def read(self, file_path):
"""
load the dataset from a hdf5 file.
"""
h5file = open_h5file(file_path)
self.inputs = h5file["inputs"].value
self.is_perm = bool(h5file.attrs['is_perm'])
self.outputs = h5file["outputs"].value
self.read_virtual(h5file)
h5file.close()
def read(self, file_path):
"""
load the dataset from a hdf5 file.
"""
h5file = open_h5file(file_path)
self.inputs = h5file["inputs"].value
self.is_perm = bool(h5file.attrs['is_perm'])
self.outputs = h5file["outputs"].value
self.read_virtual(h5file)
h5file.close()
from spynet.utils.utilities import open_h5file
if __name__ == '__main__':
"""
Compute the segmentations of the testing brains with the trained networks (with approximation of the centroids)
"""
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
from spynet.data.utils_3d.pick_target import *
from data_brain_parcellation import DatasetBrainParcellation, DataGeneratorBrain, list_miccai_files, RegionCentroids
from spynet.utils.utilities import open_h5file
if __name__ == '__main__':
"""
Evaluate a trained network (without approximating the centroids)
"""
experiment_path = "./experiments/paper_ultimate_conv/"
data_path = "./datasets/paper_ultimate_conv/"
# Load the network
net = NetworkUltimateConv()
net.init(33, 29, 5, 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"))
testing_data_path = data_path + "test.h5"
ds_testing = DatasetBrainParcellation()
ds_testing.read(testing_data_path)
scaler.scale(ds_testing.inputs)
out_pred = net.predict(ds_testing.inputs, 1000)
errors = np.argmax(out_pred, axis=1) != np.argmax(ds_testing.outputs,
axis=1)
dice = compute_dice(np.argmax(out_pred, axis=1),
np.argmax(ds_testing.outputs, axis=1), 134)
from spynet.utils.utilities import open_h5file
if __name__ == '__main__':
"""
Compute the segmentations of the testing brains with the trained networks (with approximation of the centroids)
"""
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)
if __name__ == '__main__':
mode = "drop"
experiment_path = "./experiments/mnist_example/"
data_path = "./datasets/mnist/"
testing_data_path = data_path + "test.h5"
ds_testing = Dataset.create_and_read(testing_data_path)
# Load the network
net = AutoEncoder()
net.init([28**2, 256, 28**2], dropout=True, dropout_p=[0.5], neuron_function=NeuronSigmoid())
net.load_parameters(open_h5file(experiment_path + "netdrop.net"))
i = ds_testing.inputs[0:10,:]
e = net.predict(i, 10)
print ""
print MSE(e,i)
image = PIL.Image.fromarray(tile_raster_images(X=net.ls_layers[0].ls_layer_blocks[0].w.get_value(borrow=True).T,
img_shape=(28, 28), tile_shape=(16, 16),
tile_spacing=(1, 1)))
image.save(experiment_path + "filters" + mode + ".png")
image = PIL.Image.fromarray(tile_raster_images(X=i,
img_shape=(28, 28), tile_shape=(1, 10),
tile_spacing=(1, 1)))
