diff --git a/mmdeploy/apis/calibration.py b/mmdeploy/apis/calibration.py index 5623dd5ef..efaf70410 100644 --- a/mmdeploy/apis/calibration.py +++ b/mmdeploy/apis/calibration.py @@ -57,7 +57,7 @@ def create_calib_input_data(calib_file: str, apply_marks = cfg_apply_marks(deploy_cfg) - model = task_processor.init_pytorch_model(model_checkpoint) + model = task_processor.build_pytorch_model(model_checkpoint) dataset = task_processor.build_dataset(dataset_cfg, dataset_type) # patch model diff --git a/mmdeploy/apis/inference.py b/mmdeploy/apis/inference.py index b57f4ae83..c21333464 100644 --- a/mmdeploy/apis/inference.py +++ b/mmdeploy/apis/inference.py @@ -42,7 +42,7 @@ def inference_model(model_cfg: Union[str, mmcv.Config], from mmdeploy.apis.utils import build_task_processor task_processor = build_task_processor(model_cfg, deploy_cfg, device) - model = task_processor.init_backend_model(backend_files) + model = task_processor.build_backend_model(backend_files) input_shape = get_input_shape(deploy_cfg) model_inputs, _ = task_processor.create_input(img, input_shape) diff --git a/mmdeploy/apis/pytorch2onnx.py b/mmdeploy/apis/pytorch2onnx.py index bbf056119..034f5c1d8 100644 --- a/mmdeploy/apis/pytorch2onnx.py +++ b/mmdeploy/apis/pytorch2onnx.py @@ -59,7 +59,7 @@ def torch2onnx(img: Any, from mmdeploy.apis import build_task_processor task_processor = build_task_processor(model_cfg, deploy_cfg, device) - torch_model = task_processor.init_pytorch_model(model_checkpoint) + torch_model = task_processor.build_pytorch_model(model_checkpoint) data, model_inputs = task_processor.create_input( img, input_shape, diff --git a/mmdeploy/apis/pytorch2torchscript.py b/mmdeploy/apis/pytorch2torchscript.py index 451084931..02a2c021d 100644 --- a/mmdeploy/apis/pytorch2torchscript.py +++ b/mmdeploy/apis/pytorch2torchscript.py @@ -41,7 +41,7 @@ def torch2torchscript(img: Any, from mmdeploy.apis import build_task_processor task_processor = build_task_processor(model_cfg, deploy_cfg, device) - torch_model = task_processor.init_pytorch_model(model_checkpoint) + torch_model = task_processor.build_pytorch_model(model_checkpoint) _, model_inputs = task_processor.create_input(img, input_shape) if not isinstance(model_inputs, torch.Tensor): model_inputs = model_inputs[0] diff --git a/mmdeploy/apis/visualize.py b/mmdeploy/apis/visualize.py index 20512e0f7..2bce15952 100644 --- a/mmdeploy/apis/visualize.py +++ b/mmdeploy/apis/visualize.py @@ -62,9 +62,9 @@ def visualize_model(model_cfg: Union[str, mmcv.Config], list should be str' if backend == Backend.PYTORCH: - model = task_processor.init_pytorch_model(model[0]) + model = task_processor.build_pytorch_model(model[0]) else: - model = task_processor.init_backend_model(model) + model = task_processor.build_backend_model(model) model_inputs, _ = task_processor.create_input(img, input_shape) with torch.no_grad(): diff --git a/mmdeploy/codebase/base/task.py b/mmdeploy/codebase/base/task.py index fdef8d191..e756aeba4 100644 --- a/mmdeploy/codebase/base/task.py +++ b/mmdeploy/codebase/base/task.py @@ -54,9 +54,9 @@ class BaseTask(metaclass=ABCMeta): self.visualizer = self.model_cfg.visualizer @abstractmethod - def init_backend_model(self, - model_files: Sequence[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Sequence[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -67,10 +67,10 @@ class BaseTask(metaclass=ABCMeta): """ pass - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmcls/deploy/classification.py b/mmdeploy/codebase/mmcls/deploy/classification.py index fbd67b098..248da7ddd 100644 --- a/mmdeploy/codebase/mmcls/deploy/classification.py +++ b/mmdeploy/codebase/mmcls/deploy/classification.py @@ -105,9 +105,9 @@ class Classification(BaseTask): super(Classification, self).__init__(model_cfg, deploy_cfg, device, experiment_name) - def init_backend_model(self, - model_files: Sequence[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Sequence[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: diff --git a/mmdeploy/codebase/mmdet/deploy/object_detection.py b/mmdeploy/codebase/mmdet/deploy/object_detection.py index baf388a75..9d6bc49c1 100644 --- a/mmdeploy/codebase/mmdet/deploy/object_detection.py +++ b/mmdeploy/codebase/mmdet/deploy/object_detection.py @@ -58,9 +58,9 @@ class ObjectDetection(BaseTask): device: str) -> None: super().__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -74,10 +74,10 @@ class ObjectDetection(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmdet3d/deploy/voxel_detection.py b/mmdeploy/codebase/mmdet3d/deploy/voxel_detection.py index 63eb87b7a..903b6c539 100644 --- a/mmdeploy/codebase/mmdet3d/deploy/voxel_detection.py +++ b/mmdeploy/codebase/mmdet3d/deploy/voxel_detection.py @@ -23,9 +23,9 @@ class VoxelDetection(BaseTask): device: str): super().__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Sequence[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Sequence[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -39,10 +39,10 @@ class VoxelDetection(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmedit/deploy/super_resolution.py b/mmdeploy/codebase/mmedit/deploy/super_resolution.py index 8d9140683..e7938c280 100644 --- a/mmdeploy/codebase/mmedit/deploy/super_resolution.py +++ b/mmdeploy/codebase/mmedit/deploy/super_resolution.py @@ -76,9 +76,9 @@ class SuperResolution(BaseTask): device: str): super().__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Sequence[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Sequence[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -92,9 +92,9 @@ class SuperResolution(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmocr/deploy/text_detection.py b/mmdeploy/codebase/mmocr/deploy/text_detection.py index 051d4679b..b858694c8 100644 --- a/mmdeploy/codebase/mmocr/deploy/text_detection.py +++ b/mmdeploy/codebase/mmocr/deploy/text_detection.py @@ -63,9 +63,9 @@ class TextDetection(BaseTask): device: str): super(TextDetection, self).__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -79,10 +79,10 @@ class TextDetection(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmocr/deploy/text_recognition.py b/mmdeploy/codebase/mmocr/deploy/text_recognition.py index 6e7eeb099..ce22842e8 100644 --- a/mmdeploy/codebase/mmocr/deploy/text_recognition.py +++ b/mmdeploy/codebase/mmocr/deploy/text_recognition.py @@ -75,9 +75,9 @@ class TextRecognition(BaseTask): device: str): super(TextRecognition, self).__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -91,10 +91,10 @@ class TextRecognition(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmpose/deploy/pose_detection.py b/mmdeploy/codebase/mmpose/deploy/pose_detection.py index c6d8f0d64..60d5e4770 100644 --- a/mmdeploy/codebase/mmpose/deploy/pose_detection.py +++ b/mmdeploy/codebase/mmpose/deploy/pose_detection.py @@ -86,9 +86,9 @@ class PoseDetection(BaseTask): device: str): super().__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Sequence[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Sequence[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -102,9 +102,9 @@ class PoseDetection(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmrotate/deploy/rotated_detection.py b/mmdeploy/codebase/mmrotate/deploy/rotated_detection.py index c71f50a3a..f4ef08fe7 100644 --- a/mmdeploy/codebase/mmrotate/deploy/rotated_detection.py +++ b/mmdeploy/codebase/mmrotate/deploy/rotated_detection.py @@ -85,9 +85,9 @@ class RotatedDetection(BaseTask): device: str): super(RotatedDetection, self).__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -101,10 +101,10 @@ class RotatedDetection(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/mmdeploy/codebase/mmseg/deploy/segmentation.py b/mmdeploy/codebase/mmseg/deploy/segmentation.py index e23918a9d..4c672afaa 100644 --- a/mmdeploy/codebase/mmseg/deploy/segmentation.py +++ b/mmdeploy/codebase/mmseg/deploy/segmentation.py @@ -57,9 +57,9 @@ class Segmentation(BaseTask): device: str): super(Segmentation, self).__init__(model_cfg, deploy_cfg, device) - def init_backend_model(self, - model_files: Optional[str] = None, - **kwargs) -> torch.nn.Module: + def build_backend_model(self, + model_files: Optional[str] = None, + **kwargs) -> torch.nn.Module: """Initialize backend model. Args: @@ -73,10 +73,10 @@ class Segmentation(BaseTask): model_files, self.model_cfg, self.deploy_cfg, device=self.device) return model.eval() - def init_pytorch_model(self, - model_checkpoint: Optional[str] = None, - cfg_options: Optional[Dict] = None, - **kwargs) -> torch.nn.Module: + def build_pytorch_model(self, + model_checkpoint: Optional[str] = None, + cfg_options: Optional[Dict] = None, + **kwargs) -> torch.nn.Module: """Initialize torch model. Args: diff --git a/tests/test_codebase/test_mmcls/test_classification.py b/tests/test_codebase/test_mmcls/test_classification.py index a2f8a30e2..2b3291aa4 100644 --- a/tests/test_codebase/test_mmcls/test_classification.py +++ b/tests/test_codebase/test_mmcls/test_classification.py @@ -40,7 +40,7 @@ img = np.random.rand(*img_shape, 3) @pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0]) -def test_init_pytorch_model(from_mmrazor: Any): +def test_build_pytorch_model(from_mmrazor: Any): from mmcls.models.classifiers.base import BaseClassifier if from_mmrazor is False: _task_processor = task_processor @@ -66,7 +66,7 @@ def test_init_pytorch_model(from_mmrazor: Any): assert from_mmrazor == _task_processor.from_mmrazor if from_mmrazor: pytest.importorskip('mmrazor', reason='mmrazor is not installed.') - model = _task_processor.init_pytorch_model(None) + model = _task_processor.build_pytorch_model(None) assert isinstance(model, BaseClassifier) @@ -79,12 +79,12 @@ def backend_model(): 'output': torch.rand(1, num_classes), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert isinstance(backend_model, torch.nn.Module) diff --git a/tests/test_codebase/test_mmdet/test_object_detection.py b/tests/test_codebase/test_mmdet/test_object_detection.py index 4fd5c2e17..165538f6d 100644 --- a/tests/test_codebase/test_mmdet/test_object_detection.py +++ b/tests/test_codebase/test_mmdet/test_object_detection.py @@ -51,7 +51,7 @@ img = np.random.rand(*img_shape, 3) @pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0]) -def test_init_pytorch_model(from_mmrazor: Any): +def test_build_pytorch_model(from_mmrazor: Any): from mmdet.models import BaseDetector if from_mmrazor is False: _task_processor = task_processor @@ -77,7 +77,7 @@ def test_init_pytorch_model(from_mmrazor: Any): assert from_mmrazor == _task_processor.from_mmrazor if from_mmrazor: pytest.importorskip('mmrazor', reason='mmrazor is not installed.') - model = _task_processor.init_pytorch_model(None) + model = _task_processor.build_pytorch_model(None) assert isinstance(model, BaseDetector) @@ -91,12 +91,12 @@ def backend_model(): 'labels': torch.rand(1, 10) }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): from mmdeploy.codebase.mmdet.deploy.object_detection_model import \ End2EndModel assert isinstance(backend_model, End2EndModel) @@ -131,7 +131,7 @@ def test_create_input(device): def test_run_inference(backend_model): - torch_model = task_processor.init_pytorch_model(None) + torch_model = task_processor.build_pytorch_model(None) input_dict, _ = task_processor.create_input(img, input_shape=img_shape) torch_results = task_processor.run_inference(torch_model, input_dict) backend_results = task_processor.run_inference(backend_model, input_dict) diff --git a/tests/test_codebase/test_mmdet3d/test_voxel_detection.py b/tests/test_codebase/test_mmdet3d/test_voxel_detection.py index aec5c5901..40c457212 100644 --- a/tests/test_codebase/test_mmdet3d/test_voxel_detection.py +++ b/tests/test_codebase/test_mmdet3d/test_voxel_detection.py @@ -39,9 +39,9 @@ onnx_file = NamedTemporaryFile(suffix='.onnx').name task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu') -def test_init_pytorch_model(): +def test_build_pytorch_model(): from mmdet3d.models import Base3DDetector - model = task_processor.init_pytorch_model(None) + model = task_processor.build_pytorch_model(None) assert isinstance(model, Base3DDetector) @@ -57,12 +57,12 @@ def backend_model(): 'dir_scores': torch.rand(1, 12, 32, 32) }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): from mmdeploy.codebase.mmdet3d.deploy.voxel_detection_model import \ VoxelDetectionModel assert isinstance(backend_model, VoxelDetectionModel) @@ -83,7 +83,7 @@ def test_create_input(device): reason='Only support GPU test', condition=not torch.cuda.is_available()) def test_run_inference(backend_model): task_processor.device = 'cuda:0' - torch_model = task_processor.init_pytorch_model(None) + torch_model = task_processor.build_pytorch_model(None) input_dict, _ = task_processor.create_input(pcd_path) torch_results = task_processor.run_inference(torch_model, input_dict) backend_results = task_processor.run_inference(backend_model, input_dict) @@ -98,7 +98,7 @@ def test_run_inference(backend_model): def test_visualize(): task_processor.device = 'cuda:0' input_dict, _ = task_processor.create_input(pcd_path) - torch_model = task_processor.init_pytorch_model(None) + torch_model = task_processor.build_pytorch_model(None) results = task_processor.run_inference(torch_model, input_dict) with TemporaryDirectory() as dir: filename = dir + 'tmp.bin' diff --git a/tests/test_codebase/test_mmedit/test_super_resolution.py b/tests/test_codebase/test_mmedit/test_super_resolution.py index c9ed7b86d..d776d7c26 100644 --- a/tests/test_codebase/test_mmedit/test_super_resolution.py +++ b/tests/test_codebase/test_mmedit/test_super_resolution.py @@ -37,8 +37,8 @@ onnx_file = NamedTemporaryFile(suffix='.onnx').name task_processor = build_task_processor(model_cfg, deploy_cfg, 'cpu') -def test_init_pytorch_model(): - torch_model = task_processor.init_pytorch_model(None) +def test_build_pytorch_model(): + torch_model = task_processor.build_pytorch_model(None) assert torch_model is not None @@ -51,12 +51,12 @@ def backend_model(): 'output': torch.rand(3, 50, 50), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert backend_model is not None diff --git a/tests/test_codebase/test_mmocr/test_text_detection.py b/tests/test_codebase/test_mmocr/test_text_detection.py index 48377e249..80ab4468b 100644 --- a/tests/test_codebase/test_mmocr/test_text_detection.py +++ b/tests/test_codebase/test_mmocr/test_text_detection.py @@ -37,10 +37,10 @@ img_shape = (32, 32) img = np.random.rand(*img_shape, 3).astype(np.uint8) -def test_init_pytorch_model(): +def test_build_pytorch_model(): from mmocr.models.textdet.detectors.single_stage_text_detector import \ SingleStageDetector - model = task_processor.init_pytorch_model(None) + model = task_processor.build_pytorch_model(None) assert isinstance(model, SingleStageDetector) @@ -53,12 +53,12 @@ def backend_model(): 'output': torch.rand(1, 3, *img_shape), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert isinstance(backend_model, torch.nn.Module) diff --git a/tests/test_codebase/test_mmocr/test_text_recognition.py b/tests/test_codebase/test_mmocr/test_text_recognition.py index abff31781..0ff67844f 100644 --- a/tests/test_codebase/test_mmocr/test_text_recognition.py +++ b/tests/test_codebase/test_mmocr/test_text_recognition.py @@ -37,9 +37,9 @@ img_shape = (32, 32) img = np.random.rand(*img_shape, 3).astype(np.uint8) -def test_init_pytorch_model(): +def test_build_pytorch_model(): from mmocr.models.textrecog.recognizer import BaseRecognizer - model = task_processor.init_pytorch_model(None) + model = task_processor.build_pytorch_model(None) assert isinstance(model, BaseRecognizer) @@ -52,12 +52,12 @@ def backend_model(): 'output': torch.rand(1, 9, 37), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert isinstance(backend_model, torch.nn.Module) diff --git a/tests/test_codebase/test_mmpose/test_pose_detection.py b/tests/test_codebase/test_mmpose/test_pose_detection.py index 4a8085a63..c653d962f 100644 --- a/tests/test_codebase/test_mmpose/test_pose_detection.py +++ b/tests/test_codebase/test_mmpose/test_pose_detection.py @@ -65,9 +65,9 @@ def test_create_input(): assert isinstance(inputs, tuple) and len(inputs) == 2 -def test_init_pytorch_model(): +def test_build_pytorch_model(): from mmpose.models.detectors.base import BasePose - model = task_processor.init_pytorch_model(None) + model = task_processor.build_pytorch_model(None) assert isinstance(model, BasePose) @@ -80,12 +80,12 @@ def backend_model(): 'output': torch.rand(1, num_output_channels, *heatmap_shape), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert isinstance(backend_model, torch.nn.Module) diff --git a/tests/test_codebase/test_mmrotate/test_rotated_detection.py b/tests/test_codebase/test_mmrotate/test_rotated_detection.py index 34b921a73..38889fbd9 100644 --- a/tests/test_codebase/test_mmrotate/test_rotated_detection.py +++ b/tests/test_codebase/test_mmrotate/test_rotated_detection.py @@ -48,9 +48,9 @@ img_shape = (32, 32) img = np.random.rand(*img_shape, 3) -def test_init_pytorch_model(): +def test_build_pytorch_model(): from mmrotate.models import RotatedBaseDetector - model = task_processor.init_pytorch_model(None) + model = task_processor.build_pytorch_model(None) assert isinstance(model, RotatedBaseDetector) @@ -64,12 +64,12 @@ def backend_model(): 'labels': torch.rand(1, 10) }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): from mmdeploy.codebase.mmrotate.deploy.rotated_detection_model import \ End2EndModel assert isinstance(backend_model, End2EndModel) @@ -85,7 +85,7 @@ def test_create_input(device): def test_run_inference(backend_model): - torch_model = task_processor.init_pytorch_model(None) + torch_model = task_processor.build_pytorch_model(None) input_dict, _ = task_processor.create_input(img, input_shape=img_shape) torch_results = task_processor.run_inference(torch_model, input_dict) backend_results = task_processor.run_inference(backend_model, input_dict) diff --git a/tests/test_codebase/test_mmseg/test_segmentation.py b/tests/test_codebase/test_mmseg/test_segmentation.py index c1b6769bb..a9930b5d6 100644 --- a/tests/test_codebase/test_mmseg/test_segmentation.py +++ b/tests/test_codebase/test_mmseg/test_segmentation.py @@ -40,7 +40,7 @@ img = np.random.rand(*img_shape, 3) @pytest.mark.parametrize('from_mmrazor', [True, False, '123', 0]) -def test_init_pytorch_model(from_mmrazor: Any): +def test_build_pytorch_model(from_mmrazor: Any): from mmseg.models.segmentors.base import BaseSegmentor if from_mmrazor is False: _task_processor = task_processor @@ -65,7 +65,7 @@ def test_init_pytorch_model(from_mmrazor: Any): assert from_mmrazor == _task_processor.from_mmrazor if from_mmrazor: pytest.importorskip('mmrazor', reason='mmrazor is not installed.') - model = _task_processor.init_pytorch_model(None) + model = _task_processor.build_pytorch_model(None) assert isinstance(model, BaseSegmentor) @@ -78,12 +78,12 @@ def backend_model(): 'output': torch.rand(1, 1, *img_shape), }) - yield task_processor.init_backend_model(['']) + yield task_processor.build_backend_model(['']) wrapper.recover() -def test_init_backend_model(backend_model): +def test_build_backend_model(backend_model): assert isinstance(backend_model, torch.nn.Module) diff --git a/tools/test.py b/tools/test.py index e1afc2b44..6e276aa67 100644 --- a/tools/test.py +++ b/tools/test.py @@ -100,7 +100,7 @@ def main(): dataloader = task_processor.build_dataloader(test_dataloader) # load the model of the backend - model = task_processor.init_backend_model(args.model) + model = task_processor.build_backend_model(args.model) is_device_cpu = (args.device == 'cpu')