def _RunAndVerifyBackprop(self, input_sizes, filter_sizes, output_sizes,
strides, dilations, padding, data_format, use_gpu,
err, mode):
total_input_size = 1
total_filter_size = 1
for s in input_sizes:
total_input_size *= s
for s in filter_sizes:
total_filter_size *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_input_size + 1)]
x2 = [f * 1.0 for f in range(1, total_filter_size + 1)]
default_dilations = (
dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1)
# If any dilation rate is larger than 1, only do test on the GPU
# because we currently do not have a CPU implementation for arbitrary
# dilation rates.
if default_dilations or use_gpu:
with self.cached_session(use_gpu=use_gpu) as sess:
if data_format == "NCDHW":
input_sizes = test_util.NHWCToNCHW(input_sizes)
t1 = constant_op.constant(x1, shape=input_sizes)
t2 = constant_op.constant(x2, shape=filter_sizes)
full_strides = [1] + strides + [1]
full_dilations = [1] + dilations + [1]
if data_format == "NCDHW":
full_strides = test_util.NHWCToNCHW(full_strides)
full_dilations = test_util.NHWCToNCHW(full_dilations)
actual = nn_ops.conv3d(
t1,
t2,
strides=full_strides,
dilations=full_dilations,
padding=padding,
data_format=data_format)
expected = nn_ops.convolution(
t1,
t2,
padding=padding,
strides=strides,
dilation_rate=dilations,
data_format=data_format)
if data_format == "NCDHW":
actual = test_util.NCHWToNHWC(actual)
expected = test_util.NCHWToNHWC(expected)
actual_grad = gradients_impl.gradients(actual, t1
if mode == "input" else t2)[0]
expected_grad = gradients_impl.gradients(expected, t1
if mode == "input" else t2)[0]
# "values" consists of two tensors for two backprops
actual_value = self.evaluate(actual_grad)
expected_value = self.evaluate(expected_grad)
self.assertShapeEqual(actual_value, actual_grad)
self.assertShapeEqual(expected_value, expected_grad)
print("expected = ", expected_value)
print("actual = ", actual_value)
self.assertArrayNear(expected_value.flatten(), actual_value.flatten(),
err)
def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32):
input_nhwc = math_ops.cast(inputs, dtype)
with self.cached_session(use_gpu=False):
# test NHWC (default) on CPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf, outputs)
# Run this test only if only CPU device is available
if all(x.device_type == "CPU"
for x in device_lib.list_local_devices()):
input_nchw = test_util.NHWCToNCHW(input_nhwc)
output_nchw = array_ops.depth_to_space(input_nchw,
block_size,
data_format="NCHW")
output_nhwc = test_util.NCHWToNHWC(output_nchw)
with self.assertRaisesRegex(
errors_impl.InvalidArgumentError,
"No OpKernel was registered to support Op 'DepthToSpace'"
):
output_nhwc.eval()
if test.is_gpu_available():
with self.cached_session(use_gpu=True):
# test NHWC (default) on GPU
x_tf = array_ops.depth_to_space(input_nhwc, block_size)
self.assertAllEqual(x_tf, outputs)
# test NCHW on GPU
input_nchw = test_util.NHWCToNCHW(input_nhwc)
output_nchw = array_ops.depth_to_space(input_nchw,
block_size,
data_format="NCHW")
output_nhwc = test_util.NCHWToNHWC(output_nchw)
self.assertAllEqual(output_nhwc, outputs)
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, bias,
strides, padding, activation_mode, data_format,
dtype):
"""Verifies the output values of the convolution function.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[kernel_rows, kernel_cols, input_depth, output_depth].
bias: 1-D bias tensor of length output_depth.
strides: Stride: [col_stride, row_stride]
padding: Padding type.
activation_mode: Activation mode.
data_format: Format of the data tensors.
dtype: Data type for inputs and outputs.
Returns:
Symbolic tensor value and reference value that can be used to
execute the computation and verify the results.
"""
input_size = np.prod(tensor_in_sizes)
filter_size = np.prod(filter_in_sizes)
bias_size = filter_in_sizes[-1] # equals to output depth
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, input_size + 1)]
x2 = [f * 1.0 for f in range(1, filter_size + 1)]
# This is to guarantee that there is always negative values after
# bias add so that we can test whether relu works correctly.
x3 = bias
with self.test_session(use_gpu=True):
t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
t3 = constant_op.constant(x3, shape=[bias_size], dtype=dtype)
strides = [1] + strides + [1]
if data_format == "NCHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
output = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
t1,
t2,
t3,
strides=strides,
padding=padding,
data_format=data_format,
activation_mode=activation_mode)
ref_conv_output = nn_ops.conv2d(t1,
t2,
strides=strides,
padding=padding,
data_format=data_format)
ref_bias_output = nn_ops.bias_add(ref_conv_output,
t3,
data_format=data_format)
ref_output = nn_ops.relu(ref_bias_output)
if data_format == "NCHW":
output = test_util.NCHWToNHWC(output)
ref_output = test_util.NCHWToNHWC(ref_output)
return output, ref_output
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride,
padding, data_format, use_gpu):
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_size_1 + 1)]
x2 = [f * 1.0 for f in range(1, total_size_2 + 1)]
with self.test_session(use_gpu=use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes)
t2 = constant_op.constant(x2, shape=filter_in_sizes)
if isinstance(stride, collections.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if data_format == "NCDHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
conv = nn_ops.conv3d(t1,
t2,
strides,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
return conv
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride,
padding, data_format, dtype, use_gpu):
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing numbers from 0 to 1.
# We keep the input tensor values fairly small to avoid overflowing a float16
# tensor during the conv3d
x1 = [f * 1.0 / total_size_1 for f in range(1, total_size_1 + 1)]
x2 = [f * 1.0 / total_size_2 for f in range(1, total_size_2 + 1)]
with self.test_session(use_gpu=use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
if isinstance(stride, collections.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if data_format == "NCDHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
conv = nn_ops.conv3d(t1,
t2,
strides,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
return conv
def _ComputeReferenceDilatedConv(self, tensor_in_sizes, filter_in_sizes,
stride, dilation, padding, data_format,
use_gpu):
total_size_tensor = 1
total_size_filter = 1
for s in tensor_in_sizes:
total_size_tensor *= s
for s in filter_in_sizes:
total_size_filter *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_size_tensor + 1)]
x2 = [f * 1.0 for f in range(1, total_size_filter + 1)]
with self.cached_session(use_gpu=use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes)
t2 = constant_op.constant(x2, shape=filter_in_sizes)
if isinstance(stride, collections.Iterable):
strides = list(stride)
else:
strides = [stride, stride, stride]
if data_format == "NCDHW":
t1 = test_util.NHWCToNCHW(t1)
full_strides = [1, 1] + strides
full_dilation = [1, 1] + dilation
else:
full_strides = [1] + strides + [1]
full_dilation = [1] + dilation + [1]
expected = nn_ops.convolution(
t1,
t2,
padding=padding,
strides=strides,
dilation_rate=dilation,
data_format=data_format)
computed = nn_ops.conv3d(
t1,
t2,
strides=full_strides,
dilations=full_dilation,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
expected = test_util.NCHWToNHWC(expected)
computed = test_util.NCHWToNHWC(computed)
return expected, computed
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride,
padding, data_format, dtype, use_gpu):
total_size_tensor = 1
total_size_filter = 1
for s in tensor_in_sizes:
total_size_tensor *= s
for s in filter_in_sizes:
total_size_filter *= s
# Initializes the input tensor with array containing numbers from 0 to 1.
# We keep the input tensor values fairly small to avoid overflowing float16
# during the conv3d.
x1 = [
f * 1.0 / total_size_tensor
for f in range(1, total_size_tensor + 1)
]
x2 = [
f * 1.0 / total_size_filter
for f in range(1, total_size_filter + 1)
]
with self.cached_session(use_gpu=use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
if isinstance(stride, collections_abc.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if data_format == "NCDHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
# For the 1x1x1 filter, the NDHWC conv3d will call blas kernel on GPU,
# which might lead to deviated results from the reference with tf32.
if (data_format == 'NDHWC' and use_gpu and filter_in_sizes[0] == 1
and filter_in_sizes[1] == 1 and filter_in_sizes[2] == 1
and dtype == dtypes.float32):
with ops.device('/cpu:0'):
conv = nn_ops.conv3d(t1,
t2,
strides,
padding=padding,
data_format=data_format)
else:
conv = nn_ops.conv3d(t1,
t2,
strides,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
return conv
def _SetupVal(data_format, use_gpu):
with test_util.device(use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes)
t2 = constant_op.constant(x2, shape=filter_in_sizes)
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
conv = nn_ops.conv2d(
t1, t2, strides=strides, padding=padding, data_format=data_format)
if data_format == "NCHW":
conv = test_util.NCHWToNHWC(conv)
return conv
def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes,
dilations, strides, padding, data_format, dtype,
use_ve):
"""Verifies the output values of the convolution function.
Args:
tensor_in_sizes: Input tensor dimensions in
[batch, input_rows, input_cols, input_depth].
filter_in_sizes: Filter tensor dimensions in
[kernel_rows, kernel_cols, input_depth, output_depth].
dilations: Dilated rate: [col_dilation, row_dilation]
strides: Stride: [col_stride, row_stride]
padding: Padding type.
data_format: Format of the data tensors.
dtype: Data type for inputs and outputs.
use_ve: True if the operations should be run on VE
Returns:
Symbolic tensor value that can be used to execute the computation
"""
total_size_1 = 1
total_size_2 = 1
for s in tensor_in_sizes:
total_size_1 *= s
for s in filter_in_sizes:
total_size_2 *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x1 = [f * 1.0 for f in range(1, total_size_1 + 1)]
x2 = [f * 1.0 for f in range(1, total_size_2 + 1)]
with test_util.device(use_ve=use_ve, use_gpu=False):
t1 = constant_op.constant(x1, shape=tensor_in_sizes, dtype=dtype)
t2 = constant_op.constant(x2, shape=filter_in_sizes, dtype=dtype)
strides = [1] + strides + [1]
dilations = [1] + dilations + [1]
if data_format == "NCHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
dilations = test_util.NHWCToNCHW(dilations)
conv = nn_ops.conv2d(t1,
t2,
dilations=dilations,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCHW":
conv = test_util.NCHWToNHWC(conv)
return conv
def _testOne(self, inputs, block_size, outputs, dtype=dtypes.float32):
input_nhwc = math_ops.cast(inputs, dtype)
# test NHWC (default)
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
self.assertAllEqual(self.evaluate(x_tf), outputs)
if test_util.is_gpu_available():
with test_util.force_gpu():
# test NCHW on GPU
input_nchw = test_util.NHWCToNCHW(input_nhwc)
output_nchw = array_ops.space_to_depth(input_nchw,
block_size,
data_format="NCHW")
output_nhwc = test_util.NCHWToNHWC(output_nchw)
self.assertAllEqual(self.evaluate(output_nhwc), outputs)
def _testOne(self, inputs, block_size, outputs):
input_nhwc = math_ops.to_float(inputs)
with self.test_session(use_gpu=False):
# test NHWC (default) on CPU
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
self.assertAllEqual(x_tf.eval(), outputs)
if test.is_gpu_available():
with self.test_session(use_gpu=True):
# test NHWC (default) on GPU
x_tf = array_ops.space_to_depth(input_nhwc, block_size)
self.assertAllEqual(x_tf.eval(), outputs)
# test NCHW on GPU
input_nchw = test_util.NHWCToNCHW(input_nhwc)
output_nchw = array_ops.space_to_depth(
input_nchw, block_size, data_format="NCHW")
output_nhwc = test_util.NCHWToNHWC(output_nchw)
self.assertAllEqual(output_nhwc.eval(), outputs)
def _SetupVal(data_format, use_gpu):
t1 = constant_op.constant(x1, shape=tensor_in_sizes)
t2 = constant_op.constant(x2, shape=filter_in_sizes)
t3 = constant_op.constant(x3, shape=[filter_in_sizes[-1]])
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
output = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
t1,
t2,
t3,
strides=strides,
padding=padding,
data_format=data_format,
activation_mode="Relu")
if data_format == "NCHW":
output = test_util.NCHWToNHWC(output)
return output
def _VerifyOneTest(self, pool_func, input_sizes, window, strides, padding,
data_format, expected, use_gpu):
"""Verifies the output values of the pooling function.
Args:
pool_func: Function to be called: co.MaxPool, co.AvgPool.
input_sizes: Input tensor dimensions.
window: Tuple of kernel dims: planes, rows, cols.
strides: Tuple of strides for dims: planes, rows, cols.
padding: Padding type.
data_format: The data format we use to run the pooling operation.
expected: An array containing the expected operation outputs.
use_gpu: Whether to run ops on GPU.
"""
total_size = 1
for s in input_sizes:
total_size *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x = [f * 1.0 for f in range(1, total_size + 1)]
with self.cached_session(use_gpu=use_gpu) as sess:
t = constant_op.constant(x, shape=input_sizes)
window = [1] + list(window) + [1]
strides = [1] + list(strides) + [1]
if data_format == "NCDHW":
t = test_util.NHWCToNCHW(t)
window = test_util.NHWCToNCHW(window)
strides = test_util.NHWCToNCHW(strides)
t = pool_func(
t,
ksize=window,
strides=strides,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
t = test_util.NCHWToNHWC(t)
vals = self.evaluate(t)
# Verifies values.
actual = vals.flatten()
self.assertAllClose(expected, actual)
def _SetupVal(data_format, use_gpu):
# TODO(b/79323979): re-enable memory optimization after this bug is fixed.
with self.test_session(use_gpu=use_gpu,
config=NoMemoryOptimizationConfig()):
t1 = constant_op.constant(x1, shape=tensor_in_sizes)
t2 = constant_op.constant(x2, shape=filter_in_sizes)
t3 = constant_op.constant(x3, shape=[filter_in_sizes[-1]])
strides = [1] + conv_strides + [1]
if data_format == "NCHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
output = fused_conv2d_bias_activation_op.fused_conv2d_bias_activation(
t1,
t2,
t3,
strides=strides,
padding=padding,
data_format=data_format,
activation_mode="Relu")
if data_format == "NCHW":
output = test_util.NCHWToNHWC(output)
return output
def _ConstructAndTestGradientForConfig(self, pool_func, input_sizes,
output_sizes, window, strides,
padding, data_format, use_gpu):
"""Verifies the gradients of a pooling function.
Args:
pool_func: Function to be called, co.MaxPool, co.AvgPool,
or the Lua version.
input_sizes: Input tensor dimensions.
output_sizes: Output tensor dimensions.
window: Tuple of kernel dims: planes, rows, cols.
strides: Tuple of strides for dims: planes, rows, cols.
padding: Padding type.
data_format: Data format string.
use_gpu: Whether to run on GPU.
"""
total_size = 1
for s in input_sizes:
total_size *= s
# Initializes the input tensor with array containing incrementing
# numbers from 1.
x = [f * 1.0 for f in range(1, total_size + 1)]
with self.test_session(use_gpu=use_gpu):
input_tensor = constant_op.constant(x,
shape=input_sizes,
name="input")
err_margin = 1e-3
if pool_func == nn_ops.avg_pool3d:
func_name = "avg_pool3d"
x_init_value = None
else:
x_init_value = np.asfarray(
np.arange(1, total_size + 1),
dtype=np.float32).reshape(input_sizes)
func_name = "max_pool3d"
ksize = [1, window[0], window[1], window[2], 1]
strides = [1, strides[0], strides[1], strides[2], 1]
t = input_tensor
if data_format == "NCDHW":
ksize = test_util.NHWCToNCHW(ksize)
strides = test_util.NHWCToNCHW(strides)
t = test_util.NHWCToNCHW(t)
t = pool_func(t,
ksize=ksize,
strides=strides,
padding=padding,
data_format=data_format,
name=func_name)
if data_format == "NCDHW":
t = test_util.NCHWToNHWC(t)
err = gradient_checker.compute_gradient_error(
input_tensor,
input_sizes,
t,
output_sizes,
x_init_value=x_init_value,
delta=1e-2)
print("%s gradient error = " % func_name, err)
self.assertLess(err, err_margin)
def _ConstructAndTestGradientForConfig(self, batch, input_shape,
filter_shape, in_depth, out_depth,
stride, padding, test_input,
data_format, use_gpu):
input_planes, input_rows, input_cols = input_shape
filter_planes, filter_rows, filter_cols = filter_shape
input_shape = [batch, input_planes, input_rows, input_cols, in_depth]
filter_shape = [
filter_planes, filter_rows, filter_cols, in_depth, out_depth
]
if isinstance(stride, collections.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if padding == "VALID":
output_planes = int(
math.ceil((input_planes - filter_planes + 1.0) / strides[1]))
output_rows = int(
math.ceil((input_rows - filter_rows + 1.0) / strides[2]))
output_cols = int(
math.ceil((input_cols - filter_cols + 1.0) / strides[3]))
else:
output_planes = int(math.ceil(float(input_planes) / strides[1]))
output_rows = int(math.ceil(float(input_rows) / strides[2]))
output_cols = int(math.ceil(float(input_cols) / strides[3]))
output_shape = [
batch, output_planes, output_rows, output_cols, out_depth
]
input_size = 1
for x in input_shape:
input_size *= x
filter_size = 1
for x in filter_shape:
filter_size *= x
input_data = [x * 1.0 / input_size for x in range(0, input_size)]
filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)]
if test.is_gpu_available() and use_gpu:
data_type = dtypes.float32
if test.is_gpu_available():
tolerance = 4e-3
else:
# As of Aug 2016, higher tolerance is needed for some CPU architectures.
# Runs on a single machine can also generate slightly different errors
# because of multithreading.
tolerance = 8e-3
else:
data_type = dtypes.float64
tolerance = 1e-8
with self.test_session(use_gpu=use_gpu):
orig_input_tensor = constant_op.constant(input_data,
shape=input_shape,
dtype=data_type,
name="input")
filter_tensor = constant_op.constant(filter_data,
shape=filter_shape,
dtype=data_type,
name="filter")
if data_format == "NCDHW":
input_tensor = test_util.NHWCToNCHW(orig_input_tensor)
strides = test_util.NHWCToNCHW(strides)
else:
input_tensor = orig_input_tensor
conv = nn_ops.conv3d(input_tensor,
filter_tensor,
strides,
padding,
data_format=data_format,
name="conv")
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
if test_input:
err = gradient_checker.compute_gradient_error(
orig_input_tensor, input_shape, conv, output_shape)
else:
err = gradient_checker.compute_gradient_error(
filter_tensor, filter_shape, conv, output_shape)
print("conv3d gradient error = ", err)
self.assertLess(err, tolerance)
def _ConstructAndTestGradientForConfig(self, batch, input_shape,
filter_shape, in_depth, out_depth,
stride, padding, test_input,
data_format, use_gpu):
input_planes, input_rows, input_cols = input_shape
filter_planes, filter_rows, filter_cols = filter_shape
input_shape = [batch, input_planes, input_rows, input_cols, in_depth]
filter_shape = [
filter_planes, filter_rows, filter_cols, in_depth, out_depth
]
if isinstance(stride, collections_abc.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if padding == "VALID":
output_planes = int(
math.ceil((input_planes - filter_planes + 1.0) / strides[1]))
output_rows = int(
math.ceil((input_rows - filter_rows + 1.0) / strides[2]))
output_cols = int(
math.ceil((input_cols - filter_cols + 1.0) / strides[3]))
else:
output_planes = int(math.ceil(float(input_planes) / strides[1]))
output_rows = int(math.ceil(float(input_rows) / strides[2]))
output_cols = int(math.ceil(float(input_cols) / strides[3]))
output_shape = [
batch, output_planes, output_rows, output_cols, out_depth
]
input_size = 1
for x in input_shape:
input_size *= x
filter_size = 1
for x in filter_shape:
filter_size *= x
input_data = [x * 1.0 / input_size for x in range(0, input_size)]
filter_data = [x * 1.0 / filter_size for x in range(0, filter_size)]
for data_type in self._DtypesToTest(use_gpu=use_gpu):
# TODO(mjanusz): Modify gradient_checker to also provide max relative
# error and synchronize the tolerance levels between the tests for forward
# and backward computations.
if data_type == dtypes.float64:
tolerance = 1e-8
elif data_type == dtypes.float32:
tolerance = 5e-3
elif data_type == dtypes.float16:
tolerance = 1e-3
with self.cached_session(use_gpu=use_gpu):
orig_input_tensor = constant_op.constant(input_data,
shape=input_shape,
dtype=data_type,
name="input")
filter_tensor = constant_op.constant(filter_data,
shape=filter_shape,
dtype=data_type,
name="filter")
if data_format == "NCDHW":
input_tensor = test_util.NHWCToNCHW(orig_input_tensor)
new_strides = test_util.NHWCToNCHW(strides)
else:
input_tensor = orig_input_tensor
new_strides = strides
conv = nn_ops.conv3d(input_tensor,
filter_tensor,
new_strides,
padding,
data_format=data_format,
name="conv")
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
self.assertEqual(conv.shape,
tensor_shape.TensorShape(output_shape))
if test_input:
jacob_t, jacob_n = gradient_checker.compute_gradient(
orig_input_tensor, input_shape, conv, output_shape)
else:
jacob_t, jacob_n = gradient_checker.compute_gradient(
filter_tensor, filter_shape, conv, output_shape)
if data_type != dtypes.float16:
reference_jacob_t = jacob_t
err = np.fabs(jacob_t - jacob_n).max()
else:
# Compare fp16 theoretical gradients to fp32 theoretical gradients,
# since fp16 numerical gradients are too imprecise.
err = np.fabs(jacob_t - reference_jacob_t).max()
print("conv3d gradient error = ", err)
self.assertLess(err, tolerance)
def _VerifyValues(self, tensor_in_sizes, filter_in_sizes, stride, padding,
expected):
results = []
for data_format, use_gpu in GetTestConfigs():
for dtype in self._DtypesToTest(use_gpu):
total_size_tensor = np.prod(tensor_in_sizes)
total_size_filter = np.prod(filter_in_sizes)
# Initializes the input tensor with array containing numbers from 0 to 1.
# We keep the input tensor values fairly small to avoid overflowing float16
# during the conv3d.
x1 = [
f * 1.0 / total_size_tensor
for f in range(1, total_size_tensor + 1)
]
x2 = [
f * 1.0 / total_size_filter
for f in range(1, total_size_filter + 1)
]
with self.cached_session(use_gpu=use_gpu):
t1_ph = tf.compat.v1.placeholder(dtype,
shape=tensor_in_sizes)
t1 = constant_op.constant(x1,
shape=tensor_in_sizes,
dtype=dtype)
t2_ph = tf.compat.v1.placeholder(dtype,
shape=filter_in_sizes)
t2 = constant_op.constant(x2,
shape=filter_in_sizes,
dtype=dtype)
if isinstance(stride, collections_abc.Iterable):
strides = [1] + list(stride) + [1]
else:
strides = [1, stride, stride, stride, 1]
if data_format == "NCDHW":
t1 = test_util.NHWCToNCHW(t1)
strides = test_util.NHWCToNCHW(strides)
conv = nn_ops.conv3d(t1_ph,
t2_ph,
strides,
padding=padding,
data_format=data_format)
if data_format == "NCDHW":
conv = test_util.NCHWToNHWC(conv)
sess_fn = lambda sess: sess.run(conv,
feed_dict={
t1_ph: t1.eval(),
t2_ph: t2.eval()
})
value = self.with_ngraph(sess_fn)
print("expected = ", expected)
print("actual = ", value)
tol = 1e-6
if value.dtype == np.float16:
tol = 1e-3
self.assertAllClose(expected,
value.flatten(),
atol=tol,
rtol=tol)