diff --git a/fastreid/modeling/backbones/__init__.py b/fastreid/modeling/backbones/__init__.py
index 59b6872..b7c8c62 100644
--- a/fastreid/modeling/backbones/__init__.py
+++ b/fastreid/modeling/backbones/__init__.py
@@ -4,7 +4,6 @@
@contact: sherlockliao01@gmail.com
"""
-from se_pcb_net import build_senet_pcb_backbone
from .build import build_backbone, BACKBONE_REGISTRY
from .mobilenet import build_mobilenetv2_backbone
from .osnet import build_osnet_backbone
@@ -15,3 +14,4 @@ from .resnet import build_resnet_backbone
from .resnext import build_resnext_backbone
from .shufflenet import build_shufflenetv2_backbone
from .vision_transformer import build_vit_backbone
+from .se_pcb_net import build_senet_pcb_backbone
diff --git a/fastreid/modeling/backbones/se_pcb_net.py b/fastreid/modeling/backbones/se_pcb_net.py
index 67efe23..8427a6d 100644
--- a/fastreid/modeling/backbones/se_pcb_net.py
+++ b/fastreid/modeling/backbones/se_pcb_net.py
@@ -22,13 +22,13 @@ class SePcbNet(nn.Module):
part_num: int,
embedding_dim: int,
part_dim: int,
- last_stride: Tuple[int, int]
+ last_stride: int,
):
super(SePcbNet, self).__init__()
self.part_num = part_num
self.embedding_dim = embedding_dim
self.part_dim = part_dim
- self.last_stride = last_stride
+ self.last_stride = (last_stride, last_stride)
self.cnn = pretrainedmodels.__dict__["se_resnext101_32x4d"](pretrained='imagenet')
self.cnn.layer4[0].downsample[0].stride = self.last_stride
@@ -40,7 +40,7 @@ class SePcbNet(nn.Module):
setattr(self, 'reduction_' + str(i),
nn.Sequential(
nn.Conv2d(self.embedding_dim, self.part_dim, (1, 1), bias=False),
- nn.BatchNorm2d(self.part_num),
+ nn.BatchNorm2d(self.part_dim),
nn.ReLU()
))
@@ -70,7 +70,6 @@ class SePcbNet(nn.Module):
}
def random_init(self):
- self.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
@@ -82,6 +81,8 @@ class SePcbNet(nn.Module):
m.weight.data.fill_(1)
m.bias.data.zero_()
+ self.cnn.layer0.conv1.weight.data.normal_(0, math.sqrt(2. / (7 * 7 * 64)))
+
@BACKBONE_REGISTRY.register()
def build_senet_pcb_backbone(cfg: CfgNode):
@@ -99,10 +100,10 @@ def build_senet_pcb_backbone(cfg: CfgNode):
if pretrain:
if pretrain_path:
try:
- state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))['model']
+ state_dict = torch.load(pretrain_path, map_location=torch.device('cpu'))
new_state_dict = {}
for k in state_dict:
- new_k = '.'.join(k.split('.')[2:])
+ new_k = 'cnn.' + k
if new_k in model.state_dict() and (model.state_dict()[new_k].shape == state_dict[k].shape):
new_state_dict[new_k] = state_dict[k]
state_dict = new_state_dict
diff --git a/fastreid/modeling/heads/pcb_head.py b/fastreid/modeling/heads/pcb_head.py
index 0b59f85..5c426cc 100644
--- a/fastreid/modeling/heads/pcb_head.py
+++ b/fastreid/modeling/heads/pcb_head.py
@@ -79,37 +79,31 @@ class PcbHead(nn.Module):
self.reset_parameters()
def forward(self, features, targets=None):
- full = features['full']
- parts = features['parts']
- bsz = full.size(0)
-
- # normalize
- full = self._normalize(full)
- parts = self._normalize(parts)
+ query_feature = features['query']
+ gallery_feature = features['gallery']
- # split features into pair
- query_full, gallery_full = self._split_features(full, bsz)
- query_part_0, gallery_part_0 = self._split_features(parts[0], bsz)
- query_part_1, gallery_part_1 = self._split_features(parts[1], bsz)
- query_part_2, gallery_part_2 = self._split_features(parts[2], bsz)
+ query_full, query_part_0, query_part_1, query_part_2 = torch.split(query_feature,
+ [self.full_dim, self.part_dim, self.part_dim, self.part_dim], dim=-1)
+ gallery_full, gallery_part_0, gallery_part_1, gallery_part_2 = torch.split(gallery_feature,
+ [self.full_dim, self.part_dim, self.part_dim, self.part_dim], dim=-1)
m_full = self.match_full(
- torch.cat([query_full, gallery_full, query_full - gallery_full,
+ torch.cat([query_full, gallery_full, (query_full - gallery_full).abs(),
query_full * gallery_full], dim=-1)
)
m_part_0 = self.match_part_0(
- torch.cat([query_part_0, gallery_part_0, query_part_0 - gallery_part_0,
+ torch.cat([query_part_0, gallery_part_0, (query_part_0 - gallery_part_0).abs(),
query_part_0 * gallery_part_0], dim=-1)
)
m_part_1 = self.match_part_1(
- torch.cat([query_part_1, gallery_part_1, query_part_1 - gallery_part_1,
+ torch.cat([query_part_1, gallery_part_1, (query_part_1 - gallery_part_1).abs(),
query_part_1 * gallery_part_1], dim=-1)
)
m_part_2 = self.match_part_2(
- torch.cat([query_part_2, gallery_part_2, query_part_2 - gallery_part_2,
+ torch.cat([query_part_2, gallery_part_2, (query_part_2 - gallery_part_2).abs(),
query_part_2 * gallery_part_2], dim=-1)
)
diff --git a/fastreid/modeling/meta_arch/__init__.py b/fastreid/modeling/meta_arch/__init__.py
index e825462..ed718e3 100644
--- a/fastreid/modeling/meta_arch/__init__.py
+++ b/fastreid/modeling/meta_arch/__init__.py
@@ -14,3 +14,4 @@ from .moco import MoCo
from .distiller import Distiller
from .metric import Metric
from .pcb import PCB
+from .pcb_online import PcbOnline
diff --git a/fastreid/modeling/meta_arch/pcb_online.py b/fastreid/modeling/meta_arch/pcb_online.py
new file mode 100644
index 0000000..becdce6
--- /dev/null
+++ b/fastreid/modeling/meta_arch/pcb_online.py
@@ -0,0 +1,60 @@
+# coding: utf-8
+"""
+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.
+实现和线上一模一样的PCB
+"""
+import torch
+import torch.nn.functional as F
+
+from fastreid.modeling.losses import cross_entropy_loss, log_accuracy
+from fastreid.modeling.meta_arch import Baseline
+from fastreid.modeling.meta_arch import META_ARCH_REGISTRY
+
+
+@META_ARCH_REGISTRY.register()
+class PcbOnline(Baseline):
+
+ def forward(self, batched_inputs):
+ images = self.preprocess_image(batched_inputs)
+ bsz = int(images.size(0) / 2)
+ feats = self.backbone(images)
+ feats = torch.cat((feats['full'], feats['parts'][0], feats['parts'][1], feats['parts'][2]), 1)
+ feats = F.normalize(feats, p=2.0, dim=-1)
+
+ qf = feats[0: bsz * 2: 2, ...]
+ xf = feats[1: bsz * 2: 2, ...]
+ outputs = self.heads({'query': qf, 'gallery': xf})
+
+ if self.training:
+ targets = batched_inputs['targets']
+ losses = self.losses(outputs, targets)
+ return losses
+ else:
+ return outputs
+
+ def losses(self, outputs, gt_labels):
+ """
+ Compute loss from modeling's outputs, the loss function input arguments
+ must be the same as the outputs of the model forwarding.
+ """
+ # model predictions
+ pred_class_logits = outputs['pred_class_logits'].detach()
+ cls_outputs = outputs['cls_outputs']
+
+ # Log prediction accuracy
+ log_accuracy(pred_class_logits, gt_labels)
+
+ loss_dict = {}
+ loss_names = self.loss_kwargs['loss_names']
+
+ if 'CrossEntropyLoss' in loss_names:
+ ce_kwargs = self.loss_kwargs.get('ce')
+ loss_dict['loss_cls'] = cross_entropy_loss(
+ cls_outputs,
+ gt_labels,
+ ce_kwargs.get('eps'),
+ ce_kwargs.get('alpha')
+ ) * ce_kwargs.get('scale')
+
+
+ return loss_dict
diff --git a/projects/FastShoe/configs/online-pcb.yaml b/projects/FastShoe/configs/online-pcb.yaml
index 555572d..266300f 100644
--- a/projects/FastShoe/configs/online-pcb.yaml
+++ b/projects/FastShoe/configs/online-pcb.yaml
@@ -1,8 +1,8 @@
_BASE_: base.yaml
MODEL:
- META_ARCHITECTURE: PCB
-
+ META_ARCHITECTURE: PcbOnline
+
PCB:
PART_NUM: 3
PART_DIM: 512
@@ -14,10 +14,12 @@ MODEL:
EMBEDDING_DIM: 512
BACKBONE:
- NAME: build_resnet_backbone
+ PRETRAIN: True
+ PRETRAIN_PATH: /home/apps/.cache/torch/hub/checkpoints/se_resnext101_32x4d-3b2fe3d8.pth
+ NAME: build_senet_pcb_backbone
DEPTH: 101x
NORM: BN
- LAST_STRIDE: 2
+ LAST_STRIDE: 1
FEAT_DIM: 512
PRETRAIN: True
WITH_IBN: True
@@ -46,11 +48,34 @@ INPUT:
ENABLED: True
SIZE: [270, 260]
SCALE: [0.8, 1.2]
- RATIO: [3./4, 4./3]
+ RATIO: [0.75, 1.33333333]
+
+DATALOADER:
+ NUM_WORKERS: 8
+
+SOLVER:
+ OPT: SGD
+ SCHED: CosineAnnealingLR
+
+ BASE_LR: 0.001
+ MOMENTUM: 0.9
+ NESTEROV: False
+
+ BIAS_LR_FACTOR: 1.
+ WEIGHT_DECAY: 0.0005
+ WEIGHT_DECAY_BIAS: 0.
+ ETA_MIN_LR: 0.00003
+
+ WARMUP_FACTOR: 0.1
+ WARMUP_ITERS: 1000
+
+ IMS_PER_BATCH: 40
+
+TEST:
+ IMS_PER_BATCH: 64
DATASETS:
NAMES: ("ShoeDataset",)
- TESTS: ("ShoeDataset", "OnlineDataset")
+ TESTS: ("ShoeDataset",)
OUTPUT_DIR: projects/FastShoe/logs/online-pcb
-
diff --git a/projects/FastShoe/fastshoe/data/pair_dataset.py b/projects/FastShoe/fastshoe/data/pair_dataset.py
index 5a53440..21d31f7 100644
--- a/projects/FastShoe/fastshoe/data/pair_dataset.py
+++ b/projects/FastShoe/fastshoe/data/pair_dataset.py
@@ -28,14 +28,9 @@ class PairDataset(Dataset):
self._logger.info('set {} with {} random seed: 12345'.format(self.mode, self.__class__.__name__))
seed_all_rng(12345)
- # if self.mode == 'train':
- # # make negative sample come from all negative folders when train
- # self.neg_folders = sum(self.neg_folders, list())
-
def __len__(self):
if self.mode == 'test':
return len(self.pos_folders) * 10
-
return len(self.pos_folders)
def __getitem__(self, idx):
@@ -43,9 +38,6 @@ class PairDataset(Dataset):
idx = int(idx / 10)
pf = self.pos_folders[idx]
- # if self.mode == 'train':
- # nf = self.neg_folders
- # else:
nf = self.neg_folders[idx]
label = 1