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Running
on
Zero
File size: 5,871 Bytes
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import sys
import time
import os.path as osp
import argparse
import torch
import torch.nn as nn
import torchreid
from torchreid.utils import (
Logger, check_isfile, set_random_seed, collect_env_info,
resume_from_checkpoint, load_pretrained_weights, compute_model_complexity
)
from default_config import (
imagedata_kwargs, optimizer_kwargs, videodata_kwargs, engine_run_kwargs,
get_default_config, lr_scheduler_kwargs
)
def build_datamanager(cfg):
if cfg.data.type == 'image':
return torchreid.data.ImageDataManager(**imagedata_kwargs(cfg))
else:
return torchreid.data.VideoDataManager(**videodata_kwargs(cfg))
def build_engine(cfg, datamanager, model, optimizer, scheduler):
if cfg.data.type == 'image':
if cfg.loss.name == 'softmax':
engine = torchreid.engine.ImageSoftmaxEngine(
datamanager,
model,
optimizer=optimizer,
scheduler=scheduler,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth
)
else:
engine = torchreid.engine.ImageTripletEngine(
datamanager,
model,
optimizer=optimizer,
margin=cfg.loss.triplet.margin,
weight_t=cfg.loss.triplet.weight_t,
weight_x=cfg.loss.triplet.weight_x,
scheduler=scheduler,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth
)
else:
if cfg.loss.name == 'softmax':
engine = torchreid.engine.VideoSoftmaxEngine(
datamanager,
model,
optimizer=optimizer,
scheduler=scheduler,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth,
pooling_method=cfg.video.pooling_method
)
else:
engine = torchreid.engine.VideoTripletEngine(
datamanager,
model,
optimizer=optimizer,
margin=cfg.loss.triplet.margin,
weight_t=cfg.loss.triplet.weight_t,
weight_x=cfg.loss.triplet.weight_x,
scheduler=scheduler,
use_gpu=cfg.use_gpu,
label_smooth=cfg.loss.softmax.label_smooth
)
return engine
def reset_config(cfg, args):
if args.root:
cfg.data.root = args.root
if args.sources:
cfg.data.sources = args.sources
if args.targets:
cfg.data.targets = args.targets
if args.transforms:
cfg.data.transforms = args.transforms
def check_cfg(cfg):
if cfg.loss.name == 'triplet' and cfg.loss.triplet.weight_x == 0:
assert cfg.train.fixbase_epoch == 0, \
'The output of classifier is not included in the computational graph'
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)
check_cfg(cfg)
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(
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)
)
engine = build_engine(cfg, datamanager, model, optimizer, scheduler)
engine.run(**engine_run_kwargs(cfg))
if __name__ == '__main__':
main()
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