def main():
args = parse_args()
cfg = Config.fromfile(args.config)
inference_cfg = cfg['inference']
common_cfg = cfg.get('common')
runner = InferenceRunner(inference_cfg, common_cfg)
assert runner.use_gpu, 'Please use valid gpu to export model.'
runner.load_checkpoint(args.checkpoint)
model = runner.model
shape = map(int, args.dummy_input_shape.split(','))
dummy_input = torch.randn(1, *shape)
if args.dynamic_shape:
print(f'Convert to Onnx with dynamic input shape and '
f'opset version {args.opset_version}')
else:
print(f'Convert to Onnx with constant input shape '
f'{args.dummy_input_shape} and '
f'opset version {args.opset_version}')
torch2onnx(model,
dummy_input,
args.out,
dynamic_shape=args.dynamic_shape,
opset_version=args.opset_version,
do_constant_folding=args.do_constant_folding,
verbose=args.verbose)
print(
f'Convert successfully, saved onnx file: {os.path.abspath(args.out)}')
def main():
args = parse_args()
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
multi_label = cfg.get('multi_label', False)
inference_cfg = cfg['inference']
common_cfg = cfg.get('common')
runner = InferenceRunner(inference_cfg, common_cfg)
runner.load_checkpoint(args.checkpoint)
image = cv2.imread(args.image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, c = image.shape
dummy_mask = np.zeros((h, w))
output = runner(image, [dummy_mask])
if args.need_resize:
output = inverse_resize(output, image.shape)
output = inverse_pad(output, image.shape)
result(args.image,
output,
multi_label=multi_label,
classes=CLASSES,
palette=PALETTE,
show=args.show,
out=args.out)
def main():
args = parse_args()
out_name = args.out
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
inference_cfg = cfg['inference']
common_cfg = cfg.get('common')
runner = InferenceRunner(inference_cfg, common_cfg)
assert runner.use_gpu, 'Please use valid gpu to export model.'
runner.load_checkpoint(args.checkpoint)
image = cv2.imread(args.image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, c = image.shape
dummy_mask = np.zeros((h, w))
image = runner.transform(image=image, masks=[dummy_mask])['image']
dummy_input = image.unsqueeze(0).cuda()
model = runner.model.cuda().eval()
if args.onnx:
runner.logger.info('Convert to onnx model')
torch2onnx(model, dummy_input, out_name)
else:
max_batch_size = args.max_batch_size
max_workspace_size = args.max_workspace_size
fp16_mode = args.fp16
int8_mode = args.int8
int8_calibrator = None
if int8_mode:
runner.logger.info('Convert to trt engine with int8')
if args.calibration_images:
runner.logger.info(
'Use calibration with mode {} and data {}'.format(
args.calibration_mode, args.calibration_images))
dataset = CalibDataset(args.calibration_images,
runner.transform)
int8_calibrator = CALIBRATORS[args.calibration_mode](
dataset=dataset)
else:
runner.logger.info('Use default calibration mode and data')
elif fp16_mode:
runner.logger.info('Convert to trt engine with fp16')
else:
runner.logger.info('Convert to trt engine with fp32')
trt_model = torch2trt(model,
dummy_input,
max_batch_size=max_batch_size,
max_workspace_size=max_workspace_size,
fp16_mode=fp16_mode,
int8_mode=int8_mode,
int8_calibrator=int8_calibrator)
save(trt_model, out_name)
runner.logger.info(
'Convert successfully, save model to {}'.format(out_name))
def main():
args = parse_args()
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
test_cfg = cfg['test']
inference_cfg = cfg['inference']
base_cfg = cfg['common']
runner = TestRunner(test_cfg, inference_cfg, base_cfg)
assert runner.use_gpu, 'Please use gpu for benchmark.'
runner.load_checkpoint(args.checkpoint)
image = cv2.imread(args.image)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
h, w, c = image.shape
dummy_mask = np.zeros((h, w))
image = runner.transform(image=image, masks=[dummy_mask])['image']
dummy_input = image.unsqueeze(0).cuda()
model = runner.model
shape = tuple(dummy_input.shape)
dtypes = args.dtypes
iters = args.iters
int8_calibrator = None
if args.calibration_images:
calib_dataset = CalibDataset(args.calibration_images, runner.transform)
int8_calibrator = [
CALIBRATORS[mode](dataset=calib_dataset)
for mode in args.calibration_modes
]
dataset = runner.test_dataloader.dataset
metric = Metric(runner.metric, runner.compute)
benchmark(model,
shape,
dtypes=dtypes,
iters=iters,
int8_calibrator=int8_calibrator,
dataset=dataset,
metric=metric)
def main():
args = parse_args()
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
_, fullname = os.path.split(cfg_path)
fname, ext = os.path.splitext(fullname)
root_workdir = cfg.pop('root_workdir')
workdir = os.path.join(root_workdir, fname)
os.makedirs(workdir, exist_ok=True)
train_cfg = cfg['train']
inference_cfg = cfg['inference']
common_cfg = cfg['common']
common_cfg['workdir'] = workdir
runner = TrainRunner(train_cfg, inference_cfg, common_cfg)
runner()
def main():
args = parse_args()
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
_, fullname = os.path.split(cfg_path)
fname, ext = os.path.splitext(fullname)
root_workdir = cfg.pop('root_workdir')
workdir = os.path.join(root_workdir, fname)
os.makedirs(workdir, exist_ok=True)
test_cfg = cfg['test']
inference_cfg = cfg['inference']
common_cfg = cfg['common']
common_cfg['workdir'] = workdir
common_cfg['distribute'] = args.distribute
runner = TestRunner(test_cfg, inference_cfg, common_cfg)
runner.load_checkpoint(args.checkpoint)
runner()