def test_get_cropped_input_shape(self):
# test 2D images
img_w, img_h = 30, 30
batches = 5
n_crops = 4
field = 11
# test channels_last
keras.backend.set_image_data_format('channels_last')
X = np.random.random((batches, img_w, img_h, 1))
shape = running.get_cropped_input_shape(
images=X,
num_crops=n_crops,
receptive_field=field)
new_w = img_w // n_crops + (field - 1)
new_h = img_h // n_crops + (field - 1)
self.assertEqual(shape, (new_w, new_h, 1))
# test channels_first
X = np.random.random((batches, 1, img_w, img_h))
shape = running.get_cropped_input_shape(
images=X,
num_crops=n_crops,
receptive_field=field,
data_format='channels_first')
new_w = img_w // n_crops + (field - 1)
new_h = img_h // n_crops + (field - 1)
self.assertEqual(shape, (1, new_w, new_h))
# test 3D images
frames = 30
# test channels_last
X = np.random.random((batches, frames, img_w, img_h, 1))
shape = running.get_cropped_input_shape(
images=X,
num_crops=n_crops,
receptive_field=field,
data_format='channels_last')
new_w = img_w // n_crops + (field - 1)
new_h = img_h // n_crops + (field - 1)
self.assertEqual(shape, (frames, new_w, new_h, 1))
# test channels_first
X = np.random.random((batches, 1, frames, img_w, img_h))
shape = running.get_cropped_input_shape(
images=X,
num_crops=n_crops,
receptive_field=field,
data_format='channels_first')
new_w = img_w // n_crops + (field - 1)
new_h = img_h // n_crops + (field - 1)
self.assertEqual(shape, (1, frames, new_w, new_h))
def test_process_whole_image(self, data_format, shape):
keras.backend.set_image_data_format(data_format)
num_crops = 2
receptive_field = 3
features = 3
images = np.ones(shape)
input_shape = running.get_cropped_input_shape(
images, num_crops,
receptive_field=receptive_field,
data_format=data_format)
for padding in ['reflect', 'zero']:
with self.test_session():
inputs = keras.layers.Input(shape=input_shape)
outputs = layers.TensorProduct(features)(inputs)
model = keras.models.Model(inputs=inputs,
outputs=[outputs, outputs])
output = running.process_whole_image(
model, images,
num_crops=num_crops,
receptive_field=receptive_field,
padding=padding)
if data_format == 'channels_first':
expected_shape = tuple([images.shape[0], features] +
list(images.shape[2:]))
else:
expected_shape = tuple([images.shape[0]] +
list(images.shape[1:-1]) +
[features])
self.assertEqual(output.shape, expected_shape)
with self.assertRaises(ValueError):
inputs = keras.layers.Input(shape=(3, 4, 5))
outputs = layers.TensorProduct(features)(inputs)
model = keras.models.Model(inputs=inputs, outputs=outputs)
output = running.process_whole_image(
model, images,
num_crops=num_crops,
receptive_field=receptive_field,
padding='reflect')
with self.assertRaises(ValueError):
inputs = keras.layers.Input(shape=input_shape)
outputs = layers.TensorProduct(features)(inputs)
model = keras.models.Model(inputs=inputs, outputs=outputs)
output = running.process_whole_image(
model, images,
num_crops=num_crops,
receptive_field=receptive_field,
padding=None)