conv_op.cc 16.0 KB
Newer Older
1
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
C
chengduoZH 已提交
2

3 4 5
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
C
chengduoZH 已提交
6

7
    http://www.apache.org/licenses/LICENSE-2.0
C
chengduoZH 已提交
8

9 10 11 12 13
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. */
C
chengduoZH 已提交
14

Y
Yi Wang 已提交
15
#include "paddle/fluid/operators/conv_op.h"
Y
Update  
Yi Wang 已提交
16 17 18 19

#include <string>
#include <vector>

20 21 22 23 24 25
#ifdef PADDLE_WITH_CUDA
#include "paddle/fluid/platform/cudnn_helper.h"
#endif
#ifdef PADDLE_WITH_MKLDNN
#include "paddle/fluid/platform/mkldnn_helper.h"
#endif
C
chengduoZH 已提交
26 27 28 29

namespace paddle {
namespace operators {

30
void ConvOp::InferShape(framework::InferShapeContext* ctx) const {
C
chengduoZH 已提交
31
  PADDLE_ENFORCE(ctx->HasInput("Input"),
32
                 "Input(Input) of ConvOp should not be null.");
C
chengduoZH 已提交
33
  PADDLE_ENFORCE(ctx->HasInput("Filter"),
34
                 "Input(Filter) of ConvOp should not be null.");
C
chengduoZH 已提交
35
  PADDLE_ENFORCE(ctx->HasOutput("Output"),
36
                 "Output(Output) of ConvOp should not be null.");
C
chengduoZH 已提交
37 38 39 40 41 42

  auto in_dims = ctx->GetInputDim("Input");
  auto filter_dims = ctx->GetInputDim("Filter");
  std::vector<int> strides = ctx->Attrs().Get<std::vector<int>>("strides");
  std::vector<int> paddings = ctx->Attrs().Get<std::vector<int>>("paddings");
  int groups = ctx->Attrs().Get<int>("groups");
C
chengduoZH 已提交
43
  std::vector<int> dilations = ctx->Attrs().Get<std::vector<int>>("dilations");
C
chengduoZH 已提交
44

C
chengduoZH 已提交
45 46
  PADDLE_ENFORCE(in_dims.size() == 4 || in_dims.size() == 5,
                 "Conv intput should be 4-D or 5-D tensor.");
47 48 49 50 51 52 53 54 55
  PADDLE_ENFORCE_EQ(
      in_dims.size(), filter_dims.size(),
      "Conv input dimension and filter dimension should be the same.");
  PADDLE_ENFORCE(
      in_dims.size() - strides.size() == 2U,
      "Conv input dimension and strides dimension should be consistent.");
  PADDLE_ENFORCE_EQ(
      paddings.size(), strides.size(),
      "Conv paddings dimension and Conv strides dimension should be the same.");
F
fengjiayi 已提交
56

57
  PADDLE_ENFORCE_EQ(in_dims[1], filter_dims[1] * groups,
C
chengduoZH 已提交
58
                    "The number of input channels should be equal to filter "
59
                    "channels * groups.");
F
fengjiayi 已提交
60

C
chengduoZH 已提交
61
  PADDLE_ENFORCE_EQ(
62
      filter_dims[0] % groups, 0,
C
chengduoZH 已提交
63 64 65
      "The number of output channels should be divided by groups.");

  std::vector<int64_t> output_shape({in_dims[0], filter_dims[0]});
C
chengduoZH 已提交
66
  for (size_t i = 0; i < strides.size(); ++i) {
Y
Yang Yang 已提交
67 68 69
    output_shape.push_back(ConvOutputSize(in_dims[i + 2], filter_dims[i + 2],
                                          dilations[i], paddings[i],
                                          strides[i]));
C
chengduoZH 已提交
70
  }
71
  ctx->SetOutputDim("Output", framework::make_ddim(output_shape));
72
  ctx->ShareLoD("Input", "Output");
C
chengduoZH 已提交
73 74
}

75 76
framework::OpKernelType ConvOp::GetExpectedKernelType(
    const framework::ExecutionContext& ctx) const {
77
  framework::LibraryType library{framework::LibraryType::kPlain};
C
chengduoZH 已提交
78
#ifdef PADDLE_WITH_CUDA
79
  if (platform::CanCUDNNBeUsed(ctx)) {
80
    library = framework::LibraryType::kCUDNN;
C
chengduoZH 已提交
81 82
  }
#endif
83
#ifdef PADDLE_WITH_MKLDNN
84
  if (library == framework::LibraryType::kPlain &&
85
      platform::CanMKLDNNBeUsed(ctx)) {
86
    library = framework::LibraryType::kMKLDNN;
87
  }
88
#endif
89

K
Kexin Zhao 已提交
90 91 92 93 94 95 96 97
  auto input_data_type =
      framework::ToDataType(ctx.Input<Tensor>("Input")->type());
  auto filter_data_type =
      framework::ToDataType(ctx.Input<Tensor>("Filter")->type());
  PADDLE_ENFORCE_EQ(input_data_type, filter_data_type,
                    "input and filter data type should be consistent");

  if (input_data_type == framework::proto::VarType::FP16) {
98
    PADDLE_ENFORCE_EQ(library, framework::LibraryType::kCUDNN,
K
Kexin Zhao 已提交
99 100 101
                      "float16 can only be used when CUDNN is used");
  }

102
  std::string data_format = ctx.Attr<std::string>("data_format");
103
  // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
104 105 106
  framework::DataLayout layout = framework::StringToDataLayout(data_format);
  return framework::OpKernelType(input_data_type, ctx.GetPlace(), layout,
                                 library);
107 108
}

Y
Yu Yang 已提交
109
void Conv2DOpMaker::Make() {
110 111
  AddInput(
      "Input",
C
fix doc  
chengduoZH 已提交
112 113 114 115
      "(Tensor) The input tensor of convolution operator. "
      "The format of input tensor is NCHW, where N is batch size, C is the "
      "number of channels, H is the height of the feature, "
      "and W is the width of the feature.");
116
  AddInput("Filter",
C
fix doc  
chengduoZH 已提交
117
           "(Tensor) The filter tensor of convolution operator. "
118 119
           "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, "
C
fix doc  
chengduoZH 已提交
120 121
           "H is the height of the filter, and W is the width of the filter. "
           "If the groups attribute is greater than 1, C equals the number of "
122 123
           "input image channels divided by the groups.");
  AddOutput("Output",
C
fix doc  
chengduoZH 已提交
124 125
            "(Tensor) The output tensor of convolution operator. "
            "The format of output tensor is also NCHW.");
C
chengduoZH 已提交
126 127 128 129
  AddAttr<std::vector<int>>("strides",
                            "(vector<int> default:{1, 1}), the "
                            "strides(h_stride, w_stride) of "
                            "convolution operator.")
130
      .SetDefault({1, 1});
C
chengduoZH 已提交
131 132 133 134
  AddAttr<std::vector<int>>("paddings",
                            "(vector<int> default:{0, 0}), the "
                            "paddings(h_pad, w_pad) of "
                            "convolution operator.")
135 136 137
      .SetDefault({0, 0});
  AddAttr<int>(
      "groups",
138
      "(int default:1), the groups number of the convolution operator. "
C
fix doc  
chengduoZH 已提交
139 140 141 142
      "According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
      "when group=2, the first half of the filters is only connected to the "
      "first half of the input channels, while the second half of the filters "
      "is only connected to the second half of the input channels.")
143
      .SetDefault(1);
C
chengduoZH 已提交
144
  AddAttr<std::vector<int>>("dilations",
C
chengduoZH 已提交
145 146
                            "(vector<int> default:{1, 1}), the "
                            "dilations(h_dilation, w_dilation) of "
C
chengduoZH 已提交
147
                            "convolution operator.")
C
chengduoZH 已提交
148
      .SetDefault({1, 1});
149 150 151 152
  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
153 154 155
  AddAttr<bool>("use_mkldnn",
                "(bool, default false) Only used in mkldnn kernel")
      .SetDefault(false);
156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171
  AddAttr<std::string>(
      "data_format",
      "(string, default NCHW) Only used in "
      "An optional string from: \"NHWC\", \"NCHW\". "
      "Defaults to \"NHWC\". Specify the data format of the output data, "
      "the input will be transformed automatically. ")
      .SetDefault("AnyLayout");
  // TODO(dzhwinter): need to registered layout transform function
  AddAttr<int>("workspace_size_MB",
               "Only used in cudnn kernel. Need set use_cudnn to true."
               "workspace size for cudnn, in MB, "
               "workspace is a section of GPU memory which will be "
               "allocated/freed each time the operator runs, larger "
               "workspace size can increase performance but also requires "
               "better hardware. This size should be chosen carefully.")
      .SetDefault(4096);
172
  AddComment(R"DOC(
C
fix doc  
chengduoZH 已提交
173 174
Convolution Operator.

175
The convolution operation calculates the output based on the input, filter
176
and strides, paddings, dilations, groups parameters. The size of each dimension of the
177
parameters is checked in the infer-shape.
178
Input(Input) and Output(Output) are in NCHW format. Where N is batch
C
fix doc  
chengduoZH 已提交
179
size, C is the number of channels, H is the height of the feature, and W is
180 181 182 183 184 185
the width of the feature.
Filters(Input) is MCHW format. Where M is the number of output image channels, C is
the number of input image channels, H is the height of the filter, and W
is the width of the filter.
Parameters(strides, paddings, dilations) are two elements. These two elements represent
height and width, respectively.
C
chengduoZH 已提交
186 187 188 189
The input(X) size and output(Out) size may be different.

Example:
  Input:
190 191
       Input shape: $(N, C_{in}, H_{in}, W_{in})$
       Filter shape: $(C_{out}, C_{in}, H_f, W_f)$
C
chengduoZH 已提交
192
  Output:
193 194 195 196 197 198
       Output shape: $(N, C_{out}, H_{out}, W_{out})$
  Where
$$
       H_{out}= \frac{(H_{in} + 2 * paddings[0] - (dilations[0] * (H_f - 1) + 1))}{strides[0]}+ 1 \\
       W_{out}= \frac{(W_{in} + 2 * paddings[1] - (dilations[1] * (W_f - 1) + 1))}{strides[1]}+ 1
$$
199
)DOC");
C
chengduoZH 已提交
200 201
}

Y
Yu Yang 已提交
202
void Conv3DOpMaker::Make() {
C
chengduoZH 已提交
203 204
  AddInput(
      "Input",
C
fix doc  
chengduoZH 已提交
205
      "(Tensor) The input tensor of convolution operator. "
C
chengduoZH 已提交
206
      "The format of input tensor is NCDHW. Where N is batch size, C is the "
C
fix doc  
chengduoZH 已提交
207 208 209
      "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.");
C
chengduoZH 已提交
210
  AddInput("Filter",
C
fix doc  
chengduoZH 已提交
211
           "(Tensor) The filter tensor of convolution operator. "
C
chengduoZH 已提交
212 213
           "The format of the filter tensor is MCDHW, where M is the number of "
           "output image channels, C is the number of input image channels, "
C
fix doc  
chengduoZH 已提交
214 215 216
           "D is the depth of the filter, H is the height of the filter, and W "
           "is the width of the filter."
           "If the groups attribute is greater than 1, C equals the number of "
C
chengduoZH 已提交
217 218
           "input image channels divided by the groups.");
  AddOutput("Output",
C
fix doc  
chengduoZH 已提交
219
            "(Tensor) The output tensor of convolution operator."
C
chengduoZH 已提交
220
            "The format of output tensor is also NCDHW.");
C
chengduoZH 已提交
221 222 223 224
  AddAttr<std::vector<int>>("strides",
                            "(vector<int>, default:{1, 1, 1}), the "
                            "strides(d_stride, h_stride, w_stride) of "
                            "convolution operator.")
C
chengduoZH 已提交
225
      .SetDefault({1, 1, 1});
C
chengduoZH 已提交
226 227 228 229
  AddAttr<std::vector<int>>("paddings",
                            "(vector<int>, default:{0, 0, 0}), the "
                            "paddings(d_pad, h_pad, w_pad) of convolution "
                            "operator.")
C
chengduoZH 已提交
230 231 232
      .SetDefault({0, 0, 0});
  AddAttr<int>(
      "groups",
233
      "(int default:1), the groups number of the convolution operator. "
C
fix doc  
chengduoZH 已提交
234 235 236 237
      "According to grouped convolution in Alex Krizhevsky's Deep CNN paper: "
      "when group=2, the first half of the filters is only connected to the "
      "first half of the input channels, while the second half of the filters "
      "is only connected to the second half of the input channels.")
C
chengduoZH 已提交
238
      .SetDefault(1);
C
chengduoZH 已提交
239
  AddAttr<std::vector<int>>("dilations",
C
chengduoZH 已提交
240 241
                            "(vector<int> default:{1, 1, 1}), the "
                            "dilations(d_dilation, h_dilation, w_dilation) of "
242
                            "convolution operator.")
C
chengduoZH 已提交
243
      .SetDefault({1, 1, 1});
244 245 246 247
  AddAttr<bool>(
      "use_cudnn",
      "(bool, default false) Only used in cudnn kernel, need install cudnn")
      .SetDefault(false);
248 249 250
  AddAttr<bool>("use_mkldnn",
                "(bool, default false) Only used in mkldnn kernel")
      .SetDefault(false);
251 252 253 254 255 256 257 258 259 260 261 262 263 264 265
  AddAttr<std::string>(
      "data_format",
      "(string, default NCHW) Only used in "
      "An optional string from: \"NHWC\", \"NCHW\". "
      "Defaults to \"NHWC\". Specify the data format of the output data, "
      "the input will be transformed automatically. ")
      .SetDefault("AnyLayout");
  // TODO(dzhwinter): need to registered layout transform function
  AddAttr<int>("workspace_size_MB",
               "Only used in cudnn kernel. workspace size for cudnn, in MB, "
               "workspace is a section of GPU memory which will be "
               "allocated/freed each time the operator runs, larger "
               "workspace size can increase performance but also requires "
               "better hardware. This size should be chosen carefully.")
      .SetDefault(4096);
C
fix doc  
chengduoZH 已提交
266

C
chengduoZH 已提交
267
  AddComment(R"DOC(
C
fix doc  
chengduoZH 已提交
268 269
Convolution3D Operator.

C
chengduoZH 已提交
270
The convolution operation calculates the output based on the input, filter
271
and strides, paddings, dilations, groups parameters. The size of each dimension of the
C
chengduoZH 已提交
272
parameters is checked in the infer-shape.
273
Input(Input) and output(Output) are in NCDHW format, where N is batch
C
fix doc  
chengduoZH 已提交
274
size, C is the number of channels,D is the depth of the feature, H is the height of
275 276 277 278 279 280
the feature, and W is the width of the feature.
Filters(Input) is MCDHW format, where M is the number of output image channels,
C is the number of input image channels, D is the depth of the filter,
H is the height of the filter, and W is the width of the filter.
Parameters(strides, paddings, dilations) are three elements. These three elements
represent depth, height and width, respectively.
C
fix doc  
chengduoZH 已提交
281 282 283 284
The input(X) size and output(Out) size may be different.

Example:
  Input:
285 286
       Input shape: $(N, C_{in}, D_{in}, H_{in}, W_{in})$
       Filter shape: $(C_{out}, C_{in}, D_f, H_f, W_f)$
C
fix doc  
chengduoZH 已提交
287
  Output:
288 289 290 291 292 293 294
       Output shape: $(N, C_{out}, D_{out}, H_{out}, W_{out})$
  Where
  $$
       D_{out}= \frac{(D_{in} + 2 * paddings[0] - (dilations[0] * (D_f - 1) + 1))}{ strides[0]}+ 1 \\
       H_{out}= \frac{(H_{in} + 2 * paddings[1] - (dilations[1] * (H_f - 1) + 1))}{ strides[1]}+ 1 \\
       W_{out}= \frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{ strides[2]}+ 1
  $$
C
chengduoZH 已提交
295 296 297
)DOC");
}

298 299 300 301 302 303 304 305 306 307 308
void ConvOpGrad::InferShape(framework::InferShapeContext* ctx) const {
  auto in_dims = ctx->GetInputDim("Input");
  auto filter_dims = ctx->GetInputDim("Filter");
  if (ctx->HasOutput(framework::GradVarName("Input"))) {
    ctx->SetOutputDim(framework::GradVarName("Input"), in_dims);
  }
  if (ctx->HasOutput(framework::GradVarName("Filter"))) {
    ctx->SetOutputDim(framework::GradVarName("Filter"), filter_dims);
  }
}

309 310
framework::OpKernelType ConvOpGrad::GetExpectedKernelType(
    const framework::ExecutionContext& ctx) const {
311
  framework::LibraryType library_{framework::LibraryType::kPlain};
C
chengduoZH 已提交
312
#ifdef PADDLE_WITH_CUDA
313 314
  if (platform::CanCUDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kCUDNN;
C
chengduoZH 已提交
315 316
  }
#endif
317 318 319 320
#ifdef PADDLE_WITH_MKLDNN
  if (library_ == framework::LibraryType::kPlain &&
      platform::CanMKLDNNBeUsed(ctx)) {
    library_ = framework::LibraryType::kMKLDNN;
321
  }
322
#endif
323 324

  std::string data_format = ctx.Attr<std::string>("data_format");
325
  // TODO(pzelazko-intel): enable MKLDNN layout when it's ready
326 327 328 329 330 331
  framework::DataLayout layout_ = framework::StringToDataLayout(data_format);
  return framework::OpKernelType(
      framework::ToDataType(ctx.Input<Tensor>("Input")->type()), ctx.GetPlace(),
      layout_, library_);
}

C
chengduoZH 已提交
332 333 334 335
}  // namespace operators
}  // namespace paddle

namespace ops = paddle::operators;
Y
Yang Yang 已提交
336
REGISTER_OPERATOR(conv2d, ops::ConvOp, ops::Conv2DOpMaker,
337 338
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(conv2d_grad, ops::ConvOpGrad);
339 340

// depthwise convolution op
Y
Yang Yang 已提交
341
REGISTER_OPERATOR(depthwise_conv2d, ops::ConvOp, ops::Conv2DOpMaker,
342 343
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(depthwise_conv2d_grad, ops::ConvOpGrad);
Y
Yang Yang 已提交
344
REGISTER_OPERATOR(conv3d, ops::ConvOp, ops::Conv3DOpMaker,
345 346
                  paddle::framework::DefaultGradOpDescMaker<true>);
REGISTER_OPERATOR(conv3d_grad, ops::ConvOpGrad);
347

348 349
// depthwise conv kernel
// TODO(xingzhaolong): neon kernel for mobile
Z
zlx 已提交
350
REGISTER_OP_CPU_KERNEL(
351
    depthwise_conv2d,
X
xzl 已提交
352 353 354 355
    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);

REGISTER_OP_CPU_KERNEL(
356
    depthwise_conv2d_grad,
X
xzl 已提交
357 358
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
Z
zlx 已提交
359

360
REGISTER_OP_CPU_KERNEL(
Q
QI JUN 已提交
361 362 363 364 365 366
    conv2d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    conv2d_grad,
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
C
chengduoZH 已提交
367 368

REGISTER_OP_CPU_KERNEL(
Q
QI JUN 已提交
369 370 371 372 373 374
    conv3d, ops::GemmConvKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvKernel<paddle::platform::CPUDeviceContext, double>);
REGISTER_OP_CPU_KERNEL(
    conv3d_grad,
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, float>,
    ops::GemmConvGradKernel<paddle::platform::CPUDeviceContext, double>);
反馈
建议
客服 返回
顶部