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made algos and metrics compatible with eval_model.py files #11

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8 changes: 5 additions & 3 deletions engiopt/cgan_1d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -259,17 +259,19 @@ def sample_designs(n_designs: int) -> th.Tensor:
if batches_done % args.sample_interval == 0:
# Extract 25 designs
desired_conds, designs = sample_designs(25)
fig, axes = plt.subplots(5, 5, figsize=(12, 12))
fig, axes = plt.subplots(5, 5, figsize=(24, 24))

# Flatten axes for easy indexing
axes = axes.flatten()

# Plot each tensor as a scatter plot
for j, tensor in enumerate(designs):
x, y = tensor.cpu().numpy() # Extract x and y coordinates
do = desired_conds[j].cpu()
dc = desired_conds[j].cpu()
axes[j].scatter(x, y, s=10, alpha=0.7) # Scatter plot
axes[j].title.set_text(f"m1: {do[0]:.2f}, m2: {do[1]:.2f}")
axes[j].title.set_text(
", ".join(f"{key[:3]}: {val:.2f}" for key, val in zip(problem.conditions_keys, dc))
)
axes[j].set_xticks([]) # Hide x ticks
axes[j].set_yticks([]) # Hide y ticks

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10 changes: 6 additions & 4 deletions engiopt/cgan_2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -267,18 +267,20 @@ def sample_designs(n_designs: int) -> th.Tensor:
# This saves a grid image of 25 generated designs every sample_interval
if batches_done % args.sample_interval == 0:
# Extract 25 designs
desired_objs, designs = sample_designs(25)
fig, axes = plt.subplots(5, 5, figsize=(12, 12))
desired_conds, designs = sample_designs(25)
fig, axes = plt.subplots(5, 5, figsize=(24, 24))

# Flatten axes for easy indexing
axes = axes.flatten()

# Plot each tensor as a scatter plot
for j, tensor in enumerate(designs):
img = tensor.cpu().numpy() # Extract x and y coordinates
do = desired_objs[j].cpu()
dc = desired_conds[j].cpu()
axes[j].imshow(img) # Scatter plot
axes[j].title.set_text(f"volfrac: {do[0]:.2f}, penal: {do[1]:.2f}")
axes[j].title.set_text(
", ".join(f"{key[:3]}: {val:.2f}" for key, val in zip(problem.conditions_keys, dc))
)
axes[j].set_xticks([]) # Hide x ticks
axes[j].set_yticks([]) # Hide y ticks

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8 changes: 5 additions & 3 deletions engiopt/cgan_cnn_2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -361,17 +361,19 @@ def sample_designs(n_designs: int) -> th.Tensor:
if batches_done % args.sample_interval == 0:
# Extract 25 designs
desired_conds, designs = sample_designs(25)
fig, axes = plt.subplots(5, 5, figsize=(12, 12))
fig, axes = plt.subplots(5, 5, figsize=(24, 24))

# Flatten axes for easy indexing
axes = axes.flatten()

# Plot each tensor as a scatter plot
for j, tensor in enumerate(designs):
img = tensor.cpu().numpy().reshape(design_shape[0], design_shape[1]) # Extract x and y coordinates
do = desired_conds[j].cpu()
dc = desired_conds[j].cpu()
axes[j].imshow(img) # Scatter plot
axes[j].title.set_text(f"volfrac: {do[0]:.2f}")
axes[j].title.set_text(
", ".join(f"{key[:3]}: {val:.2f}" for key, val in zip(problem.conditions_keys, dc))
)
axes[j].set_xticks([]) # Hide x ticks
axes[j].set_yticks([]) # Hide y ticks

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2 changes: 1 addition & 1 deletion engiopt/diffusion_1d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -147,7 +147,7 @@ class Args:
if batches_done % args.sample_interval == 0:
# Extract 25 designs
designs = diffusion.sample(batch_size=25)
fig, axes = plt.subplots(5, 5, figsize=(12, 12))
fig, axes = plt.subplots(5, 5, figsize=(24, 24))

# Flatten axes for easy indexing
axes = axes.flatten()
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30 changes: 10 additions & 20 deletions engiopt/diffusion_2d_cond.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,7 +15,6 @@
import numpy as np
import torch as th
from torch.nn import functional
from torchvision import transforms
import tqdm
import tyro
import wandb
Expand Down Expand Up @@ -245,7 +244,7 @@ def sample_timestep(

# Initialize UNet from Huggingface
model = UNet2DConditionModel(
sample_size=(100, 100),
sample_size=design_shape,
in_channels=1,
out_channels=1,
cross_attention_dim=64,
Expand All @@ -262,18 +261,8 @@ def sample_timestep(

# Configure data loader
training_ds = problem.dataset.with_format("torch", device=device)["train"]
filtered_ds = th.zeros(len(training_ds), 100, 100, device=device)
for i in range(len(training_ds)):
filtered_ds[i] = transforms.Resize((100, 100))(
training_ds[i]["optimal_design"].reshape(1, design_shape[0], design_shape[1])
)
filtered_ds_max = filtered_ds.max()
filtered_ds_min = filtered_ds.min()
filtered_ds *= 2
filtered_ds -= 1
filtered_ds_norm = (filtered_ds - filtered_ds_min) / (filtered_ds_max - filtered_ds_min)
training_ds = th.utils.data.TensorDataset(
filtered_ds_norm.flatten(1), *[training_ds[key] for key, _ in problem.conditions]
training_ds["optimal_design"].flatten(1), *[training_ds[key] for key in problem.conditions_keys]
)
vf_min = training_ds.tensors[1].min()
vf_max = training_ds.tensors[1].max()
Expand Down Expand Up @@ -325,7 +314,7 @@ def sample_designs(model: UNet2DConditionModel, n_designs: int = 25) -> tuple[th
"""Samples n_designs designs."""
model.eval()
with th.no_grad():
dims = (n_designs, 1, 100, 100)
dims = (n_designs, 1, design_shape[0], design_shape[1])
image = th.randn(dims, device=device) # initial image
encoder_hidden_states = th.linspace(vf_min, vf_max, n_designs, device=device)
encoder_hidden_states = encoder_hidden_states.view(n_designs, 1, 1).expand(n_designs, 1, 32)
Expand All @@ -343,7 +332,7 @@ def sample_designs(model: UNet2DConditionModel, n_designs: int = 25) -> tuple[th
for i, data in enumerate(dataloader):
# Zero the parameter gradients
optimizer.zero_grad()
designs = data[0].reshape(-1, 1, 100, 100)
designs = data[0].reshape(-1, 1, design_shape[0], design_shape[1])
x = designs.to(device)
conds = th.stack((data[1:]), dim=1).reshape(-1, 1, 1)
conds_ex = conds.expand(-1, 1, 32)
Expand Down Expand Up @@ -381,20 +370,21 @@ def sample_designs(model: UNet2DConditionModel, n_designs: int = 25) -> tuple[th
# Extract 25 designs

designs, hidden_states = sample_designs(model, 25)
fig, axes = plt.subplots(5, 5, figsize=(12, 12))
fig, axes = plt.subplots(5, 5, figsize=(24, 24))

# Flatten axes for easy indexing
axes = axes.flatten()

# Plot the image created by each output
for j, tensor in enumerate(designs):
img = tensor.cpu().numpy().reshape(100, 100) # Extract x and y coordinates
do = hidden_states[j, 0, 0].cpu()
img = tensor.cpu().numpy() # Extract x and y coordinates
dc = [hidden_states[j, 0, 0].cpu().item()]
axes[j].imshow(img.T) # image plot
axes[j].title.set_text(f"volfrac: {do:.2f}") # Set title
axes[j].title.set_text(
", ".join(f"{key}: {val:.2f}" for key, val in zip(problem.conditions_keys, dc))
)
axes[j].set_xticks([]) # Hide x ticks
axes[j].set_yticks([]) # Hide y ticks

plt.tight_layout()
img_fname = f"images/{batches_done}.png"
plt.savefig(img_fname)
Expand Down
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