def main():
# 1. Get input arguments
args = get_args()
# 2. Create config instance from args above
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available()
if args.config_file:
cfg.merge_from_file(args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
set_random_seed(cfg.train.seed)
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
# 3. Create DataManager Instance
datamanager = build_datamanager(cfg)
print('Building model: {}'.format(cfg.model.name))
model = torchreid.models.build_model(
name=cfg.model.name,
num_classes=datamanager.num_train_pids,
loss=cfg.loss.name,
pretrained=cfg.model.pretrained,
use_gpu=cfg.use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, cfg.data.height, cfg.data.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
load_pretrained_weights(model, cfg.model.load_weights)
if cfg.use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(cfg))
if cfg.model.resume and check_isfile(cfg.model.resume):
cfg.train.start_epoch = resume_from_checkpoint(cfg.model.resume,
model,
optimizer=optimizer,
scheduler=scheduler)
print('Building {}-engine for {}-reid'.format(cfg.loss.name,
cfg.data.type))
# Build engine and run
engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(cfg))
def main(args):
regex_mAP = re.compile(r'mAP: ([\.\deE+-]+)%')
regex_r1 = re.compile(r'Rank-1 : ([\.\deE+-]+)%')
regex_r5 = re.compile(r'Rank-5 : ([\.\deE+-]+)%')
regex_r10 = re.compile(r'Rank-10 : ([\.\deE+-]+)%')
regex_r20 = re.compile(r'Rank-20 : ([\.\deE+-]+)%')
final_res = defaultdict(list)
directories = listdir_nohidden(args.directory, sort=True)
num_dirs = len(directories)
for directory in directories:
fullpath = os.path.join(args.directory, directory)
filepath = glob.glob(os.path.join(fullpath, 'test.log*'))[0]
check_isfile(filepath)
print(f'Parsing {filepath}')
res = parse_file(filepath, regex_mAP, regex_r1, regex_r5, regex_r10,
regex_r20)
for key, value in res.items():
final_res[key].append(value)
print('Finished parsing')
print(f'The average results over {num_dirs} splits are shown below')
for key, values in final_res.items():
mean_val = np.mean(values)
print(f'{key}: {mean_val:.1f}')
def build_auxiliary_model(config_file,
num_classes,
use_gpu,
device_ids,
num_iter,
lr=None,
nncf_aux_config_changes=None,
aux_config_opts=None,
aux_pretrained_dict=None):
aux_cfg = get_default_config()
aux_cfg.use_gpu = use_gpu
merge_from_files_with_base(aux_cfg, config_file)
if nncf_aux_config_changes:
print(
f'applying to aux config changes from NNCF aux config {nncf_aux_config_changes}'
)
if not isinstance(nncf_aux_config_changes, CfgNode):
nncf_aux_config_changes = CfgNode(nncf_aux_config_changes)
aux_cfg.merge_from_other_cfg(nncf_aux_config_changes)
if aux_config_opts:
print(f'applying to aux config changes from command line arguments, '
f'the changes are:\n{pformat(aux_config_opts)}')
aux_cfg.merge_from_list(aux_config_opts)
print(f'\nShow auxiliary configuration\n{aux_cfg}\n')
if lr is not None:
aux_cfg.train.lr = lr
print(f"setting learning rate from main model: {lr}")
model = torchreid.models.build_model(**model_kwargs(aux_cfg, num_classes))
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(aux_cfg))
scheduler = torchreid.optim.build_lr_scheduler(
optimizer=optimizer, num_iter=num_iter, **lr_scheduler_kwargs(aux_cfg))
if aux_cfg.model.resume and check_isfile(aux_cfg.model.resume):
aux_cfg.train.start_epoch = resume_from_checkpoint(
aux_cfg.model.resume,
model,
optimizer=optimizer,
scheduler=scheduler)
elif aux_pretrained_dict is not None:
load_pretrained_weights(model, pretrained_dict=aux_pretrained_dict)
elif aux_cfg.model.load_weights and check_isfile(
aux_cfg.model.load_weights):
load_pretrained_weights(model, aux_cfg.model.load_weights)
if aux_cfg.use_gpu:
assert device_ids is not None
if len(device_ids) > 1:
model = DataParallel(model, device_ids=device_ids,
output_device=0).cuda(device_ids[0])
else:
model = model.cuda(device_ids[0])
return model, optimizer, scheduler
def main():
global args
set_random_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = torch.cuda.is_available() and not args.use_cpu
log_name = 'test.log' if args.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('** Arguments **')
arg_keys = list(args.__dict__.keys())
arg_keys.sort()
for key in arg_keys:
print('{}: {}'.format(key, args.__dict__[key]))
print('\n')
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if use_gpu:
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
datamanager = build_datamanager(args)
print('Building model: {}'.format(args.arch))
model = torchreid.models.build_model(
name=args.arch,
num_classes=datamanager.num_train_pids,
loss=args.loss.lower(),
pretrained=(not args.no_pretrained),
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
if args.resume and check_isfile(args.resume):
args.start_epoch = resume_from_checkpoint(args.resume,
model,
optimizer=optimizer)
print('Building {}-engine for {}-reid'.format(args.loss, args.app))
engine = build_engine(args, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(args))
def main():
parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config-file', type=str, default='', help='path to config file')
parser.add_argument('-s', '--sources', type=str, nargs='+', help='source datasets (delimited by space)')
parser.add_argument('-t', '--targets', type=str, nargs='+', help='target datasets (delimited by space)')
parser.add_argument('--root', type=str, default='', help='path to data root')
parser.add_argument('opts', default=None, nargs=argparse.REMAINDER,
help='Modify config options using the command-line')
args = parser.parse_args()
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available()
if args.config_file:
cfg.merge_from_file(args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
set_random_seed(cfg.train.seed)
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
datamanager = build_datamanager(cfg)
print('Building model: {}'.format(cfg.model.name))
model = torchreid.models.build_model(**model_kwargs(cfg, datamanager.num_train_pids))
num_params, flops = compute_model_complexity(model, (1, 3, cfg.data.height, cfg.data.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
if cfg.model.pretrained and not cfg.test.evaluate:
state_dict = torch.load(cfg.model.load_weights)
model.load_pretrained_weights(state_dict)
else:
load_pretrained_weights(model, cfg.model.load_weights)
if cfg.use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
scheduler = torchreid.optim.build_lr_scheduler(optimizer, **lr_scheduler_kwargs(cfg))
if cfg.model.resume and check_isfile(cfg.model.resume):
cfg.train.start_epoch = resume_from_checkpoint(
cfg.model.resume, model, optimizer=optimizer, scheduler=scheduler
)
print('Building {}-engine for {}-reid'.format(cfg.loss.name, cfg.data.type))
engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(cfg))
def main():
global args
set_random_seed(args.seed)
if not args.use_avai_gpus:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
use_gpu = (torch.cuda.is_available() and not args.use_cpu)
log_name = 'test.log' if args.evaluate else 'train.log'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('==========\nArgs:{}\n=========='.format(args))
if use_gpu:
print('Currently using GPU {}'.format(args.gpu_devices))
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
datamanager = build_datamanager(args)
print('Building model: {}'.format(args.arch))
model = torchreid.models.build_model(
name=args.arch,
num_classes=datamanager.num_train_pids,
loss=args.loss.lower(),
pretrained=(not args.no_pretrained),
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
if args.resume and check_isfile(args.resume):
args.start_epoch = resume_from_checkpoint(args.resume,
model,
optimizer=optimizer)
print('Building {}-engine for {}-reid'.format(args.loss, args.app))
engine = build_engine(args, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(args))
def __init__(
self,
model_name='',
model_path='',
image_size=(256, 128),
pixel_mean=[0.485, 0.456, 0.406],
pixel_std=[0.229, 0.224, 0.225],
pixel_norm=True,
device='cuda',
verbose=True
):
# Build model
model = build_model(
model_name,
num_classes=1,
pretrained=not (model_path and check_isfile(model_path)),
use_gpu=device.startswith('cuda')
)
model.eval()
if verbose:
num_params, flops = compute_model_complexity(
model, (1, 3, image_size[0], image_size[1])
)
print('Model: {}'.format(model_name))
print('- params: {:,}'.format(num_params))
print('- flops: {:,}'.format(flops))
if model_path and check_isfile(model_path):
load_pretrained_weights(model, model_path)
# Build transform functions
transforms = []
transforms += [T.Resize(image_size)]
transforms += [T.ToTensor()]
if pixel_norm:
transforms += [T.Normalize(mean=pixel_mean, std=pixel_std)]
preprocess = T.Compose(transforms)
to_pil = T.ToPILImage()
device = torch.device(device)
model.to(device)
# Class attributes
self.model = model
self.preprocess = preprocess
self.to_pil = to_pil
self.device = device
def main():
parser = ArgumentParser(formatter_class=ArgumentDefaultsHelpFormatter)
parser.add_argument('--config-file', '-c', type=str, required=True)
parser.add_argument('--root', '-r', type=str, required=True)
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('opts', default=None, nargs=REMAINDER)
args = parser.parse_args()
assert osp.exists(args.config_file)
assert osp.exists(args.root)
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available()
if args.config_file:
cfg.merge_from_file(args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
data_loader, num_pids = prepare_data(cfg, mode='gallery')
print('Building model: {}'.format(cfg.model.name))
model = torchreid.models.build_model(**model_kwargs(cfg, num_pids))
if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
load_pretrained_weights(model, cfg.model.load_weights)
if cfg.use_gpu:
model = model.cuda()
visualize_activation_map(model, data_loader, args.save_dir, cfg.data.width,
cfg.data.height, cfg.use_gpu)
def __init__(self,
config_path='',
model_path='',
device='cuda',
verbose=True):
# Build model
cfg = get_default_config()
merge_from_files_with_base(cfg, config_path)
cfg.use_gpu = device.startswith('cuda')
model = build_model(**model_kwargs(cfg, 1))
model.eval()
image_size = (cfg.data.height, cfg.data.width)
flops, num_params = get_model_complexity_info(
model, (3, image_size[0], image_size[1]),
as_strings=False,
verbose=False,
print_per_layer_stat=False)
if verbose:
print('Model: {}'.format(cfg.model.name))
print('- params: {:,}'.format(num_params))
print('- flops: {:,}'.format(flops))
if model_path and check_isfile(model_path):
load_pretrained_weights(model, model_path)
# Build transform functions
transforms = []
transforms += [T.Resize(image_size)]
transforms += [T.ToTensor()]
print(cfg.data.norm_mean, cfg.data.norm_std)
transforms += [
T.Normalize(mean=cfg.data.norm_mean, std=cfg.data.norm_std)
]
preprocess = T.Compose(transforms)
to_pil = T.ToPILImage()
device = torch.device(device)
model.to(device)
# Class attributes
self.model = model
self.preprocess = preprocess
self.to_pil = to_pil
self.device = device
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--root',
type=str,
default=
'/media/ddj2/ce611f70-968b-4316-9547-9bc9cf931d32/V20200108/zhejiang_train'
)
parser.add_argument('-d', '--dataset', type=str, default='rock_dataset')
parser.add_argument('-m', '--model', type=str, default='abd_resnet')
parser.add_argument('--weights', type=str)
parser.add_argument('--save-dir', type=str, default='log/resnet50_cam')
parser.add_argument('--height', type=int, default=672)
parser.add_argument('--width', type=int, default=672)
args = parser.parse_args()
os.environ["CUDA_VISIBLE_DEVICES"] = "3"
use_gpu = torch.cuda.is_available()
torchreid.data.register_image_dataset(
'rock_dataset', torchreid.data.datasets.image.rock_dataset.RockDataSet)
datamanager = torchreid.data.ImageDataManager(
root=args.root,
sources=args.dataset,
height=args.height,
width=args.width,
batch_size_train=4,
batch_size_test=4,
transforms=None,
train_sampler='SequentialSampler')
test_loader = datamanager.test_loader
model = torchreid.models.build_model(
name=args.model,
num_classes=datamanager.num_train_pids,
use_gpu=use_gpu)
if use_gpu:
model = model.cuda()
if args.weights and check_isfile(args.weights):
load_pretrained_weights(model, args.weights)
visactmap(model, test_loader, args.save_dir, args.width, args.height,
use_gpu)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--root', type=str, default='')
parser.add_argument('-m', '--model', type=str, default='resnet50')
parser.add_argument(
'--weights',
type=str,
default=
'/media/ddj2/ce611f70-968b-4316-9547-9bc9cf931d32/remote_data/PycharmProjects/ABD-Net-master/model_best.pth.tar'
)
parser.add_argument('--save-dir', type=str, default='logs/resnet50')
parser.add_argument('--height', type=int, default=672)
parser.add_argument('--width', type=int, default=672)
args = parser.parse_args()
use_gpu = torch.cuda.is_available()
test_dir = '/media/ddj2/ce611f70-968b-4316-9547-9bc9cf931d32/测试集/ceshi/crop/浙江省温州苍南县西古庵早白垩世小平田组PM201(挑选3张泛化测试用）20200114'
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
test_dataset = datasets.ImageFolder(
test_dir, transforms.Compose([
transforms.ToTensor(),
normalize,
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=8,
shuffle=False,
num_workers=8,
pin_memory=True)
model = torchreid.models.build_model(name=args.model,
num_classes=70,
use_gpu=use_gpu)
if use_gpu:
model = model.cuda()
if args.weights and check_isfile(args.weights):
load_pretrained_weights(model, args.weights)
visactmap(model, test_loader, args.save_dir, args.width, args.height,
use_gpu)
def main():
parser = argparse.ArgumentParser()
parser.add_argument('--root', type=str)
parser.add_argument('-d', '--dataset', type=str, default='market1501')
parser.add_argument('-m', '--model', type=str, default='osnet_x1_0')
parser.add_argument('--weights', type=str)
parser.add_argument('--save-dir', type=str, default='log')
parser.add_argument('--height', type=int, default=256)
parser.add_argument('--width', type=int, default=128)
args = parser.parse_args()
use_gpu = torch.cuda.is_available()
datamanager = torchreid.data.ImageDataManager(
root=args.root,
sources=args.dataset,
height=args.height,
width=args.width,
batch_size_train=100,
batch_size_test=100,
transforms=None,
train_sampler='SequentialSampler'
)
test_loader = datamanager.test_loader
model = torchreid.models.build_model(
name=args.model,
num_classes=datamanager.num_train_pids,
use_gpu=use_gpu
)
if use_gpu:
model = model.cuda()
if args.weights and check_isfile(args.weights):
load_pretrained_weights(model, args.weights)
visactmap(
model, test_loader, args.save_dir, args.width, args.height, use_gpu
)
def main():
parser = argparse.ArgumentParser()
parser.add_argument(
'--root',
type=str,
default=
'/media/ddj2/ce611f70-968b-4316-9547-9bc9cf931d32/remote_data/Market1501'
)
parser.add_argument('-d', '--dataset', type=str, default='market1501')
parser.add_argument('-m', '--model', type=str, default='abd_resnet50')
# /home/ddj2/PycharmProjects/deep-person-reid-master/saves/checkpoint_best.pth.tar
parser.add_argument(
'--weights',
type=str,
default='/media/ddj2/8b1bfd93-3f3f-4475-b279-6a9ae59c6639/'
'remote_dir/checkpoint/market_checkpoint_best.pth.tar')
parser.add_argument(
'--save-dir',
type=str,
default=
'/media/ddj2/ce611f70-968b-4316-9547-9bc9cf931d32/remote_data/Market1501/log/abd'
'-official-pretrained')
parser.add_argument('--height', type=int, default=384)
parser.add_argument('--width', type=int, default=128)
args = parser.parse_args()
new_args = {
'shallow_cam': True,
'compatibility': False,
'branches': ['global', 'abd'],
'abd_dim': 1024,
'global_dim': 1024,
'abd_np': 2,
'abd_dan': ['cam', 'pam'],
'abd_dan_no_head': False,
'dropout': 0.5,
'global_max_pooling': False,
'use_ow': True,
'margin': 1.2,
'label_smooth': True,
'flip_eval': True,
}
use_gpu = torch.cuda.is_available()
datamanager = torchreid.data.ImageDataManager(
root=args.root,
sources=args.dataset,
height=args.height,
width=args.width,
batch_size_train=100,
batch_size_test=100,
transforms=None,
train_sampler='SequentialSampler')
test_loader = datamanager.test_loader
# model = resnet_orig.resnet50(num_classes=1501)
model = torchreid.models.build_model(
name=args.model,
num_classes=datamanager.num_train_pids,
use_gpu=use_gpu,
args=new_args)
if use_gpu:
model = model.cuda()
if args.weights and check_isfile(args.weights):
load_pretrained_weights(model, args.weights)
visactmap(model, test_loader, args.save_dir, args.width, args.height,
use_gpu)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter
)
parser.add_argument(
'--config-file', type=str, default='', help='path to config file'
)
parser.add_argument(
'-s',
'--sources',
type=str,
nargs='+',
help='source datasets (delimited by space)'
)
parser.add_argument(
'-t',
'--targets',
type=str,
nargs='+',
help='target datasets (delimited by space)'
)
parser.add_argument(
'--transforms', type=str, nargs='+', help='data augmentation'
)
parser.add_argument(
'--root', type=str, default='', help='path to data root'
)
parser.add_argument(
'--gpu-devices',
type=str,
default='',
)
parser.add_argument(
'opts',
default=None,
nargs=argparse.REMAINDER,
help='Modify config options using the command-line'
)
args = parser.parse_args()
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available()
if args.config_file:
cfg.merge_from_file(args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
set_random_seed(cfg.train.seed)
if cfg.use_gpu and args.gpu_devices:
# if gpu_devices is not specified, all available gpus will be used
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu_devices
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
print('Building model: {}'.format(cfg.model.name))
model = osnet_models.build_model(
cfg.model.name, num_classes=datamanager.num_train_pids
)
num_params, flops = compute_model_complexity(
model, (1, 3, cfg.data.height, cfg.data.width)
)
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if cfg.use_gpu:
model = nn.DataParallel(model).cuda()
optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
scheduler = torchreid.optim.build_lr_scheduler(
optimizer, **lr_scheduler_kwargs(cfg)
)
if cfg.model.resume and check_isfile(cfg.model.resume):
cfg.train.start_epoch = resume_from_checkpoint(
cfg.model.resume, model, optimizer=optimizer
)
print('Building NAS engine')
engine = ImageSoftmaxNASEngine(
datamanager,
model,
optimizer,
scheduler=scheduler,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth,
mc_iter=cfg.nas.mc_iter,
init_lmda=cfg.nas.init_lmda,
min_lmda=cfg.nas.min_lmda,
lmda_decay_step=cfg.nas.lmda_decay_step,
lmda_decay_rate=cfg.nas.lmda_decay_rate,
fixed_lmda=cfg.nas.fixed_lmda
)
engine.run(**engine_run_kwargs(cfg))
print('*** Display the found architecture ***')
if cfg.use_gpu:
model.module.build_child_graph()
else:
model.build_child_graph()
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--model-name',
type=str,
default='',
help='Model name')
parser.add_argument('--weights', type=str, default='', help='Weights path')
parser.add_argument('--output',
type=str,
default='output',
help='Output path')
parser.add_argument('--resolution',
type=str,
default='128x256',
help='Resolution (WxH)')
args = parser.parse_args()
width, height = [int(i) for i in args.resolution.split('x')]
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
imagedata_kwargs = {
'root': 'reid-data',
'sources': ['market1501'],
'targets': ['market1501'],
'height': 256,
'width': 128,
'transforms': ['random_flip', 'color_jitter'],
'norm_mean': [0.485, 0.456, 0.406],
'norm_std': [0.229, 0.224, 0.225],
'use_gpu': False,
'split_id': 0,
'combineall': False,
'load_train_targets': False,
'batch_size_train': 64,
'batch_size_test': 300,
'workers': 4,
'num_instances': 4,
'train_sampler': 'RandomSampler',
'cuhk03_labeled': False,
'cuhk03_classic_split': False,
'market1501_500k': False
}
datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs)
print('Building model: {}'.format(args.model_name))
model = torchreid.models.build_model(
name=args.model_name,
num_classes=datamanager.num_train_pids,
loss='softmax',
pretrained=True,
use_gpu=False)
num_params, flops = compute_model_complexity(model, (1, 3, height, width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.weights and check_isfile(args.weights):
load_pretrained_weights(model, args.weights)
_input = torch.Tensor(1, 3, height, width)
inputs = (_input, )
print('Converting PyTorch model to ONNX...')
tmp = tempfile.mktemp(suffix='.onnx')
torch.onnx._export(model, inputs, tmp, export_params=True)
onnx_model = onnx.load(tmp)
export_path = args.output
onnx.checker.check_model(onnx_model)
print('Prepare TF model...')
tf_rep = prepare(onnx_model, strict=False)
if path.exists(export_path):
shutil.rmtree(export_path)
with tf.Session() as persisted_sess:
print("load graph")
persisted_sess.graph.as_default()
tf.import_graph_def(tf_rep.graph.as_graph_def(), name='')
i_tensors = []
o_tensors = []
inputs = {}
outputs = {}
for i in tf_rep.inputs:
t = persisted_sess.graph.get_tensor_by_name(
tf_rep.tensor_dict[i].name)
i_tensors.append(t)
tensor_info = tf.saved_model.utils.build_tensor_info(t)
inputs[t.name.split(':')[0].lower()] = tensor_info
print('input tensor [name=%s, type=%s, shape=%s]' %
(t.name, t.dtype.name, t.shape.as_list()))
print('')
for i in tf_rep.outputs:
t = persisted_sess.graph.get_tensor_by_name(
tf_rep.tensor_dict[i].name)
o_tensors.append(t)
tensor_info = tf.saved_model.utils.build_tensor_info(t)
outputs[t.name.split(':')[0]] = tensor_info
print('output tensor [name=%s, type=%s, shape=%s]' %
(t.name, t.dtype.name, t.shape.as_list()))
feed_dict = {}
for i in i_tensors:
feed_dict[i] = np.random.rand(*i.shape.as_list()).astype(
i.dtype.name)
print('test run:')
res = persisted_sess.run(o_tensors, feed_dict=feed_dict)
print(res)
# print('INPUTS')
# print(inputs)
# print('OUTPUTS')
# print(outputs)
prediction_signature = (
tf.saved_model.signature_def_utils.build_signature_def(
inputs=inputs,
outputs=outputs,
method_name=tf.saved_model.signature_constants.
PREDICT_METHOD_NAME))
builder = tf.saved_model.builder.SavedModelBuilder(export_path)
builder.add_meta_graph_and_variables(
persisted_sess, [tf.saved_model.tag_constants.SERVING],
signature_def_map={
tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY:
prediction_signature
})
builder.save()
print('Model saved to %s' % export_path)
def main():
parser = argparse.ArgumentParser(
formatter_class=argparse.ArgumentDefaultsHelpFormatter)
parser.add_argument('--config-file',
type=str,
default='',
help='path to config file')
parser.add_argument('-s',
'--sources',
type=str,
nargs='+',
help='source datasets (delimited by space)')
parser.add_argument('-t',
'--targets',
type=str,
nargs='+',
help='target datasets (delimited by space)')
parser.add_argument('--transforms',
type=str,
nargs='+',
help='data augmentation')
parser.add_argument('--root',
type=str,
default='',
help='path to data root')
parser.add_argument('opts',
default=None,
nargs=argparse.REMAINDER,
help='Modify config options using the command-line')
args = parser.parse_args()
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available()
if args.config_file:
cfg.merge_from_file(args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
set_random_seed(cfg.train.seed)
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
datamanager = torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
print('Building model-1: {}'.format(cfg.model.name))
model1 = torchreid.models.build_model(
name=cfg.model.name,
num_classes=datamanager.num_train_pids,
loss=cfg.loss.name,
pretrained=cfg.model.pretrained,
use_gpu=cfg.use_gpu)
num_params, flops = compute_model_complexity(
model1, (1, 3, cfg.data.height, cfg.data.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
print('Copying model-1 to model-2')
model2 = copy.deepcopy(model1)
if cfg.model.load_weights1 and check_isfile(cfg.model.load_weights1):
load_pretrained_weights(model1, cfg.model.load_weights1)
if cfg.model.load_weights2 and check_isfile(cfg.model.load_weights2):
load_pretrained_weights(model2, cfg.model.load_weights2)
if cfg.use_gpu:
model1 = nn.DataParallel(model1).cuda()
model2 = nn.DataParallel(model2).cuda()
optimizer1 = torchreid.optim.build_optimizer(model1,
**optimizer_kwargs(cfg))
scheduler1 = torchreid.optim.build_lr_scheduler(optimizer1,
**lr_scheduler_kwargs(cfg))
optimizer2 = torchreid.optim.build_optimizer(model2,
**optimizer_kwargs(cfg))
scheduler2 = torchreid.optim.build_lr_scheduler(optimizer2,
**lr_scheduler_kwargs(cfg))
if cfg.model.resume1 and check_isfile(cfg.model.resume1):
cfg.train.start_epoch = resume_from_checkpoint(cfg.model.resume1,
model1,
optimizer=optimizer1,
scheduler=scheduler1)
if cfg.model.resume2 and check_isfile(cfg.model.resume2):
resume_from_checkpoint(cfg.model.resume2,
model2,
optimizer=optimizer2,
scheduler=scheduler2)
print('Building DML-engine for image-reid')
engine = ImageDMLEngine(datamanager,
model1,
optimizer1,
scheduler1,
model2,
optimizer2,
scheduler2,
margin=cfg.loss.triplet.margin,
weight_t=cfg.loss.triplet.weight_t,
weight_x=cfg.loss.triplet.weight_x,
weight_ml=cfg.loss.dml.weight_ml,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth,
deploy=cfg.model.deploy)
engine.run(**engine_run_kwargs(cfg))
def main():
parser = build_base_argparser()
parser.add_argument('-e',
'--auxiliary-models-cfg',
type=str,
nargs='*',
default='',
help='path to extra config files')
parser.add_argument('--split-models',
action='store_true',
help='whether to split models on own gpu')
parser.add_argument('--enable_quantization',
action='store_true',
help='Enable NNCF quantization algorithm')
parser.add_argument('--enable_pruning',
action='store_true',
help='Enable NNCF pruning algorithm')
parser.add_argument(
'--aux-config-opts',
nargs='+',
default=None,
help='Modify aux config options using the command-line')
args = parser.parse_args()
cfg = get_default_config()
cfg.use_gpu = torch.cuda.is_available() and args.gpu_num > 0
if args.config_file:
merge_from_files_with_base(cfg, args.config_file)
reset_config(cfg, args)
cfg.merge_from_list(args.opts)
is_nncf_used = args.enable_quantization or args.enable_pruning
if is_nncf_used:
print(f'Using NNCF -- making NNCF changes in config')
cfg = make_nncf_changes_in_config(cfg, args.enable_quantization,
args.enable_pruning, args.opts)
set_random_seed(cfg.train.seed, cfg.train.deterministic)
log_name = 'test.log' if cfg.test.evaluate else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(cfg.data.save_dir, log_name))
print('Show configuration\n{}\n'.format(cfg))
if cfg.use_gpu:
torch.backends.cudnn.benchmark = True
num_aux_models = len(cfg.mutual_learning.aux_configs)
datamanager = build_datamanager(cfg, args.classes)
num_train_classes = datamanager.num_train_pids
print('Building main model: {}'.format(cfg.model.name))
model = torchreid.models.build_model(
**model_kwargs(cfg, num_train_classes))
macs, num_params = get_model_complexity_info(
model, (3, cfg.data.height, cfg.data.width),
as_strings=False,
verbose=False,
print_per_layer_stat=False)
print('Main model complexity: params={:,} flops={:,}'.format(
num_params, macs * 2))
aux_lr = cfg.train.lr # placeholder, needed for aux models, may be filled by nncf part below
if is_nncf_used:
print('Begin making NNCF changes in model')
if cfg.use_gpu:
model.cuda()
compression_ctrl, model, cfg, aux_lr, nncf_metainfo = \
make_nncf_changes_in_training(model, cfg,
args.classes,
args.opts)
should_freeze_aux_models = True
print(f'should_freeze_aux_models = {should_freeze_aux_models}')
print('End making NNCF changes in model')
else:
compression_ctrl = None
should_freeze_aux_models = False
nncf_metainfo = None
# creating optimizer and scheduler -- it should be done after NNCF part, since
# NNCF could change some parameters
optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
if cfg.lr_finder.enable and not cfg.model.resume:
scheduler = None
else:
scheduler = torchreid.optim.build_lr_scheduler(
optimizer=optimizer,
num_iter=datamanager.num_iter,
**lr_scheduler_kwargs(cfg))
# Loading model (and optimizer and scheduler in case of resuming training).
# Note that if NNCF is used, loading is done inside NNCF part, so loading here is not required.
if cfg.model.resume and check_isfile(
cfg.model.resume) and not is_nncf_used:
device_ = 'cuda' if cfg.use_gpu else 'cpu'
cfg.train.start_epoch = resume_from_checkpoint(cfg.model.resume,
model,
optimizer=optimizer,
scheduler=scheduler,
device=device_)
elif cfg.model.load_weights and not is_nncf_used:
load_pretrained_weights(model, cfg.model.load_weights)
if cfg.model.type == 'classification':
check_classification_classes(model,
datamanager,
args.classes,
test_only=cfg.test.evaluate)
model, extra_device_ids = put_main_model_on_the_device(
model, cfg.use_gpu, args.gpu_num, num_aux_models, args.split_models)
if cfg.lr_finder.enable and not cfg.test.evaluate and not cfg.model.resume:
aux_lr, model, optimizer, scheduler = run_lr_finder(
cfg,
datamanager,
model,
optimizer,
scheduler,
args.classes,
rebuild_model=True,
gpu_num=args.gpu_num,
split_models=args.split_models)
log_dir = cfg.data.tb_log_dir if cfg.data.tb_log_dir else cfg.data.save_dir
run_training(cfg,
datamanager,
model,
optimizer,
scheduler,
extra_device_ids,
aux_lr,
tb_writer=SummaryWriter(log_dir=log_dir),
should_freeze_aux_models=should_freeze_aux_models,
nncf_metainfo=nncf_metainfo,
compression_ctrl=compression_ctrl)
def main():
global args
set_random_seed(args.seed)
use_gpu = torch.cuda.is_available() and not args.use_cpu
log_name = 'test.log' if args.evaluate else 'train.log'
sys.stdout = Logger(osp.join(args.save_dir, log_name))
print('** Arguments **')
arg_keys = list(args.__dict__.keys())
arg_keys.sort()
for key in arg_keys:
print('{}: {}'.format(key, args.__dict__[key]))
print('\n')
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if use_gpu:
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
dataset_vars = init_dataset(use_gpu)
trainloader, valloader, testloader, num_attrs, attr_dict = dataset_vars
if args.weighted_bce:
print('Use weighted binary cross entropy')
print('Computing the weights ...')
bce_weights = torch.zeros(num_attrs, dtype=torch.float)
for _, attrs, _ in trainloader:
bce_weights += attrs.sum(0) # sum along the batch dim
bce_weights /= len(trainloader) * args.batch_size
print('Sample ratio for each attribute: {}'.format(bce_weights))
bce_weights = torch.exp(-1 * bce_weights)
print('BCE weights: {}'.format(bce_weights))
bce_weights = bce_weights.expand(args.batch_size, num_attrs)
criterion = nn.BCEWithLogitsLoss(weight=bce_weights)
else:
print('Use plain binary cross entropy')
criterion = nn.BCEWithLogitsLoss()
print('Building model: {}'.format(args.arch))
model = models.build_model(args.arch,
num_attrs,
pretrained=not args.no_pretrained,
use_gpu=use_gpu)
num_params, flops = compute_model_complexity(
model, (1, 3, args.height, args.width))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
if args.load_weights and check_isfile(args.load_weights):
load_pretrained_weights(model, args.load_weights)
if use_gpu:
model = nn.DataParallel(model).cuda()
criterion = criterion.cuda()
if args.evaluate:
test(model, testloader, attr_dict, use_gpu)
return
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(args))
scheduler = torchreid.optim.build_lr_scheduler(optimizer,
**lr_scheduler_kwargs(args))
start_epoch = args.start_epoch
best_result = -np.inf
if args.resume and check_isfile(args.resume):
checkpoint = torch.load(args.resume)
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
start_epoch = checkpoint['epoch']
best_result = checkpoint['label_mA']
print('Loaded checkpoint from "{}"'.format(args.resume))
print('- start epoch: {}'.format(start_epoch))
print('- label_mA: {}'.format(best_result))
time_start = time.time()
for epoch in range(start_epoch, args.max_epoch):
train(epoch, model, criterion, optimizer, scheduler, trainloader,
use_gpu)
test_outputs = test(model, testloader, attr_dict, use_gpu)
label_mA = test_outputs[0]
is_best = label_mA > best_result
if is_best:
best_result = label_mA
save_checkpoint(
{
'state_dict': model.state_dict(),
'epoch': epoch + 1,
'label_mA': label_mA,
'optimizer': optimizer.state_dict(),
},
args.save_dir,
is_best=is_best)
elapsed = round(time.time() - time_start)
elapsed = str(datetime.timedelta(seconds=elapsed))
print('Elapsed {}'.format(elapsed))
def objective(cfg, args, trial):
# Generate the trials.
# g_ = trial.suggest_int("g_", 1, 7)
# asl_pm = trial.suggest_float("asl_pm", 0, 0.5)
# m = trial.suggest_float("m", 0.01, 0.7)
# s = trial.suggest_int("s", 5, 60)
lr = trial.suggest_float("lr", 0.001, 0.5)
# t = trial.suggest_int("t", 1, 7)
# cfg.loss.softmax.m = m
# cfg.loss.softmax.s = s
# cfg.loss.asl.p_m = asl_pm
# cfg.loss.am_binary.amb_t = t
cfg.train.lr = lr
# geterate damanager
num_aux_models = len(cfg.mutual_learning.aux_configs)
datamanager = build_datamanager(cfg, args.classes)
# build the model.
num_train_classes = datamanager.num_train_pids
print('Building main model: {}'.format(cfg.model.name))
model = torchreid.models.build_model(
**model_kwargs(cfg, num_train_classes))
aux_lr = cfg.train.lr # placeholder, needed for aux models, may be filled by nncf part below
compression_ctrl = None
should_freeze_aux_models = False
nncf_metainfo = None
optimizer = torchreid.optim.build_optimizer(model, **optimizer_kwargs(cfg))
scheduler = torchreid.optim.build_lr_scheduler(
optimizer=optimizer,
num_iter=datamanager.num_iter,
**lr_scheduler_kwargs(cfg))
# Loading model (and optimizer and scheduler in case of resuming training).
if cfg.model.load_weights and check_isfile(cfg.model.load_weights):
load_pretrained_weights(model, cfg.model.load_weights)
if cfg.model.type == 'classification':
check_classification_classes(model,
datamanager,
args.classes,
test_only=cfg.test.evaluate)
model, extra_device_ids = put_main_model_on_the_device(
model, cfg.use_gpu, args.gpu_num, num_aux_models, args.split_models)
num_aux_models = len(cfg.mutual_learning.aux_configs)
num_train_classes = datamanager.num_train_pids
if num_aux_models > 0:
print(
f'Enabled mutual learning between {len(cfg.mutual_learning.aux_configs) + 1} models.'
)
models, optimizers, schedulers = [model], [optimizer], [scheduler]
for config_file, device_ids in zip(cfg.mutual_learning.aux_configs,
extra_device_ids):
aux_model, aux_optimizer, aux_scheduler = build_auxiliary_model(
config_file,
num_train_classes,
cfg.use_gpu,
device_ids,
num_iter=datamanager.num_iter,
lr=aux_lr,
aux_config_opts=args.aux_config_opts)
models.append(aux_model)
optimizers.append(aux_optimizer)
schedulers.append(aux_scheduler)
else:
models, optimizers, schedulers = model, optimizer, scheduler
print(f'Building {cfg.loss.name}-engine')
engine = build_engine(cfg,
datamanager,
models,
optimizers,
schedulers,
should_freeze_aux_models=should_freeze_aux_models,
nncf_metainfo=nncf_metainfo,
compression_ctrl=compression_ctrl,
initial_lr=aux_lr)
test_acc = AverageMeter()
obj = 0
engine.start_epoch = 0
engine.max_epoch = args.epochs
print(f"\nnext trial with [lr: {lr}]")
for engine.epoch in range(args.epochs):
np.random.seed(cfg.train.seed + engine.epoch)
avg_loss = engine.train(print_freq=20000,
fixbase_epoch=0,
open_layers=None,
lr_finder=False,
perf_monitor=None,
stop_callback=None)
top1, _ = engine.test(
engine.epoch,
lr_finder=False,
)
test_acc.update(top1)
smooth_top1 = test_acc.avg
target_metric = smooth_top1 if engine.target_metric == 'test_acc' else avg_loss
obj = top1
if not engine.per_batch_annealing:
engine.update_lr(output_avg_metric=target_metric)
trial.report(obj, engine.epoch)
# Handle pruning based on the intermediate value.
if trial.should_prune():
raise optuna.exceptions.TrialPruned()
should_exit, _ = engine.exit_on_plateau_and_choose_best(
top1, smooth_top1)
should_exit = engine.early_stoping and should_exit
if should_exit:
break
return obj
def main():
# Load model configuration
parser = argparse.ArgumentParser()
parser.add_argument('-c',
'--config',
required=True,
help='path to configuration file')
args = parser.parse_args()
with open(args.config, "r") as ymlfile:
config = yaml.load(ymlfile, Loader=yaml.FullLoader)
# Automatically add sub-folder name to config["save_dir"], with the same name
# as the config file. For example, config["save_dir"] is typically "logs",
# so this would change config["save_dir"] to "logs/exp01", for example, so that
# we don't need to change the save_dir in every single config file (it instead
# automatically generates it from the name of the config file).
experiment_number = pathlib.Path(args.config).stem
config["save_dir"] = os.path.join(config["save_dir"], experiment_number)
# Set random seeds
set_random_seed(config["seed"])
# Set up GPU
if not config["use_avai_gpus"]:
os.environ['CUDA_VISIBLE_DEVICES'] = config["gpu_devices"]
use_gpu = torch.cuda.is_available() and not config["use_cpu"]
# Set up log files
log_name = 'test.log' if config["evaluate"] else 'train.log'
log_name += time.strftime('-%Y-%m-%d-%H-%M-%S')
sys.stdout = Logger(osp.join(config["save_dir"], log_name))
# Prepare for training
print('==========\nArgs:{}\n=========='.format(config))
print('Collecting env info ...')
print('** System info **\n{}\n'.format(collect_env_info()))
if use_gpu:
torch.backends.cudnn.benchmark = True
else:
warnings.warn(
'Currently using CPU, however, GPU is highly recommended')
# Build datamanager and model
datamanager = build_datamanager(config)
print('Building model: {}'.format(config["arch"]))
model = torchreid.models.build_model(
name=config["arch"],
num_classes=datamanager.num_train_pids,
loss=config["loss"].lower(),
pretrained=(not config["no_pretrained"]),
use_gpu=use_gpu)
# Compute model complexity
num_params, flops = compute_model_complexity(
model, (1, 3, config["height"], config["width"]))
print('Model complexity: params={:,} flops={:,}'.format(num_params, flops))
# Load pretrained weights if necessary
if config["load_weights"] and check_isfile(config["load_weights"]):
load_pretrained_weights(model, config["load_weights"])
# Set up multi-gpu
if use_gpu:
model = nn.DataParallel(model).cuda()
# Model settings
optimizer = torchreid.optim.build_optimizer(model,
**optimizer_kwargs(config))
scheduler = torchreid.optim.build_lr_scheduler(
optimizer, **lr_scheduler_kwargs(config))
if config["resume"] and check_isfile(config["resume"]):
config["start_epoch"] = resume_from_checkpoint(config["resume"],
model,
optimizer=optimizer)
print('Building {}-engine for {}-reid'.format(config["loss"],
config["app"]))
engine = build_engine(config, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(config))
