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change to .shape instead of method .get_shape(). #26224

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merged 1 commit into from Mar 5, 2019
Merged

change to .shape instead of method .get_shape(). #26224

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112 changes: 56 additions & 56 deletions tensorflow/python/keras/backend_test.py
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Expand Up @@ -279,18 +279,18 @@ def test_dot(self):
x = keras.backend.ones(shape=(2, 3))
y = keras.backend.ones(shape=(3, 4))
xy = keras.backend.dot(x, y)
self.assertEqual(xy.get_shape().as_list(), [2, 4])
self.assertEqual(xy.shape.as_list(), [2, 4])

x = keras.backend.ones(shape=(32, 28, 3))
y = keras.backend.ones(shape=(3, 4))
xy = keras.backend.dot(x, y)
self.assertEqual(xy.get_shape().as_list(), [32, 28, 4])
self.assertEqual(xy.shape.as_list(), [32, 28, 4])

def test_batch_dot(self):
x = keras.backend.ones(shape=(32, 20, 1))
y = keras.backend.ones(shape=(32, 30, 20))
xy = keras.backend.batch_dot(x, y, axes=[1, 2])
self.assertEqual(xy.get_shape().as_list(), [32, 1, 30])
self.assertEqual(xy.shape.as_list(), [32, 1, 30])

# TODO(fchollet): insufficiently tested.

Expand Down Expand Up @@ -448,7 +448,7 @@ def test_concatenate(self):
a = keras.backend.variable(np.ones((1, 2, 3)))
b = keras.backend.variable(np.ones((1, 2, 2)))
y = keras.backend.concatenate([a, b], axis=-1)
self.assertEqual(y.get_shape().as_list(), [1, 2, 5])
self.assertEqual(y.shape.as_list(), [1, 2, 5])

def test_permute_dimensions(self):
compare_single_input_op_to_numpy(keras.backend.permute_dimensions,
Expand All @@ -466,15 +466,15 @@ def test_resize_images(self):
height_factor,
width_factor,
data_format)
self.assertEqual(y.get_shape().as_list(), [1, 4, 4, 3])
self.assertEqual(y.shape.as_list(), [1, 4, 4, 3])

data_format = 'channels_first'
x = keras.backend.variable(np.ones((1, 3, 2, 2)))
y = keras.backend.resize_images(x,
height_factor,
width_factor,
data_format)
self.assertEqual(y.get_shape().as_list(), [1, 3, 4, 4])
self.assertEqual(y.shape.as_list(), [1, 3, 4, 4])

# Invalid use:
with self.assertRaises(ValueError):
Expand All @@ -494,7 +494,7 @@ def test_resize_volumes(self):
height_factor,
width_factor,
data_format)
self.assertEqual(y.get_shape().as_list(), [1, 4, 4, 4, 3])
self.assertEqual(y.shape.as_list(), [1, 4, 4, 4, 3])

data_format = 'channels_first'
x = keras.backend.variable(np.ones((1, 3, 2, 2, 2)))
Expand All @@ -503,7 +503,7 @@ def test_resize_volumes(self):
height_factor,
width_factor,
data_format)
self.assertEqual(y.get_shape().as_list(), [1, 3, 4, 4, 4])
self.assertEqual(y.shape.as_list(), [1, 3, 4, 4, 4])

# Invalid use:
with self.assertRaises(ValueError):
Expand All @@ -516,18 +516,18 @@ def test_resize_volumes(self):
def test_repeat_elements(self):
x = keras.backend.variable(np.ones((1, 3, 2)))
y = keras.backend.repeat_elements(x, 3, axis=1)
self.assertEqual(y.get_shape().as_list(), [1, 9, 2])
self.assertEqual(y.shape.as_list(), [1, 9, 2])

# Use with a dynamic axis:
if not context.executing_eagerly():
x = keras.backend.placeholder(shape=(2, None, 2))
y = keras.backend.repeat_elements(x, 3, axis=1)
self.assertEqual(y.get_shape().as_list(), [2, None, 2])
self.assertEqual(y.shape.as_list(), [2, None, 2])

def test_repeat(self):
x = keras.backend.variable(np.ones((1, 3)))
y = keras.backend.repeat(x, 2)
self.assertEqual(y.get_shape().as_list(), [1, 2, 3])
self.assertEqual(y.shape.as_list(), [1, 2, 3])

def test_flatten(self):
compare_single_input_op_to_numpy(keras.backend.flatten,
Expand Down Expand Up @@ -685,30 +685,30 @@ def test_pool2d(self):
y = keras.backend.pool2d(x, (2, 2), strides=(1, 1),
padding='valid', data_format='channels_first',
pool_mode='max')
self.assertEqual(y.get_shape().as_list(), [10, 3, 9, 9])
self.assertEqual(y.shape.as_list(), [10, 3, 9, 9])

y = keras.backend.pool2d(x, (2, 2), strides=(1, 1),
padding='valid', data_format='channels_first',
pool_mode='avg')
self.assertEqual(y.get_shape().as_list(), [10, 3, 9, 9])
self.assertEqual(y.shape.as_list(), [10, 3, 9, 9])

val = np.random.random((10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool2d(x, (2, 2), strides=(1, 1),
padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 9, 9, 3])
self.assertEqual(y.shape.as_list(), [10, 9, 9, 3])

val = np.random.random((10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool2d(x, (2, 2), strides=(1, 1),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 10, 3])
self.assertEqual(y.shape.as_list(), [10, 10, 10, 3])

val = np.random.random((10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool2d(x, (2, 2), strides=(2, 2),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5, 3])
self.assertEqual(y.shape.as_list(), [10, 5, 5, 3])

with self.assertRaises(ValueError):
y = keras.backend.pool2d(x, (2, 2), strides=(2, 2),
Expand All @@ -729,30 +729,30 @@ def test_pool3d(self):
y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1),
padding='valid', data_format='channels_first',
pool_mode='max')
self.assertEqual(y.get_shape().as_list(), [10, 3, 9, 9, 9])
self.assertEqual(y.shape.as_list(), [10, 3, 9, 9, 9])

y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1),
padding='valid', data_format='channels_first',
pool_mode='avg')
self.assertEqual(y.get_shape().as_list(), [10, 3, 9, 9, 9])
self.assertEqual(y.shape.as_list(), [10, 3, 9, 9, 9])

val = np.random.random((10, 10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1),
padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 9, 9, 9, 3])
self.assertEqual(y.shape.as_list(), [10, 9, 9, 9, 3])

val = np.random.random((10, 10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool3d(x, (2, 2, 2), strides=(1, 1, 1),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 10, 10, 3])
self.assertEqual(y.shape.as_list(), [10, 10, 10, 10, 3])

val = np.random.random((10, 10, 10, 10, 3))
x = keras.backend.variable(val)
y = keras.backend.pool3d(x, (2, 2, 2), strides=(2, 2, 2),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5, 5, 3])
self.assertEqual(y.shape.as_list(), [10, 5, 5, 5, 3])

def test_conv1d(self):
val = np.random.random((10, 4, 10))
Expand All @@ -761,25 +761,25 @@ def test_conv1d(self):
k = keras.backend.variable(kernel_val)
y = keras.backend.conv1d(x, k, strides=(1,),
padding='valid', data_format='channels_first')
self.assertEqual(y.get_shape().as_list(), [10, 5, 8])
self.assertEqual(y.shape.as_list(), [10, 5, 8])

val = np.random.random((10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv1d(x, k, strides=(1,),
padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 8, 5])
self.assertEqual(y.shape.as_list(), [10, 8, 5])

val = np.random.random((10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv1d(x, k, strides=(1,),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 5])
self.assertEqual(y.shape.as_list(), [10, 10, 5])

val = np.random.random((10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv1d(x, k, strides=(2,),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5])
self.assertEqual(y.shape.as_list(), [10, 5, 5])

def test_local_conv_channels_dim(self):
filters = 3
Expand Down Expand Up @@ -899,25 +899,25 @@ def test_conv2d(self):
k = keras.backend.variable(kernel_val)
y = keras.backend.conv2d(x, k,
padding='valid', data_format='channels_first')
self.assertEqual(y.get_shape().as_list(), [10, 5, 8, 8])
self.assertEqual(y.shape.as_list(), [10, 5, 8, 8])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv2d(x, k, strides=(1, 1),
padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 8, 8, 5])
self.assertEqual(y.shape.as_list(), [10, 8, 8, 5])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv2d(x, k, strides=(1, 1),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 10, 5])
self.assertEqual(y.shape.as_list(), [10, 10, 10, 5])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv2d(x, k, strides=(2, 2),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5, 5])
self.assertEqual(y.shape.as_list(), [10, 5, 5, 5])
with self.assertRaises(ValueError):
y = keras.backend.conv2d(x, k, (2, 2),
padding='other', data_format='channels_last')
Expand All @@ -936,25 +936,25 @@ def test_separable_conv2d(self):
pk = keras.backend.variable(pointwise_kernel_val)
y = keras.backend.separable_conv2d(
x, dk, pk, padding='valid', data_format='channels_first')
self.assertEqual(y.get_shape().as_list(), [10, 5, 8, 8])
self.assertEqual(y.shape.as_list(), [10, 5, 8, 8])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.separable_conv2d(
x, dk, pk, strides=(1, 1), padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 8, 8, 5])
self.assertEqual(y.shape.as_list(), [10, 8, 8, 5])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.separable_conv2d(
x, dk, pk, strides=(1, 1), padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 10, 5])
self.assertEqual(y.shape.as_list(), [10, 10, 10, 5])

val = np.random.random((10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.separable_conv2d(
x, dk, pk, strides=(2, 2), padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5, 5])
self.assertEqual(y.shape.as_list(), [10, 5, 5, 5])
with self.assertRaises(ValueError):
y = keras.backend.separable_conv2d(
x, dk, pk, (2, 2), padding='other', data_format='channels_last')
Expand All @@ -971,25 +971,25 @@ def test_conv3d(self):
k = keras.backend.variable(kernel_val)
y = keras.backend.conv3d(x, k,
padding='valid', data_format='channels_first')
self.assertEqual(y.get_shape().as_list(), [10, 5, 8, 8, 8])
self.assertEqual(y.shape.as_list(), [10, 5, 8, 8, 8])

val = np.random.random((10, 10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv3d(x, k, strides=(1, 1, 1),
padding='valid', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 8, 8, 8, 5])
self.assertEqual(y.shape.as_list(), [10, 8, 8, 8, 5])

val = np.random.random((10, 10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv3d(x, k, strides=(1, 1, 1),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 10, 10, 10, 5])
self.assertEqual(y.shape.as_list(), [10, 10, 10, 10, 5])

val = np.random.random((10, 10, 10, 10, 4))
x = keras.backend.variable(val)
y = keras.backend.conv3d(x, k, strides=(2, 2, 2),
padding='same', data_format='channels_last')
self.assertEqual(y.get_shape().as_list(), [10, 5, 5, 5, 5])
self.assertEqual(y.shape.as_list(), [10, 5, 5, 5, 5])
with self.assertRaises(ValueError):
y = keras.backend.conv3d(x, k, (2, 2, 2),
padding='other', data_format='channels_last')
Expand Down Expand Up @@ -1049,12 +1049,12 @@ def step_function(x, states):
initial_states,
**kwargs)
# check static shape inference
self.assertEqual(last_output.get_shape().as_list(),
self.assertEqual(last_output.shape.as_list(),
[num_samples, output_dim])
self.assertEqual(outputs.get_shape().as_list(),
self.assertEqual(outputs.shape.as_list(),
[num_samples, timesteps, output_dim])
for state in new_states:
self.assertEqual(state.get_shape().as_list(),
self.assertEqual(state.shape.as_list(),
[num_samples, output_dim])

last_output_list[i].append(keras.backend.eval(last_output))
Expand Down Expand Up @@ -1147,16 +1147,16 @@ def step_function(x, states):
initial_states,
**kwargs)
# check static shape inference
self.assertEqual(last_output.get_shape().as_list(),
self.assertEqual(last_output.shape.as_list(),
[num_samples, output_dim])
self.assertEqual(outputs.get_shape().as_list(),
self.assertEqual(outputs.shape.as_list(),
[num_samples, timesteps, output_dim])
# for state in new_states:
# self.assertEqual(state.get_shape().as_list(),
# self.assertEqual(state.shape.as_list(),
# [num_samples, output_dim])
self.assertEqual(new_states[0].get_shape().as_list(),
self.assertEqual(new_states[0].shape.as_list(),
[num_samples, output_dim])
self.assertEqual(new_states[1].get_shape().as_list(),
self.assertEqual(new_states[1].shape.as_list(),
[num_samples, 2 * output_dim])

last_output_list[i].append(keras.backend.eval(last_output))
Expand Down Expand Up @@ -1328,25 +1328,25 @@ def test_normalize_batch_in_training(self):
beta = keras.backend.variable(b_val)
normed, mean, var = keras.backend.normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=1e-3)
self.assertEqual(normed.get_shape().as_list(), [10, 3, 10, 10])
self.assertEqual(mean.get_shape().as_list(), [3,])
self.assertEqual(var.get_shape().as_list(), [3,])
self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10])
self.assertEqual(mean.shape.as_list(), [3,])
self.assertEqual(var.shape.as_list(), [3,])

# case: gamma=None
gamma = None
normed, mean, var = keras.backend.normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=1e-3)
self.assertEqual(normed.get_shape().as_list(), [10, 3, 10, 10])
self.assertEqual(mean.get_shape().as_list(), [3,])
self.assertEqual(var.get_shape().as_list(), [3,])
self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10])
self.assertEqual(mean.shape.as_list(), [3,])
self.assertEqual(var.shape.as_list(), [3,])

# case: beta=None
beta = None
normed, mean, var = keras.backend.normalize_batch_in_training(
x, gamma, beta, reduction_axes, epsilon=1e-3)
self.assertEqual(normed.get_shape().as_list(), [10, 3, 10, 10])
self.assertEqual(mean.get_shape().as_list(), [3,])
self.assertEqual(var.get_shape().as_list(), [3,])
self.assertEqual(normed.shape.as_list(), [10, 3, 10, 10])
self.assertEqual(mean.shape.as_list(), [3,])
self.assertEqual(var.shape.as_list(), [3,])


@test_util.run_all_in_graph_and_eager_modes
Expand Down Expand Up @@ -1705,9 +1705,9 @@ def test_function_single_input_output(self):

def test_placeholder(self):
x = keras.backend.placeholder(shape=(3, 4))
self.assertEqual(x.get_shape().as_list(), [3, 4])
self.assertEqual(x.shape.as_list(), [3, 4])
x = keras.backend.placeholder(shape=(3, 4), sparse=True)
self.assertEqual(x.get_shape().as_list(), [3, 4])
self.assertEqual(x.shape.as_list(), [3, 4])

@test_util.run_deprecated_v1
def test_batch_normalization(self):
Expand Down