josedolot/HybridNet_Demo2

1import torch2from torch import nn3import timm4 5from hybridnets.model import BiFPN, Regressor, Classifier, BiFPNDecoder6from utils.utils import Anchors7from hybridnets.model import SegmentationHead8 9from encoders import get_encoder10 11class HybridNetsBackbone(nn.Module):12    def __init__(self, num_classes=80, compound_coef=0, seg_classes=1, backbone_name=None, **kwargs):13        super(HybridNetsBackbone, self).__init__()14        self.compound_coef = compound_coef15 16        self.seg_classes = seg_classes17 18        self.backbone_compound_coef = [0, 1, 2, 3, 4, 5, 6, 6, 7]19        self.fpn_num_filters = [64, 88, 112, 160, 224, 288, 384, 384, 384]20        self.fpn_cell_repeats = [3, 4, 5, 6, 7, 7, 8, 8, 8]21        self.input_sizes = [512, 640, 768, 896, 1024, 1280, 1280, 1536, 1536]22        self.box_class_repeats = [3, 3, 3, 4, 4, 4, 5, 5, 5]23        self.pyramid_levels = [5, 5, 5, 5, 5, 5, 5, 5, 6]24        self.anchor_scale = [1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,1.25,]25        self.aspect_ratios = kwargs.get('ratios', [(1.0, 1.0), (1.4, 0.7), (0.7, 1.4)])26        self.num_scales = len(kwargs.get('scales', [2 ** 0, 2 ** (1.0 / 3.0), 2 ** (2.0 / 3.0)]))27        conv_channel_coef = {28            # the channels of P3/P4/P5.29            0: [40, 112, 320],30            1: [40, 112, 320],31            2: [48, 120, 352],32            3: [48, 136, 384],33            4: [56, 160, 448],34            5: [64, 176, 512],35            6: [72, 200, 576],36            7: [72, 200, 576],37            8: [80, 224, 640],38        }39 40        num_anchors = len(self.aspect_ratios) * self.num_scales41 42        self.bifpn = nn.Sequential(43            *[BiFPN(self.fpn_num_filters[self.compound_coef],44                    conv_channel_coef[compound_coef],45                    True if _ == 0 else False,46                    attention=True if compound_coef < 6 else False,47                    use_p8=compound_coef > 7)48              for _ in range(self.fpn_cell_repeats[compound_coef])])49 50        self.num_classes = num_classes51        self.regressor = Regressor(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors,52                                   num_layers=self.box_class_repeats[self.compound_coef],53                                   pyramid_levels=self.pyramid_levels[self.compound_coef])54 55        '''Modified by Dat Vu'''56        # self.decoder = DecoderModule()57        self.bifpndecoder = BiFPNDecoder(pyramid_channels=self.fpn_num_filters[self.compound_coef])58 59        self.segmentation_head = SegmentationHead(60            in_channels=64,61            out_channels=self.seg_classes+1 if self.seg_classes > 1 else self.seg_classes,62            activation='softmax2d' if self.seg_classes > 1 else 'sigmoid',63            kernel_size=1,64            upsampling=4,65        )66 67        self.classifier = Classifier(in_channels=self.fpn_num_filters[self.compound_coef], num_anchors=num_anchors,68                                     num_classes=num_classes,69                                     num_layers=self.box_class_repeats[self.compound_coef],70                                     pyramid_levels=self.pyramid_levels[self.compound_coef])71 72        self.anchors = Anchors(anchor_scale=self.anchor_scale[compound_coef],73                               pyramid_levels=(torch.arange(self.pyramid_levels[self.compound_coef]) + 3).tolist(),74                               **kwargs)75 76        if backbone_name:77            # Use timm to create another backbone that you prefer78            # https://github.com/rwightman/pytorch-image-models79            self.encoder = timm.create_model(backbone_name, pretrained=True, features_only=True, out_indices=(2,3,4))  # P3,P4,P580        else:81            # EfficientNet_Pytorch82            self.encoder = get_encoder(83                'efficientnet-b' + str(self.backbone_compound_coef[compound_coef]),84                in_channels=3,85                depth=5,86                weights='imagenet',87            )88    89        self.initialize_decoder(self.bifpndecoder)90        self.initialize_head(self.segmentation_head)91        self.initialize_decoder(self.bifpn)92 93    def freeze_bn(self):94        for m in self.modules():95            if isinstance(m, nn.BatchNorm2d):96                m.eval()97 98    def forward(self, inputs):99        max_size = inputs.shape[-1]100 101        # p1, p2, p3, p4, p5 = self.backbone_net(inputs)102        p2, p3, p4, p5 = self.encoder(inputs)[-4:]  # self.backbone_net(inputs)103 104        features = (p3, p4, p5)105 106        features = self.bifpn(features)107        108        p3,p4,p5,p6,p7 = features109        110        outputs = self.bifpndecoder((p2,p3,p4,p5,p6,p7))111 112        segmentation = self.segmentation_head(outputs)113        114        regression = self.regressor(features)115        classification = self.classifier(features)116        anchors = self.anchors(inputs, inputs.dtype)117 118        return features, regression, classification, anchors, segmentation119    120    def initialize_decoder(self, module):121        for m in module.modules():122 123            if isinstance(m, nn.Conv2d):124                nn.init.kaiming_uniform_(m.weight, mode="fan_in", nonlinearity="relu")125                if m.bias is not None:126                    nn.init.constant_(m.bias, 0)127 128            elif isinstance(m, nn.BatchNorm2d):129                nn.init.constant_(m.weight, 1)130                nn.init.constant_(m.bias, 0)131 132            elif isinstance(m, nn.Linear):133                nn.init.xavier_uniform_(m.weight)134                if m.bias is not None:135                    nn.init.constant_(m.bias, 0)136 137 138    def initialize_head(self, module):139        for m in module.modules():140            if isinstance(m, (nn.Linear, nn.Conv2d)):141                nn.init.xavier_uniform_(m.weight)142                if m.bias is not None:143                    nn.init.constant_(m.bias, 0)144