diff --git a/image-inpainting/results/best_model.pt b/image-inpainting/results/best_model.pt index a85a5e3..84b2251 100644 Binary files a/image-inpainting/results/best_model.pt and b/image-inpainting/results/best_model.pt differ diff --git a/image-inpainting/src/__pycache__/architecture.cpython-313.pyc b/image-inpainting/src/__pycache__/architecture.cpython-313.pyc new file mode 100644 index 0000000..dd2aca3 Binary files /dev/null and b/image-inpainting/src/__pycache__/architecture.cpython-313.pyc differ diff --git a/image-inpainting/src/__pycache__/datasets.cpython-313.pyc b/image-inpainting/src/__pycache__/datasets.cpython-313.pyc new file mode 100644 index 0000000..c101f32 Binary files /dev/null and b/image-inpainting/src/__pycache__/datasets.cpython-313.pyc differ diff --git a/image-inpainting/src/__pycache__/train.cpython-313.pyc b/image-inpainting/src/__pycache__/train.cpython-313.pyc new file mode 100644 index 0000000..f5138a9 Binary files /dev/null and b/image-inpainting/src/__pycache__/train.cpython-313.pyc differ diff --git a/image-inpainting/src/__pycache__/utils.cpython-313.pyc b/image-inpainting/src/__pycache__/utils.cpython-313.pyc new file mode 100644 index 0000000..215c119 Binary files /dev/null and b/image-inpainting/src/__pycache__/utils.cpython-313.pyc differ diff --git a/image-inpainting/src/main.py b/image-inpainting/src/main.py index 967a616..34f8f67 100644 --- a/image-inpainting/src/main.py +++ b/image-inpainting/src/main.py @@ -32,7 +32,7 @@ if __name__ == '__main__': config_dict['print_train_stats_at'] = 10 config_dict['print_stats_at'] = 100 - config_dict['plot_at'] = 10 + config_dict['plot_at'] = 100 config_dict['validate_at'] = 100 network_config = { diff --git a/image-inpainting/src/utils.py b/image-inpainting/src/utils.py index e8e91f5..0a6f807 100644 --- a/image-inpainting/src/utils.py +++ b/image-inpainting/src/utils.py @@ -113,7 +113,7 @@ def create_predictions(model_config, state_dict_path, testset_path, device, save print(f"Processing image {i + 1}/{len(input_arrays)}") input_array = torch.from_numpy(input_arrays[i]).to( device) - output = model(input_array) + output = model(input_array.unsqueeze(0) if hasattr(input_array, 'dim') and input_array.dim() == 3 else input_array) output = output.cpu().numpy() predictions.append(output)