def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
split_line, input_shape, flops, params))
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print(f'{split_line}\nInput shape: {input_shape}\n'
f'Flops: {flops}\nParams: {params}\n{split_line}')
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify that the '
'flops computation is correct.')
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
if args.update_config is not None:
cfg.merge_from_dict(args.update_config)
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg)
model.eval()
if torch.cuda.is_available():
model.cuda()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print(f'{split_line}\nInput shape: {input_shape}\n'
f'Flops: {flops}\nParams: {params}\n{split_line}')
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify that the '
'flops computation is correct.')
if args.out:
out = list()
out.append({
'key': 'size',
'display_name': 'Size',
'value': float(params.split(' ')[0]),
'unit': 'Mp'
})
out.append({
'key': 'complexity',
'display_name': 'Complexity',
'value': 2 * float(flops.split(' ')[0]),
'unit': 'GFLOPs'
})
with open(args.out, 'w') as write_file:
json.dump(out, write_file, indent=4)
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3, ) + tuple(args.shape)
else:
raise ValueError('invalid input shape')
cfg = Config.fromfile(args.config)
# load anchors
if isinstance(cfg.model, dict) and cfg.model.get(
'type', 'FasterRCNN') == 'MyFasterRCNN':
anchors = dict()
with open(os.path.join(cfg.work_dir, 'anchors.json'), 'r') as f:
anchors = json.load(f)
# logger.info('loaded anchors: {}\n'.format(anchors))
cfg.model['anchors'] = anchors
model = build_detector(cfg.model,
train_cfg=cfg.train_cfg,
test_cfg=cfg.test_cfg).cuda()
model.eval()
if hasattr(model, 'forward_dummy'):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
'FLOPs counter is currently not currently supported with {}'.
format(model.__class__.__name__))
flops, params = get_model_complexity_info(model, input_shape)
split_line = '=' * 30
print('{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}'.format(
split_line, input_shape, flops, params))
print('!!!Please be cautious if you use the results in papers. '
'You may need to check if all ops are supported and verify that the '
'flops computation is correct.')
def main():
args = parse_args()
if len(args.shape) == 1:
input_shape = (3, args.shape[0], args.shape[0])
elif len(args.shape) == 2:
input_shape = (3,) + tuple(args.shape)
else:
raise ValueError("invalid input shape")
cfg = Config.fromfile(args.config)
model = build_detector(
cfg.model, train_cfg=cfg.train_cfg, test_cfg=cfg.test_cfg
).cuda()
model.eval()
if hasattr(model, "forward_dummy"):
model.forward = model.forward_dummy
else:
raise NotImplementedError(
"FLOPs counter is currently not currently supported with {}".format(
model.__class__.__name__
)
)
flops, params = get_model_complexity_info(model, input_shape)
split_line = "=" * 30
print(
"{0}\nInput shape: {1}\nFlops: {2}\nParams: {3}\n{0}".format(
split_line, input_shape, flops, params
)
)
print(
"!!!Please be cautious if you use the results in papers. "
"You may need to check if all ops are supported and verify that the "
"flops computation is correct."
)