# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. # # 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. import paddle import paddle.nn as nn import paddle.nn.functional as F from paddle import ParamAttr from paddle.regularizer import L2Decay from ppdet.core.workspace import register, serializable from ppdet.modeling.ops import batch_norm, mish from ..shape_spec import ShapeSpec __all__ = ['DarkNet', 'ConvBNLayer'] class ConvBNLayer(nn.Layer): def __init__(self, ch_in, ch_out, filter_size=3, stride=1, groups=1, padding=0, norm_type='bn', norm_decay=0., act="leaky", data_format='NCHW', name=''): """ conv + bn + activation layer Args: ch_in (int): input channel ch_out (int): output channel filter_size (int): filter size, default 3 stride (int): stride, default 1 groups (int): number of groups of conv layer, default 1 padding (int): padding size, default 0 norm_type (str): batch norm type, default bn norm_decay (str): decay for weight and bias of batch norm layer, default 0. act (str): activation function type, default 'leaky', which means leaky_relu data_format (str): data format, NCHW or NHWC """ super(ConvBNLayer, self).__init__() self.conv = nn.Conv2D( in_channels=ch_in, out_channels=ch_out, kernel_size=filter_size, stride=stride, padding=padding, groups=groups, data_format=data_format, bias_attr=False) self.batch_norm = batch_norm( ch_out, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self.act = act def forward(self, inputs): out = self.conv(inputs) out = self.batch_norm(out) if self.act == 'leaky': out = F.leaky_relu(out, 0.1) elif self.act == 'mish': out = mish(out) return out class DownSample(nn.Layer): def __init__(self, ch_in, ch_out, filter_size=3, stride=2, padding=1, norm_type='bn', norm_decay=0., data_format='NCHW'): """ downsample layer Args: ch_in (int): input channel ch_out (int): output channel filter_size (int): filter size, default 3 stride (int): stride, default 2 padding (int): padding size, default 1 norm_type (str): batch norm type, default bn norm_decay (str): decay for weight and bias of batch norm layer, default 0. data_format (str): data format, NCHW or NHWC """ super(DownSample, self).__init__() self.conv_bn_layer = ConvBNLayer( ch_in=ch_in, ch_out=ch_out, filter_size=filter_size, stride=stride, padding=padding, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self.ch_out = ch_out def forward(self, inputs): out = self.conv_bn_layer(inputs) return out class BasicBlock(nn.Layer): def __init__(self, ch_in, ch_out, norm_type='bn', norm_decay=0., data_format='NCHW'): """ BasicBlock layer of DarkNet Args: ch_in (int): input channel ch_out (int): output channel norm_type (str): batch norm type, default bn norm_decay (str): decay for weight and bias of batch norm layer, default 0. data_format (str): data format, NCHW or NHWC """ super(BasicBlock, self).__init__() self.conv1 = ConvBNLayer( ch_in=ch_in, ch_out=ch_out, filter_size=1, stride=1, padding=0, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self.conv2 = ConvBNLayer( ch_in=ch_out, ch_out=ch_out * 2, filter_size=3, stride=1, padding=1, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) def forward(self, inputs): conv1 = self.conv1(inputs) conv2 = self.conv2(conv1) out = paddle.add(x=inputs, y=conv2) return out class Blocks(nn.Layer): def __init__(self, ch_in, ch_out, count, norm_type='bn', norm_decay=0., name=None, data_format='NCHW'): """ Blocks layer, which consist of some BaickBlock layers Args: ch_in (int): input channel ch_out (int): output channel count (int): number of BasicBlock layer norm_type (str): batch norm type, default bn norm_decay (str): decay for weight and bias of batch norm layer, default 0. name (str): layer name data_format (str): data format, NCHW or NHWC """ super(Blocks, self).__init__() self.basicblock0 = BasicBlock( ch_in, ch_out, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self.res_out_list = [] for i in range(1, count): block_name = '{}.{}'.format(name, i) res_out = self.add_sublayer( block_name, BasicBlock( ch_out * 2, ch_out, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format)) self.res_out_list.append(res_out) self.ch_out = ch_out def forward(self, inputs): y = self.basicblock0(inputs) for basic_block_i in self.res_out_list: y = basic_block_i(y) return y DarkNet_cfg = {53: ([1, 2, 8, 8, 4])} @register @serializable class DarkNet(nn.Layer): __shared__ = ['norm_type', 'data_format'] def __init__(self, depth=53, freeze_at=-1, return_idx=[2, 3, 4], num_stages=5, norm_type='bn', norm_decay=0., data_format='NCHW'): """ Darknet, see https://pjreddie.com/darknet/yolo/ Args: depth (int): depth of network freeze_at (int): freeze the backbone at which stage filter_size (int): filter size, default 3 return_idx (list): index of stages whose feature maps are returned norm_type (str): batch norm type, default bn norm_decay (str): decay for weight and bias of batch norm layer, default 0. data_format (str): data format, NCHW or NHWC """ super(DarkNet, self).__init__() self.depth = depth self.freeze_at = freeze_at self.return_idx = return_idx self.num_stages = num_stages self.stages = DarkNet_cfg[self.depth][0:num_stages] self.conv0 = ConvBNLayer( ch_in=3, ch_out=32, filter_size=3, stride=1, padding=1, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self.downsample0 = DownSample( ch_in=32, ch_out=32 * 2, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format) self._out_channels = [] self.darknet_conv_block_list = [] self.downsample_list = [] ch_in = [64, 128, 256, 512, 1024] for i, stage in enumerate(self.stages): name = 'stage.{}'.format(i) conv_block = self.add_sublayer( name, Blocks( int(ch_in[i]), 32 * (2**i), stage, norm_type=norm_type, norm_decay=norm_decay, data_format=data_format, name=name)) self.darknet_conv_block_list.append(conv_block) if i in return_idx: self._out_channels.append(64 * (2**i)) for i in range(num_stages - 1): down_name = 'stage.{}.downsample'.format(i) downsample = self.add_sublayer( down_name, DownSample( ch_in=32 * (2**(i + 1)), ch_out=32 * (2**(i + 2)), norm_type=norm_type, norm_decay=norm_decay, data_format=data_format)) self.downsample_list.append(downsample) def forward(self, inputs): x = inputs['image'] out = self.conv0(x) out = self.downsample0(out) blocks = [] for i, conv_block_i in enumerate(self.darknet_conv_block_list): out = conv_block_i(out) if i == self.freeze_at: out.stop_gradient = True if i in self.return_idx: blocks.append(out) if i < self.num_stages - 1: out = self.downsample_list[i](out) return blocks @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]