Uploading contents of MONAI-extra-test-data
Browse files- create_data.py +100 -0
- dynunet_0/Dynunet_1.json +1 -0
- dynunet_0/Dynunet_1.pt +3 -0
- dynunet_0/dynunet_0.py +16 -0
- unet_0/Unet_0.json +1 -0
- unet_0/Unet_0.pt +3 -0
create_data.py
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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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# get next unused folder
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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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# Create model
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model = nets.__dict__[model_name](**model_params)
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model.eval()
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# Create input data
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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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# Forward pass data
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out_data = model(in_data)
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# Save in data, out data and model
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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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# Save parameters
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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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# default
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if __name__ == "__main__":
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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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dynunet_0/Dynunet_1.json
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{"spatial_dims": 3, "in_channels": 4, "out_channels": 2, "kernel_size": [3, 3, 3, 1], "strides": [2, 2, 2, 2], "upsample_kernel_size": [2, 2, 2]}
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dynunet_0/Dynunet_1.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:662e895f07075f3ab6ca8b5bf7473ba26b57a3525ef6c7b3d4ff85ff4907d63c
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size 11569537
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dynunet_0/dynunet_0.py
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from create_data import create_model_test_data
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strides = (2, 2, 2, 2)
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# network params
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params = {
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"spatial_dims": 3,
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"in_channels": 4,
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"out_channels": 2,
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"kernel_size": (3, 3, 3, 1),
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"strides": strides,
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"upsample_kernel_size": strides[1:],
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}
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# in shape
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input_shape = (1, params["in_channels"], 64, 64, 64)
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# create data
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create_model_test_data("DynUNet", params, input_shape)
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unet_0/Unet_0.json
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{"spatial_dims": 3, "in_channels": 4, "out_channels": 2, "channels": [4, 8, 16, 32], "strides": [2, 4, 1], "kernel_size": 5, "up_kernel_size": 3, "num_res_units": 2, "act": "relu", "dropout": 0.1}
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unet_0/Unet_0.pt
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version https://git-lfs.github.com/spec/v1
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oid sha256:09573859799471a60debc13635cb79a5cf6c7351c443399a65491a89294545d3
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size 7453957
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