for _, (images, _) in enumerate(dataloader): 这是一个循环,使用enumerate 函数遍历数据加载器dataloader 中的数据批次。_ 通常用于表示不需要使用的循环变量,images 是从数据批次中提取出的图像数据。images = images.to('cuda'):将图像数据images 转移到 GPU (如果有可用的 CUDA 设备),以加快计算速度。
If you guys are under a similar scenario, try to settorch.nn.parallel.DistributedDataParallel(...,broadcast_buffers=False,... ). But to be honest, I still don't know what is going wrong. I would appreciate any given ideas. BTW, I just use one GPU and one node exactly. 👍🎉2 ...
forkey, valueincfg.IMAGE_PATHS.items: ifisinstance(value, list): images = [] foriteminvalue: images.append(pygame.image.load(item)) game_images[key] = images else: game_images[key] = pygame.image.load(value) game_sounds = {} forkey, valueincfg.AUDIO_PATHS.items: ifkey =='bgm':c...
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You can go to the OSS console and view the generated images in the OSS path that you specified when you deployed the service. index 1 task_id is txt2img_0 index 1 data is b'{"images":[],"parameters":{"id_task":null,"status":0,"image_url":"/code/stable-diffusion-webui/d...
(test_images_path) img_processed_list = [] for i in range(batch_size): img = Image.open(image_files[i]) img_processed_list.append(preprocess(img, resize_size, crop_size_onnx)) if len(img_processed_list) > 1: img_data = np.concatenate(img_processed_list) elif len...
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for i, data in enumerate(train_loader): optimizer.zero_grad() # reset hidden states model.hidden = model.init_hidden() # get inputs inputs, labels = data inputs = inputs.view(-1,28,28).to(device) labels = labels.to(device) # forward+backward+optimize outputs = model(inputs) loss...
/std_vec[i] np_image = np.expand_dims(norm_img_data, axis=0)# 1xCxHxWreturnnp_image# following code loads only batch_size number of images for demonstrating ONNX inference# make sure that the data directory has at least batch_size number of imagestest_images_path ="automl_models_multi...
IMG_PATH = os.path.join(os.getcwd(), 'input', 'images-optional') if os.path.exists(IMG_PATH): for dirpath, dirnames, files in os.walk(IMG_PATH): if not files: # no image files found args.no_animation = True else: ...