# copyright (c) 2021 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. # reference: https://arxiv.org/abs/1804.02767 import paddle from paddle import ParamAttr import paddle.nn as nn import paddle.nn.functional as F from paddle.nn import Conv2D, BatchNorm, Linear, Dropout from paddle.nn import AdaptiveAvgPool2D, MaxPool2D, AvgPool2D from paddle.nn.initializer import Uniform import math from ....utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url MODEL_URLS = { "DarkNet53": "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/DarkNet53_pretrained.pdparams" } __all__ = list(MODEL_URLS.keys()) class ConvBNLayer(nn.Layer): def __init__(self, input_channels, output_channels, filter_size, stride, padding, name=None): super(ConvBNLayer, self).__init__() self._conv = Conv2D( in_channels=input_channels, out_channels=output_channels, kernel_size=filter_size, stride=stride, padding=padding, weight_attr=ParamAttr(name=name + ".conv.weights"), bias_attr=False) bn_name = name + ".bn" self._bn = BatchNorm( num_channels=output_channels, act="relu", param_attr=ParamAttr(name=bn_name + ".scale"), bias_attr=ParamAttr(name=bn_name + ".offset"), moving_mean_name=bn_name + ".mean", moving_variance_name=bn_name + ".var") def forward(self, inputs): x = self._conv(inputs) x = self._bn(x) return x class BasicBlock(nn.Layer): def __init__(self, input_channels, output_channels, name=None): super(BasicBlock, self).__init__() self._conv1 = ConvBNLayer( input_channels, output_channels, 1, 1, 0, name=name + ".0") self._conv2 = ConvBNLayer( output_channels, output_channels * 2, 3, 1, 1, name=name + ".1") def forward(self, inputs): x = self._conv1(inputs) x = self._conv2(x) return paddle.add(x=inputs, y=x) class DarkNet(nn.Layer): def __init__(self, class_num=1000): super(DarkNet, self).__init__() self.stages = [1, 2, 8, 8, 4] self._conv1 = ConvBNLayer(3, 32, 3, 1, 1, name="yolo_input") self._conv2 = ConvBNLayer( 32, 64, 3, 2, 1, name="yolo_input.downsample") self._basic_block_01 = BasicBlock(64, 32, name="stage.0.0") self._downsample_0 = ConvBNLayer( 64, 128, 3, 2, 1, name="stage.0.downsample") self._basic_block_11 = BasicBlock(128, 64, name="stage.1.0") self._basic_block_12 = BasicBlock(128, 64, name="stage.1.1") self._downsample_1 = ConvBNLayer( 128, 256, 3, 2, 1, name="stage.1.downsample") self._basic_block_21 = BasicBlock(256, 128, name="stage.2.0") self._basic_block_22 = BasicBlock(256, 128, name="stage.2.1") self._basic_block_23 = BasicBlock(256, 128, name="stage.2.2") self._basic_block_24 = BasicBlock(256, 128, name="stage.2.3") self._basic_block_25 = BasicBlock(256, 128, name="stage.2.4") self._basic_block_26 = BasicBlock(256, 128, name="stage.2.5") self._basic_block_27 = BasicBlock(256, 128, name="stage.2.6") self._basic_block_28 = BasicBlock(256, 128, name="stage.2.7") self._downsample_2 = ConvBNLayer( 256, 512, 3, 2, 1, name="stage.2.downsample") self._basic_block_31 = BasicBlock(512, 256, name="stage.3.0") self._basic_block_32 = BasicBlock(512, 256, name="stage.3.1") self._basic_block_33 = BasicBlock(512, 256, name="stage.3.2") self._basic_block_34 = BasicBlock(512, 256, name="stage.3.3") self._basic_block_35 = BasicBlock(512, 256, name="stage.3.4") self._basic_block_36 = BasicBlock(512, 256, name="stage.3.5") self._basic_block_37 = BasicBlock(512, 256, name="stage.3.6") self._basic_block_38 = BasicBlock(512, 256, name="stage.3.7") self._downsample_3 = ConvBNLayer( 512, 1024, 3, 2, 1, name="stage.3.downsample") self._basic_block_41 = BasicBlock(1024, 512, name="stage.4.0") self._basic_block_42 = BasicBlock(1024, 512, name="stage.4.1") self._basic_block_43 = BasicBlock(1024, 512, name="stage.4.2") self._basic_block_44 = BasicBlock(1024, 512, name="stage.4.3") self._pool = AdaptiveAvgPool2D(1) stdv = 1.0 / math.sqrt(1024.0) self._out = Linear( 1024, class_num, weight_attr=ParamAttr( name="fc_weights", initializer=Uniform(-stdv, stdv)), bias_attr=ParamAttr(name="fc_offset")) def forward(self, inputs): x = self._conv1(inputs) x = self._conv2(x) x = self._basic_block_01(x) x = self._downsample_0(x) x = self._basic_block_11(x) x = self._basic_block_12(x) x = self._downsample_1(x) x = self._basic_block_21(x) x = self._basic_block_22(x) x = self._basic_block_23(x) x = self._basic_block_24(x) x = self._basic_block_25(x) x = self._basic_block_26(x) x = self._basic_block_27(x) x = self._basic_block_28(x) x = self._downsample_2(x) x = self._basic_block_31(x) x = self._basic_block_32(x) x = self._basic_block_33(x) x = self._basic_block_34(x) x = self._basic_block_35(x) x = self._basic_block_36(x) x = self._basic_block_37(x) x = self._basic_block_38(x) x = self._downsample_3(x) x = self._basic_block_41(x) x = self._basic_block_42(x) x = self._basic_block_43(x) x = self._basic_block_44(x) x = self._pool(x) x = paddle.squeeze(x, axis=[2, 3]) x = self._out(x) return x def _load_pretrained(pretrained, model, model_url, use_ssld=False): if pretrained is False: pass elif pretrained is True: load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld) elif isinstance(pretrained, str): load_dygraph_pretrain(model, pretrained) else: raise RuntimeError( "pretrained type is not available. Please use `string` or `boolean` type." ) def DarkNet53(pretrained=False, use_ssld=False, **kwargs): model = DarkNet(**kwargs) _load_pretrained( pretrained, model, MODEL_URLS["DarkNet53"], use_ssld=use_ssld) return model