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)