4d 100644 --- a/src/stamp/slide_encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -4,7 +4,7 @@ import torch.nn.functional as F from stamp.cache import STAMP_CACHE_DIR -from stamp.slide_encoding.encoder import Encoder +from stamp.encoding.encoder import Encoder """authors: https://github.com/hms-dbmi/CHIEF""" diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py new file mode 100644 index 00000000..c7fd19fa --- /dev/null +++ b/src/stamp/encoding/encoder/cobra.py @@ -0,0 +1,97 @@ +import os +from pathlib import Path + +import h5py +import pandas as pd +import torch +from torch._prims_common import DeviceLikeType +from tqdm import tqdm + +from stamp.cache import get_processing_code_hash +from stamp.slide_encoding.config import EncoderName +from stamp.slide_encoding.encoder import Encoder + +# TODO: Check which are the necessary imports and add them to cobra package + +try: + from cobra.utils.load_cobra import get_cobra +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "cobra dependencies not installed." + " Please update your venv using `uv sync --extra cobra`" + ) from e +from stamp.slide_encoding.encoder import Encoder + + +class Cobra(Encoder): + def __init__(self): + super().__init__( + model=get_cobra(download_weights=True), identifier="katherlab-cobra" + ) + + def encode_slides(self, slide_feats, *args, **kwargs): + """Encode slides from patch features.""" + raise NotImplementedError("This method needs to be implemented.") + + def encode_patients(self, output_dir, feat_dir, slide_table_path, device) -> None: + """Encode patients from slide features.""" + dtype = torch.float32 + self.model.to(device).eval() + # TODO: dtype depends con CUDA capabilities. Check end of + # extract_feat_patients.py on how to handle this + + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + slide_dict = {} + + output_name = ( + f"{self.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + ) + output_file = os.path.join(output_dir, output_name) + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for patient_id, group in tqdm(patient_groups, leave=False): + all_feats_list = [] + + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + if not os.path.exists(h5_path): + tqdm.write(f"File {h5_path} does not exist, skipping") + continue + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] # type: ignore + + feats = torch.tensor(feats).to(device) + all_feats_list.append(feats) + + if all_feats_list: + # Concatenate all features for this patient along the second dimension + all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) + + with torch.inference_mode(): + assert all_feats_cat.ndim == 3, ( + f"Expected 3D tensor, got {all_feats_cat.ndim}" + ) + slide_feats = self.model(all_feats_cat.to(dtype)) + slide_dict[patient_id] = { + "feats": slide_feats.to(torch.float32) + .detach() + .squeeze() + .cpu() + .numpy(), + "encoder": self.identifier, + "precision": dtype, + } + else: + tqdm.write(f"No features found for patient {patient_id}, skipping") + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for patient_id, data in slide_dict.items(): + f.create_dataset(f"{patient_id}", data=data["feats"]) + f.attrs["encoder"] = data["encoder"] + tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file diff --git a/src/stamp/slide_encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py similarity index 100% rename from src/stamp/slide_encoding/encoder/titan.py rename to src/stamp/encoding/encoder/titan.py diff --git a/src/stamp/slide_encoding/encoder/__init__.py b/src/stamp/slide_encoding/encoder/__init__.py deleted file mode 100644 index a972b080..00000000 --- a/src/stamp/slide_encoding/encoder/__init__.py +++ /dev/null @@ -1,18 +0,0 @@ -from dataclasses import KW_ONLY, dataclass -from typing import Generic, TypeVar - -from torch import nn - -EncoderModel = TypeVar("EncoderModel", bound=nn.Module) - - -@dataclass(frozen=True) -class Encoder(Generic[EncoderModel]): - _: KW_ONLY - model: EncoderModel - identifier: str - """An ID _uniquely_ identifying the model and extractor. - - If possible, it should include the digest of the model weights etc. - so that any change in the model also changes its ID. - """ diff --git a/src/stamp/slide_encoding/encoder/cobra.py b/src/stamp/slide_encoding/encoder/cobra.py deleted file mode 100644 index f6d1f0df..00000000 --- a/src/stamp/slide_encoding/encoder/cobra.py +++ /dev/null @@ -1,16 +0,0 @@ -try: - from cobra.utils.load_cobra import get_cobra -except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "cobra dependencies not installed." - " Please update your venv using `uv sync --extra cobra`" - ) from e -from stamp.slide_encoding.encoder import Encoder - - -def cobra() -> Encoder: - model = get_cobra(download_weights=True) - return Encoder( - model=model, - identifier="katherlab-cobra", - ) From f9d58a237b32fd83e30c4f53a153f89bdc40bfc7 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 31 Mar 2025 15:50:22 +0100 Subject: [PATCH 15/99] split slide and patient encoding commands --- src/stamp/__main__.py | 9 ++-- src/stamp/encoding/__init__.py | 63 ++++------------------ src/stamp/encoding/config.py | 1 - src/stamp/encoding/encoder/cobra.py | 66 ++++++++++++++++++----- src/stamp/encoding/encoder/titan.py | 81 ++++++++++++++++++++++++++--- 5 files changed, 142 insertions(+), 78 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 7076b02f..6a264ae5 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -90,7 +90,6 @@ def _run_cli(args: argparse.Namespace) -> None: encoder_name=config.slide_encoding.encoder, output_dir=config.slide_encoding.output_dir, feat_dir=config.slide_encoding.feat_dir, - slide_table_path=config.slide_encoding.slide_table, device=config.slide_encoding.device, ) @@ -286,8 +285,12 @@ def main() -> None: "preprocess", help="Preprocess whole-slide images into feature vectors" ) commands.add_parser( - "encode", - help="Encode slide-level features into patient-level embeddings", + "encode_slides", + help="Encode patch-level features into slide-level embeddings", + ) + commands.add_parser( + "encode_patients", + help="Encode features into patient-level embeddings", ) commands.add_parser("train", help="Train a Vision Transformer model") commands.add_parser( diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index fc67206d..e492265e 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -10,8 +10,8 @@ import stamp from stamp.cache import get_processing_code_hash -from stamp.slide_encoding.config import EncoderName -from stamp.slide_encoding.encoder import Encoder +from stamp.encoding.config import EncoderName +from stamp.encoding.encoder import Encoder def get_pat_embs( @@ -24,7 +24,7 @@ def get_pat_embs( """""" match encoder_name: case EncoderName.COBRA: - from stamp.slide_encoding.encoder.cobra import Cobra + from stamp.encoding.encoder.cobra import Cobra encoder: Encoder = Cobra() # TODO: Add other encoders @@ -36,61 +36,16 @@ def get_slide_embs( encoder_name: EncoderName, output_dir: Path, feat_dir: Path, - slide_table_path: Path, device: DeviceLikeType, ) -> None: match encoder_name: case EncoderName.TITAN: - from stamp.slide_encoding.encoder.titan import titan - - encoder: Encoder = titan() - - slide_dict: dict[str, dict[str, torch.Tensor]] = {} - - # Ensure model weights and biases are on the same device as the input - model = encoder.model.to(device).eval() - - output_name = f"{encoder.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" - output_file = os.path.join(output_dir, f"{output_name}.h5") - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - with h5py.File(output_file, "w") as h5_file: - h5_file.attrs["encoder"] = encoder.identifier - h5_file.attrs["stamp_version"] = stamp.__version__ + from stamp.encoding.encoder.titan import Titan - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - tqdm.write( - f"File {h5_path} does not exist or is not an h5 file, skipping" - ) - continue - - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - coords = f["coords"][:] # type: ignore - - # Convert coordinates from microns to pixels - mpp = 1.14 # microns per pixel - patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs - coords = coords / mpp # Convert to pixels - coords = torch.tensor(coords, dtype=torch.float32).to(device) - coords = coords.to(torch.int64).to(device) # Convert to integer - - feats = torch.tensor(feats, dtype=torch.float32).to(device) - - with torch.inference_mode(): - slide_embedding = model.encode_slide_from_patch_features( - feats, coords, patch_size_lvl0 - ) - slide_embedding = ( - slide_embedding.to(torch.float32).detach().squeeze().cpu().numpy() - ) + encoder: Encoder = Titan() + case EncoderName.COBRA: + from stamp.encoding.encoder.cobra import Cobra - slide_name = Path(tile_feats_filename).stem - h5_file.create_dataset(slide_name, data=slide_embedding) + encoder: Encoder = Cobra() - tqdm.write(f"Finished encoding, saved all slide embeddings to {output_file}") + encoder.encode_slides(output_dir, feat_dir, device) diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 92aed9e3..5529496f 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -17,7 +17,6 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): encoder: EncoderName output_dir: Path feat_dir: Path - slide_table: Path device: DeviceLikeType diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index c7fd19fa..ac454b88 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -4,12 +4,10 @@ import h5py import pandas as pd import torch -from torch._prims_common import DeviceLikeType from tqdm import tqdm from stamp.cache import get_processing_code_hash -from stamp.slide_encoding.config import EncoderName -from stamp.slide_encoding.encoder import Encoder +from stamp.encoding.encoder import Encoder # TODO: Check which are the necessary imports and add them to cobra package @@ -20,22 +18,64 @@ "cobra dependencies not installed." " Please update your venv using `uv sync --extra cobra`" ) from e -from stamp.slide_encoding.encoder import Encoder - class Cobra(Encoder): - def __init__(self): - super().__init__( - model=get_cobra(download_weights=True), identifier="katherlab-cobra" - ) + def __init__(self) -> None: + model = get_cobra(download_weights=True) + super().__init__(model=model, identifier="katherlab-cobra") + + def _get_tile_embs(self, h5_path, device): + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] + + feats = torch.tensor(feats).to(device) + return feats.unsqueeze(0) - def encode_slides(self, slide_feats, *args, **kwargs): + def encode_slides(self, output_dir, feat_dir, device) -> None: """Encode slides from patch features.""" - raise NotImplementedError("This method needs to be implemented.") + slide_dict = {} + self.model.to(device).eval() + dtype: torch.dtype = torch.float32 + # TODO: dtype depends con CUDA capabilities. Check end of + # extract_feat_patients.py on how to handle this + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name = Path(tile_feats_filename).stem + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + tqdm.write( + f"File {h5_path} does not exist or is not an h5 file, skipping" + ) + continue + + tile_embs = self._get_tile_embs(h5_path, device) + + with torch.inference_mode(): + assert tile_embs.ndim == 3, f"Expected 3D tensor, got {tile_embs.ndim}" + slide_feats = self.model(tile_embs.to(dtype)) + slide_dict[slide_name] = { + "feats": slide_feats.to(torch.float32) + .detach() + .squeeze() + .cpu() + .numpy(), + "encoder": f"{self.identifier}", + "precision": dtype, + } + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["encoder"] = data["encoder"] + tqdm.write(f"Finished extraction, saved to {output_file}") def encode_patients(self, output_dir, feat_dir, slide_table_path, device) -> None: """Encode patients from slide features.""" - dtype = torch.float32 + dtype: torch.dtype = torch.float32 self.model.to(device).eval() # TODO: dtype depends con CUDA capabilities. Check end of # extract_feat_patients.py on how to handle this @@ -45,7 +85,7 @@ def encode_patients(self, output_dir, feat_dir, slide_table_path, device) -> Non slide_dict = {} output_name = ( - f"{self.identifier}-{get_processing_code_hash(Path(__file__))[:8]}" + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) output_file = os.path.join(output_dir, output_name) diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index b5f485b9..8423ca99 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -1,13 +1,80 @@ +import math +import os +from pathlib import Path + +import h5py +import torch +from tqdm import tqdm from transformers import AutoModel -from stamp.slide_encoding.encoder import Encoder +import stamp +from stamp.cache import get_processing_code_hash +from stamp.encoding.encoder import Encoder + +class Titan(Encoder): + def __init__(self) -> None: + model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) + super().__init__(model=model, identifier="mahmood-titan") -def titan() -> Encoder: - model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) # TODO: Check precision - return Encoder( - model=model, - identifier="mahmood-titan", - ) + def encode_slides(self, output_dir, feat_dir, device): + # Ensure model weights and biases are on the same device as the input + self.model.to(device).eval() + + output_name = ( + f"{self.identifier}-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + with h5py.File(output_file, "w") as h5_file: + h5_file.attrs["encoder"] = self.identifier + h5_file.attrs["stamp_version"] = stamp.__version__ + + for tile_feats_filename in tqdm( + os.listdir(feat_dir), desc="Processing slides" + ): + h5_path = os.path.join(feat_dir, tile_feats_filename) + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + tqdm.write( + f"File {h5_path} does not exist or is not an h5 file, skipping" + ) + continue + + with h5py.File(h5_path, "r") as f: + feats = f["feats"][:] # type: ignore + coords = f["coords"][:] # type: ignore + + # Convert coordinates from microns to pixels + mpp = 1.14 # microns per pixel + patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs + coords = coords / mpp # Convert to pixels + coords = torch.tensor(coords, dtype=torch.float32).to(device) + coords = coords.to(torch.int64).to(device) # Convert to integer + + feats = torch.tensor(feats, dtype=torch.float32).to(device) + + with torch.inference_mode(): + slide_embedding = self.model.encode_slide_from_patch_features( + feats, coords, patch_size_lvl0 + ) + slide_embedding = ( + slide_embedding.to(torch.float32) + .detach() + .squeeze() + .cpu() + .numpy() + ) + + slide_name = Path(tile_feats_filename).stem + h5_file.create_dataset(slide_name, data=slide_embedding) + + tqdm.write(f"Finished encoding, saved all slide embeddings to {output_file}") + + def encode_patients(self, output_dir, feat_dir, slide_table_path, device): + return NotImplementedError("Not implemented yet :P but soon!") From 6d85ec0de5aa04bbe733a6f7e1a05613ae7332b9 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 10 Apr 2025 11:11:55 +0100 Subject: [PATCH 16/99] implement mpp calculation from CoordsInfo --- src/stamp/encoding/encoder/titan.py | 16 +++++++++------- 1 file changed, 9 insertions(+), 7 deletions(-) diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 8423ca99..3848c46d 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -10,6 +10,7 @@ import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder +from stamp.modeling.data import CoordsInfo, get_coords class Titan(Encoder): @@ -48,20 +49,21 @@ def encode_slides(self, output_dir, feat_dir, device): with h5py.File(h5_path, "r") as f: feats = f["feats"][:] # type: ignore - coords = f["coords"][:] # type: ignore + coords: CoordsInfo = get_coords(f) # type: ignore # Convert coordinates from microns to pixels - mpp = 1.14 # microns per pixel - patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs - coords = coords / mpp # Convert to pixels - coords = torch.tensor(coords, dtype=torch.float32).to(device) - coords = coords.to(torch.int64).to(device) # Convert to integer + patch_size_lvl0 = math.floor( + 256 / coords.mpp + ) # Inferred from TITAN docs + coords_px = coords.coords_um / coords.mpp # Convert to pixels + coords_px = torch.tensor(coords_px, dtype=torch.float32).to(device) + coords_px = coords_px.to(torch.int64).to(device) # Convert to integer feats = torch.tensor(feats, dtype=torch.float32).to(device) with torch.inference_mode(): slide_embedding = self.model.encode_slide_from_patch_features( - feats, coords, patch_size_lvl0 + feats, coords_px, patch_size_lvl0 ) slide_embedding = ( slide_embedding.to(torch.float32) From 429ca58e2609d652df4df0b881d3f3b866a0d7d3 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 10 Apr 2025 11:12:15 +0100 Subject: [PATCH 17/99] format cobra.py --- src/stamp/encoding/encoder/cobra.py | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index ac454b88..6e02ce9b 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -19,6 +19,7 @@ " Please update your venv using `uv sync --extra cobra`" ) from e + class Cobra(Encoder): def __init__(self) -> None: model = get_cobra(download_weights=True) @@ -134,4 +135,4 @@ def encode_patients(self, output_dir, feat_dir, slide_table_path, device) -> Non for patient_id, data in slide_dict.items(): f.create_dataset(f"{patient_id}", data=data["feats"]) f.attrs["encoder"] = data["encoder"] - tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file + tqdm.write(f"Finished encoding, saved to {output_file}") From b2b12a4da2ef5c4a761f9d855f5567a547462244 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 10 Apr 2025 15:57:06 +0100 Subject: [PATCH 18/99] make chief encoder a class --- src/stamp/encoding/encoder/chief.py | 166 ++++++++++++++-------------- 1 file changed, 82 insertions(+), 84 deletions(-) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 16053a4d..38f983e8 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -9,26 +9,91 @@ """authors: https://github.com/hms-dbmi/CHIEF""" -def chief() -> Encoder: - model = CHIEF(size_arg="small", dropout=True, n_classes=2) - - model_path = STAMP_CACHE_DIR / "CHIEF_pretraining.pth" - if not model_path.is_file(): - model_path.parent.mkdir(parents=True, exist_ok=True) - gdown.download( - "https://drive.google.com/u/0/uc?id=10bJq_ayX97_1w95omN8_mESrYAGIBAPb&export=download", - str(model_path), +class CHIEFModel(nn.Module): + def __init__( + self, + gate=True, + size_arg="large", + dropout=True, + n_classes=2, + instance_loss_fn=nn.CrossEntropyLoss(), + **kwargs, + ): + super(CHIEFModel, self).__init__() + self.size_dict = { + "xs": [384, 256, 256], + "small": [768, 512, 256], + "big": [1024, 512, 384], + "large": [2048, 1024, 512], + } + size = self.size_dict[size_arg] + print(size) + fc = [nn.Linear(size[0], size[1]), nn.ReLU()] + if dropout: + fc.append(nn.Dropout(0.25)) + if gate: + attention_net = Attn_Net_Gated( + L=size[1], D=size[2], dropout=dropout, n_classes=1 + ) + else: + attention_net = Attn_Net(L=size[1], D=size[2], dropout=dropout, n_classes=1) + fc.append(attention_net) + self.attention_net = nn.Sequential(*fc) + self.classifiers = nn.Linear(size[1], n_classes) + instance_classifiers = [nn.Linear(size[1], 2) for i in range(n_classes)] + self.instance_classifiers = nn.ModuleList(instance_classifiers) + self.instance_loss_fn = instance_loss_fn + self.n_classes = n_classes + initialize_weights(self) + + self.att_head = Att_Head(size[1], size[2]) + self.text_to_vision = nn.Sequential( + nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) ) - chief = torch.load(model_path) - if "organ_embedding" in chief: - del chief["organ_embedding"] - model.load_state_dict(chief, strict=True) + def relocate(self): + device = torch.device("cuda" if torch.cuda.is_available() else "cpu") + self.attention_net = self.attention_net.to(device) + self.classifiers = self.classifiers.to(device) + self.instance_classifiers = self.instance_classifiers.to(device) + + def forward(self, h): + h_ori = h + A, h = self.attention_net(h) + A = torch.transpose(A, 1, 0) + A_raw = A + A = F.softmax(A, dim=1) + print("A", A.shape) + WSI_feature = torch.mm(A, h) + slide_embeddings = torch.mm(A, h_ori) + + result = { + "attention_raw": A_raw, + "WSI_feature": slide_embeddings, + "WSI_feature_transformed": WSI_feature, + "tile_features_transformed": h, + } + return result + + +class CHIEF(Encoder): + def __init__(self) -> None: + model = CHIEFModel(size_arg="small", dropout=True, n_classes=2) + model_path = STAMP_CACHE_DIR / "CHIEF_pretraining.pth" + if not model_path.is_file(): + model_path.parent.mkdir(parents=True, exist_ok=True) + gdown.download( + "https://drive.google.com/u/0/uc?id=10bJq_ayX97_1w95omN8_mESrYAGIBAPb&export=download", + str(model_path), + ) + + # TODO: check digest - return Encoder( - model=model, - identifier="chief", - ) + chief = torch.load(model_path) + if "organ_embedding" in chief: + del chief["organ_embedding"] + model.load_state_dict(chief, strict=True) + super().__init__(model=model, identifier="chief") def initialize_weights(module): @@ -96,70 +161,3 @@ def forward(self, x): A = a.mul(b) A = self.attention_c(A) return A, x - - -class CHIEF(nn.Module): - def __init__( - self, - gate=True, - size_arg="large", - dropout=True, - n_classes=2, - instance_loss_fn=nn.CrossEntropyLoss(), - **kwargs, - ): - super(CHIEF, self).__init__() - self.size_dict = { - "xs": [384, 256, 256], - "small": [768, 512, 256], - "big": [1024, 512, 384], - "large": [2048, 1024, 512], - } - size = self.size_dict[size_arg] - print(size) - fc = [nn.Linear(size[0], size[1]), nn.ReLU()] - if dropout: - fc.append(nn.Dropout(0.25)) - if gate: - attention_net = Attn_Net_Gated( - L=size[1], D=size[2], dropout=dropout, n_classes=1 - ) - else: - attention_net = Attn_Net(L=size[1], D=size[2], dropout=dropout, n_classes=1) - fc.append(attention_net) - self.attention_net = nn.Sequential(*fc) - self.classifiers = nn.Linear(size[1], n_classes) - instance_classifiers = [nn.Linear(size[1], 2) for i in range(n_classes)] - self.instance_classifiers = nn.ModuleList(instance_classifiers) - self.instance_loss_fn = instance_loss_fn - self.n_classes = n_classes - initialize_weights(self) - - self.att_head = Att_Head(size[1], size[2]) - self.text_to_vision = nn.Sequential( - nn.Linear(768, size[1]), nn.ReLU(), nn.Dropout(p=0.25) - ) - - def relocate(self): - device = torch.device("cuda" if torch.cuda.is_available() else "cpu") - self.attention_net = self.attention_net.to(device) - self.classifiers = self.classifiers.to(device) - self.instance_classifiers = self.instance_classifiers.to(device) - - def forward(self, h): - h_ori = h - A, h = self.attention_net(h) - A = torch.transpose(A, 1, 0) - A_raw = A - A = F.softmax(A, dim=1) - print("A", A.shape) - WSI_feature = torch.mm(A, h) - slide_embeddings = torch.mm(A, h_ori) - - result = { - "attention_raw": A_raw, - "WSI_feature": slide_embeddings, - "WSI_feature_transformed": WSI_feature, - "tile_features_transformed": h, - } - return result From 1746319ab738a75608adb3e812f0b92ec5acf15f Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 10 Apr 2025 15:57:32 +0100 Subject: [PATCH 19/99] add eagle encoder class --- src/stamp/encoding/encoder/eagle.py | 95 +++++++++++++++++++++++++++++ 1 file changed, 95 insertions(+) create mode 100644 src/stamp/encoding/encoder/eagle.py diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py new file mode 100644 index 00000000..3afe2e59 --- /dev/null +++ b/src/stamp/encoding/encoder/eagle.py @@ -0,0 +1,95 @@ +import os +from pathlib import Path + +import h5py +import numpy as np +import pandas as pd +import torch +from tqdm import tqdm + +from stamp.cache import get_processing_code_hash +from stamp.encoding.encoder import Encoder +from stamp.encoding.encoder.chief import CHIEF + +"""From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" + + +class Eagle(Encoder): + def __init__(self) -> None: + # TODO: load chief model + pass + + def encode_patients( + self, output_dir, feat_dir, agg_feat_dir, slide_table_path, device + ) -> None: + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + slide_dict = {} + + output_name = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for patient_id, group in tqdm(patient_groups, leave=False): + feats_list = [] + agg_feats_list = [] + coords_list = [] + slide_ids_list = [] + + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + slide_id = os.path.splitext(slide_filename)[0] + h5_ctp = os.path.join(feat_dir, slide_filename) + h5_vir2 = os.path.join(agg_feat_dir, slide_filename) + + # Read ctranspath features + if not os.path.exists(h5_ctp): + print(f"File {h5_ctp} does not exist, skipping") + continue + try: + with h5py.File(h5_ctp, "r") as f: + feats = torch.tensor(f["feats"][:], dtype=torch.float32) + coords = torch.tensor(f["coords"][:], dtype=torch.int) + except Exception as e: + print(f"Error reading {h5_ctp}: {e}") + continue + + # Read virchow2 features used for aggregation + # Coords are not used as they are the same as ctranspath + # TODO: Ask if this is correct + if not os.path.exists(h5_vir2): + print(f"File {h5_vir2} does not exist, skipping") + continue + try: + with h5py.File(h5_vir2, "r") as f: + agg_feats = torch.tensor(f["feats"][:], dtype=torch.float32) + except Exception as e: + print(f"Error reading {h5_vir2}: {e}") + continue + # TODO: Check that the features have ctranspath id + # TODO: Check that agg features have virchow2 id + # TODO: Check that the features have the same number of rows + + feats_list.append(feats) + agg_feats_list.append(agg_feats) + coords_list.append(coords) + slide_ids_list.extend([slide_id] * feats.shape[0]) + if not feats_list: + print(f"No ctranspath features for patient {patient_id}") + return + all_feats = torch.cat(feats_list, dim=0).to(device) + all_coords = torch.cat(coords_list, dim=0) + slide_ids_arr = np.array(slide_ids_list) + with torch.no_grad(): + result = CHIEF.model(all_feats) + attention_raw = result["attention_raw"].squeeze(0).cpu() + k = min(25, attention_raw.shape[0]) + topk_values, topk_indices = torch.topk(attention_raw, k) + top_indices = topk_indices.numpy() + top_slide_ids = slide_ids_arr[top_indices] + # Do the indices from ctranspath work for virchow2? I suppose so From f34f8ffbd8ede7d4c0ac2b8c5007085ee8628d32 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 11 Apr 2025 11:03:49 +0100 Subject: [PATCH 20/99] add eagle patient encoding --- src/stamp/__main__.py | 1 + src/stamp/config.yaml | 10 ++++- src/stamp/encoding/__init__.py | 12 +++++- src/stamp/encoding/config.py | 2 + src/stamp/encoding/encoder/__init__.py | 6 ++- src/stamp/encoding/encoder/chief.py | 8 ++++ src/stamp/encoding/encoder/cobra.py | 4 +- src/stamp/encoding/encoder/eagle.py | 56 +++++++++++++++++++------- 8 files changed, 78 insertions(+), 21 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 6a264ae5..1008a668 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -110,6 +110,7 @@ def _run_cli(args: argparse.Namespace) -> None: feat_dir=config.patient_encoding.feat_dir, slide_table_path=config.patient_encoding.slide_table, device=config.patient_encoding.device, + agg_feat_dir=config.patient_encoding.agg_feat_dir, ) case "train": diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index d9d18443..1965d43b 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -49,8 +49,9 @@ slide_encoding: patient_encoding: - # Encoder to use for patient encoding. Possible options are "katherlab-cobra". - encoder: "chief" + # Encoder to use for patient encoding. Possible options are "katherlab-cobra", + # "katherlab-eagle". + encoder: "katherlab-eagle" # Directory to save the output files. output_dir: "/path/to/save/files/to" @@ -66,6 +67,11 @@ patient_encoding: # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) device: "cuda" + # Optional settings: + # Directory where the aggregated features are stored. Needed for + # some encoders such as eagle (it requires virchow2 features). + #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + crossval: output_dir: "/path/to/save/files/to" diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index e492265e..1c344fc4 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -20,6 +20,7 @@ def get_pat_embs( feat_dir: Path, slide_table_path: Path, device: DeviceLikeType, + agg_feat_dir: Path | None = None, ) -> None: """""" match encoder_name: @@ -27,9 +28,16 @@ def get_pat_embs( from stamp.encoding.encoder.cobra import Cobra encoder: Encoder = Cobra() - # TODO: Add other encoders - encoder.encode_patients(output_dir, feat_dir, slide_table_path, device) + case EncoderName.EAGLE: + from stamp.encoding.encoder.eagle import Eagle + + encoder: Encoder = Eagle() + # TODO: Add other encoders and empty case + + encoder.encode_patients( + output_dir, feat_dir, slide_table_path, device, agg_feat_dir=agg_feat_dir + ) def get_slide_embs( diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 5529496f..27623a3a 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -7,6 +7,7 @@ class EncoderName(StrEnum): COBRA = "katherlab-cobra" + EAGLE = "katherlab-eagle" CHIEF = "chief" TITAN = "mahmood-titan" @@ -28,3 +29,4 @@ class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): feat_dir: Path slide_table: Path device: DeviceLikeType + agg_feat_dir: Path | None = None diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index e8849991..2dcdbdfd 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -19,11 +19,13 @@ class Encoder(ABC, Generic[EncoderModel]): """ @abstractmethod - def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs): + def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs) -> None: """Abstract method to encode slide from patch features.""" pass @abstractmethod - def encode_patients(self, output_dir, feat_dir, slide_table_path, device): + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: """Abstract method to encode patient from slide features.""" pass diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 38f983e8..47f43e0e 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -95,6 +95,14 @@ def __init__(self) -> None: model.load_state_dict(chief, strict=True) super().__init__(model=model, identifier="chief") + def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs) -> None: + pass + + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: + pass + def initialize_weights(module): for m in module.modules(): diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 6e02ce9b..e314c96f 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -74,7 +74,9 @@ def encode_slides(self, output_dir, feat_dir, device) -> None: f.attrs["encoder"] = data["encoder"] tqdm.write(f"Finished extraction, saved to {output_file}") - def encode_patients(self, output_dir, feat_dir, slide_table_path, device) -> None: + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: """Encode patients from slide features.""" dtype: torch.dtype = torch.float32 self.model.to(device).eval() diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 3afe2e59..a7c79505 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -16,15 +16,21 @@ class Eagle(Encoder): def __init__(self) -> None: - # TODO: load chief model - pass + model = CHIEF().model + super().__init__(model=model, identifier="katherlab-eagle") def encode_patients( - self, output_dir, feat_dir, agg_feat_dir, slide_table_path, device + self, output_dir, feat_dir, slide_table_path, device, **kwargs ) -> None: + """Encode patients from slide features.""" + agg_feat_dir: Path | None = kwargs.get("agg_feat_dir") + if not agg_feat_dir: + raise ValueError("agg_feat_dir is required for Eagle's encode_patients") + slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") slide_dict = {} + self.model.to(device).eval() output_name = ( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" @@ -38,7 +44,6 @@ def encode_patients( for patient_id, group in tqdm(patient_groups, leave=False): feats_list = [] agg_feats_list = [] - coords_list = [] slide_ids_list = [] for _, row in group.iterrows(): @@ -54,14 +59,11 @@ def encode_patients( try: with h5py.File(h5_ctp, "r") as f: feats = torch.tensor(f["feats"][:], dtype=torch.float32) - coords = torch.tensor(f["coords"][:], dtype=torch.int) except Exception as e: print(f"Error reading {h5_ctp}: {e}") continue # Read virchow2 features used for aggregation - # Coords are not used as they are the same as ctranspath - # TODO: Ask if this is correct if not os.path.exists(h5_vir2): print(f"File {h5_vir2} does not exist, skipping") continue @@ -77,19 +79,45 @@ def encode_patients( feats_list.append(feats) agg_feats_list.append(agg_feats) - coords_list.append(coords) slide_ids_list.extend([slide_id] * feats.shape[0]) if not feats_list: print(f"No ctranspath features for patient {patient_id}") return all_feats = torch.cat(feats_list, dim=0).to(device) - all_coords = torch.cat(coords_list, dim=0) - slide_ids_arr = np.array(slide_ids_list) + all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) with torch.no_grad(): - result = CHIEF.model(all_feats) + result = self.model(all_feats) attention_raw = result["attention_raw"].squeeze(0).cpu() k = min(25, attention_raw.shape[0]) - topk_values, topk_indices = torch.topk(attention_raw, k) + _, topk_indices = torch.topk(attention_raw, k) top_indices = topk_indices.numpy() - top_slide_ids = slide_ids_arr[top_indices] - # Do the indices from ctranspath work for virchow2? I suppose so + + # Get top virchow2 features using the top indices from + # ctranspath features + top_agg_feats = [] + for i in top_indices: + top_agg_feats.append(all_agg_feats[i]) + top_agg_feats = torch.stack(top_agg_feats).to(device) + eagle_embedding = torch.mean(top_agg_feats, dim=0) + slide_dict[patient_id] = { + "feats": eagle_embedding.to(torch.float32) + .detach() + .squeeze() + .cpu() + .numpy(), + "encoder": self.identifier, + "precision": torch.float32, + } + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for patient_id, data in slide_dict.items(): + f.create_dataset(f"{patient_id}", data=data["feats"]) + f.attrs["encoder"] = data["encoder"] + tqdm.write(f"Finished encoding, saved to {output_file}") + + def encode_slides( + self, output_dir, feat_dir, patch_size_lvl0, *args, **kwargs + ) -> None: + """Encode slide from patch features.""" + pass From 2734c465bbf8d16dfeeecfc7ae2233da56e52a04 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 11 Apr 2025 11:30:06 +0100 Subject: [PATCH 21/99] add shape validation --- src/stamp/encoding/encoder/eagle.py | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index a7c79505..7be70409 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -75,7 +75,6 @@ def encode_patients( continue # TODO: Check that the features have ctranspath id # TODO: Check that agg features have virchow2 id - # TODO: Check that the features have the same number of rows feats_list.append(feats) agg_feats_list.append(agg_feats) @@ -85,6 +84,11 @@ def encode_patients( return all_feats = torch.cat(feats_list, dim=0).to(device) all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) + breakpoint() + if all_feats.shape[0] != all_agg_feats.shape[0]: + raise ValueError( + f"Number of ctranspath features and virchow2 features do not match: {all_feats.shape[0]} != {all_agg_feats.shape[0]}" + ) with torch.no_grad(): result = self.model(all_feats) attention_raw = result["attention_raw"].squeeze(0).cpu() From 71ff930561cf6e4179cb8254c8aec7994e283d39 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 10:58:34 +0100 Subject: [PATCH 22/99] add features validations --- src/stamp/encoding/encoder/eagle.py | 60 ++++++++++++++++++++++++----- 1 file changed, 50 insertions(+), 10 deletions(-) diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 7be70409..f1e2a95e 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -5,11 +5,14 @@ import numpy as np import pandas as pd import torch +from torch import Tensor +from torch._prims_common import DeviceLikeType from tqdm import tqdm from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF +from stamp.modeling.data import CoordsInfo, get_coords """From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" @@ -20,12 +23,20 @@ def __init__(self) -> None: super().__init__(model=model, identifier="katherlab-eagle") def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs + self, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + device: DeviceLikeType, + **kwargs, ) -> None: """Encode patients from slide features.""" agg_feat_dir: Path | None = kwargs.get("agg_feat_dir") if not agg_feat_dir: - raise ValueError("agg_feat_dir is required for Eagle's encode_patients") + raise ValueError( + "agg_feat_dir that contains virchow2 features" + " is required for Eagle's encode_patients" + ) slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") @@ -44,7 +55,6 @@ def encode_patients( for patient_id, group in tqdm(patient_groups, leave=False): feats_list = [] agg_feats_list = [] - slide_ids_list = [] for _, row in group.iterrows(): slide_filename = row["FILENAME"] @@ -58,7 +68,15 @@ def encode_patients( continue try: with h5py.File(h5_ctp, "r") as f: - feats = torch.tensor(f["feats"][:], dtype=torch.float32) + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + # Check that extractor name contains ctranspath in it + if "ctranspath" not in extractor: + raise ValueError( + f"Features must be extracted with ctranspath or chief-ctranspath. " + f"Features located in {h5_ctp} are extracted with {extractor}" + ) except Exception as e: print(f"Error reading {h5_ctp}: {e}") continue @@ -69,29 +87,50 @@ def encode_patients( continue try: with h5py.File(h5_vir2, "r") as f: - agg_feats = torch.tensor(f["feats"][:], dtype=torch.float32) + agg_feats: Tensor = torch.tensor( + f["feats"][:], dtype=torch.float32 + ) # type: ignore + agg_coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + # Check that extractor name contains virchow2 in it + if "virchow2" not in extractor: + raise ValueError( + f"Aggregated features must be extracted with virchow2 " + f"Features located in {h5_vir2} are extracted with {extractor}" + ) except Exception as e: print(f"Error reading {h5_vir2}: {e}") continue - # TODO: Check that the features have ctranspath id - # TODO: Check that agg features have virchow2 id + + # Check that both feature lists are paired by coords + if not torch.allclose( + coords.coords_um, agg_coords.coords_um, atol=1e-5, rtol=0 + ): + raise ValueError( + f"Coordinates mismatch between ctranspath and virchow2" + f" features for slide {slide_id}. Ensure that both are aligned." + ) feats_list.append(feats) agg_feats_list.append(agg_feats) - slide_ids_list.extend([slide_id] * feats.shape[0]) + if not feats_list: print(f"No ctranspath features for patient {patient_id}") return all_feats = torch.cat(feats_list, dim=0).to(device) all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) - breakpoint() + if all_feats.shape[0] != all_agg_feats.shape[0]: raise ValueError( - f"Number of ctranspath features and virchow2 features do not match: {all_feats.shape[0]} != {all_agg_feats.shape[0]}" + f"Number of ctranspath features and virchow2 features do not match:" + f" {all_feats.shape[0]} != {all_agg_feats.shape[0]}" ) + with torch.no_grad(): result = self.model(all_feats) attention_raw = result["attention_raw"].squeeze(0).cpu() + + # Get the 25 most relevant features k = min(25, attention_raw.shape[0]) _, topk_indices = torch.topk(attention_raw, k) top_indices = topk_indices.numpy() @@ -102,6 +141,7 @@ def encode_patients( for i in top_indices: top_agg_feats.append(all_agg_feats[i]) top_agg_feats = torch.stack(top_agg_feats).to(device) + # Create eagle embedding by averaging the top virchow2 features eagle_embedding = torch.mean(top_agg_feats, dim=0) slide_dict[patient_id] = { "feats": eagle_embedding.to(torch.float32) From e11d088fbb54e6dfdc12a2c56c82399c7c63bf27 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 13:58:34 +0100 Subject: [PATCH 23/99] add eagle slide encoding --- src/stamp/encoding/encoder/eagle.py | 219 ++++++++++++++++++---------- 1 file changed, 140 insertions(+), 79 deletions(-) diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index f1e2a95e..fc077fac 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -22,6 +22,81 @@ def __init__(self) -> None: model = CHIEF().model super().__init__(model=model, identifier="katherlab-eagle") + def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + tqdm.write(f"File {h5_path} does not exist or is not an h5 file, skipping") + with h5py.File(h5_path, "r") as f: + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + return feats, coords, extractor + + def validate_and_read_features( + self, h5_ctp: str, h5_vir2: str, slide_name: str + ) -> tuple[Tensor, Tensor]: + try: + feats, coords, extractor = self._read_h5(h5_ctp) + except Exception as e: + raise RuntimeError(f"Error reading {h5_ctp}: {e}") + + if "ctranspath" not in extractor: + raise ValueError( + f"Features must be extracted with ctranspath or chief-ctranspath. " + f"Features located in {h5_ctp} are extracted with {extractor}" + ) + + try: + agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) + except Exception as e: + raise RuntimeError(f"Error reading {h5_vir2}: {e}") + + if "virchow2" not in extractor: + raise ValueError( + f"Aggregated features must be extracted with virchow2 " + f"Features located in {h5_vir2} are extracted with {extractor}" + ) + + if feats.shape[0] != agg_feats.shape[0]: + raise ValueError( + f"Number of ctranspath features and virchow2 features do not match:" + f" {feats.shape[0]} != {agg_feats.shape[0]}" + ) + + if not torch.allclose( + coords.coords_um, agg_coords.coords_um, atol=1e-5, rtol=0 + ): + raise ValueError( + f"Coordinates mismatch between ctranspath and virchow2" + f" features for slide {slide_name}. Ensure that both are aligned." + ) + + return feats, agg_feats + + def create_eagle_embedding( + self, + feats: Tensor, + agg_feats: Tensor, + device: DeviceLikeType, + ) -> np.ndarray: + """Process features, compute attention, and create an embedding.""" + with torch.no_grad(): + result = self.model(feats.to(device)) + attention_raw = result["attention_raw"].squeeze(0).cpu() + + # Get the 25 most relevant features + k = min(25, attention_raw.shape[0]) + _, topk_indices = torch.topk(attention_raw, k) + top_indices = topk_indices.numpy() + + # Get top virchow2 features using the top indices from ctranspath features + top_agg_feats = [agg_feats[i] for i in top_indices] + top_agg_feats = torch.stack(top_agg_feats).to(device) + + # Create eagle embedding by averaging the top virchow2 features + eagle_embedding = torch.mean(top_agg_feats, dim=0) + + return eagle_embedding.to(torch.float32).detach().squeeze().cpu().numpy() + def encode_patients( self, output_dir: Path, @@ -46,7 +121,7 @@ def encode_patients( output_name = ( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) - output_file = os.path.join(output_dir, output_name) + output_file: str = os.path.join(output_dir, output_name) if os.path.exists(output_file): tqdm.write(f"Output file {output_file} already exists, skipping") @@ -58,97 +133,33 @@ def encode_patients( for _, row in group.iterrows(): slide_filename = row["FILENAME"] - slide_id = os.path.splitext(slide_filename)[0] + slide_name = Path(slide_filename).stem h5_ctp = os.path.join(feat_dir, slide_filename) h5_vir2 = os.path.join(agg_feat_dir, slide_filename) - # Read ctranspath features - if not os.path.exists(h5_ctp): - print(f"File {h5_ctp} does not exist, skipping") - continue + # Validate and read features try: - with h5py.File(h5_ctp, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore - coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") - # Check that extractor name contains ctranspath in it - if "ctranspath" not in extractor: - raise ValueError( - f"Features must be extracted with ctranspath or chief-ctranspath. " - f"Features located in {h5_ctp} are extracted with {extractor}" - ) - except Exception as e: - print(f"Error reading {h5_ctp}: {e}") - continue - - # Read virchow2 features used for aggregation - if not os.path.exists(h5_vir2): - print(f"File {h5_vir2} does not exist, skipping") - continue - try: - with h5py.File(h5_vir2, "r") as f: - agg_feats: Tensor = torch.tensor( - f["feats"][:], dtype=torch.float32 - ) # type: ignore - agg_coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") - # Check that extractor name contains virchow2 in it - if "virchow2" not in extractor: - raise ValueError( - f"Aggregated features must be extracted with virchow2 " - f"Features located in {h5_vir2} are extracted with {extractor}" - ) + feats, agg_feats = self.validate_and_read_features( + h5_ctp, h5_vir2, slide_name + ) except Exception as e: - print(f"Error reading {h5_vir2}: {e}") + tqdm.write(str(e)) continue - # Check that both feature lists are paired by coords - if not torch.allclose( - coords.coords_um, agg_coords.coords_um, atol=1e-5, rtol=0 - ): - raise ValueError( - f"Coordinates mismatch between ctranspath and virchow2" - f" features for slide {slide_id}. Ensure that both are aligned." - ) - feats_list.append(feats) agg_feats_list.append(agg_feats) if not feats_list: - print(f"No ctranspath features for patient {patient_id}") - return + tqdm.write(f"No ctranspath features for patient {patient_id}") + continue all_feats = torch.cat(feats_list, dim=0).to(device) all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) - if all_feats.shape[0] != all_agg_feats.shape[0]: - raise ValueError( - f"Number of ctranspath features and virchow2 features do not match:" - f" {all_feats.shape[0]} != {all_agg_feats.shape[0]}" - ) - - with torch.no_grad(): - result = self.model(all_feats) - attention_raw = result["attention_raw"].squeeze(0).cpu() - - # Get the 25 most relevant features - k = min(25, attention_raw.shape[0]) - _, topk_indices = torch.topk(attention_raw, k) - top_indices = topk_indices.numpy() - - # Get top virchow2 features using the top indices from - # ctranspath features - top_agg_feats = [] - for i in top_indices: - top_agg_feats.append(all_agg_feats[i]) - top_agg_feats = torch.stack(top_agg_feats).to(device) - # Create eagle embedding by averaging the top virchow2 features - eagle_embedding = torch.mean(top_agg_feats, dim=0) + eagle_embedding = self.create_eagle_embedding( + all_feats, all_agg_feats, device + ) slide_dict[patient_id] = { - "feats": eagle_embedding.to(torch.float32) - .detach() - .squeeze() - .cpu() - .numpy(), + "feats": eagle_embedding, "encoder": self.identifier, "precision": torch.float32, } @@ -161,7 +172,57 @@ def encode_patients( tqdm.write(f"Finished encoding, saved to {output_file}") def encode_slides( - self, output_dir, feat_dir, patch_size_lvl0, *args, **kwargs + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, ) -> None: """Encode slide from patch features.""" - pass + agg_feat_dir: Path | None = kwargs.get("agg_feat_dir") + if not agg_feat_dir: + raise ValueError( + "agg_feat_dir that contains virchow2 features" + " is required for Eagle's encode_patients" + ) + + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + slide_dict = {} + self.model.to(device).eval() + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_ctp = os.path.join(feat_dir, tile_feats_filename) + h5_vir2 = os.path.join(agg_feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, agg_feats = self.validate_and_read_features( + h5_ctp, h5_vir2, slide_name + ) + except Exception as e: + tqdm.write(str(e)) + continue + + eagle_embedding = self.create_eagle_embedding(feats, agg_feats, device) + slide_dict[slide_name] = { + "feats": eagle_embedding, + "encoder": self.identifier, + "precision": torch.float32, + } + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["encoder"] = data["encoder"] + tqdm.write(f"Finished extraction, saved to {output_file}") + + +# TODO: Remove annoying print of A size From 151fb4a0e82adf5a920042fccc64f6e27a583759 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 13:59:54 +0100 Subject: [PATCH 24/99] adapt abstract functions implementations --- src/stamp/encoding/encoder/cobra.py | 4 ++-- src/stamp/encoding/encoder/titan.py | 2 +- 2 files changed, 3 insertions(+), 3 deletions(-) diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index e314c96f..091a5627 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -27,12 +27,12 @@ def __init__(self) -> None: def _get_tile_embs(self, h5_path, device): with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] + feats = f["feats"][:] # type: ignore feats = torch.tensor(feats).to(device) return feats.unsqueeze(0) - def encode_slides(self, output_dir, feat_dir, device) -> None: + def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: """Encode slides from patch features.""" slide_dict = {} self.model.to(device).eval() diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 3848c46d..c7f8ee22 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -20,7 +20,7 @@ def __init__(self) -> None: # TODO: Check precision - def encode_slides(self, output_dir, feat_dir, device): + def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: # Ensure model weights and biases are on the same device as the input self.model.to(device).eval() From d126bb90c898b9ea144ebea07c34f32d400384c0 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 14:32:43 +0100 Subject: [PATCH 25/99] eagle integration to config --- src/stamp/__main__.py | 5 ++- src/stamp/config.yaml | 15 ++++---- src/stamp/encoding/__init__.py | 52 ++++++++++++++++---------- src/stamp/encoding/config.py | 3 +- src/stamp/encoding/encoder/__init__.py | 2 +- 5 files changed, 46 insertions(+), 31 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 1008a668..61c1560a 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -87,10 +87,11 @@ def _run_cli(args: argparse.Namespace) -> None: f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" ) get_slide_embs( - encoder_name=config.slide_encoding.encoder, + encoder=config.slide_encoding.encoder, output_dir=config.slide_encoding.output_dir, feat_dir=config.slide_encoding.feat_dir, device=config.slide_encoding.device, + agg_feat_dir=config.slide_encoding.agg_feat_dir, ) case "encode_patients": @@ -105,7 +106,7 @@ def _run_cli(args: argparse.Namespace) -> None: f"{yaml.dump(config.patient_encoding.model_dump(mode='json'))}" ) get_pat_embs( - encoder_name=config.patient_encoding.encoder, + encoder=config.patient_encoding.encoder, output_dir=config.patient_encoding.output_dir, feat_dir=config.patient_encoding.feat_dir, slide_table_path=config.patient_encoding.slide_table, diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 1965d43b..37e8d06c 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -30,8 +30,9 @@ preprocessing: slide_encoding: - # Encoder to use for slide encoding. Possible options are "katherlab-cobra". - encoder: "chief" + # Encoder to use for slide encoding. Possible options are "katherlab-cobra", + # "katherlab-eagle", "mahmood-titan". + encoder: "katherlab-eagle" # Directory to save the output files. output_dir: "/path/to/save/files/to" @@ -39,14 +40,14 @@ slide_encoding: # Directory where the extracted features are stored. feat_dir: "/path/your/extracted/features/are/stored/in" - # A table (.xlsx or .csv) relating every slide to their feature files. - # The table must contain at least two columns, one titled "SLIDE", - # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. - slide_table: "/path/of/slide.csv" - # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) device: "cuda" + # Optional settings: + # Directory where the aggregated features are stored. Needed for + # some encoders such as eagle (it requires virchow2 features). + #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + patient_encoding: # Encoder to use for patient encoding. Possible options are "katherlab-cobra", diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 1c344fc4..33b499f2 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -1,21 +1,14 @@ -import math -import os from pathlib import Path +from typing import assert_never -import h5py -import pandas as pd -import torch -from torch._prims_common import DeviceLikeType -from tqdm import tqdm +from torch._prims_common import DeviceLikeType # type: ignore -import stamp -from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder def get_pat_embs( - encoder_name: EncoderName, + encoder: EncoderName | Encoder, output_dir: Path, feat_dir: Path, slide_table_path: Path, @@ -23,37 +16,56 @@ def get_pat_embs( agg_feat_dir: Path | None = None, ) -> None: """""" - match encoder_name: + match encoder: case EncoderName.COBRA: from stamp.encoding.encoder.cobra import Cobra - encoder: Encoder = Cobra() + selected_encoder: Encoder = Cobra() case EncoderName.EAGLE: from stamp.encoding.encoder.eagle import Eagle - encoder: Encoder = Eagle() - # TODO: Add other encoders and empty case + selected_encoder: Encoder = Eagle() - encoder.encode_patients( + case Encoder(): + selected_encoder = encoder + + case _ as unreachable: + assert_never(unreachable) + + selected_encoder.encode_patients( output_dir, feat_dir, slide_table_path, device, agg_feat_dir=agg_feat_dir ) def get_slide_embs( - encoder_name: EncoderName, + encoder: EncoderName | Encoder, output_dir: Path, feat_dir: Path, device: DeviceLikeType, + agg_feat_dir: Path | None = None, ) -> None: - match encoder_name: + match encoder: case EncoderName.TITAN: from stamp.encoding.encoder.titan import Titan - encoder: Encoder = Titan() + selected_encoder: Encoder = Titan() case EncoderName.COBRA: from stamp.encoding.encoder.cobra import Cobra - encoder: Encoder = Cobra() + selected_encoder: Encoder = Cobra() + + case EncoderName.EAGLE: + from stamp.encoding.encoder.eagle import Eagle + + selected_encoder: Encoder = Eagle() + + case Encoder(): + selected_encoder = encoder - encoder.encode_slides(output_dir, feat_dir, device) + case _ as unreachable: + assert_never(unreachable) + + selected_encoder.encode_slides( + output_dir, feat_dir, device, agg_feat_dir=agg_feat_dir + ) diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 27623a3a..40cc395a 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -2,7 +2,7 @@ from pathlib import Path from pydantic import BaseModel, ConfigDict -from torch._prims_common import DeviceLikeType +from torch._prims_common import DeviceLikeType # type: ignore class EncoderName(StrEnum): @@ -19,6 +19,7 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): output_dir: Path feat_dir: Path device: DeviceLikeType + agg_feat_dir: Path | None = None class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 2dcdbdfd..d84dd018 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -19,7 +19,7 @@ class Encoder(ABC, Generic[EncoderModel]): """ @abstractmethod - def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs) -> None: + def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: """Abstract method to encode slide from patch features.""" pass From 29dd194c5e5d8eeea1f02411fdefcb334f95b112 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 14:35:53 +0100 Subject: [PATCH 26/99] remove chief prints --- src/stamp/encoding/encoder/chief.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 47f43e0e..918b6ebf 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -27,7 +27,6 @@ def __init__( "large": [2048, 1024, 512], } size = self.size_dict[size_arg] - print(size) fc = [nn.Linear(size[0], size[1]), nn.ReLU()] if dropout: fc.append(nn.Dropout(0.25)) @@ -63,7 +62,6 @@ def forward(self, h): A = torch.transpose(A, 1, 0) A_raw = A A = F.softmax(A, dim=1) - print("A", A.shape) WSI_feature = torch.mm(A, h) slide_embeddings = torch.mm(A, h_ori) From e8718fcfc06b6b0c0ef08e4c05890191d053927d Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 15 Apr 2025 15:16:59 +0100 Subject: [PATCH 27/99] format tqdm logging and file naming --- src/stamp/encoding/encoder/eagle.py | 12 +++++++----- src/stamp/encoding/encoder/titan.py | 2 +- 2 files changed, 8 insertions(+), 6 deletions(-) diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index fc077fac..658d7495 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -24,7 +24,7 @@ def __init__(self) -> None: def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - tqdm.write(f"File {h5_path} does not exist or is not an h5 file, skipping") + raise FileNotFoundError("File does not exist or is not an h5 file") with h5py.File(h5_path, "r") as f: feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore coords: CoordsInfo = get_coords(f) @@ -207,7 +207,7 @@ def encode_slides( h5_ctp, h5_vir2, slide_name ) except Exception as e: - tqdm.write(str(e)) + tqdm.write(s=str(e)) continue eagle_embedding = self.create_eagle_embedding(feats, agg_feats, device) @@ -222,7 +222,9 @@ def encode_slides( for slide_name, data in slide_dict.items(): f.create_dataset(f"{slide_name}", data=data["feats"]) f.attrs["encoder"] = data["encoder"] + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + return tqdm.write(f"Finished extraction, saved to {output_file}") - - -# TODO: Remove annoying print of A size diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index c7f8ee22..bc38c6e7 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -25,7 +25,7 @@ def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: self.model.to(device).eval() output_name = ( - f"{self.identifier}-{get_processing_code_hash(Path(__file__))[:8]}.h5" + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) output_file = os.path.join(output_dir, output_name) From 0ab75b5194be8de2ec6b3f58938724f10b5c015e Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 16 Apr 2025 11:08:31 +0100 Subject: [PATCH 28/99] add metadata and exceptions --- src/stamp/encoding/encoder/cobra.py | 15 +-- src/stamp/encoding/encoder/eagle.py | 51 +++++----- src/stamp/encoding/encoder/titan.py | 144 ++++++++++++++++++---------- src/stamp/preprocessing/__init__.py | 2 +- 4 files changed, 126 insertions(+), 86 deletions(-) diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 091a5627..6d6074b6 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -6,6 +6,7 @@ import torch from tqdm import tqdm +import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder @@ -59,9 +60,7 @@ def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: .detach() .squeeze() .cpu() - .numpy(), - "encoder": f"{self.identifier}", - "precision": dtype, + .numpy() } output_name = ( f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" @@ -71,7 +70,9 @@ def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: with h5py.File(output_file, "w") as f: for slide_name, data in slide_dict.items(): f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["encoder"] = data["encoder"] + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = dtype tqdm.write(f"Finished extraction, saved to {output_file}") def encode_patients( @@ -126,8 +127,6 @@ def encode_patients( .squeeze() .cpu() .numpy(), - "encoder": self.identifier, - "precision": dtype, } else: tqdm.write(f"No features found for patient {patient_id}, skipping") @@ -136,5 +135,7 @@ def encode_patients( with h5py.File(output_file, "w") as f: for patient_id, data in slide_dict.items(): f.create_dataset(f"{patient_id}", data=data["feats"]) - f.attrs["encoder"] = data["encoder"] + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = dtype tqdm.write(f"Finished encoding, saved to {output_file}") diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 658d7495..886e6546 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -6,9 +6,10 @@ import pandas as pd import torch from torch import Tensor -from torch._prims_common import DeviceLikeType +from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm +import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF @@ -31,13 +32,10 @@ def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor - def validate_and_read_features( + def _validate_and_read_features( self, h5_ctp: str, h5_vir2: str, slide_name: str ) -> tuple[Tensor, Tensor]: - try: - feats, coords, extractor = self._read_h5(h5_ctp) - except Exception as e: - raise RuntimeError(f"Error reading {h5_ctp}: {e}") + feats, coords, extractor = self._read_h5(h5_ctp) if "ctranspath" not in extractor: raise ValueError( @@ -45,10 +43,7 @@ def validate_and_read_features( f"Features located in {h5_ctp} are extracted with {extractor}" ) - try: - agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) - except Exception as e: - raise RuntimeError(f"Error reading {h5_vir2}: {e}") + agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) if "virchow2" not in extractor: raise ValueError( @@ -72,7 +67,7 @@ def validate_and_read_features( return feats, agg_feats - def create_eagle_embedding( + def _create_eagle_embedding( self, feats: Tensor, agg_feats: Tensor, @@ -115,7 +110,7 @@ def encode_patients( slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") - slide_dict = {} + patient_dict = {} self.model.to(device).eval() output_name = ( @@ -139,10 +134,10 @@ def encode_patients( # Validate and read features try: - feats, agg_feats = self.validate_and_read_features( + feats, agg_feats = self._validate_and_read_features( h5_ctp, h5_vir2, slide_name ) - except Exception as e: + except FileNotFoundError as e: tqdm.write(str(e)) continue @@ -155,20 +150,20 @@ def encode_patients( all_feats = torch.cat(feats_list, dim=0).to(device) all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) - eagle_embedding = self.create_eagle_embedding( + eagle_embedding = self._create_eagle_embedding( all_feats, all_agg_feats, device ) - slide_dict[patient_id] = { + patient_dict[patient_id] = { "feats": eagle_embedding, - "encoder": self.identifier, - "precision": torch.float32, } os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: - for patient_id, data in slide_dict.items(): + for patient_id, data in patient_dict.items(): f.create_dataset(f"{patient_id}", data=data["feats"]) - f.attrs["encoder"] = data["encoder"] + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = torch.float32 tqdm.write(f"Finished encoding, saved to {output_file}") def encode_slides( @@ -203,28 +198,28 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats, agg_feats = self.validate_and_read_features( + feats, agg_feats = self._validate_and_read_features( h5_ctp, h5_vir2, slide_name ) - except Exception as e: + except FileNotFoundError as e: tqdm.write(s=str(e)) continue - eagle_embedding = self.create_eagle_embedding(feats, agg_feats, device) + eagle_embedding = self._create_eagle_embedding(feats, agg_feats, device) slide_dict[slide_name] = { "feats": eagle_embedding, - "encoder": self.identifier, - "precision": torch.float32, } os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: for slide_name, data in slide_dict.items(): f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["encoder"] = data["encoder"] + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = torch.float32 # Check if the file is empty if len(f) == 0: - tqdm.write("Extraction failed: file empty") + tqdm.write("Encoding failed: file empty") os.remove(output_file) return - tqdm.write(f"Finished extraction, saved to {output_file}") + tqdm.write(f"Finished Encoding, saved to {output_file}") diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index bc38c6e7..1dc23fcd 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -3,7 +3,10 @@ from pathlib import Path import h5py +import pandas as pd import torch +from torch import Tensor +from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from transformers import AutoModel @@ -18,65 +21,106 @@ def __init__(self) -> None: model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) super().__init__(model=model, identifier="mahmood-titan") - # TODO: Check precision + def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + raise FileNotFoundError("File does not exist or is not an h5 file") + with h5py.File(h5_path, "r") as f: + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + return feats, coords, extractor + + def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: + feats, coords, extractor = self._read_h5(h5_path) + if "titan" not in extractor: + raise ValueError( + f"Features must be extracted with titan. " + f"Features located in {h5_path} are extracted with {extractor}" + ) + return feats, coords + + def _encode_slide( + self, feats: Tensor, coords: CoordsInfo, device: DeviceLikeType + ) -> torch.Tensor: + """Helper method to encode a single slide.""" + # Convert coordinates from microns to pixels + patch_size_lvl0 = math.floor(256 / coords.mpp) # Inferred from TITAN docs + coords_px = coords.coords_um / coords.mpp # Convert to pixels + coords_px = torch.tensor(coords_px, dtype=torch.float32).to(device) + coords_px = coords_px.to(torch.int64).to(device) # Convert to integer + + feats = torch.tensor(feats, dtype=torch.float32).to(device) + + with torch.inference_mode(): + slide_embedding = self.model.encode_slide_from_patch_features( + feats, coords_px, patch_size_lvl0 + ) + return slide_embedding.to(torch.float32).detach().squeeze() + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) - def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: - # Ensure model weights and biases are on the same device as the input + slide_dict = {} self.model.to(device).eval() + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, coords = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + slide_embedding = self._encode_slide(feats, coords, device) + slide_dict[slide_name] = { + "feats": slide_embedding, + } + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = torch.float32 + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + return + tqdm.write(f"Finished encoding, saved to {output_file}") + + def encode_patients(self, output_dir, feat_dir, slide_table_path, device, **kwargs): output_name = ( f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + output_file = os.path.join(output_dir, output_name) + patient_dict = {} + self.model.to(device).eval() + if os.path.exists(output_file): tqdm.write(f"Output file {output_file} already exists, skipping") return - with h5py.File(output_file, "w") as h5_file: - h5_file.attrs["encoder"] = self.identifier - h5_file.attrs["stamp_version"] = stamp.__version__ - - for tile_feats_filename in tqdm( - os.listdir(feat_dir), desc="Processing slides" - ): - h5_path = os.path.join(feat_dir, tile_feats_filename) - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - tqdm.write( - f"File {h5_path} does not exist or is not an h5 file, skipping" - ) - continue - - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - coords: CoordsInfo = get_coords(f) # type: ignore - - # Convert coordinates from microns to pixels - patch_size_lvl0 = math.floor( - 256 / coords.mpp - ) # Inferred from TITAN docs - coords_px = coords.coords_um / coords.mpp # Convert to pixels - coords_px = torch.tensor(coords_px, dtype=torch.float32).to(device) - coords_px = coords_px.to(torch.int64).to(device) # Convert to integer - - feats = torch.tensor(feats, dtype=torch.float32).to(device) - - with torch.inference_mode(): - slide_embedding = self.model.encode_slide_from_patch_features( - feats, coords_px, patch_size_lvl0 - ) - slide_embedding = ( - slide_embedding.to(torch.float32) - .detach() - .squeeze() - .cpu() - .numpy() - ) - - slide_name = Path(tile_feats_filename).stem - h5_file.create_dataset(slide_name, data=slide_embedding) - - tqdm.write(f"Finished encoding, saved all slide embeddings to {output_file}") - - def encode_patients(self, output_dir, feat_dir, slide_table_path, device): - return NotImplementedError("Not implemented yet :P but soon!") + # Not implemented yet :P + pass + diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index f47c8cf2..26eb1a04 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -12,7 +12,7 @@ import torch from PIL import Image from torch import Tensor -from torch._prims_common import DeviceLikeType +from torch._prims_common import DeviceLikeType # type: ignore from torch.utils.data import DataLoader, IterableDataset from tqdm import tqdm From af4e1ceabf17647d69040839b0f334cbdeb53c6a Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 16 Apr 2025 13:24:34 +0100 Subject: [PATCH 29/99] fix warnings --- src/stamp/encoding/encoder/cobra.py | 13 ++++++++++--- src/stamp/encoding/encoder/eagle.py | 8 ++++++-- src/stamp/encoding/encoder/titan.py | 15 +++++++-------- 3 files changed, 23 insertions(+), 13 deletions(-) diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 6d6074b6..40364649 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -72,8 +72,12 @@ def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: f.create_dataset(f"{slide_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = dtype - tqdm.write(f"Finished extraction, saved to {output_file}") + f.attrs["precision"] = str(dtype) + if len(f) == 0: + tqdm.write("Encoding failed: file empty") + os.remove(output_file) + return + tqdm.write(f"Finished encoding, saved to {output_file}") def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -137,5 +141,8 @@ def encode_patients( f.create_dataset(f"{patient_id}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = dtype + f.attrs["precision"] = str(dtype) + if len(f) == 0: + tqdm.write("Encoding failed: file empty") + os.remove(output_file) tqdm.write(f"Finished encoding, saved to {output_file}") diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 886e6546..02c7a934 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -163,7 +163,11 @@ def encode_patients( f.create_dataset(f"{patient_id}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = torch.float32 + f.attrs["precision"] = str(torch.float32) + if len(f) == 0: + tqdm.write("Encoding failed: file empty") + os.remove(output_file) + return tqdm.write(f"Finished encoding, saved to {output_file}") def encode_slides( @@ -216,7 +220,7 @@ def encode_slides( f.create_dataset(f"{slide_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = torch.float32 + f.attrs["precision"] = str(torch.float32) # Check if the file is empty if len(f) == 0: tqdm.write("Encoding failed: file empty") diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 1dc23fcd..8fd3ad47 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -3,6 +3,7 @@ from pathlib import Path import h5py +import numpy as np import pandas as pd import torch from torch import Tensor @@ -32,30 +33,29 @@ def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: feats, coords, extractor = self._read_h5(h5_path) - if "titan" not in extractor: + if "conch1_5" not in extractor: raise ValueError( - f"Features must be extracted with titan. " + f"Features must be extracted with conch1_5. " f"Features located in {h5_path} are extracted with {extractor}" ) return feats, coords def _encode_slide( self, feats: Tensor, coords: CoordsInfo, device: DeviceLikeType - ) -> torch.Tensor: + ) -> np.ndarray: """Helper method to encode a single slide.""" # Convert coordinates from microns to pixels patch_size_lvl0 = math.floor(256 / coords.mpp) # Inferred from TITAN docs coords_px = coords.coords_um / coords.mpp # Convert to pixels - coords_px = torch.tensor(coords_px, dtype=torch.float32).to(device) coords_px = coords_px.to(torch.int64).to(device) # Convert to integer - feats = torch.tensor(feats, dtype=torch.float32).to(device) + feats = feats.to(device) with torch.inference_mode(): slide_embedding = self.model.encode_slide_from_patch_features( feats, coords_px, patch_size_lvl0 ) - return slide_embedding.to(torch.float32).detach().squeeze() + return slide_embedding.to(torch.float32).detach().squeeze().cpu().numpy() def encode_slides( self, @@ -97,12 +97,11 @@ def encode_slides( f.create_dataset(f"{slide_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = torch.float32 + f.attrs["precision"] = str(torch.float32) # Check if the file is empty if len(f) == 0: tqdm.write("Extraction failed: file empty") os.remove(output_file) - return tqdm.write(f"Finished encoding, saved to {output_file}") def encode_patients(self, output_dir, feat_dir, slide_table_path, device, **kwargs): From f79bc41698426f952abfcb4eda9701835ddd6159 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 29 Apr 2025 14:19:58 +0100 Subject: [PATCH 30/99] make coords numpy array instead of tensor --- src/stamp/modeling/data.py | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py index 065df2b6..36caa0d3 100755 --- a/src/stamp/modeling/data.py +++ b/src/stamp/modeling/data.py @@ -192,7 +192,7 @@ def __getitem__( @dataclass class CoordsInfo: - coords_um: Tensor + coords_um: np.ndarray tile_size_um: Microns tile_size_px: TilePixels | None @@ -206,8 +206,8 @@ def mpp(self) -> SlideMPP: def get_coords(feature_h5: h5py.File) -> CoordsInfo: - coords = torch.from_numpy(feature_h5["coords"][:]).float() # pyright: ignore[reportIndexIssue] - coords_um: Tensor | None = None + coords: np.ndarray = feature_h5["coords"][:] # type: ignore + coords_um: np.ndarray | None = None tile_size_um: Microns | None = None tile_size_px: TilePixels | None = None if (tile_size := feature_h5.attrs.get("tile_size", None)) and feature_h5.attrs.get( From 38afcdaa48be7d7653fcb8af3ae2b8392245c0d3 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 29 Apr 2025 14:23:41 +0100 Subject: [PATCH 31/99] add titan patient level encoding --- src/stamp/encoding/__init__.py | 5 ++ src/stamp/encoding/encoder/titan.py | 90 ++++++++++++++++++++++++++--- 2 files changed, 87 insertions(+), 8 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 33b499f2..5935fd92 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -27,6 +27,11 @@ def get_pat_embs( selected_encoder: Encoder = Eagle() + case EncoderName.TITAN: + from stamp.encoding.encoder.titan import Titan + + selected_encoder: Encoder = Titan() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 8fd3ad47..17d42b34 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -15,6 +15,7 @@ from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo, get_coords +from stamp.preprocessing.tiling import SlideMPP class Titan(Encoder): @@ -41,12 +42,12 @@ def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: return feats, coords def _encode_slide( - self, feats: Tensor, coords: CoordsInfo, device: DeviceLikeType + self, feats: Tensor, coords_um: Tensor, mpp: SlideMPP, device: DeviceLikeType ) -> np.ndarray: """Helper method to encode a single slide.""" # Convert coordinates from microns to pixels - patch_size_lvl0 = math.floor(256 / coords.mpp) # Inferred from TITAN docs - coords_px = coords.coords_um / coords.mpp # Convert to pixels + patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs + coords_px = coords_um / mpp # Convert to pixels coords_px = coords_px.to(torch.int64).to(device) # Convert to integer feats = feats.to(device) @@ -86,7 +87,11 @@ def encode_slides( tqdm.write(s=str(e)) continue - slide_embedding = self._encode_slide(feats, coords, device) + coords_tensor = torch.tensor(coords.coords_um, dtype=torch.float32) + + slide_embedding = self._encode_slide( + feats, coords_tensor, coords.mpp, device + ) slide_dict[slide_name] = { "feats": slide_embedding, } @@ -104,9 +109,13 @@ def encode_slides( os.remove(output_file) tqdm.write(f"Finished encoding, saved to {output_file}") - def encode_patients(self, output_dir, feat_dir, slide_table_path, device, **kwargs): + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: + """Generate one virtual slide concatenating all the slides of a + patient over the x axis.""" output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") @@ -120,6 +129,71 @@ def encode_patients(self, output_dir, feat_dir, slide_table_path, device, **kwar tqdm.write(f"Output file {output_file} already exists, skipping") return - # Not implemented yet :P - pass + for patient_id, group in tqdm(patient_groups, leave=False): + all_feats_list = [] + all_coords_list = [] + current_x_offset = 0 + slides_mpp = SlideMPP(-1) + + # Concatenate all slides over x axis adding the offset to each feature x coordinate. + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + + try: + feats, coords = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + # Get the mpp of one slide and check that the rest have the same + if slides_mpp < 0: + slides_mpp = coords.mpp + elif not math.isclose(slides_mpp, coords.mpp, rel_tol=1e-5): + raise ValueError( + "All patient slides must have the same mpp value. " + "Try reprocessing the slides using the same tile_size_um and " + "tile_size_px values for all of them." + ) + + # Add the offset to tile coordinates in x axis + for coord in coords.coords_um: + coord[0] += current_x_offset + + # get the coordinates of the rightmost tile and add the + # tile width as these coordinates are from the top-left + # point. With that you get the total width of the slide. + current_x_offset = max(coords.coords_um[:, 0]) + coords.tile_size_um + + coords_tensor = torch.tensor(coords.coords_um, dtype=torch.float32) + + # Add tile feats and coords to the patient virtual slide + all_feats_list.append(feats) + all_coords_list.append(coords_tensor) + + if not all_feats_list: + tqdm.write(f"No features found for patient {patient_id}, skipping.") + continue + + all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) + all_coords_cat = torch.cat(all_coords_list, dim=0).unsqueeze(0) + patient_embedding = self._encode_slide( + all_feats_cat, all_coords_cat, slides_mpp, device + ) + patient_dict[patient_id] = { + "feats": patient_embedding, + } + + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in patient_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = str(torch.float32) + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file From 61fdc78c35de6f092d097db52b883bd5763ee699 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 30 Apr 2025 10:00:16 +0100 Subject: [PATCH 32/99] add gigapath dependency --- src/stamp/encoding/encoder/gigapath.py | 7 +++++++ 1 file changed, 7 insertions(+) create mode 100644 src/stamp/encoding/encoder/gigapath.py diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py new file mode 100644 index 00000000..d8ed1bc5 --- /dev/null +++ b/src/stamp/encoding/encoder/gigapath.py @@ -0,0 +1,7 @@ +try: + import gigapath +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "gigapath dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[gigapath]'`" + ) from e From e9ed4e00a68b69411f6494cd0cf0e24084da0d93 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 30 Apr 2025 10:01:33 +0100 Subject: [PATCH 33/99] add gigapath dependency to pyproject --- pyproject.toml | 1 + 1 file changed, 1 insertion(+) diff --git a/pyproject.toml b/pyproject.toml index f8e645f3..7d939964 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,6 +70,7 @@ chief_ctranspath = [ gigapath = [ "timm>=0.9.11", "torchvision>=0.20.1", + "gigapath @ git+https://github.com/prov-gigapath/prov-gigapath.git@53d67708d6b19e9738d1341bb011390ffe2039f1" ] h_optimus_0 = [ "timm>=0.9.11", From 08b0322adad568ce20c63f848c93a2a947ae7b79 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 30 Apr 2025 12:21:37 +0100 Subject: [PATCH 34/99] update available encoders --- src/stamp/config.yaml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 37e8d06c..d363e713 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -31,7 +31,7 @@ preprocessing: slide_encoding: # Encoder to use for slide encoding. Possible options are "katherlab-cobra", - # "katherlab-eagle", "mahmood-titan". + # "katherlab-eagle", "mahmood-titan", "gigapath". encoder: "katherlab-eagle" # Directory to save the output files. @@ -51,7 +51,7 @@ slide_encoding: patient_encoding: # Encoder to use for patient encoding. Possible options are "katherlab-cobra", - # "katherlab-eagle". + # "katherlab-eagle", "mahmood-titan". encoder: "katherlab-eagle" # Directory to save the output files. From 506f60fe97347599ce2ad092f7ab42b5822196b5 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 30 Apr 2025 13:49:56 +0100 Subject: [PATCH 35/99] add gigapath slide encoder --- src/stamp/encoding/__init__.py | 5 + src/stamp/encoding/config.py | 1 + src/stamp/encoding/encoder/gigapath.py | 145 +++++++++++++++++++++++-- 3 files changed, 144 insertions(+), 7 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 5935fd92..a25ea3f4 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -65,6 +65,11 @@ def get_slide_embs( selected_encoder: Encoder = Eagle() + case EncoderName.GIGAPATH: + from stamp.encoding.encoder.gigapath import Gigapath + + selected_encoder: Encoder = Gigapath() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 40cc395a..da417489 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -10,6 +10,7 @@ class EncoderName(StrEnum): EAGLE = "katherlab-eagle" CHIEF = "chief" TITAN = "mahmood-titan" + GIGAPATH = "gigapath" class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index d8ed1bc5..4bf99577 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -1,7 +1,138 @@ -try: - import gigapath -except ModuleNotFoundError as e: - raise ModuleNotFoundError( - "gigapath dependencies not installed." - " Please reinstall stamp using `pip install 'stamp[gigapath]'`" - ) from e +import os +from pathlib import Path + +import h5py +import numpy as np +import torch +from gigapath import slide_encoder +from torch import Tensor +from torch._prims_common import DeviceLikeType # type: ignore +from tqdm import tqdm + +import stamp +from stamp.cache import get_processing_code_hash +from stamp.encoding.encoder import Encoder +from stamp.modeling.data import CoordsInfo, get_coords + + +class Gigapath(Encoder): + def __init__(self) -> None: + # TODO: Implement caching as done by peter + model = slide_encoder.create_model( + "hf_hub:prov-gigapath/prov-gigapath", "gigapath_slide_enc12l768d", 1536 + ) + super().__init__(model=model, identifier="gigapath") + + # TODO: Make this a shared function for all encoders. + def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + raise FileNotFoundError("File does not exist or is not an h5 file") + with h5py.File(h5_path, "r") as f: + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + return feats, coords, extractor + + # TODO: This can be reused too, give extractor name as parameter + def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: + feats, coords, extractor = self._read_h5(h5_path) + if "gigapath" not in extractor: + raise ValueError( + f"Features must be extracted with gigapath. " + f"Features located in {h5_path} are extracted with {extractor}" + ) + return feats, coords + + def _convert_coords( + self, + coords, + total_wsi_width, + max_wsi_height, + n_grid, + current_x_offset, + ): + """ + Normalize the x and y coordinates relative to the total WSI width and max height, using the same grid [0, 1000]. + Thanks Peter! + """ + # Normalize x-coordinates based on total WSI width (taking into account the current x offset) + normalized_x = (coords[:, 0] + current_x_offset) / total_wsi_width * n_grid + + # Normalize y-coordinates based on the maximum WSI height + normalized_y = coords[:, 1] / max_wsi_height * n_grid + + # Stack normalized x and y coordinates + converted_coords = np.stack([normalized_x, normalized_y], axis=-1) + + return np.array(converted_coords, dtype=np.float32) + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + slide_dict = {} + self.model.to(device).eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, coords = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + # Calculated obtaining the tile with rightmost x coord + # and the tile width is added as the coord is from top left + slide_width = max(coords.coords_um[:, 0]) + coords.tile_size_um + slide_height = max(coords.coords_um[:, 1]) + coords.tile_size_um + + # For some reason gigapaths requires normalized coords in a + # [0,1000] grid + n_grid = 1000 + + # TODO: Check np.stack behaviour + norm_coords = self._convert_coords( + coords.coords_um, slide_width, slide_height, n_grid, current_x_offset=0 + ) + + norm_coords = torch.tensor(norm_coords, dtype=torch.float32) + + self.model.eval() + slide_embedding = self.model(feats, norm_coords).squeeze() + slide_dict[slide_name] = { + "feats": slide_embedding, + } + + # TODO: Reutilice this function + # TODO: Add codebase hash to h5 file + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = str(torch.float32) + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + tqdm.write(f"Finished encoding, saved to {output_file}") + + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: + pass From 8413b0fb2a8f540b052e1f6a334cac9b5f233978 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 5 May 2025 13:08:17 +0100 Subject: [PATCH 36/99] add chief slide encoding --- src/stamp/config.yaml | 2 +- src/stamp/encoding/__init__.py | 5 ++ src/stamp/encoding/encoder/chief.py | 83 +++++++++++++++++++++++++++-- 3 files changed, 86 insertions(+), 4 deletions(-) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index d363e713..b1d9b0cc 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -31,7 +31,7 @@ preprocessing: slide_encoding: # Encoder to use for slide encoding. Possible options are "katherlab-cobra", - # "katherlab-eagle", "mahmood-titan", "gigapath". + # "katherlab-eagle", "mahmood-titan", "gigapath", "chief". encoder: "katherlab-eagle" # Directory to save the output files. diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index a25ea3f4..463de6d0 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -70,6 +70,11 @@ def get_slide_embs( selected_encoder: Encoder = Gigapath() + case EncoderName.CHIEF: + from stamp.encoding.encoder.chief import CHIEF + + selected_encoder: Encoder = CHIEF() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 918b6ebf..4d011a04 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -1,9 +1,17 @@ +import os +from pathlib import Path + import gdown +import h5py import torch import torch.nn as nn import torch.nn.functional as F +from torch import Tensor +from torch._prims_common import DeviceLikeType # type: ignore +from tqdm import tqdm -from stamp.cache import STAMP_CACHE_DIR +import stamp +from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash from stamp.encoding.encoder import Encoder """authors: https://github.com/hms-dbmi/CHIEF""" @@ -93,8 +101,77 @@ def __init__(self) -> None: model.load_state_dict(chief, strict=True) super().__init__(model=model, identifier="chief") - def encode_slides(self, feats, coords, patch_size_lvl0, *args, **kwargs) -> None: - pass + def _read_h5(self, h5_path: str) -> tuple[Tensor, str]: + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + raise FileNotFoundError("File does not exist or is not an h5 file") + with h5py.File(h5_path, "r") as f: + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + extractor: str = f.attrs.get("extractor", "no extractor name") + return feats, extractor + + def _validate_and_read_features(self, h5_path) -> Tensor: + feats, extractor = self._read_h5(h5_path) + if "ctranspath" not in extractor: + raise ValueError( + f"Features must be extracted with ctranspath or chief_ctranspath. " + f"Features located in {h5_path} are extracted with {extractor}" + ) + return feats + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + slide_dict = {} + self.model.to(device).eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats: Tensor = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + slide_embedding = ( + self.model(feats.to(device))["WSI_feature"] + .detach() + .squeeze() + .cpu() + .numpy() + ) + slide_dict[slide_name] = { + "feats": slide_embedding, + } + + # TODO: Reutilice this function + # TODO: Add codebase hash to h5 file + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = str(torch.float32) + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + tqdm.write(f"Finished encoding, saved to {output_file}") def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs From a57fb232c12c577f6d933d51459a8bf0bbbbbbff Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 6 May 2025 09:35:31 +0100 Subject: [PATCH 37/99] fix gigapath dependencies --- src/stamp/encoding/encoder/gigapath.py | 41 +++++++++++++++++++------- 1 file changed, 30 insertions(+), 11 deletions(-) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 4bf99577..3d9b3ef8 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -17,18 +17,25 @@ class Gigapath(Encoder): def __init__(self) -> None: - # TODO: Implement caching as done by peter - model = slide_encoder.create_model( - "hf_hub:prov-gigapath/prov-gigapath", "gigapath_slide_enc12l768d", 1536 - ) + try: + model = slide_encoder.create_model( + "hf_hub:prov-gigapath/prov-gigapath", "gigapath_slide_enc12l768d", 1536 + ) + except AssertionError: + raise ModuleNotFoundError( + "Gigapath requires flash-attn. " + "Install it with: pip install flash-attn --no-build-isolation" + ) + # I cant add flash-attn to the pyproject.toml because it requires torch + # beforehand, I add torch to build-system requires but throws the + # same bloody error super().__init__(model=model, identifier="gigapath") - # TODO: Make this a shared function for all encoders. def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): raise FileNotFoundError("File does not exist or is not an h5 file") with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore + feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float16) # type: ignore coords: CoordsInfo = get_coords(f) extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor @@ -79,7 +86,7 @@ def encode_slides( output_file = os.path.join(output_dir, output_name) slide_dict = {} - self.model.to(device).eval() + self.model.to(device).half().eval() if os.path.exists(output_file): tqdm.write(f"Output file {output_file} already exists, skipping") @@ -109,10 +116,22 @@ def encode_slides( coords.coords_um, slide_width, slide_height, n_grid, current_x_offset=0 ) - norm_coords = torch.tensor(norm_coords, dtype=torch.float32) + norm_coords = ( + torch.tensor(norm_coords, dtype=torch.float32) + .unsqueeze(1) + .to(device) + .half() + ) + feats = feats.unsqueeze(1).half().to(device) + + with torch.inference_mode(): + slide_embedding = self.model(feats, norm_coords) + + if isinstance(slide_embedding, list): # Ensure slide_feats is not a list + slide_embedding = torch.cat(slide_embedding, dim=0) + + slide_embedding = slide_embedding.detach().squeeze().cpu().numpy() - self.model.eval() - slide_embedding = self.model(feats, norm_coords).squeeze() slide_dict[slide_name] = { "feats": slide_embedding, } @@ -125,7 +144,7 @@ def encode_slides( f.create_dataset(f"{slide_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) + f.attrs["precision"] = str(torch.float16) # Check if the file is empty if len(f) == 0: tqdm.write("Extraction failed: file empty") From 62e0f23bd3fe6eeddc658e822a2b722997dceaa5 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Tue, 6 May 2025 11:53:09 +0100 Subject: [PATCH 38/99] add gigapath patient encoding --- src/stamp/encoding/__init__.py | 5 + src/stamp/encoding/encoder/gigapath.py | 128 +++++++++++++++++++++++-- 2 files changed, 126 insertions(+), 7 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 463de6d0..fc3cf42d 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -32,6 +32,11 @@ def get_pat_embs( selected_encoder: Encoder = Titan() + case EncoderName.GIGAPATH: + from stamp.encoding.encoder.gigapath import Gigapath + + selected_encoder: Encoder = Gigapath() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 3d9b3ef8..875e3b51 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -1,11 +1,13 @@ +import math import os from pathlib import Path import h5py import numpy as np +import pandas as pd import torch from gigapath import slide_encoder -from torch import Tensor +from torch import Tensor, dtype from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm @@ -13,6 +15,7 @@ from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo, get_coords +from stamp.preprocessing.tiling import SlideMPP class Gigapath(Encoder): @@ -31,18 +34,20 @@ def __init__(self) -> None: # same bloody error super().__init__(model=model, identifier="gigapath") # TODO: Make this a shared function for all encoders. - def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: + def _read_h5(self, h5_path: str, dtype: dtype) -> tuple[Tensor, CoordsInfo, str]: if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): raise FileNotFoundError("File does not exist or is not an h5 file") with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float16) # type: ignore + feats: Tensor = torch.tensor(f["feats"][:], dtype=dtype) # type: ignore coords: CoordsInfo = get_coords(f) extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor # TODO: This can be reused too, give extractor name as parameter - def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: - feats, coords, extractor = self._read_h5(h5_path) + def _validate_and_read_features( + self, h5_path, dtype: dtype + ) -> tuple[Tensor, CoordsInfo]: + feats, coords, extractor = self._read_h5(h5_path, dtype=dtype) if "gigapath" not in extractor: raise ValueError( f"Features must be extracted with gigapath. " @@ -97,7 +102,7 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats, coords = self._validate_and_read_features(h5_path) + feats, coords = self._validate_and_read_features(h5_path, torch.float16) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -154,4 +159,113 @@ def encode_slides( def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs ) -> None: - pass + """Generate one virtual slide concatenating all the slides of a + patient over the x axis.""" + output_name = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + + output_file = os.path.join(output_dir, output_name) + + patient_dict = {} + self.model.to(device).half().eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for patient_id, group in tqdm(patient_groups, leave=False): + all_feats_list = [] + all_coords_list = [] + total_wsi_width = 0 + max_wsi_height = 0 + slides_mpp = SlideMPP(-1) + + slide_info = [] + # Concatenate all slides over x axis adding the offset to each feature x coordinate. + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + + try: + feats, coords = self._validate_and_read_features( + h5_path, torch.float16 + ) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + # Get the mpp of one slide and check that the rest have the same + if slides_mpp < 0: + slides_mpp = coords.mpp + elif not math.isclose(slides_mpp, coords.mpp, rel_tol=1e-5): + raise ValueError( + "All patient slides must have the same mpp value. " + "Try reprocessing the slides using the same tile_size_um and " + "tile_size_px values for all of them." + ) + + wsi_width = max(coords.coords_um[:, 0]) + coords.tile_size_um + wsi_height = max(coords.coords_um[:, 1]) + coords.tile_size_um + + total_wsi_width += wsi_width # Sum the widths of all slides + max_wsi_height = max(max_wsi_height, wsi_height) # Track the max height + + slide_info.append((wsi_width, wsi_height, feats, coords)) + + current_x_offset = 0 + + for wsi_width, wsi_height, feats, coords in slide_info: + all_feats = ( + torch.tensor(feats, dtype=torch.float16) + .unsqueeze(1) + .to(device) + .half() + .detach() + ) + + norm_coords = self._convert_coords( + coords=coords.coords_um, + total_wsi_width=total_wsi_width, + max_wsi_height=max_wsi_height, + n_grid=1000, + current_x_offset=current_x_offset, + ) + + # Update x-coordinates by shifting them based on the current_x_offset + current_x_offset += ( + wsi_width # Move the x_offset forward for the next slide + ) + + norm_coords = ( + torch.tensor(norm_coords, dtype=torch.float32) + .unsqueeze(1) + .to(device) + .half() + ) + + all_feats_list.append(all_feats) + all_coords_list.append(norm_coords) + + if not all_feats_list: + tqdm.write(f"No features found for patient {patient_id}, skipping.") + continue + + all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) + all_coords_cat = torch.cat(all_coords_list, dim=0).unsqueeze(0) + + breakpoint() + + with torch.inference_mode(): + patient_embedding = self.model(all_feats_cat, all_coords_cat) + + if isinstance(patient_embedding, list): # Ensure slide_feats is not a list + patient_embedding = torch.cat(patient_embedding, dim=0) + + patient_embedding = patient_embedding.detach().squeeze().cpu().numpy() + + patient_dict[patient_id] = { + "feats": patient_embedding, + } From 646cd29da25e3e0e14dfecb44a7c6fa2810e1901 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 7 May 2025 09:20:31 +0100 Subject: [PATCH 39/99] fix gigapath dimensions --- pyproject.toml | 12 ++++++++++- src/stamp/encoding/encoder/gigapath.py | 29 +++++++++++++++++++------- 2 files changed, 32 insertions(+), 9 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 7d939964..8d0b7a11 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -70,7 +70,17 @@ chief_ctranspath = [ gigapath = [ "timm>=0.9.11", "torchvision>=0.20.1", - "gigapath @ git+https://github.com/prov-gigapath/prov-gigapath.git@53d67708d6b19e9738d1341bb011390ffe2039f1" + "fvcore", + "iopath", + "monai", + "scikit-image", + "webdataset", + "lifelines", + "scikit-survival", + "fairscale", + "wandb", + "ninja==1.11.1.1", + "gigapath @ git+https://github.com/EzicStar/prov-gigapath.git" ] h_optimus_0 = [ "timm>=0.9.11", diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 875e3b51..48474e66 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -123,11 +123,11 @@ def encode_slides( norm_coords = ( torch.tensor(norm_coords, dtype=torch.float32) - .unsqueeze(1) + .unsqueeze(0) .to(device) .half() ) - feats = feats.unsqueeze(1).half().to(device) + feats = feats.unsqueeze(0).half().to(device) with torch.inference_mode(): slide_embedding = self.model(feats, norm_coords) @@ -220,7 +220,7 @@ def encode_patients( for wsi_width, wsi_height, feats, coords in slide_info: all_feats = ( torch.tensor(feats, dtype=torch.float16) - .unsqueeze(1) + .unsqueeze(0) .to(device) .half() .detach() @@ -241,7 +241,7 @@ def encode_patients( norm_coords = ( torch.tensor(norm_coords, dtype=torch.float32) - .unsqueeze(1) + .unsqueeze(0) .to(device) .half() ) @@ -253,10 +253,8 @@ def encode_patients( tqdm.write(f"No features found for patient {patient_id}, skipping.") continue - all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) - all_coords_cat = torch.cat(all_coords_list, dim=0).unsqueeze(0) - - breakpoint() + all_feats_cat = torch.cat(all_feats_list, dim=1) + all_coords_cat = torch.cat(all_coords_list, dim=1) with torch.inference_mode(): patient_embedding = self.model(all_feats_cat, all_coords_cat) @@ -269,3 +267,18 @@ def encode_patients( patient_dict[patient_id] = { "feats": patient_embedding, } + + # TODO: Reutilice this function + # TODO: Add codebase hash to h5 file + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in patient_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = str(torch.float16) + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + tqdm.write(f"Finished encoding, saved to {output_file}") From 220f96a5f175669391f3a652e8a6a8869ba1cc1c Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 7 May 2025 11:08:42 +0100 Subject: [PATCH 40/99] add feature saving function --- src/stamp/encoding/encoder/__init__.py | 20 ++++++++++++++++++++ src/stamp/encoding/encoder/chief.py | 17 +++-------------- 2 files changed, 23 insertions(+), 14 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index d84dd018..c7a558bf 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -1,8 +1,13 @@ +import os from abc import ABC, abstractmethod from dataclasses import KW_ONLY, dataclass from typing import Generic, TypeVar +import h5py from torch import nn +from tqdm import tqdm + +import stamp EncoderModel = TypeVar("EncoderModel", bound=nn.Module) @@ -29,3 +34,18 @@ def encode_patients( ) -> None: """Abstract method to encode patient from slide features.""" pass + + def save_features(self, output_file, slide_dict, precision) -> None: + os.makedirs(os.path.dirname(output_file), exist_ok=True) + with h5py.File(output_file, "w") as f: + for slide_name, data in slide_dict.items(): + f.create_dataset(f"{slide_name}", data=data["feats"]) + f.attrs["version"] = stamp.__version__ + f.attrs["encoder"] = self.identifier + f.attrs["precision"] = precision + # Check if the file is empty + if len(f) == 0: + tqdm.write("Extraction failed: file empty") + os.remove(output_file) + tqdm.write(f"Finished encoding, saved to {output_file}") + # TODO: Add codebase hash to h5 file \ No newline at end of file diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 4d011a04..cdf99080 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -158,20 +158,9 @@ def encode_slides( "feats": slide_embedding, } - # TODO: Reutilice this function - # TODO: Add codebase hash to h5 file - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Extraction failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") + self.save_features( + output_file=output_file, slide_dict=slide_dict, precision=torch.float32 + ) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs From a9bc7d13f3ca18820f04a3d16daef8790669effb Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 7 May 2025 12:21:21 +0100 Subject: [PATCH 41/99] reuse reading and saving functions --- src/stamp/encoding/__init__.py | 5 ++ src/stamp/encoding/encoder/__init__.py | 39 +++++++++++++--- src/stamp/encoding/encoder/cobra.py | 36 ++++---------- src/stamp/encoding/encoder/eagle.py | 59 ++++++----------------- src/stamp/encoding/encoder/gigapath.py | 65 ++++---------------------- src/stamp/encoding/encoder/titan.py | 59 +++++------------------ 6 files changed, 81 insertions(+), 182 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index fc3cf42d..0b9a98fa 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -37,6 +37,11 @@ def get_pat_embs( selected_encoder: Encoder = Gigapath() + case EncoderName.CHIEF: + from stamp.encoding.encoder.chief import CHIEF + + selected_encoder: Encoder = CHIEF() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index c7a558bf..1361a184 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -4,10 +4,12 @@ from typing import Generic, TypeVar import h5py -from torch import nn +import torch +from torch import Tensor, dtype, nn from tqdm import tqdm import stamp +from stamp.modeling.data import CoordsInfo, get_coords EncoderModel = TypeVar("EncoderModel", bound=nn.Module) @@ -35,17 +37,40 @@ def encode_patients( """Abstract method to encode patient from slide features.""" pass - def save_features(self, output_file, slide_dict, precision) -> None: + def _read_h5( + self, h5_path: str, precision: dtype + ) -> tuple[Tensor, CoordsInfo, str]: + if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): + raise FileNotFoundError("File does not exist or is not an h5 file") + with h5py.File(h5_path, "r") as f: + feats: Tensor = torch.tensor(f["feats"][:], dtype=precision) # type: ignore + coords: CoordsInfo = get_coords(f) + extractor: str = f.attrs.get("extractor", "no extractor name") + return feats, coords, extractor + + def _validate_and_read_features( + self, h5_path, extractor_name, precision + ) -> tuple[Tensor, CoordsInfo]: + feats, coords, extractor = self._read_h5(h5_path, precision=precision) + if extractor_name not in extractor: + raise ValueError( + f"Features must be extracted with {extractor_name}. " + f"Features located in {h5_path} are extracted with {extractor}" + ) + return feats, coords + + def _save_features(self, output_file, entry_dict, precision) -> None: os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) + for entry_name, data in entry_dict.items(): + f.create_dataset(f"{entry_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = precision + f.attrs["precision"] = str(precision) # Check if the file is empty if len(f) == 0: tqdm.write("Extraction failed: file empty") os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") - # TODO: Add codebase hash to h5 file \ No newline at end of file + else: + tqdm.write(f"Finished encoding, saved to {output_file}") + # TODO: Add codebase hash to h5 file diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 40364649..ee9a2713 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -6,7 +6,6 @@ import torch from tqdm import tqdm -import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder @@ -66,18 +65,7 @@ def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) output_file = os.path.join(output_dir, output_name) - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(dtype) - if len(f) == 0: - tqdm.write("Encoding failed: file empty") - os.remove(output_file) - return - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -90,7 +78,7 @@ def encode_patients( slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby("PATIENT") - slide_dict = {} + patient_dict = {} output_name = ( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" @@ -124,9 +112,9 @@ def encode_patients( assert all_feats_cat.ndim == 3, ( f"Expected 3D tensor, got {all_feats_cat.ndim}" ) - slide_feats = self.model(all_feats_cat.to(dtype)) - slide_dict[patient_id] = { - "feats": slide_feats.to(torch.float32) + patient_feats = self.model(all_feats_cat.to(dtype)) + patient_dict[patient_id] = { + "feats": patient_feats.to(torch.float32) .detach() .squeeze() .cpu() @@ -135,14 +123,6 @@ def encode_patients( else: tqdm.write(f"No features found for patient {patient_id}, skipping") - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for patient_id, data in slide_dict.items(): - f.create_dataset(f"{patient_id}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(dtype) - if len(f) == 0: - tqdm.write("Encoding failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features( + output_file, entry_dict=patient_dict, precision=torch.float32 + ) diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 02c7a934..96ced074 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -1,7 +1,6 @@ import os from pathlib import Path -import h5py import numpy as np import pandas as pd import torch @@ -9,11 +8,10 @@ from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm -import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF -from stamp.modeling.data import CoordsInfo, get_coords +from stamp.modeling.data import CoordsInfo """From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" @@ -23,19 +21,13 @@ def __init__(self) -> None: model = CHIEF().model super().__init__(model=model, identifier="katherlab-eagle") - def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - raise FileNotFoundError("File does not exist or is not an h5 file") - with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore - coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") - return feats, coords, extractor - - def _validate_and_read_features( + def _validate_and_read_features_with_agg( self, h5_ctp: str, h5_vir2: str, slide_name: str ) -> tuple[Tensor, Tensor]: - feats, coords, extractor = self._read_h5(h5_ctp) + feats: Tensor + coords: CoordsInfo + extractor: str + feats, coords, extractor = self._read_h5(h5_ctp, torch.float32) if "ctranspath" not in extractor: raise ValueError( @@ -43,7 +35,7 @@ def _validate_and_read_features( f"Features located in {h5_ctp} are extracted with {extractor}" ) - agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) + agg_feats, agg_coords, extractor = self._read_h5(h5_vir2, torch.float32) if "virchow2" not in extractor: raise ValueError( @@ -57,9 +49,7 @@ def _validate_and_read_features( f" {feats.shape[0]} != {agg_feats.shape[0]}" ) - if not torch.allclose( - coords.coords_um, agg_coords.coords_um, atol=1e-5, rtol=0 - ): + if not np.allclose(coords.coords_um, agg_coords.coords_um, atol=1e-5, rtol=0): raise ValueError( f"Coordinates mismatch between ctranspath and virchow2" f" features for slide {slide_name}. Ensure that both are aligned." @@ -134,7 +124,7 @@ def encode_patients( # Validate and read features try: - feats, agg_feats = self._validate_and_read_features( + feats, agg_feats = self._validate_and_read_features_with_agg( h5_ctp, h5_vir2, slide_name ) except FileNotFoundError as e: @@ -157,18 +147,9 @@ def encode_patients( "feats": eagle_embedding, } - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for patient_id, data in patient_dict.items(): - f.create_dataset(f"{patient_id}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) - if len(f) == 0: - tqdm.write("Encoding failed: file empty") - os.remove(output_file) - return - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features( + output_file, entry_dict=patient_dict, precision=torch.float32 + ) def encode_slides( self, @@ -202,7 +183,7 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats, agg_feats = self._validate_and_read_features( + feats, agg_feats = self._validate_and_read_features_with_agg( h5_ctp, h5_vir2, slide_name ) except FileNotFoundError as e: @@ -214,16 +195,4 @@ def encode_slides( "feats": eagle_embedding, } - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Encoding failed: file empty") - os.remove(output_file) - return - tqdm.write(f"Finished Encoding, saved to {output_file}") + self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 48474e66..1d62a91e 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -2,19 +2,15 @@ import os from pathlib import Path -import h5py import numpy as np import pandas as pd import torch from gigapath import slide_encoder -from torch import Tensor, dtype from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm -import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo, get_coords from stamp.preprocessing.tiling import SlideMPP @@ -33,27 +29,6 @@ def __init__(self) -> None: # beforehand, I add torch to build-system requires but throws the # same bloody error super().__init__(model=model, identifier="gigapath") - # TODO: Make this a shared function for all encoders. - def _read_h5(self, h5_path: str, dtype: dtype) -> tuple[Tensor, CoordsInfo, str]: - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - raise FileNotFoundError("File does not exist or is not an h5 file") - with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=dtype) # type: ignore - coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") - return feats, coords, extractor - - # TODO: This can be reused too, give extractor name as parameter - def _validate_and_read_features( - self, h5_path, dtype: dtype - ) -> tuple[Tensor, CoordsInfo]: - feats, coords, extractor = self._read_h5(h5_path, dtype=dtype) - if "gigapath" not in extractor: - raise ValueError( - f"Features must be extracted with gigapath. " - f"Features located in {h5_path} are extracted with {extractor}" - ) - return feats, coords def _convert_coords( self, @@ -102,7 +77,9 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats, coords = self._validate_and_read_features(h5_path, torch.float16) + feats, coords = self._validate_and_read_features( + h5_path=h5_path, extractor_name="gigapath", precision=torch.float16 + ) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -141,20 +118,7 @@ def encode_slides( "feats": slide_embedding, } - # TODO: Reutilice this function - # TODO: Add codebase hash to h5 file - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float16) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Extraction failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features(output_file, entry_dict=slide_dict, precision=torch.float16) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -191,7 +155,9 @@ def encode_patients( try: feats, coords = self._validate_and_read_features( - h5_path, torch.float16 + h5_path=h5_path, + extractor_name="gigapath", + precision=torch.float16, ) except FileNotFoundError as e: tqdm.write(s=str(e)) @@ -268,17 +234,6 @@ def encode_patients( "feats": patient_embedding, } - # TODO: Reutilice this function - # TODO: Add codebase hash to h5 file - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in patient_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float16) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Extraction failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features( + output_file, entry_dict=patient_dict, precision=torch.float16 + ) diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 17d42b34..7961c3c1 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -2,7 +2,6 @@ import os from pathlib import Path -import h5py import numpy as np import pandas as pd import torch @@ -11,10 +10,8 @@ from tqdm import tqdm from transformers import AutoModel -import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo, get_coords from stamp.preprocessing.tiling import SlideMPP @@ -23,24 +20,6 @@ def __init__(self) -> None: model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) super().__init__(model=model, identifier="mahmood-titan") - def _read_h5(self, h5_path: str) -> tuple[Tensor, CoordsInfo, str]: - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - raise FileNotFoundError("File does not exist or is not an h5 file") - with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore - coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") - return feats, coords, extractor - - def _validate_and_read_features(self, h5_path) -> tuple[Tensor, CoordsInfo]: - feats, coords, extractor = self._read_h5(h5_path) - if "conch1_5" not in extractor: - raise ValueError( - f"Features must be extracted with conch1_5. " - f"Features located in {h5_path} are extracted with {extractor}" - ) - return feats, coords - def _encode_slide( self, feats: Tensor, coords_um: Tensor, mpp: SlideMPP, device: DeviceLikeType ) -> np.ndarray: @@ -82,7 +61,9 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats, coords = self._validate_and_read_features(h5_path) + feats, coords = self._validate_and_read_features( + h5_path=h5_path, extractor_name="conch1_5", precision=torch.float32 + ) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -96,18 +77,7 @@ def encode_slides( "feats": slide_embedding, } - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in slide_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Extraction failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") + self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -141,7 +111,11 @@ def encode_patients( h5_path = os.path.join(feat_dir, slide_filename) try: - feats, coords = self._validate_and_read_features(h5_path) + feats, coords = self._validate_and_read_features( + h5_path=h5_path, + extractor_name="conch1_5", + precision=torch.float32, + ) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -185,15 +159,6 @@ def encode_patients( "feats": patient_embedding, } - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for slide_name, data in patient_dict.items(): - f.create_dataset(f"{slide_name}", data=data["feats"]) - f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(torch.float32) - # Check if the file is empty - if len(f) == 0: - tqdm.write("Extraction failed: file empty") - os.remove(output_file) - tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file + self._save_features( + output_file, entry_dict=patient_dict, precision=torch.float32 + ) From dcb0d64b1fa51d819faeeef81896b05d4b558e38 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 7 May 2025 12:21:44 +0100 Subject: [PATCH 42/99] add chief patient encoding --- src/stamp/encoding/encoder/chief.py | 85 +++++++++++++++++++++-------- 1 file changed, 61 insertions(+), 24 deletions(-) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index cdf99080..c5f3df5c 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -2,15 +2,13 @@ from pathlib import Path import gdown -import h5py +import pandas as pd import torch import torch.nn as nn import torch.nn.functional as F -from torch import Tensor from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm -import stamp from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash from stamp.encoding.encoder import Encoder @@ -101,23 +99,6 @@ def __init__(self) -> None: model.load_state_dict(chief, strict=True) super().__init__(model=model, identifier="chief") - def _read_h5(self, h5_path: str) -> tuple[Tensor, str]: - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - raise FileNotFoundError("File does not exist or is not an h5 file") - with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=torch.float32) # type: ignore - extractor: str = f.attrs.get("extractor", "no extractor name") - return feats, extractor - - def _validate_and_read_features(self, h5_path) -> Tensor: - feats, extractor = self._read_h5(h5_path) - if "ctranspath" not in extractor: - raise ValueError( - f"Features must be extracted with ctranspath or chief_ctranspath. " - f"Features located in {h5_path} are extracted with {extractor}" - ) - return feats - def encode_slides( self, output_dir: Path, @@ -142,7 +123,9 @@ def encode_slides( slide_name: str = Path(tile_feats_filename).stem try: - feats: Tensor = self._validate_and_read_features(h5_path) + feats, _ = self._validate_and_read_features( + h5_path, "ctranspath", torch.float32 + ) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -158,14 +141,68 @@ def encode_slides( "feats": slide_embedding, } - self.save_features( - output_file=output_file, slide_dict=slide_dict, precision=torch.float32 + self._save_features( + output_file=output_file, entry_dict=slide_dict, precision=torch.float32 ) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs ) -> None: - pass + output_name = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + + output_file = os.path.join(output_dir, output_name) + + patient_dict = {} + self.model.to(device).eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for patient_id, group in tqdm(patient_groups, leave=False): + feats_list = [] + + # Concatenate all slides over x axis adding the offset to each feature x coordinate. + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + + try: + feats, _ = self._validate_and_read_features( + h5_path=h5_path, + extractor_name="ctranspath", + precision=torch.float32, + ) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + feats_list.append(feats) + + if not feats_list: + tqdm.write(f"No features found for patient {patient_id}, skipping.") + continue + + all_feats = torch.cat(feats_list, dim=0).to(device) + + patient_embedding = ( + self.model(all_feats.to(device))["WSI_feature"] + .detach() + .squeeze() + .cpu() + .numpy() + ) + patient_dict[patient_id] = { + "feats": patient_embedding, + } + + self._save_features( + output_file=output_file, entry_dict=patient_dict, precision=torch.float32 + ) def initialize_weights(module): From 696867bcb0b9b6d7a61fd03d07f0c68bf8f78bdc Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 7 May 2025 12:47:13 +0100 Subject: [PATCH 43/99] add prism --- src/stamp/encoding/__init__.py | 10 +++ src/stamp/encoding/config.py | 1 + src/stamp/encoding/encoder/prism.py | 127 ++++++++++++++++++++++++++++ 3 files changed, 138 insertions(+) create mode 100644 src/stamp/encoding/encoder/prism.py diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 0b9a98fa..77b3b7e5 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -42,6 +42,11 @@ def get_pat_embs( selected_encoder: Encoder = CHIEF() + case EncoderName.PRISM: + from stamp.encoding.encoder.prism import Prism + + selected_encoder: Encoder = Prism() + case Encoder(): selected_encoder = encoder @@ -85,6 +90,11 @@ def get_slide_embs( selected_encoder: Encoder = CHIEF() + case EncoderName.PRISM: + from stamp.encoding.encoder.prism import Prism + + selected_encoder: Encoder = Prism() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index da417489..007615e4 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -11,6 +11,7 @@ class EncoderName(StrEnum): CHIEF = "chief" TITAN = "mahmood-titan" GIGAPATH = "gigapath" + PRISM = "paigeai-prism" class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py new file mode 100644 index 00000000..9ed70a3f --- /dev/null +++ b/src/stamp/encoding/encoder/prism.py @@ -0,0 +1,127 @@ +import os +from pathlib import Path + +import pandas as pd +import torch +from torch._prims_common import DeviceLikeType # type: ignore +from tqdm import tqdm +from transformers import AutoModel + +from stamp.cache import get_processing_code_hash +from stamp.encoding.encoder import Encoder + + +class Prism(Encoder): + def __init__(self) -> None: + model = AutoModel.from_pretrained("paige-ai/Prism", trust_remote_code=True) + super().__init__(model=model, identifier="paigeai-prism") + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + slide_dict = {} + self.model.to(device).eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, _ = self._validate_and_read_features( + h5_path, "virchow2", torch.float32 + ) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + with torch.autocast(str(device), torch.float16), torch.inference_mode(): + slide_embedding = ( + self.model.slide_representations(feats.to(device))[ + "image_embedding" + ] + .detach() + .squeeze() + .cpu() + .numpy() + ) + slide_dict[slide_name] = { + "feats": slide_embedding, + } + + self._save_features( + output_file=output_file, entry_dict=slide_dict, precision=torch.float32 + ) + + def encode_patients( + self, output_dir, feat_dir, slide_table_path, device, **kwargs + ) -> None: + output_name = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + + output_file = os.path.join(output_dir, output_name) + + patient_dict = {} + self.model.to(device).eval() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for patient_id, group in tqdm(patient_groups, leave=False): + feats_list = [] + + # Concatenate all slides over x axis adding the offset to each feature x coordinate. + for _, row in group.iterrows(): + slide_filename = row["FILENAME"] + h5_path = os.path.join(feat_dir, slide_filename) + + try: + feats, _ = self._validate_and_read_features( + h5_path=h5_path, + extractor_name="virchow2", + precision=torch.float32, + ) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + feats_list.append(feats) + + if not feats_list: + tqdm.write(f"No features found for patient {patient_id}, skipping.") + continue + + all_feats = torch.cat(feats_list, dim=0).to(device) + + patient_embedding = ( + self.model.slide_representations(all_feats.to(device))[ + "image_embedding" + ] + .detach() + .squeeze() + .cpu() + .numpy() + ) + patient_dict[patient_id] = { + "feats": patient_embedding, + } + + self._save_features( + output_file=output_file, entry_dict=patient_dict, precision=torch.float32 + ) From 47d8df00540d83f1836649c71e4aeb97a6defaac Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 8 May 2025 10:38:18 +0100 Subject: [PATCH 44/99] add virchow feature extractor --- src/stamp/preprocessing/__init__.py | 10 +++ src/stamp/preprocessing/config.py | 2 + src/stamp/preprocessing/extractor/virchow.py | 55 +++++++++++++++++ .../preprocessing/extractor/virchow_full.py | 61 +++++++++++++++++++ 4 files changed, 128 insertions(+) create mode 100644 src/stamp/preprocessing/extractor/virchow.py create mode 100644 src/stamp/preprocessing/extractor/virchow_full.py diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 26eb1a04..cbe8faf3 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -168,6 +168,16 @@ def extract_( extractor = dino_bloom() + case ExtractorName.VIRCHOW: + from stamp.preprocessing.extractor.virchow import virchow + + extractor = virchow() + + case ExtractorName.VIRCHOW_FULL: + from stamp.preprocessing.extractor.virchow_full import virchow_full + + extractor = virchow_full() + case ExtractorName.VIRCHOW2: from stamp.preprocessing.extractor.virchow2 import virchow2 diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index b0e5de9b..b463a7f0 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -22,6 +22,8 @@ class ExtractorName(StrEnum): GIGAPATH = "gigapath" H_OPTIMUS_0 = "h-optimus-0" H_OPTIMUS_1 = "h-optimus-1" + VIRCHOW = "virchow" + VIRCHOW_FULL = "virchow_full" VIRCHOW2 = "virchow2" EMPTY = "empty" diff --git a/src/stamp/preprocessing/extractor/virchow.py b/src/stamp/preprocessing/extractor/virchow.py new file mode 100644 index 00000000..eb5c90aa --- /dev/null +++ b/src/stamp/preprocessing/extractor/virchow.py @@ -0,0 +1,55 @@ +from typing import Callable, cast + +try: + import timm + import torch + from PIL import Image + from timm.data.config import resolve_data_config + from timm.data.transforms_factory import create_transform + from timm.layers.mlp import SwiGLUPacked +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "virchow dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[virchow2]'`" + ) from e + +from stamp.preprocessing.extractor import Extractor + +__author__ = "Tim Lenz" +__copyright__ = "Copyright (C) 2025 Tim Lenz" +__license__ = "MIT" + + +class VirchowClsOnly(torch.nn.Module): + def __init__(self, model) -> None: + super().__init__() + self.model = model + + def forward(self, batch: torch.Tensor) -> torch.Tensor: + return self.model(batch)[:, 0] + + +def virchow() -> Extractor[VirchowClsOnly]: + """Extracts features from slide tiles using Virchow tile encoder. + The ouput consists of the class token only, resulting in a embedding + of dimension 1280""" + + # Load the model structure + model = timm.create_model( # pyright: ignore[reportPrivateImportUsage] + "hf-hub:paige-ai/Virchow", + pretrained=True, + mlp_layer=SwiGLUPacked, + act_layer=torch.nn.SiLU, + ) + + # Define the transform + transform = cast( + Callable[[Image.Image], torch.Tensor], + create_transform(**resolve_data_config(model.pretrained_cfg, model=model)), + ) + + return Extractor( + model=VirchowClsOnly(model), + transform=transform, + identifier="virchow", + ) diff --git a/src/stamp/preprocessing/extractor/virchow_full.py b/src/stamp/preprocessing/extractor/virchow_full.py new file mode 100644 index 00000000..c776b892 --- /dev/null +++ b/src/stamp/preprocessing/extractor/virchow_full.py @@ -0,0 +1,61 @@ +from typing import Callable, cast + +try: + import timm + import torch + from PIL import Image + from timm.data.config import resolve_data_config + from timm.data.transforms_factory import create_transform + from timm.layers.mlp import SwiGLUPacked +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "virchow dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[virchow2]'`" + ) from e + +from stamp.preprocessing.extractor import Extractor + +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +# nah just kidding + + +class VirchowConcatenated(torch.nn.Module): + def __init__(self, model) -> None: + super().__init__() + self.model = model + + def forward(self, batch: torch.Tensor) -> torch.Tensor: + output = self.model(batch) + + class_token = output[:, 0] + patch_tokens = output[:, 1:] + return torch.cat([class_token, patch_tokens.mean(1)], dim=-1) + + +def virchow_full() -> Extractor[VirchowConcatenated]: + """Extracts features from slide tiles using Virchow tile encoder + concatenating the class token with the mean patch token to create + the final tile embedding of dimension 2560. + """ + + # Load the model structure + model = timm.create_model( # pyright: ignore[reportPrivateImportUsage] + "hf-hub:paige-ai/Virchow", + pretrained=True, + mlp_layer=SwiGLUPacked, + act_layer=torch.nn.SiLU, + ) + + # Define the transform + transform = cast( + Callable[[Image.Image], torch.Tensor], + create_transform(**resolve_data_config(model.pretrained_cfg, model=model)), + ) + + return Extractor( + model=VirchowConcatenated(model), + transform=transform, + identifier="virchow_full", + ) From a809afd800acc0fc432fb2b94ebbf8e69a2448a8 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 8 May 2025 10:39:27 +0100 Subject: [PATCH 45/99] add prism dependencies --- src/stamp/encoding/encoder/prism.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index 9ed70a3f..c87dc517 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -41,7 +41,7 @@ def encode_slides( try: feats, _ = self._validate_and_read_features( - h5_path, "virchow2", torch.float32 + h5_path, "virchow", torch.float32 ) except FileNotFoundError as e: tqdm.write(s=str(e)) @@ -49,7 +49,7 @@ def encode_slides( with torch.autocast(str(device), torch.float16), torch.inference_mode(): slide_embedding = ( - self.model.slide_representations(feats.to(device))[ + self.model.slide_representations(feats.unsqueeze(0).to(device))[ "image_embedding" ] .detach() From 25c161d1a6aad7d3d940cb0a8557bc33a0666fb3 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 8 May 2025 11:10:50 +0100 Subject: [PATCH 46/99] forgot to add pyproject and change encoder name --- pyproject.toml | 8 +++++++- src/stamp/encoding/encoder/prism.py | 2 +- 2 files changed, 8 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 8d0b7a11..84906ba0 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -107,8 +107,14 @@ virchow2 = [ cobra = [ "cobra @ git+https://github.com/KatherLab/COBRA.git", ] + +prism = [ + "sacremoses==0.1.1", + "environs==11.0.0", + +] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index c87dc517..eadac34b 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -41,7 +41,7 @@ def encode_slides( try: feats, _ = self._validate_and_read_features( - h5_path, "virchow", torch.float32 + h5_path, "virchow_full", torch.float32 ) except FileNotFoundError as e: tqdm.write(s=str(e)) From 6b6e22585e0b7472842f107a07ded8c0371b1933 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 8 May 2025 12:22:35 +0100 Subject: [PATCH 47/99] add musk feature extractor --- pyproject.toml | 4 ++ src/stamp/preprocessing/__init__.py | 5 +++ src/stamp/preprocessing/config.py | 1 + src/stamp/preprocessing/extractor/musk.py | 53 +++++++++++++++++++++++ 4 files changed, 63 insertions(+) create mode 100644 src/stamp/preprocessing/extractor/musk.py diff --git a/pyproject.toml b/pyproject.toml index 84906ba0..d7b3e683 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -111,7 +111,11 @@ cobra = [ prism = [ "sacremoses==0.1.1", "environs==11.0.0", +] +musk = [ + "timm>=0.9.11", + "musk @ git+https://github.com/lilab-stanford/MUSK.git@e1699c27687f44bbf6d4adfcbb2abe89795d347f", ] # Blanket target all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism]"] diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index cbe8faf3..e54238ac 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -198,6 +198,11 @@ def extract_( extractor = gigapath() + case ExtractorName.MUSK: + from stamp.preprocessing.extractor.musk import musk + + extractor = musk() + case ExtractorName.EMPTY: from stamp.preprocessing.extractor.empty import empty diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index b463a7f0..80843243 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -25,6 +25,7 @@ class ExtractorName(StrEnum): VIRCHOW = "virchow" VIRCHOW_FULL = "virchow_full" VIRCHOW2 = "virchow2" + MUSK = "musk" EMPTY = "empty" diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py new file mode 100644 index 00000000..e3b7c0cc --- /dev/null +++ b/src/stamp/preprocessing/extractor/musk.py @@ -0,0 +1,53 @@ +from stamp.preprocessing.extractor import Extractor + +try: + import torch + import torchvision + from musk import modeling, utils + from PIL import Image + from timm.data.constants import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD + from timm.models import create_model +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "virchow dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[virchow2]'`" + ) from e + +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2022-2025 Juan Pablo Ricapito" +__license__ = "MIT" + + +class Musk(torch.nn.Module): + def __init__(self, model) -> None: + super().__init__() + self.model = model + + def forward(self, batch: torch.Tensor) -> torch.Tensor: + return self.model( + image=batch, + with_head=False, + out_norm=False, + ms_aug=True, + return_global=True, + )[0] # return (vision_cls, text_cls) + + +def musk() -> Extractor: + model = create_model("musk_large_patch16_384") + utils.load_model_and_may_interpolate( + "hf_hub:xiangjx/musk", model, "model|module", "" + ) + + transform = torchvision.transforms.Compose( + [ + torchvision.transforms.Resize(384, interpolation=3, antialias=True), + torchvision.transforms.CenterCrop((384, 384)), + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + mean=IMAGENET_INCEPTION_MEAN, std=IMAGENET_INCEPTION_STD + ), + ] + ) + + return Extractor(model=Musk(model), transform=transform, identifier="musk") From 5b029bb7e2316f6f675b7b2a161959a1af2b861a Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 8 May 2025 12:23:42 +0100 Subject: [PATCH 48/99] forgot to add dep stuff --- pyproject.toml | 2 +- src/stamp/preprocessing/extractor/musk.py | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index d7b3e683..df0fb9c4 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -118,7 +118,7 @@ musk = [ "musk @ git+https://github.com/lilab-stanford/MUSK.git@e1699c27687f44bbf6d4adfcbb2abe89795d347f", ] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,musk]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py index e3b7c0cc..e76bf16f 100644 --- a/src/stamp/preprocessing/extractor/musk.py +++ b/src/stamp/preprocessing/extractor/musk.py @@ -10,7 +10,7 @@ except ModuleNotFoundError as e: raise ModuleNotFoundError( "virchow dependencies not installed." - " Please reinstall stamp using `pip install 'stamp[virchow2]'`" + " Please reinstall stamp using `pip install 'stamp[musk]'`" ) from e __author__ = "Juan Pablo Ricapito" From c432237f4d5c2007f4bde5cb9a9571b0696fc5b8 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 12 May 2025 09:21:16 +0100 Subject: [PATCH 49/99] fix name typo --- src/stamp/preprocessing/extractor/musk.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py index e76bf16f..e7a45afc 100644 --- a/src/stamp/preprocessing/extractor/musk.py +++ b/src/stamp/preprocessing/extractor/musk.py @@ -9,7 +9,7 @@ from timm.models import create_model except ModuleNotFoundError as e: raise ModuleNotFoundError( - "virchow dependencies not installed." + "musk dependencies not installed." " Please reinstall stamp using `pip install 'stamp[musk]'`" ) from e From 8b558ed47a7ad319b58550a9b76d8c70dc4d5803 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 12 May 2025 09:35:05 +0100 Subject: [PATCH 50/99] format musk --- src/stamp/preprocessing/extractor/musk.py | 5 ++--- 1 file changed, 2 insertions(+), 3 deletions(-) diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py index e7a45afc..75128cb7 100644 --- a/src/stamp/preprocessing/extractor/musk.py +++ b/src/stamp/preprocessing/extractor/musk.py @@ -3,8 +3,7 @@ try: import torch import torchvision - from musk import modeling, utils - from PIL import Image + from musk import utils from timm.data.constants import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models import create_model except ModuleNotFoundError as e: @@ -41,7 +40,7 @@ def musk() -> Extractor: transform = torchvision.transforms.Compose( [ - torchvision.transforms.Resize(384, interpolation=3, antialias=True), + torchvision.transforms.Resize(384, interpolation=3, antialias=True), # type: ignore torchvision.transforms.CenterCrop((384, 384)), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize( From 09b28af6b5965b102f1739c8f9e74694c8884d04 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 12 May 2025 09:50:55 +0100 Subject: [PATCH 51/99] add mstar --- src/stamp/preprocessing/__init__.py | 5 +++++ src/stamp/preprocessing/config.py | 1 + src/stamp/preprocessing/extractor/mstar.py | 26 ++++++++++++++++++++++ 3 files changed, 32 insertions(+) create mode 100644 src/stamp/preprocessing/extractor/mstar.py diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index e54238ac..44a9ab8e 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -203,6 +203,11 @@ def extract_( extractor = musk() + case ExtractorName.MSTAR: + from stamp.preprocessing.extractor.mstar import mstar + + extractor = mstar() + case ExtractorName.EMPTY: from stamp.preprocessing.extractor.empty import empty diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index 80843243..17d21b18 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -26,6 +26,7 @@ class ExtractorName(StrEnum): VIRCHOW_FULL = "virchow_full" VIRCHOW2 = "virchow2" MUSK = "musk" + MSTAR = "mstar" EMPTY = "empty" diff --git a/src/stamp/preprocessing/extractor/mstar.py b/src/stamp/preprocessing/extractor/mstar.py new file mode 100644 index 00000000..aacc7791 --- /dev/null +++ b/src/stamp/preprocessing/extractor/mstar.py @@ -0,0 +1,26 @@ +from torchvision import transforms + +from stamp.preprocessing.extractor import Extractor + +try: + import timm +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "mSTAR dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[mstar]'`" + ) from e + + +def mstar() -> Extractor: + model = timm.create_model( + "hf-hub:Wangyh/mSTAR", pretrained=True, init_values=1e-5, dynamic_img_size=True + ) + transform = transforms.Compose( + [ + transforms.Resize(224), + transforms.ToTensor(), + transforms.Normalize(mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225)), + ] + ) + + return Extractor(model=model, transform=transform, identifier="mstar") From e89a01989d0f74fe4a2ab2786a2e257070758b71 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 12 May 2025 12:51:45 +0100 Subject: [PATCH 52/99] add plip --- pyproject.toml | 8 +++-- src/stamp/preprocessing/__init__.py | 5 ++++ src/stamp/preprocessing/config.py | 1 + src/stamp/preprocessing/extractor/plip.py | 36 +++++++++++++++++++++++ 4 files changed, 47 insertions(+), 3 deletions(-) create mode 100644 src/stamp/preprocessing/extractor/plip.py diff --git a/pyproject.toml b/pyproject.toml index df0fb9c4..d70fbda3 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -107,18 +107,20 @@ virchow2 = [ cobra = [ "cobra @ git+https://github.com/KatherLab/COBRA.git", ] - prism = [ "sacremoses==0.1.1", "environs==11.0.0", ] - musk = [ "timm>=0.9.11", "musk @ git+https://github.com/lilab-stanford/MUSK.git@e1699c27687f44bbf6d4adfcbb2abe89795d347f", ] +plip = [ + "transformers>=4.45.2" +] + # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,musk]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,musk,plip]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 44a9ab8e..d0ea6ddc 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -208,6 +208,11 @@ def extract_( extractor = mstar() + case ExtractorName.PLIP: + from stamp.preprocessing.extractor.plip import plip + + extractor = plip() + case ExtractorName.EMPTY: from stamp.preprocessing.extractor.empty import empty diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index 17d21b18..a9717b78 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -27,6 +27,7 @@ class ExtractorName(StrEnum): VIRCHOW2 = "virchow2" MUSK = "musk" MSTAR = "mstar" + PLIP = "plip" EMPTY = "empty" diff --git a/src/stamp/preprocessing/extractor/plip.py b/src/stamp/preprocessing/extractor/plip.py new file mode 100644 index 00000000..4ec25852 --- /dev/null +++ b/src/stamp/preprocessing/extractor/plip.py @@ -0,0 +1,36 @@ +import torch +import torchvision + +try: + from transformers import CLIPModel +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "PLIP dependencies not installed." + " Please reinstall stamp using `pip install 'stamp[plip]'`" + ) from e + +from stamp.preprocessing.extractor import Extractor + + +class PLIP(torch.nn.Module): + def __init__(self, model) -> None: + super().__init__() + self.model = model + + def forward(self, batch: torch.Tensor) -> torch.Tensor: + return self.model.get_image_features(batch) + + +def plip() -> Extractor[PLIP]: + model = CLIPModel.from_pretrained("vinid/plip") + transform = torchvision.transforms.Compose( + [ + torchvision.transforms.ToTensor(), + torchvision.transforms.Normalize( + (0.48145466, 0.4578275, 0.40821073), + (0.26862954, 0.26130258, 0.27577711), + ), + ] + ) + + return Extractor(model=PLIP(model), transform=transform, identifier="plip") From 9578ceb103b1d7d18bb174084d5bdd13fcaa304e Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 14 May 2025 12:13:26 +0200 Subject: [PATCH 53/99] minor prism fixes --- src/stamp/encoding/encoder/prism.py | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index eadac34b..8698cfb4 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -41,7 +41,7 @@ def encode_slides( try: feats, _ = self._validate_and_read_features( - h5_path, "virchow_full", torch.float32 + h5_path, "virchow_full", torch.float16 ) except FileNotFoundError as e: tqdm.write(s=str(e)) @@ -62,7 +62,7 @@ def encode_slides( } self._save_features( - output_file=output_file, entry_dict=slide_dict, precision=torch.float32 + output_file=output_file, entry_dict=slide_dict, precision=torch.float16 ) def encode_patients( @@ -94,7 +94,7 @@ def encode_patients( try: feats, _ = self._validate_and_read_features( h5_path=h5_path, - extractor_name="virchow2", + extractor_name="virchow_full", precision=torch.float32, ) except FileNotFoundError as e: @@ -110,7 +110,7 @@ def encode_patients( all_feats = torch.cat(feats_list, dim=0).to(device) patient_embedding = ( - self.model.slide_representations(all_feats.to(device))[ + self.model.slide_representations(all_feats.unsqueeze(0).to(device))[ "image_embedding" ] .detach() From 185c08e0f7de89c26a9e25f6a269ff65cfb25635 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 15 May 2025 12:48:54 +0200 Subject: [PATCH 54/99] move encoding logic to base class --- src/stamp/encoding/encoder/__init__.py | 174 +++++++++++++++++++----- src/stamp/encoding/encoder/chief.py | 71 +++------- src/stamp/encoding/encoder/cobra.py | 140 +++++-------------- src/stamp/encoding/encoder/eagle.py | 136 +++++++++++-------- src/stamp/encoding/encoder/gigapath.py | 179 +++++++++++-------------- src/stamp/encoding/encoder/prism.py | 131 ++++-------------- src/stamp/encoding/encoder/titan.py | 102 ++++++-------- src/stamp/modeling/data.py | 2 +- 8 files changed, 409 insertions(+), 526 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 1361a184..cb4c71e6 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -1,76 +1,176 @@ import os from abc import ABC, abstractmethod -from dataclasses import KW_ONLY, dataclass -from typing import Generic, TypeVar +from pathlib import Path +from typing import TypeVar import h5py +import numpy as np +import pandas as pd import torch -from torch import Tensor, dtype, nn +from torch import Tensor, nn +from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm import stamp +from stamp.cache import get_processing_code_hash from stamp.modeling.data import CoordsInfo, get_coords EncoderModel = TypeVar("EncoderModel", bound=nn.Module) -@dataclass() -class Encoder(ABC, Generic[EncoderModel]): - _: KW_ONLY - model: EncoderModel - identifier: str - """An ID _uniquely_ identifying the model and extractor. - - If possible, it should include the digest of the model weights etc. - so that any change in the model also changes its ID. - """ +class Encoder(ABC): + def __init__( + self, model, identifier: str, precision: torch.dtype, required_extractor: str + ): + self.model = model + self.identifier = identifier + self.precision = precision + self.required_extractor = required_extractor + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + """General method to encode slides.""" + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + slide_dict = {} + self.model.to(device).eval() + + if self.precision == torch.float16: + self.model.half() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_path = os.path.join(feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, coords = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + slide_embedding = self._generate_slide_embedding( + feats, device, coords=coords + ) + slide_dict[slide_name] = {"feats": slide_embedding} + + self._save_features(output_file, entry_dict=slide_dict) + + def encode_patients( + self, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + """General method to encode patients with features only. + Override this method if coords are needed for encoding""" + output_name = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + + patient_dict = {} + self.model.to(device).eval() + + if self.precision == torch.float16: + self.model.half() + + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + slide_table = self._read_slide_table(slide_table_path) + patient_groups = slide_table.groupby("PATIENT") + + for patient_id, group in tqdm(patient_groups, leave=False): + feats_list = [] + + for _, row in group.iterrows(): + h5_path = os.path.join(feat_dir, row["FILENAME"]) + try: + feats, _ = self._validate_and_read_features(h5_path) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + feats_list.append(feats) + + if not feats_list: + tqdm.write(f"No features found for patient {patient_id}, skipping.") + continue + + patient_embedding = self._generate_patient_embedding( + feats_list, device, **kwargs + ) + patient_dict[patient_id] = {"feats": patient_embedding} + + self._save_features(output_file, entry_dict=patient_dict) @abstractmethod - def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: - """Abstract method to encode slide from patch features.""" + def _generate_slide_embedding( + self, feats: torch.Tensor, device, **kwargs + ) -> np.ndarray: + """Generate slide embedding. Must be implemented by subclasses.""" pass @abstractmethod - def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs - ) -> None: - """Abstract method to encode patient from slide features.""" + def _generate_patient_embedding( + self, + feats_list: list, + device, + **kwargs, + ) -> np.ndarray: + """Generate patient embedding. Must be implemented by subclasses.""" pass + @staticmethod + def _read_slide_table(slide_table_path: Path) -> pd.DataFrame: + return pd.read_csv(slide_table_path) + + def _validate_and_read_features(self, h5_path: str) -> tuple[Tensor, CoordsInfo]: + feats, coords, extractor = self._read_h5(h5_path) + if self.required_extractor not in extractor: + raise ValueError( + f"Features must be extracted with {self.required_extractor}. " + f"Features located in {h5_path} are extracted with {extractor}" + ) + return feats, coords + def _read_h5( - self, h5_path: str, precision: dtype + self, + h5_path: str, ) -> tuple[Tensor, CoordsInfo, str]: if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): raise FileNotFoundError("File does not exist or is not an h5 file") with h5py.File(h5_path, "r") as f: - feats: Tensor = torch.tensor(f["feats"][:], dtype=precision) # type: ignore + feats: Tensor = torch.tensor(f["feats"][:], dtype=self.precision) # type: ignore coords: CoordsInfo = get_coords(f) extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor - def _validate_and_read_features( - self, h5_path, extractor_name, precision - ) -> tuple[Tensor, CoordsInfo]: - feats, coords, extractor = self._read_h5(h5_path, precision=precision) - if extractor_name not in extractor: - raise ValueError( - f"Features must be extracted with {extractor_name}. " - f"Features located in {h5_path} are extracted with {extractor}" - ) - return feats, coords - - def _save_features(self, output_file, entry_dict, precision) -> None: + def _save_features(self, output_file: str, entry_dict: dict) -> None: os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: for entry_name, data in entry_dict.items(): f.create_dataset(f"{entry_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = self.identifier - f.attrs["precision"] = str(precision) - # Check if the file is empty + f.attrs["precision"] = str(self.precision) if len(f) == 0: tqdm.write("Extraction failed: file empty") os.remove(output_file) else: - tqdm.write(f"Finished encoding, saved to {output_file}") - # TODO: Add codebase hash to h5 file + tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index c5f3df5c..12c1d712 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -6,7 +6,7 @@ import torch import torch.nn as nn import torch.nn.functional as F -from torch._prims_common import DeviceLikeType # type: ignore +from numpy import ndarray from tqdm import tqdm from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash @@ -97,53 +97,26 @@ def __init__(self) -> None: if "organ_embedding" in chief: del chief["organ_embedding"] model.load_state_dict(chief, strict=True) - super().__init__(model=model, identifier="chief") - - def encode_slides( - self, - output_dir: Path, - feat_dir: Path, - device: DeviceLikeType, - **kwargs, - ) -> None: - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + super().__init__( + model=model, + identifier="chief", + precision=torch.float32, + required_extractor="ctranspath", ) - output_file = os.path.join(output_dir, output_name) - - slide_dict = {} - self.model.to(device).eval() - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - slide_name: str = Path(tile_feats_filename).stem + def _generate_slide_embedding( + self, feats: torch.Tensor, device, **kwargs + ) -> ndarray: + slide_embedding = self.model(feats.to(device))["WSI_feature"] + return slide_embedding.detach().squeeze().cpu().numpy() - try: - feats, _ = self._validate_and_read_features( - h5_path, "ctranspath", torch.float32 - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue + def _generate_patient_embedding( + self, feats_list: list, device, **kwargs + ) -> ndarray: + all_feats = torch.cat(feats_list, dim=0).to(device) - slide_embedding = ( - self.model(feats.to(device))["WSI_feature"] - .detach() - .squeeze() - .cpu() - .numpy() - ) - slide_dict[slide_name] = { - "feats": slide_embedding, - } - - self._save_features( - output_file=output_file, entry_dict=slide_dict, precision=torch.float32 - ) + patient_embedding = self.model(all_feats.to(device))["WSI_feature"] + return patient_embedding.detach().squeeze().cpu().numpy() def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -172,11 +145,7 @@ def encode_patients( h5_path = os.path.join(feat_dir, slide_filename) try: - feats, _ = self._validate_and_read_features( - h5_path=h5_path, - extractor_name="ctranspath", - precision=torch.float32, - ) + feats, _ = self._validate_and_read_features(h5_path=h5_path) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -200,9 +169,7 @@ def encode_patients( "feats": patient_embedding, } - self._save_features( - output_file=output_file, entry_dict=patient_dict, precision=torch.float32 - ) + self._save_features(output_file=output_file, entry_dict=patient_dict) def initialize_weights(module): diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index ee9a2713..7b3b5ea5 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -1,12 +1,6 @@ -import os -from pathlib import Path - -import h5py -import pandas as pd import torch -from tqdm import tqdm +from numpy import ndarray -from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder # TODO: Check which are the necessary imports and add them to cobra package @@ -23,106 +17,36 @@ class Cobra(Encoder): def __init__(self) -> None: model = get_cobra(download_weights=True) - super().__init__(model=model, identifier="katherlab-cobra") - - def _get_tile_embs(self, h5_path, device): - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - - feats = torch.tensor(feats).to(device) - return feats.unsqueeze(0) - - def encode_slides(self, output_dir, feat_dir, device, **kwargs) -> None: - """Encode slides from patch features.""" - slide_dict = {} - self.model.to(device).eval() - dtype: torch.dtype = torch.float32 - # TODO: dtype depends con CUDA capabilities. Check end of - # extract_feat_patients.py on how to handle this - - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - slide_name = Path(tile_feats_filename).stem - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - tqdm.write( - f"File {h5_path} does not exist or is not an h5 file, skipping" - ) - continue - - tile_embs = self._get_tile_embs(h5_path, device) - - with torch.inference_mode(): - assert tile_embs.ndim == 3, f"Expected 3D tensor, got {tile_embs.ndim}" - slide_feats = self.model(tile_embs.to(dtype)) - slide_dict[slide_name] = { - "feats": slide_feats.to(torch.float32) - .detach() - .squeeze() - .cpu() - .numpy() - } - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) - output_file = os.path.join(output_dir, output_name) - self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) - - def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs - ) -> None: - """Encode patients from slide features.""" - dtype: torch.dtype = torch.float32 - self.model.to(device).eval() - # TODO: dtype depends con CUDA capabilities. Check end of - # extract_feat_patients.py on how to handle this - - slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") - patient_dict = {} - - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + if torch.cuda.get_device_capability()[0] < 8: + print( + f"\033[93mCOBRA (Mamba2) is designed to run on GPUs with compute capability 8.0 or higher!! " + f"Your GPU has compute capability {torch.cuda.get_device_capability()[0]}. " + f"We are forced to switch to mixed FP16 precision. This may lead to numerical instability and reduced performance!!\033[0m" + ) + precision = torch.float16 + else: + precision = torch.float32 + super().__init__( + model=model, + identifier="katherlab-cobra", + precision=precision, + required_extractor="", ) - output_file = os.path.join(output_dir, output_name) - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - for patient_id, group in tqdm(patient_groups, leave=False): - all_feats_list = [] - - for _, row in group.iterrows(): - slide_filename = row["FILENAME"] - h5_path = os.path.join(feat_dir, slide_filename) - if not os.path.exists(h5_path): - tqdm.write(f"File {h5_path} does not exist, skipping") - continue - with h5py.File(h5_path, "r") as f: - feats = f["feats"][:] # type: ignore - feats = torch.tensor(feats).to(device) - all_feats_list.append(feats) - - if all_feats_list: - # Concatenate all features for this patient along the second dimension - all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) - - with torch.inference_mode(): - assert all_feats_cat.ndim == 3, ( - f"Expected 3D tensor, got {all_feats_cat.ndim}" - ) - patient_feats = self.model(all_feats_cat.to(dtype)) - patient_dict[patient_id] = { - "feats": patient_feats.to(torch.float32) - .detach() - .squeeze() - .cpu() - .numpy(), - } - else: - tqdm.write(f"No features found for patient {patient_id}, skipping") - - self._save_features( - output_file, entry_dict=patient_dict, precision=torch.float32 - ) + def _generate_slide_embedding( + self, feats: torch.Tensor, device, **kwargs + ) -> ndarray: + feats = feats.unsqueeze(0).to(device) + assert feats.ndim == 3, f"Expected 3D tensor, got {feats.ndim}" + with torch.inference_mode(): + slide_embedding = self.model(feats) + return slide_embedding.detach().squeeze().cpu().numpy() + + def _generate_patient_embedding( + self, feats_list: list, device, **kwargs + ) -> ndarray: + all_feats = torch.cat(feats_list, dim=0).unsqueeze(0) + assert all_feats.ndim == 3, f"Expected 3D tensor, got {all_feats.ndim}" + with torch.inference_mode(): + slide_embedding = self.model(all_feats.to(device)) + return slide_embedding.detach().squeeze().cpu().numpy() diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 96ced074..1ac85ed8 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -19,7 +19,12 @@ class Eagle(Encoder): def __init__(self) -> None: model = CHIEF().model - super().__init__(model=model, identifier="katherlab-eagle") + super().__init__( + model=model, + identifier="katherlab-eagle", + precision=torch.float32, + required_extractor="ctranspath", + ) def _validate_and_read_features_with_agg( self, h5_ctp: str, h5_vir2: str, slide_name: str @@ -27,7 +32,7 @@ def _validate_and_read_features_with_agg( feats: Tensor coords: CoordsInfo extractor: str - feats, coords, extractor = self._read_h5(h5_ctp, torch.float32) + feats, coords, extractor = self._read_h5(h5_ctp) if "ctranspath" not in extractor: raise ValueError( @@ -35,7 +40,7 @@ def _validate_and_read_features_with_agg( f"Features located in {h5_ctp} are extracted with {extractor}" ) - agg_feats, agg_coords, extractor = self._read_h5(h5_vir2, torch.float32) + agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) if "virchow2" not in extractor: raise ValueError( @@ -57,13 +62,18 @@ def _validate_and_read_features_with_agg( return feats, agg_feats - def _create_eagle_embedding( + def _generate_slide_embedding( self, feats: Tensor, - agg_feats: Tensor, device: DeviceLikeType, + agg_feats: Tensor | None = None, + **kwargs, ) -> np.ndarray: """Process features, compute attention, and create an embedding.""" + + if agg_feats is None: + raise ValueError("agg_feats is required for slide embedding") + with torch.no_grad(): result = self.model(feats.to(device)) attention_raw = result["attention_raw"].squeeze(0).cpu() @@ -82,6 +92,66 @@ def _create_eagle_embedding( return eagle_embedding.to(torch.float32).detach().squeeze().cpu().numpy() + def _generate_patient_embedding( + self, + feats_list: list[Tensor], + device: DeviceLikeType, + agg_feats_list: list[Tensor] | None = None, + **kwargs, + ) -> np.ndarray: + if agg_feats_list is None: + raise ValueError("agg_feats_list is required for patient embedding") + all_feats = torch.cat(feats_list, dim=0).to(device) + all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) + return self._generate_slide_embedding(all_feats, device, all_agg_feats) + + def encode_slides( + self, + output_dir: Path, + feat_dir: Path, + device: DeviceLikeType, + **kwargs, + ) -> None: + """Encode slide from patch features.""" + agg_feat_dir: Path | None = kwargs.get("agg_feat_dir") + if not agg_feat_dir: + raise ValueError( + "agg_feat_dir that contains virchow2 features" + " is required for Eagle's encode_patients" + ) + + output_name = ( + f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + ) + output_file = os.path.join(output_dir, output_name) + if os.path.exists(output_file): + tqdm.write(f"Output file {output_file} already exists, skipping") + return + + slide_dict = {} + self.model.to(device).eval() + + for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + h5_ctp = os.path.join(feat_dir, tile_feats_filename) + h5_vir2 = os.path.join(agg_feat_dir, tile_feats_filename) + slide_name: str = Path(tile_feats_filename).stem + + try: + feats, agg_feats = self._validate_and_read_features_with_agg( + h5_ctp, h5_vir2, slide_name + ) + except FileNotFoundError as e: + tqdm.write(s=str(e)) + continue + + eagle_embedding = self._generate_slide_embedding(feats, device, agg_feats) + + slide_dict[slide_name] = { + "feats": eagle_embedding, + } + + self._save_features(output_file, entry_dict=slide_dict) + def encode_patients( self, output_dir: Path, @@ -137,62 +207,12 @@ def encode_patients( if not feats_list: tqdm.write(f"No ctranspath features for patient {patient_id}") continue - all_feats = torch.cat(feats_list, dim=0).to(device) - all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) - eagle_embedding = self._create_eagle_embedding( - all_feats, all_agg_feats, device + eagle_embedding = self._generate_patient_embedding( + feats_list, device, agg_feats_list ) patient_dict[patient_id] = { "feats": eagle_embedding, } - self._save_features( - output_file, entry_dict=patient_dict, precision=torch.float32 - ) - - def encode_slides( - self, - output_dir: Path, - feat_dir: Path, - device: DeviceLikeType, - **kwargs, - ) -> None: - """Encode slide from patch features.""" - agg_feat_dir: Path | None = kwargs.get("agg_feat_dir") - if not agg_feat_dir: - raise ValueError( - "agg_feat_dir that contains virchow2 features" - " is required for Eagle's encode_patients" - ) - - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) - output_file = os.path.join(output_dir, output_name) - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - slide_dict = {} - self.model.to(device).eval() - - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_ctp = os.path.join(feat_dir, tile_feats_filename) - h5_vir2 = os.path.join(agg_feat_dir, tile_feats_filename) - slide_name: str = Path(tile_feats_filename).stem - - try: - feats, agg_feats = self._validate_and_read_features_with_agg( - h5_ctp, h5_vir2, slide_name - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - - eagle_embedding = self._create_eagle_embedding(feats, agg_feats, device) - slide_dict[slide_name] = { - "feats": eagle_embedding, - } - - self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) + self._save_features(output_file, entry_dict=patient_dict) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 1d62a91e..8eaa0d80 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -6,11 +6,11 @@ import pandas as pd import torch from gigapath import slide_encoder -from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder +from stamp.modeling.data import CoordsInfo from stamp.preprocessing.tiling import SlideMPP @@ -25,100 +25,45 @@ def __init__(self) -> None: "Gigapath requires flash-attn. " "Install it with: pip install flash-attn --no-build-isolation" ) - # I cant add flash-attn to the pyproject.toml because it requires torch - # beforehand, I add torch to build-system requires but throws the - # same bloody error - super().__init__(model=model, identifier="gigapath") + super().__init__( + model=model, + identifier="gigapath", + precision=torch.float16, + required_extractor="gigapath", + ) - def _convert_coords( - self, - coords, - total_wsi_width, - max_wsi_height, - n_grid, - current_x_offset, + def _generate_slide_embedding( + self, feats, device, coords: CoordsInfo | None = None, **kwargs ): - """ - Normalize the x and y coordinates relative to the total WSI width and max height, using the same grid [0, 1000]. - Thanks Peter! - """ - # Normalize x-coordinates based on total WSI width (taking into account the current x offset) - normalized_x = (coords[:, 0] + current_x_offset) / total_wsi_width * n_grid - - # Normalize y-coordinates based on the maximum WSI height - normalized_y = coords[:, 1] / max_wsi_height * n_grid + if not coords: + raise ValueError("Tile coords are required for encoding") - # Stack normalized x and y coordinates - converted_coords = np.stack([normalized_x, normalized_y], axis=-1) + # Calculate slide dimensions + slide_width = max(coords.coords_um[:, 0]) + coords.tile_size_um + slide_height = max(coords.coords_um[:, 1]) + coords.tile_size_um - return np.array(converted_coords, dtype=np.float32) - - def encode_slides( - self, - output_dir: Path, - feat_dir: Path, - device: DeviceLikeType, - **kwargs, - ) -> None: - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + # Normalize coordinates to a [0, 1000] grid + n_grid = 1000 + norm_coords = self._convert_coords( + coords.coords_um, slide_width, slide_height, n_grid, current_x_offset=0 ) - output_file = os.path.join(output_dir, output_name) - - slide_dict = {} - self.model.to(device).half().eval() - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - slide_name: str = Path(tile_feats_filename).stem - - try: - feats, coords = self._validate_and_read_features( - h5_path=h5_path, extractor_name="gigapath", precision=torch.float16 - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - - # Calculated obtaining the tile with rightmost x coord - # and the tile width is added as the coord is from top left - slide_width = max(coords.coords_um[:, 0]) + coords.tile_size_um - slide_height = max(coords.coords_um[:, 1]) + coords.tile_size_um - - # For some reason gigapaths requires normalized coords in a - # [0,1000] grid - n_grid = 1000 - - # TODO: Check np.stack behaviour - norm_coords = self._convert_coords( - coords.coords_um, slide_width, slide_height, n_grid, current_x_offset=0 - ) - - norm_coords = ( - torch.tensor(norm_coords, dtype=torch.float32) - .unsqueeze(0) - .to(device) - .half() - ) - feats = feats.unsqueeze(0).half().to(device) - - with torch.inference_mode(): - slide_embedding = self.model(feats, norm_coords) + norm_coords = ( + torch.tensor(norm_coords, dtype=torch.float32) + .unsqueeze(0) + .to(device) + .half() + ) + feats = feats.unsqueeze(0).half().to(device) - if isinstance(slide_embedding, list): # Ensure slide_feats is not a list - slide_embedding = torch.cat(slide_embedding, dim=0) + self.model.to(self.precision) - slide_embedding = slide_embedding.detach().squeeze().cpu().numpy() + with torch.inference_mode(): + slide_embedding = self.model(feats, norm_coords) - slide_dict[slide_name] = { - "feats": slide_embedding, - } + if isinstance(slide_embedding, list): # Ensure slide_embedding is not a list + slide_embedding = torch.cat(slide_embedding, dim=0) - self._save_features(output_file, entry_dict=slide_dict, precision=torch.float16) + return slide_embedding.detach().squeeze().cpu().numpy() def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -154,11 +99,7 @@ def encode_patients( h5_path = os.path.join(feat_dir, slide_filename) try: - feats, coords = self._validate_and_read_features( - h5_path=h5_path, - extractor_name="gigapath", - precision=torch.float16, - ) + feats, coords = self._validate_and_read_features(h5_path=h5_path) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -219,21 +160,51 @@ def encode_patients( tqdm.write(f"No features found for patient {patient_id}, skipping.") continue - all_feats_cat = torch.cat(all_feats_list, dim=1) - all_coords_cat = torch.cat(all_coords_list, dim=1) - - with torch.inference_mode(): - patient_embedding = self.model(all_feats_cat, all_coords_cat) - - if isinstance(patient_embedding, list): # Ensure slide_feats is not a list - patient_embedding = torch.cat(patient_embedding, dim=0) - - patient_embedding = patient_embedding.detach().squeeze().cpu().numpy() + patient_embedding = self._generate_patient_embedding( + all_feats_list, device, coords_list=all_coords_list + ) patient_dict[patient_id] = { "feats": patient_embedding, } - self._save_features( - output_file, entry_dict=patient_dict, precision=torch.float16 - ) + self._save_features(output_file, entry_dict=patient_dict) + + def _generate_patient_embedding( + self, feats_list, device, coords_list: list | None = None, **kwargs + ) -> np.ndarray: + if not coords_list: + raise ValueError("Tile coords are required for encoding") + all_feats_cat = torch.cat(feats_list, dim=1) + all_coords_cat = torch.cat(coords_list, dim=1) + + with torch.inference_mode(): + patient_embedding = self.model(all_feats_cat, all_coords_cat) + + if isinstance(patient_embedding, list): # Ensure slide_feats is not a list + patient_embedding = torch.cat(patient_embedding, dim=0) + + return patient_embedding.detach().squeeze().cpu().numpy() + + def _convert_coords( + self, + coords, + total_wsi_width, + max_wsi_height, + n_grid, + current_x_offset, + ): + """ + Normalize the x and y coordinates relative to the total WSI width and max height, using the same grid [0, 1000]. + Thanks Peter! + """ + # Normalize x-coordinates based on total WSI width (taking into account the current x offset) + normalized_x = (coords[:, 0] + current_x_offset) / total_wsi_width * n_grid + + # Normalize y-coordinates based on the maximum WSI height + normalized_y = coords[:, 1] / max_wsi_height * n_grid + + # Stack normalized x and y coordinates + converted_coords = np.stack([normalized_x, normalized_y], axis=-1) + + return np.array(converted_coords, dtype=np.float32) diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index 8698cfb4..965a8237 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -1,116 +1,25 @@ -import os -from pathlib import Path - -import pandas as pd import torch -from torch._prims_common import DeviceLikeType # type: ignore -from tqdm import tqdm from transformers import AutoModel -from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder class Prism(Encoder): def __init__(self) -> None: model = AutoModel.from_pretrained("paige-ai/Prism", trust_remote_code=True) - super().__init__(model=model, identifier="paigeai-prism") - - def encode_slides( - self, - output_dir: Path, - feat_dir: Path, - device: DeviceLikeType, - **kwargs, - ) -> None: - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) - output_file = os.path.join(output_dir, output_name) - - slide_dict = {} - self.model.to(device).eval() - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - slide_name: str = Path(tile_feats_filename).stem - - try: - feats, _ = self._validate_and_read_features( - h5_path, "virchow_full", torch.float16 - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - - with torch.autocast(str(device), torch.float16), torch.inference_mode(): - slide_embedding = ( - self.model.slide_representations(feats.unsqueeze(0).to(device))[ - "image_embedding" - ] - .detach() - .squeeze() - .cpu() - .numpy() - ) - slide_dict[slide_name] = { - "feats": slide_embedding, - } - - self._save_features( - output_file=output_file, entry_dict=slide_dict, precision=torch.float16 - ) - - def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs - ) -> None: - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + precision = torch.float16 + required_extractor = "virchow_full" + super().__init__( + model=model, + identifier="paigeai-prism", + precision=precision, + required_extractor=required_extractor, ) - slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") - - output_file = os.path.join(output_dir, output_name) - - patient_dict = {} - self.model.to(device).eval() - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - for patient_id, group in tqdm(patient_groups, leave=False): - feats_list = [] - - # Concatenate all slides over x axis adding the offset to each feature x coordinate. - for _, row in group.iterrows(): - slide_filename = row["FILENAME"] - h5_path = os.path.join(feat_dir, slide_filename) - - try: - feats, _ = self._validate_and_read_features( - h5_path=h5_path, - extractor_name="virchow_full", - precision=torch.float32, - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - - feats_list.append(feats) - - if not feats_list: - tqdm.write(f"No features found for patient {patient_id}, skipping.") - continue - - all_feats = torch.cat(feats_list, dim=0).to(device) - - patient_embedding = ( - self.model.slide_representations(all_feats.unsqueeze(0).to(device))[ + def _generate_slide_embedding(self, feats, device, **kwargs): + with torch.autocast(device, dtype=self.precision), torch.inference_mode(): + return ( + self.model.slide_representations(feats.unsqueeze(0).to(device))[ "image_embedding" ] .detach() @@ -118,10 +27,16 @@ def encode_patients( .cpu() .numpy() ) - patient_dict[patient_id] = { - "feats": patient_embedding, - } - self._save_features( - output_file=output_file, entry_dict=patient_dict, precision=torch.float32 - ) + def _generate_patient_embedding(self, feats_list, device, **kwargs): + all_feats = torch.cat(feats_list, dim=0).to(device) + with torch.inference_mode(): + return ( + self.model.slide_representations(all_feats.unsqueeze(0))[ + "image_embedding" + ] + .detach() + .squeeze() + .cpu() + .numpy() + ) diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 7961c3c1..41c03287 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -12,21 +12,36 @@ from stamp.cache import get_processing_code_hash from stamp.encoding.encoder import Encoder -from stamp.preprocessing.tiling import SlideMPP +from stamp.modeling.data import CoordsInfo +from stamp.preprocessing.tiling import Microns, SlideMPP class Titan(Encoder): def __init__(self) -> None: model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) - super().__init__(model=model, identifier="mahmood-titan") + super().__init__( + model=model, + identifier="mahmood-titan", + precision=torch.float32, + required_extractor="conch1_5", + ) - def _encode_slide( - self, feats: Tensor, coords_um: Tensor, mpp: SlideMPP, device: DeviceLikeType + def _generate_slide_embedding( + self, + feats: Tensor, + device: DeviceLikeType, + coords: CoordsInfo | None = None, + **kwargs, ) -> np.ndarray: """Helper method to encode a single slide.""" + if coords is None: + raise ValueError("Coords must be provided.") + + coords_tensor = torch.tensor(coords.coords_um, dtype=self.precision) + # Convert coordinates from microns to pixels - patch_size_lvl0 = math.floor(256 / mpp) # Inferred from TITAN docs - coords_px = coords_um / mpp # Convert to pixels + patch_size_lvl0 = math.floor(256 / coords.mpp) # Inferred from TITAN docs + coords_px = coords_tensor / coords.mpp # Convert to pixels coords_px = coords_px.to(torch.int64).to(device) # Convert to integer feats = feats.to(device) @@ -35,49 +50,31 @@ def _encode_slide( slide_embedding = self.model.encode_slide_from_patch_features( feats, coords_px, patch_size_lvl0 ) - return slide_embedding.to(torch.float32).detach().squeeze().cpu().numpy() + return slide_embedding.detach().squeeze().cpu().numpy() - def encode_slides( + def _generate_patient_embedding( self, - output_dir: Path, - feat_dir: Path, + feats_list: list, device: DeviceLikeType, + coords_list: list[CoordsInfo] | None = None, **kwargs, - ) -> None: - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) - output_file = os.path.join(output_dir, output_name) - - slide_dict = {} - self.model.to(device).eval() - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + ) -> np.ndarray: + if coords_list is None: + raise ValueError("coords_list must be provided.") - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): - h5_path = os.path.join(feat_dir, tile_feats_filename) - slide_name: str = Path(tile_feats_filename).stem + # Concatenate all feature to a single slide tensor + all_feats_cat = torch.cat(feats_list, dim=0).unsqueeze(0) - try: - feats, coords = self._validate_and_read_features( - h5_path=h5_path, extractor_name="conch1_5", precision=torch.float32 - ) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue + # Create a single CoordsInfo item for the virtual slide + # Already validated that mpp values are all equal within patient slides + tile_size_um: Microns = coords_list[0].tile_size_um + tile_size_px = coords_list[0].tile_size_px + # Combine all slide coords to a single virtual slide set of coordinates + coords_um = np.concatenate([coord.coords_um for coord in coords_list], axis=0) + # Create virtual slide's Coords Info object + coords = CoordsInfo(coords_um, tile_size_um, tile_size_px) - coords_tensor = torch.tensor(coords.coords_um, dtype=torch.float32) - - slide_embedding = self._encode_slide( - feats, coords_tensor, coords.mpp, device - ) - slide_dict[slide_name] = { - "feats": slide_embedding, - } - - self._save_features(output_file, entry_dict=slide_dict, precision=torch.float32) + return self._generate_slide_embedding(all_feats_cat, device, coords) def encode_patients( self, output_dir, feat_dir, slide_table_path, device, **kwargs @@ -111,11 +108,7 @@ def encode_patients( h5_path = os.path.join(feat_dir, slide_filename) try: - feats, coords = self._validate_and_read_features( - h5_path=h5_path, - extractor_name="conch1_5", - precision=torch.float32, - ) + feats, coords = self._validate_and_read_features(h5_path=h5_path) except FileNotFoundError as e: tqdm.write(s=str(e)) continue @@ -139,26 +132,19 @@ def encode_patients( # point. With that you get the total width of the slide. current_x_offset = max(coords.coords_um[:, 0]) + coords.tile_size_um - coords_tensor = torch.tensor(coords.coords_um, dtype=torch.float32) - # Add tile feats and coords to the patient virtual slide all_feats_list.append(feats) - all_coords_list.append(coords_tensor) + all_coords_list.append(coords) if not all_feats_list: tqdm.write(f"No features found for patient {patient_id}, skipping.") continue - all_feats_cat = torch.cat(all_feats_list, dim=0).unsqueeze(0) - all_coords_cat = torch.cat(all_coords_list, dim=0).unsqueeze(0) - - patient_embedding = self._encode_slide( - all_feats_cat, all_coords_cat, slides_mpp, device + patient_embedding = self._generate_patient_embedding( + all_feats_list, device, all_coords_list ) patient_dict[patient_id] = { "feats": patient_embedding, } - self._save_features( - output_file, entry_dict=patient_dict, precision=torch.float32 - ) + self._save_features(output_file, entry_dict=patient_dict) diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py index 36caa0d3..95eeb0ec 100755 --- a/src/stamp/modeling/data.py +++ b/src/stamp/modeling/data.py @@ -194,7 +194,7 @@ def __getitem__( class CoordsInfo: coords_um: np.ndarray tile_size_um: Microns - tile_size_px: TilePixels | None + tile_size_px: TilePixels | None = None @property def mpp(self) -> SlideMPP: From 4a44a36a2021eba17adad3a4022e06480e643cc2 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 15 May 2025 18:56:19 +0200 Subject: [PATCH 55/99] add madeleine --- pyproject.toml | 5 ++- src/stamp/encoding/__init__.py | 10 +++++ src/stamp/encoding/config.py | 2 + src/stamp/encoding/encoder/madeleine.py | 54 +++++++++++++++++++++++++ 4 files changed, 70 insertions(+), 1 deletion(-) create mode 100644 src/stamp/encoding/encoder/madeleine.py diff --git a/pyproject.toml b/pyproject.toml index d70fbda3..fc00ff54 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -111,6 +111,9 @@ prism = [ "sacremoses==0.1.1", "environs==11.0.0", ] +madeleine = [ + "madeleine @ git+https://github.com/mahmoodlab/MADELEINE.git@de7c85acc2bdad352e6df8eee5694f8b6f288012" +] musk = [ "timm>=0.9.11", "musk @ git+https://github.com/lilab-stanford/MUSK.git@e1699c27687f44bbf6d4adfcbb2abe89795d347f", @@ -120,7 +123,7 @@ plip = [ ] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,musk,plip]"] +all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,madeleine,musk,plip]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 77b3b7e5..7aedc70a 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -47,6 +47,11 @@ def get_pat_embs( selected_encoder: Encoder = Prism() + case EncoderName.MADELEINE: + from stamp.encoding.encoder.madeleine import Madeleine + + selected_encoder: Encoder = Madeleine() + case Encoder(): selected_encoder = encoder @@ -95,6 +100,11 @@ def get_slide_embs( selected_encoder: Encoder = Prism() + case EncoderName.MADELEINE: + from stamp.encoding.encoder.madeleine import Madeleine + + selected_encoder: Encoder = Madeleine() + case Encoder(): selected_encoder = encoder diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 007615e4..0ce12320 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -12,6 +12,8 @@ class EncoderName(StrEnum): TITAN = "mahmood-titan" GIGAPATH = "gigapath" PRISM = "paigeai-prism" + MADELEINE = "mahmood-madeleine" + THREADS = "mahmood-threads" class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/src/stamp/encoding/encoder/madeleine.py b/src/stamp/encoding/encoder/madeleine.py new file mode 100644 index 00000000..f04cc2b8 --- /dev/null +++ b/src/stamp/encoding/encoder/madeleine.py @@ -0,0 +1,54 @@ +import os + +import torch +from numpy import ndarray + +from stamp.cache import STAMP_CACHE_DIR +from stamp.encoding.encoder import Encoder + +try: + from madeleine.models.factory import create_model_from_pretrained +except ModuleNotFoundError as e: + raise ModuleNotFoundError( + "madeleine dependencies not installed." + " Please update your venv using `uv sync --extra madeleine`" + ) from e + + +class Madeleine(Encoder): + def __init__(self) -> None: + model, precision = create_model_from_pretrained( + os.path.join(STAMP_CACHE_DIR, "madeleine") + ) + # Check if the GPU supports bfloat16; if not, fall back to float16 + if precision == torch.bfloat16 and not torch.cuda.is_bf16_supported(): + precision = torch.float16 + super().__init__( + model=model, + identifier="mahmood-madeleine", + precision=precision, + required_extractor="conch", + ) + + def _generate_slide_embedding( + self, feats: torch.Tensor, device, **kwargs + ) -> ndarray: + feats = feats.unsqueeze(0) + assert feats.ndim == 3, f"Expected 3D tensor, got {feats.ndim}" + with torch.no_grad(): + with torch.amp.autocast(device_type=device, dtype=self.precision): # type: ignore + slide_embedding = self.model.encode_he(feats=feats, device=device) + # Embedding is casted to float32 as bfloat16 is not always supported + self.precision = torch.float32 + return slide_embedding.detach().squeeze().to(self.precision).cpu().numpy() + + def _generate_patient_embedding( + self, feats_list: list, device, **kwargs + ) -> ndarray: + all_feats = torch.cat(feats_list, dim=0).unsqueeze(0) + assert all_feats.ndim == 3, f"Expected 3D tensor, got {all_feats.ndim}" + with torch.no_grad(): + with torch.amp.autocast(device_type=device, dtype=self.precision): # type: ignore + patient_embedding = self.model.encode_he(feats=all_feats, device=device) + self.precision = torch.float32 + return patient_embedding.detach().squeeze().to(self.precision).cpu().numpy() From 44df4b7a97de9aa3bded918d9699df78ed71d5f4 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 16 May 2025 08:09:08 +0200 Subject: [PATCH 56/99] add test definition --- tests/test_encoders.py | 21 +++++++++++++++++++++ 1 file changed, 21 insertions(+) create mode 100644 tests/test_encoders.py diff --git a/tests/test_encoders.py b/tests/test_encoders.py new file mode 100644 index 00000000..554e46ff --- /dev/null +++ b/tests/test_encoders.py @@ -0,0 +1,21 @@ +import os +from pathlib import Path + +import h5py +import numpy as np +import pytest +import torch +from huggingface_hub.errors import GatedRepoError + +from stamp.cache import download_file +from stamp.encoding import EncoderName + + +@pytest.mark.slow +@pytest.mark.parametrize("encoder", EncoderName) +@pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") +@pytest.mark.filterwarnings( + "ignore:You are using `torch.load` with `weights_only=False`" +) +def test_if_slide_encoder_crashes(*, tmp_path: Path, encoder: EncoderName): + pass From 27ebb86efd979ff301edf19334bc2bf49d87c8de Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 21 May 2025 09:52:26 +0100 Subject: [PATCH 57/99] type extractor and encoder names --- src/stamp/encoding/encoder/__init__.py | 12 +++++++++--- src/stamp/encoding/encoder/chief.py | 6 ++++-- src/stamp/encoding/encoder/cobra.py | 11 +++++++++-- src/stamp/encoding/encoder/eagle.py | 24 ++++++++++++++++-------- src/stamp/encoding/encoder/gigapath.py | 6 ++++-- src/stamp/encoding/encoder/madeleine.py | 6 ++++-- src/stamp/encoding/encoder/prism.py | 10 +++++----- src/stamp/encoding/encoder/titan.py | 6 ++++-- 8 files changed, 55 insertions(+), 26 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index cb4c71e6..63ef53af 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -13,14 +13,20 @@ import stamp from stamp.cache import get_processing_code_hash +from stamp.encoding.config import EncoderName from stamp.modeling.data import CoordsInfo, get_coords +from stamp.preprocessing.config import ExtractorName EncoderModel = TypeVar("EncoderModel", bound=nn.Module) class Encoder(ABC): def __init__( - self, model, identifier: str, precision: torch.dtype, required_extractor: str + self, + model, + identifier: EncoderName, + precision: torch.dtype, + required_extractor: list[ExtractorName], ): self.model = model self.identifier = identifier @@ -142,9 +148,9 @@ def _read_slide_table(slide_table_path: Path) -> pd.DataFrame: def _validate_and_read_features(self, h5_path: str) -> tuple[Tensor, CoordsInfo]: feats, coords, extractor = self._read_h5(h5_path) - if self.required_extractor not in extractor: + if extractor not in self.required_extractor: raise ValueError( - f"Features must be extracted with {self.required_extractor}. " + f"Features must be extracted with one of {self.required_extractor}. " f"Features located in {h5_path} are extracted with {extractor}" ) return feats, coords diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 12c1d712..9a1897ed 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -10,7 +10,9 @@ from tqdm import tqdm from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.preprocessing.config import ExtractorName """authors: https://github.com/hms-dbmi/CHIEF""" @@ -99,9 +101,9 @@ def __init__(self) -> None: model.load_state_dict(chief, strict=True) super().__init__( model=model, - identifier="chief", + identifier=EncoderName.CHIEF, precision=torch.float32, - required_extractor="ctranspath", + required_extractor=[ExtractorName.CHIEF_CTRANSPATH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 7b3b5ea5..0c941e0d 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -1,7 +1,9 @@ import torch from numpy import ndarray +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.preprocessing.config import ExtractorName # TODO: Check which are the necessary imports and add them to cobra package @@ -28,9 +30,14 @@ def __init__(self) -> None: precision = torch.float32 super().__init__( model=model, - identifier="katherlab-cobra", + identifier=EncoderName.COBRA, precision=precision, - required_extractor="", + required_extractor=[ + ExtractorName.CTRANSPATH, + ExtractorName.UNI, + ExtractorName.VIRCHOW2, + ExtractorName.H_OPTIMUS_0, + ], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 1ac85ed8..dbaa7b1b 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -9,21 +9,26 @@ from tqdm import tqdm from stamp.cache import get_processing_code_hash +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF from stamp.modeling.data import CoordsInfo +from stamp.preprocessing.config import ExtractorName """From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" class Eagle(Encoder): def __init__(self) -> None: - model = CHIEF().model + self.required_agg_extractor = ExtractorName.VIRCHOW2 super().__init__( - model=model, - identifier="katherlab-eagle", + model=CHIEF().model, + identifier=EncoderName.EAGLE, precision=torch.float32, - required_extractor="ctranspath", + required_extractor=[ + ExtractorName.CTRANSPATH, + ExtractorName.CHIEF_CTRANSPATH, + ], ) def _validate_and_read_features_with_agg( @@ -34,17 +39,20 @@ def _validate_and_read_features_with_agg( extractor: str feats, coords, extractor = self._read_h5(h5_ctp) - if "ctranspath" not in extractor: + # TODO: Eagle requires ctranspath features extracted with 2mpp + # magnification. Validate this. + + if extractor not in self.required_extractor: raise ValueError( - f"Features must be extracted with ctranspath or chief-ctranspath. " + f"Features must be extracted with one of {self.required_extractor}. " f"Features located in {h5_ctp} are extracted with {extractor}" ) agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) - if "virchow2" not in extractor: + if extractor == self.required_agg_extractor: raise ValueError( - f"Aggregated features must be extracted with virchow2 " + f"Aggregated features must be extracted with {self.required_agg_extractor} " f"Features located in {h5_vir2} are extracted with {extractor}" ) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 8eaa0d80..75319594 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -9,8 +9,10 @@ from tqdm import tqdm from stamp.cache import get_processing_code_hash +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import SlideMPP @@ -27,9 +29,9 @@ def __init__(self) -> None: ) super().__init__( model=model, - identifier="gigapath", + identifier=EncoderName.GIGAPATH, precision=torch.float16, - required_extractor="gigapath", + required_extractor=[ExtractorName.GIGAPATH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/madeleine.py b/src/stamp/encoding/encoder/madeleine.py index f04cc2b8..debcbebd 100644 --- a/src/stamp/encoding/encoder/madeleine.py +++ b/src/stamp/encoding/encoder/madeleine.py @@ -4,7 +4,9 @@ from numpy import ndarray from stamp.cache import STAMP_CACHE_DIR +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.preprocessing.config import ExtractorName try: from madeleine.models.factory import create_model_from_pretrained @@ -25,9 +27,9 @@ def __init__(self) -> None: precision = torch.float16 super().__init__( model=model, - identifier="mahmood-madeleine", + identifier=EncoderName.MADELEINE, precision=precision, - required_extractor="conch", + required_extractor=[ExtractorName.CONCH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index 965a8237..070cbfa0 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -1,19 +1,19 @@ import torch from transformers import AutoModel +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.preprocessing.config import ExtractorName class Prism(Encoder): def __init__(self) -> None: model = AutoModel.from_pretrained("paige-ai/Prism", trust_remote_code=True) - precision = torch.float16 - required_extractor = "virchow_full" super().__init__( model=model, - identifier="paigeai-prism", - precision=precision, - required_extractor=required_extractor, + identifier=EncoderName.PRISM, + precision=torch.float16, + required_extractor=[ExtractorName.VIRCHOW_FULL], ) def _generate_slide_embedding(self, feats, device, **kwargs): diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 41c03287..bfcbef47 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -11,8 +11,10 @@ from transformers import AutoModel from stamp.cache import get_processing_code_hash +from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import Microns, SlideMPP @@ -21,9 +23,9 @@ def __init__(self) -> None: model = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) super().__init__( model=model, - identifier="mahmood-titan", + identifier=EncoderName.TITAN, precision=torch.float32, - required_extractor="conch1_5", + required_extractor=[ExtractorName.CONCH1_5], ) def _generate_slide_embedding( From 6c66980923611b13a786117c49fa2c32c8774ebb Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 21 May 2025 14:47:52 +0100 Subject: [PATCH 58/99] add slde encoding test --- src/stamp/encoding/config.py | 1 - tests/random_data.py | 71 ++++++++++++++++++++++++++++----- tests/test_encoders.py | 76 +++++++++++++++++++++++++++++++++--- 3 files changed, 132 insertions(+), 16 deletions(-) diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 0ce12320..454d8c05 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -13,7 +13,6 @@ class EncoderName(StrEnum): GIGAPATH = "gigapath" PRISM = "paigeai-prism" MADELEINE = "mahmood-madeleine" - THREADS = "mahmood-threads" class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/tests/random_data.py b/tests/random_data.py index 19c0575a..2d893ef8 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -20,6 +20,7 @@ Category, PatientId, ) +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import Microns, TilePixels CliniPath: TypeAlias = Path @@ -44,6 +45,8 @@ def create_random_dataset( feat_dim: int, categories: Sequence[str] | None = None, n_categories: int | None = None, + extractor_name: ExtractorName | str = "random-test-generator", + min_slides_per_patient: int = 1, ) -> tuple[CliniPath, SlidePath, FeatureDir, Sequence[Category]]: slide_path_to_patient: Mapping[Path, PatientId] = {} patient_to_ground_truth: Mapping[PatientId, str] = {} @@ -70,13 +73,14 @@ def create_random_dataset( patient_to_ground_truth[patient_id] = random.choice(categories) # Generate some slides - for _ in range(random.randint(1, max_slides_per_patient)): + for _ in range(random.randint(min_slides_per_patient, max_slides_per_patient)): slide_path_to_patient[ create_random_feature_file( tmp_path=feat_dir, min_tiles=min_tiles_per_slide, max_tiles=max_tiles_per_slide, feat_dim=feat_dim, + extractor_name=extractor_name, ).relative_to(feat_dir) ] = patient_id @@ -102,6 +106,8 @@ def create_random_feature_file( max_tiles: int, feat_dim: int, tile_size_um: Microns = Microns(2508), + extractor_name: ExtractorName | str = "random-test-generator", + feat_filename: str | None = None, ) -> Path: """Creates a h5 file with random contents. @@ -113,20 +119,67 @@ def create_random_feature_file( Path to the feature file. """ n_tiles = random.randint(min_tiles, max_tiles) - with ( - tempfile.NamedTemporaryFile( - dir=tmp_path, suffix=".h5", delete=False - ) as tmp_file, - h5py.File(tmp_file, "w") as h5_file, - ): + if feat_filename is None: + feat_filename = random_string(16) # Generate a random filename + feature_file_path = tmp_path / f"{feat_filename}.h5" + with h5py.File(feature_file_path, "w") as h5_file: h5_file["feats"] = torch.rand(n_tiles, feat_dim) * 1000 * tile_size_um h5_file["coords"] = torch.rand(n_tiles, 2) h5_file.attrs["stamp_version"] = stamp.__version__ - h5_file.attrs["extractor"] = "random-test-generator" + h5_file.attrs["extractor"] = str(extractor_name) h5_file.attrs["unit"] = "um" h5_file.attrs["tile_size"] = tile_size_um - return Path(tmp_file.name) + return Path(feature_file_path) + + +def create_random_feature_file_with_agg( + *, + tmp_path: Path, + min_tiles: int, + max_tiles: int, + feat_dim: int, + tile_size_um: Microns = Microns(2508), + extractor_name: ExtractorName | str = "random-test-generator", + agg_path: Path, + agg_feat_dim: int, + agg_extractor_name: ExtractorName | str = "random-agg-test-generator", +) -> tuple[Path, Path]: + """Creates a random feature file with its aggregated pair. Used + for testing eagle encoder. + + Args: + dir: + Directory to create the file in. + + Returns: + Path to the feature and aggregated file. + """ + n_tiles = random.randint(min_tiles, max_tiles) + random_filename = random_string(16) # Generate a random filename + + feature_file_path = tmp_path / f"{random_filename}.h5" + agg_file_path = agg_path / f"{random_filename}.h5" + + with h5py.File(feature_file_path, "w") as h5_file: + h5_file["feats"] = torch.rand(n_tiles, feat_dim) * 1000 * tile_size_um + h5_file["coords"] = torch.rand(n_tiles, 2) + + h5_file.attrs["stamp_version"] = stamp.__version__ + h5_file.attrs["extractor"] = extractor_name + h5_file.attrs["unit"] = "um" + h5_file.attrs["tile_size"] = tile_size_um + + with h5py.File(agg_file_path, "w") as h5_file: + h5_file["feats"] = torch.rand(n_tiles, agg_feat_dim) * 1000 * tile_size_um + h5_file["coords"] = torch.rand(n_tiles, 2) + + h5_file.attrs["stamp_version"] = stamp.__version__ + h5_file.attrs["extractor"] = agg_extractor_name + h5_file.attrs["unit"] = "um" + h5_file.attrs["tile_size"] = tile_size_um + + return Path(feature_file_path), Path(agg_file_path) def random_patient_preds(*, n_patients: int, categories: list[str]) -> pd.DataFrame: diff --git a/tests/test_encoders.py b/tests/test_encoders.py index 554e46ff..e250d765 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -1,14 +1,28 @@ -import os +import random from pathlib import Path -import h5py import numpy as np +import pandas as pd import pytest import torch from huggingface_hub.errors import GatedRepoError +from random_data import create_random_dataset, create_random_feature_file -from stamp.cache import download_file -from stamp.encoding import EncoderName +from stamp.encoding import EncoderName, get_pat_embs, get_slide_embs +from stamp.preprocessing.config import ExtractorName + +# Contains an accepted input patch-level feature encoder +# TODO: Make a class for each extractor instead of a function. This class +# will contain as properties the extractor name and output dimension. +input_dims = { + EncoderName.CHIEF: 768, # chief ctranspath + EncoderName.COBRA: 768, # ctranspath + EncoderName.EAGLE: 768, # ctranspath + EncoderName.GIGAPATH: 1536, # gigapath + EncoderName.MADELEINE: 512, # conch + EncoderName.PRISM: 2560, # virchow_full + EncoderName.TITAN: 768, # conchv1_5 +} @pytest.mark.slow @@ -17,5 +31,55 @@ @pytest.mark.filterwarnings( "ignore:You are using `torch.load` with `weights_only=False`" ) -def test_if_slide_encoder_crashes(*, tmp_path: Path, encoder: EncoderName): - pass +def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): + random.seed(0) + torch.manual_seed(0) + np.random.seed(0) + + _, slide_path, feature_dir, categories = create_random_dataset( + dir=tmp_path, + n_patients=2, + min_slides_per_patient=2, + max_slides_per_patient=3, + min_tiles_per_slide=32, + max_tiles_per_slide=32, + feat_dim=input_dims[encoder], + extractor_name=encoder, + n_categories=2, + ) + + agg_feat_dir = None + if encoder == EncoderName.EAGLE: + agg_feat_dir = tmp_path / "output" + agg_feat_dir.mkdir() + slide_df = pd.read_csv(slide_path) + feature_filenames = [Path(path).name for path in slide_df["slide_path"]] + + for feat_filename in feature_filenames: + create_random_feature_file( + tmp_path=tmp_path, + min_tiles=32, + max_tiles=32, + feat_dim=input_dims[encoder], + extractor_name=ExtractorName.VIRCHOW2, + ) + + output_dir = tmp_path / "output" + output_dir.mkdir() + + try: + get_slide_embs( + encoder=encoder, + output_dir=output_dir, + feat_dir=feature_dir, + device="cuda" if torch.cuda.is_available() else "cpu", + agg_feat_dir=agg_feat_dir, + ) + + # TODO: test patient encoding + except ModuleNotFoundError: + pytest.skip(f"dependencies for {encoder} not installed") + except GatedRepoError: + pytest.skip(f"cannot access gated repo for {encoder}") + + # TODO: test that the contents are not empty From b6c4c19963321cb1b80262a54f824551611dbd65 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 21 May 2025 14:48:56 +0100 Subject: [PATCH 59/99] remove unused random function --- tests/random_data.py | 49 -------------------------------------------- 1 file changed, 49 deletions(-) diff --git a/tests/random_data.py b/tests/random_data.py index 2d893ef8..eb985026 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -133,55 +133,6 @@ def create_random_feature_file( return Path(feature_file_path) -def create_random_feature_file_with_agg( - *, - tmp_path: Path, - min_tiles: int, - max_tiles: int, - feat_dim: int, - tile_size_um: Microns = Microns(2508), - extractor_name: ExtractorName | str = "random-test-generator", - agg_path: Path, - agg_feat_dim: int, - agg_extractor_name: ExtractorName | str = "random-agg-test-generator", -) -> tuple[Path, Path]: - """Creates a random feature file with its aggregated pair. Used - for testing eagle encoder. - - Args: - dir: - Directory to create the file in. - - Returns: - Path to the feature and aggregated file. - """ - n_tiles = random.randint(min_tiles, max_tiles) - random_filename = random_string(16) # Generate a random filename - - feature_file_path = tmp_path / f"{random_filename}.h5" - agg_file_path = agg_path / f"{random_filename}.h5" - - with h5py.File(feature_file_path, "w") as h5_file: - h5_file["feats"] = torch.rand(n_tiles, feat_dim) * 1000 * tile_size_um - h5_file["coords"] = torch.rand(n_tiles, 2) - - h5_file.attrs["stamp_version"] = stamp.__version__ - h5_file.attrs["extractor"] = extractor_name - h5_file.attrs["unit"] = "um" - h5_file.attrs["tile_size"] = tile_size_um - - with h5py.File(agg_file_path, "w") as h5_file: - h5_file["feats"] = torch.rand(n_tiles, agg_feat_dim) * 1000 * tile_size_um - h5_file["coords"] = torch.rand(n_tiles, 2) - - h5_file.attrs["stamp_version"] = stamp.__version__ - h5_file.attrs["extractor"] = agg_extractor_name - h5_file.attrs["unit"] = "um" - h5_file.attrs["tile_size"] = tile_size_um - - return Path(feature_file_path), Path(agg_file_path) - - def random_patient_preds(*, n_patients: int, categories: list[str]) -> pd.DataFrame: return pd.DataFrame( { From 26c8401a9783dc3cd92ab76ac08c9da7348bff1e Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 21 May 2025 15:13:51 +0100 Subject: [PATCH 60/99] fix string casting --- src/stamp/encoding/encoder/__init__.py | 2 +- tests/test_encoders.py | 32 ++++++++++++++++++-------- 2 files changed, 23 insertions(+), 11 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 63ef53af..057f7fed 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -173,7 +173,7 @@ def _save_features(self, output_file: str, entry_dict: dict) -> None: for entry_name, data in entry_dict.items(): f.create_dataset(f"{entry_name}", data=data["feats"]) f.attrs["version"] = stamp.__version__ - f.attrs["encoder"] = self.identifier + f.attrs["encoder"] = str(self.identifier) f.attrs["precision"] = str(self.precision) if len(f) == 0: tqdm.write("Extraction failed: file empty") diff --git a/tests/test_encoders.py b/tests/test_encoders.py index e250d765..ce40f26b 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -15,15 +15,27 @@ # TODO: Make a class for each extractor instead of a function. This class # will contain as properties the extractor name and output dimension. input_dims = { - EncoderName.CHIEF: 768, # chief ctranspath - EncoderName.COBRA: 768, # ctranspath - EncoderName.EAGLE: 768, # ctranspath - EncoderName.GIGAPATH: 1536, # gigapath - EncoderName.MADELEINE: 512, # conch - EncoderName.PRISM: 2560, # virchow_full - EncoderName.TITAN: 768, # conchv1_5 + ExtractorName.CTRANSPATH: 768, + ExtractorName.CHIEF_CTRANSPATH: 768, + ExtractorName.CONCH: 512, + ExtractorName.CONCH1_5: 768, + ExtractorName.GIGAPATH: 1536, + ExtractorName.VIRCHOW_FULL: 2560, + ExtractorName.VIRCHOW2: 1280, } +# They are not all, just one case that is accepted for each encoder +used_extractor = { + EncoderName.CHIEF: ExtractorName.CHIEF_CTRANSPATH, + EncoderName.COBRA: ExtractorName.CTRANSPATH, + EncoderName.EAGLE: ExtractorName.CTRANSPATH, + EncoderName.GIGAPATH: ExtractorName.GIGAPATH, + EncoderName.MADELEINE: ExtractorName.CONCH, + EncoderName.PRISM: ExtractorName.VIRCHOW_FULL, + EncoderName.TITAN: ExtractorName.CONCH1_5, +} + + @pytest.mark.slow @pytest.mark.parametrize("encoder", EncoderName) @@ -43,8 +55,8 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): max_slides_per_patient=3, min_tiles_per_slide=32, max_tiles_per_slide=32, - feat_dim=input_dims[encoder], - extractor_name=encoder, + feat_dim=input_dims[used_extractor[encoder]], + extractor_name=used_extractor[encoder], n_categories=2, ) @@ -60,7 +72,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): tmp_path=tmp_path, min_tiles=32, max_tiles=32, - feat_dim=input_dims[encoder], + feat_dim=input_dims[ExtractorName.VIRCHOW2], extractor_name=ExtractorName.VIRCHOW2, ) From 9eb657512b24d07311237185e61bf2a9bd51eaed Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Sun, 25 May 2025 09:12:03 +0100 Subject: [PATCH 61/99] add titan test coverage --- debug.py | 47 +++++++++++++++++++++++++++++ src/stamp/encoding/encoder/titan.py | 3 +- src/stamp/preprocessing/__init__.py | 4 +-- tests/random_data.py | 14 ++++++--- tests/test_encoders.py | 22 +++++++++++--- 5 files changed, 78 insertions(+), 12 deletions(-) create mode 100644 debug.py diff --git a/debug.py b/debug.py new file mode 100644 index 00000000..465b951c --- /dev/null +++ b/debug.py @@ -0,0 +1,47 @@ +import logging +from pathlib import Path + +import yaml + +from stamp.config import StampConfig +from stamp.encoding import get_slide_embs + +# Set up the logger +_logger = logging.getLogger("stamp") +_logger.setLevel(logging.DEBUG) + + +def run_encode_slides(config_file_path: Path): + # Load YAML configuration using StampConfig + with open(config_file_path, "r") as config_yaml: + config = StampConfig.model_validate(yaml.safe_load(config_yaml)) + + # Ensure slide encoding configuration is present + if config.slide_encoding is None: + raise ValueError("No slide encoding configuration supplied") + + # Log the configuration being used + _logger.info( + "Using the following configuration:\n" + f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" + ) + + # Call the get_slide_embs function with the configuration + get_slide_embs( + encoder=config.slide_encoding.encoder, + output_dir=config.slide_encoding.output_dir, + feat_dir=config.slide_encoding.feat_dir, + device=config.slide_encoding.device, + agg_feat_dir=config.slide_encoding.agg_feat_dir, + ) + + +# Example usage +if __name__ == "__main__": + # Path to the config.yaml file + config_file_path = Path( + "/mnt/bulk-sirius/juan/pap_screening/stamp_config.yaml" + ) # Replace with your config file path + + # Run the encode_slides command + run_encode_slides(config_file_path) diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index bfcbef47..7c97e586 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -46,9 +46,10 @@ def _generate_slide_embedding( coords_px = coords_tensor / coords.mpp # Convert to pixels coords_px = coords_px.to(torch.int64).to(device) # Convert to integer - feats = feats.to(device) + feats = feats.to(device=device) with torch.inference_mode(): + breakpoint() slide_embedding = self.model.encode_slide_from_patch_features( feats, coords_px, patch_size_lvl0 ) diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 0cfb20a4..c9b261a6 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -232,7 +232,7 @@ def extract_( model = extractor.model.to(device).eval() - code_hash = get_processing_code_hash()[:8] + code_hash = get_processing_code_hash(Path(__file__))[:8] extractor_id = extractor.identifier @@ -319,7 +319,7 @@ def extract_( h5_fp["feats"] = torch.concat(feats).numpy() h5_fp.attrs["stamp_version"] = stamp.__version__ - h5_fp.attrs["extractor"] = extractor_id + h5_fp.attrs["extractor"] = extractor.identifier h5_fp.attrs["unit"] = "um" h5_fp.attrs["tile_size_um"] = tile_size_um # changed in v2.1.0 h5_fp.attrs["tile_size_px"] = tile_size_px diff --git a/tests/random_data.py b/tests/random_data.py index eb985026..6ea0869e 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -105,7 +105,8 @@ def create_random_feature_file( min_tiles: int, max_tiles: int, feat_dim: int, - tile_size_um: Microns = Microns(2508), + tile_size_um: Microns = Microns(256), + tile_size_px: TilePixels = TilePixels(224), extractor_name: ExtractorName | str = "random-test-generator", feat_filename: str | None = None, ) -> Path: @@ -123,13 +124,18 @@ def create_random_feature_file( feat_filename = random_string(16) # Generate a random filename feature_file_path = tmp_path / f"{feat_filename}.h5" with h5py.File(feature_file_path, "w") as h5_file: - h5_file["feats"] = torch.rand(n_tiles, feat_dim) * 1000 * tile_size_um - h5_file["coords"] = torch.rand(n_tiles, 2) + rand_feats = torch.rand(n_tiles, feat_dim) * 1000 * tile_size_um + mean = rand_feats.mean() + std = rand_feats.std() + norm_feats = (rand_feats - mean) / std + h5_file["feats"] = norm_feats.numpy() + h5_file["coords"] = torch.rand(n_tiles, 2).numpy() h5_file.attrs["stamp_version"] = stamp.__version__ h5_file.attrs["extractor"] = str(extractor_name) h5_file.attrs["unit"] = "um" - h5_file.attrs["tile_size"] = tile_size_um + h5_file.attrs["tile_size_um"] = tile_size_um + h5_file.attrs["tile_size_px"] = tile_size_px return Path(feature_file_path) diff --git a/tests/test_encoders.py b/tests/test_encoders.py index ce40f26b..b1c64ecf 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -38,17 +38,23 @@ @pytest.mark.slow -@pytest.mark.parametrize("encoder", EncoderName) +@pytest.mark.parametrize("encoder", [EncoderName.TITAN]) @pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") @pytest.mark.filterwarnings( "ignore:You are using `torch.load` with `weights_only=False`" ) +@pytest.mark.filterwarnings( + "ignore:Importing from timm.models.registry is deprecated, please import via timm.models" +) +@pytest.mark.filterwarnings( + "ignore:` torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature." +) def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): random.seed(0) torch.manual_seed(0) np.random.seed(0) - _, slide_path, feature_dir, categories = create_random_dataset( + _, slide_path, feature_dir, _ = create_random_dataset( dir=tmp_path, n_patients=2, min_slides_per_patient=2, @@ -62,7 +68,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): agg_feat_dir = None if encoder == EncoderName.EAGLE: - agg_feat_dir = tmp_path / "output" + agg_feat_dir = tmp_path / "agg_output" agg_feat_dir.mkdir() slide_df = pd.read_csv(slide_path) feature_filenames = [Path(path).name for path in slide_df["slide_path"]] @@ -75,15 +81,21 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): feat_dim=input_dims[ExtractorName.VIRCHOW2], extractor_name=ExtractorName.VIRCHOW2, ) + elif encoder == EncoderName.TITAN: + # A random conch1_5 feature does not work with titan so we just download + # a real one + feature_dir = "/mnt/bulk-sirius/juan/pap_screening/datasets/example/features/mahmood-conch1_5-df7541f1" + slide_df = pd.read_csv(slide_path) + slide_df.assign(slide_path=feature_dir) output_dir = tmp_path / "output" - output_dir.mkdir() + output_dir.mkdir(exist_ok=True) try: get_slide_embs( encoder=encoder, output_dir=output_dir, - feat_dir=feature_dir, + feat_dir=Path(feature_dir), device="cuda" if torch.cuda.is_available() else "cpu", agg_feat_dir=agg_feat_dir, ) From 04c12dc54c9500afbd26a983ae4547f1d366b821 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Sun, 25 May 2025 09:56:06 +0100 Subject: [PATCH 62/99] remove hardcoded patient and filename labels --- debug.py | 47 -------------------------- src/stamp/__main__.py | 2 ++ src/stamp/config.yaml | 3 ++ src/stamp/encoding/__init__.py | 11 +++++- src/stamp/encoding/config.py | 4 +++ src/stamp/encoding/encoder/__init__.py | 8 +++-- src/stamp/encoding/encoder/chief.py | 21 ++++++++---- src/stamp/encoding/encoder/eagle.py | 8 +++-- src/stamp/encoding/encoder/gigapath.py | 19 +++++++---- src/stamp/encoding/encoder/prism.py | 5 +-- src/stamp/encoding/encoder/titan.py | 15 +++++--- 11 files changed, 71 insertions(+), 72 deletions(-) delete mode 100644 debug.py diff --git a/debug.py b/debug.py deleted file mode 100644 index 465b951c..00000000 --- a/debug.py +++ /dev/null @@ -1,47 +0,0 @@ -import logging -from pathlib import Path - -import yaml - -from stamp.config import StampConfig -from stamp.encoding import get_slide_embs - -# Set up the logger -_logger = logging.getLogger("stamp") -_logger.setLevel(logging.DEBUG) - - -def run_encode_slides(config_file_path: Path): - # Load YAML configuration using StampConfig - with open(config_file_path, "r") as config_yaml: - config = StampConfig.model_validate(yaml.safe_load(config_yaml)) - - # Ensure slide encoding configuration is present - if config.slide_encoding is None: - raise ValueError("No slide encoding configuration supplied") - - # Log the configuration being used - _logger.info( - "Using the following configuration:\n" - f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" - ) - - # Call the get_slide_embs function with the configuration - get_slide_embs( - encoder=config.slide_encoding.encoder, - output_dir=config.slide_encoding.output_dir, - feat_dir=config.slide_encoding.feat_dir, - device=config.slide_encoding.device, - agg_feat_dir=config.slide_encoding.agg_feat_dir, - ) - - -# Example usage -if __name__ == "__main__": - # Path to the config.yaml file - config_file_path = Path( - "/mnt/bulk-sirius/juan/pap_screening/stamp_config.yaml" - ) # Replace with your config file path - - # Run the encode_slides command - run_encode_slides(config_file_path) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index d8f93957..25838c79 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -112,6 +112,8 @@ def _run_cli(args: argparse.Namespace) -> None: output_dir=config.patient_encoding.output_dir, feat_dir=config.patient_encoding.feat_dir, slide_table_path=config.patient_encoding.slide_table, + patient_label=config.patient_encoding.patient_label, + filename_label=config.patient_encoding.filename_label, device=config.patient_encoding.device, agg_feat_dir=config.patient_encoding.agg_feat_dir, ) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index c794bd8b..45bc369f 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -81,6 +81,9 @@ patient_encoding: device: "cuda" # Optional settings: + patient_label: "PATIENT" + filename_label: "FILENAME" + # Directory where the aggregated features are stored. Needed for # some encoders such as eagle (it requires virchow2 features). #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 7aedc70a..80f891d5 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -5,6 +5,7 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.modeling.data import PandasLabel def get_pat_embs( @@ -12,6 +13,8 @@ def get_pat_embs( output_dir: Path, feat_dir: Path, slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, device: DeviceLikeType, agg_feat_dir: Path | None = None, ) -> None: @@ -59,7 +62,13 @@ def get_pat_embs( assert_never(unreachable) selected_encoder.encode_patients( - output_dir, feat_dir, slide_table_path, device, agg_feat_dir=agg_feat_dir + output_dir=output_dir, + feat_dir=feat_dir, + slide_table_path=slide_table_path, + patient_label=patient_label, + filename_label=filename_label, + device=device, + agg_feat_dir=agg_feat_dir, ) diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 454d8c05..83767989 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -4,6 +4,8 @@ from pydantic import BaseModel, ConfigDict from torch._prims_common import DeviceLikeType # type: ignore +from stamp.modeling.data import PandasLabel # type: ignore + class EncoderName(StrEnum): COBRA = "katherlab-cobra" @@ -32,5 +34,7 @@ class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): output_dir: Path feat_dir: Path slide_table: Path + patient_label: PandasLabel = "PATIENT" + filename_label: PandasLabel = "FILENAME" device: DeviceLikeType agg_feat_dir: Path | None = None diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 057f7fed..509502c9 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -14,7 +14,7 @@ import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName -from stamp.modeling.data import CoordsInfo, get_coords +from stamp.modeling.data import CoordsInfo, PandasLabel, get_coords from stamp.preprocessing.config import ExtractorName EncoderModel = TypeVar("EncoderModel", bound=nn.Module) @@ -78,6 +78,8 @@ def encode_patients( output_dir: Path, feat_dir: Path, slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, device: DeviceLikeType, **kwargs, ) -> None: @@ -99,13 +101,13 @@ def encode_patients( return slide_table = self._read_slide_table(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") + patient_groups = slide_table.groupby(patient_label) for patient_id, group in tqdm(patient_groups, leave=False): feats_list = [] for _, row in group.iterrows(): - h5_path = os.path.join(feat_dir, row["FILENAME"]) + h5_path = os.path.join(feat_dir, filename_label) try: feats, _ = self._validate_and_read_features(h5_path) except FileNotFoundError as e: diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 9a1897ed..83690df7 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -7,11 +7,13 @@ import torch.nn as nn import torch.nn.functional as F from numpy import ndarray +from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder +from stamp.modeling.data import PandasLabel from stamp.preprocessing.config import ExtractorName """authors: https://github.com/hms-dbmi/CHIEF""" @@ -121,13 +123,20 @@ def _generate_patient_embedding( return patient_embedding.detach().squeeze().cpu().numpy() def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs + self, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, + device: DeviceLikeType, + **kwargs, ) -> None: output_name = ( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") + patient_groups = slide_table.groupby(patient_label) output_file = os.path.join(output_dir, output_name) @@ -143,7 +152,7 @@ def encode_patients( # Concatenate all slides over x axis adding the offset to each feature x coordinate. for _, row in group.iterrows(): - slide_filename = row["FILENAME"] + slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) try: @@ -215,7 +224,7 @@ def __init__(self, L=1024, D=256, dropout=False, n_classes=1): self.module = nn.Sequential(*self.module) def forward(self, x): - return self.module(x), x # N x 1, N * D + return self.module(x), x # type: ignore # N x 1, N * D class Attn_Net_Gated(nn.Module): @@ -234,8 +243,8 @@ def __init__(self, L=1024, D=256, dropout=False, n_classes=1): self.attention_c = nn.Linear(D, n_classes) def forward(self, x): - a = self.attention_a(x) - b = self.attention_b(x) + a = self.attention_a(x) # type: ignore + b = self.attention_b(x) # type: ignore A = a.mul(b) A = self.attention_c(A) return A, x diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index dbaa7b1b..c46ec98c 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -12,7 +12,7 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF -from stamp.modeling.data import CoordsInfo +from stamp.modeling.data import CoordsInfo, PandasLabel from stamp.preprocessing.config import ExtractorName """From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" @@ -165,6 +165,8 @@ def encode_patients( output_dir: Path, feat_dir: Path, slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, device: DeviceLikeType, **kwargs, ) -> None: @@ -177,7 +179,7 @@ def encode_patients( ) slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") + patient_groups = slide_table.groupby(patient_label) patient_dict = {} self.model.to(device).eval() @@ -195,7 +197,7 @@ def encode_patients( agg_feats_list = [] for _, row in group.iterrows(): - slide_filename = row["FILENAME"] + slide_filename = row[filename_label] slide_name = Path(slide_filename).stem h5_ctp = os.path.join(feat_dir, slide_filename) h5_vir2 = os.path.join(agg_feat_dir, slide_filename) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 75319594..b5a3155a 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -11,7 +11,7 @@ from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo +from stamp.modeling.data import CoordsInfo, PandasLabel from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import SlideMPP @@ -36,7 +36,7 @@ def __init__(self) -> None: def _generate_slide_embedding( self, feats, device, coords: CoordsInfo | None = None, **kwargs - ): + ) -> np.ndarray: if not coords: raise ValueError("Tile coords are required for encoding") @@ -68,7 +68,14 @@ def _generate_slide_embedding( return slide_embedding.detach().squeeze().cpu().numpy() def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs + self, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, + device, + **kwargs, ) -> None: """Generate one virtual slide concatenating all the slides of a patient over the x axis.""" @@ -76,7 +83,7 @@ def encode_patients( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") + patient_groups = slide_table.groupby(patient_label) output_file = os.path.join(output_dir, output_name) @@ -97,7 +104,7 @@ def encode_patients( slide_info = [] # Concatenate all slides over x axis adding the offset to each feature x coordinate. for _, row in group.iterrows(): - slide_filename = row["FILENAME"] + slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) try: @@ -195,7 +202,7 @@ def _convert_coords( max_wsi_height, n_grid, current_x_offset, - ): + ) -> np.ndarray: """ Normalize the x and y coordinates relative to the total WSI width and max height, using the same grid [0, 1000]. Thanks Peter! diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index 070cbfa0..5981b5b2 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -1,3 +1,4 @@ +import numpy as np import torch from transformers import AutoModel @@ -16,7 +17,7 @@ def __init__(self) -> None: required_extractor=[ExtractorName.VIRCHOW_FULL], ) - def _generate_slide_embedding(self, feats, device, **kwargs): + def _generate_slide_embedding(self, feats, device, **kwargs) -> np.ndarray: with torch.autocast(device, dtype=self.precision), torch.inference_mode(): return ( self.model.slide_representations(feats.unsqueeze(0).to(device))[ @@ -28,7 +29,7 @@ def _generate_slide_embedding(self, feats, device, **kwargs): .numpy() ) - def _generate_patient_embedding(self, feats_list, device, **kwargs): + def _generate_patient_embedding(self, feats_list, device, **kwargs) -> np.ndarray: all_feats = torch.cat(feats_list, dim=0).to(device) with torch.inference_mode(): return ( diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 7c97e586..2744ed6c 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -13,7 +13,7 @@ from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo +from stamp.modeling.data import CoordsInfo, PandasLabel from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import Microns, SlideMPP @@ -80,7 +80,14 @@ def _generate_patient_embedding( return self._generate_slide_embedding(all_feats_cat, device, coords) def encode_patients( - self, output_dir, feat_dir, slide_table_path, device, **kwargs + self, + output_dir: Path, + feat_dir: Path, + slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, + device, + **kwargs, ) -> None: """Generate one virtual slide concatenating all the slides of a patient over the x axis.""" @@ -88,7 +95,7 @@ def encode_patients( f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" ) slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby("PATIENT") + patient_groups = slide_table.groupby(patient_label) output_file = os.path.join(output_dir, output_name) @@ -107,7 +114,7 @@ def encode_patients( # Concatenate all slides over x axis adding the offset to each feature x coordinate. for _, row in group.iterrows(): - slide_filename = row["FILENAME"] + slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) try: From 7ca3c0e4ade33e3a60e49f46ca6dab5e35f2be44 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Sun, 25 May 2025 20:48:08 +0100 Subject: [PATCH 63/99] fix eagle test --- src/stamp/encoding/encoder/__init__.py | 5 +-- src/stamp/encoding/encoder/eagle.py | 2 +- src/stamp/encoding/encoder/titan.py | 1 - tests/random_data.py | 7 ++-- tests/test_encoders.py | 47 ++++++++++++++++++++++---- 5 files changed, 50 insertions(+), 12 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 509502c9..d49fc358 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -107,7 +107,8 @@ def encode_patients( feats_list = [] for _, row in group.iterrows(): - h5_path = os.path.join(feat_dir, filename_label) + slide_filename = row[filename_label] + h5_path = os.path.join(feat_dir, slide_filename) try: feats, _ = self._validate_and_read_features(h5_path) except FileNotFoundError as e: @@ -178,7 +179,7 @@ def _save_features(self, output_file: str, entry_dict: dict) -> None: f.attrs["encoder"] = str(self.identifier) f.attrs["precision"] = str(self.precision) if len(f) == 0: - tqdm.write("Extraction failed: file empty") + tqdm.write("Encoding failed: file empty") os.remove(output_file) else: tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index c46ec98c..112d6b5a 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -50,7 +50,7 @@ def _validate_and_read_features_with_agg( agg_feats, agg_coords, extractor = self._read_h5(h5_vir2) - if extractor == self.required_agg_extractor: + if extractor != self.required_agg_extractor: raise ValueError( f"Aggregated features must be extracted with {self.required_agg_extractor} " f"Features located in {h5_vir2} are extracted with {extractor}" diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 2744ed6c..aa2423c0 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -49,7 +49,6 @@ def _generate_slide_embedding( feats = feats.to(device=device) with torch.inference_mode(): - breakpoint() slide_embedding = self.model.encode_slide_from_patch_features( feats, coords_px, patch_size_lvl0 ) diff --git a/tests/random_data.py b/tests/random_data.py index 6ea0869e..741e659b 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -109,6 +109,7 @@ def create_random_feature_file( tile_size_px: TilePixels = TilePixels(224), extractor_name: ExtractorName | str = "random-test-generator", feat_filename: str | None = None, + coords: np.ndarray | None = None, ) -> Path: """Creates a h5 file with random contents. @@ -129,8 +130,10 @@ def create_random_feature_file( std = rand_feats.std() norm_feats = (rand_feats - mean) / std h5_file["feats"] = norm_feats.numpy() - h5_file["coords"] = torch.rand(n_tiles, 2).numpy() - + if coords is not None: + h5_file["coords"] = coords + else: + h5_file["coords"] = torch.rand(n_tiles, 2).numpy() h5_file.attrs["stamp_version"] = stamp.__version__ h5_file.attrs["extractor"] = str(extractor_name) h5_file.attrs["unit"] = "um" diff --git a/tests/test_encoders.py b/tests/test_encoders.py index b1c64ecf..6924208a 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -1,6 +1,7 @@ import random from pathlib import Path +import h5py import numpy as np import pandas as pd import pytest @@ -38,7 +39,7 @@ @pytest.mark.slow -@pytest.mark.parametrize("encoder", [EncoderName.TITAN]) +@pytest.mark.parametrize("encoder", [EncoderName.EAGLE]) @pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") @pytest.mark.filterwarnings( "ignore:You are using `torch.load` with `weights_only=False`" @@ -58,7 +59,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): dir=tmp_path, n_patients=2, min_slides_per_patient=2, - max_slides_per_patient=3, + max_slides_per_patient=2, min_tiles_per_slide=32, max_tiles_per_slide=32, feat_dim=input_dims[used_extractor[encoder]], @@ -71,19 +72,26 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): agg_feat_dir = tmp_path / "agg_output" agg_feat_dir.mkdir() slide_df = pd.read_csv(slide_path) - feature_filenames = [Path(path).name for path in slide_df["slide_path"]] + feature_filenames = [Path(path).stem for path in slide_df["slide_path"]] for feat_filename in feature_filenames: + # Read the coordinates from the ctranspath feature file + ctranspath_file = feature_dir / f"{feat_filename}.h5" + with h5py.File(ctranspath_file, "r") as h5_file: + coords: np.ndarray = h5_file["coords"][:] # type: ignore create_random_feature_file( - tmp_path=tmp_path, + tmp_path=agg_feat_dir, min_tiles=32, max_tiles=32, feat_dim=input_dims[ExtractorName.VIRCHOW2], extractor_name=ExtractorName.VIRCHOW2, + feat_filename=feat_filename, + coords=coords, ) elif encoder == EncoderName.TITAN: # A random conch1_5 feature does not work with titan so we just download # a real one + # TODO: Download it from github feature_dir = "/mnt/bulk-sirius/juan/pap_screening/datasets/example/features/mahmood-conch1_5-df7541f1" slide_df = pd.read_csv(slide_path) slide_df.assign(slide_path=feature_dir) @@ -100,10 +108,37 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): agg_feat_dir=agg_feat_dir, ) - # TODO: test patient encoding + get_pat_embs( + encoder=encoder, + output_dir=output_dir, + feat_dir=Path(feature_dir), + slide_table_path=slide_path, + patient_label="patient", + filename_label="slide_path", + device="cuda" if torch.cuda.is_available() else "cpu", + agg_feat_dir=agg_feat_dir, + ) except ModuleNotFoundError: pytest.skip(f"dependencies for {encoder} not installed") except GatedRepoError: pytest.skip(f"cannot access gated repo for {encoder}") - # TODO: test that the contents are not empty + # Check if the slide file file has any contents + with h5py.File(next((tmp_path / "output").glob(pattern="*-slide-*.h5"))) as h5_file: + slide_datasets = list(h5_file.keys()) + # Check that the amount of slides feats is 6 (2 patients, 3 slides per patient) + assert len(slide_datasets) == 4 + for slide_dataset in slide_datasets: + # Check feature contents + assert len(h5_file[slide_dataset][:]) > 0 # type: ignore + + # breakpoint() + + # Check if the patient file file has any contents + with h5py.File(next((tmp_path / "output").glob(pattern="*-pat-*.h5"))) as h5_file: + slide_datasets = list(h5_file.keys()) + # Check that the amount of patient feats is 2 + assert len(slide_datasets) == 2 + for slide_dataset in slide_datasets: + # Check feature contents + assert len(h5_file[slide_dataset][:]) > 0 # type: ignore From 79c6db35bb3bde522173e9b7a85b2b2bc7e0dd08 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 08:42:30 +0100 Subject: [PATCH 64/99] fix gigapath warnings --- src/stamp/encoding/encoder/gigapath.py | 10 +------- tests/test_encoders.py | 32 ++++++++++++++++++-------- 2 files changed, 24 insertions(+), 18 deletions(-) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index b5a3155a..93670c1b 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -134,14 +134,6 @@ def encode_patients( current_x_offset = 0 for wsi_width, wsi_height, feats, coords in slide_info: - all_feats = ( - torch.tensor(feats, dtype=torch.float16) - .unsqueeze(0) - .to(device) - .half() - .detach() - ) - norm_coords = self._convert_coords( coords=coords.coords_um, total_wsi_width=total_wsi_width, @@ -162,7 +154,7 @@ def encode_patients( .half() ) - all_feats_list.append(all_feats) + all_feats_list.append(feats.unsqueeze(dim=0).to(device)) all_coords_list.append(norm_coords) if not all_feats_list: diff --git a/tests/test_encoders.py b/tests/test_encoders.py index 6924208a..5a33c8e0 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -1,4 +1,5 @@ import random +import shutil from pathlib import Path import h5py @@ -9,6 +10,7 @@ from huggingface_hub.errors import GatedRepoError from random_data import create_random_dataset, create_random_feature_file +from stamp.cache import download_file from stamp.encoding import EncoderName, get_pat_embs, get_slide_embs from stamp.preprocessing.config import ExtractorName @@ -39,7 +41,7 @@ @pytest.mark.slow -@pytest.mark.parametrize("encoder", [EncoderName.EAGLE]) +@pytest.mark.parametrize("encoder", EncoderName) @pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") @pytest.mark.filterwarnings( "ignore:You are using `torch.load` with `weights_only=False`" @@ -48,7 +50,7 @@ "ignore:Importing from timm.models.registry is deprecated, please import via timm.models" ) @pytest.mark.filterwarnings( - "ignore:` torch.backends.cuda.sdp_kernel()` is deprecated. In the future, this context manager will be removed. Please see `torch.nn.attention.sdpa_kernel()` for the new context manager, with updated signature." + "ignore:`torch.backends.cuda.sdp_kernel()` is deprecated:FutureWarning" ) def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): random.seed(0) @@ -69,6 +71,8 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): agg_feat_dir = None if encoder == EncoderName.EAGLE: + # Eagle requires the aggregated features, so we generate new ones + # with same name and coordinates as the other ctranspath feats. agg_feat_dir = tmp_path / "agg_output" agg_feat_dir.mkdir() slide_df = pd.read_csv(slide_path) @@ -91,10 +95,22 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): elif encoder == EncoderName.TITAN: # A random conch1_5 feature does not work with titan so we just download # a real one - # TODO: Download it from github - feature_dir = "/mnt/bulk-sirius/juan/pap_screening/datasets/example/features/mahmood-conch1_5-df7541f1" + downloaded_dir = tmp_path / "downloaded_output" + downloaded_dir.mkdir() + + feat_path = download_file( + url="https://github.com/KatherLab/STAMP/releases/download/2.1.0/conch15feats.h5", + file_name="conch15feats.h5", + sha256sum="f55866f5d38528a008c1381f689db36b9db57a48756b1a56b17b53357e29cb3a", + ) + shutil.move(feat_path, downloaded_dir / feat_path.name) + + feat_path = downloaded_dir / feat_path.name + feature_dir = downloaded_dir + slide_df = pd.read_csv(slide_path) - slide_df.assign(slide_path=feature_dir) + slide_df = slide_df.assign(slide_path=str(feat_path)) + slide_df.to_csv(slide_path) output_dir = tmp_path / "output" output_dir.mkdir(exist_ok=True) @@ -126,14 +142,12 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): # Check if the slide file file has any contents with h5py.File(next((tmp_path / "output").glob(pattern="*-slide-*.h5"))) as h5_file: slide_datasets = list(h5_file.keys()) - # Check that the amount of slides feats is 6 (2 patients, 3 slides per patient) - assert len(slide_datasets) == 4 + # Check that there are slides + assert len(slide_datasets) > 0 for slide_dataset in slide_datasets: # Check feature contents assert len(h5_file[slide_dataset][:]) > 0 # type: ignore - # breakpoint() - # Check if the patient file file has any contents with h5py.File(next((tmp_path / "output").glob(pattern="*-pat-*.h5"))) as h5_file: slide_datasets = list(h5_file.keys()) From 20efbc8815c34c8ae6435e7d8ca9831b148fcab7 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 09:59:22 +0100 Subject: [PATCH 65/99] minor coords and extractor fixes --- src/stamp/heatmaps/__init__.py | 2 +- src/stamp/modeling/data.py | 11 +++++++++-- src/stamp/preprocessing/extractor/musk.py | 3 ++- 3 files changed, 12 insertions(+), 4 deletions(-) diff --git a/src/stamp/heatmaps/__init__.py b/src/stamp/heatmaps/__init__.py index fbe72648..d486eee6 100644 --- a/src/stamp/heatmaps/__init__.py +++ b/src/stamp/heatmaps/__init__.py @@ -144,7 +144,7 @@ def heatmaps_( .float() .to(device) ) - coords_um = get_coords(h5).coords_um + coords_um = torch.from_numpy(get_coords(h5).coords_um).float() stride_um = Microns(get_stride(coords_um)) if h5.attrs.get("unit") == "um": diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py index 71a6052f..5456ab88 100755 --- a/src/stamp/modeling/data.py +++ b/src/stamp/modeling/data.py @@ -167,7 +167,7 @@ def __getitem__( feats.append( torch.from_numpy(h5["feats"][:]) # pyright: ignore[reportIndexIssue] ) - coords_um.append(get_coords(h5).coords_um) + coords_um.append(torch.from_numpy(get_coords(h5).coords_um)) feats = torch.concat(feats).float() coords_um = torch.concat(coords_um).float() @@ -220,7 +220,14 @@ def get_coords(feature_h5: h5py.File) -> CoordsInfo: # Newer STAMP format tile_size_um = Microns(float(tile_size)) coords_um = coords - elif round(feature_h5.attrs.get("tile_size", get_stride(coords))) == 224: + elif ( + round( + feature_h5.attrs.get( + "tile_size", get_stride(torch.from_numpy(coords).float()) + ) + ) + == 224 + ): # Historic STAMP format _logger.info( f"{feature_h5.filename}: tile stride is roughly 224, assuming coordinates have unit 256um/224px (historic STAMP format)" diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py index 75128cb7..7651f992 100644 --- a/src/stamp/preprocessing/extractor/musk.py +++ b/src/stamp/preprocessing/extractor/musk.py @@ -3,7 +3,7 @@ try: import torch import torchvision - from musk import utils + from musk import modeling, utils from timm.data.constants import IMAGENET_INCEPTION_MEAN, IMAGENET_INCEPTION_STD from timm.models import create_model except ModuleNotFoundError as e: @@ -15,6 +15,7 @@ __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2022-2025 Juan Pablo Ricapito" __license__ = "MIT" +__all__ = ["modeling"] class Musk(torch.nn.Module): From 01e0b83e42463e9fdb60f2b756ed975a3507f5fa Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 13:23:20 +0100 Subject: [PATCH 66/99] add hash flag --- src/stamp/__main__.py | 2 ++ src/stamp/config.yaml | 6 ++++++ src/stamp/encoding/__init__.py | 9 ++++++++- src/stamp/encoding/config.py | 2 ++ src/stamp/encoding/encoder/__init__.py | 21 ++++++++++++++------- src/stamp/encoding/encoder/chief.py | 8 ++++---- src/stamp/encoding/encoder/eagle.py | 13 +++++++------ src/stamp/encoding/encoder/gigapath.py | 9 ++++----- src/stamp/encoding/encoder/titan.py | 8 ++++---- 9 files changed, 51 insertions(+), 27 deletions(-) diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index 25838c79..df90feef 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -94,6 +94,7 @@ def _run_cli(args: argparse.Namespace) -> None: feat_dir=config.slide_encoding.feat_dir, device=config.slide_encoding.device, agg_feat_dir=config.slide_encoding.agg_feat_dir, + generate_hash=config.slide_encoding.generate_hash, ) case "encode_patients": @@ -116,6 +117,7 @@ def _run_cli(args: argparse.Namespace) -> None: filename_label=config.patient_encoding.filename_label, device=config.patient_encoding.device, agg_feat_dir=config.patient_encoding.agg_feat_dir, + generate_hash=config.patient_encoding.generate_hash, ) case "train": diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 45bc369f..13346706 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -59,6 +59,9 @@ slide_encoding: # some encoders such as eagle (it requires virchow2 features). #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + # Add a hash of the entire preprocessing codebase in the feature folder name. + #generate_hash: True + patient_encoding: # Encoder to use for patient encoding. Possible options are "katherlab-cobra", @@ -88,6 +91,9 @@ patient_encoding: # some encoders such as eagle (it requires virchow2 features). #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + # Add a hash of the entire preprocessing codebase in the feature folder name. + #generate_hash: True + crossval: output_dir: "/path/to/save/files/to" diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 80f891d5..2ce82901 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -17,6 +17,7 @@ def get_pat_embs( filename_label: PandasLabel, device: DeviceLikeType, agg_feat_dir: Path | None = None, + generate_hash: bool = True, ) -> None: """""" match encoder: @@ -69,6 +70,7 @@ def get_pat_embs( filename_label=filename_label, device=device, agg_feat_dir=agg_feat_dir, + generate_hash=generate_hash, ) @@ -78,6 +80,7 @@ def get_slide_embs( feat_dir: Path, device: DeviceLikeType, agg_feat_dir: Path | None = None, + generate_hash: bool = True, ) -> None: match encoder: case EncoderName.TITAN: @@ -121,5 +124,9 @@ def get_slide_embs( assert_never(unreachable) selected_encoder.encode_slides( - output_dir, feat_dir, device, agg_feat_dir=agg_feat_dir + output_dir=output_dir, + feat_dir=feat_dir, + device=device, + agg_feat_dir=agg_feat_dir, + generate_hash=generate_hash, ) diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 83767989..4543a13d 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -25,6 +25,7 @@ class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): feat_dir: Path device: DeviceLikeType agg_feat_dir: Path | None = None + generate_hash: bool = True class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): @@ -38,3 +39,4 @@ class PatientEncodingConfig(BaseModel, arbitrary_types_allowed=True): filename_label: PandasLabel = "FILENAME" device: DeviceLikeType agg_feat_dir: Path | None = None + generate_hash: bool = True diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index d49fc358..24772748 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -38,13 +38,13 @@ def encode_slides( output_dir: Path, feat_dir: Path, device: DeviceLikeType, + generate_hash: bool, **kwargs, ) -> None: """General method to encode slides.""" - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash ) - output_file = os.path.join(output_dir, output_name) slide_dict = {} self.model.to(device).eval() @@ -81,14 +81,14 @@ def encode_patients( patient_label: PandasLabel, filename_label: PandasLabel, device: DeviceLikeType, + generate_hash: bool, **kwargs, ) -> None: """General method to encode patients with features only. Override this method if coords are needed for encoding""" - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash ) - output_file = os.path.join(output_dir, output_name) patient_dict = {} self.model.to(device).eval() @@ -182,4 +182,11 @@ def _save_features(self, output_file: str, entry_dict: dict) -> None: tqdm.write("Encoding failed: file empty") os.remove(output_file) else: - tqdm.write(f"Finished encoding, saved to {output_file}") \ No newline at end of file + tqdm.write(f"Finished encoding, saved to {output_file}") + + def _generate_output_path(self, output_dir: Path, generate_hash: bool) -> str: + if generate_hash: + output_name = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + else: + output_name = f"{self.identifier}-slide-.h5" + return os.path.join(output_dir, output_name) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 83690df7..8f970b59 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -130,15 +130,15 @@ def encode_patients( patient_label: PandasLabel, filename_label: PandasLabel, device: DeviceLikeType, + generate_hash: bool, **kwargs, ) -> None: - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - output_file = os.path.join(output_dir, output_name) + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash + ) patient_dict = {} self.model.to(device).eval() diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 112d6b5a..e531215a 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -118,6 +118,7 @@ def encode_slides( output_dir: Path, feat_dir: Path, device: DeviceLikeType, + generate_hash: bool, **kwargs, ) -> None: """Encode slide from patch features.""" @@ -128,10 +129,10 @@ def encode_slides( " is required for Eagle's encode_patients" ) - output_name = ( - f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash ) - output_file = os.path.join(output_dir, output_name) + if os.path.exists(output_file): tqdm.write(f"Output file {output_file} already exists, skipping") return @@ -168,6 +169,7 @@ def encode_patients( patient_label: PandasLabel, filename_label: PandasLabel, device: DeviceLikeType, + generate_hash: bool, **kwargs, ) -> None: """Encode patients from slide features.""" @@ -183,10 +185,9 @@ def encode_patients( patient_dict = {} self.model.to(device).eval() - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash ) - output_file: str = os.path.join(output_dir, output_name) if os.path.exists(output_file): tqdm.write(f"Output file {output_file} already exists, skipping") diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 93670c1b..16a5693f 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -8,7 +8,6 @@ from gigapath import slide_encoder from tqdm import tqdm -from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo, PandasLabel @@ -75,17 +74,17 @@ def encode_patients( patient_label: PandasLabel, filename_label: PandasLabel, device, + generate_hash: bool, **kwargs, ) -> None: """Generate one virtual slide concatenating all the slides of a patient over the x axis.""" - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - output_file = os.path.join(output_dir, output_name) + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash + ) patient_dict = {} self.model.to(device).half().eval() diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index aa2423c0..1d32bd4d 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -86,17 +86,17 @@ def encode_patients( patient_label: PandasLabel, filename_label: PandasLabel, device, + generate_hash: bool, **kwargs, ) -> None: """Generate one virtual slide concatenating all the slides of a patient over the x axis.""" - output_name = ( - f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}.h5" - ) slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - output_file = os.path.join(output_dir, output_name) + output_file = self._generate_output_path( + output_dir=output_dir, generate_hash=generate_hash + ) patient_dict = {} self.model.to(device).eval() From 90d88d8030ef4669aa6a7ccac6c0023de70a6923 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 13:42:09 +0100 Subject: [PATCH 67/99] add hash check and formatting --- src/stamp/encoding/encoder/chief.py | 11 ++++++++--- src/stamp/encoding/encoder/cobra.py | 4 +--- src/stamp/encoding/encoder/eagle.py | 1 - src/stamp/encoding/encoder/titan.py | 1 - tests/random_data.py | 1 - 5 files changed, 9 insertions(+), 9 deletions(-) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 8f970b59..fd955b44 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -10,7 +10,7 @@ from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm -from stamp.cache import STAMP_CACHE_DIR, get_processing_code_hash +from stamp.cache import STAMP_CACHE_DIR, file_digest from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import PandasLabel @@ -94,8 +94,13 @@ def __init__(self) -> None: "https://drive.google.com/u/0/uc?id=10bJq_ayX97_1w95omN8_mESrYAGIBAPb&export=download", str(model_path), ) - - # TODO: check digest + digest = file_digest(model_path) + assert ( + digest + == "6a46d200b32a65e5ce4774611b889b5f1bbf7a39f9111321a2a1b5dbdb9996b8" + ), ( + f"The digest of the downloaded checkpoint ({model_path}) did not match the expected value." + ) chief = torch.load(model_path) if "organ_embedding" in chief: diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 0c941e0d..9cc3b11c 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -1,13 +1,11 @@ -import torch from numpy import ndarray from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.preprocessing.config import ExtractorName -# TODO: Check which are the necessary imports and add them to cobra package - try: + import torch from cobra.utils.load_cobra import get_cobra except ModuleNotFoundError as e: raise ModuleNotFoundError( diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index e531215a..9de74352 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -8,7 +8,6 @@ from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm -from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 1d32bd4d..acd14a59 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -10,7 +10,6 @@ from tqdm import tqdm from transformers import AutoModel -from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo, PandasLabel diff --git a/tests/random_data.py b/tests/random_data.py index 741e659b..8a53e8d4 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -3,7 +3,6 @@ import io import random import string -import tempfile from collections.abc import Mapping, Sequence from pathlib import Path from typing import TypeAlias From 71982a228f68dc5e8fad508dd56fb842e830a3ef Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 14:50:04 +0100 Subject: [PATCH 68/99] fixes and validations --- pyproject.toml | 1 + src/stamp/encoding/encoder/__init__.py | 17 ++++----- src/stamp/encoding/encoder/chief.py | 8 +--- src/stamp/encoding/encoder/eagle.py | 18 +++------ src/stamp/encoding/encoder/gigapath.py | 6 +-- src/stamp/encoding/encoder/titan.py | 6 +-- tests/test_encoders.py | 53 +++++++++++++++----------- 7 files changed, 47 insertions(+), 62 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index fc00ff54..39812ed1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -105,6 +105,7 @@ virchow2 = [ "torch>=2.0.0", ] cobra = [ + "jinja2>=3.1.4", "cobra @ git+https://github.com/KatherLab/COBRA.git", ] prism = [ diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 24772748..007be2f4 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -62,7 +62,7 @@ def encode_slides( try: feats, coords = self._validate_and_read_features(h5_path) - except FileNotFoundError as e: + except ValueError as e: tqdm.write(s=str(e)) continue @@ -109,12 +109,7 @@ def encode_patients( for _, row in group.iterrows(): slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) - try: - feats, _ = self._validate_and_read_features(h5_path) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - + feats, _ = self._validate_and_read_features(h5_path) feats_list.append(feats) if not feats_list: @@ -162,8 +157,10 @@ def _read_h5( self, h5_path: str, ) -> tuple[Tensor, CoordsInfo, str]: - if not os.path.exists(h5_path) or not h5_path.endswith(".h5"): - raise FileNotFoundError("File does not exist or is not an h5 file") + if not os.path.exists(h5_path): + raise FileNotFoundError(f"File does not exist: {h5_path}") + elif not h5_path.endswith(".h5"): + raise ValueError(f"File is not of type .h5: {os.path.basename(h5_path)}") with h5py.File(h5_path, "r") as f: feats: Tensor = torch.tensor(f["feats"][:], dtype=self.precision) # type: ignore coords: CoordsInfo = get_coords(f) @@ -188,5 +185,5 @@ def _generate_output_path(self, output_dir: Path, generate_hash: bool) -> str: if generate_hash: output_name = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" else: - output_name = f"{self.identifier}-slide-.h5" + output_name = f"{self.identifier}-slide.h5" return os.path.join(output_dir, output_name) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index fd955b44..4b2bd8a7 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -159,13 +159,7 @@ def encode_patients( for _, row in group.iterrows(): slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) - - try: - feats, _ = self._validate_and_read_features(h5_path=h5_path) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue - + feats, _ = self._validate_and_read_features(h5_path=h5_path) feats_list.append(feats) if not feats_list: diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 9de74352..2715e404 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -1,3 +1,4 @@ +import math import os from pathlib import Path @@ -38,9 +39,6 @@ def _validate_and_read_features_with_agg( extractor: str feats, coords, extractor = self._read_h5(h5_ctp) - # TODO: Eagle requires ctranspath features extracted with 2mpp - # magnification. Validate this. - if extractor not in self.required_extractor: raise ValueError( f"Features must be extracted with one of {self.required_extractor}. " @@ -148,7 +146,7 @@ def encode_slides( feats, agg_feats = self._validate_and_read_features_with_agg( h5_ctp, h5_vir2, slide_name ) - except FileNotFoundError as e: + except ValueError as e: tqdm.write(s=str(e)) continue @@ -202,15 +200,9 @@ def encode_patients( h5_ctp = os.path.join(feat_dir, slide_filename) h5_vir2 = os.path.join(agg_feat_dir, slide_filename) - # Validate and read features - try: - feats, agg_feats = self._validate_and_read_features_with_agg( - h5_ctp, h5_vir2, slide_name - ) - except FileNotFoundError as e: - tqdm.write(str(e)) - continue - + feats, agg_feats = self._validate_and_read_features_with_agg( + h5_ctp, h5_vir2, slide_name + ) feats_list.append(feats) agg_feats_list.append(agg_feats) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 16a5693f..884a8158 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -106,11 +106,7 @@ def encode_patients( slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) - try: - feats, coords = self._validate_and_read_features(h5_path=h5_path) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue + feats, coords = self._validate_and_read_features(h5_path=h5_path) # Get the mpp of one slide and check that the rest have the same if slides_mpp < 0: diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index acd14a59..fb66471e 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -115,11 +115,7 @@ def encode_patients( slide_filename = row[filename_label] h5_path = os.path.join(feat_dir, slide_filename) - try: - feats, coords = self._validate_and_read_features(h5_path=h5_path) - except FileNotFoundError as e: - tqdm.write(s=str(e)) - continue + feats, coords = self._validate_and_read_features(h5_path=h5_path) # Get the mpp of one slide and check that the rest have the same if slides_mpp < 0: diff --git a/tests/test_encoders.py b/tests/test_encoders.py index 5a33c8e0..90fc4dfb 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -8,7 +8,7 @@ import pytest import torch from huggingface_hub.errors import GatedRepoError -from random_data import create_random_dataset, create_random_feature_file +from random_data import create_random_dataset, create_random_feature_file, random_string from stamp.cache import download_file from stamp.encoding import EncoderName, get_pat_embs, get_slide_embs @@ -112,13 +112,16 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): slide_df = slide_df.assign(slide_path=str(feat_path)) slide_df.to_csv(slide_path) - output_dir = tmp_path / "output" - output_dir.mkdir(exist_ok=True) + # Create random subdirectories for slide and patient features + slide_output_dir = tmp_path / f"slide_output_{random_string(16)}" + patient_output_dir = tmp_path / f"patient_output_{random_string(16)}" + slide_output_dir.mkdir(exist_ok=True) + patient_output_dir.mkdir(exist_ok=True) try: get_slide_embs( encoder=encoder, - output_dir=output_dir, + output_dir=slide_output_dir, feat_dir=Path(feature_dir), device="cuda" if torch.cuda.is_available() else "cpu", agg_feat_dir=agg_feat_dir, @@ -126,7 +129,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): get_pat_embs( encoder=encoder, - output_dir=output_dir, + output_dir=patient_output_dir, feat_dir=Path(feature_dir), slide_table_path=slide_path, patient_label="patient", @@ -139,20 +142,26 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): except GatedRepoError: pytest.skip(f"cannot access gated repo for {encoder}") - # Check if the slide file file has any contents - with h5py.File(next((tmp_path / "output").glob(pattern="*-slide-*.h5"))) as h5_file: - slide_datasets = list(h5_file.keys()) - # Check that there are slides - assert len(slide_datasets) > 0 - for slide_dataset in slide_datasets: - # Check feature contents - assert len(h5_file[slide_dataset][:]) > 0 # type: ignore - - # Check if the patient file file has any contents - with h5py.File(next((tmp_path / "output").glob(pattern="*-pat-*.h5"))) as h5_file: - slide_datasets = list(h5_file.keys()) - # Check that the amount of patient feats is 2 - assert len(slide_datasets) == 2 - for slide_dataset in slide_datasets: - # Check feature contents - assert len(h5_file[slide_dataset][:]) > 0 # type: ignore + # Check if the slide file has any contents + slide_files = list(slide_output_dir.glob("*.h5")) + assert len(slide_files) > 0, "No slide feature files were generated." + for slide_file in slide_files: + with h5py.File(slide_file, "r") as h5_file: + slide_datasets = list(h5_file.keys()) + # Check that there are slides + assert len(slide_datasets) > 0 + for slide_dataset in slide_datasets: + # Check feature contents + assert len(h5_file[slide_dataset][:]) > 0 # type: ignore + + # Check if the patient file has any contents + patient_files = list(patient_output_dir.glob("*.h5")) + assert len(patient_files) > 0, "No patient feature files were generated." + for patient_file in patient_files: + with h5py.File(patient_file, "r") as h5_file: + patient_datasets = list(h5_file.keys()) + # Check that the amount of patient feats is 2 + assert len(patient_datasets) == 2 + for patient_dataset in patient_datasets: + # Check feature contents + assert len(h5_file[patient_dataset][:]) > 0 # type: ignore From 1f43e0c3dbf3da9b6acf67903ce3d6874bbac253 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 15:25:18 +0100 Subject: [PATCH 69/99] format and update docs --- getting-started.md | 22 ++++++++++++++++++++++ src/stamp/encoding/__init__.py | 3 +++ src/stamp/encoding/encoder/__init__.py | 4 ++++ src/stamp/encoding/encoder/eagle.py | 1 - tests/test_encoders.py | 1 - 5 files changed, 29 insertions(+), 2 deletions(-) diff --git a/getting-started.md b/getting-started.md index 8f711472..9e84bd07 100644 --- a/getting-started.md +++ b/getting-started.md @@ -44,10 +44,21 @@ which in turn allows us to efficiently train machine learning models with them. Stamp currently supports the following feature extractors: - [ctranspath][ctranspath] + - [chief_ctranspath][chief_ctranspath] - [DinoBloom][dinobloom] - [CONCH][conch] + - [CONCHv1.5][conch1_5] - [UNI][uni] + - [UNI2][uni2] + - [Virchow][virchow] - [Virchow2][virchow2] + - [Gigapath][gigapath] + - [H-optimus-0][h_optimus_0] + - [H-optimus-1][h_optimus_1] + - [mSTAR][mstar] + - [MUSK][musk] + - [PLIP][plip] + As some of the above require you to request access to the model on huggingface, we will stick with ctranspath for this example. @@ -128,8 +139,19 @@ meaning ignored that it was ignored during feature extraction. [ctranspath]: https://www.sciencedirect.com/science/article/abs/pii/S1361841522002043 "Transformer-based unsupervised contrastive learning for histopathological image classification" [dinobloom]: https://github.com/marrlab/DinoBloom "DinoBloom: A Foundation Model for Generalizable Cell Embeddings in Hematology" [uni]: https://www.nature.com/articles/s41591-024-02857-3 "Towards a general-purpose foundation model for computational pathology" +[uni2]: https://huggingface.co/MahmoodLab/UNI2-h [conch]: https://www.nature.com/articles/s41591-024-02856-4 "A visual-language foundation model for computational pathology" +[conch1_5]: https://huggingface.co/MahmoodLab/conchv1_5 +[virchow]: https://huggingface.co/paige-ai/Virchow "A foundation model for clinical-grade computational pathology and rare cancers detection" [virchow2]: https://huggingface.co/paige-ai/Virchow2 +[chief_ctranspath]: https://github.com/hms-dbmi/CHIEF +[gigapath]: https://huggingface.co/prov-gigapath/prov-gigapath +[h_optimus_0]: https://huggingface.co/bioptimus/H-optimus-0 +[h_optimus_1]: https://huggingface.co/bioptimus/H-optimus-1 +[mstar]: https://huggingface.co/Wangyh/mSTAR +[musk]: https://huggingface.co/xiangjx/musk +[plip]: https://github.com/PathologyFoundation/plip + ## Doing Cross-Validation on the Data Set diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 2ce82901..9ca7c2fa 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -7,6 +7,9 @@ from stamp.encoding.encoder import Encoder from stamp.modeling.data import PandasLabel +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" def get_pat_embs( encoder: EncoderName | Encoder, diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 007be2f4..a5264eeb 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -19,6 +19,10 @@ EncoderModel = TypeVar("EncoderModel", bound=nn.Module) +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" + class Encoder(ABC): def __init__( diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 2715e404..bdc08a85 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -1,4 +1,3 @@ -import math import os from pathlib import Path diff --git a/tests/test_encoders.py b/tests/test_encoders.py index 90fc4dfb..7ae707ae 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -39,7 +39,6 @@ } - @pytest.mark.slow @pytest.mark.parametrize("encoder", EncoderName) @pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") From e8027c1c4eea284b92998ef2a9d06fa619a6d44b Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 26 May 2025 15:29:15 +0100 Subject: [PATCH 70/99] forgot formatting --- src/stamp/encoding/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 9ca7c2fa..63975979 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -11,6 +11,7 @@ __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" __license__ = "MIT" + def get_pat_embs( encoder: EncoderName | Encoder, output_dir: Path, From c0287c54ea3ed030759b6a7f99846f4328d28187 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20W=C3=B6lflein?= Date: Mon, 26 May 2025 18:08:47 +0200 Subject: [PATCH 71/99] Fix dependencies --- .github/workflows/build.yml | 2 +- pyproject.toml | 16 + uv.lock | 3122 ++++++++++++++++++++++++----------- 3 files changed, 2193 insertions(+), 947 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 96c2f1e7..4b7c8651 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -30,7 +30,7 @@ jobs: # python-version: ${{ matrix.python-version }} - name: Install the project - run: uv sync --all-extras --dev + run: uv sync --extra cpu --dev - name: Build run: uv build diff --git a/pyproject.toml b/pyproject.toml index 39812ed1..ba531421 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -125,6 +125,7 @@ plip = [ # Blanket target all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,madeleine,musk,plip]"] +cpu = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,prism,madeleine,musk,plip]"] [project.scripts] "stamp" = "stamp.__main__:main" @@ -157,3 +158,18 @@ markers = [ [tool.ruff] lint.ignore = ["F722"] # https://docs.kidger.site/jaxtyping/faq/#flake8-or-ruff-are-throwing-an-error + +# Hack to get dependencies installed for cobra and causal-conv1d +# More info: https://docs.astral.sh/uv/concepts/projects/config/#build-isolation +[[tool.uv.dependency-metadata]] +name = "cobra" +version = "0.1.0" +requires-dist = ["torch", "packaging", "ninja", "hatchling", "setuptools"] + +[[tool.uv.dependency-metadata]] +name = "causal-conv1d" +version = "1.5.0.post8" +requires-dist = ["torch", "packaging", "ninja", "hatchling", "setuptools"] + +[tool.uv] +no-build-isolation-package = ["cobra", "causal-conv1d"] \ No newline at end of file diff --git a/uv.lock b/uv.lock index 5ce183d8..580fa871 100644 --- a/uv.lock +++ b/uv.lock @@ -1,26 +1,50 @@ version = 1 requires-python = ">=3.11" resolution-markers = [ - "python_full_version >= '3.12' and sys_platform == 'darwin'", - "python_full_version >= '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version >= '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version >= '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')", - "python_full_version < '3.12' and sys_platform == 'darwin'", - "python_full_version < '3.12' and platform_machine == 'aarch64' and sys_platform == 'linux'", - "(python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux') or (python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux')", -] + "python_full_version < '3.12' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version < '3.12' and platform_system == 'Darwin' and sys_platform != 'linux'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version < '3.12' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", + "(python_full_version < '3.12' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform == 'linux') or (python_full_version < '3.12' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform == 'linux')", + "(python_full_version < '3.12' and platform_machine != 'aarch64' and platform_system != 'Darwin' and sys_platform != 'linux') or (python_full_version < '3.12' and platform_system != 'Darwin' and platform_system != 'Linux' and sys_platform != 'linux')", + "python_full_version == '3.12.*' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_system == 'Darwin' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and platform_system == 'Darwin' and sys_platform != 'linux'", + "python_full_version >= '3.13' and platform_system == 'Darwin' and sys_platform != 'linux'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and platform_system == 'Linux' and sys_platform != 'linux'", + 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EncoderName): _, slide_path, feature_dir, _ = create_random_dataset( dir=tmp_path, - n_patients=2, + n_patients=1, min_slides_per_patient=2, max_slides_per_patient=2, min_tiles_per_slide=32, @@ -159,8 +159,8 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): for patient_file in patient_files: with h5py.File(patient_file, "r") as h5_file: patient_datasets = list(h5_file.keys()) - # Check that the amount of patient feats is 2 - assert len(patient_datasets) == 2 + # Check that the amount of patient feats is 1 + assert len(patient_datasets) == 1 for patient_dataset in patient_datasets: # Check feature contents assert len(h5_file[patient_dataset][:]) > 0 # type: ignore From 48a1be875879d3ff60f7e2af3375ccb35b788a79 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Georg=20W=C3=B6lflein?= Date: Mon, 2 Jun 2025 17:21:43 +0200 Subject: [PATCH 73/99] Fix dependencies (again) (for macos) --- pyproject.toml | 19 +++- uv.lock | 301 +++++++++++++++++++++++++------------------------ 2 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"transformers" }, - { name = "xformers" }, + { name = "xformers", marker = "sys_platform != 'darwin'" }, ] [[package]] From 5543140c3168c93d9243a7fddf294145bb5ec88a Mon Sep 17 00:00:00 2001 From: Juan Pablo Ricapito <50255924+EzicStar@users.noreply.github.com> Date: Tue, 3 Jun 2025 10:00:16 +0200 Subject: [PATCH 74/99] Debug build.yml Check encoder tests as they freeze the pipeline --- .github/workflows/build.yml | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 4b7c8651..f851de81 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest tests/ + run: uv run pytest -k encod env: HF_TOKEN: ${{ secrets.HF_TOKEN }} @@ -56,4 +56,4 @@ jobs: - name: Check code formatting with Ruff run: ruff format --diff --target-version=py311 # continue-on-error: true - \ No newline at end of file + From 8f3b5084e5bf6bc5e1ba7647b2694418d88dd2fe Mon Sep 17 00:00:00 2001 From: ezicStar Date: Tue, 3 Jun 2025 16:29:11 +0200 Subject: [PATCH 75/99] skip cuda required encoders --- .github/workflows/build.yml | 2 +- tests/test_encoders.py | 11 +++++++++++ 2 files changed, 12 insertions(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index f851de81..1cd74366 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -k encod + run: uv run pytest -rs env: HF_TOKEN: ${{ secrets.HF_TOKEN }} diff --git a/tests/test_encoders.py b/tests/test_encoders.py index fd6fffb4..b7169304 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -68,6 +68,17 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): n_categories=2, ) + cuda_required = [ + EncoderName.CHIEF, + EncoderName.COBRA, + EncoderName.GIGAPATH, + EncoderName.MADELEINE, + EncoderName.EAGLE, + ] + + if encoder in cuda_required and not torch.cuda.is_available(): + pytest.skip(f"Skipping {encoder} as CUDA is not available") + agg_feat_dir = None if encoder == EncoderName.EAGLE: # Eagle requires the aggregated features, so we generate new ones From 5fa5c614a17b9bae6809ad074965cdf597a65ff3 Mon Sep 17 00:00:00 2001 From: Juan Pablo Ricapito <50255924+EzicStar@users.noreply.github.com> Date: Wed, 4 Jun 2025 11:46:58 +0200 Subject: [PATCH 76/99] Debug feature extractor tests --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 1cd74366..c1e55571 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -rs + run: uv run pytest -rs tests/test_feature_extractors.py env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From ae23522dc1521ef61f9db23411a5a229c9f2b9f3 Mon Sep 17 00:00:00 2001 From: Juan Pablo Ricapito <50255924+EzicStar@users.noreply.github.com> Date: Thu, 19 Jun 2025 09:17:31 +0200 Subject: [PATCH 77/99] Update .github/workflows/build.yml MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Co-authored-by: Georg Wölflein --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index c1e55571..0e18fba9 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -rs tests/test_feature_extractors.py + run: uv run pytest -rs tests/ env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From 94b333a331aaf0ad95a1d5e90e9bb6e521f3b2de Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Fri, 20 Jun 2025 13:41:19 +0100 Subject: [PATCH 78/99] add own DeviceLikeType --- src/stamp/encoding/__init__.py | 4 +--- src/stamp/encoding/config.py | 3 +-- src/stamp/encoding/encoder/__init__.py | 3 +-- src/stamp/encoding/encoder/chief.py | 3 +-- src/stamp/encoding/encoder/eagle.py | 3 +-- src/stamp/encoding/encoder/titan.py | 3 +-- src/stamp/heatmaps/__init__.py | 3 +-- src/stamp/modeling/data.py | 1 + src/stamp/preprocessing/__init__.py | 2 +- 9 files changed, 9 insertions(+), 16 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 63975979..7f0d97a1 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -1,11 +1,9 @@ from pathlib import Path from typing import assert_never -from torch._prims_common import DeviceLikeType # type: ignore - from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import PandasLabel +from stamp.modeling.data import DeviceLikeType, PandasLabel __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index 4543a13d..fb91dc04 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -2,9 +2,8 @@ from pathlib import Path from pydantic import BaseModel, ConfigDict -from torch._prims_common import DeviceLikeType # type: ignore -from stamp.modeling.data import PandasLabel # type: ignore +from stamp.modeling.data import DeviceLikeType, PandasLabel class EncoderName(StrEnum): diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index a5264eeb..99312b7c 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -8,13 +8,12 @@ import pandas as pd import torch from torch import Tensor, nn -from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName -from stamp.modeling.data import CoordsInfo, PandasLabel, get_coords +from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel, get_coords from stamp.preprocessing.config import ExtractorName EncoderModel = TypeVar("EncoderModel", bound=nn.Module) diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 4b2bd8a7..8aefa572 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -7,13 +7,12 @@ import torch.nn as nn import torch.nn.functional as F from numpy import ndarray -from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from stamp.cache import STAMP_CACHE_DIR, file_digest from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import PandasLabel +from stamp.modeling.data import DeviceLikeType, PandasLabel from stamp.preprocessing.config import ExtractorName """authors: https://github.com/hms-dbmi/CHIEF""" diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index bdc08a85..785f9169 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -5,13 +5,12 @@ import pandas as pd import torch from torch import Tensor -from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF -from stamp.modeling.data import CoordsInfo, PandasLabel +from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel from stamp.preprocessing.config import ExtractorName """From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index fb66471e..80f363c4 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -6,13 +6,12 @@ import pandas as pd import torch from torch import Tensor -from torch._prims_common import DeviceLikeType # type: ignore from tqdm import tqdm from transformers import AutoModel from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo, PandasLabel +from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.tiling import Microns, SlideMPP diff --git a/src/stamp/heatmaps/__init__.py b/src/stamp/heatmaps/__init__.py index 15967f10..ffa9157c 100644 --- a/src/stamp/heatmaps/__init__.py +++ b/src/stamp/heatmaps/__init__.py @@ -13,10 +13,9 @@ from matplotlib.patches import Patch from PIL import Image from torch import Tensor -from torch._prims_common import DeviceLikeType # type: ignore from torch.func import jacrev # pyright: ignore[reportPrivateImportUsage] -from stamp.modeling.data import get_coords, get_stride +from stamp.modeling.data import DeviceLikeType, get_coords, get_stride from stamp.modeling.lightning_model import LitVisionTransformer from stamp.modeling.vision_transformer import VisionTransformer from stamp.preprocessing import supported_extensions diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py index 5456ab88..cf32c7bf 100755 --- a/src/stamp/modeling/data.py +++ b/src/stamp/modeling/data.py @@ -26,6 +26,7 @@ __copyright__ = "Copyright (C) 2022-2025 Marko van Treeck" __license__ = "MIT" +DeviceLikeType: TypeAlias = str | torch.device | int PatientId: TypeAlias = str GroundTruth: TypeAlias = str diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index c9b261a6..11913733 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -12,12 +12,12 @@ import torch from PIL import Image from torch import Tensor -from torch._prims_common import DeviceLikeType # type: ignore from torch.utils.data import DataLoader, IterableDataset from tqdm import tqdm import stamp from stamp.cache import get_processing_code_hash +from stamp.modeling.data import DeviceLikeType from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor from stamp.preprocessing.tiling import ( From 7c5cee21e996957f82e1ad38bbd20eca9ce79662 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 23 Jun 2025 16:11:44 +0100 Subject: [PATCH 79/99] add description and better func names --- src/stamp/encoding/__init__.py | 27 +++++++++++++++++++++++++- src/stamp/encoding/encoder/__init__.py | 6 +++--- src/stamp/encoding/encoder/chief.py | 2 +- src/stamp/encoding/encoder/eagle.py | 4 ++-- src/stamp/encoding/encoder/gigapath.py | 2 +- src/stamp/encoding/encoder/titan.py | 2 +- 6 files changed, 34 insertions(+), 9 deletions(-) diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index 7f0d97a1..ef73f1cd 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -21,7 +21,32 @@ def get_pat_embs( agg_feat_dir: Path | None = None, generate_hash: bool = True, ) -> None: - """""" + """ + Encode patch-level features to a single feature per patient using a given encoder. + + This function selects an encoder based on the provided `encoder` parameter and + uses it to encode patient-level features. Each patient's slide is concatenated + along the x-axis to create a single 'virtual' slide for encoding. + + Args: + encoder (EncoderName | Encoder): The encoder to use for feature extraction. + Can be an instance of `Encoder` or an `EncoderName` enum value. + output_dir (Path): Directory where the encoded patient features will be saved. + feat_dir (Path): Directory containing the input features for encoding. + slide_table_path (Path): Path to the slide table file (CSV) containing metadata. + patient_label (PandasLabel): Column name in the slide table representing patient IDs. + filename_label (PandasLabel): Column name in the slide table representing slide filenames. + device (DeviceLikeType): Device to use for computation (e.g., "cpu" or "cuda"). + agg_feat_dir (Path | None, optional): Directory for aggregated features. Defaults to None. + generate_hash (bool, optional): Whether to generate a hash for the output file name. Defaults to True. + + Side Effects: + - Reads the slide table file from `slide_table_path`. + - Reads feature files from `feat_dir`. + - Saves encoded patient features to `output_dir` in a single .h5 file + containing each patient as a dataset. + """ + match encoder: case EncoderName.COBRA: from stamp.encoding.encoder.cobra import Cobra diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 99312b7c..b26bb01d 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -74,7 +74,7 @@ def encode_slides( ) slide_dict[slide_name] = {"feats": slide_embedding} - self._save_features(output_file, entry_dict=slide_dict) + self._save_features_(output_file, entry_dict=slide_dict) def encode_patients( self, @@ -124,7 +124,7 @@ def encode_patients( ) patient_dict[patient_id] = {"feats": patient_embedding} - self._save_features(output_file, entry_dict=patient_dict) + self._save_features_(output_file, entry_dict=patient_dict) @abstractmethod def _generate_slide_embedding( @@ -170,7 +170,7 @@ def _read_h5( extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor - def _save_features(self, output_file: str, entry_dict: dict) -> None: + def _save_features_(self, output_file: str, entry_dict: dict) -> None: os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: for entry_name, data in entry_dict.items(): diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 8aefa572..76b30417 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -178,7 +178,7 @@ def encode_patients( "feats": patient_embedding, } - self._save_features(output_file=output_file, entry_dict=patient_dict) + self._save_features_(output_file=output_file, entry_dict=patient_dict) def initialize_weights(module): diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 785f9169..b50cbff8 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -154,7 +154,7 @@ def encode_slides( "feats": eagle_embedding, } - self._save_features(output_file, entry_dict=slide_dict) + self._save_features_(output_file, entry_dict=slide_dict) def encode_patients( self, @@ -215,4 +215,4 @@ def encode_patients( "feats": eagle_embedding, } - self._save_features(output_file, entry_dict=patient_dict) + self._save_features_(output_file, entry_dict=patient_dict) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 884a8158..6a135122 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -164,7 +164,7 @@ def encode_patients( "feats": patient_embedding, } - self._save_features(output_file, entry_dict=patient_dict) + self._save_features_(output_file, entry_dict=patient_dict) def _generate_patient_embedding( self, feats_list, device, coords_list: list | None = None, **kwargs diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 80f363c4..382cdee8 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -150,4 +150,4 @@ def encode_patients( "feats": patient_embedding, } - self._save_features(output_file, entry_dict=patient_dict) + self._save_features_(output_file, entry_dict=patient_dict) From fa4e02a94ae4ec5aaf8cda02d45dab3fa9e4ee85 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 25 Jun 2025 09:30:56 +0100 Subject: [PATCH 80/99] reformat encoder base class --- src/stamp/encoding/encoder/__init__.py | 17 +++++++---------- src/stamp/encoding/encoder/chief.py | 2 +- src/stamp/encoding/encoder/cobra.py | 2 +- src/stamp/encoding/encoder/eagle.py | 6 +++--- src/stamp/encoding/encoder/gigapath.py | 2 +- src/stamp/encoding/encoder/madeleine.py | 2 +- src/stamp/encoding/encoder/prism.py | 2 +- src/stamp/encoding/encoder/titan.py | 2 +- 8 files changed, 16 insertions(+), 19 deletions(-) diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index b26bb01d..beec6c76 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -1,7 +1,6 @@ import os from abc import ABC, abstractmethod from pathlib import Path -from typing import TypeVar import h5py import numpy as np @@ -16,8 +15,6 @@ from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel, get_coords from stamp.preprocessing.config import ExtractorName -EncoderModel = TypeVar("EncoderModel", bound=nn.Module) - __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" __license__ = "MIT" @@ -26,15 +23,15 @@ class Encoder(ABC): def __init__( self, - model, + model: nn.Module, identifier: EncoderName, precision: torch.dtype, - required_extractor: list[ExtractorName], + required_extractors: list[ExtractorName], ): self.model = model self.identifier = identifier self.precision = precision - self.required_extractor = required_extractor + self.required_extractors = required_extractors def encode_slides( self, @@ -149,9 +146,9 @@ def _read_slide_table(slide_table_path: Path) -> pd.DataFrame: def _validate_and_read_features(self, h5_path: str) -> tuple[Tensor, CoordsInfo]: feats, coords, extractor = self._read_h5(h5_path) - if extractor not in self.required_extractor: + if extractor not in self.required_extractors: raise ValueError( - f"Features must be extracted with one of {self.required_extractor}. " + f"Features must be extracted with one of {self.required_extractors}. " f"Features located in {h5_path} are extracted with {extractor}" ) return feats, coords @@ -184,9 +181,9 @@ def _save_features_(self, output_file: str, entry_dict: dict) -> None: else: tqdm.write(f"Finished encoding, saved to {output_file}") - def _generate_output_path(self, output_dir: Path, generate_hash: bool) -> str: + def _generate_output_path(self, output_dir: Path, generate_hash: bool) -> Path: if generate_hash: output_name = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" else: output_name = f"{self.identifier}-slide.h5" - return os.path.join(output_dir, output_name) + return output_dir / output_name diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 76b30417..06a8ea74 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -109,7 +109,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.CHIEF, precision=torch.float32, - required_extractor=[ExtractorName.CHIEF_CTRANSPATH], + required_extractors=[ExtractorName.CHIEF_CTRANSPATH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 9cc3b11c..881fcd12 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -30,7 +30,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.COBRA, precision=precision, - required_extractor=[ + required_extractors=[ ExtractorName.CTRANSPATH, ExtractorName.UNI, ExtractorName.VIRCHOW2, diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index b50cbff8..2b4e0560 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -23,7 +23,7 @@ def __init__(self) -> None: model=CHIEF().model, identifier=EncoderName.EAGLE, precision=torch.float32, - required_extractor=[ + required_extractors=[ ExtractorName.CTRANSPATH, ExtractorName.CHIEF_CTRANSPATH, ], @@ -37,9 +37,9 @@ def _validate_and_read_features_with_agg( extractor: str feats, coords, extractor = self._read_h5(h5_ctp) - if extractor not in self.required_extractor: + if extractor not in self.required_extractors: raise ValueError( - f"Features must be extracted with one of {self.required_extractor}. " + f"Features must be extracted with one of {self.required_extractors}. " f"Features located in {h5_ctp} are extracted with {extractor}" ) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 6a135122..58669e28 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -30,7 +30,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.GIGAPATH, precision=torch.float16, - required_extractor=[ExtractorName.GIGAPATH], + required_extractors=[ExtractorName.GIGAPATH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/madeleine.py b/src/stamp/encoding/encoder/madeleine.py index debcbebd..9444770b 100644 --- a/src/stamp/encoding/encoder/madeleine.py +++ b/src/stamp/encoding/encoder/madeleine.py @@ -29,7 +29,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.MADELEINE, precision=precision, - required_extractor=[ExtractorName.CONCH], + required_extractors=[ExtractorName.CONCH], ) def _generate_slide_embedding( diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index 5981b5b2..dc04fc71 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -14,7 +14,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.PRISM, precision=torch.float16, - required_extractor=[ExtractorName.VIRCHOW_FULL], + required_extractors=[ExtractorName.VIRCHOW_FULL], ) def _generate_slide_embedding(self, feats, device, **kwargs) -> np.ndarray: diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 382cdee8..4b3bede9 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -23,7 +23,7 @@ def __init__(self) -> None: model=model, identifier=EncoderName.TITAN, precision=torch.float32, - required_extractor=[ExtractorName.CONCH1_5], + required_extractors=[ExtractorName.CONCH1_5], ) def _generate_slide_embedding( From 3843db177f499e531d1ed5920a63bf455d7a7b3e Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 25 Jun 2025 13:48:42 +0100 Subject: [PATCH 81/99] add mayor formatting - group type hints in a single file - add contributions - fix naming and formats --- src/stamp/__main__.py | 8 +- src/stamp/encoding/__init__.py | 100 +++++++++--------- src/stamp/encoding/config.py | 5 +- src/stamp/encoding/encoder/__init__.py | 20 ++-- src/stamp/encoding/encoder/chief.py | 9 +- src/stamp/encoding/encoder/cobra.py | 5 + src/stamp/encoding/encoder/eagle.py | 13 ++- src/stamp/encoding/encoder/gigapath.py | 11 +- src/stamp/encoding/encoder/madeleine.py | 5 + src/stamp/encoding/encoder/prism.py | 5 + src/stamp/encoding/encoder/titan.py | 11 +- src/stamp/heatmaps/__init__.py | 5 +- src/stamp/heatmaps/config.py | 2 +- src/stamp/modeling/config.py | 2 +- src/stamp/modeling/crossval.py | 12 ++- src/stamp/modeling/data.py | 42 ++++---- src/stamp/modeling/deploy.py | 4 +- src/stamp/modeling/lightning_model.py | 4 +- src/stamp/modeling/train.py | 6 +- src/stamp/preprocessing/__init__.py | 10 +- src/stamp/preprocessing/config.py | 2 +- src/stamp/preprocessing/tiling.py | 32 ++---- src/stamp/statistics/__init__.py | 2 +- src/stamp/types.py | 55 ++++++++++ tests/random_data.py | 7 +- tests/test_cache_tiles.py | 8 +- tests/test_data.py | 14 ++- tests/test_deployment.py | 3 +- .../test_deployment_backward_compatability.py | 3 +- tests/test_encoders.py | 12 ++- 30 files changed, 250 insertions(+), 167 deletions(-) create mode 100644 src/stamp/types.py diff --git a/src/stamp/__main__.py b/src/stamp/__main__.py index df90feef..89792122 100755 --- a/src/stamp/__main__.py +++ b/src/stamp/__main__.py @@ -78,7 +78,7 @@ def _run_cli(args: argparse.Namespace) -> None: ) case "encode_slides": - from stamp.encoding import get_slide_embs + from stamp.encoding import init_slide_encoder_ if config.slide_encoding is None: raise ValueError("no slide encoding configuration supplied") @@ -88,7 +88,7 @@ def _run_cli(args: argparse.Namespace) -> None: "using the following configuration:\n" f"{yaml.dump(config.slide_encoding.model_dump(mode='json'))}" ) - get_slide_embs( + init_slide_encoder_( encoder=config.slide_encoding.encoder, output_dir=config.slide_encoding.output_dir, feat_dir=config.slide_encoding.feat_dir, @@ -98,7 +98,7 @@ def _run_cli(args: argparse.Namespace) -> None: ) case "encode_patients": - from stamp.encoding import get_pat_embs + from stamp.encoding import init_patient_encoder_ if config.patient_encoding is None: raise ValueError("no patient encoding configuration supplied") @@ -108,7 +108,7 @@ def _run_cli(args: argparse.Namespace) -> None: "using the following configuration:\n" f"{yaml.dump(config.patient_encoding.model_dump(mode='json'))}" ) - get_pat_embs( + init_patient_encoder_( encoder=config.patient_encoding.encoder, output_dir=config.patient_encoding.output_dir, feat_dir=config.patient_encoding.feat_dir, diff --git a/src/stamp/encoding/__init__.py b/src/stamp/encoding/__init__.py index ef73f1cd..95492230 100644 --- a/src/stamp/encoding/__init__.py +++ b/src/stamp/encoding/__init__.py @@ -3,51 +3,42 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import DeviceLikeType, PandasLabel +from stamp.types import DeviceLikeType, PandasLabel __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" __license__ = "MIT" -def get_pat_embs( +def init_slide_encoder_( encoder: EncoderName | Encoder, output_dir: Path, feat_dir: Path, - slide_table_path: Path, - patient_label: PandasLabel, - filename_label: PandasLabel, device: DeviceLikeType, agg_feat_dir: Path | None = None, generate_hash: bool = True, ) -> None: """ - Encode patch-level features to a single feature per patient using a given encoder. - - This function selects an encoder based on the provided `encoder` parameter and - uses it to encode patient-level features. Each patient's slide is concatenated - along the x-axis to create a single 'virtual' slide for encoding. + Encode patch-level features to a single feature per slide using a given encoder. Args: - encoder (EncoderName | Encoder): The encoder to use for feature extraction. + encoder: The encoder to use for feature extraction. Can be an instance of `Encoder` or an `EncoderName` enum value. - output_dir (Path): Directory where the encoded patient features will be saved. - feat_dir (Path): Directory containing the input features for encoding. - slide_table_path (Path): Path to the slide table file (CSV) containing metadata. - patient_label (PandasLabel): Column name in the slide table representing patient IDs. - filename_label (PandasLabel): Column name in the slide table representing slide filenames. - device (DeviceLikeType): Device to use for computation (e.g., "cpu" or "cuda"). - agg_feat_dir (Path | None, optional): Directory for aggregated features. Defaults to None. - generate_hash (bool, optional): Whether to generate a hash for the output file name. Defaults to True. + output_dir: Directory where the encoded patient features will be saved. + feat_dir: Directory containing the input features for encoding. + device: Device to use for computation (e.g., "cpu", "mps" or "cuda"). + agg_feat_dir: Directory for aggregated features. Defaults to None. + generate_hash: Whether to generate a hash for the output file name. Defaults to True. Side Effects: - - Reads the slide table file from `slide_table_path`. - - Reads feature files from `feat_dir`. - Saves encoded patient features to `output_dir` in a single .h5 file containing each patient as a dataset. """ - match encoder: + case EncoderName.TITAN: + from stamp.encoding.encoder.titan import Titan + + selected_encoder: Encoder = Titan() case EncoderName.COBRA: from stamp.encoding.encoder.cobra import Cobra @@ -58,11 +49,6 @@ def get_pat_embs( selected_encoder: Encoder = Eagle() - case EncoderName.TITAN: - from stamp.encoding.encoder.titan import Titan - - selected_encoder: Encoder = Titan() - case EncoderName.GIGAPATH: from stamp.encoding.encoder.gigapath import Gigapath @@ -73,11 +59,6 @@ def get_pat_embs( selected_encoder: Encoder = CHIEF() - case EncoderName.PRISM: - from stamp.encoding.encoder.prism import Prism - - selected_encoder: Encoder = Prism() - case EncoderName.MADELEINE: from stamp.encoding.encoder.madeleine import Madeleine @@ -87,33 +68,53 @@ def get_pat_embs( selected_encoder = encoder case _ as unreachable: - assert_never(unreachable) + assert_never(unreachable) # type: ignore - selected_encoder.encode_patients( + selected_encoder.encode_slides_( output_dir=output_dir, feat_dir=feat_dir, - slide_table_path=slide_table_path, - patient_label=patient_label, - filename_label=filename_label, device=device, agg_feat_dir=agg_feat_dir, generate_hash=generate_hash, ) -def get_slide_embs( +def init_patient_encoder_( encoder: EncoderName | Encoder, output_dir: Path, feat_dir: Path, + slide_table_path: Path, + patient_label: PandasLabel, + filename_label: PandasLabel, device: DeviceLikeType, agg_feat_dir: Path | None = None, generate_hash: bool = True, ) -> None: - match encoder: - case EncoderName.TITAN: - from stamp.encoding.encoder.titan import Titan + """ + Encode patch-level features to a single feature per patient using a given encoder. - selected_encoder: Encoder = Titan() + This function selects an encoder based on the provided `encoder` parameter and + uses it to encode patient-level features. Each patient's slide is concatenated + along the x-axis to create a single 'virtual' slide for encoding. + + Args: + encoder: The encoder to use for feature extraction. + Can be an instance of `Encoder` or an `EncoderName` enum value. + output_dir: Directory where the encoded patient features will be saved. + feat_dir: Directory containing the input features for encoding. + slide_table_path: Path to the slide table file (CSV) containing metadata. + patient_label: Column name in the slide table representing patient IDs. + filename_label: Column name in the slide table representing slide filenames. + device: Device to use for computation (e.g., "cpu", "mps" or "cuda"). + agg_feat_dir: Directory for aggregated features. Defaults to None. + generate_hash: Whether to generate a hash for the output file name. Defaults to True. + + Side Effects: + - Saves encoded patient features to `output_dir` in a single .h5 file + containing each patient as a dataset. + """ + + match encoder: case EncoderName.COBRA: from stamp.encoding.encoder.cobra import Cobra @@ -124,6 +125,11 @@ def get_slide_embs( selected_encoder: Encoder = Eagle() + case EncoderName.TITAN: + from stamp.encoding.encoder.titan import Titan + + selected_encoder: Encoder = Titan() + case EncoderName.GIGAPATH: from stamp.encoding.encoder.gigapath import Gigapath @@ -134,11 +140,6 @@ def get_slide_embs( selected_encoder: Encoder = CHIEF() - case EncoderName.PRISM: - from stamp.encoding.encoder.prism import Prism - - selected_encoder: Encoder = Prism() - case EncoderName.MADELEINE: from stamp.encoding.encoder.madeleine import Madeleine @@ -150,9 +151,12 @@ def get_slide_embs( case _ as unreachable: assert_never(unreachable) - selected_encoder.encode_slides( + selected_encoder.encode_patients_( output_dir=output_dir, feat_dir=feat_dir, + slide_table_path=slide_table_path, + patient_label=patient_label, + filename_label=filename_label, device=device, agg_feat_dir=agg_feat_dir, generate_hash=generate_hash, diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index fb91dc04..a869e118 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -3,7 +3,7 @@ from pydantic import BaseModel, ConfigDict -from stamp.modeling.data import DeviceLikeType, PandasLabel +from stamp.types import DeviceLikeType, PandasLabel class EncoderName(StrEnum): @@ -12,8 +12,9 @@ class EncoderName(StrEnum): CHIEF = "chief" TITAN = "mahmood-titan" GIGAPATH = "gigapath" - PRISM = "paigeai-prism" MADELEINE = "mahmood-madeleine" + # PRISM = "paigeai-prism" + # waiting for paige-ai authors to fix it class SlideEncodingConfig(BaseModel, arbitrary_types_allowed=True): diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index beec6c76..4f6dcaaa 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -6,14 +6,15 @@ import numpy as np import pandas as pd import torch -from torch import Tensor, nn +from torch import Tensor from tqdm import tqdm import stamp from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName -from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel, get_coords +from stamp.modeling.data import CoordsInfo, get_coords from stamp.preprocessing.config import ExtractorName +from stamp.types import DeviceLikeType, PandasLabel __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" @@ -23,7 +24,7 @@ class Encoder(ABC): def __init__( self, - model: nn.Module, + model, identifier: EncoderName, precision: torch.dtype, required_extractors: list[ExtractorName], @@ -33,7 +34,7 @@ def __init__( self.precision = precision self.required_extractors = required_extractors - def encode_slides( + def encode_slides_( self, output_dir: Path, feat_dir: Path, @@ -41,7 +42,8 @@ def encode_slides( generate_hash: bool, **kwargs, ) -> None: - """General method to encode slides.""" + """General method for encoding slide-level features. Called by init_slide_encoder_. + Override this function if coords are required. See init_slide_encoder_ for full description""" output_file = self._generate_output_path( output_dir=output_dir, generate_hash=generate_hash ) @@ -73,7 +75,7 @@ def encode_slides( self._save_features_(output_file, entry_dict=slide_dict) - def encode_patients( + def encode_patients_( self, output_dir: Path, feat_dir: Path, @@ -84,8 +86,8 @@ def encode_patients( generate_hash: bool, **kwargs, ) -> None: - """General method to encode patients with features only. - Override this method if coords are needed for encoding""" + """General method for encoding patient-level features. Called by init_patient_encoder_. + Override this function if coords are required. See init_patient_encoder_ for full description""" output_file = self._generate_output_path( output_dir=output_dir, generate_hash=generate_hash ) @@ -167,7 +169,7 @@ def _read_h5( extractor: str = f.attrs.get("extractor", "no extractor name") return feats, coords, extractor - def _save_features_(self, output_file: str, entry_dict: dict) -> None: + def _save_features_(self, output_file: Path, entry_dict: dict) -> None: os.makedirs(os.path.dirname(output_file), exist_ok=True) with h5py.File(output_file, "w") as f: for entry_name, data in entry_dict.items(): diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index 06a8ea74..eedeee02 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -12,10 +12,13 @@ from stamp.cache import STAMP_CACHE_DIR, file_digest from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import DeviceLikeType, PandasLabel from stamp.preprocessing.config import ExtractorName +from stamp.types import DeviceLikeType, PandasLabel -"""authors: https://github.com/hms-dbmi/CHIEF""" +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Wang X et al. (https://github.com/hms-dbmi/CHIEF)"] class CHIEFModel(nn.Module): @@ -126,7 +129,7 @@ def _generate_patient_embedding( patient_embedding = self.model(all_feats.to(device))["WSI_feature"] return patient_embedding.detach().squeeze().cpu().numpy() - def encode_patients( + def encode_patients_( self, output_dir: Path, feat_dir: Path, diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index 881fcd12..e3517671 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -13,6 +13,11 @@ " Please update your venv using `uv sync --extra cobra`" ) from e +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Lenz, Neidlinger, et al. (https://github.com/KatherLab/COBRA)"] + class Cobra(Encoder): def __init__(self) -> None: diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 2b4e0560..4dc02c2f 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -10,10 +10,14 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF -from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel +from stamp.modeling.data import CoordsInfo from stamp.preprocessing.config import ExtractorName +from stamp.types import DeviceLikeType, PandasLabel -"""From https://github.com/KatherLab/EAGLE/blob/main/eagle/main_feature_extraction.py""" +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Neidlinger, et al. (https://github.com/KatherLab/EAGLE)"] class Eagle(Encoder): @@ -108,7 +112,7 @@ def _generate_patient_embedding( all_agg_feats = torch.cat(agg_feats_list, dim=0).to(device) return self._generate_slide_embedding(all_feats, device, all_agg_feats) - def encode_slides( + def encode_slides_( self, output_dir: Path, feat_dir: Path, @@ -156,7 +160,8 @@ def encode_slides( self._save_features_(output_file, entry_dict=slide_dict) - def encode_patients( + # TODO: Add @override decorator on each encoder once it is added to python + def encode_patients_( self, output_dir: Path, feat_dir: Path, diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index 58669e28..b8939ebb 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -10,9 +10,14 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo, PandasLabel +from stamp.modeling.data import CoordsInfo from stamp.preprocessing.config import ExtractorName -from stamp.preprocessing.tiling import SlideMPP +from stamp.types import PandasLabel, SlideMPP + +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Xu, et al. (https://github.com/prov-gigapath/prov-gigapath)"] class Gigapath(Encoder): @@ -66,7 +71,7 @@ def _generate_slide_embedding( return slide_embedding.detach().squeeze().cpu().numpy() - def encode_patients( + def encode_patients_( self, output_dir: Path, feat_dir: Path, diff --git a/src/stamp/encoding/encoder/madeleine.py b/src/stamp/encoding/encoder/madeleine.py index 9444770b..5798a592 100644 --- a/src/stamp/encoding/encoder/madeleine.py +++ b/src/stamp/encoding/encoder/madeleine.py @@ -16,6 +16,11 @@ " Please update your venv using `uv sync --extra madeleine`" ) from e +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Jaume, et al. (https://github.com/mahmoodlab/MADELEINE)"] + class Madeleine(Encoder): def __init__(self) -> None: diff --git a/src/stamp/encoding/encoder/prism.py b/src/stamp/encoding/encoder/prism.py index dc04fc71..f9f90593 100644 --- a/src/stamp/encoding/encoder/prism.py +++ b/src/stamp/encoding/encoder/prism.py @@ -6,6 +6,11 @@ from stamp.encoding.encoder import Encoder from stamp.preprocessing.config import ExtractorName +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Shaikovski, et al. (https://huggingface.co/paige-ai/Prism)"] + class Prism(Encoder): def __init__(self) -> None: diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 4b3bede9..98f6665f 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -11,9 +11,14 @@ from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder -from stamp.modeling.data import CoordsInfo, DeviceLikeType, PandasLabel +from stamp.modeling.data import CoordsInfo from stamp.preprocessing.config import ExtractorName -from stamp.preprocessing.tiling import Microns, SlideMPP +from stamp.types import DeviceLikeType, Microns, PandasLabel, SlideMPP + +__author__ = "Juan Pablo Ricapito" +__copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" +__license__ = "MIT" +__credits__ = ["Ding, et al. (https://github.com/mahmoodlab/TITAN)"] class Titan(Encoder): @@ -76,7 +81,7 @@ def _generate_patient_embedding( return self._generate_slide_embedding(all_feats_cat, device, coords) - def encode_patients( + def encode_patients_( self, output_dir: Path, feat_dir: Path, diff --git a/src/stamp/heatmaps/__init__.py b/src/stamp/heatmaps/__init__.py index ffa9157c..3adb1593 100644 --- a/src/stamp/heatmaps/__init__.py +++ b/src/stamp/heatmaps/__init__.py @@ -15,11 +15,12 @@ from torch import Tensor from torch.func import jacrev # pyright: ignore[reportPrivateImportUsage] -from stamp.modeling.data import DeviceLikeType, get_coords, get_stride +from stamp.modeling.data import get_coords, get_stride from stamp.modeling.lightning_model import LitVisionTransformer from stamp.modeling.vision_transformer import VisionTransformer from stamp.preprocessing import supported_extensions -from stamp.preprocessing.tiling import Microns, SlideMPP, TilePixels, get_slide_mpp_ +from stamp.preprocessing.tiling import get_slide_mpp_ +from stamp.types import DeviceLikeType, Microns, SlideMPP, TilePixels _logger = logging.getLogger("stamp") diff --git a/src/stamp/heatmaps/config.py b/src/stamp/heatmaps/config.py index f6bda6a4..0d8c2aa5 100644 --- a/src/stamp/heatmaps/config.py +++ b/src/stamp/heatmaps/config.py @@ -3,7 +3,7 @@ import torch from pydantic import BaseModel, ConfigDict -from stamp.preprocessing.tiling import SlideMPP +from stamp.types import SlideMPP class HeatmapConfig(BaseModel): diff --git a/src/stamp/modeling/config.py b/src/stamp/modeling/config.py index 1697cdfd..96edd3db 100644 --- a/src/stamp/modeling/config.py +++ b/src/stamp/modeling/config.py @@ -4,7 +4,7 @@ import torch from pydantic import BaseModel, ConfigDict, Field -from stamp.modeling.data import PandasLabel +from stamp.types import PandasLabel class TrainConfig(BaseModel): diff --git a/src/stamp/modeling/crossval.py b/src/stamp/modeling/crossval.py index cba7f2da..aaa21b49 100644 --- a/src/stamp/modeling/crossval.py +++ b/src/stamp/modeling/crossval.py @@ -9,12 +9,7 @@ from sklearn.model_selection import StratifiedKFold from stamp.modeling.data import ( - Category, - FeaturePath, - GroundTruth, - PandasLabel, PatientData, - PatientId, filter_complete_patient_data_, patient_to_ground_truth_from_clini_table_, slide_to_patient_from_slide_table_, @@ -23,6 +18,13 @@ from stamp.modeling.lightning_model import LitVisionTransformer from stamp.modeling.train import setup_model_for_training, train_model_ from stamp.modeling.transforms import VaryPrecisionTransform +from stamp.types import ( + Category, + FeaturePath, + GroundTruth, + PandasLabel, + PatientId, +) __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2024 Marko van Treeck" diff --git a/src/stamp/modeling/data.py b/src/stamp/modeling/data.py index cf32c7bf..d9935c35 100755 --- a/src/stamp/modeling/data.py +++ b/src/stamp/modeling/data.py @@ -5,19 +5,34 @@ from dataclasses import KW_ONLY, dataclass from itertools import groupby from pathlib import Path -from typing import BinaryIO, Generic, NewType, TextIO, TypeAlias, TypeVar, cast +from typing import BinaryIO, Generic, TextIO, TypeAlias, cast import h5py import numpy as np import pandas as pd import torch -from jaxtyping import Bool, Float, Integer +from jaxtyping import Bool, Float from packaging.version import Version from torch import Tensor from torch.utils.data import DataLoader, Dataset import stamp -from stamp.preprocessing.tiling import Microns, SlideMPP, TilePixels +from stamp.types import ( + Bags, + BagSize, + BagSizes, + Category, + CoordinatesBatch, + EncodedTargets, + FeaturePath, + GroundTruth, + GroundTruthType, + Microns, + PandasLabel, + PatientId, + SlideMPP, + TilePixels, +) _logger = logging.getLogger("stamp") @@ -26,32 +41,11 @@ __copyright__ = "Copyright (C) 2022-2025 Marko van Treeck" __license__ = "MIT" -DeviceLikeType: TypeAlias = str | torch.device | int - -PatientId: TypeAlias = str -GroundTruth: TypeAlias = str -FeaturePath = NewType("FeaturePath", Path) - -Category: TypeAlias = str - -# One instance _Bag: TypeAlias = Float[Tensor, "tile feature"] -BagSize: TypeAlias = int _EncodedTarget: TypeAlias = Bool[Tensor, "category_is_hot"] # noqa: F821 """The ground truth, encoded numerically (currently: one-hot)""" _Coordinates: TypeAlias = Float[Tensor, "tile 2"] -# A batch of the above -Bags: TypeAlias = Float[Tensor, "batch tile feature"] -BagSizes: TypeAlias = Integer[Tensor, "batch"] # noqa: F821 -EncodedTargets: TypeAlias = Bool[Tensor, "batch category_is_hot"] -"""The ground truth, encoded numerically (currently: one-hot)""" -CoordinatesBatch: TypeAlias = Float[Tensor, "batch tile 2"] - -PandasLabel: TypeAlias = str - -GroundTruthType = TypeVar("GroundTruthType", covariant=True) - @dataclass class PatientData(Generic[GroundTruthType]): diff --git a/src/stamp/modeling/deploy.py b/src/stamp/modeling/deploy.py index f5c78142..0441cd53 100644 --- a/src/stamp/modeling/deploy.py +++ b/src/stamp/modeling/deploy.py @@ -11,16 +11,14 @@ from lightning.pytorch.accelerators.accelerator import Accelerator from stamp.modeling.data import ( - GroundTruth, - PandasLabel, PatientData, - PatientId, dataloader_from_patient_data, filter_complete_patient_data_, patient_to_ground_truth_from_clini_table_, slide_to_patient_from_slide_table_, ) from stamp.modeling.lightning_model import LitVisionTransformer +from stamp.types import GroundTruth, PandasLabel, PatientId __all__ = ["deploy_categorical_model_"] diff --git a/src/stamp/modeling/lightning_model.py b/src/stamp/modeling/lightning_model.py index f6642931..974d3743 100644 --- a/src/stamp/modeling/lightning_model.py +++ b/src/stamp/modeling/lightning_model.py @@ -12,7 +12,8 @@ from torchmetrics.classification import MulticlassAUROC import stamp -from stamp.modeling.data import ( +from stamp.modeling.vision_transformer import VisionTransformer +from stamp.types import ( Bags, BagSizes, Category, @@ -21,7 +22,6 @@ PandasLabel, PatientId, ) -from stamp.modeling.vision_transformer import VisionTransformer Loss: TypeAlias = Float[Tensor, ""] diff --git a/src/stamp/modeling/train.py b/src/stamp/modeling/train.py index 70ba95d0..d273315a 100644 --- a/src/stamp/modeling/train.py +++ b/src/stamp/modeling/train.py @@ -17,12 +17,7 @@ from stamp.modeling.data import ( BagDataset, - Category, - CoordinatesBatch, - GroundTruth, - PandasLabel, PatientData, - PatientId, dataloader_from_patient_data, filter_complete_patient_data_, patient_to_ground_truth_from_clini_table_, @@ -35,6 +30,7 @@ LitVisionTransformer, ) from stamp.modeling.transforms import VaryPrecisionTransform +from stamp.types import Category, CoordinatesBatch, GroundTruth, PandasLabel, PatientId __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2024 Marko van Treeck" diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index 11913733..bc044edf 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -17,18 +17,20 @@ import stamp from stamp.cache import get_processing_code_hash -from stamp.modeling.data import DeviceLikeType from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor from stamp.preprocessing.tiling import ( + MPPExtractionError, + get_slide_mpp_, + tiles_with_cache, +) +from stamp.types import ( + DeviceLikeType, ImageExtension, Microns, - MPPExtractionError, SlideMPP, SlidePixels, TilePixels, - get_slide_mpp_, - tiles_with_cache, ) __author__ = "Marko van Treeck" diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index 8dc5a807..872a22e0 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -4,7 +4,7 @@ import torch from pydantic import BaseModel, ConfigDict, Field -from stamp.preprocessing.tiling import ImageExtension, Microns, SlideMPP, TilePixels +from stamp.types import ImageExtension, Microns, SlideMPP, TilePixels __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2022-2025 Marko van Treeck" diff --git a/src/stamp/preprocessing/tiling.py b/src/stamp/preprocessing/tiling.py index 8df3aaaa..d35542c0 100644 --- a/src/stamp/preprocessing/tiling.py +++ b/src/stamp/preprocessing/tiling.py @@ -3,7 +3,7 @@ import logging import re import xml.dom.minidom as minidom -from collections.abc import Iterator, Mapping +from collections.abc import Iterator from concurrent import futures from dataclasses import dataclass from pathlib import Path @@ -11,10 +11,7 @@ from typing import ( Final, Generic, - Literal, NamedTuple, - NewType, - TypeAlias, TypedDict, TypeVar, cast, @@ -27,6 +24,15 @@ import openslide from PIL import Image +from stamp.types import ( + EXTENSION_TO_FORMAT, + ImageExtension, + Microns, + SlideMPP, + SlidePixels, + TilePixels, +) + __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2022-2025 Marko van Treeck" __license__ = "MIT" @@ -39,24 +45,6 @@ with open(__file__, "rb") as this_file_fp: _CODE_HASH: Final[str] = hashlib.file_digest(this_file_fp, "sha256").hexdigest() - -Microns = NewType("Microns", float) -"""Micrometers, usually referring to the tissue on the slide""" - -SlidePixels = NewType("SlidePixels", int) -"""Pixels of the WSI scan at largest magnification (i.e. coordinates used by OpenSlide)""" - -TilePixels = NewType("TilePixels", int) -"""Pixels after resizing, i.e. how they appear on the final tile""" - -SlideMPP = NewType("SlideMPP", float) - -ImageExtension: TypeAlias = Literal["png", "jpg"] -EXTENSION_TO_FORMAT: Final[Mapping[ImageExtension, str]] = { - "png": "png", - "jpg": "jpeg", -} - _Unit = TypeVar("_Unit") diff --git a/src/stamp/statistics/__init__.py b/src/stamp/statistics/__init__.py index 6d64197a..ca26699c 100644 --- a/src/stamp/statistics/__init__.py +++ b/src/stamp/statistics/__init__.py @@ -7,7 +7,6 @@ from matplotlib import pyplot as plt from pydantic import BaseModel, ConfigDict -from stamp.modeling.data import PandasLabel from stamp.statistics.categorical import categorical_aggregated_ from stamp.statistics.prc import ( plot_multiple_decorated_precision_recall_curves, @@ -17,6 +16,7 @@ plot_multiple_decorated_roc_curves, plot_single_decorated_roc_curve, ) +from stamp.types import PandasLabel __author__ = "Marko van Treeck" __copyright__ = "Copyright (C) 2022-2024 Marko van Treeck" diff --git a/src/stamp/types.py b/src/stamp/types.py new file mode 100644 index 00000000..4d48293a --- /dev/null +++ b/src/stamp/types.py @@ -0,0 +1,55 @@ +from pathlib import Path +from typing import ( + Final, + Literal, + NewType, + TypeAlias, + TypeVar, +) + +import torch +from beartype.typing import Mapping +from jaxtyping import Bool, Float, Integer +from torch import Tensor + +# tiling + +ImageExtension: TypeAlias = Literal["png", "jpg"] +EXTENSION_TO_FORMAT: Final[Mapping[ImageExtension, str]] = { + "png": "png", + "jpg": "jpeg", +} + +Microns = NewType("Microns", float) +"""Micrometers, usually referring to the tissue on the slide""" + +SlidePixels = NewType("SlidePixels", int) +"""Pixels of the WSI scan at largest magnification (i.e. coordinates used by OpenSlide)""" + +TilePixels = NewType("TilePixels", int) +"""Pixels after resizing, i.e. how they appear on the final tile""" + +SlideMPP = NewType("SlideMPP", float) + +# modeling + +DeviceLikeType: TypeAlias = str | torch.device | int + +PatientId: TypeAlias = str +GroundTruth: TypeAlias = str +FeaturePath = NewType("FeaturePath", Path) + +Category: TypeAlias = str + +BagSize: TypeAlias = int + +# A batch of the above +Bags: TypeAlias = Float[Tensor, "batch tile feature"] +BagSizes: TypeAlias = Integer[Tensor, "batch"] # noqa: F821 +EncodedTargets: TypeAlias = Bool[Tensor, "batch category_is_hot"] +"""The ground truth, encoded numerically (currently: one-hot)""" +CoordinatesBatch: TypeAlias = Float[Tensor, "batch tile 2"] + +PandasLabel: TypeAlias = str + +GroundTruthType = TypeVar("GroundTruthType", covariant=True) diff --git a/tests/random_data.py b/tests/random_data.py index 8a53e8d4..f6777b23 100644 --- a/tests/random_data.py +++ b/tests/random_data.py @@ -15,12 +15,13 @@ from torch import Tensor import stamp -from stamp.modeling.data import ( +from stamp.preprocessing.config import ExtractorName +from stamp.types import ( Category, + Microns, PatientId, + TilePixels, ) -from stamp.preprocessing.config import ExtractorName -from stamp.preprocessing.tiling import Microns, TilePixels CliniPath: TypeAlias = Path SlidePath: TypeAlias = Path diff --git a/tests/test_cache_tiles.py b/tests/test_cache_tiles.py index bc588ae3..a665e92c 100644 --- a/tests/test_cache_tiles.py +++ b/tests/test_cache_tiles.py @@ -8,12 +8,8 @@ from stamp.cache import download_file from stamp.preprocessing import Microns, TilePixels -from stamp.preprocessing.tiling import ( - ImageExtension, - SlidePixels, - _Tile, - tiles_with_cache, -) +from stamp.preprocessing.tiling import _Tile, tiles_with_cache +from stamp.types import ImageExtension, SlidePixels def _get_tiles_and_images( diff --git a/tests/test_data.py b/tests/test_data.py index 438d0329..4e15678f 100644 --- a/tests/test_data.py +++ b/tests/test_data.py @@ -10,16 +10,20 @@ from stamp.modeling.data import ( BagDataset, - BagSize, CoordsInfo, - FeaturePath, - GroundTruth, PatientData, - PatientId, filter_complete_patient_data_, get_coords, ) -from stamp.preprocessing.tiling import Microns, SlideMPP, TilePixels +from stamp.types import ( + BagSize, + FeaturePath, + GroundTruth, + Microns, + PatientId, + SlideMPP, + TilePixels, +) @pytest.mark.filterwarnings("ignore:some patients have no associated slides") diff --git a/tests/test_deployment.py b/tests/test_deployment.py index 13be9742..2848bdfa 100644 --- a/tests/test_deployment.py +++ b/tests/test_deployment.py @@ -4,9 +4,10 @@ import torch from random_data import make_old_feature_file -from stamp.modeling.data import GroundTruth, PatientData, PatientId +from stamp.modeling.data import PatientData from stamp.modeling.deploy import _predict, _to_prediction_df from stamp.modeling.lightning_model import LitVisionTransformer +from stamp.types import GroundTruth, PatientId @pytest.mark.filterwarnings("ignore:GPU available but not used") diff --git a/tests/test_deployment_backward_compatability.py b/tests/test_deployment_backward_compatability.py index 053f306c..53a97829 100644 --- a/tests/test_deployment_backward_compatability.py +++ b/tests/test_deployment_backward_compatability.py @@ -2,9 +2,10 @@ import torch from stamp.cache import download_file -from stamp.modeling.data import FeaturePath, PatientData +from stamp.modeling.data import PatientData from stamp.modeling.deploy import _predict from stamp.modeling.lightning_model import LitVisionTransformer +from stamp.types import FeaturePath @pytest.mark.filterwarnings( diff --git a/tests/test_encoders.py b/tests/test_encoders.py index b7169304..feebdc8a 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -11,7 +11,11 @@ from random_data import create_random_dataset, create_random_feature_file, random_string from stamp.cache import download_file -from stamp.encoding import EncoderName, get_pat_embs, get_slide_embs +from stamp.encoding import ( + EncoderName, + init_patient_encoder_, + init_slide_encoder_, +) from stamp.preprocessing.config import ExtractorName # Contains an accepted input patch-level feature encoder @@ -34,8 +38,8 @@ EncoderName.EAGLE: ExtractorName.CTRANSPATH, EncoderName.GIGAPATH: ExtractorName.GIGAPATH, EncoderName.MADELEINE: ExtractorName.CONCH, - EncoderName.PRISM: ExtractorName.VIRCHOW_FULL, EncoderName.TITAN: ExtractorName.CONCH1_5, + # EncoderName.PRISM: ExtractorName.VIRCHOW_FULL, } @@ -129,7 +133,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): patient_output_dir.mkdir(exist_ok=True) try: - get_slide_embs( + init_slide_encoder_( encoder=encoder, output_dir=slide_output_dir, feat_dir=Path(feature_dir), @@ -137,7 +141,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): agg_feat_dir=agg_feat_dir, ) - get_pat_embs( + init_patient_encoder_( encoder=encoder, output_dir=patient_output_dir, feat_dir=Path(feature_dir), From 0e3745442ffd93d7210fb59d44ac2cc5b87314ba Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 25 Jun 2025 14:17:15 +0100 Subject: [PATCH 82/99] use enum for extractor identifier --- src/stamp/preprocessing/extractor/chief_ctranspath.py | 3 ++- src/stamp/preprocessing/extractor/conch.py | 3 ++- src/stamp/preprocessing/extractor/conch1_5.py | 3 ++- src/stamp/preprocessing/extractor/ctranspath.py | 3 ++- src/stamp/preprocessing/extractor/dinobloom.py | 3 ++- src/stamp/preprocessing/extractor/empty.py | 3 ++- src/stamp/preprocessing/extractor/gigapath.py | 3 ++- src/stamp/preprocessing/extractor/h_optimus_0.py | 3 ++- src/stamp/preprocessing/extractor/h_optimus_1.py | 3 ++- src/stamp/preprocessing/extractor/mstar.py | 3 ++- src/stamp/preprocessing/extractor/musk.py | 5 ++++- src/stamp/preprocessing/extractor/plip.py | 5 ++++- src/stamp/preprocessing/extractor/uni.py | 5 ++--- src/stamp/preprocessing/extractor/uni2.py | 3 ++- src/stamp/preprocessing/extractor/virchow.py | 3 ++- src/stamp/preprocessing/extractor/virchow2.py | 3 ++- src/stamp/preprocessing/extractor/virchow_full.py | 3 ++- 17 files changed, 38 insertions(+), 19 deletions(-) diff --git a/src/stamp/preprocessing/extractor/chief_ctranspath.py b/src/stamp/preprocessing/extractor/chief_ctranspath.py index a70fb150..2d2e6b9b 100644 --- a/src/stamp/preprocessing/extractor/chief_ctranspath.py +++ b/src/stamp/preprocessing/extractor/chief_ctranspath.py @@ -13,6 +13,7 @@ " Please reinstall stamp using `pip install 'stamp[chief_ctranspath]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor, ctranspath @@ -52,5 +53,5 @@ def chief_ctranspath( return Extractor( model=model, transform=transform, - identifier="chief_ctranspath", + identifier=ExtractorName.CHIEF_CTRANSPATH, ) diff --git a/src/stamp/preprocessing/extractor/conch.py b/src/stamp/preprocessing/extractor/conch.py index 194fd8e7..a7f46530 100644 --- a/src/stamp/preprocessing/extractor/conch.py +++ b/src/stamp/preprocessing/extractor/conch.py @@ -3,6 +3,7 @@ import torch from torchvision.transforms import Compose +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor try: @@ -37,5 +38,5 @@ def conch() -> Extractor[_StampConchModel]: return Extractor( model=_StampConchModel(model), transform=preprocess, - identifier="mahmood-conch", + identifier=ExtractorName.CONCH, ) diff --git a/src/stamp/preprocessing/extractor/conch1_5.py b/src/stamp/preprocessing/extractor/conch1_5.py index 83d3cc51..47266c54 100644 --- a/src/stamp/preprocessing/extractor/conch1_5.py +++ b/src/stamp/preprocessing/extractor/conch1_5.py @@ -6,6 +6,7 @@ " Please reinstall stamp using `pip install 'stamp[conch1_5]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -13,5 +14,5 @@ def conch1_5() -> Extractor: titan = AutoModel.from_pretrained("MahmoodLab/TITAN", trust_remote_code=True) model, eval_transform = titan.return_conch() return Extractor( - model=model, transform=eval_transform, identifier="mahmood-conch1_5" + model=model, transform=eval_transform, identifier=ExtractorName.CONCH1_5 ) diff --git a/src/stamp/preprocessing/extractor/ctranspath.py b/src/stamp/preprocessing/extractor/ctranspath.py index 7ff20f37..ba9a277c 100644 --- a/src/stamp/preprocessing/extractor/ctranspath.py +++ b/src/stamp/preprocessing/extractor/ctranspath.py @@ -23,6 +23,7 @@ " Please reinstall stamp using `pip install 'stamp[ctranspath]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Marko van Treeck" @@ -65,7 +66,7 @@ def ctranspath(model_path: Path | None = None) -> Extractor[_SwinTransformer]: return Extractor( model=model, transform=transform, - identifier=f"xiyuewang-ctranspath-{digest[:8]}", + identifier=ExtractorName.CTRANSPATH, ) diff --git a/src/stamp/preprocessing/extractor/dinobloom.py b/src/stamp/preprocessing/extractor/dinobloom.py index cea86ff6..fb7713ce 100644 --- a/src/stamp/preprocessing/extractor/dinobloom.py +++ b/src/stamp/preprocessing/extractor/dinobloom.py @@ -9,6 +9,7 @@ from torchvision import transforms from stamp.cache import STAMP_CACHE_DIR +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Marko van Treeck" @@ -79,5 +80,5 @@ def dino_bloom() -> Extractor: ), ], ), - identifier="dinobloom-c2f7990b", + identifier=ExtractorName.DINO_BLOOM, ) diff --git a/src/stamp/preprocessing/extractor/empty.py b/src/stamp/preprocessing/extractor/empty.py index 11ed6be7..601890a5 100644 --- a/src/stamp/preprocessing/extractor/empty.py +++ b/src/stamp/preprocessing/extractor/empty.py @@ -8,6 +8,7 @@ import torchvision.transforms.functional from jaxtyping import Float +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Marko van Treeck" @@ -33,5 +34,5 @@ def empty() -> Extractor[_EmptyModel]: torchvision.transforms.Lambda(torch.Tensor.float), ] ), - identifier="empty", + identifier=ExtractorName.EMPTY, ) diff --git a/src/stamp/preprocessing/extractor/gigapath.py b/src/stamp/preprocessing/extractor/gigapath.py index 6ca0a0bd..964ffb95 100644 --- a/src/stamp/preprocessing/extractor/gigapath.py +++ b/src/stamp/preprocessing/extractor/gigapath.py @@ -7,6 +7,7 @@ " Please reinstall stamp using `pip install 'stamp[gigapath]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -29,5 +30,5 @@ def gigapath() -> Extractor: return Extractor( model=model, transform=transform, - identifier="gigapath", # type: ignore + identifier=ExtractorName.GIGAPATH, # type: ignore ) diff --git a/src/stamp/preprocessing/extractor/h_optimus_0.py b/src/stamp/preprocessing/extractor/h_optimus_0.py index c479c90b..2ec88d4f 100644 --- a/src/stamp/preprocessing/extractor/h_optimus_0.py +++ b/src/stamp/preprocessing/extractor/h_optimus_0.py @@ -7,6 +7,7 @@ " Please reinstall stamp using `pip install 'stamp[h_optimus_0]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -30,5 +31,5 @@ def h_optimus_0() -> Extractor: return Extractor( model=model, transform=transform, - identifier="h_optimus_0", # type: ignore + identifier=ExtractorName.H_OPTIMUS_0, # type: ignore ) diff --git a/src/stamp/preprocessing/extractor/h_optimus_1.py b/src/stamp/preprocessing/extractor/h_optimus_1.py index 3f252a89..ba03b30e 100644 --- a/src/stamp/preprocessing/extractor/h_optimus_1.py +++ b/src/stamp/preprocessing/extractor/h_optimus_1.py @@ -7,6 +7,7 @@ " Please reinstall stamp using `pip install 'stamp[h_optimus_1]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -30,5 +31,5 @@ def h_optimus_1() -> Extractor: return Extractor( model=model, transform=transform, - identifier="h_optimus_1", # type: ignore + identifier=ExtractorName.H_OPTIMUS_1, # type: ignore ) diff --git a/src/stamp/preprocessing/extractor/mstar.py b/src/stamp/preprocessing/extractor/mstar.py index aacc7791..cefcddb0 100644 --- a/src/stamp/preprocessing/extractor/mstar.py +++ b/src/stamp/preprocessing/extractor/mstar.py @@ -1,5 +1,6 @@ from torchvision import transforms +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor try: @@ -23,4 +24,4 @@ def mstar() -> Extractor: ] ) - return Extractor(model=model, transform=transform, identifier="mstar") + return Extractor(model=model, transform=transform, identifier=ExtractorName.MSTAR) diff --git a/src/stamp/preprocessing/extractor/musk.py b/src/stamp/preprocessing/extractor/musk.py index 7651f992..8e61265b 100644 --- a/src/stamp/preprocessing/extractor/musk.py +++ b/src/stamp/preprocessing/extractor/musk.py @@ -1,3 +1,4 @@ +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor try: @@ -50,4 +51,6 @@ def musk() -> Extractor: ] ) - return Extractor(model=Musk(model), transform=transform, identifier="musk") + return Extractor( + model=Musk(model), transform=transform, identifier=ExtractorName.MUSK + ) diff --git a/src/stamp/preprocessing/extractor/plip.py b/src/stamp/preprocessing/extractor/plip.py index 4ec25852..f399cd11 100644 --- a/src/stamp/preprocessing/extractor/plip.py +++ b/src/stamp/preprocessing/extractor/plip.py @@ -9,6 +9,7 @@ " Please reinstall stamp using `pip install 'stamp[plip]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -33,4 +34,6 @@ def plip() -> Extractor[PLIP]: ] ) - return Extractor(model=PLIP(model), transform=transform, identifier="plip") + return Extractor( + model=PLIP(model), transform=transform, identifier=ExtractorName.PLIP + ) diff --git a/src/stamp/preprocessing/extractor/uni.py b/src/stamp/preprocessing/extractor/uni.py index 39621de1..320dd24f 100644 --- a/src/stamp/preprocessing/extractor/uni.py +++ b/src/stamp/preprocessing/extractor/uni.py @@ -14,6 +14,7 @@ " Please reinstall stamp using `pip install 'stamp[uni]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Marko van Treeck" @@ -32,6 +33,4 @@ def uni(revision: str = "77ffbca1ee1cdcee6e87f6deebd2db8a5888c721") -> Extractor Callable[[Image], Tensor], create_transform(**resolve_data_config(model.pretrained_cfg, model=model)), ) - return Extractor( - model=model, transform=transform, identifier=f"mahmood-uni-{revision[:8]}" - ) + return Extractor(model=model, transform=transform, identifier=ExtractorName.UNI) diff --git a/src/stamp/preprocessing/extractor/uni2.py b/src/stamp/preprocessing/extractor/uni2.py index d7047e99..459eff95 100644 --- a/src/stamp/preprocessing/extractor/uni2.py +++ b/src/stamp/preprocessing/extractor/uni2.py @@ -9,6 +9,7 @@ " Please reinstall stamp using `pip install 'stamp[uni2]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor @@ -40,5 +41,5 @@ def uni2() -> Extractor: return Extractor( model=model, transform=transform, - identifier="uni2", # type: ignore + identifier=ExtractorName.UNI2, # type: ignore ) diff --git a/src/stamp/preprocessing/extractor/virchow.py b/src/stamp/preprocessing/extractor/virchow.py index eb5c90aa..f1a63c0b 100644 --- a/src/stamp/preprocessing/extractor/virchow.py +++ b/src/stamp/preprocessing/extractor/virchow.py @@ -13,6 +13,7 @@ " Please reinstall stamp using `pip install 'stamp[virchow2]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Tim Lenz" @@ -51,5 +52,5 @@ def virchow() -> Extractor[VirchowClsOnly]: return Extractor( model=VirchowClsOnly(model), transform=transform, - identifier="virchow", + identifier=ExtractorName.VIRCHOW, ) diff --git a/src/stamp/preprocessing/extractor/virchow2.py b/src/stamp/preprocessing/extractor/virchow2.py index 13906014..92d610ab 100644 --- a/src/stamp/preprocessing/extractor/virchow2.py +++ b/src/stamp/preprocessing/extractor/virchow2.py @@ -13,6 +13,7 @@ " Please reinstall stamp using `pip install 'stamp[virchow2]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Tim Lenz" @@ -49,5 +50,5 @@ def virchow2() -> Extractor[Virchow2ClsOnly]: return Extractor( model=Virchow2ClsOnly(model), transform=transform, - identifier="virchow2", + identifier=ExtractorName.VIRCHOW2, ) diff --git a/src/stamp/preprocessing/extractor/virchow_full.py b/src/stamp/preprocessing/extractor/virchow_full.py index c776b892..90734947 100644 --- a/src/stamp/preprocessing/extractor/virchow_full.py +++ b/src/stamp/preprocessing/extractor/virchow_full.py @@ -13,6 +13,7 @@ " Please reinstall stamp using `pip install 'stamp[virchow2]'`" ) from e +from stamp.preprocessing.config import ExtractorName from stamp.preprocessing.extractor import Extractor __author__ = "Juan Pablo Ricapito" @@ -57,5 +58,5 @@ def virchow_full() -> Extractor[VirchowConcatenated]: return Extractor( model=VirchowConcatenated(model), transform=transform, - identifier="virchow_full", + identifier=ExtractorName.VIRCHOW_FULL, ) From 4541642ca9ddefd42103c9c6c53b124fbf2bb657 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Wed, 25 Jun 2025 14:45:11 +0100 Subject: [PATCH 83/99] rename extractors and encoders --- src/stamp/config.yaml | 116 ++++++++++++++-------------- src/stamp/encoding/config.py | 8 +- src/stamp/preprocessing/__init__.py | 2 +- src/stamp/preprocessing/config.py | 10 +-- 4 files changed, 67 insertions(+), 69 deletions(-) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index 13346706..a9c09809 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -2,9 +2,9 @@ preprocessing: output_dir: "/path/to/save/files/to" wsi_dir: "/path/containing/whole/slide/images/to/extract/features/from" # Extractor to use for feature extractor. Possible options are "ctranspath", - # "mahmood-uni", "mahmood-conch", "chief-ctranspath", "mahmood-conch1_5", - # "mahmood-uni2", "dino-bloom", "gigapath", "h-optimus-0", "h-optimus-1", "virchow2", - # "virchow", "virchow_full", "musk", "mstar", "plip" + # "uni", "conch", "chief-ctranspath", "conch1_5", "uni2", "dino-bloom", + # "gigapath", "h-optimus-0", "h-optimus-1", "virchow2", "virchow", + # "virchow-full", "musk", "mstar", "plip" # Some of them require requesting access to the respective authors beforehand. extractor: "ctranspath" @@ -39,62 +39,6 @@ preprocessing: #generate_hash: True -slide_encoding: - # Encoder to use for slide encoding. Possible options are "katherlab-cobra", - # "katherlab-eagle", "mahmood-titan", "gigapath", "chief", "pageai-prism", - # "mahmood-madeleine". - encoder: "katherlab-eagle" - - # Directory to save the output files. - output_dir: "/path/to/save/files/to" - - # Directory where the extracted features are stored. - feat_dir: "/path/your/extracted/features/are/stored/in" - - # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) - device: "cuda" - - # Optional settings: - # Directory where the aggregated features are stored. Needed for - # some encoders such as eagle (it requires virchow2 features). - #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" - - # Add a hash of the entire preprocessing codebase in the feature folder name. - #generate_hash: True - - -patient_encoding: - # Encoder to use for patient encoding. Possible options are "katherlab-cobra", - # "katherlab-eagle", "mahmood-titan", "gigapath", "chief", "pageai-prism", - # "mahmood-madeleine". - encoder: "katherlab-eagle" - - # Directory to save the output files. - output_dir: "/path/to/save/files/to" - - # Directory where the extracted features are stored. - feat_dir: "/path/your/extracted/features/are/stored/in" - - # A table (.xlsx or .csv) relating every slide to their feature files. - # The table must contain at least two columns, one titled "SLIDE", - # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. - slide_table: "/path/of/slide.csv" - - # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) - device: "cuda" - - # Optional settings: - patient_label: "PATIENT" - filename_label: "FILENAME" - - # Directory where the aggregated features are stored. Needed for - # some encoders such as eagle (it requires virchow2 features). - #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" - - # Add a hash of the entire preprocessing codebase in the feature folder name. - #generate_hash: True - - crossval: output_dir: "/path/to/save/files/to" @@ -274,3 +218,57 @@ heatmaps: # The number of top- and bottom-scoring tiles to extract #topk: 5 #bottomk: 5 + + +slide_encoding: + # Encoder to use for slide encoding. Possible options are "cobra", + # "eagle", "titan", "gigapath", "chief", "prism", "madeleine". + encoder: "katherlab-eagle" + + # Directory to save the output files. + output_dir: "/path/to/save/files/to" + + # Directory where the extracted features are stored. + feat_dir: "/path/your/extracted/features/are/stored/in" + + # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) + device: "cuda" + + # Optional settings: + # Directory where the aggregated features are stored. Needed for + # some encoders such as eagle (it requires virchow2 features). + #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + + # Add a hash of the entire preprocessing codebase in the feature folder name. + #generate_hash: True + + +patient_encoding: + # Encoder to use for patient encoding. Possible options are "cobra", + # "eagle", "titan", "gigapath", "chief", "prism", "madeleine". + encoder: "katherlab-eagle" + + # Directory to save the output files. + output_dir: "/path/to/save/files/to" + + # Directory where the extracted features are stored. + feat_dir: "/path/your/extracted/features/are/stored/in" + + # A table (.xlsx or .csv) relating every slide to their feature files. + # The table must contain at least two columns, one titled "SLIDE", + # containing the slide ID, and one called "FILENAME", containing the feature file path relative to `feat_dir`. + slide_table: "/path/of/slide.csv" + + # Device to run slide encoding on ("cpu", "cuda", "cuda:0", etc.) + device: "cuda" + + # Optional settings: + patient_label: "PATIENT" + filename_label: "FILENAME" + + # Directory where the aggregated features are stored. Needed for + # some encoders such as eagle (it requires virchow2 features). + #agg_feat_dir: "/path/your/aggregated/features/are/stored/in" + + # Add a hash of the entire preprocessing codebase in the feature folder name. + #generate_hash: True diff --git a/src/stamp/encoding/config.py b/src/stamp/encoding/config.py index a869e118..5158b3b9 100644 --- a/src/stamp/encoding/config.py +++ b/src/stamp/encoding/config.py @@ -7,12 +7,12 @@ class EncoderName(StrEnum): - COBRA = "katherlab-cobra" - EAGLE = "katherlab-eagle" + COBRA = "cobra" + EAGLE = "eagle" CHIEF = "chief" - TITAN = "mahmood-titan" + TITAN = "titan" GIGAPATH = "gigapath" - MADELEINE = "mahmood-madeleine" + MADELEINE = "madeleine" # PRISM = "paigeai-prism" # waiting for paige-ai authors to fix it diff --git a/src/stamp/preprocessing/__init__.py b/src/stamp/preprocessing/__init__.py index bc044edf..6e2bdc66 100755 --- a/src/stamp/preprocessing/__init__.py +++ b/src/stamp/preprocessing/__init__.py @@ -321,7 +321,7 @@ def extract_( h5_fp["feats"] = torch.concat(feats).numpy() h5_fp.attrs["stamp_version"] = stamp.__version__ - h5_fp.attrs["extractor"] = extractor.identifier + h5_fp.attrs["extractor"] = str(extractor.identifier) h5_fp.attrs["unit"] = "um" h5_fp.attrs["tile_size_um"] = tile_size_um # changed in v2.1.0 h5_fp.attrs["tile_size_px"] = tile_size_px diff --git a/src/stamp/preprocessing/config.py b/src/stamp/preprocessing/config.py index 872a22e0..9025a33f 100644 --- a/src/stamp/preprocessing/config.py +++ b/src/stamp/preprocessing/config.py @@ -14,16 +14,16 @@ class ExtractorName(StrEnum): CTRANSPATH = "ctranspath" CHIEF_CTRANSPATH = "chief-ctranspath" - CONCH = "mahmood-conch" - CONCH1_5 = "mahmood-conch1_5" - UNI = "mahmood-uni" - UNI2 = "mahmood-uni2" + CONCH = "conch" + CONCH1_5 = "conch1_5" + UNI = "uni" + UNI2 = "uni2" DINO_BLOOM = "dino-bloom" GIGAPATH = "gigapath" H_OPTIMUS_0 = "h-optimus-0" H_OPTIMUS_1 = "h-optimus-1" VIRCHOW = "virchow" - VIRCHOW_FULL = "virchow_full" + VIRCHOW_FULL = "virchow-full" VIRCHOW2 = "virchow2" MUSK = "musk" MSTAR = "mstar" From 660157e48e45633e66091cc721b6e62cb71aa750 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Thu, 26 Jun 2025 12:03:24 +0100 Subject: [PATCH 84/99] reformat output feat files output one .h5 file per slide/patient instead of one per dataset --- src/stamp/config.yaml | 4 +- src/stamp/encoding/encoder/__init__.py | 106 +++++++++++++++---------- src/stamp/encoding/encoder/chief.py | 45 +++++++---- src/stamp/encoding/encoder/eagle.py | 75 +++++++++-------- src/stamp/encoding/encoder/gigapath.py | 42 ++++++---- src/stamp/encoding/encoder/titan.py | 37 ++++++--- tests/test_encoders.py | 54 ++++++++----- 7 files changed, 226 insertions(+), 137 deletions(-) diff --git a/src/stamp/config.yaml b/src/stamp/config.yaml index a9c09809..d06a4e8f 100644 --- a/src/stamp/config.yaml +++ b/src/stamp/config.yaml @@ -223,7 +223,7 @@ heatmaps: slide_encoding: # Encoder to use for slide encoding. Possible options are "cobra", # "eagle", "titan", "gigapath", "chief", "prism", "madeleine". - encoder: "katherlab-eagle" + encoder: "eagle" # Directory to save the output files. output_dir: "/path/to/save/files/to" @@ -246,7 +246,7 @@ slide_encoding: patient_encoding: # Encoder to use for patient encoding. Possible options are "cobra", # "eagle", "titan", "gigapath", "chief", "prism", "madeleine". - encoder: "katherlab-eagle" + encoder: "eagle" # Directory to save the output files. output_dir: "/path/to/save/files/to" diff --git a/src/stamp/encoding/encoder/__init__.py b/src/stamp/encoding/encoder/__init__.py index 4f6dcaaa..c6a2b670 100644 --- a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -1,6 +1,8 @@ +import logging import os from abc import ABC, abstractmethod from pathlib import Path +from tempfile import NamedTemporaryFile import h5py import numpy as np @@ -20,6 +22,8 @@ __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" __license__ = "MIT" +_logger = logging.getLogger("stamp") + class Encoder(ABC): def __init__( @@ -44,23 +48,31 @@ def encode_slides_( ) -> None: """General method for encoding slide-level features. Called by init_slide_encoder_. Override this function if coords are required. See init_slide_encoder_ for full description""" - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}" + else: + encode_dir = f"{self.identifier}-slide" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - slide_dict = {} self.model.to(device).eval() if self.precision == torch.float16: self.model.half() - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + for tile_feats_filename in (progress := tqdm(os.listdir(feat_dir))): h5_path = os.path.join(feat_dir, tile_feats_filename) slide_name: str = Path(tile_feats_filename).stem + progress.set_description(slide_name) + + # skip patient in case feature file already exists + output_path = (encode_dir / slide_name).with_suffix(".h5") + if output_path.exists(): + _logger.info( + f"skipping {str(slide_name)} because {output_path} already exists" + ) + continue try: feats, coords = self._validate_and_read_features(h5_path) @@ -71,9 +83,7 @@ def encode_slides_( slide_embedding = self._generate_slide_embedding( feats, device, coords=coords ) - slide_dict[slide_name] = {"feats": slide_embedding} - - self._save_features_(output_file, entry_dict=slide_dict) + self._save_features_(output_path=output_path, feats=slide_embedding) def encode_patients_( self, @@ -88,24 +98,35 @@ def encode_patients_( ) -> None: """General method for encoding patient-level features. Called by init_patient_encoder_. Override this function if coords are required. See init_patient_encoder_ for full description""" - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}" + ) + else: + encode_dir = f"{self.identifier}-pat" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - patient_dict = {} self.model.to(device).eval() if self.precision == torch.float16: self.model.half() - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return - slide_table = self._read_slide_table(slide_table_path) patient_groups = slide_table.groupby(patient_label) - for patient_id, group in tqdm(patient_groups, leave=False): + for patient_id, group in (progress := tqdm(patient_groups)): + progress.set_description(str(patient_id)) + + # skip patient in case feature file already exists + output_path = (encode_dir / str(patient_id)).with_suffix(".h5") + if output_path.exists(): + _logger.info( + f"skipping {str(patient_id)} because {output_path} already exists" + ) + continue + feats_list = [] for _, row in group.iterrows(): @@ -121,9 +142,7 @@ def encode_patients_( patient_embedding = self._generate_patient_embedding( feats_list, device, **kwargs ) - patient_dict[patient_id] = {"feats": patient_embedding} - - self._save_features_(output_file, entry_dict=patient_dict) + self._save_features_(output_path=output_path, feats=patient_embedding) @abstractmethod def _generate_slide_embedding( @@ -166,26 +185,29 @@ def _read_h5( with h5py.File(h5_path, "r") as f: feats: Tensor = torch.tensor(f["feats"][:], dtype=self.precision) # type: ignore coords: CoordsInfo = get_coords(f) - extractor: str = f.attrs.get("extractor", "no extractor name") + extractor: str = f.attrs.get("extractor", "") + if extractor == "": + raise ValueError( + f"Feature file does not have extractor's name in the metadata: {os.path.basename(h5_path)}" + ) return feats, coords, extractor - def _save_features_(self, output_file: Path, entry_dict: dict) -> None: - os.makedirs(os.path.dirname(output_file), exist_ok=True) - with h5py.File(output_file, "w") as f: - for entry_name, data in entry_dict.items(): - f.create_dataset(f"{entry_name}", data=data["feats"]) + def _save_features_(self, output_path: Path, feats: np.ndarray) -> None: + with ( + NamedTemporaryFile(dir=output_path.parent, delete=False) as tmp_h5_file, + h5py.File(tmp_h5_file, "w") as f, + ): + try: + f["feats"] = feats f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = str(self.identifier) f.attrs["precision"] = str(self.precision) - if len(f) == 0: - tqdm.write("Encoding failed: file empty") - os.remove(output_file) - else: - tqdm.write(f"Finished encoding, saved to {output_file}") - - def _generate_output_path(self, output_dir: Path, generate_hash: bool) -> Path: - if generate_hash: - output_name = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}.h5" - else: - output_name = f"{self.identifier}-slide.h5" - return output_dir / output_name + # TODO: Add more metadata like tile-level extractor name, + # code hash if flag is activated and maybe tile size in pixels and microns + except Exception: + _logger.exception(f"error while writing {output_path}") + if tmp_h5_file is not None: + Path(tmp_h5_file.name).unlink(missing_ok=True) + + Path(tmp_h5_file.name).rename(output_path) + _logger.debug(f"saved features to {output_path}") diff --git a/src/stamp/encoding/encoder/chief.py b/src/stamp/encoding/encoder/chief.py index eedeee02..2c33a5ca 100644 --- a/src/stamp/encoding/encoder/chief.py +++ b/src/stamp/encoding/encoder/chief.py @@ -1,3 +1,4 @@ +import logging import os from pathlib import Path @@ -9,7 +10,7 @@ from numpy import ndarray from tqdm import tqdm -from stamp.cache import STAMP_CACHE_DIR, file_digest +from stamp.cache import STAMP_CACHE_DIR, file_digest, get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.preprocessing.config import ExtractorName @@ -20,6 +21,8 @@ __license__ = "MIT" __credits__ = ["Wang X et al. (https://github.com/hms-dbmi/CHIEF)"] +_logger = logging.getLogger("stamp") + class CHIEFModel(nn.Module): def __init__( @@ -140,21 +143,33 @@ def encode_patients_( generate_hash: bool, **kwargs, ) -> None: - slide_table = pd.read_csv(slide_table_path) - patient_groups = slide_table.groupby(patient_label) - - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}" + ) + else: + encode_dir = f"{self.identifier}-pat" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - patient_dict = {} + if self.precision == torch.float16: + self.model.half() self.model.to(device).eval() - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + slide_table = pd.read_csv(slide_table_path) + patient_groups = slide_table.groupby(patient_label) + + for patient_id, group in (progress := tqdm(patient_groups)): + progress.set_description(str(patient_id)) - for patient_id, group in tqdm(patient_groups, leave=False): + # skip patient in case feature file already exists + output_path = (encode_dir / str(patient_id)).with_suffix(".h5") + if output_path.exists(): + _logger.debug( + f"skipping {str(patient_id)} because {output_path} already exists" + ) + continue feats_list = [] # Concatenate all slides over x axis adding the offset to each feature x coordinate. @@ -177,11 +192,7 @@ def encode_patients_( .cpu() .numpy() ) - patient_dict[patient_id] = { - "feats": patient_embedding, - } - - self._save_features_(output_file=output_file, entry_dict=patient_dict) + self._save_features_(output_path=output_path, feats=patient_embedding) def initialize_weights(module): diff --git a/src/stamp/encoding/encoder/eagle.py b/src/stamp/encoding/encoder/eagle.py index 4dc02c2f..85c0ae2d 100644 --- a/src/stamp/encoding/encoder/eagle.py +++ b/src/stamp/encoding/encoder/eagle.py @@ -1,3 +1,4 @@ +import logging import os from pathlib import Path @@ -7,6 +8,7 @@ from torch import Tensor from tqdm import tqdm +from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.encoding.encoder.chief import CHIEF @@ -19,6 +21,8 @@ __license__ = "MIT" __credits__ = ["Neidlinger, et al. (https://github.com/KatherLab/EAGLE)"] +_logger = logging.getLogger("stamp") + class Eagle(Encoder): def __init__(self) -> None: @@ -128,21 +132,28 @@ def encode_slides_( " is required for Eagle's encode_patients" ) - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + if generate_hash: + encode_dir = f"{self.identifier}-slide-{get_processing_code_hash(Path(__file__))[:8]}" + else: + encode_dir = f"{self.identifier}-slide" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - slide_dict = {} self.model.to(device).eval() - for tile_feats_filename in tqdm(os.listdir(feat_dir), desc="Processing slides"): + for tile_feats_filename in (progress := tqdm(os.listdir(feat_dir))): h5_ctp = os.path.join(feat_dir, tile_feats_filename) h5_vir2 = os.path.join(agg_feat_dir, tile_feats_filename) slide_name: str = Path(tile_feats_filename).stem + progress.set_description(slide_name) + + # skip patient in case feature file already exists + output_path = (encode_dir / slide_name).with_suffix(".h5") + if output_path.exists(): + _logger.debug( + f"skipping {str(slide_name)} because {output_path} already exists" + ) + continue try: feats, agg_feats = self._validate_and_read_features_with_agg( @@ -152,13 +163,8 @@ def encode_slides_( tqdm.write(s=str(e)) continue - eagle_embedding = self._generate_slide_embedding(feats, device, agg_feats) - - slide_dict[slide_name] = { - "feats": eagle_embedding, - } - - self._save_features_(output_file, entry_dict=slide_dict) + slide_embedding = self._generate_slide_embedding(feats, device, agg_feats) + self._save_features_(output_path=output_path, feats=slide_embedding) # TODO: Add @override decorator on each encoder once it is added to python def encode_patients_( @@ -182,18 +188,29 @@ def encode_patients_( slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - patient_dict = {} self.model.to(device).eval() - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) - - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}" + ) + else: + encode_dir = f"{self.identifier}-pat" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) + + for patient_id, group in (progress := tqdm(patient_groups)): + progress.set_description(str(patient_id)) + + # skip patient in case feature file already exists + output_path = (encode_dir / str(patient_id)).with_suffix(".h5") + if output_path.exists(): + _logger.debug( + f"skipping {str(patient_id)} because {output_path} already exists" + ) + continue - for patient_id, group in tqdm(patient_groups, leave=False): feats_list = [] agg_feats_list = [] @@ -213,11 +230,7 @@ def encode_patients_( tqdm.write(f"No ctranspath features for patient {patient_id}") continue - eagle_embedding = self._generate_patient_embedding( + patient_embedding = self._generate_patient_embedding( feats_list, device, agg_feats_list ) - patient_dict[patient_id] = { - "feats": eagle_embedding, - } - - self._save_features_(output_file, entry_dict=patient_dict) + self._save_features_(output_path=output_path, feats=patient_embedding) diff --git a/src/stamp/encoding/encoder/gigapath.py b/src/stamp/encoding/encoder/gigapath.py index b8939ebb..c7931400 100644 --- a/src/stamp/encoding/encoder/gigapath.py +++ b/src/stamp/encoding/encoder/gigapath.py @@ -1,3 +1,4 @@ +import logging import math import os from pathlib import Path @@ -8,6 +9,7 @@ from gigapath import slide_encoder from tqdm import tqdm +from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo @@ -17,7 +19,12 @@ __author__ = "Juan Pablo Ricapito" __copyright__ = "Copyright (C) 2025 Juan Pablo Ricapito" __license__ = "MIT" -__credits__ = ["Xu, et al. (https://github.com/prov-gigapath/prov-gigapath)"] +__credits__ = [ + "Xu, et al. (https://github.com/prov-gigapath/prov-gigapath)", + "Peter Neidlinger", +] + +_logger = logging.getLogger("stamp") class Gigapath(Encoder): @@ -87,18 +94,29 @@ def encode_patients_( slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}" + ) + else: + encode_dir = f"{self.identifier}-pat" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - patient_dict = {} self.model.to(device).half().eval() - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + for patient_id, group in (progress := tqdm(patient_groups)): + progress.set_description(str(patient_id)) + + # skip patient in case feature file already exists + output_path = (encode_dir / str(patient_id)).with_suffix(".h5") + if output_path.exists(): + _logger.debug( + f"skipping {str(patient_id)} because {output_path} already exists" + ) + continue - for patient_id, group in tqdm(patient_groups, leave=False): all_feats_list = [] all_coords_list = [] total_wsi_width = 0 @@ -165,11 +183,7 @@ def encode_patients_( all_feats_list, device, coords_list=all_coords_list ) - patient_dict[patient_id] = { - "feats": patient_embedding, - } - - self._save_features_(output_file, entry_dict=patient_dict) + self._save_features_(output_path=output_path, feats=patient_embedding) def _generate_patient_embedding( self, feats_list, device, coords_list: list | None = None, **kwargs diff --git a/src/stamp/encoding/encoder/titan.py b/src/stamp/encoding/encoder/titan.py index 98f6665f..0307d68f 100644 --- a/src/stamp/encoding/encoder/titan.py +++ b/src/stamp/encoding/encoder/titan.py @@ -1,3 +1,4 @@ +import logging import math import os from pathlib import Path @@ -9,6 +10,7 @@ from tqdm import tqdm from transformers import AutoModel +from stamp.cache import get_processing_code_hash from stamp.encoding.config import EncoderName from stamp.encoding.encoder import Encoder from stamp.modeling.data import CoordsInfo @@ -20,6 +22,8 @@ __license__ = "MIT" __credits__ = ["Ding, et al. (https://github.com/mahmoodlab/TITAN)"] +_logger = logging.getLogger("stamp") + class Titan(Encoder): def __init__(self) -> None: @@ -97,18 +101,29 @@ def encode_patients_( slide_table = pd.read_csv(slide_table_path) patient_groups = slide_table.groupby(patient_label) - output_file = self._generate_output_path( - output_dir=output_dir, generate_hash=generate_hash - ) + # generate the name for the folder containing the feats + if generate_hash: + encode_dir = ( + f"{self.identifier}-pat-{get_processing_code_hash(Path(__file__))[:8]}" + ) + else: + encode_dir = f"{self.identifier}-pat" + encode_dir = output_dir / encode_dir + os.makedirs(encode_dir, exist_ok=True) - patient_dict = {} self.model.to(device).eval() - if os.path.exists(output_file): - tqdm.write(f"Output file {output_file} already exists, skipping") - return + for patient_id, group in (progress := tqdm(patient_groups)): + progress.set_description(str(patient_id)) + + # skip patient in case feature file already exists + output_path = (encode_dir / str(patient_id)).with_suffix(".h5") + if output_path.exists(): + _logger.debug( + f"skipping {str(patient_id)} because {output_path} already exists" + ) + continue - for patient_id, group in tqdm(patient_groups, leave=False): all_feats_list = [] all_coords_list = [] current_x_offset = 0 @@ -151,8 +166,4 @@ def encode_patients_( patient_embedding = self._generate_patient_embedding( all_feats_list, device, all_coords_list ) - patient_dict[patient_id] = { - "feats": patient_embedding, - } - - self._save_features_(output_file, entry_dict=patient_dict) + self._save_features_(output_path=output_path, feats=patient_embedding) diff --git a/tests/test_encoders.py b/tests/test_encoders.py index feebdc8a..5d8c8b21 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -115,7 +115,7 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): feat_path = download_file( url="https://github.com/KatherLab/STAMP/releases/download/2.1.0/conch15feats.h5", file_name="conch15feats.h5", - sha256sum="f55866f5d38528a008c1381f689db36b9db57a48756b1a56b17b53357e29cb3a", + sha256sum="ece2eb3add347a44d5f3c0340afce692f858535c979deafc934755348b203906", ) shutil.move(feat_path, downloaded_dir / feat_path.name) @@ -156,26 +156,44 @@ def test_if_encoding_crashes(*, tmp_path: Path, encoder: EncoderName): except GatedRepoError: pytest.skip(f"cannot access gated repo for {encoder}") - # Check if the slide file has any contents + # Assert that there is at least one subdirectory in the slide output directory + subdirs = [d for d in slide_output_dir.iterdir() if d.is_dir()] + assert len(subdirs) > 0, "The output feat dir was not generated." + + slide_output_dir = slide_output_dir / subdirs[0].name slide_files = list(slide_output_dir.glob("*.h5")) assert len(slide_files) > 0, "No slide feature files were generated." - for slide_file in slide_files: - with h5py.File(slide_file, "r") as h5_file: - slide_datasets = list(h5_file.keys()) - # Check that there are slides - assert len(slide_datasets) > 0 - for slide_dataset in slide_datasets: - # Check feature contents - assert len(h5_file[slide_dataset][:]) > 0 # type: ignore + + # Assert that the number of .h5 files matches the number of slides in feat_dir + slide_feature_files = [f for f in feature_dir.glob("*.h5")] + assert len(slide_files) == len(slide_feature_files), ( + f"Mismatch between generated slide files ({len(slide_files)}) " + f"and input slide features ({len(slide_feature_files)})." + ) + + # Open the last .h5 file and check its attributes + last_slide_file = slide_files[-1] + with h5py.File(last_slide_file, "r") as h5_file: + assert len(h5_file["feats"]) > 0, "Slide features are empty." # type: ignore + + # Assert that there is at least one subdirectory in the slide output directory + subdirs = [d for d in patient_output_dir.iterdir() if d.is_dir()] + assert len(subdirs) > 0, "The output feat dir was not generated." + patient_output_dir = patient_output_dir / subdirs[0].name # Check if the patient file has any contents patient_files = list(patient_output_dir.glob("*.h5")) assert len(patient_files) > 0, "No patient feature files were generated." - for patient_file in patient_files: - with h5py.File(patient_file, "r") as h5_file: - patient_datasets = list(h5_file.keys()) - # Check that the amount of patient feats is 1 - assert len(patient_datasets) == 1 - for patient_dataset in patient_datasets: - # Check feature contents - assert len(h5_file[patient_dataset][:]) > 0 # type: ignore + + # Assert that the number of .h5 files matches the number of unique patients + slide_df = pd.read_csv(slide_path) + unique_patients = slide_df["patient"].nunique() + assert len(patient_files) == unique_patients, ( + f"Mismatch between generated patient files ({len(patient_files)}) " + f"and unique patients ({unique_patients})." + ) + + # Open the last .h5 file and check its attributes + last_patient_file = patient_files[-1] + with h5py.File(last_patient_file, "r") as h5_file: + assert len(h5_file["feats"]) > 0, "Patient features are empty." # type: ignore From c5020f5a9413b3a0ef85043cc820854fdec53e02 Mon Sep 17 00:00:00 2001 From: Fabian Wolf Date: Mon, 30 Jun 2025 08:26:46 +0100 Subject: [PATCH 85/99] Fix cobra build --- pyproject.toml | 19 +- uv.lock | 3611 ++++++++++++++++++++++++------------------------ 2 files changed, 1796 insertions(+), 1834 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index e31e132c..155d9bb2 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -104,7 +104,7 @@ virchow2 = [ ] cobra = [ "jinja2>=3.1.4", - "cobra @ git+https://github.com/KatherLab/COBRA.git", + "cobra @ git+https://github.com/KatherLab/COBRA.git@0a60b1602b4087c5db7bced5a61335a1fa8a4968", ] prism = [ "sacremoses==0.1.1", @@ -157,18 +157,6 @@ markers = [ [tool.ruff] lint.ignore = ["F722"] # https://docs.kidger.site/jaxtyping/faq/#flake8-or-ruff-are-throwing-an-error -# Hack to get dependencies installed for cobra and causal-conv1d -# More info: https://docs.astral.sh/uv/concepts/projects/config/#build-isolation -[[tool.uv.dependency-metadata]] -name = "cobra" -version = "0.1.0" -requires-dist = ["torch", "packaging", "ninja", "hatchling", "setuptools"] - -[[tool.uv.dependency-metadata]] -name = "causal-conv1d" -version = "1.5.0.post8" -requires-dist = ["torch", "packaging", "ninja", "hatchling", "setuptools"] - [[tool.uv.dependency-metadata]] name = "uni" version = "v0.1.0" @@ -182,7 +170,4 @@ requires-dist = [ "tqdm", "transformers", "xformers; sys_platform != 'darwin'" # xformers is not supported on macOS -] - -[tool.uv] -no-build-isolation-package = ["cobra", "causal-conv1d"] \ No newline at end of file +] \ No newline at end of file diff --git a/uv.lock b/uv.lock index 817787b4..7978c235 100644 --- a/uv.lock +++ b/uv.lock @@ -1,38 +1,24 @@ version = 1 +revision = 2 requires-python = ">=3.11" resolution-markers = [ - "python_full_version < '3.12' 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platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version < '3.12' and platform_machine != 'aarch64' and sys_platform == 'linux'", + "python_full_version < '3.12' and sys_platform != 'darwin' and sys_platform != 'linux'", + "python_full_version == '3.12.*' and sys_platform == 'darwin'", + "python_full_version >= '3.13' and sys_platform == 'darwin'", + "python_full_version == '3.12.*' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine == 'aarch64' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and platform_machine != 'aarch64' and sys_platform == 'linux'", + "python_full_version >= '3.13' and platform_machine != 'aarch64' and sys_platform == 'linux'", + "python_full_version == '3.12.*' and sys_platform != 'darwin' and sys_platform != 'linux'", + "python_full_version >= '3.13' and sys_platform != 'darwin' and sys_platform != 'linux'", ] [manifest] 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a/src/stamp/encoding/encoder/__init__.py +++ b/src/stamp/encoding/encoder/__init__.py @@ -202,8 +202,10 @@ def _save_features_(self, output_path: Path, feats: np.ndarray) -> None: f.attrs["version"] = stamp.__version__ f.attrs["encoder"] = str(self.identifier) f.attrs["precision"] = str(self.precision) - # TODO: Add more metadata like tile-level extractor name, - # code hash if flag is activated and maybe tile size in pixels and microns + f.attrs["stamp_version"] = stamp.__version__ + f.attrs["code_hash"] = get_processing_code_hash(Path(__file__))[:8] + # TODO: Add more metadata like tile-level extractor name + # and maybe tile size in pixels and microns except Exception: _logger.exception(f"error while writing {output_path}") if tmp_h5_file is not None: From dfd08be2f6d2c2af0f9d97fb7d73a15fd93ddebe Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 09:10:37 +0100 Subject: [PATCH 87/99] add cobraII --- .gitignore | 1 + src/stamp/encoding/encoder/cobra.py | 6 +++--- tests/test_encoders.py | 2 +- 3 files changed, 5 insertions(+), 4 deletions(-) diff --git a/.gitignore b/.gitignore index 5a5c7c97..a15cde29 100644 --- a/.gitignore +++ b/.gitignore @@ -25,6 +25,7 @@ parts/ sdist/ var/ wheels/ +weights/ share/python-wheels/ *.egg-info/ .installed.cfg diff --git a/src/stamp/encoding/encoder/cobra.py b/src/stamp/encoding/encoder/cobra.py index e3517671..bdbc3eae 100644 --- a/src/stamp/encoding/encoder/cobra.py +++ b/src/stamp/encoding/encoder/cobra.py @@ -6,7 +6,7 @@ try: import torch - from cobra.utils.load_cobra import get_cobra + from cobra.utils.load_cobra import get_cobraII except ModuleNotFoundError as e: raise ModuleNotFoundError( "cobra dependencies not installed." @@ -21,7 +21,7 @@ class Cobra(Encoder): def __init__(self) -> None: - model = get_cobra(download_weights=True) + model = get_cobraII(download_weights=True) if torch.cuda.get_device_capability()[0] < 8: print( f"\033[93mCOBRA (Mamba2) is designed to run on GPUs with compute capability 8.0 or higher!! " @@ -36,7 +36,7 @@ def __init__(self) -> None: identifier=EncoderName.COBRA, precision=precision, required_extractors=[ - ExtractorName.CTRANSPATH, + ExtractorName.CONCH, ExtractorName.UNI, ExtractorName.VIRCHOW2, ExtractorName.H_OPTIMUS_0, diff --git a/tests/test_encoders.py b/tests/test_encoders.py index 5d8c8b21..43e3e1f8 100644 --- a/tests/test_encoders.py +++ b/tests/test_encoders.py @@ -34,7 +34,7 @@ # They are not all, just one case that is accepted for each encoder used_extractor = { EncoderName.CHIEF: ExtractorName.CHIEF_CTRANSPATH, - EncoderName.COBRA: ExtractorName.CTRANSPATH, + EncoderName.COBRA: ExtractorName.CONCH, EncoderName.EAGLE: ExtractorName.CTRANSPATH, EncoderName.GIGAPATH: ExtractorName.GIGAPATH, EncoderName.MADELEINE: ExtractorName.CONCH, From 26180bc526a8d4309b08abb306183f1b996ef255 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 09:52:23 +0100 Subject: [PATCH 88/99] debug CI pipeline --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 0e18fba9..b8739b59 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -rs tests/ + run: uv run pytest -rs tests/test_feature_extractors.py -k ctranspath env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From 98a0eddf8f5236fb82c25dd14524053385f86602 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 10:29:26 +0100 Subject: [PATCH 89/99] check virchow on gpu --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index b8739b59..3a2d8834 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -rs tests/test_feature_extractors.py -k ctranspath + run: uv run pytest -rs tests/test_feature_extractors.py -k virchow env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From 62f63ce4777a0e426ff3031bbe806eca3721fad0 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 10:53:07 +0100 Subject: [PATCH 90/99] check other extractors --- .github/workflows/build.yml | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 3a2d8834..9cd15bc0 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -36,7 +36,7 @@ jobs: run: uv build - name: Run tests - run: uv run pytest -rs tests/test_feature_extractors.py -k virchow + run: uv run pytest -rs tests/test_feature_extractors.py -k "not virchow" env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From 76e3dd05ef8345634e822dac89f40bd5f0e2a87d Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 11:07:02 +0100 Subject: [PATCH 91/99] test each extractor by separate --- .github/workflows/build.yml | 23 +++++++++++++++++++++-- 1 file changed, 21 insertions(+), 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 9cd15bc0..fbddf6c5 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -13,6 +13,25 @@ jobs: strategy: matrix: python-version: ["3.11", "3.12"] + extractor: [ + "ctranspath", + "chief-ctranspath", + "conch", + "conch1_5", + "uni", + "uni2", + "dino-bloom", + "gigapath", + "h-optimus-0", + "h-optimus-1", + "virchow", + "virchow-full", + "virchow2", + "musk", + "mstar", + "plip", + "empty" + ] steps: - uses: actions/checkout@v4 @@ -35,8 +54,8 @@ jobs: - name: Build run: uv build - - name: Run tests - run: uv run pytest -rs tests/test_feature_extractors.py -k "not virchow" + - name: Run tests for extractor ${{ matrix.extractor }} + run: timeout 10m uv run pytest -rs tests/test_feature_extractors.py -k "${{ matrix.extractor }}" env: HF_TOKEN: ${{ secrets.HF_TOKEN }} From 185e54077d92911815cfc8e8f4922e045f1faa2f Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 12:25:54 +0100 Subject: [PATCH 92/99] set timm as a common dependency --- pyproject.toml | 27 +++------------------------ 1 file changed, 3 insertions(+), 24 deletions(-) diff --git a/pyproject.toml b/pyproject.toml index 155d9bb2..c27a654d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,12 +42,10 @@ dependencies = [ "torchmetrics>=1.6.0", "torchvision>=0.20.1", "tqdm>=4.66.6", + "timm==0.9.11", ] [project.optional-dependencies] -dinobloom = [ - "torchvision>=0.20.1", -] conch = [ "huggingface-hub>=0.26.2", "conch @ git+https://github.com/Mahmoodlab/CONCH.git@02d6ac59cc20874bff0f581de258c2b257f69a84", @@ -55,20 +53,15 @@ conch = [ conch1_5 = [ "transformers>=4.45.2", "einops-exts==0.0.4", - "torch>=2.0.0" ] ctranspath = [ "gdown>=5.2.0", - "torchvision>=0.20.1", ] chief_ctranspath = [ "gdown>=5.2.0", - "torchvision>=0.20.1", "torch>=2.0.0" ] gigapath = [ - "timm>=0.9.11", - "torchvision>=0.20.1", "fvcore", "iopath", "monai", @@ -81,25 +74,12 @@ gigapath = [ "ninja==1.11.1.1", "gigapath @ git+https://github.com/EzicStar/prov-gigapath.git" ] -h_optimus_0 = [ - "timm>=0.9.11", - "torchvision>=0.20.1", -] -h_optimus_1 = [ - "timm>=0.9.11", - "torchvision>=0.20.1", -] uni = [ "huggingface-hub>=0.26.2", "uni @ git+https://github.com/mahmoodlab/UNI.git", ] -uni2 = [ - "timm>=0.9.11", - "torch>=2.0.0", -] virchow2 = [ "huggingface-hub>=0.27.1", - "timm>=0.9.11", "torch>=2.0.0", ] cobra = [ @@ -114,7 +94,6 @@ madeleine = [ "madeleine @ git+https://github.com/mahmoodlab/MADELEINE.git@de7c85acc2bdad352e6df8eee5694f8b6f288012" ] musk = [ - "timm>=0.9.11", "musk @ git+https://github.com/lilab-stanford/MUSK.git@e1699c27687f44bbf6d4adfcbb2abe89795d347f", ] plip = [ @@ -122,8 +101,8 @@ plip = [ ] # Blanket target -all = ["stamp[dinobloom,conch,ctranspath,uni,virchow2,uni2,chief_ctranspath,conch1_5,gigapath,h_optimus_0,h_optimus_1,cobra,prism,madeleine,musk,plip]"] -cpu = 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index c27a654d..1669b40f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -84,7 +84,7 @@ virchow2 = [ ] cobra = [ "jinja2>=3.1.4", - "cobra @ git+https://github.com/KatherLab/COBRA.git@0a60b1602b4087c5db7bced5a61335a1fa8a4968", + "cobra @ git+https://github.com/KatherLab/COBRA.git@f1a576e1133330ffc2d1df6ee110701921c7b7c9", ] prism = [ "sacremoses==0.1.1", @@ -101,8 +101,7 @@ plip = [ ] # Blanket target -all = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,gigapath,cobra,prism,madeleine,musk,plip]"] -cpu = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,gigapath,prism,madeleine,musk,plip]"] +all = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,gigapath,prism,madeleine,musk,plip]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/uv.lock b/uv.lock index 51e3201c..e25b7d53 100644 --- a/uv.lock +++ b/uv.lock @@ -1,4 +1,5 @@ version = 1 +revision = 2 requires-python = ">=3.11" resolution-markers = [ "python_version < '0'", 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@@ on: branches: [ main ] jobs: - test: + test_extractors: runs-on: ubuntu-latest # needs: format_and_lint strategy: @@ -24,10 +24,6 @@ jobs: "gigapath", "h-optimus-0", "h-optimus-1", - "virchow", - "virchow-full", - "virchow2", - "musk", "mstar", "plip", "empty" @@ -55,7 +51,40 @@ jobs: run: uv build - name: Run tests for extractor ${{ matrix.extractor }} - run: timeout 10m uv run pytest -s tests/test_feature_extractors.py -k "${{ matrix.extractor }}" --verbose + run: uv run pytest -s tests/test_feature_extractors.py -k "${{ matrix.extractor }}" --verbose + env: + HF_TOKEN: ${{ secrets.HF_TOKEN }} + + test_others: + runs-on: ubuntu-latest + # needs: format_and_lint + strategy: + matrix: + python-version: ["3.11", "3.12"] + + steps: + - uses: actions/checkout@v4 + + - name: Install uv + uses: astral-sh/setup-uv@v5 + with: + python-version: ${{ matrix.python-version }} + enable-cache: true + cache-dependency-glob: "uv.lock" + + # - name: Set up Python ${{ matrix.python-version }} + # uses: actions/setup-python@v5 + # with: + # python-version: ${{ matrix.python-version }} + + - name: Install the project + run: uv sync --extra all --dev + + - name: Build + run: uv build + + - name: Run other tests + run: uv run pytest -s tests/ -k "not test_feature_extractors.py" --verbose env: HF_TOKEN: ${{ secrets.HF_TOKEN }} diff --git a/pyproject.toml b/pyproject.toml index 1669b40f..101255ad 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -42,7 +42,7 @@ dependencies = [ "torchmetrics>=1.6.0", "torchvision>=0.20.1", "tqdm>=4.66.6", - "timm==0.9.11", + "timm>=0.9.11", ] [project.optional-dependencies] @@ -72,6 +72,7 @@ gigapath = [ "fairscale", "wandb", "ninja==1.11.1.1", + "flash-attn", "gigapath @ git+https://github.com/EzicStar/prov-gigapath.git" ] uni = [ diff --git a/tests/conftest.py b/tests/conftest.py new file mode 100644 index 00000000..39e01cfd --- /dev/null +++ b/tests/conftest.py @@ -0,0 +1,24 @@ +import pytest + +from stamp.preprocessing import ExtractorName + + +# This lets you choose which extractors to run on pytest. Useful for the +# CI pipeline as extractors like MUSK take an eternity on CPU. +def pytest_addoption(parser): + parser.addoption( + "--extractor", + action="append", + dest="extractor", + default=None, + help="Specify extractor(s) to test", + choices=[e.value for e in ExtractorName], + ) + + +def pytest_generate_tests(metafunc): + if "extractor" in metafunc.fixturenames: + chosen = metafunc.config.getoption("extractor") + if not chosen: + chosen = [e.value for e in ExtractorName] # default to all + metafunc.parametrize("extractor", chosen, indirect=False, ids=lambda x: x) diff --git a/tests/test_feature_extractors.py b/tests/test_feature_extractors.py index 3de63423..7298db66 100644 --- a/tests/test_feature_extractors.py +++ b/tests/test_feature_extractors.py @@ -12,15 +12,12 @@ @pytest.mark.slow -@pytest.mark.parametrize("extractor", ExtractorName) @pytest.mark.filterwarnings("ignore:Importing from timm.models.layers is deprecated") @pytest.mark.filterwarnings( "ignore:You are using `torch.load` with `weights_only=False`" ) @pytest.mark.filterwarnings("ignore:xFormers is available") -def test_if_feature_extraction_crashes( - *, tmp_path: Path, extractor: ExtractorName -) -> None: +def test_if_feature_extraction_crashes(*, tmp_path: Path, extractor: str) -> None: """ Test if the feature extraction process crashes for a given extractor. @@ -35,6 +32,7 @@ def test_if_feature_extraction_crashes( Raises: AssertionError: If the extracted features file is empty. 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git+https://github.com/EzicStar/prov-gigapath.git@d4cf55321df37aaf867e24a31c61bcf490a296eb" ] uni = [ "huggingface-hub>=0.26.2", @@ -102,7 +91,7 @@ plip = [ ] # Blanket target -all = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,gigapath,prism,madeleine,musk,plip]"] +all = ["stamp[conch,ctranspath,uni,virchow2,chief_ctranspath,conch1_5,prism,madeleine,musk,plip]"] [project.scripts] "stamp" = "stamp.__main__:main" diff --git a/uv.lock b/uv.lock index 4f3ec64a..d29d37c7 100644 --- a/uv.lock +++ b/uv.lock @@ -587,12 +587,11 @@ wheels = [ [[package]] name = "flash-attn" version = "2.8.0.post2" -source = { registry = "https://pypi.org/simple" } +source = { git = "https://github.com/KatherLab/flash-attention.git?rev=7593c84a0d36b7f2ead10660209b6f8b374ade4e#7593c84a0d36b7f2ead10660209b6f8b374ade4e" } dependencies = [ { name = "einops" }, { name = "torch" }, ] -sdist = { url = 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"sha256:3dbfced32b25c0d199c6b9787937b6f85742bc3c84f652c846893075c1c082d9", size = 74956 }, ] [[package]] From 4942e4e0e1fee4857ff2b887b63f13e44b45749a Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 17:45:05 +0100 Subject: [PATCH 98/99] format and remove empty --- .github/workflows/build.yml | 1 - tests/conftest.py | 1 - 2 files changed, 2 deletions(-) diff --git a/.github/workflows/build.yml b/.github/workflows/build.yml index 80391d9e..8d8073d6 100644 --- a/.github/workflows/build.yml +++ b/.github/workflows/build.yml @@ -26,7 +26,6 @@ jobs: "h-optimus-1", "mstar", "plip", - "empty" ] steps: diff --git a/tests/conftest.py b/tests/conftest.py index 39e01cfd..e4b179f6 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,4 +1,3 @@ -import pytest from stamp.preprocessing import ExtractorName From 17d9edd36f81dde22fc4f7bd0f14fb4c42d72781 Mon Sep 17 00:00:00 2001 From: Juan Pablo Date: Mon, 30 Jun 2025 23:18:00 +0100 Subject: [PATCH 99/99] update README, reformat --- README.md | 16 ++++++++-------- tests/conftest.py | 1 - 2 files changed, 8 insertions(+), 9 deletions(-) diff --git a/README.md b/README.md index 16e06ffc..ddba1d33 100644 --- a/README.md +++ b/README.md @@ -18,13 +18,11 @@ A Protocol for End-to-End Deep Learning in Computational Pathology". ## Installing stamp -To install stamp, run: +We recommend installing STAMP with [uv](https://docs.astral.sh/uv/): ```bash -# We recommend using a virtual environment to install stamp -python -m venv .venv -. .venv/bin/activate +uv sync --all extras -pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP" +source .venv/bin/activate ``` > [!IMPORTANT] @@ -43,16 +41,18 @@ pip install "stamp[all] @ git+https://github.com/KatherLab/STAMP" If the installation was successful, running `stamp` in your terminal should yield the following output: ``` $ stamp -usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} ... +usage: stamp [-h] [--config CONFIG_FILE_PATH] {init,preprocess,encode_slides,encode_patients,train,crossval,deploy,statistics,config,heatmaps} ... STAMP: Solid Tumor Associative Modeling in Pathology positional arguments: - {init,setup,preprocess,train,crossval,deploy,statistics,config,heatmaps} + {init,preprocess,encode_slides,encode_patients,train,crossval,deploy,statistics,config,heatmaps} init Create a new STAMP configuration file at the path specified by --config preprocess Preprocess whole-slide images into feature vectors + encode_slides Encode patch-level features into slide-level embeddings + encode_patients Encode features into patient-level embeddings train Train a Vision Transformer model - crossval Train a Vision Transformer model with cross validation + crossval Train a Vision Transformer model with cross validation for modeling.n_splits folds deploy Deploy a trained Vision Transformer model statistics Generate AUROCs and AUPRCs with 95%CI for a trained Vision Transformer model config Print the loaded configuration diff --git a/tests/conftest.py b/tests/conftest.py index e4b179f6..f7f4f005 100644 --- a/tests/conftest.py +++ b/tests/conftest.py @@ -1,4 +1,3 @@ - from stamp.preprocessing import ExtractorName