# 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 from paddle.regularizer import L2Decay from paddle import ParamAttr from ..layers import AnchorGeneratorSSD class SepConvLayer(nn.Layer): def __init__(self, in_channels, out_channels, kernel_size=3, padding=1, conv_decay=0.): super(SepConvLayer, self).__init__() self.dw_conv = nn.Conv2D( in_channels=in_channels, out_channels=in_channels, kernel_size=kernel_size, stride=1, padding=padding, groups=in_channels, weight_attr=ParamAttr(regularizer=L2Decay(conv_decay)), bias_attr=False) self.bn = nn.BatchNorm2D( in_channels, weight_attr=ParamAttr(regularizer=L2Decay(0.)), bias_attr=ParamAttr(regularizer=L2Decay(0.))) self.pw_conv = nn.Conv2D( in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=1, padding=0, weight_attr=ParamAttr(regularizer=L2Decay(conv_decay)), bias_attr=False) def forward(self, x): x = self.dw_conv(x) x = F.relu6(self.bn(x)) x = self.pw_conv(x) return x class SSDExtraHead(nn.Layer): def __init__(self, in_channels=256, out_channels=([256, 512], [256, 512], [128, 256], [128, 256], [128, 256]), strides=(2, 2, 2, 1, 1), paddings=(1, 1, 1, 0, 0)): super(SSDExtraHead, self).__init__() self.convs = nn.LayerList() for out_channel, stride, padding in zip(out_channels, strides, paddings): self.convs.append( self._make_layers(in_channels, out_channel[0], out_channel[1], stride, padding)) in_channels = out_channel[-1] def _make_layers(self, c_in, c_hidden, c_out, stride_3x3, padding_3x3): return nn.Sequential( nn.Conv2D(c_in, c_hidden, 1), nn.ReLU(), nn.Conv2D(c_hidden, c_out, 3, stride_3x3, padding_3x3), nn.ReLU()) def forward(self, x): out = [x] for conv_layer in self.convs: out.append(conv_layer(out[-1])) return out @register class SSDHead(nn.Layer): """ SSDHead Args: num_classes (int): Number of classes in_channels (list): Number of channels per input feature anchor_generator (dict): Configuration of 'AnchorGeneratorSSD' instance kernel_size (int): Conv kernel size padding (int): Conv padding use_sepconv (bool): Use SepConvLayer if true conv_decay (float): Conv regularization coeff loss (object): 'SSDLoss' instance use_extra_head (bool): If use ResNet34 as baskbone, you should set `use_extra_head`=True """ __shared__ = ['num_classes'] __inject__ = ['anchor_generator', 'loss'] def __init__(self, num_classes=80, in_channels=(512, 1024, 512, 256, 256, 256), anchor_generator=AnchorGeneratorSSD().__dict__, kernel_size=3, padding=1, use_sepconv=False, conv_decay=0., loss='SSDLoss', use_extra_head=False): super(SSDHead, self).__init__() # add background class self.num_classes = num_classes + 1 self.in_channels = in_channels self.anchor_generator = anchor_generator self.loss = loss self.use_extra_head = use_extra_head if self.use_extra_head: self.ssd_extra_head = SSDExtraHead() self.in_channels = [256, 512, 512, 256, 256, 256] if isinstance(anchor_generator, dict): self.anchor_generator = AnchorGeneratorSSD(**anchor_generator) self.num_priors = self.anchor_generator.num_priors self.box_convs = [] self.score_convs = [] for i, num_prior in enumerate(self.num_priors): box_conv_name = "boxes{}".format(i) if not use_sepconv: box_conv = self.add_sublayer( box_conv_name, nn.Conv2D( in_channels=self.in_channels[i], out_channels=num_prior * 4, kernel_size=kernel_size, padding=padding)) else: box_conv = self.add_sublayer( box_conv_name, SepConvLayer( in_channels=self.in_channels[i], out_channels=num_prior * 4, kernel_size=kernel_size, padding=padding, conv_decay=conv_decay)) self.box_convs.append(box_conv) score_conv_name = "scores{}".format(i) if not use_sepconv: score_conv = self.add_sublayer( score_conv_name, nn.Conv2D( in_channels=self.in_channels[i], out_channels=num_prior * self.num_classes, kernel_size=kernel_size, padding=padding)) else: score_conv = self.add_sublayer( score_conv_name, SepConvLayer( in_channels=self.in_channels[i], out_channels=num_prior * self.num_classes, kernel_size=kernel_size, padding=padding, conv_decay=conv_decay)) self.score_convs.append(score_conv) @classmethod def from_config(cls, cfg, input_shape): return {'in_channels': [i.channels for i in input_shape], } def forward(self, feats, image, gt_bbox=None, gt_class=None): if self.use_extra_head: assert len(feats) == 1, \ ("If you set use_extra_head=True, backbone feature " "list length should be 1.") feats = self.ssd_extra_head(feats[0]) box_preds = [] cls_scores = [] for feat, box_conv, score_conv in zip(feats, self.box_convs, self.score_convs): box_pred = box_conv(feat) box_pred = paddle.transpose(box_pred, [0, 2, 3, 1]) box_pred = paddle.reshape(box_pred, [0, -1, 4]) box_preds.append(box_pred) cls_score = score_conv(feat) cls_score = paddle.transpose(cls_score, [0, 2, 3, 1]) cls_score = paddle.reshape(cls_score, [0, -1, self.num_classes]) cls_scores.append(cls_score) prior_boxes = self.anchor_generator(feats, image) if self.training: return self.get_loss(box_preds, cls_scores, gt_bbox, gt_class, prior_boxes) else: return (box_preds, cls_scores), prior_boxes def get_loss(self, boxes, scores, gt_bbox, gt_class, prior_boxes): return self.loss(boxes, scores, gt_bbox, gt_class, prior_boxes)