| # Copyright 2017 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. |
| # ============================================================================== |
| """Tests for Conv2D via the XLA JIT. |
| |
| The canned results in these tests are created by running each test using the |
| Tensorflow CPU device and saving the output. |
| """ |
| |
| from absl.testing import parameterized |
| import numpy as np |
| |
| from tensorflow.compiler.tests import test_utils |
| from tensorflow.compiler.tests import xla_test |
| from tensorflow.python.framework import dtypes |
| from tensorflow.python.ops import array_ops |
| from tensorflow.python.ops import gen_nn_ops |
| from tensorflow.python.ops import nn_ops |
| from tensorflow.python.platform import googletest |
| |
| DATA_FORMATS = ( |
| ("_data_format_NHWC", "NHWC"), |
| ("_data_format_NCHW", "NCHW"), |
| ) |
| |
| |
| class Conv2DTest(xla_test.XLATestCase, parameterized.TestCase): |
| |
| def _VerifyValues(self, |
| input_sizes=None, |
| filter_sizes=None, |
| strides=None, |
| dilations=None, |
| padding=None, |
| data_format_src="NHWC", |
| data_format_dst="NHWC", |
| expected=None): |
| """Tests that tf.nn.conv2d produces the expected value. |
| |
| Args: |
| input_sizes: Input tensor dimensions in |
| [batch, input_rows, input_cols, input_depth]. |
| filter_sizes: Filter tensor dimensions in |
| [kernel_rows, kernel_cols, input_depth, output_depth]. |
| strides: Strides. |
| dilations: RHS dilations. |
| padding: Padding type. |
| data_format_src: Data format input is in. |
| data_format_dst: Data format verification will run and input is converted |
| to. |
| expected: Expected output. |
| """ |
| |
| total_size_1 = np.prod(input_sizes) |
| total_size_2 = np.prod(filter_sizes) |
| x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes) |
| x2 = np.arange(1, total_size_2 + 1, dtype=np.float32).reshape(filter_sizes) |
| strides = [1] + strides + [1] |
| if dilations is None: |
| dilations = [1, 1] |
| dilations = [1] + dilations + [1] |
| |
| # Convert between data formats. |
| expected = test_utils.ConvertBetweenDataFormats(expected, data_format_src, |
| data_format_dst) |
| x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, |
| data_format_dst) |
| input_sizes = test_utils.PermuteDimsBetweenDataFormats( |
| input_sizes, data_format_src, data_format_dst) |
| strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, |
| data_format_dst) |
| dilations = test_utils.PermuteDimsBetweenDataFormats( |
| dilations, data_format_src, data_format_dst) |
| |
| with self.session() as sess: |
| t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) |
| t2 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) |
| with self.test_scope(): |
| out = nn_ops.conv2d( |
| t1, |
| t2, |
| strides=strides, |
| padding=padding, |
| data_format=data_format_dst, |
| dilations=dilations) |
| |
| value = sess.run(out, {t1: x1, t2: x2}) |
| self.assertAllClose(expected, value, 1e-3) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x1Filter(self, data_format): |
| expected_output = np.reshape([ |
| 30.0, 36.0, 42.0, 66.0, 81.0, 96.0, 102.0, 126.0, 150.0, 138.0, 171.0, |
| 204.0, 174.0, 216.0, 258.0, 210.0, 261.0, 312.0 |
| ], [1, 2, 3, 3]) |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[1, 1, 3, 3], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Filter(self, data_format): |
| expected_output = np.reshape( |
| [2271.0, 2367.0, 2463.0, 2901.0, 3033.0, 3165.0], [1, 1, 2, 3]) |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Filter2x1Dilation(self, data_format): |
| expected_output = np.array([[[[72], [82], [92]], [[112], [122], [132]]]]) |
| self._VerifyValues( |
| input_sizes=[1, 4, 4, 1], |
| filter_sizes=[2, 2, 1, 1], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2Filter(self, data_format): |
| expected_output = np.reshape([ |
| 231.0, 252.0, 273.0, 384.0, 423.0, 462.0, 690.0, 765.0, 840.0, 843.0, |
| 936.0, 1029.0 |
| ], [1, 2, 2, 3]) |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[1, 2, 3, 3], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2(self, data_format): |
| expected_output = np.reshape([2271.0, 2367.0, 2463.0], [1, 1, 1, 3]) |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| strides=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2Same(self, data_format): |
| expected_output = np.reshape( |
| [2271.0, 2367.0, 2463.0, 1230.0, 1305.0, 1380.0], [1, 1, 2, 3]) |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| strides=[2, 2], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DEmptyDilation(self, data_format): |
| self._VerifyValues( |
| input_sizes=[0, 2, 3, 3], |
| filter_sizes=[1, 1, 3, 3], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.zeros([0, 2, 3, 3])) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterDilation(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| strides=[1, 1], |
| dilations=[1, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.reshape([2667, 2781, 2895], [1, 1, 1, 3])) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterDilation(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[1, 2, 3, 3], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.array([[[[231, 252, 273], [384, 423, 462]], |
| [[690, 765, 840], [843, 936, 1029]]]])) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DKernelSizeMatchesInputSizeDilation(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[2, 2, 1, 2], |
| strides=[1, 1], |
| dilations=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.reshape([108, 128], [1, 1, 1, 2])) |
| |
| |
| class Conv2DBackpropInputTest(xla_test.XLATestCase, parameterized.TestCase): |
| |
| def _VerifyValues(self, |
| input_sizes=None, |
| filter_sizes=None, |
| out_backprop_sizes=None, |
| strides=None, |
| dilations=None, |
| padding=None, |
| data_format_src="NHWC", |
| data_format_dst="NHWC", |
| expected=None): |
| """Tests that gen_nn_ops.conv2d_backprop_input produces the expected output. |
| |
| Args: |
| input_sizes: Input tensor dimensions in |
| [batch, input_rows, input_cols, input_depth]. |
| filter_sizes: Filter tensor dimensions in |
| [kernel_rows, kernel_cols, input_depth, output_depth]. |
| out_backprop_sizes: Output gradients tensor dimensions. |
| strides: Strides. |
| dilations: Dilations. |
| padding: Padding type. |
| data_format_src: Data format input is in. |
| data_format_dst: Data format verification will run and input is converted |
| to. |
| expected: Expected output. |
| """ |
| |
| total_size_1 = np.prod(filter_sizes) |
| total_size_2 = np.prod(out_backprop_sizes) |
| x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(filter_sizes) |
| x2 = np.arange( |
| 1, total_size_2 + 1, dtype=np.float32).reshape(out_backprop_sizes) |
| strides = [1] + strides + [1] |
| if dilations is not None: |
| dilations = [1] + dilations + [1] |
| |
| expected = np.reshape(expected, input_sizes) |
| |
| # Convert between data formats. |
| expected = test_utils.ConvertBetweenDataFormats(expected, data_format_src, |
| data_format_dst) |
| x2 = test_utils.ConvertBetweenDataFormats(x2, data_format_src, |
| data_format_dst) |
| input_sizes = test_utils.PermuteDimsBetweenDataFormats( |
| input_sizes, data_format_src, data_format_dst) |
| out_backprop_sizes = test_utils.PermuteDimsBetweenDataFormats( |
| out_backprop_sizes, data_format_src, data_format_dst) |
| strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, |
| data_format_dst) |
| if dilations is not None: |
| dilations = test_utils.PermuteDimsBetweenDataFormats( |
| dilations, data_format_src, data_format_dst) |
| |
| with self.session() as sess: |
| t1 = array_ops.placeholder(dtypes.float32, shape=filter_sizes) |
| t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) |
| with self.test_scope(): |
| out = gen_nn_ops.conv2d_backprop_input( |
| input_sizes=input_sizes, |
| filter=t1, |
| out_backprop=t2, |
| strides=strides, |
| dilations=dilations, |
| padding=padding, |
| data_format=data_format_dst) |
| |
| value = sess.run(out, {t1: x1, t2: x2}) |
| self.assertAllEqual(input_sizes, value.shape) |
| self.assertAllClose(expected, value, 1e-3) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x1Filter(self, data_format): |
| expected_output = [ |
| 5, 11, 17, 11, 25, 39, 17, 39, 61, 23, 53, 83, 29, 67, 105, 35, 81, 127, |
| 41, 95, 149, 47, 109, 171, 53, 123, 193, 59, 137, 215, 65, 151, 237, 71, |
| 165, 259, 77, 179, 281, 83, 193, 303, 89, 207, 325, 95, 221, 347. |
| ] |
| self._VerifyValues( |
| input_sizes=[1, 4, 4, 3], |
| filter_sizes=[1, 1, 3, 2], |
| out_backprop_sizes=[1, 4, 4, 2], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width5(self, data_format): |
| expected_output = [1, 2, 0, 2, 4] |
| self._VerifyValues( |
| input_sizes=[1, 1, 5, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width6(self, data_format): |
| expected_output = [1, 2, 0, 2, 4, 0] |
| self._VerifyValues( |
| input_sizes=[1, 1, 6, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width7(self, data_format): |
| expected_output = [1, 2, 0, 2, 4, 0, 0] |
| self._VerifyValues( |
| input_sizes=[1, 1, 7, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterC1Same(self, data_format): |
| expected_output = [1, 4, 7, 7, 23, 33] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 2, 3, 1], |
| strides=[1, 1], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Filter(self, data_format): |
| expected_output = [ |
| 14, 32, 50, 100, 163, 226, 167, 212, 257, 122, 140, 158, 478, 541, 604, |
| 437, 482, 527 |
| ] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| out_backprop_sizes=[1, 1, 2, 3], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterSame(self, data_format): |
| expected_output = [ |
| 14, 32, 50, 100, 163, 226, 217, 334, 451, 190, 307, 424, 929, 1217, |
| 1505, 1487, 1883, 2279 |
| ] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| out_backprop_sizes=[1, 2, 3, 3], |
| strides=[1, 1], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2Filter(self, data_format): |
| expected_output = [1, 4, 4, 3, 10, 8, 5, 16, 12] |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 3, 2, 1], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterSame(self, data_format): |
| expected_output = [1, 4, 7, 4, 13, 16, 7, 22, 25] |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 3, 3, 1], |
| strides=[1, 1], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2(self, data_format): |
| expected_output = [1, 2, 5, 4, 6, 0, 0, 0, 0, 0, 3, 6, 13, 8, 12] |
| self._VerifyValues( |
| input_sizes=[1, 3, 5, 1], |
| filter_sizes=[1, 3, 1, 1], |
| out_backprop_sizes=[1, 2, 2, 1], |
| strides=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2Same(self, data_format): |
| expected_output = [1, 2, 2, 3, 4, 6] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[2, 2], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth3ValidBackpropInputStride1x1Dilation2x1( |
| self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 6, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 5, 1], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[1, 4, 7, 10, 13, 10, 0, 0, 0, 0, 0, 0, 3, 10, 17, 24, 31, 20]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth1ValidBackpropInputDilation1x2(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 1, 1], |
| strides=[1, 1], |
| dilations=[1, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[1, 0, 2, 3, 0, 4]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DEmptyBackpropInputDilation1x2(self, data_format): |
| self._VerifyValues( |
| input_sizes=[0, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[0, 1, 1, 1], |
| strides=[1, 1], |
| dilations=[1, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.zeros([0])) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth3ValidBackpropInputDilation2x1(self, data_format): |
| # The GPU version of this test is not very stable. So adjusting the |
| # error threshold to 1e-4. |
| self._VerifyValues( |
| input_sizes=[1, 3, 2, 3], |
| filter_sizes=[2, 2, 3, 3], |
| out_backprop_sizes=[1, 1, 1, 3], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[ |
| 14, 32, 50, 68, 86, 104, 0, 0, 0, 0, 0, 0, 122, 140, 158, 176, 194, |
| 212 |
| ]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DKernelSizeMatchesInputSizeBackpropInputDilation2x2( |
| self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[2, 2, 1, 2], |
| out_backprop_sizes=[1, 1, 1, 2], |
| strides=[1, 1], |
| dilations=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[5, 0, 11, 0, 0, 0, 17, 0, 23]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DGroupedFilter(self, data_format): |
| expected_output = [ |
| 5, 17, 29, 25, 53, 81, 41, 53, 65, 109, 137, 165, 77, 89, 101, 193, 221, |
| 249, 113, 125, 137, 277, 305, 333 |
| ] |
| self._VerifyValues( |
| input_sizes=[1, 2, 2, 6], |
| filter_sizes=[2, 2, 3, 4], |
| out_backprop_sizes=[1, 1, 1, 4], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| |
| class Conv2DBackpropFilterTest(xla_test.XLATestCase, parameterized.TestCase): |
| |
| def _VerifyValues(self, |
| input_sizes=None, |
| filter_sizes=None, |
| out_backprop_sizes=None, |
| strides=None, |
| dilations=None, |
| padding=None, |
| data_format_src="NHWC", |
| data_format_dst="NHWC", |
| expected=None): |
| """Tests that gen_nn_ops.conv2d_backprop_filter produces the right output. |
| |
| Args: |
| input_sizes: Input tensor dimensions in |
| [batch, input_rows, input_cols, input_depth]. |
| filter_sizes: Filter tensor dimensions in |
| [kernel_rows, kernel_cols, input_depth, output_depth]. |
| out_backprop_sizes: Output gradients tensor dimensions. |
| strides: Stride. |
| dilations: Dilations. |
| padding: Padding type. |
| data_format_src: Data format input is in. |
| data_format_dst: Data format verification will run and input is converted |
| to. |
| expected: Expected output. |
| """ |
| |
| total_size_1 = np.prod(input_sizes) |
| total_size_2 = np.prod(out_backprop_sizes) |
| x1 = np.arange(1, total_size_1 + 1, dtype=np.float32).reshape(input_sizes) |
| x2 = np.arange( |
| 1, total_size_2 + 1, dtype=np.float32).reshape(out_backprop_sizes) |
| strides = [1] + strides + [1] |
| if dilations is not None: |
| dilations = [1] + dilations + [1] |
| |
| expected = np.reshape(expected, filter_sizes) |
| |
| # Convert between data formats. |
| x1 = test_utils.ConvertBetweenDataFormats(x1, data_format_src, |
| data_format_dst) |
| x2 = test_utils.ConvertBetweenDataFormats(x2, data_format_src, |
| data_format_dst) |
| input_sizes = test_utils.PermuteDimsBetweenDataFormats( |
| input_sizes, data_format_src, data_format_dst) |
| out_backprop_sizes = test_utils.PermuteDimsBetweenDataFormats( |
| out_backprop_sizes, data_format_src, data_format_dst) |
| strides = test_utils.PermuteDimsBetweenDataFormats(strides, data_format_src, |
| data_format_dst) |
| if dilations is not None: |
| dilations = test_utils.PermuteDimsBetweenDataFormats( |
| dilations, data_format_src, data_format_dst) |
| |
| with self.session() as sess: |
| t1 = array_ops.placeholder(dtypes.float32, shape=input_sizes) |
| t2 = array_ops.placeholder(dtypes.float32, shape=out_backprop_sizes) |
| with self.test_scope(): |
| tensor = gen_nn_ops.conv2d_backprop_filter( |
| input=t1, |
| filter_sizes=filter_sizes, |
| out_backprop=t2, |
| strides=strides, |
| dilations=dilations, |
| padding=padding, |
| data_format=data_format_dst) |
| |
| value = sess.run(tensor, {t1: x1, t2: x2}) |
| self.assertAllEqual(filter_sizes, value.shape) |
| self.assertAllClose(expected, value, 1e-3) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x1Filter(self, data_format): |
| expected_output = [8056, 8432, 8312, 8704, 8568, 8976] |
| self._VerifyValues( |
| input_sizes=[1, 4, 4, 3], |
| filter_sizes=[1, 1, 3, 2], |
| out_backprop_sizes=[1, 4, 4, 2], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2Filter(self, data_format): |
| expected_output = [120, 141] |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 3, 2, 1], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterDepth1(self, data_format): |
| expected_output = [5, 8, 14, 17] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Filter(self, data_format): |
| expected_output = [ |
| 17, 22, 27, 22, 29, 36, 27, 36, 45, 32, 43, 54, 37, 50, 63, 42, 57, 72, |
| 62, 85, 108, 67, 92, 117, 72, 99, 126, 77, 106, 135, 82, 113, 144, 87, |
| 120, 153 |
| ] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 3], |
| filter_sizes=[2, 2, 3, 3], |
| out_backprop_sizes=[1, 1, 2, 3], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width5(self, data_format): |
| expected_output = [9, 12] |
| self._VerifyValues( |
| input_sizes=[1, 1, 5, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width6(self, data_format): |
| expected_output = [9, 12] |
| self._VerifyValues( |
| input_sizes=[1, 1, 6, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x2FilterStride3Width7(self, data_format): |
| expected_output = [9, 12] |
| self._VerifyValues( |
| input_sizes=[1, 1, 7, 1], |
| filter_sizes=[1, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[3, 3], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x3Filter(self, data_format): |
| expected_output = [5, 8, 11] |
| self._VerifyValues( |
| input_sizes=[1, 1, 4, 1], |
| filter_sizes=[1, 3, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x3FilterSame(self, data_format): |
| expected_output = [20, 30, 20] |
| self._VerifyValues( |
| input_sizes=[1, 1, 4, 1], |
| filter_sizes=[1, 3, 1, 1], |
| out_backprop_sizes=[1, 1, 4, 1], |
| strides=[1, 1], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D1x3FilterSameOutbackprop2(self, data_format): |
| expected_output = [7, 10, 3] |
| self._VerifyValues( |
| input_sizes=[1, 1, 4, 1], |
| filter_sizes=[1, 3, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[2, 2], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterC1Same(self, data_format): |
| expected_output = [91, 58, 32, 17] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 2, 3, 1], |
| strides=[1, 1], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2(self, data_format): |
| expected_output = [92, 102, 112] |
| self._VerifyValues( |
| input_sizes=[1, 3, 5, 1], |
| filter_sizes=[1, 3, 1, 1], |
| out_backprop_sizes=[1, 2, 2, 1], |
| strides=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2FilterStride2Same(self, data_format): |
| expected_output = [7, 2, 16, 5] |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 2, 1], |
| strides=[2, 2], |
| padding="SAME", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth3ValidBackpropFilterStride1x1Dilation2x1( |
| self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 6, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 5, 1], |
| strides=[1, 1], |
| dilations=[2, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[55, 70, 235, 250]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth1ValidBackpropFilterDilation1x2(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 1], |
| out_backprop_sizes=[1, 1, 1, 1], |
| strides=[1, 1], |
| dilations=[1, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[1, 3, 4, 6]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DEmptyBackpropFilterDilation1x2(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 2, 3, 1], |
| filter_sizes=[2, 2, 1, 0], |
| out_backprop_sizes=[1, 1, 1, 0], |
| strides=[1, 1], |
| dilations=[1, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=np.zeros([0])) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2D2x2Depth3ValidBackpropFilterDilation2x2(self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 4, 3], |
| filter_sizes=[2, 2, 3, 3], |
| out_backprop_sizes=[1, 1, 2, 3], |
| strides=[1, 1], |
| dilations=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[ |
| 17, 22, 27, 22, 29, 36, 27, 36, 45, 47, 64, 81, 52, 71, 90, 57, 78, |
| 99, 137, 190, 243, 142, 197, 252, 147, 204, 261, 167, 232, 297, 172, |
| 239, 306, 177, 246, 315 |
| ]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DKernelSizeMatchesInputSizeBackpropFilterDilation2x2( |
| self, data_format): |
| self._VerifyValues( |
| input_sizes=[1, 3, 3, 1], |
| filter_sizes=[2, 2, 1, 2], |
| out_backprop_sizes=[1, 1, 1, 2], |
| strides=[1, 1], |
| dilations=[2, 2], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=[1, 2, 3, 6, 7, 14, 9, 18]) |
| |
| @parameterized.named_parameters(*DATA_FORMATS) |
| def testConv2DGroupedFilter(self, data_format): |
| expected_output = [1, 4, 3, 8, 5, 12, 7, 16] |
| self._VerifyValues( |
| input_sizes=[1, 2, 2, 2], |
| filter_sizes=[2, 2, 1, 2], |
| out_backprop_sizes=[1, 1, 1, 2], |
| strides=[1, 1], |
| padding="VALID", |
| data_format_src="NHWC", |
| data_format_dst=data_format, |
| expected=expected_output) |
| |
| |
| if __name__ == "__main__": |
| googletest.main() |
