Skip to content

P
Paddle

PaddlePaddle / Paddle
大约 1 年 前同步成功

通知 2299
Star 20931
Fork 5422
P
Paddle
提交 3aa331d9 编写于 作者: L liym27 提交者： Guo Sheng
浏览文件
操作

fix conv2d and conv3d: (#20042) 

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
上级 02c6edc0
展开全部 隐藏空白更改
内联 并排
Showing with 3801 addition and 316 deletion
cmake/operators.cmake
浏览文件 @ 3aa331d9
......@@ -9,6 +9,7 @@ function(op_library TARGET)
set(miopen_hip_cc_srcs)
set(cu_cc_srcs)
set(cudnn_cu_cc_srcs)
set(cudnn_cu_srcs)
set(CUDNN_FILE)
set(mkldnn_cc_srcs)
set(MKLDNN_FILE)
......@@ -44,6 +45,9 @@ function(op_library TARGET)
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu.cc)
list(APPEND cudnn_cu_cc_srcs ${CUDNN_FILE}.cu.cc)
endif()
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${CUDNN_FILE}.cu)
list(APPEND cudnn_cu_srcs ${CUDNN_FILE}.cu)
endif()
if(WITH_AMD_GPU)
string(REPLACE "_op" "_miopen_op" MIOPEN_FILE "${TARGET}")
if (EXISTS ${CMAKE_CURRENT_SOURCE_DIR}/${MIOPEN_FILE}.hip.cc)
......@@ -60,6 +64,8 @@ function(op_library TARGET)
foreach(src ${op_library_SRCS})
if (${src} MATCHES ".*\\.hip.cu$")
list(APPEND hip_cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu$")
list(APPEND cudnn_cu_srcs ${src})
elseif (${src} MATCHES ".*\\.cu$")
list(APPEND cu_srcs ${src})
elseif(${src} MATCHES ".*_cudnn_op.cu.cc$")
......@@ -97,7 +103,7 @@ function(op_library TARGET)
set(DEPS_OPS ${TARGET} ${DEPS_OPS} PARENT_SCOPE)
endif()
if (WITH_GPU)
nv_library(${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}
${op_common_deps})
elseif (WITH_AMD_GPU)
hip_library(${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()
# 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
if (WITH_AMD_GPU AND ${miopen_hip_cc_srcs_len} GREATER 0)
file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(${TARGET}, MIOPEN);\n")
......
paddle/fluid/API.spec
浏览文件 @ 3aa331d9
......@@ -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.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.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.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.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', '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_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/operators/conv_op.cc
浏览文件 @ 3aa331d9
......@@ -31,53 +31,76 @@ namespace paddle {
namespace operators {
void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of ConvOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of ConvOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of ConvOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
"Input(Input) of ConvOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("Filter"), true,
"Input(Filter) of ConvOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
"Output(Output) of ConvOp should not be null.");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
std::string padding_algorithm =
ctx->Attrs().Get<std::string>("padding_algorithm");
int groups = ctx->Attrs().Get<int>("groups");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
const std::string data_format = ctx->Attrs().Get<std::string>("data_format");
const bool channel_last = (data_format == "NHWC" || data_format == "NDHWC");
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
"Conv intput should be 4-D or 5-D tensor, get %u",
in_dims.size());
PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
"Conv intput should be 4-D or 5-D tensor, get %u",
in_dims.size());
PADDLE_ENFORCE_EQ(
in_dims.size(), filter_dims.size(),
"Conv input dimension and filter dimension should be the same.");
PADDLE_ENFORCE(
in_dims.size() - strides.size() == 2U,
"Conv input dimension and strides dimension should be consistent.");
PADDLE_ENFORCE_EQ(
paddings.size(), strides.size(),
"Conv paddings dimension and Conv strides dimension should be the same.");
in_dims.size() - strides.size() == 2U, true,
"Conv input dimension and strides dimension should be consistent.");
const auto input_channels =
channel_last ? in_dims[in_dims.size() - 1] : in_dims[1];
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[1] * groups,
PADDLE_ENFORCE_EQ(input_channels, filter_dims[1] * groups,
"The number of input channels should be equal to filter "
"channels * groups.");
PADDLE_ENFORCE_EQ(
filter_dims[0] % groups, 0,
"The number of output channels should be divided by groups.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
for (size_t i = 0; i < strides.size(); ++i) {
framework::DDim in_data_dims;
if (channel_last) {
in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
} else {
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
}
framework::DDim filter_data_dims =
framework::slice_ddim(filter_dims, 2, filter_dims.size());
std::vector<int> ksize = framework::vectorize<int>(filter_data_dims);
UpdatePaddingAndDilation(&paddings, &dilations, padding_algorithm,
in_data_dims, strides, ksize);
std::vector<int64_t> output_shape({in_dims[0]});
if (!channel_last) {
output_shape.push_back(filter_dims[0]);
}
for (size_t i = 0; i < in_data_dims.size(); ++i) {
if ((!ctx->IsRuntime()) &&
(in_dims[i + 2] <= 0 || filter_dims[i + 2] <= 0)) {
(in_data_dims[i] <= 0 || filter_dims[i + 2] <= 0)) {
output_shape.push_back(-1);
} else {
output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
dilations[i], paddings[i],
strides[i]));
output_shape.push_back(ConvOutputSize(in_data_dims[i], filter_dims[i + 2],
dilations[i], paddings[2 * i],
paddings[2 * i + 1], strides[i]));
}
}
if (channel_last) {
output_shape.push_back(filter_dims[0]);
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
ctx->ShareLoD("Input", "Output");
}
......@@ -89,7 +112,8 @@ framework::OpKernelType ConvOp::GetExpectedKernelType(
framework::LibraryType library{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
auto input_data_type = ctx.Input<Tensor>("Input")->type();
std::string data_format = ctx.Attr<std::string>("data_format");
std::string data_format =
"AnyLayout"; // todo enable data layout when it's ready
framework::DataLayout layout = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
......@@ -142,12 +166,12 @@ void Conv2DOpMaker::Make() {
"(bool, default false) Set to true for inference only, false "
"for training. Some layers may run faster when this is true.")
.SetDefault(false);
AddInput(
"Input",
"(Tensor) The input tensor of convolution operator. "
"The format of input tensor is NCHW, where N is batch size, C is the "
"number of channels, H is the height of the feature, "
"and W is the width of the feature.");
AddInput("Input",
"(Tensor) The input tensor of convolution operator. "
"The format of input tensor is NCHW or NHWC, where N is batch size, "
"C is the "
"number of channels, H is the height of the feature, "
"and W is the width of the feature.");
AddInput("Filter",
"(Tensor) The filter tensor of convolution operator. "
"The format of the filter tensor is MCHW, where M is the number of "
......@@ -167,7 +191,7 @@ void Conv2DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator. "
"The format of output tensor is also NCHW.");
"It has same data fromat and data type as the Input.");
AddAttr<std::vector<int>>("strides",
"(vector<int> default:{1, 1}), the "
"strides(h_stride, w_stride) of "
......@@ -175,9 +199,16 @@ void Conv2DOpMaker::Make() {
.SetDefault({1, 1});
AddAttr<std::vector<int>>("paddings",
"(vector<int> default:{0, 0}), the "
"paddings(h_pad, w_pad) of "
"paddings(pad_height_top, pad_height_bottom, "
"pad_width_left, pad_wifth_right) of "
"convolution operator.")
.SetDefault({0, 0});
AddAttr<std::string>(
"padding_algorithm",
"(string, default \"EXPLICIT\") An optional string from: \"EXPLICIT\","
"\"SAME\",\"VALID\". Set to \"EXPLICIT\" for explicit padding. "
"Set to \"SAME\" or \"VALID\" for algorithm of padding. ")
.SetDefault("EXPLICIT");
AddAttr<int>(
"groups",
"(int default:1), the groups number of the convolution operator. "
......@@ -254,7 +285,7 @@ void Conv2DOpMaker::Make() {
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
.SetDefault("NCHW");
// TODO(dzhwinter): need to registered layout transform function
AddAttr<int>("workspace_size_MB",
"Only used in cudnn kernel. Need set use_cudnn to true."
......@@ -269,13 +300,14 @@ void Conv2DOpMaker::Make() {
"convolution, whether enable exhaustive search "
"for cuDNN convolution or not, default is False.")
.SetDefault(false);
AddComment(R"DOC(
Convolution Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and Output(Output) are in NCHW format. Where N is batch
Input(Input) and Output(Output) are in NCHW or NHWC format. Where N is batch
size, C is the number of channels, H is the height of the feature, and W is
the width of the feature.
Filters(Input) is MCHW format. Where M is the number of output image channels, C is
......@@ -293,8 +325,8 @@ Example:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
H_{out}= \frac{(H_{in} + pad_height_top + pad_height_bottom - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
W_{out}= \frac{(W_{in} + pad_width_left + pad_width_right - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
)DOC");
Apply();
......@@ -308,7 +340,8 @@ void Conv3DOpMaker::Make() {
AddInput(
"Input",
"(Tensor) The input tensor of convolution operator. "
"The format of input tensor is NCDHW. Where N is batch size, C is the "
"The format of input tensor is NCDHW or NDHWC. Where N is batch size, C "
"is the "
"number of channels, D is the depth of the feature, H is the height of "
"the feature, "
"and W is the width of the feature.");
......@@ -327,17 +360,25 @@ void Conv3DOpMaker::Make() {
.AsDispensable();
AddOutput("Output",
"(Tensor) The output tensor of convolution operator."
"The format of output tensor is also NCDHW.");
"It has same data fromat and data type as the Input.");
AddAttr<std::vector<int>>("strides",
"(vector<int>, default:{1, 1, 1}), the "
"strides(d_stride, h_stride, w_stride) of "
"convolution operator.")
.SetDefault({1, 1, 1});
AddAttr<std::vector<int>>("paddings",
"(vector<int>, default:{0, 0, 0}), the "
"paddings(d_pad, h_pad, w_pad) of convolution "
"operator.")
AddAttr<std::vector<int>>(
"paddings",
"(vector<int>, default:{0, 0, 0}), the "
"paddings(pad_depth_front, pad_depth_back, pad_height_top, "
"pad_height_bottom, pad_width_left, pad_width_right) of convolution "
"operator.")
.SetDefault({0, 0, 0});
AddAttr<std::string>(
"padding_algorithm",
"(string, default \"EXPLICIT\") An optional string from: \"EXPLICIT\","
"\"SAME\",\"VALID\". Set to \"EXPLICIT\" for explicit padding. "
"Set to \"SAME\" or \"VALID\" for algorithm of padding. ")
.SetDefault("EXPLICIT");
AddAttr<int>(
"groups",
"(int default:1), the groups number of the convolution operator. "
......@@ -375,11 +416,11 @@ void Conv3DOpMaker::Make() {
.SetDefault(false);
AddAttr<std::string>(
"data_format",
"(string, default NCHW) Only used in "
"An optional string from: \"NHWC\", \"NCHW\". "
"Defaults to \"NHWC\". Specify the data format of the output data, "
"(string, default NCDHW) Only used in "
"An optional string from: \"NDHWC\", \"NCDHW\". "
"Defaults to \"NDHWC\". Specify the data format of the output data, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
.SetDefault("NCDHW");
AddAttr<bool>("force_fp32_output",
"(bool, default false) Only used in mkldnn INT8 kernel")
.SetDefault(false);
......@@ -402,7 +443,7 @@ Convolution3D Operator.
The convolution operation calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and output(Output) are in NCDHW format, where N is batch
Input(Input) and output(Output) are in NCDHW or NDHWC format, where N is batch
size, C is the number of channels,D is the depth of the feature, H is the height of
the feature, and W is the width of the feature.
Filters(Input) is MCDHW format, where M is the number of output image channels,
......@@ -420,9 +461,9 @@ Example:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out}= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
H_{out}= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
D_{out}= \frac{(D_{in} + pad_depth_front + pad_depth_back - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
H_{out}= \frac{(H_{in} + pad_height_top + pad_height_bottom - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
W_{out}= \frac{(W_{in} + pad_width_left + pad_width_right - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
$$
)DOC");
Apply();
......@@ -445,7 +486,7 @@ framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
framework::OpKernelType::kDefaultCustomizedTypeValue;
framework::LibraryType library_{framework::LibraryType::kPlain};
// TODO(pzelazko-intel): enable MKLDNN layout when it's ready
std::string data_format = ctx.Attr<std::string>("data_format");
std::string data_format = "AnyLayout";
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
......@@ -623,7 +664,7 @@ framework::OpKernelType ConvOpDoubleGrad::GetExpectedKernelType(
int customized_type_value =
framework::OpKernelType::kDefaultCustomizedTypeValue;
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
std::string data_format = "AnyLayout";
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
#ifdef PADDLE_WITH_CUDA
......
paddle/fluid/operators/conv_op.h
浏览文件 @ 3aa331d9
此差异已折叠。
paddle/fluid/operators/math/im2col.cc
浏览文件 @ 3aa331d9
......@@ -33,15 +33,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col->dims().size() == 5);
PADDLE_ENFORCE_EQ(im.dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE_EQ(col->dims().size(), 5,
"The dimension of col should be 5.");
if (stride[0] == 1 && stride[1] == 1 && dilation[0] == 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);
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);
return;
}
......@@ -65,8 +68,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
PADDLE_ENFORCE_EQ(im->dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE_EQ(col.dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
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 std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col->dims().size() == 5);
PADDLE_ENFORCE_EQ(im.dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE_EQ(col->dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
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>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE(im->dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
PADDLE_ENFORCE_EQ(im->dims().size(), 3, "The dimension of im should be 3.");
PADDLE_ENFORCE_EQ(col.dims().size(), 5,
"The dimension of col should be 5.");
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
......
paddle/fluid/operators/math/vol2col.cc
浏览文件 @ 3aa331d9
......@@ -34,9 +34,10 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const {
PADDLE_ENFORCE(vol.dims().size() == 4);
PADDLE_ENFORCE(col->dims().size() == 7);
PADDLE_ENFORCE_EQ(vol.dims().size(), 4,
"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_depth = vol.dims()[1];
int input_height = vol.dims()[2];
......@@ -50,28 +51,35 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
int channels_col =
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))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"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))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"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))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"mismatching.");
const T* vol_data = vol.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 c_in = c / filter_width / filter_height / filter_depth;
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) {
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) {
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 =
((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>& paddings,
framework::Tensor* vol) const {
PADDLE_ENFORCE(vol->dims().size() == 4);
PADDLE_ENFORCE(col.dims().size() == 7);
PADDLE_ENFORCE_EQ(vol->dims().size(), 4,
"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_depth = vol->dims()[1];
int input_height = vol->dims()[2];
......@@ -136,21 +145,29 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
int channels_col =
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))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"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))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"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))) /
strides[2] +
1,
......@@ -166,11 +183,11 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
int d_offset = (c / filter_width / filter_height) % filter_depth;
int cIm = c / filter_width / filter_height / filter_depth;
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) {
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) {
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 &&
w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {
......
paddle/fluid/operators/math/vol2col.cu
浏览文件 @ 3aa331d9
......@@ -92,27 +92,34 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
int output_height = col->dims()[5];
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))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"Mismatching.");
"mismatching.");
int num_outputs =
input_channels * output_depth * output_height * output_width;
......@@ -122,9 +129,8 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
vol2col<T><<<blocks, threads, 0, context.stream()>>>(
num_outputs, vol.data<T>(), input_depth, input_height, input_width,
dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0],
paddings[1], paddings[2], output_depth, output_height, output_width,
col->data<T>());
filter_width, strides[0], strides[1], strides[2], pad_d_forth, pad_h_up,
pad_w_left, output_depth, output_height, output_width, col->data<T>());
}
};
......@@ -218,27 +224,35 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
int output_height = col.dims()[5];
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))) /
strides[0] +
1,
output_depth,
"input_depth and output_depth are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_height + 2 * paddings[1] -
"mismatching.");
PADDLE_ENFORCE_EQ((input_height + pad_h_up + pad_h_down -
((dilations[1] * (filter_height - 1) + 1))) /
strides[1] +
1,
output_height,
"input_height and output_height are "
"Mismatching.");
PADDLE_ENFORCE_EQ((input_width + 2 * paddings[2] -
"mismatching.");
PADDLE_ENFORCE_EQ((input_width + pad_w_left + pad_w_right -
((dilations[2] * (filter_width - 1) + 1))) /
strides[2] +
1,
output_width,
"input_width and output_width are "
"Mismatching.");
"mismatching.");
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()>>>(
num_kernels, col.data<T>(), input_depth, input_height, input_width,
dilations[0], dilations[1], dilations[2], filter_depth, filter_height,
filter_width, strides[0], strides[1], strides[2], paddings[0],
paddings[1], paddings[2], output_depth, output_height, output_width,
vol->data<T>());
filter_width, strides[0], strides[1], strides[2], pad_d_forth, pad_h_up,
pad_w_left, output_depth, output_height, output_width, vol->data<T>());
}
};
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 3aa331d9
......@@ -2259,11 +2259,12 @@ def conv2d(input,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
name=None,
data_format="NCHW"):
"""
The convolution2D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input and
Output are in NCHW format, where N is batch size, C is the number of
Output are in NCHW or NHWC format, where N is batch size, C is the number of
channels, H is the height of the feature, and W is the width of the feature.
Filter is in MCHW format, where M is the number of output image channels,
C is the number of input image channels, H is the height of the filter,
......@@ -2284,7 +2285,7 @@ def conv2d(input,
Where:
* :math:`X`: Input value, a tensor with NCHW format.
* :math:`X`: Input value, a tensor with NCHW or NHWC format.
* :math:`W`: Filter value, a tensor with MCHW format.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
......@@ -2314,7 +2315,7 @@ def conv2d(input,
padding mode is 'SAME' and 'VALID' can reference this link<https://github.com/PaddlePaddle/models/blob/develop/PaddleCV/PaddleGAN/network/base_network.py#L181>`_
Args:
input (Variable): The input image with [N, C, H, W] format.
input (Variable): The input image with [N, C, H, W] or [N, H, W, C] format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple): The filter size. If filter_size
......@@ -2324,9 +2325,14 @@ def conv2d(input,
stride (int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_height, stride_width). Otherwise,
stride_height = stride_width = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain two integers, (padding_height, padding_width). Otherwise,
padding_height = padding_width = padding. Default: padding = 0.
padding (string|int|list|tuple): The padding size. If `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_height, pad_width]` or
`[pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`, and when `data_format` is `"NCHW"`,
`padding` can be in the form `[[0,0], [0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
when `data_format` is `"NHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_height, dilation_width). Otherwise,
dilation_height = dilation_width = dilation. Default: dilation = 1.
......@@ -2350,7 +2356,10 @@ def conv2d(input,
act (str): Activation type, if it is set to None, activation is not appended.
Default: None
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None
will be named automatically. Default: None.
data_format (str): The data format of the input and output data. An optional string from: `"NCHW"`, `"NHWC"`.
The default is `"NCHW"`. When it is `"NCHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_height, input_width]`.
Returns:
Variable: The tensor variable storing the convolution and \
......@@ -2368,8 +2377,23 @@ def conv2d(input,
conv2d = fluid.layers.conv2d(input=data, num_filters=2, filter_size=3, act="relu")
"""
num_channels = input.shape[1]
if not isinstance(use_cudnn, bool):
raise ValueError("Attr(use_cudnn) should be True or False. Received "
"Attr(use_cudnn): %s. " % str(use_cudnn))
if data_format not in ["NCHW", "NHWC"]:
raise ValueError(
"Attr(data_format) should be 'NCHW' or 'NHWC'. Received "
"Attr(data_format): %s." % str(data_format))
channel_last = (data_format == "NHWC")
num_channels = input.shape[3] if channel_last else input.shape[1]
if num_channels < 0:
raise ValueError(
"The channel dimmention of the input(%s) should be defined. "
"Received: %s." % (str(input.shape), str(num_channels)))
assert param_attr is not False, "param_attr should not be False here."
l_type = 'conv2d'
if (num_channels == groups and num_filters % num_channels == 0 and
not use_cudnn):
......@@ -2382,18 +2406,61 @@ def conv2d(input,
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
raise ValueError(
"The number of input channels must be divisible by Attr(groups). "
"Received: number of channels(%s), groups(%s)." %
(str(num_channels), str(groups)))
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 2, 'filter_size')
stride = utils.convert_to_list(stride, 2, 'stride')
padding = utils.convert_to_list(padding, 2, 'padding')
dilation = utils.convert_to_list(dilation, 2, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
# padding
def _update_padding(padding, data_format):
def is_list_or_tuple(ele):
if isinstance(ele, list) or isinstance(ele, tuple):
return True
return False
if is_list_or_tuple(padding) and len(padding) == 4:
if is_list_or_tuple(padding[0]) and (data_format == "NCHW"):
if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
raise ValueError(
"Non-zero padding(%s) in the batch or channel dimensions "
"is not supported." % str(padding))
padding = padding[2:4]
padding = [ele for a_list in padding for ele in a_list]
elif is_list_or_tuple(padding[0]) and (data_format == "NHWC"):
if not (padding[0] == [0, 0] and padding[3] == [0, 0]):
raise ValueError(
"Non-zero padding(%s) in the batch or channel dimensions "
"is not supported." % str(padding))
padding = padding[1:3]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 4, 'padding')
else:
padding = utils.convert_to_list(padding, 2, 'padding')
padding = [padding[0], padding[0], padding[1], padding[1]]
return padding
padding_algorithm = "EXPLICIT"
if isinstance(padding, str):
padding = padding.upper()
if padding not in ["SAME", "VALID"]:
raise ValueError(
"Unknown padding: '%s'. It can only be 'SAME' or 'VALID'." %
str(padding))
if padding == "VALID":
padding_algorithm = "VALID"
padding = [0, 0, 0, 0]
elif padding == "SAME":
padding_algorithm = "SAME"
padding = [0, 0, 0, 0]
padding = _update_padding(padding, data_format)
input_shape = input.shape
filter_shape = [num_filters, int(num_filter_channels)] + filter_size
def _get_default_param_initializer():
......@@ -2423,7 +2490,9 @@ def conv2d(input,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': False,
'fuse_relu_before_depthwise_conv': False
'fuse_relu_before_depthwise_conv': False,
"padding_algorithm": padding_algorithm,
"data_format": data_format,
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
......@@ -2442,13 +2511,14 @@ def conv3d(input,
bias_attr=None,
use_cudnn=True,
act=None,
name=None):
name=None,
data_format="NCDHW"):
"""
**Convlution3D Layer**
The convolution3D layer calculates the output based on the input, filter
and strides, paddings, dilations, groups parameters. Input(Input) and
Output(Output) are in NCDHW format. Where N is batch size C is the number of
Output(Output) are in NCDHW or NDHWC format. Where N is batch size C is the number of
channels, D is the depth of the feature, H is the height of the feature,
and W is the width of the feature. Convlution3D is similar with Convlution2D
but adds one dimension(depth). If bias attribution and activation type are
......@@ -2463,7 +2533,7 @@ def conv3d(input,
In the above equation:
* :math:`X`: Input value, a tensor with NCDHW format.
* :math:`X`: Input value, a tensor with NCDHW or NDHWC format.
* :math:`W`: Filter value, a tensor with MCDHW format.
* :math:`\\ast`: Convolution operation.
* :math:`b`: Bias value, a 2-D tensor with shape [M, 1].
......@@ -2490,7 +2560,7 @@ def conv3d(input,
W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1
Args:
input (Variable): The input image with [N, C, D, H, W] format.
input (Variable): The input image with [N, C, D, H, W] or [N, D, H, W, C]format.
num_filters(int): The number of filter. It is as same as the output
image channel.
filter_size (int|tuple): The filter size. If filter_size is a tuple,
......@@ -2500,9 +2570,15 @@ def conv3d(input,
stride (int|tuple): The stride size. If stride is a tuple, it must
contain three integers, (stride_depth, stride_height, stride_width). Otherwise,
stride_depth = stride_height = stride_width = stride. Default: stride = 1.
padding (int|tuple): The padding size. If padding is a tuple, it must
contain three integers, (padding_depth, padding_height, padding_width). Otherwise,
padding_depth = padding_height = padding_width = padding. Default: padding = 0.
padding (string|int|list|tuple): The padding size. f `padding` is a string, either 'VALID' or
'SAME' which is the padding algorithm. If padding size is a tuple or list,
it could be in three forms: `[pad_depth, pad_height, pad_width]` or
`[pad_depth_front, pad_depth_back, pad_height_top, pad_height_bottom, pad_width_left, pad_width_right]`,
and when `data_format` is `"NCDHW"`, `pool_padding` can be in the form
`[[0,0], [0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right]]`.
when `data_format` is `"NDHWC"`, `pool_padding` can be in the form
`[[0,0], [pad_depth_front, pad_depth_back], [pad_height_top, pad_height_bottom], [pad_width_left, pad_width_right], [0,0]]`.
Default: padding = 0.
dilation (int|tuple): The dilation size. If dilation is a tuple, it must
contain three integers, (dilation_depth, dilation_height, dilation_width). Otherwise,
dilation_depth = dilation_height = dilation_width = dilation. Default: dilation = 1.
......@@ -2527,6 +2603,9 @@ def conv3d(input,
Default: None.
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically. Default: None.
data_format (str): The data format of the input and output data. An optional string from: `"NCDHW"`, `"NDHWC"`.
The default is `"NCDHW"`. When it is `"NCDHW"`, the data is stored in the order of:
`[batch_size, input_channels, input_depth, input_height, input_width]`.
Returns:
Variable: The tensor variable storing the convolution and \
......@@ -2549,22 +2628,85 @@ def conv3d(input,
helper = LayerHelper(l_type, **locals())
dtype = helper.input_dtype()
num_channels = input.shape[1]
if not isinstance(use_cudnn, bool):
raise ValueError("Attr(use_cudnn) should be True or False. Received "
"Attr(use_cudnn): %s. " % str(use_cudnn))
if data_format not in ["NCDHW", "NDHWC"]:
raise ValueError(
"Attr(data_format) should be 'NCDHW' or 'NDHWC'. Received "
"Attr(data_format): %s." % str(data_format))
channel_last = (data_format == "NDHWC")
num_channels = input.shape[4] if channel_last else input.shape[1]
if num_channels < 0:
raise ValueError(
"The channel dimmention of the input(%s) should be defined. "
"Received: %s." % (str(input.shape), str(num_channels)))
if groups is None:
num_filter_channels = num_channels
else:
if num_channels % groups != 0:
raise ValueError("num_channels must be divisible by groups.")
raise ValueError(
"The number of input channels must be divisible by Attr(groups). "
"Received: number of channels(%s), groups(%s)." %
(str(num_channels), str(groups)))
num_filter_channels = num_channels // groups
filter_size = utils.convert_to_list(filter_size, 3, 'filter_size')
stride = utils.convert_to_list(stride, 3, 'stride')
padding = utils.convert_to_list(padding, 3, 'padding')
dilation = utils.convert_to_list(dilation, 3, 'dilation')
if not isinstance(use_cudnn, bool):
raise ValueError("use_cudnn should be True or False")
def _update_padding(padding, data_format):
def is_list_or_tuple(ele):
if isinstance(ele, list) or isinstance(ele, tuple):
return True
return False
if is_list_or_tuple(padding) and len(padding) == 5:
if is_list_or_tuple(padding[0]) and (data_format == "NCDHW"):
if not (padding[0] == [0, 0] and padding[1] == [0, 0]):
raise ValueError(
"Non-zero padding(%s) in the batch or channel dimensions "
"is not supported." % str(padding))
padding = padding[2:5]
padding = [ele for a_list in padding for ele in a_list]
elif is_list_or_tuple(padding[0]) and (data_format == "NDHWC"):
if not (padding[0] == [0, 0] and padding[4] == [0, 0]):
raise ValueError(
"Non-zero padding(%s) in the batch or channel dimensions "
"is not supported." % str(padding))
padding = padding[1:4]
padding = [ele for a_list in padding for ele in a_list]
padding = utils.convert_to_list(padding, 6, 'padding')
elif is_list_or_tuple(padding) and len(padding) == 6:
padding = utils.convert_to_list(padding, 6, 'padding')
else:
padding = utils.convert_to_list(padding, 3, 'padding')
padding = [
padding[0], padding[0], padding[1], padding[1], padding[2],
padding[2]
]
return padding
padding_algorithm = "EXPLICIT"
if isinstance(padding, str):
padding = padding.upper()
if padding not in ["SAME", "VALID"]:
raise ValueError(
"Unknown padding: '%s'. It can only be 'SAME' or 'VALID'." %
str(padding))
if padding == "VALID":
padding_algorithm = "VALID"
padding = [0, 0, 0, 0, 0, 0]
elif padding == "SAME":
padding_algorithm = "SAME"
padding = [0, 0, 0, 0, 0, 0]
padding = _update_padding(padding, data_format)
input_shape = input.shape
filter_shape = [num_filters, num_filter_channels] + filter_size
......@@ -2596,7 +2738,9 @@ def conv3d(input,
'dilations': dilation,
'groups': groups,
'use_cudnn': use_cudnn,
'use_mkldnn': False
'use_mkldnn': False,
"padding_algorithm": padding_algorithm,
"data_format": data_format,
})
pre_act = helper.append_bias_op(pre_bias, dim_start=1, dim_end=2)
......
python/paddle/fluid/tests/unittests/test_conv_nn_grad.py
浏览文件 @ 3aa331d9
......@@ -28,11 +28,38 @@ from decorator_helper import prog_scope
class TestConvDoubleGradCheck(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 4, 7, 8]
shape = [2, 4, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 4, 1, bias_attr=False)
y = layers.conv2d(x, 2, 1, groups=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
places = []
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConvDoubleGradCheck(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 4, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 2, 1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
......@@ -53,11 +80,11 @@ class TestConvDoubleGradCheck(unittest.TestCase):
class TestConvDoubleGradCheckTest1(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 3, 4, 5]
shape = [2, 3, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(x, 4, 1, padding=1, bias_attr=False)
y = layers.conv2d(x, 2, 1, padding=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
......@@ -82,7 +109,7 @@ class TestConv3DDoubleGradCheck(unittest.TestCase):
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(x, 4, 1, bias_attr=False)
y = layers.conv3d(x, 2, 1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
......@@ -107,7 +134,326 @@ class TestConv3DDoubleGradCheckTest1(unittest.TestCase):
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(x, 4, 1, padding=1, bias_attr=False)
y = layers.conv3d(x, 2, 1, padding=1, bias_attr=False)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv2DoubleGradCheck_AsyPadding(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 0, 0, 1],
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv2DoubleGradCheck_PaddingSAME(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
num_filters=2,
filter_size=1,
padding="SAME",
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv2DoubleGradCheck_PaddingVALID(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
num_filters=2,
filter_size=1,
padding="VALID",
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv2DoubleGradCheck_ChannelLast(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 1],
bias_attr=False,
use_cudnn=True,
groups=1,
data_format="NHWC")
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv2DoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv2d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 0, 1, 0],
bias_attr=False,
use_cudnn=True,
groups=1,
data_format="NHWC")
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv3DDoubleGradCheck_AsyPadding(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 2, 2, 2]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 0, 0, 1, 1, 2],
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv3DoubleGradCheck_PaddingSAME(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 2, 2, 2]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
num_filters=2,
filter_size=1,
padding="SAME",
groups=1,
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv3DoubleGradCheck_PaddingVALID(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 3, 3, 2]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
num_filters=2,
filter_size=1,
padding="VALID",
bias_attr=False,
use_cudnn=True)
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv3DDoubleGradCheck_ChannelLast(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 2, 2, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 1, 1],
bias_attr=False,
use_cudnn=True,
groups=1,
data_format="NDHWC")
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
w_arr = []
for p in w:
w_arr.append(np.random.uniform(-1, 1, p.shape).astype(dtype))
gradient_checker.double_grad_check(
[x] + w, y, x_init=[x_arr] + w_arr, place=place, eps=eps)
def test_grad(self):
places = [fluid.CPUPlace()]
if core.is_compiled_with_cuda():
places.append(fluid.CUDAPlace(0))
for p in places:
self.func(p)
class TestConv3dDoubleGradCheck_ChannelLast_AsyPadding(unittest.TestCase):
@prog_scope()
def func(self, place):
shape = [2, 2, 2, 2, 3]
eps = 0.005
dtype = np.float64
x = layers.data('x', shape, False, dtype)
y = layers.conv3d(
input=x,
num_filters=2,
filter_size=1,
padding=[1, 0, 1, 0, 1, 0],
bias_attr=False,
use_cudnn=True,
groups=1,
data_format="NDHWC")
x_arr = np.random.uniform(-1, 1, shape).astype(dtype)
w = fluid.default_main_program().global_block().all_parameters()
......
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