def __init__(self, trt_file: str, device_id: int):
super().__init__()
from mmcv.tensorrt import TRTWraper, load_tensorrt_plugin
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with TensorRT from source.')
model = TRTWraper(
trt_file, input_names=['input'], output_names=['output'])
self.device_id = device_id
self.model = model
def __init__(self, engine_file, class_names, device_id, output_names=None):
super(TensorRTDetector, self).__init__(class_names, device_id)
warnings.warn('`output_names` is deprecated and will be removed in '
'future releases.')
from mmcv.tensorrt import TRTWraper, load_tensorrt_plugin
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with TensorRT from source.')
output_names = ['dets', 'labels']
model = TRTWraper(engine_file, ['input'], output_names)
with_masks = False
# if TensorRT has totally 4 inputs/outputs, then
# the detector should have `mask` output.
if len(model.engine) == 4:
model.output_names = output_names + ['masks']
with_masks = True
self.model = model
self.with_masks = with_masks
def __init__(self,
trt_file: str,
cfg: Any,
device_id: int,
show_score: bool = False):
if 'type' in cfg.model:
cfg.model.pop('type')
EncodeDecodeRecognizer.__init__(self, **(cfg.model))
from mmcv.tensorrt import TRTWrapper, load_tensorrt_plugin
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with TensorRT from source.')
model = TRTWrapper(trt_file,
input_names=['input'],
output_names=['output'])
self.model = model
self.device_id = device_id
self.cfg = cfg
def __init__(self,
trt_file: str,
cfg: Any,
device_id: int,
show_score: bool = False):
EncodeDecodeRecognizer.__init__(self, cfg.model.preprocessor,
cfg.model.backbone, cfg.model.encoder,
cfg.model.decoder, cfg.model.loss,
cfg.model.label_convertor,
cfg.train_cfg, cfg.test_cfg, 40,
cfg.model.pretrained)
from mmcv.tensorrt import TRTWrapper, load_tensorrt_plugin
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with TensorRT from source.')
model = TRTWrapper(trt_file,
input_names=['input'],
output_names=['output'])
self.model = model
self.device_id = device_id
self.cfg = cfg
def __init__(self,
trt_file: str,
cfg: Any,
device_id: int,
show_score: bool = False):
SingleStageTextDetector.__init__(self, cfg.model.backbone,
cfg.model.neck, cfg.model.bbox_head,
cfg.model.train_cfg,
cfg.model.test_cfg,
cfg.model.pretrained)
TextDetectorMixin.__init__(self, show_score)
from mmcv.tensorrt import TRTWrapper, load_tensorrt_plugin
try:
load_tensorrt_plugin()
except (ImportError, ModuleNotFoundError):
warnings.warn('If input model has custom op from mmcv, \
you may have to build mmcv with TensorRT from source.')
model = TRTWrapper(trt_file,
input_names=['input'],
output_names=['output'])
self.model = model
self.device_id = device_id
self.cfg = cfg
