# 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 ppdet.core.workspace import register, serializable from ppdet.modeling.layers import DropBlock from ..backbones.darknet import ConvBNLayer from ..shape_spec import ShapeSpec __all__ = ['YOLOv3FPN', 'PPYOLOFPN', 'PPYOLOTinyFPN', 'PPYOLOPAN'] def add_coord(x, data_format): b = paddle.shape(x)[0] if data_format == 'NCHW': h, w = x.shape[2], x.shape[3] else: h, w = x.shape[1], x.shape[2] gx = paddle.cast(paddle.arange(w) / ((w - 1.) * 2.0) - 1., x.dtype) gy = paddle.cast(paddle.arange(h) / ((h - 1.) * 2.0) - 1., x.dtype) if data_format == 'NCHW': gx = gx.reshape([1, 1, 1, w]).expand([b, 1, h, w]) gy = gy.reshape([1, 1, h, 1]).expand([b, 1, h, w]) else: gx = gx.reshape([1, 1, w, 1]).expand([b, h, w, 1]) gy = gy.reshape([1, h, 1, 1]).expand([b, h, w, 1]) gx.stop_gradient = True gy.stop_gradient = True return gx, gy class YoloDetBlock(nn.Layer): def __init__(self, ch_in, channel, norm_type, freeze_norm=False, name='', data_format='NCHW'): """ YOLODetBlock layer for yolov3, see https://arxiv.org/abs/1804.02767 Args: ch_in (int): input channel channel (int): base channel norm_type (str): batch norm type freeze_norm (bool): whether to freeze norm, default False name (str): layer name data_format (str): data format, NCHW or NHWC """ super(YoloDetBlock, self).__init__() self.ch_in = ch_in self.channel = channel assert channel % 2 == 0, \ "channel {} cannot be divided by 2".format(channel) conv_def = [ ['conv0', ch_in, channel, 1, '.0.0'], ['conv1', channel, channel * 2, 3, '.0.1'], ['conv2', channel * 2, channel, 1, '.1.0'], ['conv3', channel, channel * 2, 3, '.1.1'], ['route', channel * 2, channel, 1, '.2'], ] self.conv_module = nn.Sequential() for idx, (conv_name, ch_in, ch_out, filter_size, post_name) in enumerate(conv_def): self.conv_module.add_sublayer( conv_name, ConvBNLayer( ch_in=ch_in, ch_out=ch_out, filter_size=filter_size, padding=(filter_size - 1) // 2, norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name + post_name)) self.tip = ConvBNLayer( ch_in=channel, ch_out=channel * 2, filter_size=3, padding=1, norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name + '.tip') def forward(self, inputs): route = self.conv_module(inputs) tip = self.tip(route) return route, tip class SPP(nn.Layer): def __init__(self, ch_in, ch_out, k, pool_size, norm_type='bn', freeze_norm=False, name='', act='leaky', data_format='NCHW'): """ SPP layer, which consist of four pooling layer follwed by conv layer Args: ch_in (int): input channel of conv layer ch_out (int): output channel of conv layer k (int): kernel size of conv layer norm_type (str): batch norm type freeze_norm (bool): whether to freeze norm, default False name (str): layer name act (str): activation function data_format (str): data format, NCHW or NHWC """ super(SPP, self).__init__() self.pool = [] self.data_format = data_format for size in pool_size: pool = self.add_sublayer( '{}.pool1'.format(name), nn.MaxPool2D( kernel_size=size, stride=1, padding=size // 2, data_format=data_format, ceil_mode=False)) self.pool.append(pool) self.conv = ConvBNLayer( ch_in, ch_out, k, padding=k // 2, norm_type=norm_type, freeze_norm=freeze_norm, name=name, act=act, data_format=data_format) def forward(self, x): outs = [x] for pool in self.pool: outs.append(pool(x)) if self.data_format == "NCHW": y = paddle.concat(outs, axis=1) else: y = paddle.concat(outs, axis=-1) y = self.conv(y) return y class CoordConv(nn.Layer): def __init__(self, ch_in, ch_out, filter_size, padding, norm_type, freeze_norm=False, name='', data_format='NCHW'): """ CoordConv layer, see https://arxiv.org/abs/1807.03247 Args: ch_in (int): input channel ch_out (int): output channel filter_size (int): filter size, default 3 padding (int): padding size, default 0 norm_type (str): batch norm type, default bn name (str): layer name data_format (str): data format, NCHW or NHWC """ super(CoordConv, self).__init__() self.conv = ConvBNLayer( ch_in + 2, ch_out, filter_size=filter_size, padding=padding, norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name) self.data_format = data_format def forward(self, x): gx, gy = add_coord(x, self.data_format) if self.data_format == 'NCHW': y = paddle.concat([x, gx, gy], axis=1) else: y = paddle.concat([x, gx, gy], axis=-1) y = self.conv(y) return y class PPYOLODetBlock(nn.Layer): def __init__(self, cfg, name, data_format='NCHW'): """ PPYOLODetBlock layer Args: cfg (list): layer configs for this block name (str): block name data_format (str): data format, NCHW or NHWC """ super(PPYOLODetBlock, self).__init__() self.conv_module = nn.Sequential() for idx, (conv_name, layer, args, kwargs) in enumerate(cfg[:-1]): kwargs.update( name='{}.{}'.format(name, conv_name), data_format=data_format) self.conv_module.add_sublayer(conv_name, layer(*args, **kwargs)) conv_name, layer, args, kwargs = cfg[-1] kwargs.update( name='{}.{}'.format(name, conv_name), data_format=data_format) self.tip = layer(*args, **kwargs) def forward(self, inputs): route = self.conv_module(inputs) tip = self.tip(route) return route, tip class PPYOLOTinyDetBlock(nn.Layer): def __init__(self, ch_in, ch_out, name, drop_block=False, block_size=3, keep_prob=0.9, data_format='NCHW'): """ PPYOLO Tiny DetBlock layer Args: ch_in (list): input channel number ch_out (list): output channel number name (str): block name drop_block: whether user DropBlock block_size: drop block size keep_prob: probability to keep block in DropBlock data_format (str): data format, NCHW or NHWC """ super(PPYOLOTinyDetBlock, self).__init__() self.drop_block_ = drop_block self.conv_module = nn.Sequential() cfgs = [ # name, in channels, out channels, filter_size, # stride, padding, groups ['.0', ch_in, ch_out, 1, 1, 0, 1], ['.1', ch_out, ch_out, 5, 1, 2, ch_out], ['.2', ch_out, ch_out, 1, 1, 0, 1], ['.route', ch_out, ch_out, 5, 1, 2, ch_out], ] for cfg in cfgs: conv_name, conv_ch_in, conv_ch_out, filter_size, stride, padding, \ groups = cfg self.conv_module.add_sublayer( name + conv_name, ConvBNLayer( ch_in=conv_ch_in, ch_out=conv_ch_out, filter_size=filter_size, stride=stride, padding=padding, groups=groups, name=name + conv_name)) self.tip = ConvBNLayer( ch_in=ch_out, ch_out=ch_out, filter_size=1, stride=1, padding=0, groups=1, name=name + conv_name) if self.drop_block_: self.drop_block = DropBlock( block_size=block_size, keep_prob=keep_prob, data_format=data_format, name=name + '.dropblock') def forward(self, inputs): if self.drop_block_: inputs = self.drop_block(inputs) route = self.conv_module(inputs) tip = self.tip(route) return route, tip class PPYOLODetBlockCSP(nn.Layer): def __init__(self, cfg, ch_in, ch_out, act, norm_type, name, data_format='NCHW'): """ PPYOLODetBlockCSP layer Args: cfg (list): layer configs for this block ch_in (int): input channel ch_out (int): output channel act (str): default mish name (str): block name data_format (str): data format, NCHW or NHWC """ super(PPYOLODetBlockCSP, self).__init__() self.data_format = data_format self.conv1 = ConvBNLayer( ch_in, ch_out, 1, padding=0, act=act, norm_type=norm_type, name=name + '.left', data_format=data_format) self.conv2 = ConvBNLayer( ch_in, ch_out, 1, padding=0, act=act, norm_type=norm_type, name=name + '.right', data_format=data_format) self.conv3 = ConvBNLayer( ch_out * 2, ch_out * 2, 1, padding=0, act=act, norm_type=norm_type, name=name, data_format=data_format) self.conv_module = nn.Sequential() for idx, (layer_name, layer, args, kwargs) in enumerate(cfg): kwargs.update(name=name + layer_name, data_format=data_format) self.conv_module.add_sublayer(layer_name, layer(*args, **kwargs)) def forward(self, inputs): conv_left = self.conv1(inputs) conv_right = self.conv2(inputs) conv_left = self.conv_module(conv_left) if self.data_format == 'NCHW': conv = paddle.concat([conv_left, conv_right], axis=1) else: conv = paddle.concat([conv_left, conv_right], axis=-1) conv = self.conv3(conv) return conv, conv @register @serializable class YOLOv3FPN(nn.Layer): __shared__ = ['norm_type', 'data_format'] def __init__(self, in_channels=[256, 512, 1024], norm_type='bn', freeze_norm=False, data_format='NCHW'): """ YOLOv3FPN layer Args: in_channels (list): input channels for fpn norm_type (str): batch norm type, default bn data_format (str): data format, NCHW or NHWC """ super(YOLOv3FPN, self).__init__() assert len(in_channels) > 0, "in_channels length should > 0" self.in_channels = in_channels self.num_blocks = len(in_channels) self._out_channels = [] self.yolo_blocks = [] self.routes = [] self.data_format = data_format for i in range(self.num_blocks): name = 'yolo_block.{}'.format(i) in_channel = in_channels[-i - 1] if i > 0: in_channel += 512 // (2**i) yolo_block = self.add_sublayer( name, YoloDetBlock( in_channel, channel=512 // (2**i), norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name)) self.yolo_blocks.append(yolo_block) # tip layer output channel doubled self._out_channels.append(1024 // (2**i)) if i < self.num_blocks - 1: name = 'yolo_transition.{}'.format(i) route = self.add_sublayer( name, ConvBNLayer( ch_in=512 // (2**i), ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name)) self.routes.append(route) def forward(self, blocks, for_mot=False): assert len(blocks) == self.num_blocks blocks = blocks[::-1] yolo_feats = [] # add embedding features output for multi-object tracking model if for_mot: emb_feats = [] for i, block in enumerate(blocks): if i > 0: if self.data_format == 'NCHW': block = paddle.concat([route, block], axis=1) else: block = paddle.concat([route, block], axis=-1) route, tip = self.yolo_blocks[i](block) yolo_feats.append(tip) if for_mot: # add embedding features output emb_feats.append(route) if i < self.num_blocks - 1: route = self.routes[i](route) route = F.interpolate( route, scale_factor=2., data_format=self.data_format) if for_mot: return {'yolo_feats': yolo_feats, 'emb_feats': emb_feats} else: return yolo_feats @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], } @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels] @register @serializable class PPYOLOFPN(nn.Layer): __shared__ = ['norm_type', 'data_format'] def __init__(self, in_channels=[512, 1024, 2048], norm_type='bn', freeze_norm=False, data_format='NCHW', coord_conv=False, conv_block_num=2, drop_block=False, block_size=3, keep_prob=0.9, spp=False): """ PPYOLOFPN layer Args: in_channels (list): input channels for fpn norm_type (str): batch norm type, default bn data_format (str): data format, NCHW or NHWC coord_conv (bool): whether use CoordConv or not conv_block_num (int): conv block num of each pan block drop_block (bool): whether use DropBlock or not block_size (int): block size of DropBlock keep_prob (float): keep probability of DropBlock spp (bool): whether use spp or not """ super(PPYOLOFPN, self).__init__() assert len(in_channels) > 0, "in_channels length should > 0" self.in_channels = in_channels self.num_blocks = len(in_channels) # parse kwargs self.coord_conv = coord_conv self.drop_block = drop_block self.block_size = block_size self.keep_prob = keep_prob self.spp = spp self.conv_block_num = conv_block_num self.data_format = data_format if self.coord_conv: ConvLayer = CoordConv else: ConvLayer = ConvBNLayer if self.drop_block: dropblock_cfg = [[ 'dropblock', DropBlock, [self.block_size, self.keep_prob], dict() ]] else: dropblock_cfg = [] self._out_channels = [] self.yolo_blocks = [] self.routes = [] for i, ch_in in enumerate(self.in_channels[::-1]): if i > 0: ch_in += 512 // (2**i) channel = 64 * (2**self.num_blocks) // (2**i) base_cfg = [] c_in, c_out = ch_in, channel for j in range(self.conv_block_num): base_cfg += [ [ 'conv{}'.format(2 * j), ConvLayer, [c_in, c_out, 1], dict( padding=0, norm_type=norm_type, freeze_norm=freeze_norm) ], [ 'conv{}'.format(2 * j + 1), ConvBNLayer, [c_out, c_out * 2, 3], dict( padding=1, norm_type=norm_type, freeze_norm=freeze_norm) ], ] c_in, c_out = c_out * 2, c_out base_cfg += [[ 'route', ConvLayer, [c_in, c_out, 1], dict( padding=0, norm_type=norm_type, freeze_norm=freeze_norm) ], [ 'tip', ConvLayer, [c_out, c_out * 2, 3], dict( padding=1, norm_type=norm_type, freeze_norm=freeze_norm) ]] if self.conv_block_num == 2: if i == 0: if self.spp: spp_cfg = [[ 'spp', SPP, [channel * 4, channel, 1], dict( pool_size=[5, 9, 13], norm_type=norm_type, freeze_norm=freeze_norm) ]] else: spp_cfg = [] cfg = base_cfg[0:3] + spp_cfg + base_cfg[ 3:4] + dropblock_cfg + base_cfg[4:6] else: cfg = base_cfg[0:2] + dropblock_cfg + base_cfg[2:6] elif self.conv_block_num == 0: if self.spp and i == 0: spp_cfg = [[ 'spp', SPP, [c_in * 4, c_in, 1], dict( pool_size=[5, 9, 13], norm_type=norm_type, freeze_norm=freeze_norm) ]] else: spp_cfg = [] cfg = spp_cfg + dropblock_cfg + base_cfg name = 'yolo_block.{}'.format(i) yolo_block = self.add_sublayer(name, PPYOLODetBlock(cfg, name)) self.yolo_blocks.append(yolo_block) self._out_channels.append(channel * 2) if i < self.num_blocks - 1: name = 'yolo_transition.{}'.format(i) route = self.add_sublayer( name, ConvBNLayer( ch_in=channel, ch_out=256 // (2**i), filter_size=1, stride=1, padding=0, norm_type=norm_type, freeze_norm=freeze_norm, data_format=data_format, name=name)) self.routes.append(route) def forward(self, blocks, for_mot=False): assert len(blocks) == self.num_blocks blocks = blocks[::-1] yolo_feats = [] # add embedding features output for multi-object tracking model if for_mot: emb_feats = [] for i, block in enumerate(blocks): if i > 0: if self.data_format == 'NCHW': block = paddle.concat([route, block], axis=1) else: block = paddle.concat([route, block], axis=-1) route, tip = self.yolo_blocks[i](block) yolo_feats.append(tip) if for_mot: # add embedding features output emb_feats.append(route) if i < self.num_blocks - 1: route = self.routes[i](route) route = F.interpolate( route, scale_factor=2., data_format=self.data_format) if for_mot: return {'yolo_feats': yolo_feats, 'emb_feats': emb_feats} else: return yolo_feats @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], } @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels] @register @serializable class PPYOLOTinyFPN(nn.Layer): __shared__ = ['norm_type', 'data_format'] def __init__(self, in_channels=[80, 56, 34], detection_block_channels=[160, 128, 96], norm_type='bn', data_format='NCHW', **kwargs): """ PPYOLO Tiny FPN layer Args: in_channels (list): input channels for fpn detection_block_channels (list): channels in fpn norm_type (str): batch norm type, default bn data_format (str): data format, NCHW or NHWC kwargs: extra key-value pairs, such as parameter of DropBlock and spp """ super(PPYOLOTinyFPN, self).__init__() assert len(in_channels) > 0, "in_channels length should > 0" self.in_channels = in_channels[::-1] assert len(detection_block_channels ) > 0, "detection_block_channelslength should > 0" self.detection_block_channels = detection_block_channels self.data_format = data_format self.num_blocks = len(in_channels) # parse kwargs self.drop_block = kwargs.get('drop_block', False) self.block_size = kwargs.get('block_size', 3) self.keep_prob = kwargs.get('keep_prob', 0.9) self.spp_ = kwargs.get('spp', False) if self.spp_: self.spp = SPP(self.in_channels[0] * 4, self.in_channels[0], k=1, pool_size=[5, 9, 13], norm_type=norm_type, name='spp') self._out_channels = [] self.yolo_blocks = [] self.routes = [] for i, ( ch_in, ch_out ) in enumerate(zip(self.in_channels, self.detection_block_channels)): name = 'yolo_block.{}'.format(i) if i > 0: ch_in += self.detection_block_channels[i - 1] yolo_block = self.add_sublayer( name, PPYOLOTinyDetBlock( ch_in, ch_out, name, drop_block=self.drop_block, block_size=self.block_size, keep_prob=self.keep_prob)) self.yolo_blocks.append(yolo_block) self._out_channels.append(ch_out) if i < self.num_blocks - 1: name = 'yolo_transition.{}'.format(i) route = self.add_sublayer( name, ConvBNLayer( ch_in=ch_out, ch_out=ch_out, filter_size=1, stride=1, padding=0, norm_type=norm_type, data_format=data_format, name=name)) self.routes.append(route) def forward(self, blocks, for_mot=False): assert len(blocks) == self.num_blocks blocks = blocks[::-1] yolo_feats = [] # add embedding features output for multi-object tracking model if for_mot: emb_feats = [] for i, block in enumerate(blocks): if i == 0 and self.spp_: block = self.spp(block) if i > 0: if self.data_format == 'NCHW': block = paddle.concat([route, block], axis=1) else: block = paddle.concat([route, block], axis=-1) route, tip = self.yolo_blocks[i](block) yolo_feats.append(tip) if for_mot: # add embedding features output emb_feats.append(route) if i < self.num_blocks - 1: route = self.routes[i](route) route = F.interpolate( route, scale_factor=2., data_format=self.data_format) if for_mot: return {'yolo_feats': yolo_feats, 'emb_feats': emb_feats} else: return yolo_feats @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], } @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels] @register @serializable class PPYOLOPAN(nn.Layer): __shared__ = ['norm_type', 'data_format'] def __init__(self, in_channels=[512, 1024, 2048], norm_type='bn', data_format='NCHW', act='mish', conv_block_num=3, drop_block=False, block_size=3, keep_prob=0.9, spp=False): """ PPYOLOPAN layer with SPP, DropBlock and CSP connection. Args: in_channels (list): input channels for fpn norm_type (str): batch norm type, default bn data_format (str): data format, NCHW or NHWC act (str): activation function, default mish conv_block_num (int): conv block num of each pan block drop_block (bool): whether use DropBlock or not block_size (int): block size of DropBlock keep_prob (float): keep probability of DropBlock spp (bool): whether use spp or not """ super(PPYOLOPAN, self).__init__() assert len(in_channels) > 0, "in_channels length should > 0" self.in_channels = in_channels self.num_blocks = len(in_channels) # parse kwargs self.drop_block = drop_block self.block_size = block_size self.keep_prob = keep_prob self.spp = spp self.conv_block_num = conv_block_num self.data_format = data_format if self.drop_block: dropblock_cfg = [[ 'dropblock', DropBlock, [self.block_size, self.keep_prob], dict() ]] else: dropblock_cfg = [] # fpn self.fpn_blocks = [] self.fpn_routes = [] fpn_channels = [] for i, ch_in in enumerate(self.in_channels[::-1]): if i > 0: ch_in += 512 // (2**(i - 1)) channel = 512 // (2**i) base_cfg = [] for j in range(self.conv_block_num): base_cfg += [ # name, layer, args [ '{}.0'.format(j), ConvBNLayer, [channel, channel, 1], dict( padding=0, act=act, norm_type=norm_type) ], [ '{}.1'.format(j), ConvBNLayer, [channel, channel, 3], dict( padding=1, act=act, norm_type=norm_type) ] ] if i == 0 and self.spp: base_cfg[3] = [ 'spp', SPP, [channel * 4, channel, 1], dict( pool_size=[5, 9, 13], act=act, norm_type=norm_type) ] cfg = base_cfg[:4] + dropblock_cfg + base_cfg[4:] name = 'fpn.{}'.format(i) fpn_block = self.add_sublayer( name, PPYOLODetBlockCSP(cfg, ch_in, channel, act, norm_type, name, data_format)) self.fpn_blocks.append(fpn_block) fpn_channels.append(channel * 2) if i < self.num_blocks - 1: name = 'fpn_transition.{}'.format(i) route = self.add_sublayer( name, ConvBNLayer( ch_in=channel * 2, ch_out=channel, filter_size=1, stride=1, padding=0, act=act, norm_type=norm_type, data_format=data_format, name=name)) self.fpn_routes.append(route) # pan self.pan_blocks = [] self.pan_routes = [] self._out_channels = [512 // (2**(self.num_blocks - 2)), ] for i in reversed(range(self.num_blocks - 1)): name = 'pan_transition.{}'.format(i) route = self.add_sublayer( name, ConvBNLayer( ch_in=fpn_channels[i + 1], ch_out=fpn_channels[i + 1], filter_size=3, stride=2, padding=1, act=act, norm_type=norm_type, data_format=data_format, name=name)) self.pan_routes = [route, ] + self.pan_routes base_cfg = [] ch_in = fpn_channels[i] + fpn_channels[i + 1] channel = 512 // (2**i) for j in range(self.conv_block_num): base_cfg += [ # name, layer, args [ '{}.0'.format(j), ConvBNLayer, [channel, channel, 1], dict( padding=0, act=act, norm_type=norm_type) ], [ '{}.1'.format(j), ConvBNLayer, [channel, channel, 3], dict( padding=1, act=act, norm_type=norm_type) ] ] cfg = base_cfg[:4] + dropblock_cfg + base_cfg[4:] name = 'pan.{}'.format(i) pan_block = self.add_sublayer( name, PPYOLODetBlockCSP(cfg, ch_in, channel, act, norm_type, name, data_format)) self.pan_blocks = [pan_block, ] + self.pan_blocks self._out_channels.append(channel * 2) self._out_channels = self._out_channels[::-1] def forward(self, blocks, for_mot=False): assert len(blocks) == self.num_blocks blocks = blocks[::-1] fpn_feats = [] # add embedding features output for multi-object tracking model if for_mot: emb_feats = [] for i, block in enumerate(blocks): if i > 0: if self.data_format == 'NCHW': block = paddle.concat([route, block], axis=1) else: block = paddle.concat([route, block], axis=-1) route, tip = self.fpn_blocks[i](block) fpn_feats.append(tip) if for_mot: # add embedding features output emb_feats.append(route) if i < self.num_blocks - 1: route = self.fpn_routes[i](route) route = F.interpolate( route, scale_factor=2., data_format=self.data_format) pan_feats = [fpn_feats[-1], ] route = fpn_feats[self.num_blocks - 1] for i in reversed(range(self.num_blocks - 1)): block = fpn_feats[i] route = self.pan_routes[i](route) if self.data_format == 'NCHW': block = paddle.concat([route, block], axis=1) else: block = paddle.concat([route, block], axis=-1) route, tip = self.pan_blocks[i](block) pan_feats.append(tip) if for_mot: return {'yolo_feats': pan_feats[::-1], 'emb_feats': emb_feats} else: return pan_feats[::-1] @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], } @property def out_shape(self): return [ShapeSpec(channels=c) for c in self._out_channels]