Added SegFormer3D arch compatible with HF Transformers structure

.gitignore

@@ -1,12 +1,4 @@
-/data/brats2017_seg/BraTS2017_Training_Data/*
-/data/brats2017_seg/brats2017_raw_data/train/*
-
-/data/brats2021_seg/BraTS2021_Training_Data/*
-/data/brats2021_seg/brats2021_raw_data/train
-
-
-/visualization/*
-
+.vscode/
 
 **/wandb
 **/__pycache__

architectures/build_architecture.py

@@ -3,6 +3,8 @@
 we import each of the architectures into this file. Once we have that
 we can use a keyword from the config file to build the model.
 """
+
+
 ######################################################################
 def build_architecture(config):
     if config["model_name"] == "segformer3d":

architectures/configuration_segformer3d.py

@@ -0,0 +1,75 @@
+from transformers import PretrainedConfig
+from typing import List, Union
+
+
+class SegFormer3DConfig(PretrainedConfig):
+    model_type = "segformer3d"
+
+    def __init__(
+        self,
+        in_channels: int = 4,
+        sr_ratios: List[int] = [4, 2, 1, 1],
+        embed_dims: List[int] = [32, 64, 160, 256],
+        patch_kernel_size: List[Union[int, List[int]]] = [
+            [7, 7, 7],
+            [3, 3, 3],
+            [3, 3, 3],
+            [3, 3, 3],
+        ],
+        patch_stride: List[Union[int, List[int]]] = [
+            [4, 4, 4],
+            [2, 2, 2],
+            [2, 2, 2],
+            [2, 2, 2],
+        ],
+        patch_padding: List[Union[int, List[int]]] = [
+            [3, 3, 3],
+            [1, 1, 1],
+            [1, 1, 1],
+            [1, 1, 1],
+        ],
+        mlp_ratios: List[int] = [4, 4, 4, 4],
+        num_heads: List[int] = [1, 2, 5, 8],
+        depths: List[int] = [2, 2, 2, 2],
+        decoder_head_embedding_dim: int = 256,
+        num_classes: int = 3,
+        decoder_dropout: float = 0.0,
+        qkv_bias: bool = True,
+        attention_dropout: float = 0.0,
+        projection_dropout: float = 0.0,
+        **kwargs
+    ):
+        """
+        Args:
+            in_channels (int): Number of input channels
+            sr_ratios (List[int]): Spatial reduction ratios for each stage
+            embed_dims (List[int]): Embedding dimensions for each stage
+            patch_kernel_size (List[int]): Kernel sizes for patch embedding
+            patch_stride (List[int]): Stride values for patch embedding
+            patch_padding (List[int]): Padding values for patch embedding
+            mlp_ratios (List[int]): MLP expansion ratios for each stage
+            num_heads (List[int]): Number of attention heads for each stage
+            depths (List[int]): Number of transformer blocks per stage
+            decoder_head_embedding_dim (int): Embedding dimension in decoder head
+            num_classes (int): Number of output classes
+            decoder_dropout (float): Dropout rate in decoder
+            qkv_bias (bool): Whether to use bias in QKV projections
+            attention_dropout (float): Dropout rate for attention
+            projection_dropout (float): Dropout rate for projections
+        """
+        super().__init__(**kwargs)
+        self.in_channels = in_channels
+        self.sr_ratios = sr_ratios
+        self.embed_dims = embed_dims
+        self.patch_kernel_size = patch_kernel_size
+        self.patch_stride = patch_stride
+        self.patch_padding = patch_padding
+        self.mlp_ratios = mlp_ratios
+        self.num_heads = num_heads
+        self.depths = depths
+        self.decoder_head_embedding_dim = decoder_head_embedding_dim
+        self.num_classes = num_classes
+        self.decoder_dropout = decoder_dropout
+        self.qkv_bias = qkv_bias
+        self.attention_dropout = attention_dropout
+        self.projection_dropout = projection_dropout