mirror of https://github.com/JDAI-CV/fast-reid.git
56 lines
1.6 KiB
Python
56 lines
1.6 KiB
Python
# 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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from torch import nn
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import torch.nn.functional as F
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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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from ...layers import GeneralizedMeanPoolingP
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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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self._cfg = cfg
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# backbone
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self.backbone = build_backbone(cfg)
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# head
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if cfg.MODEL.HEADS.POOL_LAYER == 'avgpool':
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pool_layer = nn.AdaptiveAvgPool2d(1)
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elif cfg.MODEL.HEADS.POOL_LAYER == 'maxpool':
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pool_layer = nn.AdaptiveMaxPool2d(1)
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elif cfg.MODEL.HEADS.POOL_LAYER == 'gempool':
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pool_layer = GeneralizedMeanPoolingP()
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else:
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pool_layer = nn.Identity()
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self.heads = build_reid_heads(cfg, 2048, pool_layer)
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def forward(self, inputs):
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images = inputs["images"]
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targets = inputs["targets"]
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if not self.training:
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pred_feat = self.inference(images)
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return pred_feat, targets, inputs["camid"]
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# training
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features = self.backbone(images) # (bs, 2048, 16, 8)
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logits, global_feat = self.heads(features, targets)
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return logits, global_feat, targets
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def inference(self, images):
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assert not self.training
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features = self.backbone(images) # (bs, 2048, 16, 8)
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pred_feat = self.heads(features)
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return F.normalize(pred_feat)
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def losses(self, outputs):
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return self.heads.losses(self._cfg, *outputs)
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