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Merge pull request #4887 from chengduoZH/fix_im2col_kocf_for_sequence

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Add up, down, left and right padding for im2col.
revert-4814-Add_sequence_project_op
qingqing01 7 years ago committed by GitHub
parent 8fdc315acc 09662da0be
commit 23bf6b2c15
5 changed files with 225 additions and 70 deletions
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8
paddle/operators/conv2d_op.h
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@ -114,7 +114,7 @@ class GemmConv2DKernel : public framework::OpKernel<T> {
// im2col
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
im2col(context.device_context(), in_slice, col, strides[0], strides[1],
paddings[0], paddings[1]);
paddings[0], paddings[0], paddings[1], paddings[1]);
// gemm
Tensor out_slice = out_batch.Slice(g * out_step, (g + 1) * out_step);
@ -213,7 +213,8 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
Tensor in_grad_slice =
in_grad_batch.Slice(g * in_step, (g + 1) * in_step);
col2im(context.device_context(), in_grad_slice, col, strides[0],
strides[1], paddings[0], paddings[1]);
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
}
}
}
@ -235,7 +236,8 @@ class GemmConvGrad2DKernel : public framework::OpKernel<T> {
out_grad_batch.Slice(g * out_step, (g + 1) * out_step);
Tensor in_slice = in_batch.Slice(g * in_step, (g + 1) * in_step);
im2col(context.device_context(), in_slice, col, strides[0],
strides[1], paddings[0], paddings[1]);
strides[1], paddings[0], paddings[0], paddings[1],
paddings[1]);
// gemm
Tensor filter_grad_slice =

114
paddle/operators/math/im2col.cc
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@ -29,8 +29,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width) {
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
@ -41,6 +41,22 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
"inconsistent.");
int channels_col = input_channels * filter_height * filter_width;
const T* im_data = im.data<T>();
@ -52,16 +68,14 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int c_im = c / filter_width / filter_height;
for (int h = 0; h < output_height; ++h) {
for (int w = 0; w < output_width; ++w) {
int im_row_idx = h * stride_height + h_offset;
int im_col_idx = w * stride_width + w_offset;
if ((im_row_idx - padding_height) < 0 ||
(im_row_idx - padding_height) >= input_height ||
(im_col_idx - padding_width) < 0 ||
(im_col_idx - padding_width) >= input_width) {
int im_row_idx = h * stride_height + h_offset - padding_up;
int im_col_idx = w * stride_width + w_offset - padding_left;
if (im_row_idx < 0 || im_row_idx >= input_height || im_col_idx < 0 ||
im_col_idx >= input_width) {
col_data[(c * output_height + h) * output_width + w] = T(0);
} else {
im_row_idx += c_im * input_height - padding_height;
im_col_idx -= padding_width;
im_row_idx += c_im * input_height;
col_data[(c * output_height + h) * output_width + w] =
im_data[im_row_idx * input_width + im_col_idx];
}
@ -82,7 +96,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_height, int padding_width) {
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
@ -92,6 +107,22 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
int filter_width = col.dims()[2];
int output_height = col.dims()[3];
int output_width = col.dims()[4];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
"inconsistent.");
int channels_col = input_channels * filter_height * filter_width;
T* im_data = im.data<T>();
@ -103,14 +134,12 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
int c_im = c / filter_width / filter_height;
for (int h = 0; h < output_height; ++h) {
for (int w = 0; w < output_width; ++w) {
int im_row_idx = h * stride_height + h_offset;
int im_col_idx = w * stride_width + w_offset;
if ((im_row_idx - padding_height) >= 0 &&
(im_row_idx - padding_height) < input_height &&
(im_col_idx - padding_width) >= 0 &&
(im_col_idx - padding_width) < input_width) {
im_row_idx += c_im * input_height - padding_height;
im_col_idx -= padding_width;
int im_row_idx = h * stride_height + h_offset - padding_up;
int im_col_idx = w * stride_width + w_offset - padding_left;
if ((im_row_idx) >= 0 && (im_row_idx) < input_height &&
(im_col_idx) >= 0 && (im_col_idx) < input_width) {
im_row_idx += c_im * input_height;
im_data[im_row_idx * input_width + im_col_idx] +=
col_data[(c * output_height + h) * output_width + w];
}
@ -140,8 +169,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width) {
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
@ -152,6 +181,21 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
"inconsistent.");
const T* im_data = im.data<T>();
T* col_data = col.data<T>();
@ -163,10 +207,10 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride_height + filter_row_idx - padding_height;
col_row_idx * stride_height + filter_row_idx - padding_up;
int im_col_offset =
col_col_idx * stride_width + filter_col_idx - padding_width;
int col_offset = (((col_row_idx * output_width + col_col_idx) *
col_col_idx * stride_width + filter_col_idx - padding_left;
int col_offset = ((((col_row_idx)*output_width + col_col_idx) *
input_channels +
channel) *
filter_height +
@ -201,7 +245,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_height, int padding_width) {
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
@ -212,6 +257,21 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE_EQ(
(input_height + padding_up + padding_down - filter_height) /
stride_height +
1,
output_height,
"Output_height and padding(padding_up, padding_down) are "
"inconsistent.");
PADDLE_ENFORCE_EQ(
(input_width + padding_left + padding_right - filter_width) /
stride_width +
1,
output_width,
"output_width and padding(padding_left, padding_right) are "
"inconsistent.");
T* im_data = im.data<T>();
const T* col_data = col.data<T>();
@ -223,9 +283,9 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
for (int filter_col_idx = 0; filter_col_idx < filter_width;
++filter_col_idx) {
int im_row_offset =
col_row_idx * stride_height + filter_row_idx - padding_height;
col_row_idx * stride_height + filter_row_idx - padding_up;
int im_col_offset =
col_col_idx * stride_width + filter_col_idx - padding_width;
col_col_idx * stride_width + filter_col_idx - padding_left;
int col_offset = (((col_row_idx * output_width + col_col_idx) *
input_channels +
channel) *

75
paddle/operators/math/im2col.cu
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@ -66,8 +66,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width) {
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
@ -79,6 +79,15 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
int output_height = col.dims()[3];
int output_width = col.dims()[4];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
int num_outputs = input_channels * output_height * output_width;
int blocks = (num_outputs + 1024 - 1) / 1024;
int block_x = 512;
@ -89,8 +98,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kCFO,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), num_outputs, input_height, input_width, filter_height,
filter_width, stride_height, stride_width, padding_height,
padding_width, output_height, output_width, col.data<T>());
filter_width, stride_height, stride_width, padding_up, padding_left,
output_height, output_width, col.data<T>());
}
};
@ -152,7 +161,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_height, int padding_width) {
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
@ -164,8 +174,18 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
int output_height = col.dims()[3];
int output_width = col.dims()[4];
size_t num_kernels = input_channels * (input_height + 2 * padding_height) *
(input_width + 2 * padding_width);
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
size_t num_kernels = input_channels *
(input_height + padding_up + padding_down) *
(input_width + padding_left + padding_right);
size_t blocks = (num_kernels + 1024 - 1) / 1024;
size_t block_x = 512;
@ -178,10 +198,10 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kCFO,
col2im<T><<<grid, threads, 0,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
num_kernels, col.data<T>(), input_height + 2 * padding_height,
input_width + 2 * padding_width, input_channels, filter_height,
filter_width, stride_height, stride_width, padding_height,
padding_width, output_height, output_width, im.data<T>());
num_kernels, col.data<T>(), input_height + padding_up + padding_down,
input_width + padding_left + padding_left, input_channels,
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width, im.data<T>());
}
};
@ -238,8 +258,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width) {
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
@ -250,6 +270,15 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
int block_dim_x = 0;
int block_dim_y = 0;
if (filter_height <= 4 && filter_width <= 4) {
@ -274,8 +303,8 @@ class Im2ColFunctor<paddle::operators::math::ColFormat::kOCF,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
filter_height, filter_width, stride_height, stride_width,
padding_height, padding_width, output_height, output_width);
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width);
}
};
@ -322,7 +351,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_height, int padding_width) {
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right) {
PADDLE_ENFORCE(im.dims().size() == 3);
PADDLE_ENFORCE(col.dims().size() == 5);
int input_channels = im.dims()[0];
@ -333,6 +363,15 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
int output_height = col.dims()[0];
int output_width = col.dims()[1];
PADDLE_ENFORCE((input_height + padding_up + padding_down - filter_height) /
stride_height +
1 ==
output_height);
PADDLE_ENFORCE((input_width + padding_left + padding_right - filter_width) /
stride_width +
1 ==
output_width);
int block_dim_x = 0;
int block_dim_y = 0;
if (filter_height <= 4 && filter_width <= 4) {
@ -357,8 +396,8 @@ class Col2ImFunctor<paddle::operators::math::ColFormat::kOCF,
reinterpret_cast<const platform::CUDADeviceContext&>(context)
.stream()>>>(
im.data<T>(), col.data<T>(), input_channels, input_height, input_width,
filter_height, filter_width, stride_height, stride_width,
padding_height, padding_width, output_height, output_width);
filter_height, filter_width, stride_height, stride_width, padding_up,
padding_left, output_height, output_width);
}
};

7
paddle/operators/math/im2col.h
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@ -74,8 +74,8 @@ class Im2ColFunctor {
public:
void operator()(const platform::DeviceContext& context,
const framework::Tensor& im, framework::Tensor& col,
int stride_height, int stride_width, int padding_height,
int padding_width);
int stride_height, int stride_width, int padding_up,
int padding_down, int padding_left, int padding_right);
};
template <ColFormat Format, typename Place, typename T>
@ -83,7 +83,8 @@ class Col2ImFunctor {
public:
void operator()(const platform::DeviceContext& context, framework::Tensor& im,
const framework::Tensor& col, int stride_height,
int stride_width, int padding_height, int padding_width);
int stride_width, int padding_up, int padding_down,
int padding_left, int padding_right);
};
} // namespace math

91
paddle/operators/math/im2col_test.cc
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@ -35,6 +35,12 @@ void testIm2col() {
*
* output_ocf = [0, 1, 3, 4
* 1, 2, 4, 5]
*
* col2im_cfo = [0, 2, 2
* 3, 4, 5]
*
* col2im_ocf = [0, 2, 2
* 3, 4, 5]
*/
int input_height = 2;
int input_width = 3;
@ -59,7 +65,7 @@ void testIm2col() {
new paddle::platform::CUDADeviceContext(paddle::platform::GPUPlace());
#else
PADDLE_THROW("no GPU support");
#endif // PADDLE_ONLY_CPU
#endif // PADDLE_WITH_CUDA
}
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
@ -71,6 +77,7 @@ void testIm2col() {
output_ocf.mutable_data<float>(
{output_height, output_width, 1, filter_size, filter_size}, *place);
// Im2Col
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO, Place, float>
im2col;
@ -78,8 +85,13 @@ void testIm2col() {
paddle::operators::math::ColFormat::kOCF, Place, float>
im2col_ocf;
im2col(*context, input, output_cfo, stride, stride, padding, padding);
im2col_ocf(*context, input, output_ocf, stride, stride, padding, padding);
im2col(*context, input, output_cfo, stride, stride, padding, padding, padding,
padding);
im2col_ocf(*context, input, output_ocf, stride, stride, padding, padding,
padding, padding);
float out_cfo_data[] = {0, 1, 1, 2, 3, 4, 4, 5};
float out_ocf_data[] = {0, 1, 3, 4, 1, 2, 4, 5};
float* out_cfo_ptr;
if (paddle::platform::is_cpu_place(*place)) {
@ -88,14 +100,9 @@ void testIm2col() {
output_tmp.CopyFrom(output_cfo, paddle::platform::CPUPlace(), *context);
out_cfo_ptr = output_tmp.data<float>();
}
EXPECT_EQ(out_cfo_ptr[0], 0);
EXPECT_EQ(out_cfo_ptr[1], 1);
EXPECT_EQ(out_cfo_ptr[2], 1);
EXPECT_EQ(out_cfo_ptr[3], 2);
EXPECT_EQ(out_cfo_ptr[4], 3);
EXPECT_EQ(out_cfo_ptr[5], 4);
EXPECT_EQ(out_cfo_ptr[6], 4);
EXPECT_EQ(out_cfo_ptr[7], 5);
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(out_cfo_ptr[i], out_cfo_data[i]);
}
float* out_ocf_ptr;
if (paddle::platform::is_cpu_place(*place)) {
@ -104,14 +111,60 @@ void testIm2col() {
output_tmp.CopyFrom(output_ocf, paddle::platform::CPUPlace(), *context);
out_ocf_ptr = output_tmp.data<float>();
}
EXPECT_EQ(out_ocf_ptr[0], 0);
EXPECT_EQ(out_ocf_ptr[1], 1);
EXPECT_EQ(out_ocf_ptr[2], 3);
EXPECT_EQ(out_ocf_ptr[3], 4);
EXPECT_EQ(out_ocf_ptr[4], 1);
EXPECT_EQ(out_ocf_ptr[5], 2);
EXPECT_EQ(out_ocf_ptr[6], 4);
EXPECT_EQ(out_ocf_ptr[7], 5);
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(out_ocf_ptr[i], out_ocf_data[i]);
}
// Col2Im: kCFO
paddle::operators::math::Col2ImFunctor<
paddle::operators::math::ColFormat::kCFO, Place, float>
col2im;
paddle::operators::math::Col2ImFunctor<
paddle::operators::math::ColFormat::kOCF, Place, float>
col2im_ocf;
float col2im_data[] = {0, 2, 2, 3, 8, 5};
memset(input_ptr, 0, 6 * sizeof(float));
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
} else {
input.CopyFrom(input_tmp, *place, *context);
}
col2im(*context, input, output_cfo, stride, stride, padding, padding, padding,
padding);
float* in_ptr;
if (paddle::platform::is_cpu_place(*place)) {
in_ptr = input.data<float>();
} else {
input_tmp.CopyFrom(input, paddle::platform::CPUPlace(), *context);
in_ptr = input_tmp.data<float>();
}
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(in_ptr[i], col2im_data[i]);
}
// Col2Im: kOCF
memset(input_ptr, 0, 6 * sizeof(float));
if (paddle::platform::is_cpu_place(*place)) {
input = input_tmp;
} else {
input.CopyFrom(input_tmp, *place, *context);
}
col2im_ocf(*context, input, output_ocf, stride, stride, padding, padding,
padding, padding);
if (paddle::platform::is_cpu_place(*place)) {
in_ptr = input.data<float>();
} else {
input_tmp.CopyFrom(input, paddle::platform::CPUPlace(), *context);
in_ptr = input_tmp.data<float>();
}
for (int i = 0; i < 6; ++i) {
EXPECT_EQ(in_ptr[i], col2im_data[i]);
}
}
TEST(math, im2col) {

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