Create encoder_criteria_id_loss.py

encoder_criteria_id_loss.py

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+import torch
+from torch import nn
+from model.encoder.encoders.model_irse import Backbone
+
+
+class IDLoss(nn.Module):
+    def __init__(self, model_paths):
+        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))
+        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):
+        n_samples = y_hat.shape[0]
+        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
+        count = 0
+        for i in range(n_samples):
+            diff_target = y_hat_feats[i].dot(y_feats[i])
+            loss += 1 - diff_target
+            count += 1
+
+        return loss / count