1 # Copyright 2015 The TensorFlow Authors. All Rights Reserved. 2 # 3 # Licensed under the Apache License, Version 2.0 (the "License"); 4 # you may not use this file except in compliance with the License. 5 # You may obtain a copy of the License at 6 # 7 # http://www.apache.org/licenses/LICENSE-2.0 8 # 9 # Unless required by applicable law or agreed to in writing, software 10 # distributed under the License is distributed on an "AS IS" BASIS, 11 # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 12 # See the License for the specific language governing permissions and 13 # limitations under the License. 14 # ============================================================================== 15 """Tests for convolution related functionality in tensorflow.ops.nn.""" 16 17 from __future__ import absolute_import 18 from __future__ import division 19 from __future__ import print_function 20 21 import numpy as np 22 23 from tensorflow.python.framework import constant_op 24 from tensorflow.python.framework import dtypes 25 from tensorflow.python.ops import array_ops 26 from tensorflow.python.ops import gradient_checker 27 from tensorflow.python.ops import nn_ops 28 import tensorflow.python.ops.nn_grad # pylint: disable=unused-import 29 from tensorflow.python.platform import test 30 31 32 class Conv2DBackpropFilterGradTest(test.TestCase): 33 34 def testGradient(self): 35 with self.test_session(): 36 for padding in ["SAME", "VALID"]: 37 for stride in [1, 2]: 38 np.random.seed(1) 39 in_shape = [5, 8, 6, 4] 40 in_val = constant_op.constant( 41 2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32) 42 filter_shape = [3, 3, 4, 6] 43 # Make a convolution op with the current settings, just to easily get 44 # the shape of the output. 45 conv_out = nn_ops.conv2d( 46 in_val, 47 array_ops.zeros(filter_shape), 48 strides=[1, stride, stride, 1], 49 padding=padding) 50 out_backprop_shape = conv_out.get_shape().as_list() 51 out_backprop_val = constant_op.constant( 52 2 * np.random.random_sample(out_backprop_shape) - 1, 53 dtype=dtypes.float32) 54 output = nn_ops.conv2d_backprop_filter( 55 in_val, 56 filter_shape, 57 out_backprop_val, 58 strides=[1, stride, stride, 1], 59 padding=padding) 60 err = gradient_checker.compute_gradient_error( 61 [in_val, out_backprop_val], [in_shape, out_backprop_shape], 62 output, filter_shape) 63 print("conv2d_backprop_filter gradient err = %g " % err) 64 err_tolerance = 2e-3 65 self.assertLess(err, err_tolerance) 66 67 def testGradientDilatedConv(self): 68 if test.is_gpu_available(cuda_only=True): 69 with self.test_session(use_gpu=True): 70 for padding in ["SAME", "VALID"]: 71 for stride in [1, 2]: 72 np.random.seed(1) 73 in_shape = [5, 8, 6, 4] 74 in_val = constant_op.constant( 75 2 * np.random.random_sample(in_shape) - 1, dtype=dtypes.float32) 76 filter_shape = [3, 3, 4, 6] 77 # Make a convolution op with the current settings, 78 # just to easily get the shape of the output. 79 conv_out = nn_ops.conv2d( 80 in_val, 81 array_ops.zeros(filter_shape), 82 dilations=[1, 2, 2, 1], 83 strides=[1, stride, stride, 1], 84 padding=padding) 85 out_backprop_shape = conv_out.get_shape().as_list() 86 out_backprop_val = constant_op.constant( 87 2 * np.random.random_sample(out_backprop_shape) - 1, 88 dtype=dtypes.float32) 89 output = nn_ops.conv2d_backprop_filter( 90 in_val, 91 filter_shape, 92 out_backprop_val, 93 dilations=[1, 2, 2, 1], 94 strides=[1, stride, stride, 1], 95 padding=padding) 96 err = gradient_checker.compute_gradient_error( 97 [in_val, out_backprop_val], [in_shape, out_backprop_shape], 98 output, filter_shape) 99 print("conv2d_backprop_filter gradient err = %g " % err) 100 err_tolerance = 2e-3 101 self.assertLess(err, err_tolerance) 102 103 104 if __name__ == "__main__": 105 test.main() 106
