def load_data(args):
"""
Modify this to load your data and labels
"""
validation_data_params = {
"dim": (args.patch_dim, args.patch_dim, args.patch_dim),
"batch_size": 8,
"n_in_channels": args.number_input_channels,
"n_out_channels": 1,
"train_test_split": args.train_test_split,
"augment": False,
"shuffle": False,
"seed": args.random_seed
}
testing_generator = DataGenerator("train", args.data_path,
**validation_data_params)
testing_generator.print_info()
batch_idx = 0
imgs, msks = testing_generator.get_batch(batch_idx)
fileIDs = testing_generator.get_batch_fileIDs(batch_idx)
"""
OpenVINO uses channels first tensors (NCHWD).
TensorFlow usually does channels last (NHWDC).
So we need to transpose the axes.
"""
imgs = imgs.transpose((0, 4, 1, 2, 3))
msks = msks.transpose((0, 4, 1, 2, 3))
return imgs, msks, fileIDs
training_data_params = {
"dim": (args.patch_height, args.patch_width, args.patch_depth),
"batch_size": args.bz,
"n_in_channels": args.number_input_channels,
"n_out_channels": 1,
"train_test_split": args.train_test_split,
"validate_test_split": args.validate_test_split,
"augment": True,
"shuffle": True,
"seed": hvd.rank()
}
training_generator = DataGenerator("train", args.data_path,
**training_data_params)
if (hvd.rank() == 0):
training_generator.print_info()
validation_data_params = {
"dim": (args.patch_height, args.patch_width, args.patch_depth),
"batch_size": 1,
"n_in_channels": args.number_input_channels,
"n_out_channels": 1,
"train_test_split": args.train_test_split,
"validate_test_split": args.validate_test_split,
"augment": False,
"shuffle": False,
"seed": args.random_seed
}
validation_generator = DataGenerator("validate", args.data_path,
**validation_data_params)
unet_model = unet(channels_last = True) # channels first or last
model = K.models.load_model(args.saved_model, custom_objects=unet_model.custom_objects)
print("Loading images and masks from test set")
validation_data_params = {"dim": (args.patch_height, args.patch_width, args.patch_depth),
"batch_size": 1,
"n_in_channels": args.number_input_channels,
"n_out_channels": 1,
"train_test_split": args.train_test_split,
"augment": False,
"shuffle": False, "seed": args.random_seed}
testing_generator = DataGenerator("test", args.data_path,
**validation_data_params)
testing_generator.print_info()
m = model.evaluate_generator(testing_generator, verbose=1,
max_queue_size=args.num_prefetched_batches,
workers=args.num_data_loaders,
use_multiprocessing=False)
print("\n\nTest metrics")
print("============")
for idx, name in enumerate(model.metrics_names):
print("{} = {:.4f}".format(name, m[idx]))
save_directory = "predictions_directory"
try:
os.stat(save_directory)
