def test_custom_classes_override_default(dataset, classes):
dataset_class = DATASETS.get(dataset)
original_classes = dataset_class.CLASSES
# Test setting classes as a tuple
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=classes,
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == classes
# Test setting classes as a list
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=list(classes),
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == list(classes)
# Test overriding not a subset
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=[classes[0]],
test_mode=True)
assert custom_dataset.CLASSES != original_classes
assert custom_dataset.CLASSES == [classes[0]]
# Test default behavior
if dataset_class is CustomDataset:
with pytest.raises(AssertionError):
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
test_mode=True)
else:
custom_dataset = dataset_class(
pipeline=[],
img_dir=MagicMock(),
split=MagicMock(),
classes=None,
test_mode=True)
assert custom_dataset.CLASSES == original_classes
def onnx2tensorrt(onnx_file: str,
trt_file: str,
config: dict,
input_config: dict,
fp16: bool = False,
verify: bool = False,
show: bool = False,
dataset: str = 'CityscapesDataset',
workspace_size: int = 1,
verbose: bool = False):
import tensorrt as trt
min_shape = input_config['min_shape']
max_shape = input_config['max_shape']
# create trt engine and wrapper
opt_shape_dict = {'input': [min_shape, min_shape, max_shape]}
max_workspace_size = get_GiB(workspace_size)
trt_engine = onnx2trt(
onnx_file,
opt_shape_dict,
log_level=trt.Logger.VERBOSE if verbose else trt.Logger.ERROR,
fp16_mode=fp16,
max_workspace_size=max_workspace_size)
save_dir, _ = osp.split(trt_file)
if save_dir:
os.makedirs(save_dir, exist_ok=True)
save_trt_engine(trt_engine, trt_file)
print(f'Successfully created TensorRT engine: {trt_file}')
if verify:
inputs = _prepare_input_img(input_config['input_path'],
config.data.test.pipeline,
shape=min_shape[2:])
imgs = inputs['imgs']
img_metas = inputs['img_metas']
img_list = [img[None, :] for img in imgs]
img_meta_list = [[img_meta] for img_meta in img_metas]
# update img_meta
img_list, img_meta_list = _update_input_img(img_list, img_meta_list)
if max_shape[0] > 1:
# concate flip image for batch test
flip_img_list = [_.flip(-1) for _ in img_list]
img_list = [
torch.cat((ori_img, flip_img), 0)
for ori_img, flip_img in zip(img_list, flip_img_list)
]
# Get results from ONNXRuntime
ort_custom_op_path = get_onnxruntime_op_path()
session_options = ort.SessionOptions()
if osp.exists(ort_custom_op_path):
session_options.register_custom_ops_library(ort_custom_op_path)
sess = ort.InferenceSession(onnx_file, session_options)
sess.set_providers(['CPUExecutionProvider'], [{}]) # use cpu mode
onnx_output = sess.run(['output'],
{'input': img_list[0].detach().numpy()})[0][0]
# Get results from TensorRT
trt_model = TRTWraper(trt_file, ['input'], ['output'])
with torch.no_grad():
trt_outputs = trt_model({'input': img_list[0].contiguous().cuda()})
trt_output = trt_outputs['output'][0].cpu().detach().numpy()
if show:
dataset = DATASETS.get(dataset)
assert dataset is not None
palette = dataset.PALETTE
show_result_pyplot(input_config['input_path'],
(onnx_output[0].astype(np.uint8), ),
palette=palette,
title='ONNXRuntime',
block=False)
show_result_pyplot(input_config['input_path'],
(trt_output[0].astype(np.uint8), ),
palette=palette,
title='TensorRT')
np.testing.assert_allclose(onnx_output,
trt_output,
rtol=1e-03,
atol=1e-05)
print('TensorRT and ONNXRuntime output all close.')
if __name__ == '__main__':
assert is_tensorrt_plugin_loaded(), 'TensorRT plugin should be compiled.'
args = parse_args()
if not args.input_img:
args.input_img = osp.join(osp.dirname(__file__), '../demo/demo.png')
# check arguments
assert osp.exists(args.config), 'Config {} not found.'.format(args.config)
assert osp.exists(args.model), \
'ONNX model {} not found.'.format(args.model)
assert args.workspace_size >= 0, 'Workspace size less than 0.'
assert DATASETS.get(args.dataset) is not None, \
'Dataset {} does not found.'.format(args.dataset)
for max_value, min_value in zip(args.max_shape, args.min_shape):
assert max_value >= min_value, \
'max_shape should be larger than min shape'
input_config = {
'min_shape': args.min_shape,
'max_shape': args.max_shape,
'input_path': args.input_img
}
cfg = mmcv.Config.fromfile(args.config)
onnx2tensorrt(args.model,
args.trt_file,
cfg,
