行人属性识别二：添加新网络训练和自定义数据集训练
序言
上一篇记录了训练过程，但是项目中提供的模型网络都是偏大的，如果想要在边缘设备上部署，还是比较吃力的，所以本文记录如何加入新的网络模型进行训练，以repvgg为例，加入mobilenet、shufflenet等网络同配置。
以及抽取pa100k数据集部分属性作为自己数据集进行训练，对于自主标记的数据集，按照相同格式准备即可。
一、添加新网络repvgg
本项目中用到网络结构其实和分类网络没有太大区别，查看网络的构建，同样是卷积+全连接结构，所以构建起来就简单多了，只需要替换backbone即可。
在models/backbone中添加repvgg.py文件，文件内容如下，提供的backbone只有两个：RepVGG_A0、RepVGG_A0_m：
import torch.nn as nn
import numpy as np
import torch
import torch.nn.functional as F
from models.registry import BACKBONE
__all__ = ['RepVGG_A0','RepVGG_A0_m']
def conv_bn(in_channels, out_channels, kernel_size, stride, padding, groups=1):
# 3 x 3的卷积层
result = nn.Sequential()
result.add_module('conv', nn.Conv2d(in_channels=in_channels, out_channels=out_channels,
kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False))
result.add_module('bn', nn.BatchNorm2d(num_features=out_channels))
return result
class RepVGGBlock(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size,
stride=1, padding=0, dilation=1, groups=1, padding_mode='zeros', deploy=False):
super(RepVGGBlock, self).__init__()
self.deploy = deploy # 部署
self.groups = groups
self.in_channels = in_channels
assert kernel_size == 3
assert padding == 1
padding_11 = padding - kernel_size // 2
self.nonlinearity = nn.ReLU() # 激活
if deploy:
self.rbr_reparam = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride,
padding=padding, dilation=dilation, groups=groups, bias=True, padding_mode=padding_mode)
else:
self.rbr_identity = nn.BatchNorm2d(num_features=in_channels) if out_channels == in_channels and stride == 1 else None
self.rbr_dense = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups)
self.rbr_1x1 = conv_bn(in_channels=in_channels, out_channels=out_channels, kernel_size=1, stride=stride, padding=padding_11, groups=groups)
# print('RepVGG Block, identity = ', self.rbr_identity)
def forward(self, inputs):
if hasattr(self, 'rbr_reparam'):
return self.nonlinearity(self.rbr_reparam(inputs))
if self.rbr_identity is None:
id_out = 0
else:
id_out = self.rbr_identity(inputs)
return self.nonlinearity(self.rbr_dense(inputs) + self.rbr_1x1(inputs) + id_out)
# This func derives the equivalent kernel and bias in a DIFFERENTIABLE way.
# You can get the equivalent kernel and bias at any time and do whatever you want,
# for example, apply some penalties or constraints during training, just like you do to the other models.
# May be useful for quantization or pruning.
def get_equivalent_kernel_bias(self):
kernel3x3, bias3x3 = self._fuse_bn_tensor(self.rbr_dense) # 融合3*3卷积和BN
kernel1x1, bias1x1 = self._fuse_bn_tensor(self.rbr_1x1) # 融合1*1卷积和BN
kernelid, biasid = self._fuse_bn_tensor(self.rbr_identity) # 融合值为1的3*3卷积和BN
return kernel3x3 + self._pad_1x1_to_3x3_tensor(kernel1x1) + kernelid, bias3x3 + bias1x1 + biasid
def _pad_1x1_to_3x3_tensor(self, kernel1x1):
if kernel1x1 is None:
return 0
else:
return torch.nn.functional.pad(kernel1x1, [1,1,1,1])
def _fuse_bn_tensor(self, branch):
if branch is None:
return 0, 0
if isinstance(branch, nn.Sequential):
kernel = branch.conv.weight
running_mean = branch.bn.running_mean
running_var = branch.bn.running_var
gamma = branch.bn.weight
beta = branch.bn.bias
eps = branch.bn.eps
else:
assert isinstance(branch, nn.BatchNorm2d)
if not hasattr(self, 'id_tensor'):
input_dim = self.in_channels // self.groups
kernel_value = np.zeros((self.in_channels, input_dim, 3, 3), dtype=np.float32)
for i in range(self.in_channels):
kernel_value[i, i % input_dim, 1, 1] = 1
self.id_tensor = torch.from_numpy(kernel_value).to(branch.weight.device)
kernel = self.id_tensor
running_mean = branch.running_mean
running_var = branch.running_var
gamma = branch.weight
beta = branch.bias
eps = branch.eps
std = (running_var + eps).sqrt()
t = (gamma / std).reshape(-1, 1, 1, 1)
return kernel * t, beta - running_mean * gamma / std
def repvgg_convert(self):
kernel, bias = self.get_equivalent_kernel_bias()
return kernel.detach().cpu().numpy(), bias.detach().cpu().numpy(),
class RepVGG(nn.Module):
def __init__(self, num_blocks, num_classes=1000, width_multiplier=None, override_groups_map=None, deploy=False):
super(RepVGG, self).__init__()
assert len(width_multiplier) == 4
self.deploy = deploy
self.override_groups_map = override_groups_map or dict()
assert 0 not in self.override_groups_map
self.in_planes = min(64, int(64 * width_multiplier[0]))
self.out_planes = int(512 * width_multiplier[3])
self.stage0 = RepVGGBlock(in_channels=3, out_channels=self.in_planes, kernel_size=3, stride=2, padding=1, deploy=self.deploy)
self.cur_layer_idx = 1
self.stage1 = self._make_stage(int(64 * width_multiplier[0]), num_blocks[0], stride=2)
self.stage2 = self._make_stage(int(128 * width_multiplier[1]), num_blocks[1], stride=2)
self.stage3 = self._make_stage(int(256 * width_multiplier[2]), num_blocks[2], stride=2)
self.stage4 = self._make_stage(int(512 * width_multiplier[3]), num_blocks[3], stride=2)
# self.gap = nn.AdaptiveAvgPool2d(output_size=1)
# self.linear = nn.Linear(int(512 * width_multiplier[3]), num_classes)
def _make_stage(self, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
blocks = []
for stride in strides:
cur_groups = self.override_groups_map.get(self.cur_layer_idx, 1)
blocks.append(RepVGGBlock(in_channels=self.in_planes, out_channels=planes, kernel_size=3,
stride=stride, padding=1, groups=cur_groups, deploy=self.deploy))
self.in_planes = planes
self.cur_layer_idx += 1
return nn.Sequential(*blocks)
def forward(self, x):
out = self.stage0(x)
out = self.stage1(out)
out = self.stage2(out)
out = self.stage3(out)
out = self.stage4(out)
# out = self.gap(out)
# out = out.view(out.size(0), -1)
# out = self.linear(out)
return out
optional_groupwise_layers = [2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26]
g2_map = {l: 2 for l in optional_groupwise_layers}
g4_map = {l: 4 for l in optional_groupwise_layers}
def create_RepVGG_A0(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=num_classes,
width_multiplier=[0.75, 0.75, 0.75, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_A0_m(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=num_classes,
width_multiplier=[0.75, 0.75, 0.75, 1], override_groups_map=None, deploy=deploy)
def create_RepVGG_A0_s(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=num_classes,
width_multiplier=[0.75, 0.75, 0.5, 1], override_groups_map=None, deploy=deploy)
def create_RepVGG_A1(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=num_classes,
width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_A2(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[2, 4, 14, 1], num_classes=num_classes,
width_multiplier=[1.5, 1.5, 1.5, 2.75], override_groups_map=None, deploy=deploy)
def create_RepVGG_B0(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=num_classes,
width_multiplier=[1, 1, 1, 2.5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B1(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=num_classes,
width_multiplier=[2, 2, 2, 4], override_groups_map=None, deploy=deploy)
def create_RepVGG_B1g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2, 2, 2, 4], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B1g4(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=num_classes,
width_multiplier=[2, 2, 2, 4], override_groups_map=g4_map, deploy=deploy)
def create_RepVGG_B2(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=num_classes,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B2g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B2g4(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[2.5, 2.5, 2.5, 5], override_groups_map=g4_map, deploy=deploy)
def create_RepVGG_B3(num_classes=1000,deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=num_classes,
width_multiplier=[3, 3, 3, 5], override_groups_map=None, deploy=deploy)
def create_RepVGG_B3g2(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[3, 3, 3, 5], override_groups_map=g2_map, deploy=deploy)
def create_RepVGG_B3g4(deploy=False):
return RepVGG(num_blocks=[4, 6, 16, 1], num_classes=1000,
width_multiplier=[3, 3, 3, 5], override_groups_map=g4_map, deploy=deploy)
func_dict = {
'RepVGG-A0': create_RepVGG_A0,
'RepVGG-A0_s': create_RepVGG_A0_s,
'RepVGG-A0_m': create_RepVGG_A0_m,
'RepVGG-A1': create_RepVGG_A1,
'RepVGG-A2': create_RepVGG_A2,
'RepVGG-B0': create_RepVGG_B0,
'RepVGG-B1': create_RepVGG_B1,
'RepVGG-B1g2': create_RepVGG_B1g2,
'RepVGG-B1g4': create_RepVGG_B1g4,
'RepVGG-B2': create_RepVGG_B2,
'RepVGG-B2g2': create_RepVGG_B2g2,
'RepVGG-B2g4': create_RepVGG_B2g4,
'RepVGG-B3': create_RepVGG_B3,
'RepVGG-B3g2': create_RepVGG_B3g2,
'RepVGG-B3g4': create_RepVGG_B3g4,
}
def get_RepVGG_func_by_name(name):
return func_dict[name]
# Use like this:
# train_model = create_RepVGG_A0(deploy=False)
# train train_model
# deploy_model = repvgg_convert(train_model, create_RepVGG_A0, save_path='repvgg_deploy.pth')
def repvgg_model_convert(model:torch.nn.Module, build_func, save_path=None,num_classes=1000):
converted_weights = {}
for name, module in model.named_modules():
if hasattr(module, 'repvgg_convert'):
kernel, bias = module.repvgg_convert()
converted_weights[name + '.rbr_reparam.weight'] = kernel
converted_weights[name + '.rbr_reparam.bias'] = bias
elif isinstance(module, torch.nn.Linear):
converted_weights[name + '.weight'] = module.weight.detach().cpu().numpy()
converted_weights[name + '.bias'] = module.bias.detach().cpu().numpy()
else:
print(name, type(module))
del model
deploy_model = build_func(num_classes=num_classes,deploy=True)
for name, param in deploy_model.named_parameters():
print('deploy param: ', name, param.size(), np.mean(converted_weights[name]))
param.data = torch.from_numpy(converted_weights[name]).float()
if save_path is not None:
torch.save(deploy_model.state_dict(), save_path,_use_new_zipfile_serialization=False)
return deploy_model
@BACKBONE.register("repvgg_a0")
def RepVGG_A0(model_path = "/home/cai/project/Rethinking_of_PAR/model/RepVGG-A0-train.pth"):
model = create_RepVGG_A0()
if model_path is not None:
pretrained_params = torch.load(model_path,map_location=torch.device("cpu"))
# pretrained_params= \
# {k: v for k, v in pretrained_params.items() if
# k in net.state_dict().keys() and net.state_dict()[k].numel() == v.numel()}
model.load_state_dict(pretrained_params, strict=False)
return model
@BACKBONE.register("repvgg_a0_m")
def RepVGG_A0_m(model_path = "/home/cai/project/Rethinking_of_PAR/model/RepVGG-A0-train.pth"):
model = create_RepVGG_A0_m()
if model_path is not None:
pretrained_params = torch.load(model_path,map_location=torch.device("cpu"))
# pretrained_params= \
# {k: v for k, v in pretrained_params.items() if
# k in net.state_dict().keys() and net.state_dict()[k].numel() == v.numel()}
model.load_state_dict(pretrained_params, strict=False)
return model
需要注意的是这两个部分，在初始化网络前要加载好预训练模型，其他网络也是类似：
然后在models/model_factory.py中添加，这两个网络最后层输出的对应通道数：
在train.py中导入
最后修改配置文件，添加repvgg_a0网络，就可以运行了：
二、自定义数据集
pa100k数据集中包含了26种属性，有一些属性对我来说没有用，不想训练这么多，所以抽取其中18中属性进行训练，那么该如何准备呢？
首先，先通过以下脚本将pa100k的mat标签文件转换为txt形式：
import pandas as pd
import scipy
from scipy import io
def mat2txt(data, key):
subdata = data[key]
dfdata = pd.DataFrame(subdata)
dfdata.to_csv("/home/cai/data/PA100K/%s.txt" % key, index=False)
if __name__ == "__main__":
data = scipy.io.loadmat("/home/cai/data/PA100K/annotation.mat")
key_list = ["attributes", "test_images_name", "test_label",
"train_images_name", "train_label",
"val_images_name", "val_label"]
for key in key_list:
mat2txt(data, key)
得到如下几个文件：
在通过如下文件对属性进行剔除，并声称新的txt文件：
# 根据txt生成标签
import os
txts = [["train_images_name", "train_label"],["test_images_name", "test_label"],["val_images_name", "val_label"]]
txt_path = "/home/cai/data/PA100K"
for txt_list in txts:
file1 = open(os.path.join(txt_path,txt_list[0]+".txt"),"r")
file2 = open(os.path.join(txt_path,txt_list[1]+".txt"),"r")
save_file = open(txt_list[0].split("_")[0]+".txt","w")
label1_list = []
label2_list = []
for line1 in file1.readlines():
label1_list.append(line1)
for line2 in file2.readlines():
label2_list.append(line2)
for i,label in enumerate(label1_list):
if i ==0 :
continue
label1 = label1_list[i].split('\n')[0].split('\'')
label2 = label2_list[i].split(',')
for i,ind in enumerate([12,15,16,17,18,19,20,25]): # 这是要剔除的相应属性索引
label2.pop(ind-i)
label2 = ",".join(label2)
save_file.write("/home/cai/data/PA100K/PA100k/data/"+label1[1]+"\t"+label2+"\n")
file1.close()
file2.close()
save_file.close()
得到三个文件train.txt、test.txt、val.txt，内容如下，如果是自己标注的数据集，想办法转成这个格式即可：
然后根据dataset/pedes_attr/preprocess/format_pa100k.py编写format_mydata.py文件，在生成pkl之前需要对数据集属性进行重排，重排规则看dataset/pedes_attr/annotation.md，其实不排也可以，不排的话将generate_data_description函数中label_txt,reorder=Flase即可，相应的修改位置已经在代码中标明：
import os
import numpy as np
import random
import pickle
from easydict import EasyDict
from scipy.io import loadmat
np.random.seed(0)
random.seed(0)
classes_name = ['Female','AgeOver60','Age18-60','AgeLess18','Front','Side','Back','Hat','Glasses','HandBag','ShoulderBag','Backpack'
,'ShortSleeve','LongSleeve','LongCoat','Trousers','Shorts','Skirt&Dress'] # 属性类别
group_order = [7, 8, 12, 13, 14, 15, 16, 17, 9, 10, 11, 1, 2, 3, 0, 4, 5, 6] # 需要对新属性进行重排，属性重排后的索引顺序（不懂的看上一篇）
# clas_name = ['Hat','Glasses','ShortSleeve','LongSleeve','LongCoat','Trousers','Shorts','Skirt&Dress','HandBag','ShoulderBag','Backpack'
# ,'AgeOver60','Age18-60','AgeLess18','Female','Front','Side','Back']
def make_dir(path):
if os.path.exists(path):
pass
else:
os.mkdir(path)
def generate_data_description(save_dir, label_txt,reorder):
"""
create a dataset description file, which consists of images, labels
"""
image_name = []
image_label = []
file = open(label_txt,"r")
for line in file.readlines():
name = line.split("\t")[0]
label = line.split('\t')[1].split('\n')[0].split(',')
label = list(map(int, label))
image_name.append(name)
image_label.append(label)
dataset = EasyDict()
dataset.description = 'pa100k'
dataset.reorder = 'group_order'
dataset.root = os.path.join(save_dir, 'data')
dataset.image_name = image_name
dataset.label = np.array(image_label)
dataset.attr_name = classes_name
dataset.label_idx = EasyDict()
dataset.label_idx.eval = list(range(len(classes_name)))
if reorder:
dataset.label_idx.eval = group_order
dataset.partition = EasyDict()
dataset.partition.train = np.arange(0, 80000) # np.array(range(80000)) # 数据集数量划分，自己根据自己数据集来，这是pa100k的划分
dataset.partition.val = np.arange(80000, 90000) # np.array(range(80000, 90000))
dataset.partition.test = np.arange(90000, 100000) # np.array(range(90000, 100000))
dataset.partition.trainval = np.arange(0, 90000) # np.array(range(90000))
dataset.weight_train = np.mean(dataset.label[dataset.partition.train], axis=0).astype(np.float32)
dataset.weight_trainval = np.mean(dataset.label[dataset.partition.trainval], axis=0).astype(np.float32)
with open(os.path.join(save_dir, 'dataset_all.pkl'), 'wb+') as f:
pickle.dump(dataset, f)
if __name__ == "__main__":
# save_dir = '/mnt/data1/jiajian/datasets/attribute/PA100k/'
save_dir = '/home/cai/data/PA100K/MyData/' # 数据集图片存放路径 MyData/data MyData/dataset_all.pkl
label_txt = "/home/cai/project/Rethinking_of_PAR/data/MyData/label.txt" # train.txt test.txt val.txt 合并成label.txt
generate_data_description(save_dir, label_txt,reorder=True)
修改完成后需要到该文件下添加自己数据集名字，我的数据集取名为MyData：
在configs中新建mydata.yaml，内容根据pa100k的来修改，示例如下，加入了新的网络和数据集：
最后运行代码训练：
python train.py --cfg configs/pedes_baseline/mydata.yaml
