def Img_transform(self, name, size, split='train'):
# if len(args.crop_size) == 1:
# crop_size = (args.crop_size[0] , args.crop_size[0]) ## W x H
# else:
# crop_size = (args.crop_size[1] , args.crop_size[0])
assert (isinstance(size, tuple) and len(size) == 2)
if name in ['CS', 'IDD']:
if split == 'train':
t = [
transforms.Resize(size),
transforms.RandomCrop((512, 512)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]
else:
t = [transforms.Resize(size), transforms.ToTensor()]
return transforms.Compose(t)
if split == 'train':
t = [
transforms.Resize(size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]
else:
t = [transforms.Resize(size), transforms.ToTensor()]
return transforms.Compose(t)
def __init__(self, data, labels, is_train=True):
super(Cifar10, self).__init__()
self.data, self.labels = data, labels
self.is_train = is_train
assert len(self.data) == len(self.labels)
mean, std = (0.4914, 0.4822, 0.4465), (0.2471, 0.2435, 0.2616)
if is_train:
self.trans_weak = T.Compose([
T.Resize((32, 32)),
T.PadandRandomCrop(border=4, cropsize=(32, 32)),
T.RandomHorizontalFlip(p=0.5),
T.Normalize(mean, std),
T.ToTensor(),
])
self.trans_strong = T.Compose([
T.Resize((32, 32)),
T.PadandRandomCrop(border=4, cropsize=(32, 32)),
T.RandomHorizontalFlip(p=0.5),
RandomAugment(2, 10),
T.Normalize(mean, std),
T.ToTensor(),
])
else:
self.trans = T.Compose([
T.Resize((32, 32)),
T.Normalize(mean, std),
T.ToTensor(),
])
def Img_transform(self, name, size, split='train'):
assert (isinstance(size, tuple) and len(size) == 2)
if name in ['CS', 'IDD', 'MAP', 'ADE', 'IDD20K']:
if split == 'train':
t = [ #transforms.RandomScale(1.1),
#transforms.RandomRotate(3),
#transforms.Resize((640,640)),
#RandomAffine(1,(0.04,0.04),None,1,resample=Image.NEAREST,fillcolor=255),
#Resize((512,512),Image.NEAREST),
#RandomHorizontalFlip(),
#ToTensor()
transforms.Resize(size),
#transforms.RandomCrop((512,512)),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]
else:
t = [transforms.Resize(size), transforms.ToTensor()]
return transforms.Compose(t)
if split == 'train':
t = [
transforms.Resize(size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor()
]
else:
t = [transforms.Resize(size), transforms.ToTensor()]
return transforms.Compose(t)
def __init__(self, root, mode='train'):
self.samples = []
lines = os.listdir(os.path.join(root, 'GT'))
for line in lines:
rgbpath = os.path.join(root, 'RGB', line[:-4] + '.jpg')
tpath = os.path.join(root, 'T', line[:-4] + '.jpg')
maskpath = os.path.join(root, 'GT', line)
self.samples.append([rgbpath, tpath, maskpath])
if mode == 'train':
self.transform = transform.Compose(
transform.Normalize(mean1=mean_rgb,
mean2=mean_t,
std1=std_rgb,
std2=std_t), transform.Resize(400, 400),
transform.RandomHorizontalFlip(), transform.ToTensor())
elif mode == 'test':
self.transform = transform.Compose(
transform.Normalize(mean1=mean_rgb,
mean2=mean_t,
std1=std_rgb,
std2=std_t), transform.Resize(400, 400),
transform.ToTensor())
else:
raise ValueError
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def __init__(self, cfg):
self.cfg = cfg
if self.cfg.mode == 'train':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.Resize(size=448), transform.RandomHorizontalFlip(),
transform.ToTensor())
elif self.cfg.mode == 'test' or self.cfg.mode == 'val':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.ToTensor())
else:
raise ValueError
def __init__(self, data, labels, is_train=True):
super(Cifar10, self).__init__()
self.data, self.labels = data, labels
self.is_train = is_train
assert len(self.data) == len(self.labels)
mean, std = (0.4914, 0.4822, 0.4465), (0.2471, 0.2435, 0.2616)
# mean, std = (-0.0172, -0.0356, -0.1069), (0.4940, 0.4869, 0.5231) # [-1, 1]
if is_train:
self.trans_reg = transforms.Compose([
transforms.RandomResizedCrop(32),
transforms.RandomHorizontalFlip(p=0.5),
transforms.RandomApply(
[transforms.ColorJitter(0.4, 0.4, 0.4, 0.1)], p=0.8),
transforms.RandomGrayscale(p=0.2),
transforms.ToTensor(),
transforms.Normalize([0.4914, 0.4822, 0.4465],
[0.2023, 0.1994, 0.2010])
])
self.trans_weak = T.Compose([
T.Resize((32, 32)),
T.PadandRandomCrop(border=4, cropsize=(32, 32)),
T.RandomHorizontalFlip(p=0.5),
T.Normalize(mean, std),
T.ToTensor(),
])
self.trans_strong = T.Compose([
T.Resize((32, 32)),
T.PadandRandomCrop(border=4, cropsize=(32, 32)),
T.RandomHorizontalFlip(p=0.5),
RandomAugment(2, 10),
T.Normalize(mean, std),
T.ToTensor(),
])
else:
self.trans = T.Compose([
T.Resize((32, 32)),
T.Normalize(mean, std),
T.ToTensor(),
])
def __init__(self, cfg):
with open(cfg.datapath + '/' + cfg.mode + '.txt', 'r') as lines:
self.samples = []
for line in lines:
imagepath = cfg.datapath + '/image/' + line.strip() + '.jpg'
maskpath = cfg.datapath + '/scribble/' + line.strip() + '.png'
self.samples.append([imagepath, maskpath])
if cfg.mode == 'train':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.Resize(320, 320), transform.RandomHorizontalFlip(),
transform.RandomCrop(320, 320), transform.ToTensor())
elif cfg.mode == 'test':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.Resize(320, 320), transform.ToTensor())
else:
raise ValueError
def __init__(self, cfg):
with open(os.path.join(cfg.datapath, cfg.mode + '.txt'), 'r') as lines:
self.samples = []
for line in lines:
imagepath = os.path.join(cfg.datapath, 'image',
line.strip() + '.jpg')
maskpath = os.path.join(cfg.datapath, 'mask',
line.strip() + '.png')
self.samples.append([imagepath, maskpath])
if cfg.mode == 'train':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.Resize(320, 320), transform.RandomHorizontalFlip(),
transform.RandomCrop(288, 288), transform.ToTensor())
elif cfg.mode == 'test':
self.transform = transform.Compose(
transform.Normalize(mean=cfg.mean, std=cfg.std),
transform.Resize(320, 320), transform.ToTensor())
else:
raise ValueError
def __init__(self, root, mode='train'):
self.samples = []
lines = os.listdir(os.path.join(root, mode + '_images'))
self.mode = mode
for line in lines:
rgbpath = os.path.join(root, mode + '_images', line)
tpath = os.path.join(root, mode + '_depth', line[:-4] + '.png')
maskpath = os.path.join(root, mode + '_masks', line[:-4] + '.png')
self.samples.append([rgbpath, tpath, maskpath])
if mode == 'train':
self.transform = transform.Compose(
transform.Normalize(mean1=mean_rgb, std1=std_rgb),
transform.Resize(256, 256), transform.RandomHorizontalFlip(),
transform.ToTensor())
elif mode == 'test':
self.transform = transform.Compose(
transform.Normalize(mean1=mean_rgb, std1=std_rgb),
transform.Resize(256, 256), transform.ToTensor())
else:
raise ValueError
def __init__(self, data, labels, n_guesses=1, is_train=True):
super(Cifar10, self).__init__()
self.data, self.labels = data, labels
self.n_guesses = n_guesses
assert len(self.data) == len(self.labels)
assert self.n_guesses >= 1
# mean, std = (0.4914, 0.4822, 0.4465), (0.2471, 0.2435, 0.2616) # [0, 1]
mean, std = (-0.0172, -0.0356, -0.1069), (0.4940, 0.4869, 0.5231
) # [-1, 1]
if is_train:
self.trans = T.Compose([
T.Resize((32, 32)),
T.PadandRandomCrop(border=4, cropsize=(32, 32)),
T.RandomHorizontalFlip(p=0.5),
T.Normalize(mean, std),
T.ToTensor(),
])
else:
self.trans = T.Compose([
T.Resize((32, 32)),
T.Normalize(mean, std),
T.ToTensor(),
])
def main():
#数据集加载
dataset = Market1501()
#训练数据处理器
transform_train = T.Compose([
T.Random2DTransform(height, width), #尺度统一，随机裁剪
T.RandomHorizontalFlip(), #水平翻转
T.ToTensor(), #图片转张量
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]), #归一化，参数固定
])
#测试数据处理器
transform_test = T.Compose([
T.Resize((height, width)), #尺度统一
T.ToTensor(), #图片转张量
T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224,
0.225]), #归一化，参数固定
])
#train数据集吞吐器
train_data_loader = DataLoader(
ImageDataset(dataset.train,
transform=transform_train), #自定义的数据集，使用训练数据处理器
batch_size=train_batch_size, #一个批次的大小（一个批次有多少个图片张量）
drop_last=True, #丢弃最后无法称为一整个批次的数据
)
print("train_data_loader inited")
#query数据集吞吐器
query_data_loader = DataLoader(
ImageDataset(dataset.query,
transform=transform_test), #自定义的数据集，使用测试数据处理器
batch_size=test_batch_size, #一个批次的大小（一个批次有多少个图片张量）
shuffle=False, #不重排
drop_last=True, #丢弃最后无法称为一整个批次的数据
)
print("query_data_loader inited")
#gallery数据集吞吐器
gallery_data_loader = DataLoader(
ImageDataset(dataset.gallery,
transform=transform_test), #自定义的数据集，使用测试数据处理器
batch_size=test_batch_size, #一个批次的大小（一个批次有多少个图片张量）
shuffle=False, #不重排
drop_last=True, #丢弃最后无法称为一整个批次的数据
)
print("gallery_data_loader inited\n")
#加载模型
model = ReIDNet(num_classes=751,
loss={'softmax'}) #指定分类的数量，与使用的损失函数以便决定模型输出何种计算结果
print("=>ReIDNet loaded")
print("Model size: {:.5f}M\n".format(
sum(p.numel() for p in model.parameters()) / 1000000.0))
#损失函数
criterion_class = nn.CrossEntropyLoss()
"""
优化器
参数1，待优化的参数
参数2，学习率
参数3，权重衰减
"""
optimizer = torch.optim.SGD(model.parameters(),
lr=train_lr,
weight_decay=5e-04)
"""
动态学习率
参数1，指定使用的优化器
参数2，mode，可选择‘min’（min表示当监控量停止下降的时候，学习率将减小）或者‘max’（max表示当监控量停止上升的时候，学习率将减小）
参数3，factor，代表学习率每次降低多少
参数4，patience，容忍网路的性能不提升的次数，高于这个次数就降低学习率
参数5，min_lr，学习率的下限
"""
scheduler = lr_scheduler.ReduceLROnPlateau(optimizer,
mode='min',
factor=dy_step_gamma,
patience=10,
min_lr=0.0001)
#如果是测试
if evaluate:
test(model, query_data_loader, gallery_data_loader)
return 0
#如果是训练
print('————model start training————\n')
bt = time.time() #训练的开始时间
for epoch in range(start_epoch, end_epoch):
model.train(True)
train(epoch, model, criterion_class, optimizer, scheduler,
train_data_loader)
et = time.time() #训练的结束时间
print('**模型训练结束, 保存最终参数到{}**\n'.format(final_model_path))
torch.save(model.state_dict(), final_model_path)
print('————训练总用时{:.2f}小时————'.format((et - bt) / 3600.0))
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print("Using {} device training.".format(device.type))
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = parser_data.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
train_dataset = VOC2012DataSet(VOC_root,
data_transform['train'],
train_set='train.txt')
# 注意训练时，batch_size必须大于1
batch_size = parser_data.batch_size
assert batch_size > 1, "batch size must be greater than 1"
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using %g dataloader workers' % nw)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw,
collate_fn=train_dataset.collate_fn)
val_dataset = VOC2012DataSet(VOC_root,
data_transform['val'],
train_set='val.txt')
val_data_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=nw,
collate_fn=train_dataset.collate_fn)
model = create_model(num_classes=21, device=device)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.0005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.3)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
val_data = None
# 如果电脑内存充裕，可提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
# val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50,
train_loss=train_loss,
train_lr=learning_rate)
lr_scheduler.step()
utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data,
mAP_list=val_map)
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd300-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
def main():
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(device)
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"train": transform.Compose([transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()]),
"val": transform.Compose([transform.Resize(),
transform.ToTensor(),
transform.Normalization()])
}
voc_path = "../"
train_dataset = VOC2012DataSet(voc_path, data_transform['train'], True)
# 注意训练时，batch_size必须大于1
train_data_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=8,
shuffle=True,
num_workers=0,
collate_fn=utils.collate_fn)
val_dataset = VOC2012DataSet(voc_path, data_transform['val'], False)
val_data_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=utils.collate_fn)
model = create_model(num_classes=21, device=device)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params, lr=0.002,
momentum=0.9, weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.3)
train_loss = []
learning_rate = []
val_map = []
val_data = None
# 如果电脑内存充裕，可提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
# val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(20):
utils.train_one_epoch(model=model, optimizer=optimizer,
data_loader=train_data_loader,
device=device, epoch=epoch,
print_freq=50, train_loss=train_loss,
train_lr=learning_rate, warmup=True)
lr_scheduler.step()
utils.evaluate(model=model, data_loader=val_data_loader,
device=device, data_set=val_data, mAP_list=val_map)
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch}
torch.save(save_files, "./save_weights/ssd300-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
def main(args):
print(args)
# mp.spawn(main_worker, args=(args,), nprocs=args.world_size, join=True)
init_distributed_mode(args)
device = torch.device(args.device)
results_file = "results{}.txt".format(
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
# Data loading code
print("Loading data")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = args.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
# load train data set
train_data_set = VOC2012DataSet(VOC_root,
data_transform["train"],
train_set='train.txt')
# load validation data set
val_data_set = VOC2012DataSet(VOC_root,
data_transform["val"],
train_set='val.txt')
print("Creating data loaders")
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_data_set)
test_sampler = torch.utils.data.distributed.DistributedSampler(
val_data_set)
else:
train_sampler = torch.utils.data.RandomSampler(train_data_set)
test_sampler = torch.utils.data.SequentialSampler(val_data_set)
if args.aspect_ratio_group_factor >= 0:
# 统计所有图像比例在bins区间中的位置索引
group_ids = create_aspect_ratio_groups(
train_data_set, k=args.aspect_ratio_group_factor)
train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids,
args.batch_size)
else:
train_batch_sampler = torch.utils.data.BatchSampler(train_sampler,
args.batch_size,
drop_last=True)
data_loader = torch.utils.data.DataLoader(
train_data_set,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
collate_fn=train_data_set.collate_fn)
data_loader_test = torch.utils.data.DataLoader(
val_data_set,
batch_size=1,
sampler=test_sampler,
num_workers=args.workers,
collate_fn=train_data_set.collate_fn)
print("Creating model")
model = create_model(num_classes=args.num_classes + 1, device=device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_step_size,
gamma=args.lr_gamma)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
# 如果传入resume参数，即上次训练的权重地址，则接着上次的参数训练
if args.resume:
# If map_location is missing, torch.load will first load the module to CPU
# and then copy each parameter to where it was saved,
# which would result in all processes on the same machine using the same set of devices.
checkpoint = torch.load(
args.resume, map_location='cpu') # 读取之前保存的权重文件(包括优化器以及学习率策略)
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
utils.evaluate(model, data_loader_test, device=device)
return
train_loss = []
learning_rate = []
val_map = []
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
mean_loss, lr = utils.train_one_epoch(model, optimizer, data_loader,
device, epoch, args.print_freq)
# only first process to save training info
if args.rank in [-1, 0]:
train_loss.append(mean_loss.item())
learning_rate.append(lr)
# update learning rate
lr_scheduler.step()
# evaluate after every epoch
coco_info = utils.evaluate(model, data_loader_test, device=device)
if args.rank in [-1, 0]:
# write into txt
with open(results_file, "a") as f:
result_info = [
str(round(i, 4))
for i in coco_info + [mean_loss.item(), lr]
]
txt = "epoch:{} {}".format(epoch, ' '.join(result_info))
f.write(txt + "\n")
val_map.append(coco_info[1]) # pascal mAP
if args.output_dir:
# 只在主节点上执行保存权重操作
save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args,
'epoch': epoch
}, os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if args.rank in [-1, 0]:
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print(device)
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
night_root = parser_data.data_path
train_dataset = NightDataSet(night_root,
data_transform['train'],
train_set='train.txt')
# aa = train_dataset[1]
# 注意训练时，batch_size必须大于1
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=8,
shuffle=True,
num_workers=4,
collate_fn=utils.collate_fn)
val_dataset = NightDataSet(night_root,
data_transform['val'],
train_set='val.txt')
# bb = val_dataset[2]
val_data_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=4,
shuffle=False,
num_workers=0,
collate_fn=utils.collate_fn)
model = create_model(num_classes=3, device=device)
print(model)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.5)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
train_val_map = []
val_data = None
# 如果电脑内存充裕，可提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
# val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50,
train_loss=train_loss,
train_lr=learning_rate)
lr_scheduler.step()
if epoch >= 20 or epoch == 10:
utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data,
mAP_list=val_map)
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd512-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
import numpy as np
import torchvision
import torch
print(torch.__version__)
print(torchvision.__version__)
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
# def normalize(tensor):
# # Subtract the mean, and scale to the interval [-1,1]
# tensor_minusmean = tensor - tensor.mean()
# return tensor_minusmean/tensor_minusmean.abs().max()
transform_video = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)),
T.RandomHorizontalFlip(), normalize,
T.RandomCrop((112, 112))
])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
root = ET.parse(
'/root/yangsen-data/LIRIS-ACCEDE-movies/ACCEDEmovies.xml').getroot()
movie_length = {}
def get_sec(time_str: str) -> int:
"""Get Seconds from time."""
h, m, s = time_str.split(':')
return int(h) * 3600 + int(m) * 60 + int(s)
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print("Using {} device training.".format(device.type))
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
results_file = "results{}.txt".format(
datetime.datetime.now().strftime("%Y%m%d-%H%M%S"))
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = parser_data.data_path
# check voc root
if os.path.exists(os.path.join(VOC_root, "VOCdevkit")) is False:
raise FileNotFoundError(
"VOCdevkit dose not in path:'{}'.".format(VOC_root))
train_dataset = VOC2012DataSet(VOC_root,
data_transform['train'],
train_set='train.txt')
# 注意训练时，batch_size必须大于1
batch_size = parser_data.batch_size
assert batch_size > 1, "batch size must be greater than 1"
# 防止最后一个batch_size=1，如果最后一个batch_size=1就舍去
drop_last = True if len(train_dataset) % batch_size == 1 else False
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0,
8]) # number of workers
print('Using %g dataloader workers' % nw)
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=nw,
collate_fn=train_dataset.collate_fn,
drop_last=drop_last)
val_dataset = VOC2012DataSet(VOC_root,
data_transform['val'],
train_set='val.txt')
val_data_loader = torch.utils.data.DataLoader(
val_dataset,
batch_size=batch_size,
shuffle=False,
num_workers=nw,
collate_fn=train_dataset.collate_fn)
model = create_model(num_classes=args.num_classes + 1, device=device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.0005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.3)
# 如果指定了上次训练保存的权重文件地址，则接着上次结果接着训练
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
# 提前加载验证集数据，以免每次验证时都要重新加载一次数据，节省时间
val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
mean_loss, lr = utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50)
train_loss.append(mean_loss.item())
learning_rate.append(lr)
# update learning rate
lr_scheduler.step()
coco_info = utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data)
# write into txt
with open(results_file, "a") as f:
result_info = [
str(round(i, 4)) for i in coco_info + [mean_loss.item(), lr]
]
txt = "epoch:{} {}".format(epoch, ' '.join(result_info))
f.write(txt + "\n")
val_map.append(coco_info[1]) # pascal mAP
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd300-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
def main(parser_data):
device = torch.device(
parser_data.device if torch.cuda.is_available() else "cpu")
print(device)
if not os.path.exists("save_weights"):
os.mkdir("save_weights")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
XRay_root = parser_data.data_path
train_dataset = XRayDataset(XRay_root,
data_transform['train'],
train_set='train.txt')
# Note that the batch_size must be greater than 1
train_data_loader = torch.utils.data.DataLoader(
train_dataset,
batch_size=8,
shuffle=True,
num_workers=4,
collate_fn=utils.collate_fn)
val_dataset = XRayDataset(XRay_root,
data_transform['val'],
train_set='val.txt')
val_data_loader = torch.utils.data.DataLoader(val_dataset,
batch_size=1,
shuffle=False,
num_workers=0,
collate_fn=utils.collate_fn)
model = create_model(num_classes=6, device=device)
model.to(device)
# define optimizer
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=0.0005,
momentum=0.9,
weight_decay=0.0005)
# learning rate scheduler
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=5,
gamma=0.3)
# If the address of the weight file saved by the last training is specified, the training continues with the last result
if parser_data.resume != "":
checkpoint = torch.load(parser_data.resume)
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
parser_data.start_epoch = checkpoint['epoch'] + 1
print("the training process from epoch{}...".format(
parser_data.start_epoch))
train_loss = []
learning_rate = []
val_map = []
val_data = None
# If your computer has sufficient memory, you can save time by loading the validation set data in advance to avoid having to reload the data each time you validate
# val_data = get_coco_api_from_dataset(val_data_loader.dataset)
for epoch in range(parser_data.start_epoch, parser_data.epochs):
utils.train_one_epoch(model=model,
optimizer=optimizer,
data_loader=train_data_loader,
device=device,
epoch=epoch,
print_freq=50,
train_loss=train_loss,
train_lr=learning_rate)
lr_scheduler.step()
utils.evaluate(model=model,
data_loader=val_data_loader,
device=device,
data_set=val_data,
mAP_list=val_map)
# save weights
save_files = {
'model': model.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch
}
torch.save(save_files, "./save_weights/ssd300-{}.pth".format(epoch))
# plot loss and lr curve
if len(train_loss) != 0 and len(learning_rate) != 0:
from plot_curve import plot_loss_and_lr
plot_loss_and_lr(train_loss, learning_rate)
# plot mAP curve
if len(val_map) != 0:
from plot_curve import plot_map
plot_map(val_map)
import pandas as pd
import traceback
import transform as T
import numpy as np
import torchvision
import torch
from tensorboardX import SummaryWriter
print(torch.__version__)
print(torchvision.__version__)
normalize = T.Normalize(mean=[0.43216, 0.394666, 0.37645],
std=[0.22803, 0.22145, 0.216989])
transform_video = torchvision.transforms.Compose([
T.ToFloatTensorInZeroOne(),
T.Resize((128, 171)),
T.RandomHorizontalFlip(),
normalize,
T.RandomCrop((112, 112))
])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(torchaudio.__version__)
trainIter = 0
evalIter = 0
def normalize(tensor):
# Subtract the mean, and scale to the interval [-1,1]
tensor_minusmean = tensor - tensor.mean()
return tensor_minusmean/tensor_minusmean.abs().max()
class LIRIS_ACCEDE(torch.utils.data.Dataset):
def main():
print("------------------------------")
print("START")
print("------------------------------")
composed_transforms_tr = standard_transforms.Compose([
tr.RandomHorizontalFlip(),
tr.ScaleNRotate(rots=(-15, 15), scales=(.75, 1.5)),
# tr.RandomResizedCrop(img_size),
tr.FixedResize(img_size),
tr.Normalize(mean=mean, std=std),
tr.ToTensor()
]) # data pocessing and data augumentation
voc_train_dataset = VOCSegmentation(
base_dir=data_dir, split='train',
transform=composed_transforms_tr) # get data
#return {'image': _img, 'gt': _target}
print("Data loaded...")
print("Dataset:{}".format(dataset))
print("------------------------------")
voc_train_loader = DataLoader(voc_train_dataset,
batch_size=batch_size,
shuffle=True,
num_workers=1)
iter_dataset = iter(voc_train_loader)
train = next(iter_dataset)
print("Input size {}".format(train['image'].shape))
print("Output size {}".format(train['gt'].shape))
print("Model start training...")
print("------------------------------")
print("Model info:")
print("If use CUDA : {}".format(use_gpu))
print('Initial learning rate {} | batch size {} | epoch num {}'.format(
0.0001, batch_size, epoches))
print("------------------------------")
model = fpn_unet(input_bands=input_bands, n_classes=num_class)
model_id = 0
# load model
if find_new_file(model_dir) is not None:
model.load_state_dict(torch.load(find_new_file(model_dir)))
# model.load_state_dict(torch.load('./pth/best2.pth'))
print('load the model %s' % find_new_file(model_dir))
model_id = re.findall(r'\d+', find_new_file(model_dir))
model_id = int(model_id[0])
print('Current model ID {}'.format(model_id))
model.cuda()
criterion = torch.nn.CrossEntropyLoss() #define loss
optimizer = torch.optim.Adam(model.parameters(),
lr=0.0001) #define optimizer
model.cuda()
model.train()
f = open('log.txt', 'w')
for epoch in range(epoches):
cur_log = ''
running_loss = 0.0
start = time.time()
lr = adjust_learning_rate(base_lr, optimizer, epoch, model_id, power)
print("Current learning rate : {}".format(lr))
for i, batch_data in tqdm.tqdm(enumerate(voc_train_loader)): #get data
images, labels = batch_data['image'], batch_data['gt']
labels = labels.view(images.size()[0], img_size, img_size).long()
i += images.size()[0]
images = Variable(images).cuda()
labels = Variable(labels).cuda()
optimizer.zero_grad()
outputs = model(images)
losses = criterion(outputs, labels) # calculate loss
losses.backward()
optimizer.step()
running_loss += losses
print("Epoch [%d] all Loss: %.4f" %
(epoch + 1 + model_id, running_loss / i))
cur_log += 'epoch:{}, '.format(str(epoch)) + 'learning_rate:{}'.format(
str(lr)) + ', ' + 'train_loss:{}'.format(
str(running_loss.item() / i)) + ', '
torch.save(model.state_dict(),
os.path.join(model_dir, '%d.pth' % (model_id + epoch + 1)))
print("Model Saved")
# iou, acc, recall, precision = test_my(input_bands, model_name, model_dir, img_size, num_class)
# cur_log += 'iou:{}'.format(str(iou)) + ', ' + 'acc:{}'.format(str(acc))+'\n' + ', ' + 'recall:{}'.format(str(recall))+'\n' + ', ' + 'precision:{}'.format(str(precision))
end = time.time()
time_cha = end - start
left_steps = epoches - epoch - model_id
print('the left time is %d hours, and %d minutes' %
(int(left_steps * time_cha) / 3600,
(int(left_steps * time_cha) % 3600) / 60))
print(cur_log)
f.writelines(str(cur_log))
current_pos + batch_size)
if self.transform:
samples = self.transform(samples)
yield samples, labels
if __name__ == '__main__':
# just for debug the dataset
tic = time.time()
background = np.ones(18, )
background = -9999 * background
counts = 0
mean = unpickle('mean_channal.pkl')
tfs = T.Compose([
T.Normalize(mean=mean),
T.RandomHorizontalFlip(0.5),
])
datasets = CustomDataset('training.h5', transform=None)
for samples, labels in datasets.load_data(batch_size=256, shuffle=False):
# background += np.sum(samples, axis = (0,1,2))
counts += labels.shape[0]
print("counts:", counts)
# compute mean,std by channel
print("len:", len(datasets))
# mean = background / len(datasets)
# print("mean:", mean.shape)
toc = time.time()
print("elasped time is %.3f" % (toc - tic))
# pdb.set_trace()
# all_data = np.concatenate([datasets.s1,datasets.s2], axis=3)
# with open("mean_channal.pkl", 'wb') as f:
save_best_only=False,
save_weights_only=False,
mode='auto',
period=1)
#model = resnet.ResnetBuilder.build_resnet_18((18, 32, 32), nb_classes)
network = model.create_model('resnet50',
input_shape=(18, 32, 32),
num_outputs=nb_classes)
network.compile(loss='sparse_categorical_crossentropy',
optimizer=optimizers.SGD(lr=0.1, momentum=0.9, nesterov=True),
metrics=['accuracy'])
mean = unpickle("mean_channal.pkl")
trfs = T.Compose([T.Normalize(mean), T.RandomHorizontalFlip(0.5)])
training_data = CustomDataset('/mnt/img1/yangqh/Germany_cloud/training.h5',
transform=None)
validation_data = CustomDataset('/mnt/img1/yangqh/Germany_cloud/training.h5',
transform=None)
##############此处改成train set 的目录
network.fit_generator(
training_data.load_data(batch_size=batch_size),
steps_per_epoch=len(training_data) // batch_size,
validation_data=validation_data.load_data(batch_size=batch_size),
validation_steps=len(validation_data) // batch_size,
epochs=nb_epoch,
verbose=1,
max_q_size=100,
callbacks=[checkpoint, lr_reducer, early_stopper, csv_logger])
network.save('final.h5')
def main(args):
print(args)
# mp.spawn(main_worker, args=(args,), nprocs=args.world_size, join=True)
utils.init_distributed_mode(args)
device = torch.device(args.device)
# Data loading code
print("Loading data")
data_transform = {
"train":
transform.Compose([
transform.SSDCropping(),
transform.Resize(),
# transform.ColorJitter(),
transform.ToTensor(),
transform.RandomHorizontalFlip(),
transform.Normalization(),
transform.AssignGTtoDefaultBox()
]),
"val":
transform.Compose([
transform.Resize(),
transform.ToTensor(),
transform.Normalization()
])
}
VOC_root = args.data_path
# load train data set
train_data_set = VOC2012DataSet(VOC_root, data_transform["train"], True)
# load validation data set
val_data_set = VOC2012DataSet(VOC_root, data_transform["val"], False)
print("Creating data loaders")
if args.distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_data_set)
test_sampler = torch.utils.data.distributed.DistributedSampler(
val_data_set)
else:
train_sampler = torch.utils.data.RandomSampler(train_data_set)
test_sampler = torch.utils.data.SequentialSampler(val_data_set)
if args.aspect_ratio_group_factor >= 0:
# 统计所有图像比例在bins区间中的位置索引
group_ids = create_aspect_ratio_groups(
train_data_set, k=args.aspect_ratio_group_factor)
train_batch_sampler = GroupedBatchSampler(train_sampler, group_ids,
args.batch_size)
else:
train_batch_sampler = torch.utils.data.BatchSampler(train_sampler,
args.batch_size,
drop_last=True)
data_loader = torch.utils.data.DataLoader(
train_data_set,
batch_sampler=train_batch_sampler,
num_workers=args.workers,
collate_fn=utils.collate_fn)
data_loader_test = torch.utils.data.DataLoader(val_data_set,
batch_size=4,
sampler=test_sampler,
num_workers=args.workers,
collate_fn=utils.collate_fn)
print("Creating model")
model = create_model(num_classes=21)
model.to(device)
model_without_ddp = model
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
params = [p for p in model.parameters() if p.requires_grad]
optimizer = torch.optim.SGD(params,
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer,
step_size=args.lr_step_size,
gamma=args.lr_gamma)
# lr_scheduler = torch.optim.lr_scheduler.MultiStepLR(optimizer, milestones=args.lr_steps, gamma=args.lr_gamma)
# 如果传入resume参数，即上次训练的权重地址，则接着上次的参数训练
if args.resume:
# If map_location is missing, torch.load will first load the module to CPU
# and then copy each parameter to where it was saved,
# which would result in all processes on the same machine using the same set of devices.
checkpoint = torch.load(
args.resume, map_location='cpu') # 读取之前保存的权重文件(包括优化器以及学习率策略)
model_without_ddp.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['optimizer'])
lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
args.start_epoch = checkpoint['epoch'] + 1
if args.test_only:
utils.evaluate(model, data_loader_test, device=device)
return
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
train_sampler.set_epoch(epoch)
utils.train_one_epoch(model, optimizer, data_loader, device, epoch,
args.print_freq)
lr_scheduler.step()
if args.output_dir:
# 只在主节点上执行保存权重操作
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'args': args,
'epoch': epoch
}, os.path.join(args.output_dir, 'model_{}.pth'.format(epoch)))
# evaluate after every epoch
utils.evaluate(model, data_loader_test, device=device)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
if args.distributed:
torch.cuda.set_device(args.local_rank)
torch.distributed.init_process_group(backend='nccl', init_method='env://')
synchronize()
img_mean = [0.485, 0.456, 0.406]
img_std = [0.229, 0.224, 0.225]
device = 'cuda'
# torch.backends.cudnn.deterministic = True
train_trans = transform.Compose(
[
transform.RandomScale(0.5, 2.0),
# transform.Resize(args.size, None),
transform.RandomHorizontalFlip(),
transform.RandomCrop(args.size),
transform.RandomBrightness(0.04),
transform.ToTensor(),
transform.Normalize(img_mean, img_std),
transform.Pad(args.size)
]
)
valid_trans = transform.Compose(
[transform.ToTensor(), transform.Normalize(img_mean, img_std)]
)
train_set = ADE20K(args.path, 'train', train_trans)
valid_set = ADE20K(args.path, 'valid', valid_trans)
