1 /* Copyright 2017 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 16 #include "tensorflow/compiler/xla/client/lib/conv_grad_size_util.h" 17 #include "tensorflow/compiler/xla/status_macros.h" 18 #include "tensorflow/core/lib/core/errors.h" 19 20 namespace xla { 21 22 namespace { 23 24 StatusOr<SpatialDimensionOutputSizeAndPadding> GetWindowedOutputSize( 25 int64 input_size, int64 filter_size, int64 dilation_rate, int64 stride, 26 Padding padding_type) { 27 if (stride <= 0) { 28 return tensorflow::errors::InvalidArgument("Stride must be > 0, but got ", 29 stride); 30 } 31 if (dilation_rate < 1) { 32 return tensorflow::errors::InvalidArgument( 33 "Dilation rate must be >= 1, but got ", dilation_rate); 34 } 35 36 int64 effective_filter_size = (filter_size - 1) * dilation_rate + 1; 37 SpatialDimensionOutputSizeAndPadding dim; 38 switch (padding_type) { 39 case Padding::kValid: 40 dim.output_size = (input_size - effective_filter_size + stride) / stride; 41 dim.pad_before = dim.pad_after = 0; 42 break; 43 case Padding::kSame: 44 dim.output_size = (input_size + stride - 1) / stride; 45 const int64 padding_needed = 46 std::max(int64{0}, (dim.output_size - 1) * stride + 47 effective_filter_size - input_size); 48 // For odd values of total padding, add more padding on the "after" side 49 // of the given dimension. 50 dim.pad_before = padding_needed / 2; 51 dim.pad_after = padding_needed - dim.pad_before; 52 break; 53 } 54 if (dim.output_size < 0) { 55 return tensorflow::errors::InvalidArgument( 56 "Computed output size would be negative: ", dim.output_size, 57 " [input_size: ", input_size, 58 ", effective_filter_size: ", effective_filter_size, 59 ", stride: ", stride, "]"); 60 } 61 return dim; 62 } 63 64 } // namespace 65 66 StatusOr<SpatialDimensionOutputSizeAndPadding> 67 ConvGradExtractAndVerifyDimension(int64 input_size, int64 filter_size, 68 int64 output_size, int64 dilation, 69 int64 stride, Padding padding) { 70 TF_ASSIGN_OR_RETURN(SpatialDimensionOutputSizeAndPadding output_dim, 71 GetWindowedOutputSize(input_size, filter_size, dilation, 72 stride, padding)); 73 if (output_size != output_dim.output_size) { 74 return tensorflow::errors::InvalidArgument( 75 "Size of out_backprop doesn't match computed: ", "actual = ", 76 output_size, ", computed = ", output_dim.output_size, 77 " input: ", input_size, " filter: ", filter_size, 78 " output: ", output_size, " stride: ", stride, " dilation: ", dilation); 79 } 80 81 SpatialDimensionOutputSizeAndPadding dim; 82 int64 effective_filter_size = (filter_size - 1) * dilation + 1; 83 dim.output_size = (output_dim.output_size - 1) * stride + 1; 84 const auto padded_out_size = input_size + effective_filter_size - 1; 85 dim.pad_before = effective_filter_size - 1 - output_dim.pad_before; 86 dim.pad_after = padded_out_size - dim.output_size - dim.pad_before; 87 VLOG(2) << "expanded_out = " << dim.output_size 88 << ", effective_filter_size = " << effective_filter_size 89 << ", padded_out = " << padded_out_size 90 << ", pad_before = " << dim.pad_before 91 << ", pad_after = " << dim.pad_after << ", dilation = " << dilation 92 << ", strides = " << stride; 93 return dim; 94 } 95 96 } // namespace xla 97
