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 = Config.fromfile(args.config)
if torch.cuda.is_available():
device = torch.cuda.current_device()
else:
device = 'cpu'
model = build_detector(cfg.model)
load_weights(model, args.checkpoint)
model.to(device)
model.forward = model.forward_impl
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()
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()
out_name = args.out
cfg_path = args.config
cfg = Config.fromfile(cfg_path)
deploy_cfg = cfg['deploy']
common_cfg = cfg.get('common')
runner = InferenceRunner(deploy_cfg, common_cfg)
assert runner.use_gpu, 'Please use valid gpu to export model.'
runner.load_checkpoint(args.checkpoint)
image = cv2.imread(args.image)
aug = runner.transform(image=image, label='')
image, label = aug['image'], aug['label']
image = image.unsqueeze(0).cuda()
dummy_input = (image, runner.converter.test_encode(['']))
model = runner.model.cuda().eval()
need_text = runner.need_text
if not need_text:
dummy_input = dummy_input[0]
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.converter,
runner.transform, need_text)
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)
deploy_cfg = cfg['deploy']
common_cfg = cfg.get('common')
if torch.cuda.is_available():
device = torch.cuda.current_device()
deploy_cfg['gpu_id'] = str(device)
else:
raise AssertionError('Please use gpu for benchmark.')
runner = InferenceRunner(deploy_cfg, common_cfg)
runner.load_checkpoint(args.checkpoint)
C, H, W = [int(_.strip()) for _ in args.dummy_input_shape.split(',')]
dummy_image = np.random.random_integers(0, 255, (H, W, C)).astype(np.uint8)
aug = runner.transform(image=dummy_image, label='')
image, label = aug['image'], aug['label']
image = image.unsqueeze(0).cuda()
dummy_input = (image, runner.converter.test_encode(['']))
model = runner.model.cuda().eval()
need_text = runner.need_text
if not need_text:
dummy_input = dummy_input[0]
if args.dynamic_shape:
print(f'Convert to Onnx with dynamic input shape and opset version'
f'{args.opset_version}')
else:
print(f'Convert to Onnx with constant input shape'
f' {args.dummy_input_shape} and opset version '
f'{args.opset_version}')
torch2onnx(
model,
dummy_input,
args.out,
verbose=args.verbose,
dynamic_shape=args.dynamic_shape,
opset_version=args.opset_version,
do_constant_folding=args.do_constant_folding,
)
runner.logger.info(
f'Convert successfully, saved onnx file: {os.path.abspath(args.out)}')