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61 lines
2.1 KiB
61 lines
2.1 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "DepthwiseConvLayer.h"
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#include "paddle/utils/Logging.h"
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#include "paddle/utils/Stat.h"
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namespace paddle {
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REGISTER_LAYER(depthwise_conv, DepthwiseConvLayer);
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bool DepthwiseConvLayer::init(const LayerMap &layerMap,
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const ParameterMap ¶meterMap) {
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/* Initialize the basic convolutional parent class */
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ExpandConvBaseLayer::init(layerMap, parameterMap);
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size_t numInputs = config_.inputs_size();
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inputShape_.resize(numInputs);
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filterShape_.resize(numInputs);
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outputShape_.resize(numInputs);
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for (int i = 0; i < config_.inputs_size(); i++) {
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std::vector<size_t> paddings = {(size_t)paddingY_[i], (size_t)padding_[i]};
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std::vector<size_t> strides = {(size_t)strideY_[i], (size_t)stride_[i]};
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createFunction(forward_,
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"DepthwiseConv",
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FuncConfig()
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.set("paddings", paddings)
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.set("strides", strides)
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.set("groups", (size_t)groups_[i]));
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createFunction(backward_,
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"DepthwiseConvGradInput",
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FuncConfig()
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.set("paddings", paddings)
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.set("strides", strides)
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.set("groups", (size_t)groups_[i]));
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createFunction(backward_,
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"DepthwiseConvGradFilter",
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FuncConfig()
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.set("paddings", paddings)
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.set("strides", strides)
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.set("groups", (size_t)groups_[i]));
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}
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return true;
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}
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} // namespace paddle
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