def make_coco_transforms(image_set, cautious):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
max_size = 1333
if image_set == "train":
horizontal = [] if cautious else [T.RandomHorizontalFlip()]
return T.Compose(horizontal + [
T.RandomSelect(
T.RandomResize(scales, max_size=max_size),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, max_size, respect_boxes=cautious),
T.RandomResize(scales, max_size=max_size),
]),
),
normalize,
])
if image_set == "val":
return T.Compose([
T.RandomResize([800], max_size=max_size),
normalize,
])
raise ValueError(f"unknown {image_set}")
def make_coco_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')
def make_coco_transforms(image_set):
# T 是项目中的datatsets/transforms.py模块，以上各个数据增强的方法在该模
# 块中的实现和 torchvision.transforms 中的差不多，其中ToTensor()会
# 将图像的通道维度排列在第一个维度，并且像素值归一化到0-1范围内；而Normalize()
# 则会根据指定的均值和标准差对图像进行归一化，同时将标签的bbox转换为Cx Cy W H形式，后归一化到0-1，此处不再进行解析，感兴趣的可以去参考源码。
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f'unknown {image_set}')
def make_yolo_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
if image_set == 'train':
return T.Compose([
T.RandomShuffle(keep=0.7),
T.RandomSelect(
T.RandomRotation(180),
T.RandomGrayscale(p=0.07),
),
T.RandomSelect(
T.RandomSizeCrop(384, 600),
T.RandomGrayscale(p=0.07),
),
T.RandomResize([400, 450, 500, 550, 600, 650]),
normalize,
])
if image_set == 'val':
return T.Compose([T.RandomResize([640]), normalize])
raise ValueError(f'unknown {image_set}')
def make_support_transforms():
"""
Transforms for support images during the training phase.
For transforms for support images during inference, please check dataset_support.py
"""
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [
448, 464, 480, 496, 512, 528, 544, 560, 576, 592, 608, 624, 640, 656,
672
]
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomColorJitter(p=0.25),
T.RandomSelect(
T.RandomResize(scales, max_size=672),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=672),
])),
normalize,
])
def make_clevr_transforms(image_set, cautious=False):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [256, 288, 320, 352, 384]
if image_set == "train":
horizontal = [] if cautious else [T.RandomHorizontalFlip()]
return T.Compose(horizontal + [
T.RandomSelect(
T.RandomResize(scales, max_size=512),
T.Compose([
T.RandomResize([320, 352, 384]),
T.RandomSizeCrop(256, 512, respect_boxes=cautious),
T.RandomResize(scales, max_size=512),
]),
),
normalize,
])
if image_set == "val":
return T.Compose([
# T.RandomResize([480], max_size=1333),
normalize,
])
raise ValueError(f"unknown {image_set}")
def make_coco_transforms(image_set):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768]
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomResize(scales, max_size=800),
T.PhotometricDistort(),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
# To suit the GPU memory the scale might be different
T.RandomResize([300], max_size=540), #for r50
#T.RandomResize([280], max_size=504),#for r101
]),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([360], max_size=640),
normalize,
])
raise ValueError(f'unknown {image_set}')
def initialise(args):
# Load model and loss function
detr, criterion, postprocessors = build_model(args)
class_embed = torch.nn.Linear(256, 81, bias=True)
if os.path.exists(args.pretrained):
print(f"Load pre-trained model from {args.pretrained}")
detr.load_state_dict(torch.load(args.pretrained)['model_state_dict'])
w, b = detr.class_embed.state_dict().values()
keep = [
1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15, 16, 17, 18, 19, 20,
21, 22, 23, 24, 25, 27, 28, 31, 32, 33, 34, 35, 36, 37, 38, 39, 40,
41, 42, 43, 44, 46, 47, 48, 49, 50, 51, 52, 53, 54, 55, 56, 57, 58,
59, 60, 61, 62, 63, 64, 65, 67, 70, 72, 73, 74, 75, 76, 77, 78, 79,
80, 81, 82, 84, 85, 86, 87, 88, 89, 90, 91
]
class_embed.load_state_dict(dict(weight=w[keep], bias=b[keep]))
detr.class_embed = class_embed
if os.path.exists(args.resume):
print(f"Resume from model at {args.resume}")
detr.load_state_dict(torch.load(args.resume)['model_state_dict'])
# Prepare dataset transforms
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if args.partition == 'train2015':
transforms = T.Compose([
T.RandomHorizontalFlip(),
T.ColorJitter(.4, .4, .4),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])),
normalize,
])
if args.partition == 'test2015':
transforms = T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
# Load dataset
dataset = HICODetObject(
pocket.data.HICODet(
root=os.path.join(args.data_root,
f'hico_20160224_det/images/{args.partition}'),
anno_file=os.path.join(args.data_root,
f'instances_{args.partition}.json'),
target_transform=pocket.ops.ToTensor(input_format='dict')),
transforms)
return detr, criterion, postprocessors['bbox'], dataset
def make_coco_transforms(image_set):
"""coco dataset preprocessing
Parameters
----------
image_set : {str-like, scalar} "train" or "val"
Returns
-------
result : {T.Compose list} the image transform operation list
"""
# -------------------
# normalize the image
#
normalize = T.Compose([
# processing method
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406],
[0.229, 0.224, 0.225]) # R,G,B每层的归一化用到的均值和方差
])
# -------------------
# -------------------
#
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
# -------------------
# -------------------
# according to the image set types,
# determine the preprocessing steps of the image
#
if image_set == 'train':
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
])),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
# -------------------
raise ValueError(f'unknown {image_set}')
def make_default_transforms(image_set, crop=False):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
# normalize = T.Compose([T.ToTensor()])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
if image_set == "train":
if not crop:
trans = [
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1333),
]),
),
normalize,
]
else:
trans = [
T.RandomHorizontalFlip(),
T.RandomResize([256, 324]),
T.RandomSizeCrop(224, 224),
normalize,
]
return T.Compose(trans)
if image_set in ["test", "val"]:
return T.Compose([
T.RandomResize([800], max_size=1333),
normalize,
])
raise ValueError(f"unknown {image_set}")
def make_coco_transforms(image_set, args):
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.538, 0.494, 0.453], [0.257, 0.263, 0.273])
])
scales = [
480, 512, 544, 576, 608, 640, 672, 680, 690, 704, 736, 768, 788, 800
]
test_size = 1100
max = 1333
if args.eval:
return T.Compose([
T.RandomResize([test_size], max_size=max),
normalize,
])
else:
if image_set == 'train':
return T.Compose([
T.RandomSelect(
T.RandomHorizontalFlip(),
T.RandomVerticalFlip(),
),
T.RandomSelect(
T.RandomResize(scales, max_size=max),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=max),
])),
T.ColorJitter(),
normalize,
])
if image_set == 'val':
return T.Compose([
T.RandomResize([test_size], max_size=max),
normalize,
])
raise ValueError(f'unknown {image_set}')
def make_transforms():
"""
Transforms for query images during the few-shot fine-tuning stage.
"""
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
return T.Compose([
T.RandomHorizontalFlip(),
T.RandomColorJitter(p=0.3333),
T.RandomSelect(
T.RandomResize(scales, max_size=1152),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
T.RandomResize(scales, max_size=1152),
])),
normalize,
])
def main(args):
utils.init_distributed_mode(args)
print("git:\n {}\n".format(utils.get_sha()))
if args.frozen_weights is not None:
assert args.masks, "Frozen training is meant for segmentation only"
print(args)
device = torch.device(args.device)
# fix the seed for reproducibility
seed = args.seed + utils.get_rank()
torch.manual_seed(seed)
np.random.seed(seed)
random.seed(seed)
Dataset = get_dataset(args.dataset, args.task)
f = open(args.data_cfg)
data_config = json.load(f)
trainset_paths = data_config['train']
dataset_root = data_config['root']
f.close()
normalize = T.Compose([
T.ToTensor(),
T.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
])
scales = [480, 512, 544, 576, 608, 640, 672, 704, 736, 768, 800]
transforms = T.Compose([
T.RandomHorizontalFlip(),
T.RandomSelect(
T.RandomResize(scales, max_size=1333),
T.Compose([
T.RandomResize([400, 500, 600]),
T.RandomSizeCrop(384, 600),
# T.RandomSizeCrop_MOT(384, 600),
T.RandomResize(scales, max_size=1333),
])),
normalize,
])
dataset_train = Dataset(args,
dataset_root,
trainset_paths, (1088, 608),
augment=True,
transforms=transforms)
args.nID = dataset_train.nID
model, criterion, postprocessors = build_model(args)
model.to(device)
model_without_ddp = model
# dataset_train = build_dataset(image_set='train', args=args)
# dataset_val = build_dataset(image_set='val', args=args)
if args.distributed:
if args.cache_mode:
sampler_train = samplers.NodeDistributedSampler(dataset_train)
# sampler_val = samplers.NodeDistributedSampler(dataset_val, shuffle=False)
else:
sampler_train = samplers.DistributedSampler(dataset_train)
# sampler_val = samplers.DistributedSampler(dataset_val, shuffle=False)
else:
sampler_train = torch.utils.data.RandomSampler(dataset_train)
# sampler_val = torch.utils.data.SequentialSampler(dataset_val)
batch_sampler_train = torch.utils.data.BatchSampler(sampler_train,
args.batch_size,
drop_last=True)
data_loader_train = DataLoader(dataset_train,
batch_sampler=batch_sampler_train,
collate_fn=utils.collate_fn,
num_workers=args.num_workers,
pin_memory=True)
# data_loader_val = DataLoader(dataset_val, args.batch_size, sampler=sampler_val,
# drop_last=False, collate_fn=utils.collate_fn, num_workers=args.num_workers,
# pin_memory=True)
# data_loader_train = torch.utils.data.DataLoader(
# dataset_train,
# batch_size=args.batch_size,
# shuffle=True,
# num_workers=args.num_workers,
# pin_memory=True,
# drop_last=True
# )
# lr_backbone_names = ["backbone.0", "backbone.neck", "input_proj", "transformer.encoder"]
def match_name_keywords(n, name_keywords):
out = False
for b in name_keywords:
if b in n:
out = True
break
return out
for n, p in model_without_ddp.named_parameters():
print(n)
# 用于将classifer不更新参数
# for name,p in model_without_ddp.named_parameters():
# if name.startswith('classifier'):
# p.requires_grad = False
param_dicts = [{
"params": [
p for n, p in model_without_ddp.named_parameters()
if not match_name_keywords(n, args.lr_backbone_names)
and not match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
"lr":
args.lr,
}, {
"params": [
p for n, p in model_without_ddp.named_parameters() if
match_name_keywords(n, args.lr_backbone_names) and p.requires_grad
],
"lr":
args.lr_backbone,
}, {
"params": [
p for n, p in model_without_ddp.named_parameters()
if match_name_keywords(n, args.lr_linear_proj_names)
and p.requires_grad
],
"lr":
args.lr * args.lr_linear_proj_mult,
}]
if args.sgd:
optimizer = torch.optim.SGD(param_dicts,
lr=args.lr,
momentum=0.9,
weight_decay=args.weight_decay)
else:
optimizer = torch.optim.AdamW(param_dicts,
lr=args.lr,
weight_decay=args.weight_decay)
lr_scheduler = torch.optim.lr_scheduler.StepLR(optimizer, args.lr_drop)
# optimizer.add_param_group({'params': criterion.parameters()})
if args.distributed:
model = torch.nn.parallel.DistributedDataParallel(
model, device_ids=[args.gpu])
model_without_ddp = model.module
if args.frozen_weights is not None:
checkpoint = torch.load(args.frozen_weights, map_location='cpu')
model_without_ddp.detr.load_state_dict(checkpoint['model'])
output_dir = Path(args.output_dir)
if args.resume:
if args.resume.startswith('https'):
checkpoint = torch.hub.load_state_dict_from_url(args.resume,
map_location='cpu',
check_hash=True)
else:
checkpoint = torch.load(args.resume, map_location='cpu')
model_dict = model_without_ddp.state_dict() #当前模型参数
pretrained_dict = {
k: v
for k, v in checkpoint['model'].items() if k not in [
"class_embed.0.weight", "class_embed.0.bias",
"class_embed.1.weight", "class_embed.1.bias",
"class_embed.2.weight", "class_embed.2.bias",
"class_embed.3.weight", "class_embed.3.bias",
"class_embed.4.weight", "class_embed.4.bias",
"class_embed.5.weight", "class_embed.5.bias"
]
}
model_dict.update(pretrained_dict)
# missing_keys, unexpected_keys = model_without_ddp.load_state_dict(checkpoint['model'], strict=False)
missing_keys, unexpected_keys = model_without_ddp.load_state_dict(
model_dict, strict=False)
unexpected_keys = [
k for k in unexpected_keys
if not (k.endswith('total_params') or k.endswith('total_ops'))
]
if len(missing_keys) > 0:
print('Missing Keys: {}'.format(missing_keys))
if len(unexpected_keys) > 0:
print('Unexpected Keys: {}'.format(unexpected_keys))
if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
args.start_epoch = checkpoint['epoch'] + 1
# optimizer.load_state_dict(checkpoint['optimizer'])
# if not args.eval and 'optimizer' in checkpoint and 'lr_scheduler' in checkpoint and 'epoch' in checkpoint:
# import copy
# p_groups = copy.deepcopy(optimizer.param_groups)
# # optimizer.load_state_dict(checkpoint['optimizer'])
# for pg, pg_old in zip(optimizer.param_groups, p_groups):
# pg['lr'] = pg_old['lr']
# pg['initial_lr'] = pg_old['initial_lr']
# # print(optimizer.param_groups)
# lr_scheduler.load_state_dict(checkpoint['lr_scheduler'])
# # todo: this is a hack for doing experiment that resume from checkpoint and also modify lr scheduler (e.g., decrease lr in advance).
# args.override_resumed_lr_drop = True
# if args.override_resumed_lr_drop:
# print('Warning: (hack) args.override_resumed_lr_drop is set to True, so args.lr_drop would override lr_drop in resumed lr_scheduler.')
# lr_scheduler.step_size = args.lr_drop
# lr_scheduler.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
# lr_scheduler.step(lr_scheduler.last_epoch)
# model.add_module('id')
# [p for p in model.named_parameters() if not p[1].requires_grad]
# 用于将classifer不更新参数
# optimizer = torch.optim.SGD(filter(lambda x: "classifier" not in x[0], model.parameters()), lr=args.lr,
# momentum=0.9, weight_decay=1e-4)
# model.classifier.training = False
n_parameters = sum(p.numel() for p in model.parameters()
if p.requires_grad)
print('number of params:', n_parameters)
print("Start training")
start_time = time.time()
for epoch in range(args.start_epoch, args.epochs):
if args.distributed:
sampler_train.set_epoch(epoch)
train_stats = train_one_epoch(args, model, criterion,
data_loader_train, optimizer, device,
epoch, args.clip_max_norm)
lr_scheduler.step()
if args.output_dir:
checkpoint_paths = [output_dir / 'checkpoint.pth']
# extra checkpoint before LR drop and every 5 epochs
if (epoch + 1) % args.lr_drop == 0 or (epoch + 1) % 5 == 0:
checkpoint_paths.append(output_dir /
f'checkpoint{epoch:04}.pth')
for checkpoint_path in checkpoint_paths:
utils.save_on_master(
{
'model': model_without_ddp.state_dict(),
'optimizer': optimizer.state_dict(),
'lr_scheduler': lr_scheduler.state_dict(),
'epoch': epoch,
'args': args,
}, checkpoint_path)
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=int(total_time)))
print('Training time {}'.format(total_time_str))
