Add a convolution Function based on gemm.

paddle/function/GemmConvOp.cpp

+/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
+
+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. */
+
+#include "ConvOp.h"
+#include "paddle/math/MathFunctions.h"
+#include "paddle/math/MemoryHandle.h"
+
+namespace paddle {
+
+/*
+ * imData = [input_channels, input_height, input_width]
+ * colData = [input_channels, filter_height, filter_width,
+ *            output_height, output_width]
+ */
+template <class T>
+class Im2ColFunctor {
+public:
+  void operator()(const T* imData,
+                  int inputChannels,
+                  int inputHeight,
+                  int inputWidth,
+                  int filterHeight,
+                  int filterWidth,
+                  int strideHeight,
+                  int strideWidth,
+                  int paddingHeight,
+                  int paddingWidth,
+                  int outputHeight,
+                  int outputWidth,
+                  T* colData) {
+    int channelsCol = inputChannels * filterHeight * filterWidth;
+
+    for (int c = 0; c < channelsCol; ++c) {
+      int wOffset = c % filterWidth;
+      int hOffset = (c / filterWidth) % filterHeight;
+      int c_im = c / filterHeight / filterWidth;
+      for (int h = 0; h < outputHeight; ++h) {
+        for (int w = 0; w < outputWidth; ++w) {
+          // no c_im*height to Exclude the channel number
+          int imgRowIdx = h * strideHeight + hOffset;
+          int imgColIdx = w * strideWidth + wOffset;
+          if ((imgRowIdx - paddingHeight) < 0 ||
+              (imgRowIdx - paddingHeight) >= inputHeight ||
+              (imgColIdx - paddingWidth) < 0 ||
+              (imgColIdx - paddingWidth) >= inputWidth) {
+            colData[(c * outputHeight + h) * outputWidth + w] = T(0);
+          } else {
+            imgRowIdx += c_im * inputHeight - paddingHeight;
+            imgColIdx -= paddingWidth;
+            colData[(c * outputHeight + h) * outputWidth + w] =
+                imData[imgRowIdx * inputWidth + imgColIdx];
+          }
+        }
+      }
+    }
+  }
+};
+
+/*
+ * Function Arguments:
+ *
+ * \param inputs[0]  Input image data, is NCHW format, where N is batch size,
+ *                   C is the number of channels, H and W is the height and
+ *                   width of input image.
+ * \param inputs[1]  Filter data, is MCHW, where M is the number of output
+ *                   channels, C is the number of input channels, H and W
+ *                   is height and width of filter.
+ * \param outputs[0] Output image data, is NCHW format, where N is batch size,
+  *                  C is the number of channels, H and W is the height and
+ *                   width of output image.
+ */
+template <DeviceType Device>
+class GemmConvFunction : public ConvFunctionBase {
+public:
+  void init(const FuncConfig& config) override {
+    ConvFunctionBase::init(config);
+  }
+
+  void calc(const BufferArgs& inputs, const BufferArgs& outputs) override {
+    check(inputs, outputs);
+    CHECK_EQ(outputs[0].getArgType(), ASSIGN_TO);
+
+    size_t batchSize = inputs[0].shape()[0];
+    size_t inputChannels = inputs[0].shape()[1];
+    size_t inputHeight = inputs[0].shape()[2];
+    size_t inputWidth = inputs[0].shape()[3];
+    size_t filterHeight = inputs[1].shape()[2];
+    size_t filterWidth = inputs[1].shape()[2];
+    size_t outputChannels = outputs[0].shape()[1];
+    size_t outputHeight = outputs[0].shape()[2];
+    size_t outputWidth = outputs[0].shape()[3];
+
+    real* inputData = inputs[0].data<real>();
+    real* filterData = inputs[1].data<real>();
+    real* outputData = outputs[0].data<real>();
+
+    size_t size =
+        inputChannels * filterHeight * filterWidth * outputHeight * outputWidth;
+    resizeBuffer(size);
+    real* colData = reinterpret_cast<real*>(memory_->getBuf());
+
+    Im2ColFunctor<real> im2col;
+    for (size_t i = 0; i < batchSize; i++) {
+      im2col(inputData,
+             inputChannels,
+             inputHeight,
+             inputWidth,
+             filterHeight,
+             filterWidth,
+             stride_,
+             stride_,
+             padding_,
+             padding_,
+             outputHeight,
+             outputWidth,
+             colData);
+
+      int M = outputChannels;
+      int N = outputHeight * outputWidth;
+      int K = inputChannels * filterHeight * filterWidth;
+      gemm<real>(CblasNoTrans,
+                 CblasNoTrans,
+                 M,
+                 N,
+                 K,
+                 1.0f,
+                 filterData,
+                 K,
+                 colData,
+                 N,
+                 0.0f,
+                 outputData,
+                 N);
+      inputData += inputChannels * inputHeight * inputWidth;
+      outputData += outputChannels * outputHeight * outputWidth;
+    }
+  }
+
+  void resizeBuffer(size_t newSize) {
+    if (!memory_ || newSize * sizeof(real) > memory_->getAllocSize()) {
+      memory_ = std::make_shared<CpuMemoryHandle>(newSize * sizeof(real));
+    }
+  }
+
+private:
+  CpuMemHandlePtr memory_;
+};
+
+REGISTER_TYPED_FUNC(GemmConv, CPU, GemmConvFunction);
+
+}  // namespace paddle