train_dataset, shuffle=True, collate_fn=data_collator, batch_size=args.per_device_train_batch_size9 changes: 8 additions & 1 deletion 9 examples/pytorch/question-answering/run_qa_beam_search_no_trainer.py Original file line numberDiff line numberDiff line change ...
disable_flashinfer_sampling=True, disable_radix_cache=False, disable_regex_jump_forward=False, disable_cuda_graph=False, disable_disk_cache=False, enable_mixed_chunk=False, enable_torch_compile=False, enable_p2p_check=False, enable_mla=False, attention_reduce_in_fp32=False, efficient_weight_load...
Running on local URL:http://127.0.0.1:7860 To create a public link, setshare=Trueinlaunch(). Folder 100_Abey Ja: 16 images found Folder 100_Abey Ja: 1600 steps max_train_steps = 1600 stop_text_encoder_training = 0 lr_warmup_steps = 160 ...
Hi, I was trying to use FP16 and INT8. I understand this is how you prepare a FP32 model. model = onnx.load("/path/to/model.onnx") engine = backend.prepare(model, device='CUDA:1') input_data = np.random.random(size=(32, 3, 224, 224)).ast...
load(ckpt_path, weights_only=True, map_location=device) checkpoint = torch.load(ckpt_path, weights_only=True) if use_ema == True: ema_model = EMA(model, include_online_model = False).to(device) if use_ema: if ckpt_type == "safetensors": ema_model.load_state_dict(checkpoint) ...
fp16data := gocv.FP16BlobFromImage(img, 1.0/128.0, image.Pt(299, 299), 128.0, true, false)// load image tensor into graph on NCS stick loadStatus := graph.LoadTensor(fp16Blob.ToBytes()) loadStatus := graph.LoadTensor(fp16data) ...
epoch_runner(data_loaders[i], **kwargs) File "/usr/local/lib/python3.6/dist-packages/mmcv/runner/runner.py", line 264, in train self.model, data_batch, train_mode=True, **kwargs) File "/data2/TDL/paper_fabric/code/mmdetection/mmdet/apis/train.py", line 38, in batch_processor ...