engine/BiRefNet/models/backbones/build_backbone.py

from collections import OrderedDict

import torch
import torch.nn as nn
from torchvision.models import (
    ResNet50_Weights,
    VGG16_BN_Weights,
    VGG16_Weights,
    resnet50,
    vgg16,
    vgg16_bn,
)

from engine.BiRefNet.config import Config
from engine.BiRefNet.models.backbones.pvt_v2 import (
    pvt_v2_b0,
    pvt_v2_b1,
    pvt_v2_b2,
    pvt_v2_b5,
)
from engine.BiRefNet.models.backbones.swin_v1 import (
    swin_v1_b,
    swin_v1_l,
    swin_v1_s,
    swin_v1_t,
)

config = Config()


def build_backbone(bb_name, pretrained=True, params_settings=""):
    if bb_name == "vgg16":
        bb_net = list(
            vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children()
        )[0]
        bb = nn.Sequential(
            OrderedDict(
                {
                    "conv1": bb_net[:4],
                    "conv2": bb_net[4:9],
                    "conv3": bb_net[9:16],
                    "conv4": bb_net[16:23],
                }
            )
        )
    elif bb_name == "vgg16bn":
        bb_net = list(
            vgg16_bn(
                pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None
            ).children()
        )[0]
        bb = nn.Sequential(
            OrderedDict(
                {
                    "conv1": bb_net[:6],
                    "conv2": bb_net[6:13],
                    "conv3": bb_net[13:23],
                    "conv4": bb_net[23:33],
                }
            )
        )
    elif bb_name == "resnet50":
        bb_net = list(
            resnet50(
                pretrained=ResNet50_Weights.DEFAULT if pretrained else None
            ).children()
        )
        bb = nn.Sequential(
            OrderedDict(
                {
                    "conv1": nn.Sequential(*bb_net[0:3]),
                    "conv2": bb_net[4],
                    "conv3": bb_net[5],
                    "conv4": bb_net[6],
                }
            )
        )
    else:
        bb = eval("{}({})".format(bb_name, params_settings))
        if pretrained:
            bb = load_weights(bb, bb_name)
    return bb


def load_weights(model, model_name):
    save_model = torch.load(
        config.weights[model_name], map_location="cpu", weights_only=True
    )
    model_dict = model.state_dict()
    state_dict = {
        k: v if v.size() == model_dict[k].size() else model_dict[k]
        for k, v in save_model.items()
        if k in model_dict.keys()
    }
    # to ignore the weights with mismatched size when I modify the backbone itself.
    if not state_dict:
        save_model_keys = list(save_model.keys())
        sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None
        state_dict = {
            k: v if v.size() == model_dict[k].size() else model_dict[k]
            for k, v in save_model[sub_item].items()
            if k in model_dict.keys()
        }
        if not state_dict or not sub_item:
            print(
                "Weights are not successully loaded. Check the state dict of weights file."
            )
            return None
        else:
            print(
                'Found correct weights in the "{}" item of loaded state_dict.'.format(
                    sub_item
                )
            )
    model_dict.update(state_dict)
    model.load_state_dict(model_dict)
    return model