diff --git a/paddle/function/DepthwiseConvOp.cpp b/paddle/function/DepthwiseConvOp.cpp index ad332d2931bab144591f10190ec1c07cb6d7940c..d4272c72f2404739a02d3458f00a771aa8d3d2c0 100644 --- a/paddle/function/DepthwiseConvOp.cpp +++ b/paddle/function/DepthwiseConvOp.cpp @@ -18,11 +18,6 @@ limitations under the License. */ namespace paddle { -/* - * imData = [input_channels, input_height, input_width] - * colData = [input_channels, filter_height, filter_width, - * output_height, output_width] - */ template class DepthwiseConvFunctor { public: @@ -33,6 +28,8 @@ public: int outputChannels, int outputHeight, int outputWidth, + int inputHeight, + int inputWidth, int filterHeight, int filterWidth, int strideH, @@ -40,7 +37,7 @@ public: int paddingH, int paddingW, T* outputData) { - // NO_IMPLEMENTATION + // TODO(zhaolong) : cpu implementation of depthwise convolution } }; @@ -118,8 +115,8 @@ public: size_t batchSize = input[0]; // size_t inputChannels = input[1]; - // size_t inputHeight = input[2]; - // size_t inputWidth = input[3]; + size_t inputHeight = input[2]; + size_t inputWidth = input[3]; size_t filterHeight = getFilterHeight(filter); size_t filterWidth = getFilterWidth(filter); size_t outputChannels = output[1]; @@ -139,6 +136,8 @@ public: outputChannels, outputHeight, outputWidth, + inputHeight, + inputWidth, filterHeight, filterWidth, strideH(), @@ -233,8 +232,8 @@ public: } void calc(const BufferArgs& inputs, const BufferArgs& outputs) override { - CHECK_EQ(numInputs_, inputs.size()); - CHECK_EQ(numOutputs_, outputs.size()); + // CHECK_EQ(numInputs_, inputs.size()); + // CHECK_EQ(numOutputs_, outputs.size()); check(inputs, outputs); const TensorShape& output = inputs[0].shape(); const TensorShape& input = inputs[1].shape(); diff --git a/paddle/function/DepthwiseConvOp.h b/paddle/function/DepthwiseConvOp.h index 8af1db974de520491eb75762a75f7c63f93c73f1..44290682def458aa51789b3ab12e8c5ac2c6a802 100644 --- a/paddle/function/DepthwiseConvOp.h +++ b/paddle/function/DepthwiseConvOp.h @@ -18,11 +18,6 @@ limitations under the License. */ namespace paddle { -/* - * imData = [input_channels, input_height, input_width] - * colData = [input_channels, filter_height, filter_width, - * output_height, output_width] - */ template class DepthwiseConvFunctor { public: @@ -33,6 +28,8 @@ public: int outputChannels, int outputHeight, int outputWidth, + int inputHeight, + int intputWidth, int filterHeight, int filterWidth, int strideH, diff --git a/paddle/function/DepthwiseConvOpGpu.cu b/paddle/function/DepthwiseConvOpGpu.cu index 1b2d5d99ed2c557a1a41f916aa6e546a4bad8a43..08fe9221ac036d9eea324e6ce050d36ee0452d6e 100644 --- a/paddle/function/DepthwiseConvOpGpu.cu +++ b/paddle/function/DepthwiseConvOpGpu.cu @@ -14,73 +14,95 @@ limitations under the License. */ #include "ConvOp.h" #include "DepthwiseConvOp.h" +#include "GemmFunctor.h" +#include "paddle/math/MemoryHandle.h" namespace paddle { template -__global__ void ConvolutionDepthwiseWeightForward(const int nthreads, - const T* const bottom_data, const T* const weight_data, - const int num, const int channels, const int top_height, - const int top_width, const int bottom_height, const int bottom_width, - const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, T* const top_data) { +__global__ +void ConvolutionDepthwiseForward(const int nthreads, + const T* const inputData, const T* const filterData, + const int batchSize, const int outputChannels, const int outputHeight, + const int outputWidth, const int inputHeight, const int inputWidth, + const int filterHeight, const int filterWidth, const int strideH, + const int strideW, const int paddingH, const int paddingW, + T* const outputData) { int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; if(index < nthreads) { - const int n = index / channels / top_height / top_width; - const int c = (index / top_height / top_width) % channels; - const int h = (index / top_width) % top_height; - const int w = index % top_width; - const T* weight = weight_data + c * kernel_h * kernel_w; + const int n = index / outputChannels / outputHeight / outputWidth; + const int c = (index / outputHeight / outputWidth) % outputChannels; + const int h = (index / outputWidth) % outputHeight; + const int w = index % outputWidth; + const T* weight = filterData + c * filterHeight * filterWidth; T value = 0; - for (int kh = 0; kh < kernel_h; ++kh) { - for (int kw = 0; kw < kernel_w; ++kw) { - const int h_in = -pad_h + h * stride_h + kh * dilation_h; - const int w_in = -pad_w + w * stride_w + kw * dilation_w; - if ((h_in >= 0) && (h_in < bottom_height) - && (w_in >= 0) && (w_in < bottom_width)) { - const int offset = ((n * channels + c) * bottom_height + h_in) - * bottom_width + w_in; - value += (*weight) * bottom_data[offset]; - } - ++weight; - } - } - top_data[index] = value; + const int h_in_start = -paddingH + h * strideH; + const int w_in_start = -paddingW + w * strideW; + const int h_in_end = -paddingH + h * strideH + filterHeight - 1; + const int w_in_end = -paddingW + w * strideW + filterWidth - 1; + if ((h_in_start >= 0) && (h_in_end < inputHeight) + &&(w_in_start >= 0) && (w_in_end < inputWidth)) { + for (int kh = 0; kh < filterHeight; ++kh) { + for (int kw = 0; kw < filterWidth; ++kw) { + const int h_in = -paddingH + h * strideH + kh; + const int w_in = -paddingW + w * strideW + kw; + const int offset = ((n * outputChannels + c) * inputHeight + h_in) + * inputWidth + w_in; + value += (*weight) * inputData[offset]; + ++weight; + } + } + }else{ + for (int kh = 0; kh < filterHeight; ++kh) { + for (int kw = 0; kw < filterWidth; ++kw) { + const int h_in = -paddingH + h * strideH + kh; + const int w_in = -paddingW + w * strideW + kw; + if ((h_in >= 0) && (h_in < inputHeight) + && (w_in >= 0) && (w_in < inputWidth)) { + const int offset = ((n * outputChannels + c) * inputHeight + h_in) + * inputWidth + w_in; + value += (*weight) * inputData[offset]; + } + ++weight; + } + } + } + outputData[index] = value; } } template -__global__ void ConvolutionDepthwiseBottomBackward(const int nthreads, +__global__ +void ConvolutionDepthwiseInputBackward(const int nthreads, const T* const top_diff, const T* const weight_data, - const int num, const int channels, const int top_height, - const int top_width, const int bottom_height, const int bottom_width, - const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, T* const bottom_diff) { + const int num, const int outputChannels, const int outputHeight, + const int outputWidth, const int inputHeight, const int inputWidth, + const int filterHeight, const int filterWidth, const int strideH, + const int strideW, const int paddingH, const int paddingW, + T* const bottom_diff) { int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; if(index < nthreads) { - const int n = index / channels / bottom_height / bottom_width; - const int c = (index / bottom_height / bottom_width) % channels; - const int h = (index / bottom_width) % bottom_height; - const int w = index % bottom_width; - const T* weight = weight_data + c * kernel_h * kernel_w; + const int n = index / outputChannels / inputHeight / inputWidth; + const int c = (index / inputHeight / inputWidth) % outputChannels; + const int h = (index / inputWidth) % inputHeight; + const int w = index % inputWidth; + const T* weight = weight_data + c * filterHeight * filterWidth; T value = 0; - for (int kh = 0; kh < kernel_h; ++kh) { - for (int kw = 0; kw < kernel_w; ++kw) { - const int h_out_s = h + pad_h - kh * dilation_h; - const int w_out_s = w + pad_w - kw * dilation_w; - if (((h_out_s % stride_h) == 0) && ((w_out_s % stride_w) == 0)) { - const int h_out = h_out_s / stride_h; - const int w_out = w_out_s / stride_w; - //it affect the effectives - if ((h_out >= 0) && (h_out < top_height) - && (w_out >= 0) && (w_out < top_width)) { - const int offset = ((n * channels + c) * top_height + h_out) - * top_width + w_out; + for (int kh = 0; kh < filterHeight; ++kh) { + for (int kw = 0; kw < filterWidth; ++kw) { + const int h_out_s = h + paddingH - kh; + const int w_out_s = w + paddingW - kw; + if (((h_out_s % strideH) == 0) && ((w_out_s % strideW) == 0)) { + const int h_out = h_out_s / strideH; + const int w_out = w_out_s / strideW; + // TODO(zhaolong) : the 'if' affect the effectiveness, it needs to optimize + if ((h_out >= 0) && (h_out < outputHeight) + && (w_out >= 0) && (w_out < outputWidth)) { + const int offset = ((n * outputChannels + c) * outputHeight + h_out) + * outputWidth + w_out; value += (*weight) * top_diff[offset]; } } @@ -92,32 +114,33 @@ __global__ void ConvolutionDepthwiseBottomBackward(const int nthreads, } template -__global__ void ConvolutionDepthwiseWeightBackward(const int num_i, const int nthreads, - const T* const top_diff, const T* const bottom_data, - const int num, const int channels, const int top_height, - const int top_width, const int bottom_height, const int bottom_width, - const int kernel_h, const int kernel_w, const int stride_h, - const int stride_w, const int pad_h, const int pad_w, - const int dilation_h, const int dilation_w, T* const buffer_data) { +__global__ +void ConvolutionDepthwiseFilterBackward(const int num_i, const int nthreads, + const T* const top_diff, const T* const inputData, + const int num, const int outputChannels, const int outputHeight, + const int outputWidth, const int inputHeight, const int inputWidth, + const int filterHeight, const int filterWidth, const int strideH, + const int strideW, const int paddingH, const int paddingW, + T* const buffer_data) { int index = (blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x; if (index < nthreads) { - const int h = (index / top_width) % top_height; - const int w = index % top_width; - const int kh = (index / kernel_w / top_height / top_width) - % kernel_h; - const int kw = (index / top_height / top_width) % kernel_w; - const int h_in = -pad_h + h * stride_h + kh * dilation_h; - const int w_in = -pad_w + w * stride_w + kw * dilation_w; - if ((h_in >= 0) && (h_in < bottom_height) - && (w_in >= 0) && (w_in < bottom_width)) { - const int c = index / kernel_h / kernel_w / top_height / top_width; + const int h = (index / outputWidth) % outputHeight; + const int w = index % outputWidth; + const int kh = (index / filterWidth / outputHeight / outputWidth) + % filterHeight; + const int kw = (index / outputHeight / outputWidth) % filterWidth; + const int h_in = -paddingH + h * strideH + kh; + const int w_in = -paddingW + w * strideW + kw; + if ((h_in >= 0) && (h_in < inputHeight) + && (w_in >= 0) && (w_in < inputWidth)) { + const int c = index / filterHeight / filterWidth / outputHeight / outputWidth; const int n = num_i; - const int top_offset = ((n * channels + c) * top_height + h) - * top_width + w; - const int bottom_offset = ((n * channels + c) * bottom_height + h_in) - * bottom_width + w_in; - buffer_data[index] = top_diff[top_offset] * bottom_data[bottom_offset]; + const int top_offset = ((n * outputChannels + c) * outputHeight + h) + * outputWidth + w; + const int bottom_offset = ((n * outputChannels + c) * inputHeight + h_in) + * inputWidth + w_in; + buffer_data[index] = top_diff[top_offset] * inputData[bottom_offset]; } else { buffer_data[index] = 0; } @@ -134,6 +157,8 @@ public: int outputChannels, int outputHeight, int outputWidth, + int inputHeight, + int inputWidth, int filterHeight, int filterWidth, int strideH, @@ -148,7 +173,7 @@ public: dim3 threads(1024, 1); dim3 grid(blockX, blockY); - ConvolutionDepthwiseWeightForward + ConvolutionDepthwiseForward <<< grid, threads, 0, STREAM_DEFAULT >>>( outputSize, inputData, @@ -157,6 +182,8 @@ public: outputChannels, outputHeight, outputWidth, + inputHeight, + inputWidth, filterHeight, filterWidth, strideH, @@ -193,7 +220,7 @@ public: dim3 threads(1024, 1); dim3 grid(blockX, blockY); - ConvolutionDepthwiseBottomBackward + ConvolutionDepthwiseInputBackward // NOLINT_NEXT_LINE(whitespace/operators) <<< grid, threads, 0, STREAM_DEFAULT >>>( inputSize, @@ -244,10 +271,10 @@ public: dim3 threads(1024, 1); dim3 grid(blockX, blockY); - ConvolutionDepthwiseWeightBackward + ConvolutionDepthwiseFilterBackward <<< grid, threads, 0, STREAM_DEFAULT >>>( - i, - size, + num_i, + colDataSize, outputGrad, inputData, batchSize, @@ -264,8 +291,8 @@ public: paddingW, colData ); - GemmFunctor gemm; - int M = size / outputHeight / outputWidth; + GemmFunctor gemm; + int M = colDataSize / outputHeight / outputWidth; int N = 1; int K = outputHeight * outputWidth; gemm(CblasNoTrans, @@ -273,23 +300,25 @@ public: M, N, K, - 1.0f, + (T)1.0, colData, K, multiplierData, N, - 1.0f, + (T)1.0, filterGrad, N); //gemv } }; -template class DepthwiseConvGradInputFunctor; -template class DepthwiseConvGradInputFunctor; -template class DepthwiseConvFunctor; -template class DepthwiseConvFunctor; -template class DepthwiseConvGradFilterFunctor; -template class DepthwiseConvGradFilterFunctor; +#ifdef PADDLE_TYPE_DOUBLE +using real=double; +#else +using real=float; +#endif +template class DepthwiseConvGradInputFunctor; +template class DepthwiseConvFunctor; +template class DepthwiseConvGradFilterFunctor; } // namespace paddle diff --git a/paddle/gserver/layers/ConvBaseLayer.cpp b/paddle/gserver/layers/ConvBaseLayer.cpp index e161d89c38a290000a2cbdb2905e56901ae4c144..765c627c3083fd4e551ca01a4e300b2972af27d2 100644 --- a/paddle/gserver/layers/ConvBaseLayer.cpp +++ b/paddle/gserver/layers/ConvBaseLayer.cpp @@ -21,7 +21,8 @@ bool ConvBaseLayer::init(const LayerMap& layerMap, const ParameterMap& parameterMap) { /* Initialize the basic parent class */ Layer::init(layerMap, parameterMap); - isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv") + isDeconv_ = (config_.type() == "exconv" || config_.type() == "cudnn_conv" || + config_.type() == "depthwise_conv") ? false : true; diff --git a/paddle/gserver/layers/DepthwiseConvLayer.cpp b/paddle/gserver/layers/DepthwiseConvLayer.cpp index 9df8a9df7cc4e0ec76eb71efb7f9a575510e6b6f..f07100d94978959d36327ecd6c54fb3f672b8fa1 100644 --- a/paddle/gserver/layers/DepthwiseConvLayer.cpp +++ b/paddle/gserver/layers/DepthwiseConvLayer.cpp @@ -15,6 +15,7 @@ limitations under the License. */ #include "DepthwiseConvLayer.h" #include "paddle/utils/Logging.h" #include "paddle/utils/Stat.h" +#include namespace paddle { @@ -79,6 +80,7 @@ void DepthwiseConvLayer::forward(PassType passType) { Layer::forward(passType); size_t batchSize = inputLayers_[0]->getOutputValue()->getHeight(); + // std::cout << "outputSize" << getOutputSize() <