mirror of https://github.com/JDAI-CV/fast-reid.git
实现和线上模型一模一样的PCBNet
zuchen.wang
parent
5a075c1fe8
commit
65cfc515d9
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@ -4,7 +4,6 @@
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@contact: sherlockliao01@gmail.com
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"""
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from se_pcb_net import build_senet_pcb_backbone
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from .build import build_backbone, BACKBONE_REGISTRY
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from .mobilenet import build_mobilenetv2_backbone
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from .osnet import build_osnet_backbone
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@ -15,3 +14,4 @@ from .resnet import build_resnet_backbone
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from .resnext import build_resnext_backbone
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from .shufflenet import build_shufflenetv2_backbone
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from .vision_transformer import build_vit_backbone
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from .se_pcb_net import build_senet_pcb_backbone
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@ -22,13 +22,13 @@ class SePcbNet(nn.Module):
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part_num: int,
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embedding_dim: int,
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part_dim: int,
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last_stride: Tuple[int, int]
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last_stride: int,
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):
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super(SePcbNet, self).__init__()
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self.part_num = part_num
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self.embedding_dim = embedding_dim
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self.part_dim = part_dim
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self.last_stride = last_stride
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self.last_stride = (last_stride, last_stride)
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self.cnn = pretrainedmodels.__dict__["se_resnext101_32x4d"](pretrained='imagenet')
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self.cnn.layer4[0].downsample[0].stride = self.last_stride
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@ -40,7 +40,7 @@ class SePcbNet(nn.Module):
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setattr(self, 'reduction_' + str(i),
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nn.Sequential(
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nn.Conv2d(self.embedding_dim, self.part_dim, (1, 1), bias=False),
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nn.BatchNorm2d(self.part_num),
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nn.BatchNorm2d(self.part_dim),
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nn.ReLU()
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))
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@ -70,7 +70,6 @@ class SePcbNet(nn.Module):
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}
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def random_init(self):
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self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
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for m in self.modules():
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if isinstance(m, nn.Conv2d):
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n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
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@ -82,6 +81,8 @@ class SePcbNet(nn.Module):
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m.weight.data.fill_(1)
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m.bias.data.zero_()
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self.cnn.layer0.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
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@BACKBONE_REGISTRY.register()
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def build_senet_pcb_backbone(cfg: CfgNode):
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@ -99,10 +100,10 @@ def build_senet_pcb_backbone(cfg: CfgNode):
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if pretrain:
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if pretrain_path:
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try:
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state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))['model']
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state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))
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new_state_dict = {}
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for k in state_dict:
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new_k = '.'.join(k.split('.')[2:])
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new_k = 'cnn.' + k
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if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
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new_state_dict[new_k] = state_dict[k]
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state_dict = new_state_dict
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@ -79,37 +79,31 @@ class PcbHead(nn.Module):
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self.reset_parameters()
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def forward(self, features, targets=None):
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full = features['full']
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parts = features['parts']
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bsz = full.size(0)
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query_feature = features['query']
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gallery_feature = features['gallery']
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# normalize
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full = self._normalize(full)
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parts = self._normalize(parts)
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# split features into pair
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query_full, gallery_full = self._split_features(full, bsz)
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query_part_0, gallery_part_0 = self._split_features(parts[0], bsz)
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query_part_1, gallery_part_1 = self._split_features(parts[1], bsz)
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query_part_2, gallery_part_2 = self._split_features(parts[2], bsz)
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query_full, query_part_0, query_part_1, query_part_2 = torch.split(query_feature,
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[self.full_dim, self.part_dim, self.part_dim, self.part_dim], dim=-1)
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gallery_full, gallery_part_0, gallery_part_1, gallery_part_2 = torch.split(gallery_feature,
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[self.full_dim, self.part_dim, self.part_dim, self.part_dim], dim=-1)
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m_full = self.match_full(
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torch.cat([query_full, gallery_full, query_full - gallery_full,
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torch.cat([query_full, gallery_full, (query_full - gallery_full).abs(),
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query_full * gallery_full], dim=-1)
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)
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m_part_0 = self.match_part_0(
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torch.cat([query_part_0, gallery_part_0, query_part_0 - gallery_part_0,
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torch.cat([query_part_0, gallery_part_0, (query_part_0 - gallery_part_0).abs(),
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query_part_0 * gallery_part_0], dim=-1)
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)
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m_part_1 = self.match_part_1(
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torch.cat([query_part_1, gallery_part_1, query_part_1 - gallery_part_1,
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torch.cat([query_part_1, gallery_part_1, (query_part_1 - gallery_part_1).abs(),
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query_part_1 * gallery_part_1], dim=-1)
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)
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m_part_2 = self.match_part_2(
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torch.cat([query_part_2, gallery_part_2, query_part_2 - gallery_part_2,
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torch.cat([query_part_2, gallery_part_2, (query_part_2 - gallery_part_2).abs(),
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query_part_2 * gallery_part_2], dim=-1)
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)
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@ -14,3 +14,4 @@ from .moco import MoCo
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from .distiller import Distiller
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from .metric import Metric
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from .pcb import PCB
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from .pcb_online import PcbOnline
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@ -0,0 +1,60 @@
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# coding: utf-8
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"""
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Sun, Y. , Zheng, L. , Yang, Y. , Tian, Q. , & Wang, S. . (2017). Beyond part models: person retrieval with refined part pooling (and a strong convolutional baseline). Springer, Cham.
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实现和线上一模一样的PCB
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"""
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import torch
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import torch.nn.functional as F
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from fastreid.modeling.losses import cross_entropy_loss, log_accuracy
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from fastreid.modeling.meta_arch import Baseline
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from fastreid.modeling.meta_arch import META_ARCH_REGISTRY
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@META_ARCH_REGISTRY.register()
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class PcbOnline(Baseline):
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def forward(self, batched_inputs):
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images = self.preprocess_image(batched_inputs)
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bsz = int(images.size(0) / 2)
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feats = self.backbone(images)
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feats = torch.cat((feats['full'], feats['parts'][0], feats['parts'][1], feats['parts'][2]), 1)
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feats = F.normalize(feats, p=2.0, dim=-1)
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qf = feats[0: bsz * 2: 2, ...]
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xf = feats[1: bsz * 2: 2, ...]
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outputs = self.heads({'query': qf, 'gallery': xf})
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if self.training:
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targets = batched_inputs['targets']
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losses = self.losses(outputs, targets)
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return losses
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else:
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return outputs
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def losses(self, outputs, gt_labels):
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"""
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Compute loss from modeling's outputs, the loss function input arguments
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must be the same as the outputs of the model forwarding.
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"""
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# model predictions
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pred_class_logits = outputs['pred_class_logits'].detach()
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cls_outputs = outputs['cls_outputs']
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# Log prediction accuracy
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log_accuracy(pred_class_logits, gt_labels)
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loss_dict = {}
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loss_names = self.loss_kwargs['loss_names']
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if 'CrossEntropyLoss' in loss_names:
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ce_kwargs = self.loss_kwargs.get('ce')
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loss_dict['loss_cls'] = cross_entropy_loss(
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cls_outputs,
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gt_labels,
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ce_kwargs.get('eps'),
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ce_kwargs.get('alpha')
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) * ce_kwargs.get('scale')
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return loss_dict
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@ -1,7 +1,7 @@
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_BASE_: base.yaml
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MODEL:
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META_ARCHITECTURE: PCB
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META_ARCHITECTURE: PcbOnline
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PCB:
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PART_NUM: 3
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@ -14,10 +14,12 @@ MODEL:
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EMBEDDING_DIM: 512
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BACKBONE:
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NAME: build_resnet_backbone
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PRETRAIN: True
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PRETRAIN_PATH: /home/apps/.cache/torch/hub/checkpoints/se_resnext101_32x4d-3b2fe3d8.pth
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NAME: build_senet_pcb_backbone
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DEPTH: 101x
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NORM: BN
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LAST_STRIDE: 2
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LAST_STRIDE: 1
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FEAT_DIM: 512
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PRETRAIN: True
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WITH_IBN: True
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@ -46,11 +48,34 @@ INPUT:
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ENABLED: True
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SIZE: [270, 260]
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SCALE: [0.8, 1.2]
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RATIO: [3./4, 4./3]
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RATIO: [0.75, 1.33333333]
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DATALOADER:
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NUM_WORKERS: 8
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SOLVER:
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OPT: SGD
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SCHED: CosineAnnealingLR
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BASE_LR: 0.001
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MOMENTUM: 0.9
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NESTEROV: False
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BIAS_LR_FACTOR: 1.
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WEIGHT_DECAY: 0.0005
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WEIGHT_DECAY_BIAS: 0.
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ETA_MIN_LR: 0.00003
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WARMUP_FACTOR: 0.1
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WARMUP_ITERS: 1000
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IMS_PER_BATCH: 40
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TEST:
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IMS_PER_BATCH: 64
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DATASETS:
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NAMES: ("ShoeDataset",)
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TESTS: ("ShoeDataset", "OnlineDataset")
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TESTS: ("ShoeDataset",)
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OUTPUT_DIR: projects/FastShoe/logs/online-pcb
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@ -28,14 +28,9 @@ class PairDataset(Dataset):
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self._logger.info('set {} with {} random seed: 12345'.format(self.mode, self.__class__.__name__))
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seed_all_rng(12345)
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# if self.mode == 'train':
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# # make negative sample come from all negative folders when train
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# self.neg_folders = sum(self.neg_folders, list())
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def __len__(self):
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if self.mode == 'test':
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return len(self.pos_folders) * 10
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return len(self.pos_folders)
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def __getitem__(self, idx):
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@ -43,9 +38,6 @@ class PairDataset(Dataset):
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idx = int(idx / 10)
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pf = self.pos_folders[idx]
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# if self.mode == 'train':
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# nf = self.neg_folders
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# else:
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nf = self.neg_folders[idx]
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label = 1
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