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tensorflow/tensorflow/core/kernels/fractional_avg_pool_op.cc
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| /* 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. | |
| ==============================================================================*/ | |
| #define EIGEN_USE_THREADS | |
| #include <algorithm> | |
| #include <cmath> | |
| #include <random> | |
| #include <vector> | |
| #include "tensorflow/core/kernels/fractional_pool_common.h" | |
| #include "third_party/eigen3/unsupported/Eigen/CXX11/Tensor" | |
| #include "tensorflow/core/framework/numeric_op.h" | |
| #include "tensorflow/core/framework/op_kernel.h" | |
| #include "tensorflow/core/lib/random/random.h" | |
| #include "tensorflow/core/platform/logging.h" | |
| #include "tensorflow/core/platform/mutex.h" | |
| #include "tensorflow/core/util/guarded_philox_random.h" | |
| namespace tensorflow { | |
| typedef Eigen::ThreadPoolDevice CPUDevice; | |
| template <typename T> | |
| class FractionalAvgPoolOp : public OpKernel { | |
| public: | |
| explicit FractionalAvgPoolOp(OpKernelConstruction* context) | |
| : OpKernel(context) { | |
| OP_REQUIRES_OK(context, context->GetAttr("pooling_ratio", &pooling_ratio_)); | |
| OP_REQUIRES_OK(context, context->GetAttr("pseudo_random", &pseudo_random_)); | |
| OP_REQUIRES_OK(context, context->GetAttr("overlapping", &overlapping_)); | |
| OP_REQUIRES(context, pooling_ratio_.size() == 4, | |
| errors::InvalidArgument( | |
| "pooling_ratio field must specify 4 dimensions")); | |
| OP_REQUIRES( | |
| context, pooling_ratio_[0] == 1 || pooling_ratio_[3] == 1, | |
| errors::Unimplemented("Fractional average pooling is not yet " | |
| "supported on the batch nor channel dimension.")); | |
| OP_REQUIRES_OK(context, context->GetAttr("deterministic", &deterministic_)); | |
| OP_REQUIRES_OK(context, context->GetAttr("seed", &seed_)); | |
| OP_REQUIRES_OK(context, context->GetAttr("seed2", &seed2_)); | |
| if (deterministic_) { | |
| // If both seeds are not set when deterministic_ is true, force set seeds. | |
| if ((seed_ == 0) && (seed2_ == 0)) { | |
| seed_ = random::New64(); | |
| seed2_ = random::New64(); | |
| } | |
| } else { | |
| OP_REQUIRES( | |
| context, (seed_ == 0) && (seed2_ == 0), | |
| errors::InvalidArgument( | |
| "Both seed and seed2 should be 0 if deterministic is false.")); | |
| } | |
| } | |
| void Compute(OpKernelContext* context) override { | |
| typedef Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>> | |
| ConstEigenMatrixMap; | |
| typedef Eigen::Map<Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>> | |
| EigenMatrixMap; | |
| constexpr int tensor_in_and_out_dims = 4; | |
| const Tensor& tensor_in = context->input(0); | |
| OP_REQUIRES(context, tensor_in.dims() == tensor_in_and_out_dims, | |
| errors::InvalidArgument("tensor_in must be 4-dimensional")); | |
| std::vector<int> input_size(tensor_in_and_out_dims); | |
| std::vector<int> output_size(tensor_in_and_out_dims); | |
| for (int i = 0; i < tensor_in_and_out_dims; ++i) { | |
| input_size[i] = tensor_in.dim_size(i); | |
| OP_REQUIRES( | |
| context, pooling_ratio_[i] <= input_size[i], | |
| errors::InvalidArgument( | |
| "Pooling ratio cannot be bigger than input tensor dim size.")); | |
| } | |
| // Output size. | |
| for (int i = 0; i < tensor_in_and_out_dims; ++i) { | |
| output_size[i] = | |
| static_cast<int>(std::floor(input_size[i] / pooling_ratio_[i])); | |
| DCHECK_GT(output_size[i], 0); | |
| } | |
| // Generate pooling sequence. | |
| std::vector<int64_t> row_cum_seq; | |
| std::vector<int64_t> col_cum_seq; | |
| GuardedPhiloxRandom generator; | |
| generator.Init(seed_, seed2_); | |
| row_cum_seq = GeneratePoolingSequence(input_size[1], output_size[1], | |
| &generator, pseudo_random_); | |
| col_cum_seq = GeneratePoolingSequence(input_size[2], output_size[2], | |
| &generator, pseudo_random_); | |
| // Prepare output. | |
| Tensor* output_tensor = nullptr; | |
| OP_REQUIRES_OK(context, context->allocate_output( | |
| 0, | |
| TensorShape({output_size[0], output_size[1], | |
| output_size[2], output_size[3]}), | |
| &output_tensor)); | |
| Tensor* output_row_seq_tensor = nullptr; | |
| OP_REQUIRES_OK( | |
| context, context->allocate_output( | |
| 1, TensorShape({static_cast<int64_t>(row_cum_seq.size())}), | |
| &output_row_seq_tensor)); | |
| Tensor* output_col_seq_tensor = nullptr; | |
| OP_REQUIRES_OK( | |
| context, context->allocate_output( | |
| 2, TensorShape({static_cast<int64_t>(col_cum_seq.size())}), | |
| &output_col_seq_tensor)); | |
| ConstEigenMatrixMap in_mat(tensor_in.flat<T>().data(), input_size[3], | |
| input_size[2] * input_size[1] * input_size[0]); | |
| EigenMatrixMap out_mat(output_tensor->flat<T>().data(), output_size[3], | |
| output_size[2] * output_size[1] * output_size[0]); | |
| // out_count corresponds to number of elements in each pooling cell. | |
| Eigen::Matrix<T, Eigen::Dynamic, 1> out_count(out_mat.cols()); | |
| // Initializes the output tensor and out_count with 0. | |
| out_mat.setZero(); | |
| out_count.setZero(); | |
| auto output_row_seq_flat = output_row_seq_tensor->flat<int64_t>(); | |
| auto output_col_seq_flat = output_col_seq_tensor->flat<int64_t>(); | |
| // Set output tensors. | |
| for (int i = 0; i < row_cum_seq.size(); ++i) { | |
| output_row_seq_flat(i) = row_cum_seq[i]; | |
| } | |
| for (int i = 0; i < col_cum_seq.size(); ++i) { | |
| output_col_seq_flat(i) = col_cum_seq[i]; | |
| } | |
| // For both input and output, | |
| // 0: batch | |
| // 1: row / row | |
| // 2: col / col | |
| // 3: depth / channel | |
| const int64_t row_max = input_size[1] - 1; | |
| const int64_t col_max = input_size[2] - 1; | |
| for (int64_t b = 0; b < input_size[0]; ++b) { | |
| // row sequence. | |
| for (int64_t hs = 0; hs < row_cum_seq.size() - 1; ++hs) { | |
| // row start and end. | |
| const int64_t row_start = row_cum_seq[hs]; | |
| int64_t row_end = | |
| overlapping_ ? row_cum_seq[hs + 1] : row_cum_seq[hs + 1] - 1; | |
| row_end = std::min(row_end, row_max); | |
| // col sequence. | |
| for (int64_t ws = 0; ws < col_cum_seq.size() - 1; ++ws) { | |
| const int64_t out_offset = | |
| (b * output_size[1] + hs) * output_size[2] + ws; | |
| // col start and end. | |
| const int64_t col_start = col_cum_seq[ws]; | |
| int64_t col_end = | |
| overlapping_ ? col_cum_seq[ws + 1] : col_cum_seq[ws + 1] - 1; | |
| col_end = std::min(col_end, col_max); | |
| for (int64_t h = row_start; h <= row_end; ++h) { | |
| for (int64_t w = col_start; w <= col_end; ++w) { | |
| const int64_t in_offset = | |
| (b * input_size[1] + h) * input_size[2] + w; | |
| out_mat.col(out_offset) += in_mat.col(in_offset); | |
| out_count(out_offset)++; | |
| } | |
| } | |
| } | |
| } | |
| } | |
| DCHECK_GT(out_count.minCoeff(), 0); | |
| out_mat.array().rowwise() /= out_count.transpose().array(); | |
| } | |
| private: | |
| bool deterministic_; | |
| int64_t seed_; | |
| int64_t seed2_; | |
| std::vector<float> pooling_ratio_; | |
| bool pseudo_random_; | |
| bool overlapping_; | |
| }; | |
| #define REGISTER_FRACTIONALAVGPOOL(type) \ | |
| REGISTER_KERNEL_BUILDER( \ | |
| Name("FractionalAvgPool").Device(DEVICE_CPU).TypeConstraint<type>("T"), \ | |
| FractionalAvgPoolOp<type>) | |
| REGISTER_FRACTIONALAVGPOOL(int32); | |
| REGISTER_FRACTIONALAVGPOOL(int64_t); | |
| REGISTER_FRACTIONALAVGPOOL(float); | |
| REGISTER_FRACTIONALAVGPOOL(double); | |
| #undef REGISTER_FRACTIONALAVGPOOL | |
| template <class T> | |
| class FractionalAvgPoolGradOp : public OpKernel { | |
| public: | |
| explicit FractionalAvgPoolGradOp(OpKernelConstruction* context) | |
| : OpKernel(context) { | |
| OP_REQUIRES_OK(context, context->GetAttr("overlapping", &overlapping_)); | |
| } | |
| void Compute(OpKernelContext* context) override { | |
| // Here's the basic idea: | |
| // Batch and depth dimension are independent from row and col dimension. And | |
| // because FractionalAvgPool currently only support pooling along row and | |
| // col, we can basically think of this 4D tensor backpropagation as | |
| // operation of a series of 2D planes. | |
| // | |
| // For each element of a 'slice' (2D plane) of output_backprop, we need to | |
| // figure out its contributors when doing FractionalAvgPool operation. This | |
| // can be done based on row_pooling_sequence, col_pooling_seq and | |
| // overlapping. | |
| // Once we figure out the original contributors, we just need to evenly | |
| // divide the value of this element among these contributors. | |
| // | |
| // Internally, we divide the out_backprop tensor and store it in a temporary | |
| // tensor of double type. And cast it to the corresponding type. | |
| typedef Eigen::Map<const Eigen::Matrix<T, Eigen::Dynamic, Eigen::Dynamic>> | |
| ConstEigenMatrixMap; | |
| typedef Eigen::Map<Eigen::Matrix<double, Eigen::Dynamic, Eigen::Dynamic>> | |
| EigenDoubleMatrixMap; | |
| // Grab the inputs. | |
| const Tensor& orig_input_tensor_shape = context->input(0); | |
| OP_REQUIRES(context, | |
| orig_input_tensor_shape.dims() == 1 && | |
| orig_input_tensor_shape.NumElements() == 4, | |
| errors::InvalidArgument("original input tensor shape must be" | |
| "1-dimensional and 4 elements")); | |
| const Tensor& out_backprop = context->input(1); | |
| const Tensor& row_seq_tensor = context->input(2); | |
| const Tensor& col_seq_tensor = context->input(3); | |
| const int64_t out_batch = out_backprop.dim_size(0); | |
| const int64_t out_rows = out_backprop.dim_size(1); | |
| const int64_t out_cols = out_backprop.dim_size(2); | |
| const int64_t out_depth = out_backprop.dim_size(3); | |
| OP_REQUIRES(context, row_seq_tensor.NumElements() > out_rows, | |
| errors::InvalidArgument("Given out_backprop shape ", | |
| out_backprop.shape().DebugString(), | |
| ", row_seq_tensor must have at least ", | |
| out_rows + 1, " elements, but got ", | |
| row_seq_tensor.NumElements())); | |
| OP_REQUIRES(context, col_seq_tensor.NumElements() > out_cols, | |
| errors::InvalidArgument("Given out_backprop shape ", | |
| out_backprop.shape().DebugString(), | |
| ", col_seq_tensor must have at least ", | |
| out_cols + 1, " elements, but got ", | |
| col_seq_tensor.NumElements())); | |
| auto row_seq_tensor_flat = row_seq_tensor.flat<int64_t>(); | |
| auto col_seq_tensor_flat = col_seq_tensor.flat<int64_t>(); | |
| auto orig_input_tensor_shape_flat = orig_input_tensor_shape.flat<int64_t>(); | |
| const int64_t in_batch = orig_input_tensor_shape_flat(0); | |
| const int64_t in_rows = orig_input_tensor_shape_flat(1); | |
| const int64_t in_cols = orig_input_tensor_shape_flat(2); | |
| const int64_t in_depth = orig_input_tensor_shape_flat(3); | |
| OP_REQUIRES( | |
| context, in_batch != 0, | |
| errors::InvalidArgument("Batch dimension of input must not be 0")); | |
| OP_REQUIRES( | |
| context, in_rows != 0, | |
| errors::InvalidArgument("Rows dimension of input must not be 0")); | |
| OP_REQUIRES( | |
| context, in_cols != 0, | |
| errors::InvalidArgument("Columns dimension of input must not be 0")); | |
| OP_REQUIRES( | |
| context, in_depth != 0, | |
| errors::InvalidArgument("Depth dimension of input must not be 0")); | |
| constexpr int tensor_in_and_out_dims = 4; | |
| // Transform orig_input_tensor_shape into TensorShape | |
| TensorShape in_shape; | |
| for (auto i = 0; i < tensor_in_and_out_dims; ++i) { | |
| in_shape.AddDim(orig_input_tensor_shape_flat(i)); | |
| } | |
| // Create intermediate in_backprop. | |
| Tensor in_backprop_tensor_temp; | |
| OP_REQUIRES_OK(context, context->forward_input_or_allocate_temp( | |
| {0}, DataTypeToEnum<double>::v(), in_shape, | |
| &in_backprop_tensor_temp)); | |
| in_backprop_tensor_temp.flat<double>().setZero(); | |
| // Transform 4D tensor to 2D matrix. | |
| EigenDoubleMatrixMap in_backprop_tensor_temp_mat( | |
| in_backprop_tensor_temp.flat<double>().data(), in_depth, | |
| in_cols * in_rows * in_batch); | |
| ConstEigenMatrixMap out_backprop_mat(out_backprop.flat<T>().data(), | |
| out_depth, | |
| out_cols * out_rows * out_batch); | |
| // Loop through each element of out_backprop and evenly distribute the | |
| // element to the corresponding pooling cell. | |
| const int64_t in_max_row_index = in_rows - 1; | |
| const int64_t in_max_col_index = in_cols - 1; | |
| for (int64_t b = 0; b < out_batch; ++b) { | |
| for (int64_t r = 0; r < out_rows; ++r) { | |
| const int64_t in_row_start = row_seq_tensor_flat(r); | |
| int64_t in_row_end = overlapping_ ? row_seq_tensor_flat(r + 1) | |
| : row_seq_tensor_flat(r + 1) - 1; | |
| in_row_end = std::min(in_row_end, in_max_row_index); | |
| for (int64_t c = 0; c < out_cols; ++c) { | |
| const int64_t in_col_start = col_seq_tensor_flat(c); | |
| int64_t in_col_end = overlapping_ ? col_seq_tensor_flat(c + 1) | |
| : col_seq_tensor_flat(c + 1) - 1; | |
| in_col_end = std::min(in_col_end, in_max_col_index); | |
| const int64_t num_elements_in_pooling_cell = | |
| (in_row_end - in_row_start + 1) * (in_col_end - in_col_start + 1); | |
| const int64_t out_index = (b * out_rows + r) * out_cols + c; | |
| // Now we can evenly distribute out_backprop(b, h, w, *) to | |
| // in_backprop(b, hs:he, ws:we, *). | |
| for (int64_t in_r = in_row_start; in_r <= in_row_end; ++in_r) { | |
| for (int64_t in_c = in_col_start; in_c <= in_col_end; ++in_c) { | |
| const int64_t in_index = (b * in_rows + in_r) * in_cols + in_c; | |
| // Walk through each channel (depth). | |
| for (int64_t d = 0; d < out_depth; ++d) { | |
| const double out_backprop_element = static_cast<double>( | |
| out_backprop_mat.coeffRef(d, out_index)); | |
| double& in_backprop_ref = | |
| in_backprop_tensor_temp_mat.coeffRef(d, in_index); | |
| in_backprop_ref += | |
| out_backprop_element / num_elements_in_pooling_cell; | |
| } | |
| } | |
| } | |
| } | |
| } | |
| } | |
| // Depending on the type, cast double to type T. | |
| Tensor* in_backprop_tensor = nullptr; | |
| OP_REQUIRES_OK(context, context->forward_input_or_allocate_output( | |
| {0}, 0, in_shape, &in_backprop_tensor)); | |
| auto in_backprop_tensor_flat = in_backprop_tensor->flat<T>(); | |
| auto in_backprop_tensor_temp_flat = in_backprop_tensor_temp.flat<double>(); | |
| for (int64_t i = 0; i < in_backprop_tensor_flat.size(); ++i) { | |
| in_backprop_tensor_flat(i) = | |
| static_cast<T>(in_backprop_tensor_temp_flat(i)); | |
| } | |
| } | |
| private: | |
| bool overlapping_; | |
| }; | |
| #define REGISTER_FRACTIONALAVGPOOLGRAD(type) \ | |
| REGISTER_KERNEL_BUILDER(Name("FractionalAvgPoolGrad") \ | |
| .Device(DEVICE_CPU) \ | |
| .TypeConstraint<type>("T"), \ | |
| FractionalAvgPoolGradOp<type>) | |
| REGISTER_FRACTIONALAVGPOOLGRAD(int32); | |
| REGISTER_FRACTIONALAVGPOOLGRAD(int64_t); | |
| REGISTER_FRACTIONALAVGPOOLGRAD(float); | |
| REGISTER_FRACTIONALAVGPOOLGRAD(double); | |
| #undef REGISTER_FRACTIONALAVGPOOLGRAD | |
| } // namespace tensorflow |