def compute_dilation2d(image_tensor, kernel_tensor):
return nn_ops.dilation2d(image_tensor,
kernel_tensor,
strides=strides,
rates=rates,
padding=padding,
name="dilation2d")
def _ConstructAndTestGradient(self, image_shape, kernel_shape, strides,
rates, padding, use_gpu):
"""Verifies the gradients of the dilation function.
Args:
image_shape: Input shape, [batch, in_height, in_width, channels].
kernel_shape: Filter shape, [filter_height, filter_width, channels].
strides: Output strides, specified as [stride_height, stride_width].
rates: Atrous rates, specified as [rate_height, rate_width].
padding: Padding type.
use_gpu: Whether we are running on GPU.
"""
assert image_shape[3] == kernel_shape[2]
np.random.seed(1) # Make it reproducible.
image = np.random.random_sample(image_shape).astype(np.float32)
kernel = np.random.random_sample(kernel_shape).astype(np.float32)
image_init = np.random.random_sample(image_shape).astype(np.float32)
kernel_init = np.random.random_sample(kernel_shape).astype(np.float32)
strides = [1] + strides + [1]
rates = [1] + rates + [1]
with self.test_session(use_gpu=use_gpu):
image_tensor = constant_op.constant(image,
shape=image_shape,
name="input")
kernel_tensor = constant_op.constant(kernel,
shape=kernel_shape,
name="filter")
out_tensor = nn_ops.dilation2d(image_tensor,
kernel_tensor,
strides=strides,
rates=rates,
padding=padding,
name="dilation2d")
out_shape = out_tensor.eval().shape
# Small delta is necessary for argmax to remain the same.
err = gradient_checker.compute_gradient_error(
[image_tensor, kernel_tensor], [image_shape, kernel_shape],
out_tensor,
out_shape, [image_init, kernel_init],
delta=1e-3)
print("Dilation gradient error = %f" % err)
self.assertLess(err, 1e-4)
def _ConstructAndTestGradient(self, image_shape, kernel_shape, strides, rates,
padding, use_gpu):
"""Verifies the gradients of the dilation function.
Args:
image_shape: Input shape, [batch, in_height, in_width, channels].
kernel_shape: Filter shape, [filter_height, filter_width, channels].
strides: Output strides, specified as [stride_height, stride_width].
rates: Atrous rates, specified as [rate_height, rate_width].
padding: Padding type.
use_gpu: Whether we are running on GPU.
"""
assert image_shape[3] == kernel_shape[2]
np.random.seed(1) # Make it reproducible.
image = np.random.random_sample(image_shape).astype(np.float32)
kernel = np.random.random_sample(kernel_shape).astype(np.float32)
image_init = np.random.random_sample(image_shape).astype(np.float32)
kernel_init = np.random.random_sample(kernel_shape).astype(np.float32)
strides = [1] + strides + [1]
rates = [1] + rates + [1]
with self.cached_session(use_gpu=use_gpu):
image_tensor = constant_op.constant(
image, shape=image_shape, name="input")
kernel_tensor = constant_op.constant(
kernel, shape=kernel_shape, name="filter")
out_tensor = nn_ops.dilation2d(
image_tensor,
kernel_tensor,
strides=strides,
rates=rates,
padding=padding,
name="dilation2d")
out_shape = self.evaluate(out_tensor).shape
# Small delta is necessary for argmax to remain the same.
err = gradient_checker.compute_gradient_error(
[image_tensor, kernel_tensor], [image_shape, kernel_shape],
out_tensor,
out_shape, [image_init, kernel_init],
delta=1e-3)
print("Dilation gradient error = %f" % err)
self.assertLess(err, 1e-4)
def _VerifyValues(self, image, kernel, strides, rates, padding, out,
use_gpu):
"""Verifies the output values of the dilation function.
Args:
image: Input tensor with shape: [batch, in_height, in_width, channels].
kernel: Filter tensor with shape: [filter_height, filter_width, channels].
strides: Output strides, specified as [stride_height, stride_width].
rates: Atrous rates, specified as [rate_height, rate_width].
padding: Padding type.
out: Expected output.
use_gpu: Whether we are running on GPU.
"""
strides = [1] + strides + [1]
rates = [1] + rates + [1]
with self.test_session(use_gpu=use_gpu):
out_tensor = nn_ops.dilation2d(constant_op.constant(image),
constant_op.constant(kernel),
strides=strides,
rates=rates,
padding=padding,
name="dilation2d")
self.assertAllClose(out, out_tensor.eval())
def _VerifyValues(self, image, kernel, strides, rates, padding, out, use_gpu):
"""Verifies the output values of the dilation function.
Args:
image: Input tensor with shape: [batch, in_height, in_width, channels].
kernel: Filter tensor with shape: [filter_height, filter_width, channels].
strides: Output strides, specified as [stride_height, stride_width].
rates: Atrous rates, specified as [rate_height, rate_width].
padding: Padding type.
out: Expected output.
use_gpu: Whether we are running on GPU.
"""
strides = [1] + strides + [1]
rates = [1] + rates + [1]
with self.cached_session(use_gpu=use_gpu):
out_tensor = nn_ops.dilation2d(
constant_op.constant(image),
constant_op.constant(kernel),
strides=strides,
rates=rates,
padding=padding,
name="dilation2d")
self.assertAllClose(out, self.evaluate(out_tensor))