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