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 __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()
for module in [self.facenet, self.face_pool]:
for param in module.parameters():
param.requires_grad = False
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[:, :, 0:196, 94:290] # Crop shoulder and reshape to square image
rescale = 196 / 256
# (35, 223) and (32, 220) is position on 256x256 img.
# Current img size is 196x196
x = x[:, :,
int(35 * rescale):int(223 * rescale),
int(32 * rescale):int(220 * rescale)] # 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
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, label=None, weights=None):
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()
total_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])
if label is None:
id_logs.append({'diff_target': float(diff_target),
'diff_input': float(diff_input),
'diff_views': float(diff_views)})
else:
id_logs.append({f'diff_target_{label}': float(diff_target),
f'diff_input_{label}': float(diff_input),
f'diff_views_{label}': float(diff_views)})
loss = 1 - diff_target
if weights is not None:
loss = weights[i] * loss
total_loss += loss
id_diff = float(diff_target) - float(diff_views)
sim_improvement += id_diff
count += 1
return total_loss / count, sim_improvement / count, id_logs
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()
for module in [self.facenet, self.face_pool]:
for param in module.parameters():
param.requires_grad = False
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