mirror of https://github.com/JDAI-CV/fast-reid.git
80 lines
2.8 KiB
Python
80 lines
2.8 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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import torch
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from torch import nn
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from fastreid.layers import GeneralizedMeanPoolingP, AdaptiveAvgMaxPool2d
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from fastreid.modeling.backbones import build_backbone
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from fastreid.modeling.heads import build_reid_heads
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from fastreid.modeling.losses import reid_losses
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from .build import META_ARCH_REGISTRY
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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.register_buffer("pixel_mean", torch.Tensor(cfg.MODEL.PIXEL_MEAN).view(1, -1, 1, 1))
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self.register_buffer("pixel_std", torch.Tensor(cfg.MODEL.PIXEL_STD).view(1, -1, 1, 1))
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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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pool_type = cfg.MODEL.HEADS.POOL_LAYER
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if pool_type == 'avgpool': pool_layer = nn.AdaptiveAvgPool2d(1)
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elif pool_type == 'maxpool': pool_layer = nn.AdaptiveMaxPool2d(1)
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elif pool_type == 'gempool': pool_layer = GeneralizedMeanPoolingP()
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elif pool_type == "avgmaxpool": pool_layer = AdaptiveAvgMaxPool2d(1)
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elif pool_type == "identity": pool_layer = nn.Identity()
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else:
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raise KeyError(f"{pool_type} is invalid, please choose from "
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f"'avgpool', 'maxpool', 'gempool', 'avgmaxpool' and 'identity'.")
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in_feat = cfg.MODEL.HEADS.IN_FEAT
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num_classes = cfg.MODEL.HEADS.NUM_CLASSES
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self.heads = build_reid_heads(cfg, in_feat, num_classes, pool_layer)
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@property
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def device(self):
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return self.pixel_mean.device
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def forward(self, batched_inputs):
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if not self.training:
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pred_feat = self.inference(batched_inputs)
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try:
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return pred_feat, batched_inputs["targets"], batched_inputs["camid"]
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except KeyError:
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return pred_feat
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images = self.preprocess_image(batched_inputs)
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targets = batched_inputs["targets"].long()
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# training
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features = self.backbone(images) # (bs, 2048, 16, 8)
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return self.heads(features, targets)
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def inference(self, batched_inputs):
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assert not self.training
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images = self.preprocess_image(batched_inputs)
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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 pred_feat
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def preprocess_image(self, batched_inputs):
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"""
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Normalize and batch the input images.
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"""
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# images = [x["images"] for x in batched_inputs]
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images = batched_inputs["images"]
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images.sub_(self.pixel_mean).div_(self.pixel_std)
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return images
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def losses(self, outputs):
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logits, feat, targets = outputs
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return reid_losses(self._cfg, logits, feat, targets)
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