| # Copyright 2016 The TensorFlow Authors. All Rights Reserved. |
| # |
| # Licensed under the Apache License, Version 2.0 (the "License"); |
| # you may not use this file except in compliance with the License. |
| # You may obtain a copy of the License at |
| # |
| # http://www.apache.org/licenses/LICENSE-2.0 |
| # |
| # Unless required by applicable law or agreed to in writing, software |
| # distributed under the License is distributed on an "AS IS" BASIS, |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. |
| # See the License for the specific language governing permissions and |
| # limitations under the License. |
| # ============================================================================== |
| """Functional tests for 3d convolutional operations.""" |
| |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import math |
| |
| import numpy as np |
| |
| from tensorflow.python.framework import constant_op |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.framework import errors_impl |
| from tensorflow.python.framework import tensor_shape |
| from tensorflow.python.framework import test_util |
| from tensorflow.python.ops import gradient_checker |
| from tensorflow.python.ops import gradients_impl |
| from tensorflow.python.ops import nn_ops |
| import tensorflow.python.ops.nn_grad # pylint: disable=unused-import |
| from tensorflow.python.platform import test |
| from tensorflow.python.util.compat import collections_abc |
| from tensorflow.python.eager import context |
| |
| |
| def GetTestConfigs(): |
| """Get all the valid tests configs to run. |
| |
| Returns: |
| all the valid test configs as tuples of data_format and use_gpu. |
| """ |
| test_configs = [("NDHWC", False), ("NDHWC", True)] |
| if test.is_gpu_available(cuda_only=True): |
| # "NCDHW" format is only supported on CUDA. |
| test_configs += [("NCDHW", True)] |
| return test_configs |
| |
| |
| @test_util.run_all_without_tensor_float_32( |
| "Tests Conv3d, which in some cases is implemented with a matmul. With " |
| "TensorFloat-32, tests fail in some of those cases (and as of August 13 " |
| "2020, only those cases)") |
| class Conv3DTest(test.TestCase): |
| |
| def _DtypesToTest(self, use_gpu): |
| # double datatype is currently not supported for convolution ops |
| # on the ROCm platform |
| optional_float64 = [] if test.is_built_with_rocm() else [dtypes.float64] |
| if use_gpu: |
| if not test_util.GpuSupportsHalfMatMulAndConv(): |
| return optional_float64 + [dtypes.float32] |
| else: |
| # It is important that float32 comes before float16 here, |
| # as we will be using its gradients as reference for fp16 gradients. |
| return optional_float64 + [dtypes.float32, dtypes.float16] |
| else: |
| return optional_float64 + [dtypes.float32, dtypes.float16] |
| |
| def _SetupValuesForDevice(self, tensor_in_sizes, filter_in_sizes, stride, |
| padding, data_format, dtype, 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 = 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) |
| 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 _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): |
| result = self._SetupValuesForDevice( |
| tensor_in_sizes, |
| filter_in_sizes, |
| stride, |
| padding, |
| data_format, |
| dtype, |
| use_gpu=use_gpu) |
| results.append(result) |
| |
| with self.cached_session() as sess: |
| values = self.evaluate(results) |
| for value in values: |
| 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) |
| |
| def _ComputeReferenceDilatedConv(self, tensor_in_sizes, filter_in_sizes, |
| stride, dilation, padding, data_format, |
| 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 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_abc.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 _VerifyDilatedConvValues(self, tensor_in_sizes, filter_in_sizes, stride, |
| padding, dilations): |
| expected_results = [] |
| computed_results = [] |
| default_dilations = ( |
| dilations[0] == 1 and dilations[1] == 1 and dilations[2] == 1) |
| for data_format, use_gpu in GetTestConfigs(): |
| # 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: |
| expected, computed = self._ComputeReferenceDilatedConv( |
| tensor_in_sizes, filter_in_sizes, stride, dilations, padding, |
| data_format, use_gpu) |
| expected_results.append(expected) |
| computed_results.append(computed) |
| tolerance = 1e-2 if use_gpu else 1e-5 |
| with self.cached_session() as sess: |
| expected_values = self.evaluate(expected_results) |
| computed_values = self.evaluate(computed_results) |
| for e_value, c_value in zip(expected_values, computed_values): |
| print("expected = ", e_value) |
| print("actual = ", c_value) |
| self.assertAllClose( |
| e_value.flatten(), c_value.flatten(), atol=tolerance, rtol=1e-6) |
| |
| def _CreateNumpyTensor(self, sizes): |
| return np.asarray([f * 1.0 for f in range(1, |
| np.prod(sizes) + 1)], |
| dtype=np.float32).reshape(sizes) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testConv3DExpandedBatch(self): |
| tensor_in_sizes_batch = [10, 2, 3, 1, 3] |
| tensor_in_sizes_expanded_batch = [2, 5, 2, 3, 1, 3] |
| filter_in_sizes = [1, 1, 1, 3, 3] |
| filter_in = self._CreateNumpyTensor(filter_in_sizes) |
| x1 = self._CreateNumpyTensor(tensor_in_sizes_batch) |
| x2 = x1.reshape(tensor_in_sizes_expanded_batch) |
| conv1 = nn_ops.conv3d_v2( |
| x1, filter_in, strides=[1, 1, 1, 1, 1], padding="VALID") |
| conv2 = nn_ops.conv3d_v2( |
| x2, filter_in, strides=[1, 1, 1, 1, 1], padding="VALID") |
| self.assertEqual(conv1.shape, tensor_in_sizes_batch) |
| self.assertEqual(conv2.shape, tensor_in_sizes_expanded_batch) |
| self.assertAllClose(conv1, self.evaluate(conv2).reshape(conv1.shape)) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testConvolutionClass3DExpandedBatch(self): |
| tensor_in_sizes_batch = [10, 2, 3, 1, 3] |
| tensor_in_sizes_expanded_batch = [2, 5, 2, 3, 1, 3] |
| filter_in_sizes = [1, 1, 1, 3, 3] |
| filter_in = self._CreateNumpyTensor(filter_in_sizes) |
| x1 = self._CreateNumpyTensor(tensor_in_sizes_batch) |
| x2 = x1.reshape(tensor_in_sizes_expanded_batch) |
| convolver1 = nn_ops.Convolution( |
| input_shape=x1.shape, |
| filter_shape=filter_in.shape, |
| strides=[1, 1, 1], |
| padding="VALID") |
| self.assertEqual(convolver1.num_batch_dims, 1) |
| convolver2 = nn_ops.Convolution( |
| input_shape=x2.shape, |
| filter_shape=filter_in.shape, |
| strides=[1, 1, 1], |
| padding="VALID") |
| self.assertEqual(convolver2.num_batch_dims, 2) |
| conv1 = convolver1(x1, filter_in) |
| conv2 = convolver2(x2, filter_in) |
| self.assertEqual(conv1.shape, tensor_in_sizes_batch) |
| self.assertEqual(conv2.shape, tensor_in_sizes_expanded_batch) |
| self.assertAllClose(conv1, self.evaluate(conv2).reshape(conv1.shape)) |
| |
| @test_util.run_in_graph_and_eager_modes |
| def testConvolutionWith2SpatialDimensionsAndExpandedBatch(self): |
| tensor_in_sizes_batch = [10, 2, 3, 1, 3] |
| tensor_in_sizes_expanded_batch = [2, 5, 2, 3, 1, 3] |
| filter_in_sizes = [1, 1, 1, 3, 3] |
| filter_in = self._CreateNumpyTensor(filter_in_sizes) |
| x1 = self._CreateNumpyTensor(tensor_in_sizes_batch) |
| x2 = x1.reshape(tensor_in_sizes_expanded_batch) |
| conv1 = nn_ops.convolution( |
| x1, filter_in, strides=[1, 1, 1], padding="VALID") |
| conv2 = nn_ops.convolution( |
| x2, filter_in, strides=[1, 1, 1], padding="VALID") |
| self.assertEqual(conv1.shape, tensor_in_sizes_batch) |
| self.assertEqual(conv2.shape, tensor_in_sizes_expanded_batch) |
| self.assertAllClose(conv1, self.evaluate(conv2).reshape(conv1.shape)) |
| |
| def testConv3D1x1x1Filter(self): |
| expected_output = [ |
| 0.18518519, 0.22222222, 0.25925926, 0.40740741, 0.5, 0.59259259, |
| 0.62962963, 0.77777778, 0.92592593, 0.85185185, 1.05555556, 1.25925926, |
| 1.07407407, 1.33333333, 1.59259259, 1.2962963, 1.61111111, 1.92592593 |
| ] |
| |
| # These are equivalent to the Conv2D1x1 case. |
| self._VerifyValues( |
| tensor_in_sizes=[1, 2, 3, 1, 3], |
| filter_in_sizes=[1, 1, 1, 3, 3], |
| stride=1, |
| padding="VALID", |
| expected=expected_output) |
| self._VerifyValues( |
| tensor_in_sizes=[1, 2, 1, 3, 3], |
| filter_in_sizes=[1, 1, 1, 3, 3], |
| stride=1, |
| padding="VALID", |
| expected=expected_output) |
| self._VerifyValues( |
| tensor_in_sizes=[1, 1, 2, 3, 3], |
| filter_in_sizes=[1, 1, 1, 3, 3], |
| stride=1, |
| padding="VALID", |
| expected=expected_output) |
| |
| def testConv3D1x1x1Filter2x1x1Dilation(self): |
| ctx = context.context() |
| is_eager = ctx is not None and ctx.executing_eagerly() |
| if test.is_gpu_available(cuda_only=True) or \ |
| (test_util.IsMklEnabled() and is_eager is False): |
| self._VerifyDilatedConvValues( |
| tensor_in_sizes=[1, 3, 6, 1, 1], |
| filter_in_sizes=[1, 1, 1, 1, 1], |
| stride=1, |
| padding="VALID", |
| dilations=[2, 1, 1]) |
| |
| # Expected values computed using scipy's correlate function. |
| def testConv3D2x2x2Filter(self): |
| expected_output = [ |
| 3.77199074, 3.85069444, 3.92939815, 4.2650463, 4.35763889, 4.45023148, |
| 6.73032407, 6.89236111, 7.05439815, 7.22337963, 7.39930556, 7.57523148, |
| 9.68865741, 9.93402778, 10.17939815, 10.18171296, 10.44097222, |
| 10.70023148 |
| ] |
| # expected_shape = [1, 3, 1, 2, 5] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 4, 2, 3, 3], # b, z, y, x, fin |
| filter_in_sizes=[2, 2, 2, 3, 3], # z, y, x, fin, fout |
| stride=1, |
| padding="VALID", |
| expected=expected_output) |
| |
| def testConv3D2x2x2Filter1x2x1Dilation(self): |
| ctx = context.context() |
| is_eager = ctx is not None and ctx.executing_eagerly() |
| if test.is_gpu_available(cuda_only=True) or \ |
| (test_util.IsMklEnabled() and is_eager is False): |
| self._VerifyDilatedConvValues( |
| tensor_in_sizes=[1, 4, 6, 3, 1], |
| filter_in_sizes=[2, 2, 2, 1, 1], |
| stride=1, |
| padding="VALID", |
| dilations=[1, 2, 1]) |
| |
| def testConv3DStrides(self): |
| expected_output = [ |
| 0.06071429, 0.08988095, 0.10238095, 0.11488095, 0.12738095, 0.13988095, |
| 0.08452381, 0.26071429, 0.35238095, 0.36488095, 0.37738095, 0.38988095, |
| 0.40238095, 0.23452381, 0.46071429, 0.61488095, 0.62738095, 0.63988095, |
| 0.65238095, 0.66488095, 0.38452381, 1.12738095, 1.48988095, 1.50238095, |
| 1.51488095, 1.52738095, 1.53988095, 0.88452381, 1.32738095, 1.75238095, |
| 1.76488095, 1.77738095, 1.78988095, 1.80238095, 1.03452381, 1.52738095, |
| 2.01488095, 2.02738095, 2.03988095, 2.05238095, 2.06488095, 1.18452381, |
| 2.19404762, 2.88988095, 2.90238095, 2.91488095, 2.92738095, 2.93988095, |
| 1.68452381, 2.39404762, 3.15238095, 3.16488095, 3.17738095, 3.18988095, |
| 3.20238095, 1.83452381, 2.59404762, 3.41488095, 3.42738095, 3.43988095, |
| 3.45238095, 3.46488095, 1.98452381 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 5, 8, 7, 1], |
| filter_in_sizes=[1, 2, 3, 1, 1], |
| stride=[2, 3, 1], # different stride for each spatial dimension |
| padding="SAME", |
| expected=expected_output) |
| |
| def testConv3D2x2x2FilterStride2(self): |
| expected_output = [ |
| 3.77199074, 3.85069444, 3.92939815, 9.68865741, 9.93402778, 10.17939815 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 4, 2, 3, 3], |
| filter_in_sizes=[2, 2, 2, 3, 3], |
| stride=2, |
| padding="VALID", |
| expected=expected_output) |
| |
| def testConv3DStride3(self): |
| expected_output = [ |
| 1.51140873, 1.57167659, 1.63194444, 1.56349206, 1.62673611, 1.68998016, |
| 1.6155754, 1.68179563, 1.74801587, 1.9280754, 2.01215278, 2.09623016, |
| 1.98015873, 2.0672123, 2.15426587, 2.03224206, 2.12227183, 2.21230159, |
| 4.4280754, 4.65500992, 4.88194444, 4.48015873, 4.71006944, 4.93998016, |
| 4.53224206, 4.76512897, 4.99801587, 4.84474206, 5.09548611, 5.34623016, |
| 4.8968254, 5.15054563, 5.40426587, 4.94890873, 5.20560516, 5.46230159 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 6, 7, 8, 2], |
| filter_in_sizes=[3, 2, 1, 2, 3], |
| stride=3, |
| padding="VALID", |
| expected=expected_output) |
| |
| def testConv3D2x2x2FilterStride2Same(self): |
| expected_output = [ |
| 3.77199074, 3.85069444, 3.92939815, 2.0162037, 2.06597222, 2.11574074, |
| 9.68865741, 9.93402778, 10.17939815, 4.59953704, 4.73263889, 4.86574074 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 4, 2, 3, 3], |
| filter_in_sizes=[2, 2, 2, 3, 3], |
| stride=2, |
| padding="SAME", |
| expected=expected_output) |
| |
| def _TestConv3DEmptyTensorOutputShape(self): |
| """Verifies the output shape of the Conv3D op when output tensor is empty. |
| |
| Args: none |
| """ |
| input_shape = [0, 112, 112, 112, 32] |
| filter_shape = [3, 3, 3, 32, 64] |
| |
| output_shape = [0, 112, 112, 112, 64] |
| input_data = 1 |
| filter_data = 1 |
| for data_type in self._DtypesToTest(False): |
| 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") |
| conv = nn_ops.conv3d( |
| input_tensor, |
| filter_tensor, |
| strides=[1, 1, 1, 1, 1], |
| dilations=[1, 1, 1, 1, 1], |
| padding="SAME", |
| data_format="NDHWC", |
| name="conv") |
| values = self.evaluate(conv) |
| self.assertEqual(values.shape, tensor_shape.TensorShape(output_shape)) |
| |
| def testKernelSmallerThanStride(self): |
| expected_output = [ |
| 0.03703704, 0.11111111, 0.25925926, 0.33333333, 0.7037037, 0.77777778, |
| 0.92592593, 1. |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 3, 3, 3, 1], |
| filter_in_sizes=[1, 1, 1, 1, 1], |
| stride=2, |
| padding="SAME", |
| expected=expected_output) |
| self._VerifyValues( |
| tensor_in_sizes=[1, 3, 3, 3, 1], |
| filter_in_sizes=[1, 1, 1, 1, 1], |
| stride=2, |
| padding="VALID", |
| expected=expected_output) |
| |
| expected_output = [ |
| 0.54081633, 0.58017493, 0.28061224, 0.81632653, 0.85568513, 0.40306122, |
| 0.41873178, 0.4340379, 0.19642857, 2.46938776, 2.50874636, 1.1377551, |
| 2.74489796, 2.78425656, 1.26020408, 1.16873178, 1.1840379, 0.51785714, |
| 1.09511662, 1.10604956, 0.44642857, 1.17164723, 1.18258017, 0.47704082, |
| 0.3691691, 0.37244898, 0.125 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 7, 7, 7, 1], |
| filter_in_sizes=[2, 2, 2, 1, 1], |
| stride=3, |
| padding="SAME", |
| expected=expected_output) |
| |
| expected_output = [ |
| 0.540816, 0.580175, 0.816327, 0.855685, 2.469388, 2.508746, 2.744898, |
| 2.784257 |
| ] |
| self._VerifyValues( |
| tensor_in_sizes=[1, 7, 7, 7, 1], |
| filter_in_sizes=[2, 2, 2, 1, 1], |
| stride=3, |
| padding="VALID", |
| expected=expected_output) |
| |
| def testKernelSizeMatchesInputSize(self): |
| self._VerifyValues( |
| tensor_in_sizes=[1, 2, 1, 2, 1], |
| filter_in_sizes=[2, 1, 2, 1, 2], |
| stride=1, |
| padding="VALID", |
| expected=[1.5625, 1.875]) |
| |
| def testZeroSizedFilterThrowsIllegalArgument(self): |
| tensor_in_sizes = [1, 1, 1, 1, 1] |
| x1 = self._CreateNumpyTensor(tensor_in_sizes) |
| filter_in = np.ones((1, 1, 0, 1, 1), dtype=np.float32) |
| with self.assertRaisesRegex( |
| errors_impl.InvalidArgumentError, "filter must not have zero elements" |
| "|has a non-positive dimension"): |
| self.evaluate( |
| nn_ops.conv3d(x1, filter_in, strides=[1, 1, 1, 1, 1], padding="SAME")) |
| |
| 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 ConstructAndTestGradient(self, **kwargs): |
| for data_format, use_gpu in GetTestConfigs(): |
| self._ConstructAndTestGradientForConfig(data_format=data_format, |
| use_gpu=use_gpu, **kwargs) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientValidPaddingStrideOne(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(3, 5, 4), |
| filter_shape=(3, 3, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=1, |
| padding="VALID", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientValidPaddingStrideOne(self): |
| self.ConstructAndTestGradient( |
| batch=4, |
| input_shape=(4, 6, 5), |
| filter_shape=(2, 2, 2), |
| in_depth=2, |
| out_depth=3, |
| stride=1, |
| padding="VALID", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientValidPaddingStrideTwo(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(6, 3, 5), |
| filter_shape=(3, 3, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=2, |
| padding="VALID", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientValidPaddingStrideTwo(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(7, 6, 5), |
| filter_shape=(2, 2, 2), |
| in_depth=2, |
| out_depth=3, |
| stride=2, |
| padding="VALID", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientValidPaddingStrideThree(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(3, 7, 6), |
| filter_shape=(3, 3, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=3, |
| padding="VALID", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientValidPaddingStrideThree(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(4, 4, 7), |
| filter_shape=(4, 4, 4), |
| in_depth=2, |
| out_depth=3, |
| stride=3, |
| padding="VALID", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientSamePaddingStrideOne(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(3, 2, 2), |
| filter_shape=(3, 2, 1), |
| in_depth=2, |
| out_depth=1, |
| stride=1, |
| padding="SAME", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientSamePaddingStrideOne(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(3, 6, 5), |
| filter_shape=(2, 2, 2), |
| in_depth=2, |
| out_depth=3, |
| stride=1, |
| padding="SAME", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientSamePaddingStrideTwo(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(6, 3, 4), |
| filter_shape=(3, 3, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=2, |
| padding="SAME", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientSamePaddingStrideTwo(self): |
| self.ConstructAndTestGradient( |
| batch=4, |
| input_shape=(7, 3, 5), |
| filter_shape=(2, 2, 2), |
| in_depth=2, |
| out_depth=3, |
| stride=2, |
| padding="SAME", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientSamePaddingStrideThree(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(9, 3, 6), |
| filter_shape=(3, 3, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=3, |
| padding="SAME", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientSamePaddingStrideThree(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(9, 4, 7), |
| filter_shape=(4, 4, 4), |
| in_depth=2, |
| out_depth=3, |
| stride=3, |
| padding="SAME", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientSamePaddingDifferentStrides(self): |
| self.ConstructAndTestGradient( |
| batch=1, |
| input_shape=(5, 8, 7), |
| filter_shape=(1, 2, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=[2, 3, 1], |
| padding="SAME", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientKernelSizeMatchesInputSize(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(5, 4, 3), |
| filter_shape=(5, 4, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=1, |
| padding="VALID", |
| test_input=False) |
| |
| @test_util.run_deprecated_v1 |
| def testInputGradientKernelSizeMatchesInputSize(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(5, 4, 3), |
| filter_shape=(5, 4, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=1, |
| padding="VALID", |
| test_input=True) |
| |
| def disabledtestFilterGradientSamePaddingDifferentStrides(self): |
| self.ConstructAndTestGradient( |
| batch=1, |
| input_shape=(5, 8, 7), |
| filter_shape=(1, 2, 3), |
| in_depth=2, |
| out_depth=3, |
| stride=[2, 3, 1], |
| padding="SAME", |
| test_input=False) |
| |
| # Test the fast path in gemm_pack_rhs/gemm_pack_colmajor_block, when channel |
| # dimension is a multiple of packet size. |
| @test_util.run_deprecated_v1 |
| def testInputGradientValidPaddingStrideOneFastPath(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(3, 5, 4), |
| filter_shape=(2, 2, 2), |
| in_depth=8, |
| out_depth=2, |
| stride=1, |
| padding="VALID", |
| test_input=True) |
| |
| @test_util.run_deprecated_v1 |
| def testFilterGradientValidPaddingStrideOneFastPath(self): |
| self.ConstructAndTestGradient( |
| batch=2, |
| input_shape=(4, 6, 5), |
| filter_shape=(2, 2, 2), |
| in_depth=8, |
| out_depth=2, |
| stride=1, |
| padding="VALID", |
| test_input=False) |
| |
| # Testing for backprops |
| 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) |
| |
| @test_util.run_deprecated_v1 |
| def testConv3D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1(self): |
| if test.is_gpu_available(cuda_only=True): |
| for (data_format, use_gpu) in GetTestConfigs(): |
| self._RunAndVerifyBackprop( |
| input_sizes=[1, 3, 6, 1, 1], |
| filter_sizes=[2, 2, 1, 1, 1], |
| output_sizes=[1, 1, 5, 1, 1], |
| strides=[1, 1, 1], |
| dilations=[2, 1, 1], |
| padding="VALID", |
| data_format=data_format, |
| use_gpu=use_gpu, |
| err=1e-5, |
| mode="filter") |
| |
| @test_util.run_deprecated_v1 |
| def testConv3D2x2Depth3ValidBackpropInputStride1x1Dilation2x1(self): |
| if test.is_gpu_available(cuda_only=True): |
| for (data_format, use_gpu) in GetTestConfigs(): |
| self._RunAndVerifyBackprop( |
| input_sizes=[1, 3, 6, 1, 1], |
| filter_sizes=[2, 2, 1, 1, 1], |
| output_sizes=[1, 1, 5, 1, 1], |
| strides=[1, 1, 1], |
| dilations=[2, 1, 1], |
| padding="VALID", |
| data_format=data_format, |
| use_gpu=use_gpu, |
| err=1e-5, |
| mode="input") |
| |
| |
| if __name__ == "__main__": |
| test.main() |
