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* [TOP] complete level2 * [TOP] add split
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| /*! | ||
| * Copyright (c) 2017 by Contributors | ||
| * \file convolution.cc | ||
| * \brief Convolution operators | ||
| */ | ||
| #include <nnvm/op.h> | ||
| #include <nnvm/node.h> | ||
| #include <nnvm/op_attr_types.h> | ||
| #include <nnvm/top/nn.h> | ||
| #include "./nn_common.h" | ||
| #include "../op_common.h" | ||
| #include "../elemwise_op_common.h" | ||
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| namespace nnvm { | ||
| namespace top { | ||
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| // conv2d | ||
| DMLC_REGISTER_PARAMETER(ConvParam); | ||
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| inline bool Conv2DInferShape(const nnvm::NodeAttrs& attrs, | ||
| std::vector<TShape>* in_shape, | ||
| std::vector<TShape>* out_shape) { | ||
| const ConvParam& param = nnvm::get<ConvParam>(attrs.parsed); | ||
| if (param.use_bias) { | ||
| CHECK_EQ(in_shape->size(), 3U) << "Input:[data, weight, bias]"; | ||
| } else { | ||
| CHECK_EQ(in_shape->size(), 2U) << "Input:[data, weight]"; | ||
| } | ||
| CHECK_EQ(out_shape->size(), 1U); | ||
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| TShape dshape = in_shape->at(0); | ||
| if (dshape.ndim() == 0) return false; | ||
| dshape = ConvertLayout(dshape, param.layout, kNCHW); | ||
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| CHECK_EQ(dshape.ndim(), 4U) << "Input data should be 4D"; | ||
| CHECK_EQ(param.kernel_size.ndim(), 2U); | ||
| CHECK_EQ(param.strides.ndim(), 2U) | ||
| << "incorrect stride size: " << param.strides; | ||
| CHECK_EQ(param.dilation.ndim(), 2U) | ||
| << "incorrect dilate size: " << param.dilation; | ||
| CHECK_EQ(dshape[1] % param.groups, 0U) | ||
| << "input channels must divide group size"; | ||
| CHECK_EQ(param.channels % param.groups, 0U) | ||
| << "output channels must divide group size"; | ||
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| TShape wshape({param.channels / param.groups, | ||
| dshape[1] / param.groups, | ||
| param.kernel_size[0], | ||
| param.kernel_size[1]}); | ||
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| wshape = ConvertLayout(wshape, kNCHW, param.layout); | ||
| wshape[0] *= param.groups; | ||
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| NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, ConvParam::kWeight, wshape); | ||
| if (param.use_bias) { | ||
| NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, | ||
| ConvParam::kBias, TShape({param.channels})); | ||
| } | ||
| // dilation | ||
| dim_t dilated_ksize_y = 1 + (param.kernel_size[0] - 1) * param.dilation[0]; | ||
| dim_t dilated_ksize_x = 1 + (param.kernel_size[1] - 1) * param.dilation[1]; | ||
| TShape oshape({dshape[0], param.channels, 0, 0}); | ||
| if (dshape[2] != 0) { | ||
| oshape[2] = (dshape[2] + param.padding[0] * 2 - dilated_ksize_y) / param.strides[0] + 1; | ||
| } | ||
| if (dshape[3] != 0) { | ||
| oshape[3] = (dshape[3] + param.padding[1] * 2 - dilated_ksize_x) / param.strides[1] + 1; | ||
| } | ||
| NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, | ||
| ConvertLayout(oshape, kNCHW, param.layout)); | ||
| // Perform incomplete shape inference. Fill in the missing values in data shape. | ||
| // 1) We can always fill in the batch_size. | ||
| // 2) We can back-calculate the input height/width if the corresponding stride is 1. | ||
| oshape = ConvertLayout((*out_shape)[0], param.layout, kNCHW); | ||
| dshape[0] = oshape[0]; | ||
| if (oshape[2] && param.strides[0] == 1) { | ||
| dshape[2] = oshape[2] + dilated_ksize_y - 1 - 2 * param.padding[0]; | ||
| } | ||
| if (oshape[3] && param.strides[1] == 1) { | ||
| dshape[3] = oshape[3] + dilated_ksize_x - 1 - 2 * param.padding[1]; | ||
| } | ||
| NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, ConvParam::kData, | ||
| ConvertLayout(dshape, kNCHW, param.layout)); | ||
| // Check whether the kernel sizes are valid | ||
| if (dshape[2] != 0) { | ||
| CHECK_LE(dilated_ksize_y, dshape[2] + 2 * param.padding[0]) | ||
| << "kernel size exceed input"; | ||
| } | ||
| if (dshape[3] != 0) { | ||
| CHECK_LE(dilated_ksize_x, dshape[3] + 2 * param.padding[1]) | ||
| << "kernel size exceed input"; | ||
| } | ||
| return true; | ||
| } | ||
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| NNVM_REGISTER_OP(conv2d) | ||
| .describe(R"code(2D convolution layer (e.g. spatial convolution over images). | ||
| This layer creates a convolution kernel that is convolved | ||
| with the layer input to produce a tensor of | ||
| outputs. If `use_bias` is True, | ||
| a bias vector is created and added to the outputs. | ||
| - **data**: This depends on the `layout` parameter. Input is 4D array of shape | ||
| (batch_size, in_channels, height, width) if `layout` is `NCHW`. | ||
| - **weight**: (channels, in_channels, kernel_size[0], kernel_size[1]) | ||
| - **bias**: (channels,) | ||
| - **out**: This depends on the `layout` parameter. Output is 4D array of shape | ||
| (batch_size, channels, out_height, out_width) if `layout` is `NCHW`. | ||
| )code" NNVM_ADD_FILELINE) | ||
| .add_argument("data", "4D Tensor", "Input data.") | ||
| .add_argument("weight", "4D Tensor", "Weight matrix.") | ||
| .add_argument("bias", "1D Tensor", "Bias parameter.") | ||
| .add_arguments(ConvParam::__FIELDS__()) | ||
| .set_attr_parser(ParamParser<ConvParam>) | ||
| .set_num_outputs(1) | ||
| .set_num_inputs(UseBiasNumInputs<ConvParam>) | ||
| .set_attr<FListInputNames>("FListInputNames", UseBiasListInputNames<ConvParam>) | ||
| .set_attr<FInferShape>("FInferShape", Conv2DInferShape) | ||
| .set_attr<FInferType>("FInferType", ElemwiseType<-1, 1>) | ||
| .set_support_level(2); | ||
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| DMLC_REGISTER_PARAMETER(ConvTransposeParam); | ||
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| inline bool ConvTransposeInferShape(const nnvm::NodeAttrs& attrs, | ||
| std::vector<TShape>* in_shape, | ||
| std::vector<TShape>* out_shape) { | ||
| const ConvTransposeParam& param = nnvm::get<ConvTransposeParam>(attrs.parsed); | ||
| if (param.use_bias) { | ||
| CHECK_EQ(in_shape->size(), 3U) << "Input:[data, weight, bias]"; | ||
| } else { | ||
| CHECK_EQ(in_shape->size(), 2U) << "Input:[data, weight]"; | ||
| } | ||
| CHECK_EQ(out_shape->size(), 1U); | ||
| const TShape& dshape = (*in_shape)[ConvTransposeParam::kData]; | ||
| if (dshape.ndim() == 0) return false; | ||
| TShape dshape_nchw = ConvertLayout(dshape, param.layout, kNCHW); | ||
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| CHECK_EQ(dshape_nchw[1] % param.groups, 0U) | ||
| << "input num_filter must divide group size"; | ||
| CHECK_EQ(param.channels % param.groups, 0U) | ||
| << "output num_filter must divide group size"; | ||
| CHECK_EQ(param.kernel_size.ndim(), 2U) | ||
| << "incorrect kernel size: " << param.kernel_size; | ||
| CHECK_EQ(param.strides.ndim(), 2U) | ||
| << "incorrect stride size: " << param.strides; | ||
| CHECK_EQ(param.dilation.ndim(), 2U) | ||
| << "incorrect dilate size: " << param.dilation; | ||
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| TShape wshape({dshape_nchw[1], | ||
| param.channels / param.groups, | ||
| param.kernel_size[0], param.kernel_size[1]}); | ||
| wshape = ConvertLayout(wshape, kNCHW, param.layout); | ||
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| NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, ConvTransposeParam::kWeight, wshape); | ||
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| if (param.use_bias) { | ||
| NNVM_ASSIGN_INPUT_SHAPE(attrs, *in_shape, | ||
| ConvTransposeParam::kBias, | ||
| TShape({param.channels})); | ||
| } | ||
| // dilation | ||
| dim_t dilated_ksize_y = 1 + (param.kernel_size[0] - 1) * param.dilation[0]; | ||
| dim_t dilated_ksize_x = 1 + (param.kernel_size[1] - 1) * param.dilation[1]; | ||
| // output shape. | ||
| TShape oshape({dshape_nchw[0], param.channels, 0, 0}); | ||
| oshape[2] = (param.strides[0] * (dshape_nchw[2] - 1) + dilated_ksize_y - | ||
| 2 * param.padding[0] + param.output_padding[0]); | ||
|
|
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| oshape[3] = (param.strides[1] * (dshape_nchw[3] - 1) + dilated_ksize_x - | ||
| 2 * param.padding[1] + param.output_padding[1]); | ||
| NNVM_ASSIGN_OUTPUT_SHAPE(attrs, *out_shape, 0, | ||
| ConvertLayout(oshape, kNCHW, param.layout)); | ||
| return true; | ||
| } | ||
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| NNVM_REGISTER_OP(conv2d_transpose) | ||
| .describe(R"code(Transposed 2D convolution layer (sometimes called Deconvolution). | ||
| The need for transposed convolutions generally arises | ||
| from the desire to use a transformation going in the opposite direction | ||
| of a normal convolution, i.e., from something that has the shape of the | ||
| output of some convolution to something that has the shape of its input | ||
| while maintaining a connectivity pattern that is compatible with | ||
| said convolution. | ||
| - **data**: This depends on the `layout` parameter. Input is 4D array of shape | ||
| (batch_size, in_channels, height, width) if `layout` is `NCHW`. | ||
| - **weight**: (channels, in_channels, kernel_size[0], kernel_size[1]) | ||
| - **bias**: (channels,) | ||
| - **out**: This depends on the `layout` parameter. Output is 4D array of shape | ||
| (batch_size, channels, out_height, out_width) if `layout` is `NCHW`. | ||
| out_height and out_width are calculated as:: | ||
| out_height = (height-1)*strides[0]-2*padding[0]+kernel_size[0]+output_padding[0] | ||
| out_width = (width-1)*strides[1]-2*padding[1]+kernel_size[1]+output_padding[1] | ||
| )code" NNVM_ADD_FILELINE) | ||
| .add_argument("data", "4D Tensor", "Input data.") | ||
| .add_argument("weight", "4D Tensor", "Weight matrix.") | ||
| .add_argument("bias", "1D Tensor", "Bias parameter.") | ||
| .add_arguments(ConvTransposeParam::__FIELDS__()) | ||
| .set_attr_parser(ParamParser<ConvTransposeParam>) | ||
| .set_num_outputs(1) | ||
| .set_num_inputs(UseBiasNumInputs<ConvTransposeParam>) | ||
| .set_attr<FListInputNames>("FListInputNames", UseBiasListInputNames<ConvTransposeParam>) | ||
| .set_attr<FInferShape>("FInferShape", ConvTransposeInferShape) | ||
| .set_attr<FInferType>("FInferType", ElemwiseType<-1, 1>) | ||
| .set_support_level(2); | ||
|
|
||
| } // namespace top | ||
| } // namespace nnvm |
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