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Convolution op and forward calculation.

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update-doc-pybind
hedaoyuan 8 years ago
parent 544458e011
commit c9d8cb4e90
6 changed files with 285 additions and 0 deletions
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96
paddle/operators/conv_op.cc
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/gemm_conv_op.h"
namespace paddle {
namespace operators {
int outputSize(int input_size, int filter_size, int padding, int stride) {
int output_size = (input_size - filter_size + 2 * padding) / stride + 1;
return output_size;
}
class Conv2DOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {
auto *in = ctx.Input<framework::Tensor>("Input");
auto *filter = ctx.Input<framework::Tensor>("Filter");
auto *out = ctx.Output<framework::Tensor>("Output");
PADDLE_ENFORCE_EQ(in->dims().size(), 4, "Conv2DOp intput should be 4-D.");
PADDLE_ENFORCE_EQ(filter->dims().size(), 4,
"Conv2DOp filter should be 4-D.");
std::vector<int> strides = Attr<std::vector<int>>("strides");
std::vector<int> paddings = Attr<std::vector<int>>("paddings");
auto output_height =
outputSize(in->dims()[2], filter->dims()[2], paddings[0], strides[0]);
auto output_width =
outputSize(in->dims()[3], filter->dims()[3], paddings[1], strides[1]);
out->Resize(
{in->dims()[0], filter->dims()[0], output_height, output_width});
}
};
class Conv2DOppMaker : public framework::OpProtoAndCheckerMaker {
public:
Conv2DOppMaker(framework::OpProto *proto,
framework::OpAttrChecker *op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput(
"Input",
"The input tensor of convolution operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of channels, H and W is the height and width of image.");
AddInput(
"Filter",
"The filter tensor of convolution operator."
"The format of the filter tensor is MCHW, where M is the number of "
"output "
"image channels, C is the number of input image channels, H and W is "
" height and width of filter.");
AddOutput("Output",
"The output tensor of convolution operator."
"The format of output tensor is also NCHW.");
AddComment(R"DOC(
The convolution operation calculates the output based on
the input, filter and strides, paddings parameters.
)DOC");
AddAttr<std::vector<int>>("strides", "strides of convolution operator.");
AddAttr<std::vector<int>>("paddings", "paddings of convolution operator.");
}
};
class Conv2DOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
protected:
void InferShape(const framework::InferShapeContext &ctx) const override {}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP(conv2d, ops::Conv2DOp, ops::Conv2DOppMaker, conv2d_grad,
ops::Conv2DOpGrad);
REGISTER_OP_CPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::CPUPlace, float>);
REGISTER_OP_CPU_KERNEL(
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::CPUPlace, float>);

22
paddle/operators/conv_op.cu
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/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/operators/gemm_conv_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(conv2d,
ops::GemmConvKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
conv2d_grad, ops::GemmConvGradKernel<paddle::platform::GPUPlace, float>);

103
paddle/operators/gemm_conv_op.h
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
template <typename Place, typename T>
class GemmConvKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
const Tensor* input = context.Input<Tensor>("Input");
Tensor* filter = const_cast<Tensor*>(context.Input<Tensor>("Filter"));
Tensor* output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
paddle::framework::Tensor col;
paddle::framework::Tensor in_slice;
paddle::framework::Tensor out_slice;
std::vector<int> strides = context.Attr<std::vector<int>>("strides");
std::vector<int> paddings = context.Attr<std::vector<int>>("paddings");
int batch_size = input->dims()[0];
int input_channels = input->dims()[1];
int filter_height = filter->dims()[filter->dims().size() - 2];
int filter_width = filter->dims()[filter->dims().size() - 1];
int output_height = output->dims()[2];
int output_width = output->dims()[3];
paddle::operators::math::Im2ColFunctor<
paddle::operators::math::ColFormat::kCFO, Place, T>
im2col;
framework::DDim col_shape = {input_channels, filter_height, filter_width,
output_height, output_width};
col.mutable_data<float>(col_shape, context.GetPlace());
auto* device_context =
const_cast<platform::DeviceContext*>(context.device_context_);
framework::DDim input_shape = {input->dims()[1], input->dims()[2],
input->dims()[3]};
framework::DDim filter_matrix_shape = {
filter->dims()[0],
filter->dims()[1] * filter->dims()[2] * filter->dims()[3]};
framework::DDim col_matrix_shape = {
input_channels * filter_height * filter_width,
output_height * output_width};
framework::DDim output_matrix_shape = {
output->dims()[1], output->dims()[2] * output->dims()[3]};
filter->Resize(filter_matrix_shape);
// convolution opperator: im2col + gemm
for (int i = 0; i < batch_size; i++) {
// im2col
in_slice = input->Slice<T>(i, i + 1);
in_slice.Resize(input_shape);
col.Resize(col_shape);
im2col(in_slice, col, strides[0], strides[1], paddings[0], paddings[1],
device_context);
// gemm
out_slice = output->Slice<T>(i, i + 1);
out_slice.Resize(output_matrix_shape);
col.Resize(col_matrix_shape);
math::matmul<Place, T>(*filter, false, col, false, T(1.0), &out_slice,
T(0.0), device_context);
}
}
};
template <typename Place, typename T>
class GemmConvGradKernel : public framework::OpKernel {
public:
void Compute(const framework::ExecutionContext& context) const override {
#if 0
auto input = context.Input<Tensor>("Input");
auto filter = context.Input<Tensor>("Filter");
auto output = context.Output<Tensor>("Output");
output->mutable_data<T>(context.GetPlace());
#endif
}
};
} // namespace operators
} // namespace paddle

1
paddle/pybind/pybind.cc
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@ -51,6 +51,7 @@ USE_CPU_ONLY_OP(gather);
USE_CPU_ONLY_OP(scatter);
USE_OP(top_k);
USE_OP(squared_l2_distance);
USE_OP(conv2d);
namespace paddle {
namespace framework {

1
python/paddle/v2/framework/tests/CMakeLists.txt
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@ -35,3 +35,4 @@ py_test(test_lookup_table SRCS test_lookup_table.py)
py_test(test_scale_and_identity_op SRCS test_scale_and_identity_op.py)
py_test(mnist SRCS mnist.py)
py_test(test_squared_l2_distance_op SRCS test_squared_l2_distance_op.py)
py_test(test_conv2d SRCS test_conv2d_op.py)

62
python/paddle/v2/framework/tests/test_conv2d_op.py
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import unittest
import numpy as np
from gradient_checker import GradientChecker, create_op
from op_test_util import OpTestMeta
class TestConv2dOp(unittest.TestCase):
__metaclass__ = OpTestMeta
def setUp(self):
self.type = "conv2d"
batch_size = 2
input_channels = 3
input_height = 5
input_width = 5
output_channels = 6
filter_height = 3
filter_width = 3
stride = 1
padding = 0
output_height = (input_height - filter_height + 2 * padding
) / stride + 1
output_width = (input_width - filter_width + 2 * padding) / stride + 1
input = np.random.random((batch_size, input_channels, input_height,
input_width)).astype("float32")
filter = np.random.random(
(output_channels, input_channels, filter_height,
filter_width)).astype("float32")
output = np.ndarray(
(batch_size, output_channels, output_height, output_width))
for batchid in xrange(batch_size):
for channelid in xrange(output_channels):
for rowid in xrange(output_height):
for colid in xrange(output_width):
start_h = (rowid * stride) - padding
start_w = (colid * stride) - padding
output_value = 0.0
for inchannelid in xrange(input_channels):
for frowid in xrange(filter_height):
for fcolid in xrange(filter_width):
input_value = 0.0
inrowid = start_h + frowid
incolid = start_w + fcolid
if ((inrowid >= 0 and
inrowid < input_height) and
(incolid >= 0 and
incolid < input_width)):
input_value = input[batchid][
inchannelid][inrowid][incolid]
filter_value = filter[channelid][
inchannelid][frowid][fcolid]
output_value += input_value * filter_value
output[batchid][channelid][rowid][colid] = output_value
self.inputs = {'Input': input, 'Filter': filter}
self.outputs = {'Output': output}
self.attrs = {'strides': [1, 1], 'paddings': [0, 0]}
if __name__ == '__main__':
unittest.main()
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