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import torch
from torch import nn
from configs.paths_config import model_paths
from models.encoders.model_irse import Backbone
class IDLoss(nn.Module):
def __init__(self):
super(IDLoss, self).__init__()
print('Loading ResNet ArcFace')
self.facenet = Backbone(input_size=112, num_layers=50, drop_ratio=0.6, mode='ir_se')
self.facenet.load_state_dict(torch.load(model_paths['ir_se50']))
self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
self.facenet.eval()
def extract_feats(self, x):
x = x[:, :, 35:223, 32:220] # Crop interesting region
x = self.face_pool(x)
x_feats = self.facenet(x)
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) # Otherwise use the feature from there
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
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