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import torch
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from pdb import set_trace as st
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from torch import nn
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from .model_irse import Backbone
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from .paths_config import model_paths
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class IDLoss(nn.Module):
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def __init__(self, device):
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super().__init__()
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print('Loading ResNet ArcFace')
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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)
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try:
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face_net_model = torch.load(model_paths['ir_se50'],
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map_location=device)
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except Exception as e:
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face_net_model = torch.load(model_paths['ir_se50_hwc'],
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map_location=device)
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self.facenet.load_state_dict(face_net_model)
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self.face_pool = torch.nn.AdaptiveAvgPool2d((112, 112))
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self.facenet.eval()
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def extract_feats(self, x):
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x = x[:, :, 35:223, 32:220]
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x = self.face_pool(x)
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x_feats = self.facenet(x)
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return x_feats
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def forward(self, y_hat, y, x):
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n_samples, _, H, W = x.shape
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assert H == W == 256, 'idloss needs 256*256 input images'
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x_feats = self.extract_feats(x)
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y_feats = self.extract_feats(y)
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y_hat_feats = self.extract_feats(y_hat)
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y_feats = y_feats.detach()
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loss = 0
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sim_improvement = 0
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id_logs = []
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count = 0
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for i in range(n_samples):
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diff_target = y_hat_feats[i].dot(y_feats[i])
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diff_input = y_hat_feats[i].dot(x_feats[i])
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diff_views = y_feats[i].dot(x_feats[i])
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id_logs.append({
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'diff_target': float(diff_target),
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'diff_input': float(diff_input),
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'diff_views': float(diff_views)
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})
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loss += 1 - diff_target
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id_diff = float(diff_target) - float(diff_views)
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sim_improvement += id_diff
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count += 1
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return loss / count, sim_improvement / count, id_logs
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