experiment_path = "./experiments/test_iter_0/" data_path = "./datasets/test_iter/" cf_data = imp.load_source("cf_data", data_path + "cfg_testing_data_creation.py") # Load the network net = NetworkUltimateConv() net.init(29, 29, 13, 134, 135) net.load_parameters(open_h5file(experiment_path + "net.net")) n_out = net.n_out # Load the scaler scaler = pickle.load(open(experiment_path + "s.scaler", "rb")) # Files on which to evaluate the network file_list = list_miccai_files(**{"mode": "folder", "path": "./datasets/miccai/2/"}) n_files = len(file_list) # Options for the generation of the dataset # The generation/evaluation of the dataset has to be split into batches as a whole brain does not fit into memory batch_size = 50000 select_region = SelectWholeBrain() extract_vx = ExtractVoxelAll(1) pick_vx = PickVoxel(select_region, extract_vx) pick_patch = create_pick_features(cf_data) pick_tg = create_pick_target(cf_data) # Create the data generator data_gen = DataGeneratorBrain() data_gen.init_from(file_list, pick_vx, pick_patch, pick_tg)
data_path = "./datasets/test_iter/" cf_data = imp.load_source("cf_data", data_path + "cfg_testing_data_creation.py") # Load the network net = NetworkUltimateConv() net.init(29, 29, 13, 134, 135) net.load_parameters(open_h5file(experiment_path + "net.net")) n_out = net.n_out # Load the scaler scaler = pickle.load(open(experiment_path + "s.scaler", "rb")) # Files on which to evaluate the network file_list = list_miccai_files(**{ "mode": "folder", "path": "./datasets/miccai/2/" }) n_files = len(file_list) # Options for the generation of the dataset # The generation/evaluation of the dataset has to be split into batches as a whole brain does not fit into memory batch_size = 50000 select_region = SelectWholeBrain() extract_vx = ExtractVoxelAll(1) pick_vx = PickVoxel(select_region, extract_vx) pick_patch = create_pick_features(cf_data) pick_tg = create_pick_target(cf_data) # Create the data generator data_gen = DataGeneratorBrain() data_gen.init_from(file_list, pick_vx, pick_patch, pick_tg)
experiment_path = "./experiments/best_so_far/" data_path = "./datasets/test_iter/" cf_data = imp.load_source("cf_data", data_path + "cfg_testing_data_creation.py") # Load the network net = NetworkUltimateConv() net.init(29, 29, 13, 134, 135) net.load_parameters(open_h5file(experiment_path + "net.net")) n_out = net.n_out # Load the scaler scaler = pickle.load(open(experiment_path + "s.scaler", "rb")) # scaler = None # Files on which to evaluate the network file_list = list_miccai_files(**{"mode": "idx_files", "path": "./datasets/miccai/2/", "idx_files": range(20)}) n_files = len(file_list) # Options for the generation of the dataset # The generation/evaluation of the dataset has to be split into batches as a whole brain does not fit into memory batch_size = 50000 select_region = SelectWholeBrain() extract_vx = ExtractVoxelAll(1) pick_vx = PickVoxel(select_region, extract_vx) pick_patch = create_pick_features(cf_data) pick_tg = create_pick_target(cf_data) # Create the data generator data_gen = DataGeneratorBrain() data_gen.init_from(file_list, pick_vx, pick_patch, pick_tg)