# with tqdm(dataloaders[phase], desc = phase, file=sys.stdout, disable=not (self.verbose)) as iterator: for i in trange(batch_count + 1,disable=not (self.verbose)): top = i * self.batch_size bottom = min(self.input_array.shape[0], (i+1) * self.batch_size) if top < self...
When using enumerate we now provide start value of 1 where it is appropriate, e.g. when counting source code lines, rather than adding count+1 on every usage, making code more readable. Organisational Do not recommend Anaconda on Windows anymore, it seems barely possible to get anything insta...
return tensor.detach().cpu().tolist()for batch in tqdm(eval_dataloader): model.eval() batch = tuple(t.to(device) for t in batch) with torch.no_grad(): inputs = { "input_ids": batch[0], "attention_mask": batch[1], "token_type_ids": batch[2] } example_indices = batch[3]...
&& mkdir -p /var/run/sshdRUNpip install -U pytorch-lightning transformers yacs ujson pkuseg pypinyin deepspeed datasets tqdm wandb && pip uninstall -y torch && pip install torch==1.10.0+cu113 torchvision==0.11.1+cu113 torchaudio==0.10.0+cu113 -f https://download.pytorch.org/whl/cu113/t...
(fp16=training_args.fp16)model,optimizer,train_dataloader=accelerator.prepare(model,optimizer,train_dataloader)model.train()forstep,batchinenumerate(tqdm(train_dataloader),start=1):inputs=torch.tensor(batch['input_ids'])inputs=inputs.to(model.device)outputs=model(inputs,return_dict=True)logits=...
self.symbol_to_id = {s: i for i, s in enumerate(symbols)} self.hps = hps self.device = device # load state_dict checkpoint_dict = load_or_download_model(language, device, use_hf=use_hf, ckpt_path=ckpt_path) self.model.load_state_dict(checkpoint_dict['model'], strict=True) lang...
size(0) for i, v in enumerate(self.data)} def __getitem__(self, index): return self.data[index] def __len__(self): return self.len def create_mask_from_lengths(lengths): max_length = torch.max(lengths).item() idx = torch.arange(max_length).unsqueeze(0) mask = idx < lengths...
for i, choice in enumerate(choices): test_df[f'prob_{choice}'] = (all_probs[f'prob_{choice}']) test_df[f"prob_{choice}"] = all_probs[f"prob_{choice}"] if score: test_df["correctness"] = score os.makedirs(save_result_dir, exist_ok=True) test_df.to_csv(os.path.join( ...
num_inference_steps=4# 生成图像images=pipe(prompt=prompt,num_inference_steps=num_inference_steps,guidance_scale=8.0,lcm_origin_steps=50,output_type="pil").images# 保存生成的每个图像fori,img_pilinenumerate(images):# 保存图片img_pil.save(f"generated_image_{i+1}.png")print("Images saved ...
" for idx, key in enumerate(auto_device_map):\n", " if idx <= transition_layer:\n", " shared_device_map[key] = 0\n", " else:\n", " shared_device_map[key] = 1\n", " return shared_device_map\n", "\n", "def create_intertwined_device_map():\n", " device_map = {...