2.0,3.0]# 将列表转换为NumPy数组my_array=np.array(my_list,dtype=np.float32)# 现在my_array是一个32位浮点数的NumPy数组print(my_array)```### 使用TensorFlow```pythonimporttensorflow as tf# 假设你有一个Python列表my_list=[1.0,2.0,3.0]# 将列表转换为TensorFlow张量my_tensor=tf.convert_to_tensor...
min=0, max=255).byte().permute(1,2,0).cpu().numpy()) image = torchvision.transforms.functional.to_pil_image(tensor) # Equivalently way # PIL.Image -> torch.Tensor path = r'./figure.jpg' tensor
non_blocking=True), \data[1].to(device=device, non_blocking=True)# convert to float32 and normalizeinputs = (inputs.to(torch.float32) / 255. - 0.5) / 0.5outputs = model(inputs)loss = criterion(outputs, labels)optimizer.zero_grad()loss.backwar...
img=Image.open(img_path).convert('RGB')#将图像读入并转换成RGB形式 img=img.resize(img_size,img_size)#调整读入图像像素大小 img=transforms.ToTensor()(img)#将图像转化为tensor img=img.unsqueeze(0)#在0维上增加一个维度returnimg defshow_img(img):#图像输出 img=img.squeeze(0)#将多余的0维通道...
edge_index=torch.tensor([# 这里表示节点0和1有连接,因为是无向图 # 那么1和0也有连接 # 上下对应着看[0,1,1,2],[1,0,2,1],],# 指定数据类型 dtype=torch.long)# 节点的属性信息 x=torch.tensor([# 三个节点 # 每个节点的属性向量维度为1[-1],[0],[1],])# 实例化为一个图结构的数据 ...
will change `input_ids` from shape [B, L] to [B, L, D]def batch_embedding_calls(input_ids, embedding_layer, batch_size=256):# Check if input_ids is already a tensor, if not convert itif not isinstance(input_ids, torch.Tensor):...
input_ids, token_type_ids=convert_text_to_ids(tokenizer, text) input_ids=seq_padding(tokenizer, input_ids) token_type_ids=seq_padding(tokenizer, token_type_ids)#标签形状为 (batch_size, 1)label = label.unsqueeze(1)#需要 LongTensorinput_ids, token_type_ids, label =input_ids.long(), to...
"model.safetensors") tensors = {} with safe_open("model.safetensors", framework="pt") as f: for k in f.keys(): tensors[k] = f.get_tensor(k) print(list(tensors.keys())) # ['a.bias', 'a.weight'] model_restored = Model() load_model(model_restored, "model.safetensors"...
QuantStub在observer阶段会记录参数值,DeQuantStub在prepare阶段相当于Identity;而在convert API调用过程中,会分别被替换为nnq.Quantize和nnq.DeQuantize。在这个章节要介绍的推理过程中,QuantStub,也就是nnq.Quantize在做什么工作呢?如下所示: def forward(self, X): return torch.quantize_per_tensor(X, float(sel...
cutting of an image (224, 224) from the original image transforms.RandomHorizontalFlip(), #Reversal at 0.5 probability level transforms.ToTensor(), #Convert a PIL. Image with a range of [0,255] or numpy. ndarray to a shape of [C, H, W], and a FloadTensor with a range o...