def testConstructGradientWithLargeVolumes(self, use_tape):
with test_util.AbstractGradientTape(use_tape=use_tape) as tape:
batch_size = 4
planes = 8
height = 32
width = 32
ksize = 5
shape = (batch_size, planes, height, width, 1)
volumes = variables.Variable(
np.random.uniform(size=np.prod(shape)).reshape(shape),
name='inputs')
tape.watch(volumes)
patches = array_ops.extract_volume_patches(
volumes,
ksizes=[1, ksize, ksize, ksize, 1],
strides=[1, 1, 1, 1, 1],
padding='SAME')
# Github issue: #20146
# tf.extract_volume_patches() gradient very slow at graph construction
# time.
gradients = tape.gradient(patches, volumes)
# Won't time out.
self.assertIsNotNone(gradients)
def _VerifyValues(self, image, ksizes, strides, padding, patches):
"""Tests input-output pairs for the ExtractVolumePatches op.
Args:
image: Input tensor with shape:
[batch, in_planes, in_rows, in_cols, depth].
ksizes: Patch size specified as: [ksize_planes, ksize_rows, ksize_cols].
strides: Output strides, specified as:
[stride_planes, stride_rows, stride_cols].
padding: Padding type.
patches: Expected output.
Note:
rates are not supported as of now.
"""
ksizes = [1] + ksizes + [1]
strides = [1] + strides + [1]
out_tensor = array_ops.extract_volume_patches(
constant_op.constant(image),
ksizes=ksizes,
strides=strides,
padding=padding,
name="im2col_3d")
self.assertAllClose(patches, self.evaluate(out_tensor))
def call(self, inputs, **kwargs):
# region Getting parameters for extraction
if self.rank == 1:
sizes = (1, 1, *self.kernel_size, 1)
strides = (1, 1, *self.strides, 1)
rates = (1, 1, *self.dilation_rate, 1)
else:
sizes = (1, *self.kernel_size, 1)
strides = (1, *self.strides, 1)
rates = (1, *self.dilation_rate, 1)
# endregion
# region Extraction
if self.rank == 1:
expanded_inputs = tf.expand_dims(inputs, axis=1)
outputs = extract_image_patches(expanded_inputs, sizes=sizes, strides=strides, rates=rates, padding=self.padding)
outputs = tf.squeeze(outputs, axis=1)
elif self.rank == 2:
outputs = extract_image_patches(inputs, sizes=sizes, strides=strides, rates=rates, padding=self.padding)
elif self.rank == 3:
outputs = extract_volume_patches(inputs, ksizes=sizes, strides=strides, padding=self.padding)
else:
raise AttributeError("Invalid rank : self.rank is {}.".format(self.rank))
# endregion
if len(outputs.shape) != len(inputs.shape):
raise ValueError(outputs.shape, inputs.shape, self.name)
return outputs
def testConstructGradientWithLargeVolumess(self):
batch_size = 4
planes = 8
height = 32
width = 32
ksize = 5
volumes = variable_scope.get_variable(
'inputs', (batch_size, planes, height, width, 1))
patches = array_ops.extract_volume_patches(
volumes,
ksizes=[1, ksize, ksize, ksize, 1],
strides=[1, 1, 1, 1, 1],
padding='SAME')
# Github issue: #20146
# tf.extract_volume_patches() gradient very slow at graph construction time
gradients = gradients_impl.gradients(patches, volumes)
# Won't time out.
self.assertIsNotNone(gradients)
def testGradient(self, in_shape, ksizes, strides):
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
with self.cached_session():
np.random.seed(random_seed)
in_val = constant_op.constant(np.random.random(in_shape),
dtype=dtypes.float32)
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_volume_patches(
in_val, ksizes, strides, padding)
out_shape = out_val.get_shape().as_list()
err = gradient_checker.compute_gradient_error(
in_val, in_shape, out_val, out_shape)
print('extract_volume_patches gradient err: %.4e' % err)
self.assertLess(err, 1e-4)
def testGradient(self):
# Set graph seed for determinism.
random_seed = 42
random_seed_lib.set_random_seed(random_seed)
with self.cached_session():
for test_case in self._TEST_CASES:
np.random.seed(random_seed)
in_shape = test_case['in_shape']
in_val = constant_op.constant(
np.random.random(in_shape), dtype=dtypes.float32)
for padding in ['VALID', 'SAME']:
out_val = array_ops.extract_volume_patches(
in_val, test_case['ksizes'], test_case['strides'], padding)
out_shape = out_val.get_shape().as_list()
err = gradient_checker.compute_gradient_error(in_val, in_shape,
out_val, out_shape)
print('extract_volume_patches gradient err: %.4e' % err)
self.assertLess(err, 1e-4)
def extract(in_val,
ksizes=ksizes,
strides=strides,
padding=padding):
return array_ops.extract_volume_patches(
in_val, ksizes, strides, padding)