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提交 87a0a45d 编写于 作者: Z Zhang Ting 提交者： Aurelius84
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[cherry-pick] conv_transpose supports channel_last input, test=release/1.6 (#20072) (#20178)

上级 546a0d3c
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Showing with 2789 addition and 435 deletion
paddle/fluid/API.spec
浏览文件 @ 87a0a45d
......@@ -153,8 +153,8 @@ paddle.fluid.layers.batch_norm (ArgSpec(args=['input', 'act', 'is_test', 'moment
paddle.fluid.layers.instance_norm (ArgSpec(args=['input', 'epsilon', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1e-05, None, None, None)), ('document', '02972097e089629efdb0ed9404fd36ae'))
paddle.fluid.layers.data_norm (ArgSpec(args=['input', 'act', 'epsilon', 'param_attr', 'data_layout', 'in_place', 'name', 'moving_mean_name', 'moving_variance_name', 'do_model_average_for_mean_and_var'], varargs=None, keywords=None, defaults=(None, 1e-05, None, 'NCHW', False, None, None, None, False)), ('document', '2460b30fb87037555208fa8ac6fc1787'))
paddle.fluid.layers.beam_search_decode (ArgSpec(args=['ids', 'scores', 'beam_size', 'end_id', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '83e08f21af41ac8bac37aeab1f86fdd0'))
paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', 'ab58296b567bf0c686084add7f3280a4'))
paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None)), ('document', 'fe15dbfb17d97d3d29b2fa7ee6390ee6'))
paddle.fluid.layers.conv2d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCHW')), ('document', '9391d75358b6cba0cc5d22a01a223420'))
paddle.fluid.layers.conv3d_transpose (ArgSpec(args=['input', 'num_filters', 'output_size', 'filter_size', 'padding', 'stride', 'dilation', 'groups', 'param_attr', 'bias_attr', 'use_cudnn', 'act', 'name', 'data_format'], varargs=None, keywords=None, defaults=(None, None, 0, 1, 1, None, None, None, True, None, None, 'NCDHW')), ('document', '74bce3cd4224e6ff133d54508dc7f150'))
paddle.fluid.layers.sequence_expand (ArgSpec(args=['x', 'y', 'ref_level', 'name'], varargs=None, keywords=None, defaults=(-1, None)), ('document', '10e122eb755c2bd1f78ef2332b28f1a0'))
paddle.fluid.layers.sequence_expand_as (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '858c432e7cbd8bb952cc2eb555457d50'))
paddle.fluid.layers.sequence_pad (ArgSpec(args=['x', 'pad_value', 'maxlen', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'df08b9c499ab3a90f95d08ab5b6c6c62'))
......
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @ 87a0a45d
......@@ -16,6 +16,7 @@ limitations under the License. */
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/data_layout.h"
#include "paddle/fluid/platform/cudnn_workspace_helper.h"
#ifdef PADDLE_WITH_MKLDNN
......@@ -25,13 +26,15 @@ limitations under the License. */
namespace paddle {
namespace operators {
using DataLayout = framework::DataLayout;
void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE(ctx->HasInput("Input"),
"Input(Input) of ConvTransposeOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Filter"),
"Input(Filter) of ConvTransposeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Output"),
"Output(Output) of ConvTransposeOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("Input"), true,
"Input(Input) of ConvTransposeOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasInput("Filter"), true,
"Input(Filter) of ConvTransposeOp should not be null.");
PADDLE_ENFORCE_EQ(ctx->HasOutput("Output"), true,
"Output(Output) of ConvTransposeOp should not be null.");
auto in_dims = ctx->GetInputDim("Input");
auto filter_dims = ctx->GetInputDim("Filter");
......@@ -41,52 +44,75 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
int groups = ctx->Attrs().Get<int>("groups");
std::string padding_algorithm =
ctx->Attrs().Get<std::string>("padding_algorithm");
const DataLayout data_layout = framework::StringToDataLayout(
ctx->Attrs().Get<std::string>("data_format"));
PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
PADDLE_ENFORCE_EQ(in_dims.size() == 4 || in_dims.size() == 5, true,
"ConvTransposeOp intput should be 4-D or 5-D tensor.");
PADDLE_ENFORCE_EQ(in_dims.size(), filter_dims.size(),
"ConvTransposeOp input dimension and filter dimension "
"should be the same.");
PADDLE_ENFORCE(in_dims.size() - strides.size() == 2U,
"ConvTransposeOp input dimension and strides dimension should "
"be consistent.");
PADDLE_ENFORCE_EQ(
in_dims.size() - strides.size(), 2U,
"ConvTransposeOp input dimension and strides dimension should "
"be consistent.");
if (output_size.size())
PADDLE_ENFORCE_EQ(output_size.size(), strides.size(),
"ConvTransposeOp output_size dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(paddings.size(), strides.size(),
"ConvTransposeOp paddings dimension and strides "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(paddings.size(), dilations.size(),
"ConvTransposeOp paddings dimension and dilations "
"dimension should be the same.");
PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[0],
"In ConvTransposeOp, The number of input channels should "
"be equal to the number of filter's channels.");
std::vector<int64_t> output_shape({in_dims[0], filter_dims[1] * groups});
const int64_t C =
(data_layout == DataLayout::kNCHW ? in_dims[1]
: in_dims[in_dims.size() - 1]);
PADDLE_ENFORCE_EQ(
C, filter_dims[0],
"The number of input channels of Op(ConvTransposeOp) should "
"be equal to the number of filter's channels.");
framework::DDim in_data_dims;
if (data_layout == DataLayout::kNCHW) {
in_data_dims = framework::slice_ddim(in_dims, 2, in_dims.size());
} else {
in_data_dims = framework::slice_ddim(in_dims, 1, in_dims.size() - 1);
}
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 (data_layout == DataLayout::kNCHW) {
output_shape.push_back(filter_dims[1] * groups);
}
const int offset = (data_layout == DataLayout::kNCHW ? 2 : 1);
for (size_t i = 0; i < strides.size(); ++i) {
auto filter_extent = dilations[i] * (filter_dims[i + 2] - 1) + 1;
auto infer_shape =
(in_dims[i + 2] - 1) * strides[i] - 2 * paddings[i] + filter_extent;
auto infer_shape = (in_dims[i + offset] - 1) * strides[i] -
paddings[2 * i] - paddings[2 * i + 1] + filter_extent;
if (output_size.size()) {
PADDLE_ENFORCE((output_size[i] >= infer_shape &&
output_size[i] < infer_shape + strides[i]),
"ConvTransposeOp output_size should be "
"in appropriate range.");
PADDLE_ENFORCE_EQ((output_size[i] >= infer_shape &&
output_size[i] < infer_shape + strides[i]),
true,
"output_size of Op(ConvTransposeOp) should be "
"in appropriate range.");
output_shape.push_back(output_size[i]);
} else {
output_shape.push_back(infer_shape);
}
}
if (data_layout == DataLayout::kNHWC) {
output_shape.push_back(filter_dims[1] * groups);
}
ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
}
framework::OpKernelType ConvTransposeOp::GetExpectedKernelType(
const framework::ExecutionContext& ctx) const {
framework::LibraryType library_{framework::LibraryType::kPlain};
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
bool use_cudnn = ctx.Attr<bool>("use_cudnn");
use_cudnn &= platform::is_gpu_place(ctx.GetPlace());
#ifdef PADDLE_WITH_CUDA
......@@ -115,12 +141,11 @@ void Conv2DTransposeOpMaker::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 transpose operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of input channels, H is the height of the feature, and "
"W is the width of the feature.");
AddInput("Input",
"(Tensor) The input tensor of convolution transpose operator. "
"The format of input tensor is NCHW or NHWC. Where N is batch size, "
"C is the number of input channels, H is the height of the feature, "
"and W is the width of the feature.");
AddInput(
"Filter",
"(Tensor) The filter tensor of convolution transpose operator. "
......@@ -137,7 +162,7 @@ void Conv2DTransposeOpMaker::Make() {
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW.");
"The format of output tensor is the same as input tensor.");
AddAttr<std::vector<int>>("output_size",
"(vector<int> default: []), the "
"size of the output tensor")
......@@ -182,10 +207,15 @@ void Conv2DTransposeOpMaker::Make() {
"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, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
// TODO(dzhwinter): need to registered layout transform function
"Specify that the data format of the input and output data is "
"channel_first or channel_last.")
.SetDefault("NCHW");
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>("workspace_size_MB",
"Used in cudnn kernel only. workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......@@ -199,7 +229,7 @@ Convolution2D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and dilations, strides, paddings, 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 batchsize, C is the
Input(Input) and output(Output) are in NCHW or NHWC format. Where N is batchsize, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
Filter(Input) is in MCHW format. Where M is the number of input feature channels,
C is the number of output feature channels, H is the height of the filter,
......@@ -216,19 +246,19 @@ For an example:
Output shape: $(N, C_{out}, H_{out}, W_{out})$
Where
$$
H_{out} = (H_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (H_f - 1) + 1 \\
W_{out} = (W_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (W_f - 1) + 1
H_{out} = (H_{in} - 1) * strides[0] - pad_height_top - pad_height_bottom + dilations[0] * (H_f - 1) + 1 \\
W_{out} = (W_{in} - 1) * strides[1] - pad_width_left - pad_width_right + dilations[1] * (W_f - 1) + 1
$$
)DOC");
}
void Conv3DTransposeOpMaker::Make() {
AddInput("Input",
"(Tensor) The input tensor of convolution transpose operator."
"The format of input tensor is NCDHW. 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.");
AddInput(
"Input",
"(Tensor) The input tensor of convolution transpose operator."
"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.");
AddInput("Filter",
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is MCDHW, where M is the number of "
......@@ -240,7 +270,7 @@ void Conv3DTransposeOpMaker::Make() {
"the convolution3d transpose scenario.");
AddOutput("Output",
"(Tensor) The output tensor of convolution transpose operator."
"The format of output tensor is also NCDHW."
"The format of output tensor is the same as input tensor."
"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.");
......@@ -278,10 +308,15 @@ void Conv3DTransposeOpMaker::Make() {
"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, "
"the input will be transformed automatically. ")
.SetDefault("AnyLayout");
// TODO(dzhwinter): need to registered layout transform function
"Specify that the data format of the input and output data is "
"channel_first or channel_last.")
.SetDefault("NCHW");
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>("workspace_size_MB",
"Used in cudnn kernel only. workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
......@@ -295,7 +330,7 @@ Convolution3D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and dilations, strides, paddings, 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 size, C is the
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.
Filter(Input) is in MCDHW format. Where M is the number of input feature channels,
......@@ -313,9 +348,9 @@ Example:
Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
Where
$$
D_{out} = (D_{in} - 1) * strides[0] - 2 * paddings[0] + dilations[0] * (D_f - 1) + 1 \\
H_{out} = (H_{in} - 1) * strides[1] - 2 * paddings[1] + dilations[1] * (H_f - 1) + 1 \\
W_{out} = (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1
D_{out} = (D_{in} - 1) * strides[0] - pad_depth_front - pad_depth_back + dilations[0] * (D_f - 1) + 1 \\
H_{out} = (H_{in} - 1) * strides[1] - pad_height_top - pad_height_bottom + dilations[1] * (H_f - 1) + 1 \\
W_{out} = (W_{in} - 1) * strides[2] - pad_width_left - pad_width_right + dilations[2] * (W_f - 1) + 1
$$
)DOC");
}
......@@ -348,8 +383,7 @@ framework::OpKernelType ConvTransposeOpGrad::GetExpectedKernelType(
library_ = framework::LibraryType::kPlain;
}
std::string data_format = ctx.Attr<std::string>("data_format");
framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
framework::DataLayout layout_ = framework::DataLayout::kAnyLayout;
return framework::OpKernelType(ctx.Input<Tensor>("Input")->type(),
ctx.GetPlace(), layout_, library_);
}
......
paddle/fluid/operators/math/depthwise_conv.h
浏览文件 @ 87a0a45d
......@@ -22,6 +22,8 @@ namespace paddle {
namespace operators {
namespace math {
using DataLayout = framework::DataLayout;
/*
* \brief Compute the depthwise convolution which include
* forward process and backpropagation process
......@@ -34,7 +36,8 @@ class DepthwiseConvFunctor {
const framework::Tensor& filter,
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations, framework::Tensor* output);
const std::vector<int>& dilations, framework::Tensor* output,
const DataLayout data_layout = DataLayout::kNCHW);
};
template <typename DeviceContext, typename T,
......@@ -47,7 +50,8 @@ class DepthwiseConvInputGradFunctor {
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
framework::Tensor* input_grad);
framework::Tensor* input_grad,
const DataLayout data_layout = DataLayout::kNCHW);
};
template <typename DeviceContext, typename T,
......@@ -59,7 +63,8 @@ class DepthwiseConvFilterGradFunctor {
const std::vector<int>& strides,
const std::vector<int>& paddings,
const std::vector<int>& dilations,
framework::Tensor* filter_grad);
framework::Tensor* filter_grad,
const DataLayout data_layout = DataLayout::kNCHW);
};
} // namespace math
......
paddle/fluid/operators/math/im2col.cc
浏览文件 @ 87a0a45d
......@@ -32,7 +32,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
const std::vector<int>& padding, framework::Tensor* col,
const DataLayout data_layout) {
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.");
......@@ -41,16 +42,16 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
dilation[1] == 1) {
if (padding[0] == 0 && padding[1] == 0 && padding[2] == 0 &&
padding[3] == 0) {
im2col_sh1sw1dh1dw1ph0pw0<T>(im, col);
im2col_sh1sw1dh1dw1ph0pw0<T>(im, col, data_layout);
return;
} else if (padding[0] == 1 && padding[1] == 1 && padding[2] == 1 &&
padding[3] == 1) {
im2col_sh1sw1dh1dw1ph1pw1<T>(im, col);
im2col_sh1sw1dh1dw1ph1pw1<T>(im, col, data_layout);
return;
}
// TODO(TJ): complete padding >=2
}
im2col_common<T>(im, dilation, stride, padding, col);
im2col_common<T>(im, dilation, stride, padding, col, data_layout);
}
};
......@@ -67,13 +68,17 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
const std::vector<int>& padding, framework::Tensor* im,
const DataLayout data_layout) {
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];
int im_channels =
(data_layout == DataLayout::kNCHW ? im->dims()[0] : im->dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im->dims()[1] : im->dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im->dims()[2] : im->dims()[1]);
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
......@@ -109,7 +114,15 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
if ((im_row_idx) >= 0 && (im_row_idx) < im_height &&
(im_col_idx) >= 0 && (im_col_idx) < im_width) {
im_data[(im_row_idx + c_im * im_height) * im_width + im_col_idx] +=
int im_offset;
if (data_layout == DataLayout::kNCHW) {
im_offset =
(c_im * im_height + im_row_idx) * im_width + im_col_idx;
} else {
im_offset =
(im_row_idx * im_width + im_col_idx) * im_channels + c_im;
}
im_data[im_offset] +=
col_data[(c * col_height + h) * col_width + w];
}
}
......@@ -139,7 +152,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
void operator()(const platform::CPUDeviceContext& context,
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
const std::vector<int>& padding, framework::Tensor* col,
const DataLayout data_layout) {
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.");
......@@ -202,7 +216,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
const std::vector<int>& padding, framework::Tensor* im,
const DataLayout data_layout) {
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.");
......
paddle/fluid/operators/math/im2col.cu
浏览文件 @ 87a0a45d
......@@ -26,27 +26,41 @@ __global__ void im2col(const T* data_im, int num_outs, int im_height,
int im_width, int dilation_h, int dilation_w,
int filter_height, int filter_width, int stride_height,
int stride_width, int padding_height, int padding_width,
int col_height, int col_width, T* data_col) {
int col_height, int col_width, T* data_col,
const DataLayout data_layout) {
int input_channels = num_outs / col_height / col_width;
int channels_col = input_channels * filter_height * filter_width;
const int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
if (index < num_outs) {
int w_out = index % col_width;
int h_out = (index / col_width) % col_height;
int channel_in = index / col_width / col_height;
int w_out = (data_layout == DataLayout::kNCHW
? index % col_width
: (index / input_channels) % col_width);
int h_out = (data_layout == DataLayout::kNCHW
? (index / col_width) % col_height
: (index / input_channels / col_width) % col_height);
int channel_in =
(data_layout == DataLayout::kNCHW ? index / col_width / col_height
: index % input_channels);
int channel_out = channel_in * filter_height * filter_width;
int h_in = h_out * stride_height - padding_height;
int w_in = w_out * stride_width - padding_width;
data_col += (channel_out * col_height + h_out) * col_width + w_out;
data_im += (channel_in * im_height + h_in) * im_width + w_in;
for (int i = 0; i < filter_height; ++i) {
for (int j = 0; j < filter_width; ++j) {
int rIdx = h_in + i * dilation_h;
int cIdx = w_in + j * dilation_w;
int im_idx;
if (data_layout == DataLayout::kNCHW) {
im_idx = (channel_in * im_height + rIdx) * im_width + cIdx;
} else {
im_idx = (rIdx * im_width + cIdx) * input_channels + channel_in;
}
*data_col =
(rIdx >= im_height || rIdx < 0 || cIdx >= im_width || cIdx < 0)
? 0
: data_im[i * dilation_h * im_width + j * dilation_w];
: data_im[im_idx];
data_col += col_height * col_width;
}
}
......@@ -65,13 +79,18 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE_EQ(im.dims().size(), 3);
PADDLE_ENFORCE_EQ(col->dims().size(), 5);
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
const std::vector<int>& padding, framework::Tensor* col,
const DataLayout data_layout) {
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 =
(data_layout == DataLayout::kNCHW ? im.dims()[0] : im.dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im.dims()[1] : im.dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im.dims()[2] : im.dims()[1]);
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int col_height = col->dims()[3];
......@@ -86,7 +105,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
im2col<T><<<grid, threads, 0, context.stream()>>>(
im.data<T>(), num_outputs, im_height, im_width, dilation[0],
dilation[1], filter_height, filter_width, stride[0], stride[1],
padding[0], padding[1], col_height, col_width, col->data<T>());
padding[0], padding[1], col_height, col_width, col->data<T>(),
data_layout);
}
};
......@@ -95,18 +115,27 @@ __global__ void col2im(int n, const T* data_col, int im_height, int im_width,
int dilation_h, int dilation_w, int filter_height,
int filter_width, int stride_height, int stride_width,
int padding_height, int padding_width, int col_height,
int col_width, T* data_im) {
int col_width, T* data_im,
const DataLayout data_layout) {
const int index =
(blockIdx.x * gridDim.y + blockIdx.y) * blockDim.x + threadIdx.x;
const int d_filter_height = dilation_h * (filter_height - 1) + 1;
const int d_filter_width = dilation_w * (filter_width - 1) + 1;
int input_channels = n / im_height / im_width;
if (index < n) {
T val = 0;
int w = index % im_width + padding_width;
int h = (index / im_width) % im_height + padding_height;
int c = index / (im_width * im_height);
int w = (data_layout == DataLayout::kNCHW
? index % im_width + padding_width
: (index / input_channels) % im_width + padding_width);
int h = (data_layout == DataLayout::kNCHW
? (index / im_width) % im_height + padding_height
: (index / input_channels / im_width) % im_height +
padding_height);
int c = (data_layout == DataLayout::kNCHW ? index / im_width / im_height
: index % input_channels);
// compute the start and end of the output
int w_col_start =
......@@ -151,13 +180,18 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE_EQ(im->dims().size(), 3);
PADDLE_ENFORCE_EQ(col.dims().size(), 5);
int im_channels = im->dims()[0];
int im_height = im->dims()[1];
int im_width = im->dims()[2];
const std::vector<int>& padding, framework::Tensor* im,
const DataLayout data_layout) {
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 =
(data_layout == DataLayout::kNCHW ? im->dims()[0] : im->dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im->dims()[1] : im->dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im->dims()[2] : im->dims()[1]);
int filter_height = col.dims()[1];
int filter_width = col.dims()[2];
int col_height = col.dims()[3];
......@@ -191,7 +225,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
col2im<T><<<grid, threads, 0, context.stream()>>>(
num_kernels, col.data<T>(), im_height, im_width, dilation[0],
dilation[1], filter_height, filter_width, stride[0], stride[1],
padding[0], padding[2], col_height, col_width, im->data<T>());
padding[0], padding[1], col_height, col_width, im->data<T>(),
data_layout);
}
};
......@@ -248,9 +283,12 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
void operator()(const platform::CUDADeviceContext& context,
const framework::Tensor& im, const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col) {
PADDLE_ENFORCE_EQ(im.dims().size(), 3);
PADDLE_ENFORCE_EQ(col->dims().size(), 5);
const std::vector<int>& padding, framework::Tensor* col,
const DataLayout data_layout) {
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];
......@@ -330,9 +368,12 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im) {
PADDLE_ENFORCE_EQ(im->dims().size(), 3);
PADDLE_ENFORCE_EQ(col.dims().size(), 5);
const std::vector<int>& padding, framework::Tensor* im,
const DataLayout data_layout) {
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/im2col.h
浏览文件 @ 87a0a45d
......@@ -23,6 +23,8 @@ namespace paddle {
namespace operators {
namespace math {
using DataLayout = framework::DataLayout;
/* The storage format of the coldata in the Im2ColFunctor and Col2ImFunctor. */
enum class ColFormat { kCFO = 0, kOCF = 1 };
......@@ -86,7 +88,8 @@ class Im2ColFunctor {
void operator()(const DeviceContext& context, const framework::Tensor& im,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* col);
const std::vector<int>& padding, framework::Tensor* col,
const DataLayout data_layout = DataLayout::kNCHW);
};
template <ColFormat Format, typename DeviceContext, typename T>
......@@ -95,7 +98,8 @@ class Col2ImFunctor {
void operator()(const DeviceContext& context, const framework::Tensor& col,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding, framework::Tensor* im);
const std::vector<int>& padding, framework::Tensor* im,
const DataLayout data_layout = DataLayout::kNCHW);
};
} // namespace math
......
paddle/fluid/operators/math/im2col_cfo_cpu.h
浏览文件 @ 87a0a45d
......@@ -30,10 +30,14 @@ inline void im2col_common(const framework::Tensor& im,
const std::vector<int>& dilation,
const std::vector<int>& stride,
const std::vector<int>& padding,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
framework::Tensor* col,
const DataLayout data_layout = DataLayout::kNCHW) {
int im_channels =
(data_layout == DataLayout::kNCHW ? im.dims()[0] : im.dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im.dims()[1] : im.dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im.dims()[2] : im.dims()[1]);
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
......@@ -50,8 +54,14 @@ inline void im2col_common(const framework::Tensor& im,
int im_row_idx = h * stride[0] - padding[0] + h_offset * dilation[0];
for (int w = 0; w < output_width; ++w) {
int im_col_idx = w * stride[1] - padding[1] + w_offset * dilation[1];
int im_idx;
if (data_layout == DataLayout::kNCHW) {
im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
} else {
im_idx = (im_row_idx * im_width + im_col_idx) * im_channels + c_im;
}
int col_idx = (c * output_height + h) * output_width + w;
int im_idx = (im_row_idx + c_im * im_height) * im_width + im_col_idx;
col_data[col_idx] = (im_row_idx < 0 || im_row_idx >= im_height ||
im_col_idx < 0 || im_col_idx >= im_width)
? static_cast<T>(0)
......@@ -65,11 +75,15 @@ inline void im2col_common(const framework::Tensor& im,
* im2col algorithm with strides == 1, dilations == 1, paddings == 0
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
inline void im2col_sh1sw1dh1dw1ph0pw0(
const framework::Tensor& im, framework::Tensor* col,
const DataLayout data_layout = DataLayout::kNCHW) {
int im_channels =
(data_layout == DataLayout::kNCHW ? im.dims()[0] : im.dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im.dims()[1] : im.dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im.dims()[2] : im.dims()[1]);
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
......@@ -89,7 +103,14 @@ inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
const T* src_data = src_data_ic;
for (int kh = 0; kh < filter_height; ++kh) {
for (int kw = 0; kw < filter_width; ++kw) {
std::memcpy(dst_data, src_data + kw, copy_size);
if (data_layout == DataLayout::kNCHW) {
std::memcpy(dst_data, src_data + kw, copy_size);
} else {
for (int kow = 0; kow < output_width; ++kow) {
dst_data[kow] =
im_data[((oh + kh) * im_width + kw + kow) * im_channels + ic];
}
}
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
......@@ -107,10 +128,14 @@ inline void im2col_sh1sw1dh1dw1ph0pw0(const framework::Tensor& im,
*/
template <typename T>
inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
framework::Tensor* col) {
int im_channels = im.dims()[0];
int im_height = im.dims()[1];
int im_width = im.dims()[2];
framework::Tensor* col,
const DataLayout data_layout) {
int im_channels =
(data_layout == DataLayout::kNCHW ? im.dims()[0] : im.dims()[2]);
int im_height =
(data_layout == DataLayout::kNCHW ? im.dims()[1] : im.dims()[0]);
int im_width =
(data_layout == DataLayout::kNCHW ? im.dims()[2] : im.dims()[1]);
int filter_height = col->dims()[1];
int filter_width = col->dims()[2];
int output_height = col->dims()[3];
......@@ -180,7 +205,17 @@ inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
dst_data = dst_data + col_matrix_width;
continue;
}
std::memcpy(dst_data + plw, src_data, copy_size);
if (data_layout == DataLayout::kNCHW) {
std::memcpy(dst_data + plw, src_data, copy_size);
} else {
for (int kow = 0; kow < output_width - plw - prw; ++kow) {
dst_data[plw + kow] =
im_data[(((oh - plh > 0 ? oh - plh : 0) + kh) * im_width +
kow) *
im_channels +
ic];
}
}
dst_data = dst_data + col_matrix_width;
src_data = src_data + im_width;
}
......@@ -226,19 +261,49 @@ inline void im2col_sh1sw1dh1dw1ph1pw1(const framework::Tensor& im,
// TODO(TJ): reuse plw-kw outside this for
// try to unify
for (int kw = 0; kw < plw; ++kw) {
std::memcpy(dst_data + (plw - kw), src_data,
sizeof(T) * (output_width - (plw - kw)));
if (data_layout == DataLayout::kNCHW) {
std::memcpy(dst_data + (plw - kw), src_data,
sizeof(T) * (output_width - (plw - kw)));
} else {
for (int kow = 0; kow < output_width - (plw - kw); ++kow) {
dst_data[plw - kw + kow] =
im_data[(((oh - plh > 0 ? oh - plh : 0) + kh) * im_width +
kow) *
im_channels +
ic];
}
}
dst_data = dst_data + col_matrix_width;
}
for (int kw = plw; kw < filter_width - prw; ++kw) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * output_width);
if (data_layout == DataLayout::kNCHW) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * output_width);
} else {
for (int kow = 0; kow < output_width; ++kow) {
dst_data[kow] =
im_data[(((oh - plh > 0 ? oh - plh : 0) + kh) * im_width +
kw - plw + kow) *
im_channels +
ic];
}
}
dst_data = dst_data + col_matrix_width;
}
int i = 1;
for (int kw = filter_width - prw; kw < filter_width; ++kw, ++i) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * (output_width - i));
if (data_layout == DataLayout::kNCHW) {
std::memcpy(dst_data, src_data + (kw - plw),
sizeof(T) * (output_width - i));
} else {
for (int kow = 0; kow < output_width - i; ++kow) {
dst_data[kow] =
im_data[(((oh - plh > 0 ? oh - plh : 0) + kh) * im_width +
kw - plw + kow) *
im_channels +
ic];
}
}
dst_data = dst_data + col_matrix_width;
}
src_data = src_data + im_width;
......
paddle/fluid/operators/math/vol2col.cc
浏览文件 @ 87a0a45d
......@@ -32,16 +32,21 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const {
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
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];
int input_width = vol.dims()[3];
int input_channels =
(data_layout == DataLayout::kNCHW ? vol.dims()[0] : vol.dims()[3]);
int input_depth =
(data_layout == DataLayout::kNCHW ? vol.dims()[1] : vol.dims()[0]);
int input_height =
(data_layout == DataLayout::kNCHW ? vol.dims()[2] : vol.dims()[1]);
int input_width =
(data_layout == DataLayout::kNCHW ? vol.dims()[3] : vol.dims()[2]);
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
......@@ -59,6 +64,7 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
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] +
......@@ -97,10 +103,16 @@ class Vol2ColFunctor<platform::CPUDeviceContext, T> {
int col_idx =
((c * output_depth + d) * output_height + h) * output_width + w;
int vol_idx =
((c_in * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
int vol_idx;
if (data_layout == DataLayout::kNCHW) {
vol_idx = ((c_in * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
} else {
vol_idx = ((d_pad * input_height + h_pad) * input_width + w_pad) *
input_channels +
c_in;
}
col_data[col_idx] =
(h_pad < 0 || h_pad >= input_height || w_pad < 0 ||
w_pad >= input_width || d_pad < 0 || d_pad >= input_depth)
......@@ -126,16 +138,21 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const {
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
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];
int input_width = vol->dims()[3];
int input_channels =
(data_layout == DataLayout::kNCHW ? vol->dims()[0] : vol->dims()[3]);
int input_depth =
(data_layout == DataLayout::kNCHW ? vol->dims()[1] : vol->dims()[0]);
int input_height =
(data_layout == DataLayout::kNCHW ? vol->dims()[2] : vol->dims()[1]);
int input_width =
(data_layout == DataLayout::kNCHW ? vol->dims()[3] : vol->dims()[2]);
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
......@@ -191,11 +208,17 @@ class Col2VolFunctor<platform::CPUDeviceContext, T> {
if (h_pad >= 0 && h_pad < input_height && w_pad >= 0 &&
w_pad < input_width && d_pad >= 0 && d_pad < input_depth) {
int vol_idx =
((cIm * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
int vol_idx;
if (data_layout == DataLayout::kNCHW) {
vol_idx = ((cIm * input_depth + d_pad) * input_height + h_pad) *
input_width +
w_pad;
} else {
vol_idx =
((d_pad * input_height + h_pad) * input_width + w_pad) *
input_channels +
cIm;
}
int col_idx =
((c * output_depth + d) * output_height + h) * output_width +
w;
......
paddle/fluid/operators/math/vol2col.cu
浏览文件 @ 87a0a45d
......@@ -28,7 +28,12 @@ __global__ void vol2col(int num_kernels, const T* data_vol, int depth,
int filter_width, int stride_depth, int stride_height,
int stride_width, int padding_depth, int padding_height,
int padding_width, int output_detph, int output_height,
int output_width, T* data_col) {
int output_width, T* data_col,
const DataLayout data_layout) {
int input_channels =
num_kernels / output_detph / output_height / output_width;
int channels_col =
input_channels * filter_depth * filter_height * filter_width;
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
int w_out = index % output_width;
......@@ -43,18 +48,22 @@ __global__ void vol2col(int num_kernels, const T* data_vol, int depth,
data_col += ((channel_out * output_detph + d_out) * output_height + h_out) *
output_width +
w_out;
data_vol += ((channel_in * depth + d_in) * height + h_in) * width + w_in;
for (int k = 0; k < filter_depth; ++k) {
for (int i = 0; i < filter_height; ++i) {
for (int j = 0; j < filter_width; ++j) {
int d = d_in + k * dilation_d;
int h = h_in + i * dilation_h;
int w = w_in + j * dilation_w;
int col_idx = (k * dilation_d * height + i * dilation_h) * width +
j * dilation_w;
int vol_idx;
if (data_layout == DataLayout::kNCHW) {
vol_idx = ((channel_in * depth + d) * height + h) * width + w;
} else {
vol_idx =
((d * height + h) * width + w) * input_channels + channel_in;
}
*data_col = (d >= 0 && d < depth && h >= 0 && h < height && w >= 0 &&
w < width)
? data_vol[col_idx]
? data_vol[vol_idx]
: 0;
data_col += output_detph * output_height * output_width;
}
......@@ -64,7 +73,10 @@ __global__ void vol2col(int num_kernels, const T* data_vol, int depth,
}
/*
* im = [input_channels,intpu_depth, input_height, input_width]
* im = [input_channels,intpu_depth, input_height, input_width] for
* channels_first
* im = [input_depth, input_height, input_width, input_channels] for
* channels_last
* col =
* [input_channels, filter_depth, filter_height, filter_width,
* output_depth, output_height, output_width]
......@@ -76,15 +88,21 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const {
PADDLE_ENFORCE_EQ(vol.dims().size(), 4);
PADDLE_ENFORCE_EQ(col->dims().size(), 7);
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout) const {
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];
int input_width = vol.dims()[3];
int input_channels =
(data_layout == DataLayout::kNCHW ? vol.dims()[0] : vol.dims()[3]);
int input_depth =
(data_layout == DataLayout::kNCHW ? vol.dims()[1] : vol.dims()[0]);
int input_height =
(data_layout == DataLayout::kNCHW ? vol.dims()[2] : vol.dims()[1]);
int input_width =
(data_layout == DataLayout::kNCHW ? vol.dims()[3] : vol.dims()[2]);
int filter_depth = col->dims()[1];
int filter_height = col->dims()[2];
int filter_width = col->dims()[3];
......@@ -130,7 +148,8 @@ class Vol2ColFunctor<platform::CUDADeviceContext, T> {
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], pad_d_forth, pad_h_up,
pad_w_left, output_depth, output_height, output_width, col->data<T>());
pad_w_left, output_depth, output_height, output_width, col->data<T>(),
data_layout);
}
};
......@@ -141,18 +160,27 @@ __global__ void col2vol(int num_kernels, const T* data_col, int depth,
int filter_width, int stride_depth, int stride_height,
int stride_width, int padding_depth, int padding_height,
int padding_width, int output_detph, int output_height,
int output_width, T* data_vol) {
int output_width, T* data_vol,
const DataLayout data_layout) {
const int d_filter_depth = dilation_d * (filter_depth - 1) + 1;
const int d_filter_height = dilation_h * (filter_height - 1) + 1;
const int d_filter_width = dilation_w * (filter_width - 1) + 1;
int input_channels = num_kernels / depth / height / width;
for (int index = blockIdx.x * blockDim.x + threadIdx.x; index < num_kernels;
index += blockDim.x * gridDim.x) {
T src_val = 0;
int w = index % width + padding_width;
int h = (index / width) % height + padding_height;
int d = (index / width / height) % depth + padding_depth;
int c = index / width / height / depth;
int w = (data_layout == DataLayout::kNCHW
? index % width + padding_width
: (index / input_channels) % width + padding_width);
int h = (data_layout == DataLayout::kNCHW
? (index / width) % height + padding_height
: (index / input_channels / width) % height + padding_height);
int d = (data_layout == DataLayout::kNCHW
? (index / width / height) % depth + padding_depth
: index / input_channels / width / height + padding_depth);
int c = (data_layout == DataLayout::kNCHW ? index / width / height / depth
: index % input_channels);
// compute the start and end of the output
int w_col_start =
......@@ -196,7 +224,10 @@ __global__ void col2vol(int num_kernels, const T* data_col, int depth,
}
/*
* im = [input_channels, input_depth, input_height, input_width]
* im = [input_channels,intpu_depth, input_height, input_width] for
* channels_first
* im = [input_depth, input_height, input_width, input_channels] for
* channels_last
* col =
* [input_channels, filter_depth, filter_height, filter_width,
* output_depth, output_height, output_width]
......@@ -208,15 +239,21 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const {
PADDLE_ENFORCE_EQ(vol->dims().size(), 4);
PADDLE_ENFORCE_EQ(col.dims().size(), 7);
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout) const {
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];
int input_width = vol->dims()[3];
int input_channels =
(data_layout == DataLayout::kNCHW ? vol->dims()[0] : vol->dims()[3]);
int input_depth =
(data_layout == DataLayout::kNCHW ? vol->dims()[1] : vol->dims()[0]);
int input_height =
(data_layout == DataLayout::kNCHW ? vol->dims()[2] : vol->dims()[1]);
int input_width =
(data_layout == DataLayout::kNCHW ? vol->dims()[3] : vol->dims()[2]);
int filter_depth = col.dims()[1];
int filter_height = col.dims()[2];
int filter_width = col.dims()[3];
......@@ -263,7 +300,8 @@ class Col2VolFunctor<platform::CUDADeviceContext, T> {
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], pad_d_forth, pad_h_up,
pad_w_left, output_depth, output_height, output_width, vol->data<T>());
pad_w_left, output_depth, output_height, output_width, vol->data<T>(),
data_layout);
}
};
......
paddle/fluid/operators/math/vol2col.h
浏览文件 @ 87a0a45d
......@@ -22,6 +22,9 @@ limitations under the License. */
namespace paddle {
namespace operators {
namespace math {
using DataLayout = framework::DataLayout;
/*
* \brief Converts the feature data of four dimensions(CDHW) into a colData of
* seven dimensions in the Vol2ColFunctor calculation,
......@@ -70,8 +73,8 @@ class Vol2ColFunctor {
void operator()(const DeviceContext& context, const framework::Tensor& vol,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* col) const;
const std::vector<int>& paddings, framework::Tensor* col,
const DataLayout data_layout = DataLayout::kNCHW) const;
};
template <typename DeviceContext, typename T>
......@@ -80,8 +83,8 @@ class Col2VolFunctor {
void operator()(const DeviceContext& context, const framework::Tensor& col,
const std::vector<int>& dilations,
const std::vector<int>& strides,
const std::vector<int>& paddings,
framework::Tensor* vol) const;
const std::vector<int>& paddings, framework::Tensor* vol,
const DataLayout data_layout = DataLayout::kNCHW) const;
};
} // namespace math
......
python/paddle/fluid/layers/nn.py
浏览文件 @ 87a0a45d
此差异已折叠。
python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
浏览文件 @ 87a0a45d
......@@ -18,10 +18,19 @@ import unittest
import numpy as np
import paddle.fluid.core as core
import paddle.fluid as fluid
from op_test import OpTest
def conv3dtranspose_forward_naive(input_, filter_, attrs):
padding_algorithm = attrs['padding_algorithm']
if padding_algorithm not in ["SAME", "VALID", "EXPLICIT"]:
raise ValueError("Unknown Attr(padding_algorithm): '%s'. "
"It can only be 'SAME' or 'VALID'." %
str(padding_algorithm))
if attrs['data_format'] == 'NHWC':
input_ = np.transpose(input_, [0, 4, 1, 2, 3])
in_n, in_c, in_d, in_h, in_w = input_.shape
f_c, f_out_c, f_d, f_h, f_w = filter_.shape
groups = attrs['groups']
......@@ -32,6 +41,39 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
stride, pad, dilations = attrs['strides'], attrs['paddings'], attrs[
'dilations']
def _get_padding_with_SAME(input_shape, kernel_size, kernel_stride):
padding = []
for input_size, filter_size, stride_size in zip(
input_shape, kernel_size, kernel_stride):
out_size = int((input_size + stride_size - 1) / stride_size)
pad_sum = np.max((
(out_size - 1) * stride_size + filter_size - input_size, 0))
pad_0 = int(pad_sum / 2)
pad_1 = int(pad_sum - pad_0)
padding.append(pad_0)
padding.append(pad_1)
return padding
ksize = filter_.shape[2:5]
if padding_algorithm == "VALID":
pad = [0, 0, 0, 0, 0, 0]
elif padding_algorithm == "SAME":
dilation = [1, 1, 1]
input_data_shape = []
if attrs['data_format'] == "NCHW":
input_data_shape = input_.shape[2:5]
elif attrs['data_format'] == "NHWC":
input_data_shape = input_.shape[1:4]
pad = _get_padding_with_SAME(input_data_shape, ksize, stride)
pad_d_0, pad_d_1 = pad[0], pad[0]
pad_h_0, pad_h_1 = pad[1], pad[1]
pad_w_0, pad_w_1 = pad[2], pad[2]
if len(pad) == 6:
pad_d_0, pad_d_1 = pad[0], pad[1]
pad_h_0, pad_h_1 = pad[2], pad[3]
pad_w_0, pad_w_1 = pad[4], pad[5]
d_bolck_d = dilations[0] * (f_d - 1) + 1
d_bolck_h = dilations[1] * (f_h - 1) + 1
d_bolck_w = dilations[2] * (f_w - 1) + 1
......@@ -62,8 +104,10 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
out[n, g * f_out_c + k, d1:d2:dilations[0], i1:i2:
dilations[1], j1:j2:dilations[2]] += tmp_out
out = out[:, :, pad[0]:out_d - pad[0], pad[1]:out_h - pad[1], pad[2]:out_w -
pad[2]]
out = out[:, :, pad_d_0:out_d - pad_d_1, pad_h_0:out_h - pad_h_1, pad_w_0:
out_w - pad_w_1]
if attrs['data_format'] == 'NHWC':
out = np.transpose(out, [0, 2, 3, 4, 1])
return out
......@@ -71,6 +115,9 @@ class TestConv3dTransposeOp(OpTest):
def setUp(self):
# init as conv transpose
self.use_cudnn = False
self.data_format = 'NCHW'
self.pad = [0, 0, 0]
self.padding_algorithm = "EXPLICIT"
self.init_op_type()
self.init_test_case()
......@@ -81,10 +128,11 @@ class TestConv3dTransposeOp(OpTest):
self.attrs = {
'strides': self.stride,
'paddings': self.pad,
'padding_algorithm': self.padding_algorithm,
'dilations': self.dilations,
'groups': self.groups,
'use_cudnn': self.use_cudnn,
'data_format': 'AnyLayout' # TODO(dzhwinter) : should be fix latter
'data_format': self.data_format
}
output = conv3dtranspose_forward_naive(input_, filter_,
......@@ -154,7 +202,7 @@ class TestConv3dTransposeOp(OpTest):
self.op_type = "conv3d_transpose"
class TestWithPad(TestConv3dTransposeOp):
class TestWithSymmetricPad(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
......@@ -165,6 +213,39 @@ class TestWithPad(TestConv3dTransposeOp):
self.filter_size = [f_c, 6, 3, 3, 3]
class TestWithAsymmetricPad(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 0, 1, 0, 1, 2]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
class TestWithSAMEPad(TestConv3dTransposeOp):
def init_test_case(self):
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.padding_algorithm = 'SAME'
class TestWithVALIDPad(TestConv3dTransposeOp):
def init_test_case(self):
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.padding_algorithm = 'VALID'
class TestWithGroups(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
......@@ -198,6 +279,78 @@ class TestWithDilation(TestConv3dTransposeOp):
self.filter_size = [f_c, 6, 3, 3, 3]
class Test_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
class TestWithSymmetricPad_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
class TestWithAsymmetricPad_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 0, 1, 0, 1, 2]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
class TestWithGroups_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 2
self.input_size = [2, 5, 5, 5, 4] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 3, 3, 3, 3]
self.data_format = 'NHWC'
class TestWithStride_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NCDHW
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
class TestWithDilation_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [2, 2, 2]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NCDHW
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
# ------------ test_cudnn ------------
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
......@@ -209,7 +362,7 @@ class TestCUDNN(TestConv3dTransposeOp):
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithPad(TestWithPad):
class TestCUDNNWithSymmetricPad(TestWithSymmetricPad):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
......@@ -224,6 +377,57 @@ class TestCUDNNWithPad(TestWithPad):
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithAsymmetricPad(TestWithAsymmetricPad):
def init_test_case(self):
self.pad = [1, 1, 1, 0, 0, 2]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 4, 4, 4] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithSAMEPad(TestWithSAMEPad):
def init_test_case(self):
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.padding_algorithm = 'SAME'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithVALIDPad(TestWithVALIDPad):
def init_test_case(self):
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 3, 5, 5, 5] # NCDHW
f_c = self.input_size[1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.padding_algorithm = 'VALID'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithStride(TestWithStride):
......@@ -272,5 +476,222 @@ class TestCUDNNWithGroups(TestWithGroups):
# def init_op_type(self):
# self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNN_NHWC(TestConv3dTransposeOp):
def init_test_case(self):
self.pad = [0, 0, 0]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithSymmetricPad_NHWC(TestWithSymmetricPad):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithAsymmetricPad_NHWC(TestWithAsymmetricPad):
def init_test_case(self):
self.pad = [1, 0, 1, 0, 0, 2]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NDHWC
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithStride_NHWC(TestWithStride):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [2, 2, 2]
self.dilations = [1, 1, 1]
self.groups = 1
self.input_size = [2, 5, 5, 5, 3] # NCDHW
f_c = self.input_size[-1]
self.filter_size = [f_c, 6, 3, 3, 3]
self.data_format = 'NHWC'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
@unittest.skipIf(not core.is_compiled_with_cuda(),
"core is not compiled with CUDA")
class TestCUDNNWithGroups_NHWC(TestWithGroups):
def init_test_case(self):
self.pad = [1, 1, 1]
self.stride = [1, 1, 1]
self.dilations = [1, 1, 1]
self.groups = 2
self.input_size = [2, 5, 5, 5, 4] # NCHW
f_c = self.input_size[-1]
self.filter_size = [f_c, 3, 3, 3, 3]
self.data_format = 'NHWC'
def init_op_type(self):
self.use_cudnn = True
self.op_type = "conv3d_transpose"
class TestConv3dTransposeAPI(OpTest):
def test_case1(self):
data1 = fluid.layers.data(
name='data1', shape=[3, 5, 5, 5], dtype='float32')
data2 = fluid.layers.data(
name='data2', shape=[5, 5, 5, 3], dtype='float32')
out1 = fluid.layers.conv3d_transpose(
input=data1,
groups=1,
num_filters=6,
filter_size=3,
data_format='NCDHW')
out2 = fluid.layers.conv3d_transpose(
input=data2,
groups=1,
num_filters=6,
filter_size=3,
data_format='NDHWC')
out3 = fluid.layers.conv3d_transpose(
input=data1,
groups=1,
num_filters=6,
filter_size=3,
padding=[[0, 0], [0, 0], [1, 1], [0, 0], [1, 1]],
data_format='NCDHW')
out4 = fluid.layers.conv3d_transpose(
input=data2,
groups=3,
num_filters=6,
filter_size=3,
padding=[[0, 0], [0, 0], [1, 1], [1, 2], [0, 0]],
data_format='NDHWC')
out5 = fluid.layers.conv3d_transpose(
input=data2,
groups=1,
num_filters=6,
filter_size=3,
padding='SAME',
data_format='NCDHW')
out6 = fluid.layers.conv3d_transpose(
input=data2,
groups=1,
num_filters=6,
filter_size=3,
padding='VALID',
data_format='NDHWC')
out7 = fluid.layers.conv3d_transpose(
input=data2,
groups=1,
num_filters=6,
output_size=[7, 7, 7],
padding=[0, 0, 0],
data_format='NDHWC')
data1_np = np.random.random((2, 3, 5, 5, 5)).astype("float32")
data2_np = np.random.random((2, 5, 5, 5, 3)).astype("float32")
if core.is_compiled_with_cuda():
place = core.CUDAPlace(0)
else:
place = core.CPUPlace()
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
results = exe.run(
fluid.default_main_program(),
feed={"data1": data1_np,
"data2": data2_np},
fetch_list=[out1, out2, out3, out4, out5, out6, out7],
return_numpy=True)
self.assertIsNotNone(results[0])
self.assertIsNotNone(results[1])
self.assertIsNotNone(results[2])
self.assertIsNotNone(results[3])
self.assertIsNotNone(results[4])
self.assertIsNotNone(results[5])
self.assertIsNotNone(results[6])
class TestConv3dTransposeOpException(OpTest):
def test_exception(self):
data = fluid.layers.data(
name='data', shape=[3, 5, 5, 5], dtype="float32")
def attr_data_format():
out = fluid.layers.conv2d_transpose(
input=data,
groups=1,
num_filters=6,
filter_size=3,
data_format="NCDW")
self.assertRaises(ValueError, attr_data_format)
def attr_padding_str():
out = fluid.layers.conv2d_transpose(
input=data,
groups=1,
num_filters=6,
filter_size=3,
padding='Vald')
self.assertRaises(ValueError, attr_padding_str)
def attr_padding_list():
out = fluid.layers.conv2d_transpose(
input=data,
groups=1,
num_filters=6,
filter_size=3,
padding=[[1, 1], [1, 1], [0, 0], [0, 0], [1, 1]])
self.assertRaises(ValueError, attr_padding_list)
def attr_padding_with_data_format():
out = fluid.layers.conv2d_transpose(
input=data,
groups=1,
num_filters=6,
filter_size=3,
padding=[[1, 1], [0, 0], [0, 0], [1, 0], [1, 1]],
data_format='NDHWC')
self.assertRaises(ValueError, attr_padding_with_data_format)
if __name__ == '__main__':
unittest.main()
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