参考代码：
https://github.com/JDAI-CV/fast-reid
0.环境
ubuntu16.04
cuda9.0
python3.6
torch==1.1.0
torchvision==0.3.0
Cython
yacs
tensorboard
future
termcolor
sklearn
tqdm
opencv-python==4.1.0.25
matplotlib
scikit-image
numpy==1.16.4安装apex（不要直接通过pip安装）：
git clone https://www.github.com/nvidia/apex
cd apex
# python setup.py install --cuda_ext --cpp_ext
pip install -v --no-cache-dir ./
1.准备数据
参考https://blog.csdn.net/qq_35975447/article/details/106664593
数据目录结构如下：
fast-reid
datasets
Market-1501-v15.09.15
bounding_box_train
bounding_box_test
query
2.修改
2.1 RandomErasing
我的torchvision是0.3.0版本的，所以需要修改一些地方。如果torchvision不是高版本的，需要
修改三处：
（1）fastreid/data/transforms/build.py line 66-77
# fastreid/data/transforms/build.py line 66-77
# res.append(ToTensor()) # gmt modify
if do_rea:
res.append(RandomErasing(probability=rea_prob, mean=rea_value)) # gmt modify
if do_rpt:
res.append(RandomPatch(prob_happen=rpt_prob))
else:
size_test = cfg.INPUT.SIZE_TEST
res.append(T.Resize(size_test, interpolation=3))
# res.append(ToTensor()) # gmt modify
res.append(ToTensor()) # gmt modify
return T.Compose(res)（2） fastreid/data/transforms/transforms.py line 7
# fastreid/data/transforms/transforms.py line 7
# __all__ = ['ToTensor', 'RandomPatch', 'AugMix', ] # gmt modify
__all__ = ['ToTensor', 'RandomErasing', 'RandomPatch', 'AugMix', ] # gmt modify
（3）fastreid/data/transforms/transforms.py line 44-86
# fastreid/data/transforms/transforms.py line 44-86
class RandomErasing(object):
""" Randomly selects a rectangle region in an image and erases its pixels.
'Random Erasing Data Augmentation' by Zhong et al.
See https://arxiv.org/pdf/1708.04896.pdf
Args:
probability: The probability that the Random Erasing operation will be performed.
sl: Minimum proportion of erased area against input image.
sh: Maximum proportion of erased area against input image.
r1: Minimum aspect ratio of erased area.
mean: Erasing value.
"""
def __init__(self, probability=0.5, sl=0.02, sh=0.4, r1=0.3, mean=255 * (0.49735, 0.4822, 0.4465)):
self.probability = probability
self.mean = mean
self.sl = sl
self.sh = sh
self.r1 = r1
def __call__(self, img):
img = np.asarray(img, dtype=np.float32).copy()
if random.uniform(0, 1) > self.probability:
return img
for attempt in range(100):
area = img.shape[0] * img.shape[1]
target_area = random.uniform(self.sl, self.sh) * area
aspect_ratio = random.uniform(self.r1, 1 / self.r1)
h = int(round(math.sqrt(target_area * aspect_ratio)))
w = int(round(math.sqrt(target_area / aspect_ratio)))
if w < img.shape[1] and h < img.shape[0]:
x1 = random.randint(0, img.shape[0] - h)
y1 = random.randint(0, img.shape[1] - w)
if img.shape[2] == 3:
img[x1:x1 + h, y1:y1 + w, 0] = self.mean[0]
img[x1:x1 + h, y1:y1 + w, 1] = self.mean[1]
img[x1:x1 + h, y1:y1 + w, 2] = self.mean[2]
else:
img[x1:x1 + h, y1:y1 + w, 0] = self.mean[0]
return img
return img
2.2 CUDA float类型
# "./fastreid/layers/circle_softmax.py", line 38
pred_class_logits = targets * s_p + (1.0 - targets) * s_n
# 改为：
pred_class_logits = targets.float() * s_p.float() + (1.0 - targets.float()) * s_n.float()
2.3 cross_entroy_loss
# fastreid/modeling/losses/cross_entroy_loss.py line 50
non_zero_cnt = max(loss.nonzero().size(0), 1) # gmt add
# non_zero_cnt = max(loss.nonzero(as_tuple=False).size(0), 1)2.4 data/commom.py(多数据训练的）
# line 26-27
self.pids = sorted(list(pid_set))
self.cams = sorted(list(cam_set))
#改为：
self.pids = sorted(list(pid_set), key=lambda x: str(x))
self.cams = sorted(list(cam_set), key=lambda x: str(x))2.5 config
由于sbs后面会调用bot的yaml文件，所以这里要注释掉bot中yaml的pretrain_model（路径：configs/Base-bagtricks.yml）:
3.训练
CUDA_VISIBLE_DEVICES="0,1" python ./tools/train_net.py --config-file='./configs/Market1501/sbs_R101-ibn.yml'
对比最后一项，可以看到mAP是可以复现对应结果的。
复现BOT R101-ibn（默认参数，步骤还是要按之前的来）:
CUDA_VISIBLE_DEVICES="0" python ./tools/train_net.py --config-file='./configs/Market1501/bagtricks_R101-ibn.yml'