| import torch
|
| from pdb import set_trace as st
|
| from torch import nn
|
| from .model_irse import Backbone
|
| from .paths_config import model_paths
|
|
|
|
|
| class IDLoss(nn.Module):
|
|
|
| def __init__(self, device):
|
|
|
| super().__init__()
|
| print('Loading ResNet ArcFace')
|
| self.facenet = Backbone(input_size=112,
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| num_layers=50,
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| drop_ratio=0.6,
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| mode='ir_se').to(device)
|
|
|
| try:
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| face_net_model = torch.load(model_paths['ir_se50'],
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| map_location=device)
|
| except Exception as e:
|
| face_net_model = torch.load(model_paths['ir_se50_hwc'],
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| map_location=device)
|
|
|
| self.facenet.load_state_dict(face_net_model)
|
|
|
| self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
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| self.facenet.eval()
|
|
|
| def extract_feats(self, x):
|
| x = x[:, :, 35:223, 32:220]
|
| x = self.face_pool(x)
|
| x_feats = self.facenet(x)
|
| return x_feats
|
|
|
| def forward(self, y_hat, y, x):
|
| n_samples, _, H, W = x.shape
|
| assert H == W == 256, 'idloss needs 256*256 input images'
|
|
|
| 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
|
| id_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])
|
| id_logs.append({
|
| 'diff_target': float(diff_target),
|
| 'diff_input': float(diff_input),
|
| 'diff_views': float(diff_views)
|
| })
|
| loss += 1 - diff_target
|
| id_diff = float(diff_target) - float(diff_views)
|
| sim_improvement += id_diff
|
| count += 1
|
|
|
| return loss / count, sim_improvement / count, id_logs
|
|
|
