################################## Save & Test #################################
################################################################################
# Generate Loss Graph
graphics.draw_loss(all_train_loss, all_val_loss, loss_output_path, mode="ae")
# Save Model
torch.save(model.state_dict(), model_output_path)
# Plot Animation Sample
fig, axis = graphics.make_grid(("Sample", sample), 4, 4)
plt.savefig(animation_sample_image_name)
# Create & Save Animation
Writer = animation.writers["ffmpeg"]
writer = Writer()
anim = graphics.make_animation(graphics.make_grid, all_samples)
anim.save(animation_output_path, writer=writer)
model.eval()
# Evaluate on Test Images
# Save Generated Images & Calculate Metrics
# Testing Loop - Standard
all_mse = []
all_ssim = []
with torch.no_grad():
for iteration, batch in enumerate(tqdm(test_dataloader)):
# Move batch to device
filenames, image = batch
image = image.to(device)
all_val_fusion_loss,
fusion_loss_output_path,
mode="vae")
# Save Model
torch.save(model.state_dict(), model_output_path)
# Plot Animation Sample
fig, axis = graphics.make_grid(("Sample", sample), 4, 4)
plt.savefig(animation_sample_image_name)
fusion_sample = [x for y in fusion_sample for x in y]
fig, axis = graphics.make_grid(("Fusion Sample", fusion_sample), 4, 3)
plt.savefig(fusion_animation_sample_image_name)
# Create & Save Animation
anim = graphics.make_animation(graphics.make_grid, all_samples, width=3)
fusion_anim = graphics.make_animation(graphics.make_grid,
all_fusion_samples,
width=3)
anim.save(animation_output_path)
fusion_anim.save(fusion_animation_output_path)
model.eval()
# Evaluate on Test Images
# Save Generated Images & Calculate Metrics
# Testing Loop - Standard
all_mse = []
all_ssim = []
with torch.no_grad():
for iteration, batch in enumerate(tqdm(test_dataloader)):