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celinelee
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python_fn_calls

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folder_name.split(sep='.')
join(path.rsplit(folder_name, 1)
os.makedirs(new_path, exist_ok=True)
self.model.state_dict()
os.path.join(path_no_folder_name, folder_name_no_ext, model_metadata["model_paths"][0])
print("Saved Pytorch model.")
os.path.join(self.temp_path, "onnx_model_temp.onnx")
os.path.join(path_no_folder_name, folder_name_no_ext, model_metadata["model_paths"][0])
print("Saved ONNX model.")
open(os.path.join(new_path, folder_name_no_ext + ".json")
json.dump(model_metadata, outfile)
self.__extract_trailing(path)
open(os.path.join(path, model_name + ".json")
json.load(metadata_file)
self.__load_from_pth(self.model, os.path.join(path, metadata["model_paths"][0])
print("Loaded Pytorch model.")
self.__load_rpn_from_onnx(os.path.join(path, metadata["model_paths"][0])
print("Loaded ONNX model.")
reset(self)
self.tracker.reset()
Logger(silent, verbose, logging_path)
hasattr(input_dataset_iterator, "nID")
os.path.join(self.temp_path, "checkpoints")
os.makedirs(checkpoints_path, exist_ok=True)
os.path.join(checkpoints_path, f"checkpoint_{self.checkpoint_load_iter}.pth")
else (-1 if silent else 10)
logger.close()
Logger(silent, verbose, logging_path)
evaluate(self.infer, dataset)
logger.log(Logger.LOG_WHEN_NORMAL, result)
logger.close()
infer(self, batch, frame_ids=None, img_size=(1088, 608)
ValueError("No model loaded or created")
self.model.eval()
isinstance(batch, Image)
isinstance(batch, list)
ValueError("Input batch should be an engine.Image or a list of engine.Image")
len(batch)
zip(batch, frame_ids)
image.convert("channels_last", "bgr")
letterbox(img0, height=img_size[1], width=img_size[0])
transpose(2, 0, 1)
np.ascontiguousarray(img, dtype=np.float32)
torch.from_numpy(img)
to(self.device)
unsqueeze(0)
self.tracker.update(blob, img0)
online_tlwhs.append(tlwh)
online_ids.append(tid)
online_scores.append(t.score)
results.append(result)
optimize(self, do_constant_folding=False, img_size=(1088, 608)
Exception("Can not optimize the model while DCNv2 implementation is not optimizable")
UserWarning("No model is loaded, cannot optimize. Load or train a model first.")
UserWarning("Model is already optimized in ONNX.")
os.path.join(self.temp_path, "onnx_model_temp.onnx")
os.makedirs(self.temp_path, exist_ok=True)
os.path.join(self.temp_path, "onnx_model_temp.onnx")
self.__load_rpn_from_onnx(os.path.join(self.temp_path, "onnx_model_rpn_temp.onnx")
download(model_name, path, server_url=None)
ValueError("Unknown model_name: " + model_name)
os.makedirs(path, exist_ok=True)
os.path.join(path, model_name)
os.makedirs(model_dir, exist_ok=True)
print("Downloaded model", model_name, "to", model_dir)
__convert_to_onnx(self, input_shape, output_name, do_constant_folding=False, verbose=False)
torch.randn(input_shape)
to(self.device)
self.heads.keys()
__load_from_onnx(self, path)
ort.InferenceSession(path)
onnx.load(path)
onnx.checker.check_model(self.model)
onnx.helper.printable_graph(self.model.graph)
__load_from_pth(self, model, path, use_original_dict=False)
torch.load(path, map_location=self.device)
model.load_state_dict(all_params if use_original_dict else all_params["state_dict"])
isinstance(dataset, ExternalDataset)
dataset.dataset_type.lower()
ExternalDataset (" + str(dataset)
T.Compose([T.ToTensor()
isinstance(dataset, DatasetIterator)
isinstance(val_dataset, ExternalDataset)
val_dataset.dataset_type.lower()
ExternalDataset (" + str(val_dataset)
isinstance(val_dataset, DatasetIterator)
isinstance(dataset, ExternalDataset)
dataset.dataset_type.lower()
ExternalDataset (" + str(dataset)
__create_model(self)
heads.update({'reg': 2})
create_model(self.backbone, heads, self.head_conv)
self.model.to(self.device)
heads.keys()
torch.optim.Adam(self.model.parameters()
__extract_trailing(path)
ntpath.split(path)
ntpath.basename(head)
logging.getLogger(__name__)
pytest.fixture(scope="module")