Python mmcv.mkdir_or_exist() Examples
The following are 30
code examples of mmcv.mkdir_or_exist().
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Example #1
| Source File: cityscapes.py From mmdetection with Apache License 2.0 | 6 votes |
|
def main():
args = parse_args()
cityscapes_path = args.cityscapes_path
out_dir = args.out_dir if args.out_dir else cityscapes_path
mmcv.mkdir_or_exist(out_dir)
img_dir = osp.join(cityscapes_path, args.img_dir)
gt_dir = osp.join(cityscapes_path, args.gt_dir)
set_name = dict(
train='instancesonly_filtered_gtFine_train.json',
val='instancesonly_filtered_gtFine_val.json',
test='instancesonly_filtered_gtFine_test.json')
for split, json_name in set_name.items():
print(f'Converting {split} into {json_name}')
with mmcv.Timer(
print_tmpl='It tooks {}s to convert Cityscapes annotation'):
files = collect_files(
osp.join(img_dir, split), osp.join(gt_dir, split))
image_infos = collect_annotations(files, nproc=args.nproc)
cvt_annotations(image_infos, osp.join(out_dir, json_name))
Example #2
| Source File: test_ensemble.py From kaggle-imaterialist with MIT License | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(bytearray(tmpdir.encode()),
dtype=torch.uint8,
device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #3
| Source File: test.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #4
| Source File: pascal_voc.py From Libra_R-CNN with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #5
| Source File: test_robustness.py From FoveaBox with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #6
| Source File: test.py From FoveaBox with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #7
| Source File: pascal_voc.py From FoveaBox with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #8
| Source File: utils.py From FNA with Apache License 2.0 | 5 votes |
|
def create_work_dir(work_dir):
if mmcv.is_str(work_dir):
work_dir = osp.abspath(work_dir)
mmcv.mkdir_or_exist(work_dir)
elif work_dir is None:
work_dir = None
else:
raise TypeError('"work_dir" must be a str or None')
Example #9
| Source File: pascal_voc.py From FNA with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #10
| Source File: test_robustness.py From Cascade-RPN with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #11
| Source File: test.py From Cascade-RPN with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #12
| Source File: pascal_voc.py From Cascade-RPN with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #13
| Source File: test_robustness.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #14
| Source File: test.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #15
| Source File: pascal_voc.py From Feature-Selective-Anchor-Free-Module-for-Single-Shot-Object-Detection with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #16
| Source File: test.py From kaggle-imaterialist with MIT License | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(bytearray(tmpdir.encode()),
dtype=torch.uint8,
device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #17
| Source File: pascal_voc.py From kaggle-imaterialist with MIT License | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #18
| Source File: pascal_voc.py From Reasoning-RCNN with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #19
| Source File: test_recognizer_heavy.py From mmaction with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
tmpdir = osp.join(tmpdir, args.out.split('.')[0])
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
print('rank {} begin dump'.format(rank), flush=True)
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
print('rank {} finished dump'.format(rank), flush=True)
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
return ordered_results
Example #20
| Source File: test_recognizer.py From mmaction with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
global args
rank, world_size = get_dist_info()
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
tmpdir = osp.join(tmpdir, args.out.split('.')[0])
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
print('rank {} begin dump'.format(rank), flush=True)
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
print('rank {} finished dump'.format(rank), flush=True)
dist.barrier()
if rank != 0:
return None
else:
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
ordered_results = ordered_results[:size]
return ordered_results
Example #21
| Source File: pascal_voc.py From hrnet with MIT License | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #22
| Source File: voc_test.py From CenterNet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #23
| Source File: visdrone_test.py From CenterNet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #24
| Source File: visdrone_test_1.py From CenterNet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #25
| Source File: test.py From CenterNet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #26
| Source File: pascal_voc.py From CenterNet with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #27
| Source File: test_robustness.py From ttfnet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #28
| Source File: test.py From ttfnet with Apache License 2.0 | 5 votes |
|
def collect_results(result_part, size, tmpdir=None):
rank, world_size = get_dist_info()
# create a tmp dir if it is not specified
if tmpdir is None:
MAX_LEN = 512
# 32 is whitespace
dir_tensor = torch.full((MAX_LEN, ),
32,
dtype=torch.uint8,
device='cuda')
if rank == 0:
tmpdir = tempfile.mkdtemp()
tmpdir = torch.tensor(
bytearray(tmpdir.encode()), dtype=torch.uint8, device='cuda')
dir_tensor[:len(tmpdir)] = tmpdir
dist.broadcast(dir_tensor, 0)
tmpdir = dir_tensor.cpu().numpy().tobytes().decode().rstrip()
else:
mmcv.mkdir_or_exist(tmpdir)
# dump the part result to the dir
mmcv.dump(result_part, osp.join(tmpdir, 'part_{}.pkl'.format(rank)))
dist.barrier()
# collect all parts
if rank != 0:
return None
else:
# load results of all parts from tmp dir
part_list = []
for i in range(world_size):
part_file = osp.join(tmpdir, 'part_{}.pkl'.format(i))
part_list.append(mmcv.load(part_file))
# sort the results
ordered_results = []
for res in zip(*part_list):
ordered_results.extend(list(res))
# the dataloader may pad some samples
ordered_results = ordered_results[:size]
# remove tmp dir
shutil.rmtree(tmpdir)
return ordered_results
Example #29
| Source File: pascal_voc.py From ttfnet with Apache License 2.0 | 5 votes |
|
def main():
args = parse_args()
devkit_path = args.devkit_path
out_dir = args.out_dir if args.out_dir else devkit_path
mmcv.mkdir_or_exist(out_dir)
years = []
if osp.isdir(osp.join(devkit_path, 'VOC2007')):
years.append('2007')
if osp.isdir(osp.join(devkit_path, 'VOC2012')):
years.append('2012')
if '2007' in years and '2012' in years:
years.append(['2007', '2012'])
if not years:
raise IOError('The devkit path {} contains neither "VOC2007" nor '
'"VOC2012" subfolder'.format(devkit_path))
for year in years:
if year == '2007':
prefix = 'voc07'
elif year == '2012':
prefix = 'voc12'
elif year == ['2007', '2012']:
prefix = 'voc0712'
for split in ['train', 'val', 'trainval']:
dataset_name = prefix + '_' + split
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, split,
osp.join(out_dir, dataset_name + '.pkl'))
if not isinstance(year, list):
dataset_name = prefix + '_test'
print('processing {} ...'.format(dataset_name))
cvt_annotations(devkit_path, year, 'test',
osp.join(out_dir, dataset_name + '.pkl'))
print('Done!')
Example #30
| Source File: eval_hooks.py From AugFPN with Apache License 2.0 | 5 votes |
|
def before_run(self, runner):
self.lock_dir = osp.join(runner.work_dir, '.lock_map_hook')
if runner.rank == 0:
if osp.exists(self.lock_dir):
shutil.rmtree(self.lock_dir)
mmcv.mkdir_or_exist(self.lock_dir)
