Skip to content

P
Paddle

Crayon鑫 / Paddle
与 Fork 源项目一致

Fork自 PaddlePaddle / Paddle

通知 1
Star 1
Fork 0
P
Paddle
未验证 提交 d7319c22 编写于 作者: Z Zhaolong Xing 提交者： GitHub
浏览文件
操作

Merge pull request #5165 from NHZlX/add_dilation 

Add dilation for exconv layer
上级 3e6f7684 f3818bd3
隐藏空白更改
内联 并排
Showing with 299 addition and 157 deletion
paddle/function/ConvOp.h
浏览文件 @ d7319c22
......@@ -61,6 +61,7 @@ public:
// function arguments
strides_ = config.get<std::vector<size_t>>("strides");
paddings_ = config.get<std::vector<size_t>>("paddings");
dilations_ = config.get<std::vector<size_t>>("dilations");
groups_ = config.get<size_t>("groups");
// number of inputs and outputs
......@@ -118,6 +119,7 @@ protected:
std::vector<size_t> strides_;
std::vector<size_t> paddings_;
std::vector<size_t> dilations_;
/// Group size, refer to grouped convolution in
/// Alex Krizhevsky's paper: when group=2, the first half of the
......@@ -133,6 +135,10 @@ protected:
inline int paddingW() const { return paddings_[1]; }
inline int dilationH() const { return dilations_[0]; }
inline int dilationW() const { return dilations_[1]; }
// A temporary memory in convolution calculation.
MemoryHandlePtr memory_;
......
paddle/function/ConvOpTest.h
浏览文件 @ d7319c22
......@@ -79,45 +79,59 @@ void Convolution(const std::string& conv1,
if (outputChannels < inputChannels) continue;
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (padding >= filterSize) break;
for (size_t dilation : {1, 3}) {
if (padding >= filterSize) break;
size_t filterS = (filterSize - 1) * dilation + 1;
// NNPACK only supports stride = 1 if batchSize > 1
if ((conv1 == "NNPACKConv-CPU" || conv2 == "NNPACKConv-CPU") &&
batchSize > 1 && stride > 1)
break;
if (inputSize + 2 * padding < filterS) break;
size_t outputSize =
(inputSize - filterSize + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize << " stride=" << stride
<< " padding=" << padding;
if ((conv1 == "NaiveConv-CPU" || conv2 == "NaiveConv-CPU" ||
conv1 == "NNPACKConv-CPU" ||
conv2 == "NNPACKConv-CPU") &&
dilation > 1)
break;
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
// NNPACK only supports stride = 1 if batchSize > 1
if ((conv1 == "NNPACKConv-CPU" ||
conv2 == "NNPACKConv-CPU") &&
batchSize > 1 && stride > 1)
break;
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
size_t outputSize =
(inputSize - filterS + 2 * padding + stride) / stride;
VLOG(3) << " batchSize=" << batchSize
<< " inputChannels=" << inputChannels
<< " inputHeight=" << inputSize
<< " inputWidth=" << inputSize
<< " outputChannels=" << outputChannels
<< " filterHeight=" << filterSize
<< " filterWidth=" << filterSize
<< " outputHeight=" << outputSize
<< " outputWidth=" << outputSize
<< " stride=" << stride << " padding=" << padding;
function(test, input, filter, output);
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {dilation, dilation};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)1)
.set("algo", (std::string) "auto"));
TensorShape input{
batchSize, inputChannels, inputSize, inputSize};
TensorShape filter{
outputChannels, inputChannels, filterSize, filterSize};
TensorShape output{
batchSize, outputChannels, outputSize, outputSize};
function(test, input, filter, output);
}
}
}
}
......@@ -144,6 +158,7 @@ void Convolution2(const std::string& conv1,
for (size_t outputChannels : {7}) {
size_t stride = 1;
size_t padding = 0;
size_t dilation = 1;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
......@@ -162,6 +177,7 @@ void Convolution2(const std::string& conv1,
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {dilation, dilation};
Compare2Function<DType1, DType2> test(
conv1,
conv2,
......@@ -169,6 +185,7 @@ void Convolution2(const std::string& conv1,
.set("paddings", paddings)
.set("strides", strides)
.set("groups", (size_t)1)
.set("dilations", dilations)
.set("algo", (std::string) "auto"));
TensorShape input{
......@@ -223,6 +240,7 @@ void DepthwiseConvolution(const std::string& conv1,
std::vector<size_t> paddings = {padding, padding};
std::vector<size_t> strides = {stride, stride};
std::vector<size_t> dilations = {1, 1};
size_t groups = inputChannels;
Compare2Function<DType1, DType2> test(
conv1,
......@@ -231,6 +249,7 @@ void DepthwiseConvolution(const std::string& conv1,
.set("paddings", paddings)
.set("strides", strides)
.set("groups", groups)
.set("dilations", dilations)
.set("algo", (std::string) "auto"));
TensorShape input{
......
paddle/function/GemmConvOp.cpp
浏览文件 @ d7319c22
......@@ -100,7 +100,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
} else {
colData = inputData + g * inputOffset;
}
......@@ -223,7 +225,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
}
}
inputGrad += inputChannels * inputHeight * inputWidth;
......@@ -310,7 +314,9 @@ public:
strideH(),
strideW(),
paddingH(),
paddingW());
paddingW(),
dilationH(),
dilationW());
} else {
colData = inputData + g * inputOffset;
}
......
paddle/function/Im2Col.h
浏览文件 @ d7319c22
......@@ -78,7 +78,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1);
};
template <ColFormat Format, DeviceType Device, class T>
......@@ -91,7 +93,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth);
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1);
};
} // namespace paddle
paddle/function/Im2ColOp.cpp
浏览文件 @ d7319c22
......@@ -31,7 +31,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -47,8 +49,8 @@ public:
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
int imRowIdx = h * strideHeight + hOffset * dilationHeight;
int imColIdx = w * strideWidth + wOffset * dilationWidth;
if ((imRowIdx - paddingHeight) < 0 ||
(imRowIdx - paddingHeight) >= inputHeight ||
(imColIdx - paddingWidth) < 0 ||
......@@ -81,7 +83,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -97,8 +101,8 @@ public:
int c_im = c / filterWidth / filterHeight;
for (int h = 0; h < outputHeight; ++h) {
for (int w = 0; w < outputWidth; ++w) {
int imRowIdx = h * strideHeight + hOffset;
int imColIdx = w * strideWidth + wOffset;
int imRowIdx = h * strideHeight + hOffset * dilationHeight;
int imColIdx = w * strideWidth + wOffset * dilationWidth;
if ((imRowIdx - paddingHeight) >= 0 &&
(imRowIdx - paddingHeight) < inputHeight &&
(imColIdx - paddingWidth) >= 0 &&
......@@ -134,7 +138,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -147,9 +153,10 @@ public:
for (int channel = 0; channel < inputChannels; ++channel) {
for (int filterH = 0; filterH < filterHeight; ++filterH) {
for (int filterW = 0; filterW < filterWidth; ++filterW) {
int imRowOffset =
outputH * strideHeight + filterH - paddingHeight;
int imColOffset = outputW * strideWidth + filterW - paddingWidth;
int imRowOffset = outputH * strideHeight +
filterH * dilationHeight - paddingHeight;
int imColOffset = outputW * strideWidth +
filterW * dilationWidth - paddingWidth;
int colDataOffset =
(((outputH * outputWidth + outputW) * inputChannels +
channel) *
......@@ -189,7 +196,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight = 1,
int dilationWidth = 1) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -202,9 +211,10 @@ public:
for (int channel = 0; channel < inputChannels; ++channel) {
for (int filterH = 0; filterH < filterHeight; ++filterH) {
for (int filterW = 0; filterW < filterWidth; ++filterW) {
int imRowOffset =
outputH * strideHeight + filterH - paddingHeight;
int imColOffset = outputW * strideWidth + filterW - paddingWidth;
int imRowOffset = outputH * strideHeight +
filterH * dilationHeight - paddingHeight;
int imColOffset = outputW * strideWidth +
filterW * dilationWidth - paddingWidth;
int colDataOffset =
(((outputH * outputWidth + outputW) * inputChannels +
channel) *
......
paddle/function/Im2ColOpGpu.cu
浏览文件 @ d7319c22
......@@ -28,6 +28,8 @@ __global__ void im2col(const T* data_im,
int strideW,
int paddingH,
int paddingW,
int dilationH,
int dilationW,
int height_col,
int width_col,
T* data_col) {
......@@ -44,8 +46,8 @@ __global__ void im2col(const T* data_im,
data_col += (channel_out * height_col + h_out) * width_col + w_out;
for (int i = 0; i < blockH; ++i) {
for (int j = 0; j < blockW; ++j) {
int rIdx = int(h_in + i);
int cIdx = int(w_in + j);
int rIdx = int(h_in + i * dilationH);
int cIdx = int(w_in + j * dilationW);
if ((rIdx - (int)paddingH) >= (int)height ||
(rIdx - (int)paddingH) < 0 ||
(cIdx - (int)paddingW) >= (int)width ||
......@@ -77,7 +79,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -102,6 +106,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth,
colData);
......@@ -121,6 +127,8 @@ __global__ void col2im(size_t n,
size_t strideW,
size_t paddingH,
size_t paddingW,
size_t dilationH,
size_t dilationW,
size_t height_col,
size_t width_col,
T* data_im) {
......@@ -131,23 +139,34 @@ __global__ void col2im(size_t n,
int w = int(index % width);
int h = int((index / width) % height);
int c = int(index / (width * height));
int filterH = (blockH - 1) * dilationH + 1;
int filterW = (blockW - 1) * dilationW + 1;
if ((w - (int)paddingW) >= 0 &&
(w - (int)paddingW) < (width - 2 * paddingW) &&
(h - (int)paddingH) >= 0 && (h - paddingH) < (height - 2 * paddingH)) {
// compute the start and end of the output
int w_col_start =
(w < (int)blockW) ? 0 : (w - int(blockW)) / (int)strideW + 1;
(w < (int)filterW) ? 0 : (w - int(filterW)) / (int)strideW + 1;
int w_col_end = min((int)(w / (int)strideW + 1), (int)(width_col));
int h_col_start =
(h < (int)blockH) ? 0 : (h - (int)blockH) / (int)strideH + 1;
(h < (int)filterH) ? 0 : (h - (int)filterH) / (int)strideH + 1;
int h_col_end = min(int(h / strideH + 1), int(height_col));
for (int h_col = h_col_start; h_col < h_col_end; ++h_col) {
for (int w_col = w_col_start; w_col < w_col_end; ++w_col) {
// the col location: [c * width * height + h_out, w_out]
int c_col = int(c * blockH * blockW) +
(h - h_col * (int)strideH) * (int)blockW +
(w - w_col * (int)strideW);
val += data_col[(c_col * height_col + h_col) * width_col + w_col];
int h_k = (h - h_col * strideH);
int w_k = (w - w_col * strideW);
if (h_k % dilationH == 0 && w_k % dilationW == 0) {
h_k /= dilationH;
w_k /= dilationW;
int c_col =
(((c * blockH + h_k) * blockW + w_k) * height_col + h_col) *
width_col +
w_col;
val += data_col[c_col];
}
}
}
h -= paddingH;
......@@ -173,7 +192,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -205,6 +226,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth,
imData);
......@@ -229,6 +252,8 @@ __global__ void im2colOCF(const T* imData,
int strideWidth,
int paddingHeight,
int paddingWidth,
int dilationHeight,
int dilationWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
......@@ -237,8 +262,10 @@ __global__ void im2colOCF(const T* imData,
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int widthOffset =
idx * dilationHeight + swId * strideWidth - paddingWidth;
int heightOffset =
idy * dilationWidth + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
......@@ -273,7 +300,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -312,6 +341,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Im2ColFunctor GPU failed");
......@@ -330,6 +361,8 @@ __global__ void col2imOCF(T* imData,
int strideWidth,
int paddingHeight,
int paddingWidth,
int dilationHeight,
int dilationWidth,
int outputHeight,
int outputWidth) {
int swId = blockIdx.x;
......@@ -338,8 +371,10 @@ __global__ void col2imOCF(T* imData,
channelId += blockDim.z) {
for (int idy = threadIdx.y; idy < filterHeight; idy += blockDim.y) {
for (int idx = threadIdx.x; idx < filterWidth; idx += blockDim.x) {
int widthOffset = idx + swId * strideWidth - paddingWidth;
int heightOffset = idy + shId * strideHeight - paddingHeight;
int widthOffset =
idx * dilationWidth + swId * strideWidth - paddingWidth;
int heightOffset =
idy * dilationHeight + shId * strideHeight - paddingHeight;
int imOffset = widthOffset + heightOffset * inputWidth +
channelId * inputHeight * inputWidth;
......@@ -372,7 +407,9 @@ public:
int strideHeight,
int strideWidth,
int paddingHeight,
int paddingWidth) {
int paddingWidth,
int dilationHeight,
int dilationWidth) {
int inputChannels = imShape[0];
int inputHeight = imShape[1];
int inputWidth = imShape[2];
......@@ -411,6 +448,8 @@ public:
strideWidth,
paddingHeight,
paddingWidth,
dilationHeight,
dilationWidth,
outputHeight,
outputWidth);
CHECK_SYNC("Col2ImFunctor GPU failed");
......
paddle/function/Im2ColTest.cpp
浏览文件 @ d7319c22
......@@ -29,82 +29,98 @@ void TestIm2ColFunctor() {
for (size_t filterWidth : {3, 7}) {
for (size_t stride : {1, 2}) {
for (size_t padding : {0, 1}) {
if (inputHeight <= filterHeight || inputWidth <= filterWidth)
break;
if (padding >= filterHeight || padding >= filterWidth) break;
size_t outputHeight =
(inputHeight - filterHeight + 2 * padding + stride) /
stride;
size_t outputWidth =
(inputWidth - filterWidth + 2 * padding + stride) / stride;
TensorShape imShape =
TensorShape({channels, inputHeight, inputWidth});
TensorShape colShape1 = TensorShape({channels,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
TensorShape colShape2 = TensorShape({outputHeight,
outputWidth,
channels,
filterHeight,
filterWidth});
size_t height = channels * filterHeight * filterWidth;
size_t width = outputHeight * outputWidth;
VectorPtr input1 = Vector::create(imShape.getElements(), false);
VectorPtr input2 = Vector::create(imShape.getElements(), false);
MatrixPtr output1 = Matrix::create(height, width, false, false);
MatrixPtr output2 = Matrix::create(width, height, false, false);
input1->uniform(0.001, 1);
input2->copyFrom(*input1);
Im2ColFunctor<kCFO, Device, T> im2Col1;
Im2ColFunctor<kOCF, Device, T> im2Col2;
im2Col1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding);
im2Col2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding);
// The transposition of the result of ColFormat == kCFO
// is equal to the result of ColFormat == kOCF.
MatrixPtr test;
output2->transpose(test, true);
autotest::TensorCheckErr(*output1, *test);
Col2ImFunctor<kCFO, Device, T> col2Im1;
Col2ImFunctor<kOCF, Device, T> col2Im2;
col2Im1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding);
col2Im2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding);
autotest::TensorCheckErr(*input1, *input2);
for (size_t dilation : {1, 3}) {
size_t filterSizeH = (filterHeight - 1) * dilation + 1;
size_t filterSizeW = (filterWidth - 1) * dilation + 1;
if (inputHeight + 2 * padding < filterSizeH ||
inputWidth + 2 * padding < filterSizeW)
break;
if (padding >= filterSizeH || padding >= filterSizeW) break;
size_t outputHeight =
(inputHeight - filterSizeH + 2 * padding) / stride + 1;
size_t outputWidth =
(inputWidth - filterSizeW + 2 * padding) / stride + 1;
TensorShape imShape =
TensorShape({channels, inputHeight, inputWidth});
TensorShape colShape1 = TensorShape({channels,
filterHeight,
filterWidth,
outputHeight,
outputWidth});
TensorShape colShape2 = TensorShape({outputHeight,
outputWidth,
channels,
filterHeight,
filterWidth});
size_t height = channels * filterHeight * filterWidth;
size_t width = outputHeight * outputWidth;
VectorPtr input1 =
Vector::create(imShape.getElements(), false);
VectorPtr input2 =
Vector::create(imShape.getElements(), false);
MatrixPtr output1 =
Matrix::create(height, width, false, false);
MatrixPtr output2 =
Matrix::create(width, height, false, false);
input1->uniform(0.001, 1);
input2->copyFrom(*input1);
Im2ColFunctor<kCFO, Device, T> im2Col1;
Im2ColFunctor<kOCF, Device, T> im2Col2;
im2Col1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation);
im2Col2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding,
dilation,
dilation);
// The transposition of the result of ColFormat == kCFO
// is equal to the result of ColFormat == kOCF.
MatrixPtr test;
output2->transpose(test, true);
autotest::TensorCheckErr(*output1, *test);
Col2ImFunctor<kCFO, Device, T> col2Im1;
Col2ImFunctor<kOCF, Device, T> col2Im2;
col2Im1(input1->getData(),
imShape,
output1->getData(),
colShape1,
stride,
stride,
padding,
padding,
dilation,
dilation);
col2Im2(input2->getData(),
imShape,
output2->getData(),
colShape2,
stride,
stride,
padding,
padding,
dilation,
dilation);
autotest::TensorCheckErr(*input1, *input2);
}
}
}
}
......
paddle/gserver/layers/ExpandConvLayer.cpp
浏览文件 @ d7319c22
......@@ -79,6 +79,10 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
for (int i = 0; i < config_.inputs_size(); i++) {
std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
std::vector<size_t> dilations = {(size_t)dilationY_[i],
(size_t)dilation_[i]};
bool useDilation = ((size_t)dilationY_[i] > 1 || (size_t)dilation_[i] > 1);
// Convolution Layer uses the GemmConv function by default.
convType = "GemmConv";
......@@ -97,13 +101,14 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
#if defined(__ARM_NEON__) || defined(__ARM_NEON)
if ((filterSize_[i] == filterSizeY_[i]) &&
(filterSize_[i] == 3 || filterSize_[i] == 4) &&
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2)) {
(stride_[i] == strideY_[i]) && (stride_[i] == 1 || stride_[i] == 2) &&
!useDilation) {
convType = "NeonDepthwiseConv";
}
#endif
}
if (FLAGS_use_nnpack && !isDeconv_) {
if (FLAGS_use_nnpack && !isDeconv_ && !useDilation) {
createFunction(forward_,
"NNPACKConv",
FuncConfig()
......@@ -117,6 +122,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
......@@ -124,6 +130,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
createFunction(backward_,
......@@ -131,6 +138,7 @@ bool ExpandConvLayer::init(const LayerMap &layerMap,
FuncConfig()
.set("paddings", paddings)
.set("strides", strides)
.set("dilations", dilations)
.set("groups", (size_t)groups_[i]));
}
}
......
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @ d7319c22
......@@ -434,7 +434,7 @@ void testConvLayer(const string& type, bool trans, bool useGpu) {
config.layerConfig.set_partial_sum(1);
config.layerConfig.set_shared_biases(true);
int dilation = 1;
int dilation = 2;
if (type == "cudnn_conv") {
#if CUDNN_VERSION >= 6000
dilation = 2;
......
python/paddle/trainer/config_parser.py
浏览文件 @ d7319c22
......@@ -1200,8 +1200,14 @@ def TestData(data_config, async_load_data=None):
#caffe_mode: compute the output size using floor instead of ceil,
# which is consistent of caffe and CuDNN's convention.
def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode):
output = (2 * padding + img_size - filter_size) / float(stride)
def cnn_output_size(img_size,
filter_size,
padding,
stride,
caffe_mode,
dilation=1):
filter_s = (filter_size - 1) * dilation + 1
output = (2 * padding + img_size - filter_s) / float(stride)
if caffe_mode:
return 1 + int(math.floor(output))
else:
......@@ -1210,8 +1216,14 @@ def cnn_output_size(img_size, filter_size, padding, stride, caffe_mode):
#calcualte image_size based on output_size for de-convolution (ConvTransLayer).
#It is the reverse function of cnn_output_size
def cnn_image_size(output_size, filter_size, padding, stride, caffe_mode):
img_size = (output_size - 1) * stride + filter_size - 2 * padding
def cnn_image_size(output_size,
filter_size,
padding,
stride,
caffe_mode,
dilation=1):
filter_s = (filter_size - 1) * dilation + 1
img_size = (output_size - 1) * stride + filter_s - 2 * padding
if not caffe_mode:
img_size = img_size + 1
return img_size
......@@ -1376,6 +1388,12 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
conv_conf.stride_y = conv.stride_y
conv_conf.groups = conv.groups
conv_conf.caffe_mode = conv.caffe_mode
if not conv.dilation:
conv.dilation = 1
conv.dilation_y = 1
else:
conv_conf.dilation = conv.dilation
conv_conf.dilation_y = conv.dilation_y
if not trans:
conv_conf.filter_channels = conv.channels / conv.groups
......@@ -1383,20 +1401,20 @@ def parse_conv(conv, input_layer_name, conv_conf, num_filters, trans=False):
get_img_size(input_layer_name, conv.channels)
conv_conf.output_x = cnn_output_size(
conv_conf.img_size, conv_conf.filter_size, conv_conf.padding,
conv_conf.stride, conv_conf.caffe_mode)
conv_conf.stride, conv_conf.caffe_mode, conv.dilation)
conv_conf.output_y = cnn_output_size(
conv_conf.img_size_y, conv_conf.filter_size_y, conv_conf.padding_y,
conv_conf.stride_y, conv_conf.caffe_mode)
conv_conf.stride_y, conv_conf.caffe_mode, conv.dilation_y)
else:
conv_conf.filter_channels = num_filters / conv.groups
conv_conf.output_x, conv_conf.output_y = \
get_img_size(input_layer_name, conv.channels)
conv_conf.img_size = cnn_image_size(
conv_conf.output_x, conv_conf.filter_size, conv_conf.padding,
conv_conf.stride, conv_conf.caffe_mode)
conv_conf.stride, conv_conf.caffe_mode, conv.dilation)
conv_conf.img_size_y = cnn_image_size(
conv_conf.output_y, conv_conf.filter_size_y, conv_conf.padding_y,
conv_conf.stride_y, conv_conf.caffe_mode)
conv_conf.stride_y, conv_conf.caffe_mode, conv.dilation_y)
#caffe_mode: compute the output size using floor instead of ceil,
......
python/paddle/trainer_config_helpers/layers.py
浏览文件 @ d7319c22
......@@ -2571,7 +2571,9 @@ def img_conv_layer(input,
if layer_type:
if dilation > 1 or dilation_y > 1:
assert layer_type in ["cudnn_conv", "cudnn_convt"]
assert layer_type in [
"cudnn_conv", "cudnn_convt", "exconv", "exconvt"
]
if trans:
assert layer_type in ["exconvt", "cudnn_convt"]
else:
......
python/paddle/trainer_config_helpers/tests/configs/protostr/img_layers.protostr
浏览文件 @ d7319c22
......@@ -28,6 +28,8 @@ layers {
stride_y: 1
output_y: 227
img_size_y: 256
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/img_trans_layers.protostr
浏览文件 @ d7319c22
......@@ -28,6 +28,8 @@ layers {
stride_y: 1
output_y: 227
img_size_y: 256
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_bilinear_interp.protostr
浏览文件 @ d7319c22
......@@ -28,6 +28,8 @@ layers {
stride_y: 1
output_y: 48
img_size_y: 48
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_maxout.protostr
浏览文件 @ d7319c22
......@@ -30,6 +30,8 @@ layers {
stride_y: 1
output_y: 48
img_size_y: 48
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......@@ -105,6 +107,8 @@ layers {
stride_y: 1
output_y: 24
img_size_y: 24
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_1__.wbias"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_pad.protostr
浏览文件 @ d7319c22
......@@ -30,6 +30,8 @@ layers {
stride_y: 1
output_y: 48
img_size_y: 48
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......
python/paddle/trainer_config_helpers/tests/configs/protostr/test_roi_pool_layer.protostr
浏览文件 @ d7319c22
......@@ -36,6 +36,8 @@ layers {
stride_y: 1
output_y: 14
img_size_y: 14
dilation: 1
dilation_y: 1
}
}
bias_parameter_name: "___conv_0__.wbias"
......
Markdown is supported
0% 或 .
You are about to add 0 people to the discussion. Proceed with caution.
先完成此消息的编辑！
想要评论请 注册 或