beta=1.0, cls_batch_num=6, cls_image_shape='3, 48, 192', cls_model_dir='/home/ocr_model/cls_infer', cls_thresh=0.9, cpu_threads=10, crop_res_save_dir='./output', det=True, det_algorithm='DB', det_db_box_thresh=
os.environ['MXNET_USE_TENSORRT'] ='0' executor = sym.simple_bind(ctx=mx.gpu(0), data=batch_shape, grad_req='null', force_rebind=True) executor.copy_params_from(arg_params, aux_params) # Warmup print('Warming up MXNet') fori in range(0,10): y_gen = executor.forward(is_train=...
”" Input [Text 1]: " In this essay I will explain " Output [Text 1 Beam 0]: "3 different types of love. The first type is called Eros, the second Philia and last Agape. Socrates believes that true love is a desire to obtain what one lacks or needs in their life. The word ...
dummy_convert(cfg, only_backbone = True) # only backbone + FPN dummy_convert(cfg, only_backbone = False) # all Ifonly_backbone = True, you can convert it successfully that only with backbone + FPN. However, ifonly_backbone = False, it means including whole model that...
//std::vector<std::string> output_blobnames; //output_blobnames.push_back(OUTPUT_BLOB_NAME_1); //output_blobnames.push_back(OUTPUT_BLOB_NAME_2); //caffeToGIEModel(prototxt, caffemodel, output_blobnames, 1, &plugin_factory, gieModelStream, true); ...
(prototxt, caffemodel, output_blobnames, 1, &plugin_factory, gieModelStream, true);//caffeToGIEModel_serialize(prototxt, caffemodel, output_blobnames, 1, &plugin_factory, gieModelStream, true, s);plugin_factory.destroyPlugin();//std::vector<std::string>().swap(output_blobnames);//...
PyTorch Hub– Force-reloadmodel = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True) Notebooks– View updated notebooks Docker–sudo docker pull ultralytics/yolov5:latestto update your image github-actionsbotadded theStalelabelJun 13, 2022 ...
"export_params": true, "keep_initializers_as_inputs": false, "opset_version": 11, "save_file": "end2end.onnx", "input_names": [ "input" ], "output_names": [ "output" ], "input_shape": [ 256, 256 ], "optimize": true ...
fullgraph=True)pipe.enable_vae_slicing()pipe.enable_vae_tiling()pipe.enable_model_cpu_offload()defload_from_single(local_dir):pipe=StableDiffusionXLPipeline.from_single_file(f'{local_dir}/sd_xl_base_1.0.safetensors',config=download_config(local_dir),local_files_only=True,torch_dtype=torch....
model = alexnet(pretrained=True).eval().cuda() # create example data x = torch.ones((1, 3, 224, 224)).cuda() # convert to TensorRT feeding sample data as input model_trt = torch2trt(model, [x]) Execute We can execute the returned TRTModule just like the original PyTorch model y...