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lee
2025-07-17 14:33:18 +08:00
parent 09f41f6289
commit 54898e30ec
12 changed files with 233 additions and 34 deletions
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6
.idea/CopilotChatHistory.xml generated
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@ -3,6 +3,12 @@
<component name="CopilotChatHistory">
<option name="conversations">
<list>
<Conversation>
<option name="createTime" value="1752195523240" />
<option name="id" value="0197f6fde2a87e68b893b3a36dfc838f" />
<option name="title" value="新对话 2025年7月11日 08:58:43" />
<option name="updateTime" value="1752195523240" />
</Conversation>
<Conversation>
<option name="createTime" value="1752114061266" />
<option name="id" value="0197f222dfd27515a3dbfea638532ee5" />

20
configs/compare.yml
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@ -15,25 +15,25 @@ base:
# 模型配置
models:
backbone: 'resnet34'
channel_ratio: 1.0
backbone: 'resnet18'
channel_ratio: 0.75
# 训练参数
training:
epochs: 600 # 总训练轮次
batch_size: 128 # 批次大小
lr: 0.001 # 初始学习率
lr: 0.007 # 初始学习率
optimizer: "sgd" # 优化器类型
metric: 'arcface' # 损失函数类型（可选：arcface/cosface/sphereface/softmax）
loss: "cross_entropy" # 损失函数类型（可选：cross_entropy/cross_entropy_smooth/center_loss/center_loss_smooth/arcface/cosface/sphereface/softmax）
lr_step: 10 # 学习率调整间隔（epoch）
lr_decay: 0.98 # 学习率衰减率
lr_decay: 0.95 # 学习率衰减率
weight_decay: 0.0005 # 权重衰减
scheduler: "cosine" # 学习率调度器（可选：cosine/cosine_warm/step/None）
num_workers: 32 # 数据加载线程数
checkpoints: "./checkpoints/resnet34_20250612_scale=1.0/" # 模型保存目录
restore: false
restore_model: "resnet18_test/epoch_600.pth" # 模型恢复路径
checkpoints: "./checkpoints/resnet18_20250717_scale=0.75_nosub/" # 模型保存目录
restore: true
restore_model: "./checkpoints/resnet18_20250716_scale=0.75_nosub/best.pth" # 模型恢复路径
cosine_t_0: 10 # 初始周期长度
cosine_t_mult: 1 # 周期长度倍率
cosine_eta_min: 0.00001 # 最小学习率
@ -49,8 +49,8 @@ data:
train_batch_size: 128 # 训练批次大小
val_batch_size: 128 # 验证批次大小
num_workers: 32 # 数据加载线程数
data_train_dir: "../data_center/contrast_learning/data_base/train" # 训练数据集根目录
data_val_dir: "../data_center/contrast_learning/data_base/val" # 验证数据集根目录
data_train_dir: "../data_center/contrast_data/v2/train" # 训练数据集根目录
data_val_dir: "../data_center/contrast_data/v2/val" # 验证数据集根目录
transform:
img_size: 224 # 图像尺寸
@ -62,7 +62,7 @@ transform:
# 日志与监控
logging:
logging_dir: "./logs/resnet50_log" # 日志保存目录
logging_dir: "./logs/resnet18_scale=0.75_nosub_log" # 日志保存目录
tensorboard: true # 是否启用TensorBoard
checkpoint_interval: 30 # 检查点保存间隔（epoch）

49
configs/similar_analysis.yml Normal file
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@ -0,0 +1,49 @@
# configs/similar_analysis.yml
# 专为模型训练对比设计的配置文件
# 支持对比不同训练策略（如蒸馏vs独立训练）
# 基础配置
base:
experiment_name: "model_comparison" # 实验名称（用于结果保存目录）
device: "cuda" # 训练设备（cuda/cpu）
embedding_size: 256 # 特征维度
pin_memory: true # 是否启用pin_memory
distributed: true # 是否启用分布式训练
# 模型配置
models:
backbone: 'resnet18'
channel_ratio: 0.75
model_path: "../checkpoints/resnet18_1009/best.pth"
heatmap:
feature_layer: "layer4"
show_heatmap: true
# 数据配置
data:
dataset: "imagenet" # 数据集名称（示例用，可替换为实际数据集）
train_batch_size: 128 # 训练批次大小
val_batch_size: 8 # 验证批次大小
num_workers: 32 # 数据加载线程数
data_dir: "/home/lc/data_center/image_analysis/error_compare_subimg"
image_joint_pth: "/home/lc/data_center/image_analysis/error_compare_result"
transform:
img_size: 224 # 图像尺寸
img_mean: 0.5 # 图像均值
img_std: 0.5 # 图像方差
RandomHorizontalFlip: 0.5 # 随机水平翻转概率
RandomRotation: 180 # 随机旋转角度
ColorJitter: 0.5 # 随机颜色抖动强度
# 日志与监控
logging:
logging_dir: "./logs/resnet18_scale=0.75_nosub_log" # 日志保存目录
tensorboard: true # 是否启用TensorBoard
checkpoint_interval: 30 # 检查点保存间隔（epoch）
# 分布式训练（可选）
distributed:
enabled: false # 是否启用分布式训练
backend: "nccl" # 分布式后端（nccl/gloo）

12
configs/sub_data.yml
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@ -11,16 +11,22 @@ data:
max_files_ratio: 0.1
min_files: 10
split_ratio: 0.9
combine_scr_dir: "../../data_center/contrast_data/v1/val" # 合并数据集源目录·
combine_dst_dir: "../../data_center/contrast_data/v2/val" # 合并数据集目标目录
extend:
extend_same_dir: true
extend_extra: true
extend_extra_dir: "../../data_center/contrast_data/v1/extra"
extend_extra_dir: "../../data_center/contrast_data/v1/extra" # 扩展测试集数据
extend_train: true
extend_train_dir: "../../data_center/contrast_data/v1/train"
extend_train_dir: "../../data_center/contrast_data/v1/train" # 训练接数据扩展
limit:
count_limit: true
limit_count: 200
limit_dir: "../../data_center/contrast_data/v1/train"
limit_dir: "../../data_center/contrast_data/v1/train" # 限制单个样本数量
control:
combine: true # 是否进行子类数据集合并
split: false # 子类数据集拆解与扩增

2
configs/test.yml
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@ -8,7 +8,7 @@ base:
log_level: "info" # 日志级别（debug/info/warning/error）
embedding_size: 256 # 特征维度
pin_memory: true # 是否启用pin_memory
distributed: false # 是否启用分布式训练
distributed: false # 是否启用分布式训练 启用热力图时不能用分布式训练
# 模型配置
models:

4
configs/write_feature.yml
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@ -22,7 +22,7 @@ data:
test_batch_size: 128 # 验证批次大小
num_workers: 32 # 数据加载线程数
half: true # 是否启用半精度数据
img_dirs_path: "/personalDocument/lic/base+stlib"
img_dirs_path: "/home/lc/data_center/baseStlib/pic/stlib_base" # base标准库图片存储路径
# img_dirs_path: "/home/lc/contrast_nettest/data/feature_json"
xlsx_pth: false # 过滤商品， 默认None不进行过滤
@ -42,7 +42,7 @@ logging:
save:
json_bin: "../search_library/yunhedian_05-09.json" # 保存整个json文件
json_path: "../feature_json/base+stlib/" # 保存单个json文件路径
json_path: "/home/lc/data_center/baseStlib/feature_json/stlib_base" # 保存单个json文件路径
error_barcodes: "error_barcodes.txt"
barcodes_statistics: "../search_library/barcodes_statistics.txt"
create_single_json: true # 是否保存单个json文件

25
data_preprocessing/combine_sub_class.py Normal file
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@ -0,0 +1,25 @@
import os
import shutil
def combine_dirs(conf):
source_root = conf['data']['combine_scr_dir']
target_root = conf['data']['combine_dst_dir']
for roots, dir_names, files in os.walk(source_root):
for dir_name in dir_names:
source_dir = os.path.join(roots, dir_name)
target_dir = os.path.join(target_root, dir_name.split('_')[0])
if not os.path.exists(target_dir):
os.mkdir(target_dir)
for filename in os.listdir(source_dir):
print(filename)
source_file = os.sep.join([source_dir, filename])
target_file = os.sep.join([target_dir, filename])
shutil.copy(source_file, target_file)
# print(f"已复制目录 {source_dir} 到 {target_dir}")
# if __name__ == '__main__':
# source_root = r'scatter_mini'
# target_root = r'C:\Users\123\Desktop\scatter-1'
# # combine_dirs(conf)

10
data_preprocessing/sub_data_preprocessing.py
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@ -1,14 +1,18 @@
from create_extra import split_subdirs
from data_split import split_directory
from extend import ImageExtendProcessor
from combine_sub_class import combine_dirs
import yaml
def data_preprocessing(conf):
# split_subdirs(conf)
# image_ex = ImageExtendProcessor(conf)
# image_ex.control_number()
if conf['control']['split']:
split_subdirs(conf)
image_ex = ImageExtendProcessor(conf)
image_ex.control_number()
split_directory(conf)
if conf['control']['combine']:
combine_dirs(conf)
if __name__ == '__main__':

2
test_ori.py
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@ -17,7 +17,7 @@ from configs import trainer_tools
import yaml
from datetime import datetime
with open('configs/test.yml', 'r') as f:
with open('../configs/test.yml', 'r') as f:
conf = yaml.load(f, Loader=yaml.FullLoader)
# Constants from config

9
tools/image_joint.py
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@ -3,7 +3,7 @@ from tools.getHeatMap import cal_cam
import os
def merge_imgs(img1_path, img2_path, conf, similar=None, label=None, cam=None):
def merge_imgs(img1_path, img2_path, conf, similar=None, label=None, cam=None, save_path=None):
save = True
position = (50, 50) # 文字的左上角坐标
color = (255, 0, 0) # 红色文字，格式为 RGB
@ -11,21 +11,24 @@ def merge_imgs(img1_path, img2_path, conf, similar=None, label=None, cam=None):
# os.makedirs(os.sep.join([save_path, str(label)]))
# save_path = os.sep.join([save_path, str(label)])
# img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
if save_path is None:
save_path = conf['data']['image_joint_pth']
if not conf['heatmap']['show_heatmap']:
img1 = Image.open(img1_path)
img2 = Image.open(img2_path)
img1 = img1.resize((224, 224))
img2 = img2.resize((224, 224))
save_path = conf['data']['image_joint_pth']
# save_path = conf['data']['image_joint_pth']
else:
assert cam is not None, 'cam is None'
img1 = cam.get_hot_map(img1_path)
img2 = cam.get_hot_map(img2_path)
save_path = conf['heatmap']['image_joint_pth']
# save_path = conf['heatmap']['image_joint_pth']
# print('img1_path', img1)
# print('img2_path', img2)
if not os.path.exists(os.sep.join([save_path, str(label)])):
os.makedirs(os.sep.join([save_path, str(label)]))
if save_path is None:
save_path = os.sep.join([save_path, str(label)])
img_name = os.path.basename(img1_path).split('.')[0] + '_' + os.path.basename(img2_path).split('.')[0] + '.png'
assert img1.height == img2.height

106
tools/similar_analysis.py Normal file
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@ -0,0 +1,106 @@
from configs.utils import trainer_tools
from test_ori import group_image, featurize, cosin_metric
from tools.dataset import get_transform
from tools.getHeatMap import cal_cam
from tools.image_joint import merge_imgs
import torch.nn as nn
import torch
from collections import ChainMap
import yaml
import os
class analysis:
def __init__(self):
with open('../configs/similar_analysis.yml', 'r') as f:
self.conf = yaml.load(f, Loader=yaml.FullLoader)
self.model = self.initialize_model(self.conf)
_, self.test_transform = get_transform(self.conf)
self.cam = cal_cam(self.model, self.conf)
def initialize_model(self, conf):
"""初始化模型和度量方法"""
tr_tools = trainer_tools(conf)
backbone_mapping = tr_tools.get_backbone()
if conf['models']['backbone'] in backbone_mapping:
model = backbone_mapping[conf['models']['backbone']]()
else:
raise ValueError('不支持该模型: {}'.format({conf['models']['backbone']}))
try:
model.load_state_dict(torch.load(conf['models']['model_path'],
map_location=conf['base']['device']))
except:
state_dict = torch.load(conf['models']['model_path'],
map_location=conf['base']['device'])
new_state_dict = {}
for k, v in state_dict.items():
new_key = k.replace("module.", "")
new_state_dict[new_key] = v
model.load_state_dict(new_state_dict, strict=False)
return model.eval()
def get_feature(self, img_pth):
group = group_image([img_pth], self.conf['data']['val_batch_size'])
feature = featurize(group[0], self.test_transform, self.model, self.conf['base']['device'])
return feature
def get_similarity(self, feature_dict1, feature_dict2):
similarity = cosin_metric(feature_dict1, feature_dict2)
print(f"Similarity: {similarity}")
return similarity
def get_feature_map(self, all_imgs):
feature_dicts = {}
for img_pth in all_imgs:
feature_dict = self.get_feature(img_pth)
feature_dicts = dict(ChainMap(feature_dict, feature_dicts))
return feature_dicts
def get_image_map(self):
all_compare_img = []
for root, dirs, files in os.walk(self.conf['data']['data_dir']):
if len(dirs) == 2:
dir_pth_1 = os.sep.join([root, dirs[0]])
dir_pth_2 = os.sep.join([root, dirs[1]])
for img_name_1 in os.listdir(dir_pth_1):
for img_name_2 in os.listdir(dir_pth_2):
all_compare_img.append((os.sep.join([dir_pth_1, img_name_1]),
os.sep.join([dir_pth_2, img_name_2])))
return all_compare_img
def create_total_feature(self):
all_imgs = []
for root, dirs, files in os.walk(self.conf['data']['data_dir']):
if len(dirs) == 2:
for dir_name in dirs:
dir_pth = os.sep.join([root, dir_name])
for img_name in os.listdir(dir_pth):
all_imgs.append(os.sep.join([dir_pth, img_name]))
return all_imgs
def get_contrast_result(self, feature_dicts, all_compare_img):
for img_pth1, img_pth2 in all_compare_img:
feature_dict1 = feature_dicts[img_pth1]
feature_dict2 = feature_dicts[img_pth2]
similarity = self.get_similarity(feature_dict1.cpu().numpy(),
feature_dict2.cpu().numpy())
dir_name = img_pth1.split(os.sep)[-3]
save_path = os.sep.join([self.conf['data']['image_joint_pth'], dir_name])
if similarity > 0.7:
merge_imgs(img_pth1,
img_pth2,
self.conf,
similarity,
label=None,
cam=self.cam,
save_path=save_path)
print(similarity)
if __name__ == '__main__':
ana = analysis()
all_imgs = ana.create_total_feature()
feature_dicts = ana.get_feature_map(all_imgs)
all_compare_img = ana.get_image_map()
ana.get_contrast_result(feature_dicts, all_compare_img)

4
train_compare.py
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@ -17,7 +17,7 @@ import yaml
from datetime import datetime
def load_configuration(config_path='configs/scatter.yml'):
def load_configuration(config_path='configs/compare.yml'):
"""加载配置文件"""
with open(config_path, 'r') as f:
return yaml.load(f, Loader=yaml.FullLoader)
@ -74,7 +74,7 @@ def train_one_epoch(model, metric, criterion, optimizer, dataloader, device, sca
data = data.to(device)
labels = labels.to(device)
with torch.cuda.amp.autocast():
# with torch.cuda.amp.autocast():
embeddings = model(data)
if not conf['training']['metric'] == 'softmax':
thetas = metric(embeddings, labels)