| import torch |
| import torch.nn as nn |
| from collections import OrderedDict |
| from torchvision.models import vgg16, vgg16_bn, VGG16_Weights, VGG16_BN_Weights, resnet50, ResNet50_Weights |
| from models.backbones.pvt_v2 import pvt_v2_b0, pvt_v2_b1, pvt_v2_b2, pvt_v2_b5 |
| from models.backbones.swin_v1 import swin_v1_t, swin_v1_s, swin_v1_b, swin_v1_l |
| from config import Config |
|
|
|
|
| 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') |
| 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()} |
| |
| 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 |
|
|
