def main():
parser = argparse.ArgumentParser(description='Benchmark dataloading')
parser.add_argument('config', help='train config file path')
args = parser.parse_args()
cfg = Config.fromfile(args.config)
# init logger before other steps
logger = get_root_logger()
logger.info(f'Config: {cfg.text}')
dataset = build_dataset(cfg.data.train)
data_loaders = [
build_dataloader(ds,
cfg.data.samples_per_gpu,
cfg.data.workers_per_gpu,
dist=False,
drop_last=cfg.data.get('drop_last', False),
seed=0) for ds in dataset
]
# Start progress bar after first 5 batches
prog_bar = mmcv.ProgressBar(len(dataset) - 5 * cfg.data.samples_per_gpu,
start=False)
for data_loader in data_loaders:
for i, data in enumerate(data_loader):
if i == 5:
prog_bar.start()
for _ in data['imgs']:
if i < 5:
continue
prog_bar.update()
def main():
args = parse_args()
cfg = Config.fromfile(args.config)
if args.cfg_options is not None:
cfg.merge_from_dict(args.cfg_options)
# init distributed env first, since logger depends on the dist info.
distributed = False
# build the dataloader
dataset = build_dataset(cfg.data.test)
loader_cfg = {
**dict((k, cfg.data[k]) for k in ['workers_per_gpu'] if k in cfg.data),
**dict(
samples_per_gpu=1,
drop_last=False,
shuffle=False,
dist=distributed),
**cfg.data.get('test_dataloader', {})
}
data_loader = build_dataloader(dataset, **loader_cfg)
# build the model
if args.backend == 'onnxruntime':
model = ONNXRuntimeEditing(args.model, cfg=cfg, device_id=0)
elif args.backend == 'tensorrt':
model = TensorRTEditing(args.model, cfg=cfg, device_id=0)
args.save_image = args.save_path is not None
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(
model,
data_loader,
save_path=args.save_path,
save_image=args.save_image)
print()
# print metrics
stats = dataset.evaluate(outputs)
for stat in stats:
print('Eval-{}: {}'.format(stat, stats[stat]))
# save result pickle
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
def test_build_dataloader():
dataset = ToyDataset()
samples_per_gpu = 3
# dist=True, shuffle=True, 1GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=2)
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, DistributedSampler)
assert dataloader.sampler.shuffle
# dist=True, shuffle=False, 1GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=2,
shuffle=False)
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, DistributedSampler)
assert not dataloader.sampler.shuffle
# dist=True, shuffle=True, 8GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=2,
num_gpus=8)
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert dataloader.num_workers == 2
# dist=False, shuffle=True, 1GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=samples_per_gpu,
workers_per_gpu=2,
dist=False)
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, RandomSampler)
assert dataloader.num_workers == 2
# dist=False, shuffle=False, 1GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=3,
workers_per_gpu=2,
shuffle=False,
dist=False)
assert dataloader.batch_size == samples_per_gpu
assert len(dataloader) == int(math.ceil(len(dataset) / samples_per_gpu))
assert isinstance(dataloader.sampler, SequentialSampler)
assert dataloader.num_workers == 2
# dist=False, shuffle=True, 8GPU
dataloader = build_dataloader(dataset,
samples_per_gpu=3,
workers_per_gpu=2,
num_gpus=8,
dist=False)
assert dataloader.batch_size == samples_per_gpu * 8
assert len(dataloader) == int(math.ceil(
len(dataset) / samples_per_gpu / 8))
assert isinstance(dataloader.sampler, RandomSampler)
assert dataloader.num_workers == 16
def main():
args = parse_args()
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
rank, _ = get_dist_info()
# set random seeds
if args.seed is not None:
if rank == 0:
print('set random seed to', args.seed)
set_random_seed(args.seed, deterministic=args.deterministic)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
loader_cfg = {
**dict((k, cfg.data[k]) for k in ['workers_per_gpu'] if k in cfg.data),
**dict(samples_per_gpu=1,
drop_last=False,
shuffle=False,
dist=distributed),
**cfg.data.get('test_dataloader', {})
}
data_loader = build_dataloader(dataset, **loader_cfg)
# build the model and load checkpoint
model = build_model(cfg.model, train_cfg=None, test_cfg=cfg.test_cfg)
args.save_image = args.save_path is not None
empty_cache = cfg.get('empty_cache', False)
if not distributed:
_ = load_checkpoint(model, args.checkpoint, map_location='cpu')
model = MMDataParallel(model, device_ids=[0])
outputs = single_gpu_test(model,
data_loader,
save_path=args.save_path,
save_image=args.save_image)
else:
find_unused_parameters = cfg.get('find_unused_parameters', False)
model = DistributedDataParallelWrapper(
model,
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
device_id = torch.cuda.current_device()
_ = load_checkpoint(
model,
args.checkpoint,
map_location=lambda storage, loc: storage.cuda(device_id))
outputs = multi_gpu_test(model,
data_loader,
args.tmpdir,
args.gpu_collect,
save_path=args.save_path,
save_image=args.save_image,
empty_cache=empty_cache)
if rank == 0:
print('')
# print metrics
stats = dataset.evaluate(outputs)
for stat in stats:
print('Eval-{}: {}'.format(stat, stats[stat]))
# save result pickle
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)
def main():
args = parse_args()
checkpoint_list = os.listdir(args.checkpoint_dir)
print(checkpoint_list)
for checkpoint in checkpoint_list:
if '.pth' in checkpoint:
cfg = mmcv.Config.fromfile(args.config)
# set cudnn_benchmark
if cfg.get('cudnn_benchmark', False):
torch.backends.cudnn.benchmark = True
cfg.model.pretrained = None
# init distributed env first, since logger depends on the dist info.
if args.launcher == 'none':
distributed = False
else:
distributed = True
init_dist(args.launcher, **cfg.dist_params)
rank, _ = get_dist_info()
# set random seeds
if args.seed is not None:
if rank == 0:
print('set random seed to', args.seed)
set_random_seed(args.seed, deterministic=args.deterministic)
# build the dataloader
# TODO: support multiple images per gpu (only minor changes are needed)
dataset = build_dataset(cfg.data.test)
data_loader = build_dataloader(dataset,
samples_per_gpu=1,
workers_per_gpu=cfg.data.get(
'val_workers_per_gpu',
cfg.data.workers_per_gpu),
dist=distributed,
shuffle=False)
# build the model and load checkpoint
model = build_model(cfg.model,
train_cfg=None,
test_cfg=cfg.test_cfg)
args.save_image = args.save_path is not None
# distributed test
find_unused_parameters = cfg.get('find_unused_parameters', False)
model = DistributedDataParallelWrapper(
model,
device_ids=[torch.cuda.current_device()],
broadcast_buffers=False,
find_unused_parameters=find_unused_parameters)
device_id = torch.cuda.current_device()
_ = load_checkpoint(
model,
os.path.join(args.checkpoint_dir, checkpoint),
map_location=lambda storage, loc: storage.cuda(device_id))
outputs = multi_gpu_test(model,
data_loader,
args.tmpdir,
args.gpu_collect,
save_path=args.save_path,
save_image=args.save_image)
if rank == 0:
# print metrics
stats = dataset.evaluate(outputs)
write_file = open(
os.path.join(args.checkpoint_dir, 'eval_result_new.txt'),
'a')
for stat in stats:
print('{}: Eval-{}: {}'.format(checkpoint, stat,
stats[stat]))
write_file.write('{}: Eval-{}: {} '.format(
checkpoint, stat, stats[stat]))
write_file.write('\n')
write_file.close()
# save result pickle
if args.out:
print('writing results to {}'.format(args.out))
mmcv.dump(outputs, args.out)