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| import torch | |
| from torch import nn | |
| import torch.nn.functional as F | |
| from configs.paths_config import model_paths | |
| class MocoLoss(nn.Module): | |
| def __init__(self, opts): | |
| super(MocoLoss, self).__init__() | |
| print("Loading MOCO model from path: {}".format(model_paths["moco"])) | |
| self.model = self.__load_model() | |
| self.model.eval() | |
| for param in self.model.parameters(): | |
| param.requires_grad = False | |
| def __load_model(): | |
| import torchvision.models as models | |
| model = models.__dict__["resnet50"]() | |
| # freeze all layers but the last fc | |
| for name, param in model.named_parameters(): | |
| if name not in ['fc.weight', 'fc.bias']: | |
| param.requires_grad = False | |
| checkpoint = torch.load(model_paths['moco'], map_location="cpu") | |
| state_dict = checkpoint['state_dict'] | |
| # rename moco pre-trained keys | |
| for k in list(state_dict.keys()): | |
| # retain only encoder_q up to before the embedding layer | |
| if k.startswith('module.encoder_q') and not k.startswith('module.encoder_q.fc'): | |
| # remove prefix | |
| state_dict[k[len("module.encoder_q."):]] = state_dict[k] | |
| # delete renamed or unused k | |
| del state_dict[k] | |
| msg = model.load_state_dict(state_dict, strict=False) | |
| assert set(msg.missing_keys) == {"fc.weight", "fc.bias"} | |
| # remove output layer | |
| model = nn.Sequential(*list(model.children())[:-1]).cuda() | |
| return model | |
| def extract_feats(self, x): | |
| x = F.interpolate(x, size=224) | |
| x_feats = self.model(x) | |
| x_feats = nn.functional.normalize(x_feats, dim=1) | |
| x_feats = x_feats.squeeze() | |
| return x_feats | |
| def forward(self, y_hat, y, x): | |
| n_samples = x.shape[0] | |
| x_feats = self.extract_feats(x) | |
| y_feats = self.extract_feats(y) | |
| y_hat_feats = self.extract_feats(y_hat) | |
| y_feats = y_feats.detach() | |
| loss = 0 | |
| sim_improvement = 0 | |
| sim_logs = [] | |
| count = 0 | |
| for i in range(n_samples): | |
| diff_target = y_hat_feats[i].dot(y_feats[i]) | |
| diff_input = y_hat_feats[i].dot(x_feats[i]) | |
| diff_views = y_feats[i].dot(x_feats[i]) | |
| sim_logs.append({'diff_target': float(diff_target), | |
| 'diff_input': float(diff_input), | |
| 'diff_views': float(diff_views)}) | |
| loss += 1 - diff_target | |
| sim_diff = float(diff_target) - float(diff_views) | |
| sim_improvement += sim_diff | |
| count += 1 | |
| return loss / count, sim_improvement / count, sim_logs | |