def load_model(parameters):
"""
Load trained patch classifier
"""
if (parameters["device_type"] == "gpu") and torch.has_cudnn:
device = torch.device("cuda:{}".format(parameters["gpu_number"]))
else:
device = torch.device("cpu")
model = models.ModifiedDenseNet121(num_classes=parameters['number_of_classes'])
model.load_from_path(parameters["initial_parameters"])
model = model.to(device)
model.eval()
return model, device
def load_model_and_produce_heatmaps(parameters):
"""
Loads trained patch classifier and generates heatmaps for all exams
"""
# set random seed at the beginning of program
random.seed(parameters['seed'])
if (parameters["device_type"] == "gpu") and torch.has_cudnn:
device = torch.device("cuda:{}".format(parameters["gpu_number"]))
else:
device = torch.device("cpu")
model = models.ModifiedDenseNet121(num_classes=parameters['number_of_classes'])
model.load_from_path(parameters["initial_parameters"])
model = model.to(device)
model.eval()
# Load exam info
exam_list = pickling.unpickle_from_file(parameters['data_file'])
# Create heatmaps
making_heatmap_with_large_minibatch_potential(parameters, model, exam_list, device)
