# 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 __future__ import absolute_import from __future__ import division from __future__ import print_function 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.cached_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.cached_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]) 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.cached_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]) if __name__ == "__main__": googletest.main()