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import torch |
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import torch.nn as nn |
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from collections import OrderedDict |
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from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights |
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from models.backbones.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5 |
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from models.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l |
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from config import Config |
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config = Config() |
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def build_backbone(bb_name, pretrained=True, params_settings=''): |
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if bb_name == 'vgg16': |
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bb_net = list(vgg16(pretrained=VGG16_Weights.DEFAULT if pretrained else None).children())[0] |
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bb = nn.Sequential(OrderedDict({'conv1': bb_net[:4], 'conv2': bb_net[4:9], 'conv3': bb_net[9:16], 'conv4': bb_net[16:23]})) |
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elif bb_name == 'vgg16bn': |
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bb_net = list(vgg16_bn(pretrained=VGG16_BN_Weights.DEFAULT if pretrained else None).children())[0] |
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bb = nn.Sequential(OrderedDict({'conv1': bb_net[:6], 'conv2': bb_net[6:13], 'conv3': bb_net[13:23], 'conv4': bb_net[23:33]})) |
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elif bb_name == 'resnet50': |
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bb_net = list(resnet50(pretrained=ResNet50_Weights.DEFAULT if pretrained else None).children()) |
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bb = nn.Sequential(OrderedDict({'conv1': nn.Sequential(*bb_net[0:3]), 'conv2': bb_net[4], 'conv3': bb_net[5], 'conv4': bb_net[6]})) |
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else: |
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bb = eval('{}({})'.format(bb_name, params_settings)) |
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if pretrained: |
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bb = load_weights(bb, bb_name) |
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return bb |
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def load_weights(model, model_name): |
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save_model = torch.load(config.weights[model_name], map_location='cpu') |
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model_dict = model.state_dict() |
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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()} |
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if not state_dict: |
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save_model_keys = list(save_model.keys()) |
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sub_item = save_model_keys[0] if len(save_model_keys) == 1 else None |
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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()} |
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if not state_dict or not sub_item: |
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print('Weights are not successully loaded. Check the state dict of weights file.') |
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return None |
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else: |
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print('Found correct weights in the "{}" item of loaded state_dict.'.format(sub_item)) |
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model_dict.update(state_dict) |
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model.load_state_dict(model_dict) |
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return model |
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