scale= (x_max – x_min) / (q_max – q_min)zeroPt= round(q_min – x_min / scale)x_q= clamp( round(x_float / scale) + zeroPt, q_min, q_max )x_deq= (x_q – zeroPt) * scale 在QAT期间,伪量化操作表示为: x_fake = (round(x_float/scale)+zeroPt – zeroPt) *scale ...
只要你需要torch.Tensor,首先尝试在要使用它们的设备上创建它们。不要使用原生Python或NumPy创建数据,然后将其转换为torch.Tensor。在大多数情况下,如果你要在GPU中使用它们,直接在GPU中创建它们。 #Randomnumbersbetween0and1 #Sameasnp.random.rand([10,5]) tensor=torch.rand([10,5],device=torch.device('cuda...
# Random numbers between0and1# Sameasnp.random.rand([10,5])tensor=torch.rand([10,5],device=torch.device('cuda:0'))# Random numbers from normal distributionwithmean0and variance1# Sameasnp.random.randn([10,5])tensor=torch.randn([10,5],device=torch.device('cuda:0')) 唯一的语法差异...
boxes (torch.Tensor): 形状为(N, 4)的张量,包含N个边界框的左上角和右下角坐标 返回: torch.Tensor: 形状为(N, 4)的张量,包含N个边界框的中心点坐标、宽度和高度 """ # 分别提取所有边界框的左上角和右下角坐标 x1, y1, x2, y2 = boxes[:,0], boxes[:,1], boxes[:,2], boxes[:,3] ...
tensor([0,10,20,30],dtype=torch.uint8) 1. 2. 3. 4. 参数介绍 input:要量化的tensor。 scale:应用在量化公式的缩放大小。 zero_point:以整数值表示的偏移量,该值映射为浮点数零。 dtype:返回张量的所需数据类型。必须是量化的dtypes之一:torch.quint8,torch.qint...
Tensor, multiclass: bool = False): # Dice loss (objective to minimize) between 0 and 1 ...
Tensor(sample['target']) def __len__(self): return len(self.data) 然后,我们可以使用PyTorch DataLoader来遍历数据。使用DataLoader的好处是它在内部自动进行批处理和数据的打乱,所以我们不必自己实现它,代码如下: 代码语言:javascript 代码运行次数:0 运行 AI代码解释 # 这里我们为我们的模型定义属性 BATCH_...
Changetorch.Tensor.new_tensor()to be on the given Tensor's device by default (#144958) This function was always creating the new Tensor on the "cpu" device and will now use the same device as the current Tensor object. This behavior is now consistent with other.new_*methods. ...
between runs easily. e.g. pass in 'runs/exp1', 'runs/exp2', etc. for each new experiment to compare across them. comment (string): Comment log_dir suffix appended to the default ``log_dir``. If ``log_dir`` is assigned, this argument has no effect. ...
v1.6 - v1.8.1:pre 1.6 & 1.9.0 >>> mha = torch.nn.MultiheadAttention(4, 2, bias=False) >>> print(mha.out_proj.bias) Parameter containing: tensor([0., 0., 0., 0.], requires_grad=True) >>> mha = torch.nn.MultiheadAttention(4, 2, bias=False) >>> print(mha.out_proj...