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import json |
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import os |
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import torch |
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from typing import Any, Dict, Sequence |
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import monai.networks.nets as nets |
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def create_model_test_data( |
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model_name: str, |
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model_params: Dict[str, Any], |
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input_shape: Sequence[int], |
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) -> None: |
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""" |
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Create test data to check model consistency |
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Args: |
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model_class: Name of model to be tested. |
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model_params: Dictionary of parameters to construct object. |
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input_shape: Tuple of dimensions (B, C, H, W, [D]). |
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.. code-block:: python |
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# network params |
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unet_params = { |
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"dimensions" : 3, |
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"in_channels" : 4, |
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"out_channels" : 2, |
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"channels": (4, 8, 16, 32), |
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"strides": (2, 4, 1), |
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"kernel_size" : 5, |
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"up_kernel_size" : 3, |
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"num_res_units": 2, |
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"act": "relu", |
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"dropout": 0.1, |
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} |
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# in shape |
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input_shape = (1, unet_params["in_channels"], 64, 64, 64) |
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# create data |
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create_model_test_data("UNet", unet_params, input_shape) |
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""" |
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model_name = model_name.lower() |
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base_folder = os.path.dirname(os.path.abspath(__file__)) |
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i=0 |
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while True: |
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out_folder = os.path.join(base_folder, f"{model_name}_{i}") |
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if not os.path.isdir(out_folder): |
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print("\n\nCreating output folder: " + out_folder) |
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os.mkdir(out_folder) |
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break |
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i += 1 |
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out_path_no_ext = os.path.join(out_folder, f"{model_name}_{i}") |
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model = nets.__dict__[model_name](**model_params) |
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model.eval() |
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num_elements = int(torch.Tensor(input_shape).prod()) |
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in_data = torch.arange(num_elements).reshape(input_shape).float() |
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out_data = model(in_data) |
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data_path = out_path_no_ext + ".pt" |
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to_save = {"in_data": in_data, "out_data": out_data, "model": model.state_dict()} |
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print("Writing data output to .pt: " + data_path) |
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torch.save(to_save, data_path) |
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json_params = out_path_no_ext + ".json" |
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with open(json_params, "w+") as f: |
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print("Writing network parameters to .json: " + json_params) |
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json.dump(model_params, f) |
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if __name__ == "__main__": |
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unet_params = { |
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"dimensions" : 3, |
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"in_channels" : 4, |
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"out_channels" : 2, |
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"channels": (4, 8, 16, 32), |
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"strides": (2, 4, 1), |
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"kernel_size" : 5, |
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"up_kernel_size" : 3, |
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"num_res_units": 2, |
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"act": "relu", |
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"dropout": 0.1, |
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} |
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input_shape = (1, unet_params["in_channels"], 64, 64, 64) |
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create_model_test_data("UNet", unet_params, input_shape) |
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