def testFuseResizeAndConv(self):
with self.cached_session() as sess:
inputs = [1, 4, 2, 5, 3, 6, -1, -4, -2, -5, -3, -6]
input_op = constant_op.constant(
np.array(inputs), shape=[1, 2, 3, 2], dtype=dtypes.float32)
resize_op = image_ops.resize_bilinear(
input_op, [12, 4], align_corners=False)
weights = [1, 2, 3, 4, 0.1, 0.2, 0.3, 0.4]
weights_op = constant_op.constant(
np.array(weights), shape=[1, 2, 2, 2], dtype=dtypes.float32)
nn_ops.conv2d(
resize_op, weights_op, [1, 1, 1, 1], padding="VALID", name="output")
original_graph_def = sess.graph_def
original_result = sess.run(["output:0"])
optimized_graph_def = optimize_for_inference_lib.fuse_resize_and_conv(
original_graph_def, ["output"])
with self.cached_session() as sess:
_ = importer.import_graph_def(
optimized_graph_def, input_map={}, name="optimized")
optimized_result = sess.run(["optimized/output:0"])
self.assertAllClose(original_result, optimized_result)
for node in optimized_graph_def.node:
self.assertNotEqual("Conv2D", node.op)
self.assertNotEqual("MirrorPad", node.op)
def preprocess_image(
images, height=INCEPTION_DEFAULT_IMAGE_SIZE,
width=INCEPTION_DEFAULT_IMAGE_SIZE, scope=None):
"""Prepare a batch of images for evaluation.
This is the preprocessing portion of the graph from
http://download.tensorflow.org/models/image/imagenet/inception-2015-12-05.tgz.
Note that it expects Tensors in [0, 255]. This function maps pixel values to
[-1, 1] and resizes to match the InceptionV1 network.
Args:
images: 3-D or 4-D Tensor of images. Values are in [0, 255].
height: Integer. Height of resized output image.
width: Integer. Width of resized output image.
scope: Optional scope for name_scope.
Returns:
3-D or 4-D float Tensor of prepared image(s). Values are in [-1, 1].
"""
is_single = images.shape.ndims == 3
with ops.name_scope(scope, 'preprocess', [images, height, width]):
if not images.dtype.is_floating:
images = math_ops.to_float(images)
if is_single:
images = array_ops.expand_dims(images, axis=0)
resized = image_ops.resize_bilinear(images, [height, width])
resized = (resized - 128.0) / 128.0
if is_single:
resized = array_ops.squeeze(resized, axis=0)
return resized
def testGradOnUnsupportedType(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.uint8)
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
grad = gradients_impl.gradients(input_tensor, [resize_out])
self.assertEqual([None], grad)
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
with self.test_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
err = gradient_checker.compute_gradient_error(
input_tensor, in_shape, resize_out, out_shape, x_init_value=x)
self.assertLess(err, 1e-3)
def testShapeIsCorrectAfterOp(self):
in_shape = [1, 2, 2, 1]
out_shape = [1, 4, 6, 1]
x = np.arange(0, 4).reshape(in_shape).astype(np.float32)
with self.test_session() as sess:
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
self.assertEqual(out_shape, list(resize_out.get_shape()))
resize_out = sess.run(resize_out)
self.assertEqual(out_shape, list(resize_out.shape))
def testGradFromResizeToSmallerInBothDims(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape).astype(np.float32)
with self.cached_session():
input_tensor = constant_op.constant(x, shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor,
out_shape[1:3])
err = gradient_checker.compute_gradient_error(input_tensor,
in_shape,
resize_out,
out_shape,
x_init_value=x)
self.assertLess(err, 1e-3)
def testShapes(self, batch_size, channel_count):
smaller_shape = [batch_size, 2, 3, channel_count]
larger_shape = [batch_size, 4, 6, channel_count]
for in_shape, out_shape, align_corners, half_pixel_centers in \
self._itGen(smaller_shape, larger_shape):
# Input values should not influence shapes
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(np.float32)
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
self.assertEqual(out_shape, list(resized_tensor.get_shape()))
grad_tensor = gradients_impl.gradients(resized_tensor, input_tensor)[0]
self.assertEqual(in_shape, list(grad_tensor.get_shape()))
with self.cached_session():
resized_values = self.evaluate(resized_tensor)
self.assertEqual(out_shape, list(resized_values.shape))
grad_values = self.evaluate(grad_tensor)
self.assertEqual(in_shape, list(grad_values.shape))
def testCompareGpuVsCpu(self):
in_shape = [2, 4, 6, 3]
out_shape = [2, 8, 16, 3]
size = np.prod(in_shape)
x = 1.0 / size * np.arange(0, size).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
grad = {}
for use_gpu in [False, True]:
with self.cached_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor, out_shape[1:3], align_corners=align_corners)
grad[use_gpu] = gradient_checker.compute_gradient(
input_tensor, in_shape, resized_tensor, out_shape, x_init_value=x)
self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
def testCompareGpuVsCpu(self):
in_shape = [2, 4, 6, 3]
out_shape = [2, 8, 16, 3]
size = np.prod(in_shape)
x = 1.0 / size * np.arange(0, size).reshape(in_shape).astype(np.float32)
for align_corners in [True, False]:
grad = {}
for use_gpu in [False, True]:
with self.test_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor, out_shape[1:3], align_corners=align_corners)
grad[use_gpu] = gradient_checker.compute_gradient(
input_tensor, in_shape, resized_tensor, out_shape, x_init_value=x)
self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
def testTypes(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape)
with self.cached_session() as sess:
for dtype in [np.float16, np.float32, np.float64]:
input_tensor = constant_op.constant(x.astype(dtype), shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
grad = sess.run(gradients_impl.gradients(resize_out, input_tensor))[0]
self.assertAllEqual(in_shape, grad.shape)
# Not using gradient_checker.compute_gradient as I didn't work out
# the changes required to compensate for the lower precision of
# float16 when computing the numeric jacobian.
# Instead, we just test the theoretical jacobian.
self.assertAllEqual([[[[1.], [0.], [1.], [0.], [1.], [0.]], [[0.], [
0.
], [0.], [0.], [0.], [0.]], [[1.], [0.], [1.], [0.], [1.], [0.]],
[[0.], [0.], [0.], [0.], [0.], [0.]]]], grad)
def testTypes(self):
in_shape = [1, 4, 6, 1]
out_shape = [1, 2, 3, 1]
x = np.arange(0, 24).reshape(in_shape)
with self.cached_session() as sess:
for dtype in [np.float16, np.float32, np.float64]:
input_tensor = constant_op.constant(x.astype(dtype), shape=in_shape)
resize_out = image_ops.resize_bilinear(input_tensor, out_shape[1:3])
grad = sess.run(gradients_impl.gradients(resize_out, input_tensor))[0]
self.assertAllEqual(in_shape, grad.shape)
# Not using gradient_checker.compute_gradient as I didn't work out
# the changes required to compensate for the lower precision of
# float16 when computing the numeric jacobian.
# Instead, we just test the theoretical jacobian.
self.assertAllEqual([[[[1.], [0.], [1.], [0.], [1.], [0.]], [[0.], [
0.
], [0.], [0.], [0.], [0.]], [[1.], [0.], [1.], [0.], [1.], [0.]],
[[0.], [0.], [0.], [0.], [0.], [0.]]]], grad)
def _getJacobians(self,
in_shape,
out_shape,
align_corners=False,
half_pixel_centers=False,
dtype=np.float32,
use_gpu=False,
force_gpu=False):
with self.cached_session(use_gpu=use_gpu, force_gpu=force_gpu) as sess:
# Input values should not influence gradients
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(dtype)
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor,
out_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
# compute_gradient will use a random tensor as the init value
return gradient_checker.compute_gradient(input_tensor, in_shape,
resized_tensor, out_shape)
def preprocess_image(image,
height=INCEPTION_V3_DEFAULT_IMG_SIZE,
width=INCEPTION_V3_DEFAULT_IMG_SIZE,
central_fraction=0.875,
scope=None):
"""Prepare one image for evaluation.
If height and width are specified it would output an image with that size by
applying resize_bilinear.
If central_fraction is specified it would crop the central fraction of the
input image.
Args:
image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
[0, 1], otherwise it would converted to tf.float32 assuming that the range
is [0, MAX], where MAX is largest positive representable number for
int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
height: integer
width: integer
central_fraction: Optional Float, fraction of the image to crop.
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with ops.name_scope(scope, 'eval_image', [image, height, width]):
if image.dtype != dtypes.float32:
image = image_ops.convert_image_dtype(image, dtype=dtypes.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = image_ops.central_crop(image,
central_fraction=central_fraction)
# Resize the image to the specified height and width.
image = array_ops.expand_dims(image, 0)
image = image_ops.resize_bilinear(image, [height, width],
align_corners=False)
image = array_ops.squeeze(image, [0])
image = (image - 0.5) * 2.0
return image
def testCompareGpuVsCpu(self):
in_shape = [2, 4, 6, 3]
out_shape = [2, 8, 16, 3]
size = np.prod(in_shape)
x = 1.0 / size * np.arange(0, size).reshape(in_shape).astype(np.float32)
# Align corners will be deprecated for tf2.0 and the false version is not
# supported by XLA.
align_corner_options = [True
] if test_util.is_xla_enabled() else [True, False]
for align_corners in align_corner_options:
grad = {}
for use_gpu in [False, True]:
with self.cached_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x, shape=in_shape)
resized_tensor = image_ops.resize_bilinear(
input_tensor, out_shape[1:3], align_corners=align_corners)
grad[use_gpu] = gradient_checker.compute_gradient(
input_tensor, in_shape, resized_tensor, out_shape, x_init_value=x)
self.assertAllClose(grad[False], grad[True], rtol=1e-4, atol=1e-4)
def preprocess_image(
image, height=INCEPTION_V3_DEFAULT_IMG_SIZE,
width=INCEPTION_V3_DEFAULT_IMG_SIZE, central_fraction=0.875, scope=None):
"""Prepare one image for evaluation.
If height and width are specified it would output an image with that size by
applying resize_bilinear.
If central_fraction is specified it would crop the central fraction of the
input image.
Args:
image: 3-D Tensor of image. If dtype is tf.float32 then the range should be
[0, 1], otherwise it would converted to tf.float32 assuming that the range
is [0, MAX], where MAX is largest positive representable number for
int(8/16/32) data type (see `tf.image.convert_image_dtype` for details).
height: integer
width: integer
central_fraction: Optional Float, fraction of the image to crop.
scope: Optional scope for name_scope.
Returns:
3-D float Tensor of prepared image.
"""
with ops.name_scope(scope, 'eval_image', [image, height, width]):
if image.dtype != dtypes.float32:
image = image_ops.convert_image_dtype(image, dtype=dtypes.float32)
# Crop the central region of the image with an area containing 87.5% of
# the original image.
image = image_ops.central_crop(image, central_fraction=central_fraction)
# Resize the image to the specified height and width.
image = array_ops.expand_dims(image, 0)
image = image_ops.resize_bilinear(image, [height, width],
align_corners=False)
image = array_ops.squeeze(image, [0])
image = (image - 0.5) * 2.0
return image
def _testShapesParameterized(self, use_tape):
TEST_CASES = [[1, 1], [2, 3], [5, 4]] # pylint: disable=invalid-name
for batch_size, channel_count in TEST_CASES:
smaller_shape = [batch_size, 2, 3, channel_count]
larger_shape = [batch_size, 4, 6, channel_count]
for in_shape, out_shape, _, _ in self._itGen(smaller_shape, larger_shape):
with test_util.AbstractGradientTape(use_tape=use_tape) as tape:
# Input values should not influence shapes
x = np.arange(np.prod(in_shape)).reshape(in_shape).astype(np.float32)
input_tensor = constant_op.constant(x, shape=in_shape)
tape.watch(input_tensor)
resized_tensor = image_ops.resize_bilinear(input_tensor,
out_shape[1:3])
self.assertEqual(out_shape, list(resized_tensor.get_shape()))
grad_tensor = tape.gradient(resized_tensor, input_tensor)
self.assertEqual(in_shape, list(grad_tensor.get_shape()))
with self.cached_session():
resized_values = self.evaluate(resized_tensor)
self.assertEqual(out_shape, list(resized_values.shape))
grad_values = self.evaluate(grad_tensor)
self.assertEqual(in_shape, list(grad_values.shape))
def func(in_tensor):
return image_ops.resize_bilinear(
in_tensor,
out_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
def testDeterministicGradients(self, align_corners, half_pixel_centers,
data_type):
if not align_corners and test_util.is_xla_enabled():
# Align corners is deprecated in TF2.0, but align_corners==False is not
# supported by XLA.
self.skipTest(
'align_corners==False not currently supported by XLA')
with self.session(force_gpu=True):
seed = (hash(align_corners) % 256 +
hash(half_pixel_centers) % 256 + hash(data_type) % 256)
np.random.seed(seed)
input_shape = (1, 25, 12, 3) # NHWC
output_shape = (1, 200, 250, 3)
input_image = self._randomDataOp(input_shape, data_type)
repeat_count = 3
if context.executing_eagerly():
def resize_bilinear_gradients(local_seed):
np.random.seed(local_seed)
upstream_gradients = self._randomDataOp(
output_shape, dtypes.float32)
with backprop.GradientTape(persistent=True) as tape:
tape.watch(input_image)
output_image = image_ops.resize_bilinear(
input_image,
output_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
gradient_injector_output = output_image * upstream_gradients
return tape.gradient(gradient_injector_output, input_image)
for i in range(repeat_count):
local_seed = seed + i # select different upstream gradients
result_a = resize_bilinear_gradients(local_seed)
result_b = resize_bilinear_gradients(local_seed)
self.assertAllEqual(result_a, result_b)
else: # graph mode
upstream_gradients = array_ops.placeholder(
dtypes.float32,
shape=output_shape,
name='upstream_gradients')
output_image = image_ops.resize_bilinear(
input_image,
output_shape[1:3],
align_corners=align_corners,
half_pixel_centers=half_pixel_centers)
gradient_injector_output = output_image * upstream_gradients
# The gradient function behaves as if grad_ys is multiplied by the op
# gradient result, not passing the upstream gradients through the op's
# gradient generation graph. This is the reason for using the
# gradient injector
resize_bilinear_gradients = gradients_impl.gradients(
gradient_injector_output,
input_image,
grad_ys=None,
colocate_gradients_with_ops=True)[0]
for i in range(repeat_count):
feed_dict = {
upstream_gradients: self._randomNDArray(output_shape)
}
result_a = resize_bilinear_gradients.eval(
feed_dict=feed_dict)
result_b = resize_bilinear_gradients.eval(
feed_dict=feed_dict)
self.assertAllEqual(result_a, result_b)
def _resize_image(image, height, width): image = array_ops.expand_dims(image, 0) image = image_ops.resize_bilinear(image, [height, width]) return array_ops.squeeze(image, [0])
def _resize_image(image, height, width): image = array_ops.expand_dims(image, 0) image = image_ops.resize_bilinear(image, [height, width]) return array_ops.squeeze(image, [0])
