isegm/model/is_deeplab_model.py

import torch.nn as nn

from isegm.utils.serialization import serialize
from .is_model import ISModel
from .modeling.deeplab_v3 import DeepLabV3Plus
from .modeling.basic_blocks import SepConvHead
from isegm.model.modifiers import LRMult


class DeeplabModel(ISModel):
    @serialize
    def __init__(self, backbone='resnet50', deeplab_ch=256, aspp_dropout=0.5,
                 backbone_norm_layer=None, backbone_lr_mult=0.1, norm_layer=nn.BatchNorm2d, **kwargs):
        super().__init__(norm_layer=norm_layer, **kwargs)

        self.feature_extractor = DeepLabV3Plus(backbone=backbone, ch=deeplab_ch, project_dropout=aspp_dropout,
                                               norm_layer=norm_layer, backbone_norm_layer=backbone_norm_layer)
        self.feature_extractor.backbone.apply(LRMult(backbone_lr_mult))
        self.head = SepConvHead(1, in_channels=deeplab_ch, mid_channels=deeplab_ch // 2,
                                num_layers=2, norm_layer=norm_layer)

    def backbone_forward(self, image, coord_features=None):
        backbone_features = self.feature_extractor(image, coord_features)

        return {'instances': self.head(backbone_features[0])}