mirror of
https://github.com/JDAI-CV/fast-reid.git
synced 2025-06-03 14:50:47 +08:00
71 lines
2.4 KiB
Python
71 lines
2.4 KiB
Python
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# encoding: utf-8
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"""
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@author: liaoxingyu
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@contact: sherlockliao01@gmail.com
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"""
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import torch
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from torch import nn
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from .build import META_ARCH_REGISTRY
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from ..backbones import build_backbone
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from ..heads import build_reid_heads
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@META_ARCH_REGISTRY.register()
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class Baseline(nn.Module):
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def __init__(self, cfg):
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super().__init__()
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assert len(cfg.MODEL.PIXEL_MEAN) == len(cfg.MODEL.PIXEL_STD)
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num_channels = len(cfg.MODEL.PIXEL_MEAN)
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pixel_mean = torch.tensor(cfg.MODEL.PIXEL_MEAN).view(1, num_channels, 1, 1)
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self.register_buffer('pixel_mean', pixel_mean)
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pixel_std = torch.tensor(cfg.MODEL.PIXEL_STD).view(1, num_channels, 1, 1)
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self.register_buffer('pixel_std', pixel_std)
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self.normalizer = lambda x: (x - self.pixel_mean) / self.pixel_std
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self.backbone = build_backbone(cfg)
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self.heads = build_reid_heads(cfg)
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def forward(self, inputs, labels=None):
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inputs = self.normalizer(inputs)
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# images = self.preprocess_image(batched_inputs)
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global_feat = self.backbone(inputs) # (bs, 2048, 16, 8)
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if self.training:
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outputs = self.heads(global_feat, labels)
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return outputs
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else:
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pred_features = self.heads(global_feat)
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return pred_features
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def load_params_wo_fc(self, state_dict):
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if 'classifier.weight' in state_dict:
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state_dict.pop('classifier.weight')
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if 'amsoftmax.weight' in state_dict:
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state_dict.pop('amsoftmax.weight')
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res = self.load_state_dict(state_dict, strict=False)
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print(f'missing keys {res.missing_keys}')
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print(f'unexpected keys {res.unexpected_keys}')
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# assert str(res.missing_keys) == str(['classifier.weight',]), 'issue loading pretrained weights'
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# def unfreeze_all_layers(self, ):
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# self.train()
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# for p in self.parameters():
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# p.requires_grad_()
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#
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# def unfreeze_specific_layer(self, names):
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# if isinstance(names, str):
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# names = [names]
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#
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# for name, module in self.named_children():
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# if name in names:
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# module.train()
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# for p in module.parameters():
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# p.requires_grad_()
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# else:
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# module.eval()
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# for p in module.parameters():
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# p.requires_grad_(False)
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