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)
