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fix conv2d and conv3d: (#20042)

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1.support asymmetric padding;
    2.support padding algorithm:"SAME" and "VALID";
    3.support channel_last: data_format NHWC and NDHWC;
    4.change doc of python API and c++;

    test=develop, test=document_preview
fix-python-transpose
liym27 6 years ago committed by Guo Sheng
parent 02c6edc0d5
commit 3aa331d97e
13 changed files with 3801 additions and 839 deletions
Show Stats Download Patch File Download Diff File

14
cmake/operators.cmake
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@ -9,6 +9,7 @@ function(op_library TARGET)
set(miopen_hip_cc_srcs) set(miopen_hip_cc_srcs)
set(cu_cc_srcs) set(cu_cc_srcs)
set(cudnn_cu_cc_srcs) set(cudnn_cu_cc_srcs)
set(cudnn_cu_srcs)
set(CUDNN_FILE) set(CUDNN_FILE)
set(mkldnn_cc_srcs) set(mkldnn_cc_srcs)
set(MKLDNN_FILE) set(MKLDNN_FILE)
@ -44,6 +45,9 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc) if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc)
list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc) list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc)
endif() endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu)
list(APPEND cudnn_cu_srcs ${CUDNN_FILE}.cu)
endif()
if(WITH_AMD_GPU) if(WITH_AMD_GPU)
string(REPLACE "_op" "_miopen_op" MIOPEN_FILE "${TARGET}") string(REPLACE "_op" "_miopen_op" MIOPEN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MIOPEN_FILE}.hip.cc) if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MIOPEN_FILE}.hip.cc)
@ -60,6 +64,8 @@ function(op_library TARGET)
foreach(src ${op_library_SRCS}) foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.hip.cu$") if (${src} MATCHES ".*\\.hip.cu$")
list(APPEND hip_cu_srcs ${src}) list(APPEND hip_cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu$")
list(APPEND cudnn_cu_srcs ${src})
elseif (${src} MATCHES ".*\\.cu$") elseif (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src}) list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu.cc$") elseif(${src} MATCHES ".*_cudnn_op.cu.cc$")
@ -97,7 +103,7 @@ function(op_library TARGET)
set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE) set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE)
endif() endif()
if (WITH_GPU) if (WITH_GPU)
nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${mkldnn_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS} nv_library(${TARGET} SRCS ${cc_srcs} ${cu_cc_srcs} ${cudnn_cu_cc_srcs} ${cudnn_cu_srcs} ${mkldnn_cc_srcs} ${cu_srcs} DEPS ${op_library_DEPS}
${op_common_deps}) ${op_common_deps})
elseif (WITH_AMD_GPU) elseif (WITH_AMD_GPU)
hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cu_srcs} ${miopen_hip_cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS} hip_library(${TARGET} SRCS ${cc_srcs} ${hip_cu_srcs} ${miopen_hip_cc_srcs} ${mkldnn_cc_srcs} DEPS ${op_library_DEPS}
@ -160,6 +166,12 @@ function(op_library TARGET)
endif() endif()
endif() endif()
# pybind USE_OP_DEVICE_KERNEL for CUDNN
list(LENGTH cudnn_cu_srcs cudnn_cu_srcs_len)
if (WITH_GPU AND ${cudnn_cu_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, CUDNN);\n")
endif()
# pybind USE_OP_DEVICE_KERNEL for MIOPEN # pybind USE_OP_DEVICE_KERNEL for MIOPEN
if (WITH_AMD_GPU AND ${miopen_hip_cc_srcs_len} GREATER 0) if (WITH_AMD_GPU AND ${miopen_hip_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MIOPEN);\n") file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MIOPEN);\n")

4
paddle/fluid/API.spec
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@ -140,8 +140,8 @@ paddle.fluid.layers.bpr_loss (ArgSpec(args=['input', 'label', 'name'], varargs=N
paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9')) paddle.fluid.layers.square_error_cost (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', 'bbb9e708bab250359864fefbdf48e9d9'))
paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c')) paddle.fluid.layers.chunk_eval (ArgSpec(args=['input', 'label', 'chunk_scheme', 'num_chunk_types', 'excluded_chunk_types', 'seq_length'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'b02844e0ad4bd713c5fe6802aa13219c'))
paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', '2bf23e7884c380c3b27f2709aa322cb9')) paddle.fluid.layers.sequence_conv (ArgSpec(args=['input', 'num_filters', 'filter_size', 'filter_stride', 'padding', 'padding_start', 'bias_attr', 'param_attr', 'act', 'name'], varargs=None, keywords=None, defaults=(3, 1, True, None, None, None, None, None)), ('document', '2bf23e7884c380c3b27f2709aa322cb9'))
paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '06de9adb5994f6f8cb806c75b55550af')) paddle.fluid.layers.conv2d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCHW')), ('document', 'b8da17862ba02b5297a37d2edd571d76'))
paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None)), ('document', '71b09227709475fa178c1739dff64af6')) paddle.fluid.layers.conv3d (ArgSpec(args=['input', 'num_filters', 'filter_size', 'stride', 'padding', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(1, 0, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '73a15322d460ef9aa90d4d237b0bc5d5'))
paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82')) paddle.fluid.layers.sequence_pool (ArgSpec(args=['input', 'pool_type', 'is_test', 'pad_value'], varargs=None, keywords=None, defaults=(False, 0.0)), ('document', 'e90a93251c52dc4e6fb34fb3991b3f82'))
paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711')) paddle.fluid.layers.sequence_softmax (ArgSpec(args=['input', 'use_cudnn', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'eaa9d0bbd3d4e017c8bc4ecdac483711'))
paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5')) paddle.fluid.layers.softmax (ArgSpec(args=['input', 'use_cudnn', 'name', 'axis'], varargs=None, keywords=None, defaults=(False, None, -1)), ('document', 'cee673c79e3ff4582656a24e04f841e5'))

1063
paddle/fluid/operators/conv_cudnn_op.cu
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523
paddle/fluid/operators/conv_cudnn_op.cu.cc
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121
paddle/fluid/operators/conv_op.cc
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599
paddle/fluid/operators/conv_op.h
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26
paddle/fluid/operators/math/im2col.cc
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@ -33,15 +33,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
const framework::Tensor& im, const std::vector<int>& dilation, const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) { const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3); PADDLE_ENFORCE_EQ(im.dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE(col->dims().size() == 5); PADDLE_ENFORCE_EQ(col->dims().size(), 5,
"The dimension of col should be 5.");
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 && if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 1 &&
dilation[1] == 1) { dilation[1] == 1) {
if (padding[0] == 0 && padding[1] == 0) { if (padding[0] == 0 && padding[1] == 0 && padding[2] == 0 &&
padding[3] == 0) {
im2col_sh1sw1dh1dw1ph0pw0<T>(im, col); im2col_sh1sw1dh1dw1ph0pw0<T>(im, col);
return; return;
} else if (padding[0] == 1 && padding[1] == 1) { } else if (padding[0] == 1 && padding[1] == 1 && padding[2] == 1 &&
padding[3] == 1) {
im2col_sh1sw1dh1dw1ph1pw1<T>(im, col); im2col_sh1sw1dh1dw1ph1pw1<T>(im, col);
return; return;
} }
@ -65,8 +68,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
const std::vector<int>& dilation, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) { const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3); PADDLE_ENFORCE_EQ(im->dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE(col.dims().size() == 5); PADDLE_ENFORCE_EQ(col.dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im->dims()[0]; int im_channels = im->dims()[0];
int im_height = im->dims()[1]; int im_height = im->dims()[1];
int im_width = im->dims()[2]; int im_width = im->dims()[2];
@ -136,8 +140,9 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
const framework::Tensor& im, const std::vector<int>& dilation, const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) { const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3); PADDLE_ENFORCE_EQ(im.dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE(col->dims().size() == 5); PADDLE_ENFORCE_EQ(col->dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im.dims()[0]; int im_channels = im.dims()[0];
int im_height = im.dims()[1]; int im_height = im.dims()[1];
int im_width = im.dims()[2]; int im_width = im.dims()[2];
@ -198,8 +203,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
const std::vector<int>& dilation, const std::vector<int>& dilation,
const std::vector<int>& stride, const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) { const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3); PADDLE_ENFORCE_EQ(im->dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE(col.dims().size() == 5); PADDLE_ENFORCE_EQ(col.dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im->dims()[0]; int im_channels = im->dims()[0];
int im_height = im->dims()[1]; int im_height = im->dims()[1];
int im_width = im->dims()[2]; int im_width = im->dims()[2];

55
paddle/fluid/operators/math/vol2col.cc
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@ -34,9 +34,10 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
framework::Tensor* col) const { framework::Tensor* col) const {
PADDLE_ENFORCE(vol.dims().size() == 4); PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
PADDLE_ENFORCE(col->dims().size() == 7); "The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col->dims().size(), 7,
"The dimension of col should be 7.");
int input_channels = vol.dims()[0]; int input_channels = vol.dims()[0];
int input_depth = vol.dims()[1]; int input_depth = vol.dims()[1];
int input_height = vol.dims()[2]; int input_height = vol.dims()[2];
@ -50,28 +51,35 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
int channels_col = int channels_col =
input_channels * filter_depth * filter_height * filter_width; input_channels * filter_depth * filter_height * filter_width;
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] - // changed
bool paddings_size_is_6 = (paddings.size() == 6);
int pad_d_forth = paddings_size_is_6 ? paddings[0] : paddings[0];
int pad_d_back = paddings_size_is_6 ? paddings[1] : paddings[0];
int pad_h_up = paddings_size_is_6 ? paddings[2] : paddings[1];
int pad_h_down = paddings_size_is_6 ? paddings[3] : paddings[1];
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) / ((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] + strides[0] +
1, 1,
output_depth, output_depth,
"input_depth and output_depth are " "input_depth and output_depth are "
"mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) / ((dilations[1] * (filter_height - 1) + 1))) /
strides[1] + strides[1] +
1, 1,
output_height, output_height,
"input_height and output_height are " "input_height and output_height are "
"mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) / ((dilations[2] * (filter_width - 1) + 1))) /
strides[2] + strides[2] +
1, 1,
output_width, output_width,
"input_width and output_width are " "input_width and output_width are "
"mismatching."); "mismatching.");
const T* vol_data = vol.data<T>(); const T* vol_data = vol.data<T>();
T* col_data = col->data<T>(); T* col_data = col->data<T>();
@ -81,11 +89,11 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
int d_offset = (c / filter_width / filter_height) % filter_depth; int d_offset = (c / filter_width / filter_height) % filter_depth;
int c_in = c / filter_width / filter_height / filter_depth; int c_in = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) { for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0]; int d_pad = d * strides[0] - pad_d_forth + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) { for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1]; int h_pad = h * strides[1] - pad_h_up + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) { for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2]; int w_pad = w * strides[2] - pad_w_left + w_offset * dilations[2];
int col_idx = int col_idx =
((c * output_depth + d) * output_height + h) * output_width + w; ((c * output_depth + d) * output_height + h) * output_width + w;
@ -120,9 +128,10 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides, const std::vector<int>& strides,
const std::vector<int>& paddings, const std::vector<int>& paddings,
framework::Tensor* vol) const { framework::Tensor* vol) const {
PADDLE_ENFORCE(vol->dims().size() == 4); PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
PADDLE_ENFORCE(col.dims().size() == 7); "The dimension of vol should be 4.");
PADDLE_ENFORCE_EQ(col.dims().size(), 7,
"The dimension of col should be 7.");
int input_channels = vol->dims()[0]; int input_channels = vol->dims()[0];
int input_depth = vol->dims()[1]; int input_depth = vol->dims()[1];
int input_height = vol->dims()[2]; int input_height = vol->dims()[2];
@ -136,21 +145,29 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
int channels_col = int channels_col =
input_channels * filter_depth * filter_height * filter_width; input_channels * filter_depth * filter_height * filter_width;
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] - bool paddings_size_is_6 = (paddings.size() == 6);
int pad_d_forth = paddings_size_is_6 ? paddings[0] : paddings[0];
int pad_d_back = paddings_size_is_6 ? paddings[1] : paddings[0];
int pad_h_up = paddings_size_is_6 ? paddings[2] : paddings[1];
int pad_h_down = paddings_size_is_6 ? paddings[3] : paddings[1];
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) / ((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] + strides[0] +
1, 1,
output_depth, output_depth,
"input_depth and output_depth are " "input_depth and output_depth are "
"mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) / ((dilations[1] * (filter_height - 1) + 1))) /
strides[1] + strides[1] +
1, 1,
output_height, output_height,
"input_height and output_height are " "input_height and output_height are "
"mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) / ((dilations[2] * (filter_width - 1) + 1))) /
strides[2] + strides[2] +
1, 1,
@ -166,11 +183,11 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
int d_offset = (c / filter_width / filter_height) % filter_depth; int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth; int cIm = c / filter_width / filter_height / filter_depth;
for (int d = 0; d < output_depth; ++d) { for (int d = 0; d < output_depth; ++d) {
int d_pad = d * strides[0] - paddings[0] + d_offset * dilations[0]; int d_pad = d * strides[0] - pad_d_forth + d_offset * dilations[0];
for (int h = 0; h < output_height; ++h) { for (int h = 0; h < output_height; ++h) {
int h_pad = h * strides[1] - paddings[1] + h_offset * dilations[1]; int h_pad = h * strides[1] - pad_h_up + h_offset * dilations[1];
for (int w = 0; w < output_width; ++w) { for (int w = 0; w < output_width; ++w) {
int w_pad = w * strides[2] - paddings[2] + w_offset * dilations[2]; int w_pad = w * strides[2] - pad_w_left + w_offset * dilations[2];
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 && if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 && d_pad < input_depth) { w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {

49
paddle/fluid/operators/math/vol2col.cu
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@ -92,27 +92,34 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
int output_height = col->dims()[5]; int output_height = col->dims()[5];
int output_width = col->dims()[6]; int output_width = col->dims()[6];
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] - bool paddings_size_is_6 = (paddings.size() == 6);
int pad_d_forth = paddings_size_is_6 ? paddings[0] : paddings[0];
int pad_d_back = paddings_size_is_6 ? paddings[1] : paddings[0];
int pad_h_up = paddings_size_is_6 ? paddings[2] : paddings[1];
int pad_h_down = paddings_size_is_6 ? paddings[3] : paddings[1];
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) / ((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] + strides[0] +
1, 1,
output_depth, output_depth,
"input_depth and output_depth are " "input_depth and output_depth are "
"Mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) / ((dilations[1] * (filter_height - 1) + 1))) /
strides[1] + strides[1] +
1, 1,
output_height, output_height,
"input_height and output_height are " "input_height and output_height are "
"Mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) / ((dilations[2] * (filter_width - 1) + 1))) /
strides[2] + strides[2] +
1, 1,
output_width, output_width,
"input_width and output_width are " "input_width and output_width are "
"Mismatching."); "mismatching.");
int num_outputs = int num_outputs =
input_channels * output_depth * output_height * output_width; input_channels * output_depth * output_height * output_width;
@ -122,9 +129,8 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
vol2col<T><<<blocks, threads, 0, context.stream()>>>( vol2col<T><<<blocks, threads, 0, context.stream()>>>(
num_outputs, vol.data<T>(), input_depth, input_height, input_width, num_outputs, vol.data<T>(), input_depth, input_height, input_width,
dilations[0], dilations[1], dilations[2], filter_depth, filter_height, dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0], filter_width, strides[0], strides[1], strides[2], pad_d_forth, pad_h_up,
paddings[1], paddings[2], output_depth, output_height, output_width, pad_w_left, output_depth, output_height, output_width, col->data<T>());
col->data<T>());
} }
}; };
@ -218,27 +224,35 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
int output_height = col.dims()[5]; int output_height = col.dims()[5];
int output_width = col.dims()[6]; int output_width = col.dims()[6];
PADDLE_ENFORCE_EQ((input_depth + 2 * paddings[0] - bool paddings_size_is_6 = (paddings.size() == 6);
int pad_d_forth = paddings_size_is_6 ? paddings[0] : paddings[0];
int pad_d_back = paddings_size_is_6 ? paddings[1] : paddings[0];
int pad_h_up = paddings_size_is_6 ? paddings[2] : paddings[1];
int pad_h_down = paddings_size_is_6 ? paddings[3] : paddings[1];
int pad_w_left = paddings_size_is_6 ? paddings[4] : paddings[2];
int pad_w_right = paddings_size_is_6 ? paddings[5] : paddings[2];
PADDLE_ENFORCE_EQ((input_depth + pad_d_forth + pad_d_back -
((dilations[0] * (filter_depth - 1) + 1))) / ((dilations[0] * (filter_depth - 1) + 1))) /
strides[0] + strides[0] +
1, 1,
output_depth, output_depth,
"input_depth and output_depth are " "input_depth and output_depth are "
"Mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] - PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) / ((dilations[1] * (filter_height - 1) + 1))) /
strides[1] + strides[1] +
1, 1,
output_height, output_height,
"input_height and output_height are " "input_height and output_height are "
"Mismatching."); "mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] - PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) / ((dilations[2] * (filter_width - 1) + 1))) /
strides[2] + strides[2] +
1, 1,
output_width, output_width,
"input_width and output_width are " "input_width and output_width are "
"Mismatching."); "mismatching.");
int num_kernels = input_channels * input_depth * input_height * input_width; int num_kernels = input_channels * input_depth * input_height * input_width;
@ -248,9 +262,8 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
col2vol<T><<<blocks, threads, 0, context.stream()>>>( col2vol<T><<<blocks, threads, 0, context.stream()>>>(
num_kernels, col.data<T>(), input_depth, input_height, input_width, num_kernels, col.data<T>(), input_depth, input_height, input_width,
dilations[0], dilations[1], dilations[2], filter_depth, filter_height, dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0], filter_width, strides[0], strides[1], strides[2], pad_d_forth, pad_h_up,
paddings[1], paddings[2], output_depth, output_height, output_width, pad_w_left, output_depth, output_height, output_width, vol->data<T>());
vol->data<T>());
} }
}; };

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python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
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