m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

conv_transpose supports channel_last input, test=develop, test=document_preview (#20072)

Browse Source
fix-python-transpose
Zhang Ting 6 years ago committed by hong
parent c9139c3db3
commit cf6919bf6e
18 changed files with 2789 additions and 700 deletions
Show Stats Download Patch File Download Diff File

4
paddle/fluid/API.spec
Unescape View File

@ -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'))

586
paddle/fluid/operators/conv_transpose_cudnn_op.cu
View File

File diff suppressed because it is too large Load Diff

265
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
View File

File diff suppressed because it is too large Load Diff

156
paddle/fluid/operators/conv_transpose_op.cc
View File

File diff suppressed because it is too large Load Diff

0
paddle/fluid/operators/conv_transpose_op.cu.cc → paddle/fluid/operators/conv_transpose_op.cu
Unescape View File

480
paddle/fluid/operators/conv_transpose_op.h
View File

File diff suppressed because it is too large Load Diff

274
paddle/fluid/operators/math/depthwise_conv.cu
View File

File diff suppressed because it is too large Load Diff

11
paddle/fluid/operators/math/depthwise_conv.h
Unescape View File

@ -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

37
paddle/fluid/operators/math/im2col.cc
Unescape View File

@ -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.");

105
paddle/fluid/operators/math/im2col.cu
Unescape View File

@ -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];

8
paddle/fluid/operators/math/im2col.h
Unescape View File

@ -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

109
paddle/fluid/operators/math/im2col_cfo_cpu.h
Unescape View File

@ -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;

65
paddle/fluid/operators/math/vol2col.cc
Unescape View File

@ -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;

98
paddle/fluid/operators/math/vol2col.cu
Unescape View File

@ -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);
}
};

11
paddle/fluid/operators/math/vol2col.h
Unescape View File

@ -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

279
python/paddle/fluid/layers/nn.py
View File

File diff suppressed because it is too large Load Diff

570
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
View File

File diff suppressed because it is too large Load Diff

431
python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
View File

File diff suppressed because it is too large Load Diff
Write Preview
Loading…
Cancel
Save