在上述代码中,我们首先创建了一个输入张量input_tensor,然后使用torch.max函数获取最大值和最大值的索引。最大值保存在max_value变量中,最大值的索引保存在max_index变量中。最后,我们使用item()方法将张量的值转换为Python标量,并打印出最大值和最大值的索引。
max(x, dim=1, keepdim=True) print(x_value_index) >>> torch.return_types.max( values=tensor([ [[0.5524, 0.2290, 0.4652, 0.4948, 0.7163]], [[0.4045, 0.5833, 0.7844, 0.5605, 0.6278]], [[0.9522, 0.7801, 0.5938, 0.1807, 0.8103]] ]), indices=tensor([ [[0, 1, 1, 0, 0]], [...
max_value,index= torch.max(x,dim=1)#返回的是两个值,一个是每一行最大值的tensor组,另一个是最大值所在的位置print(max_value,index) max_lie_value= torch.max(x,dim=0)[0].numpy()#每一列最大值max_hang_value = torch.max(x,dim=1)[0].numpy()#每一行最大值print('max_lie_value:',...
torch.max(input,dim,keepdim=False,out=None) -> (Tensor,LongTensor) Returns a namedtuple(values, indices)wherevaluesis the maximum value of each row of theinputtensor in the given dimensiondim. Andindicesis the index location of each maximum value found (argmax). IfkeepdimisTrue, the outpu...
max_pool2d 0.01% 492.109us 9.49% 874.295ms 8.743ms 100 aten::adaptive_avg_pool2d 0.01% 469.736us 0.10% 9.673ms 96.733us 100 aten::ones_like 0.00% 460.352us 0.01% 1.377ms 13.766us 100 SumBackward0 0.00% 399.188us 0.01% 1.206ms 12.057us 100 aten::flatten 0.00% 397.053us 0.02% 1.917ms ...
update({id(p): i for i, p in enumerate(group['params'], start_index) if id(p) not in param_mappings}) packed['params'] = [param_mappings[id(p)] for p in group['params']] start_index += len(packed['params']) return packed param_groups = [pack_group(g) for g in self....
>>> torch.max(a) tensor(0.7445) 1. 2. 3. 4. 5. torch.max(input,dim,keepdim=False,out=None) -> (Tensor,LongTensor) Returns a namedtuple(values, indices)wherevaluesis the maximum value of each row of theinputtensor in the given dimensiondim. Andindicesis the index location of each...
print("全局最大值:{}".format(torch.max(x))) #求y轴方向的最大值 print("y方向最大值:{}".format(torch.max(x,dim=0))) #求最大的2个元素 print("最大的n元素及其相对索引(在dim索引方向上):\n{}".format(torch.topk(x,2,dim=0))) ...
torch.nn.MaxPool1d( ** 一维maxpooling (初始化类) kernel_size,*卷积核尺寸 stride=None,*步长,默认=kernel_size padding=0,*zero padding dilation=1,*膨胀卷积中,膨胀系数(卷积核间隔) return_indices=False,*是否同时返回max位置的索引;一般在torch.nn.MaxUnpool1d中很有用(maxpool逆计算) ...
(self,idx:torch.Tensor,input_pos:torch.Tensor)-> torch.Tensor:B, T = idx.sizecos, sin =self.rope_cachecos = cos.index_select(0, input_pos)sin = sin.index_select(0, input_pos)mask =self.mask_cache.index_select(2, input_pos)mask = mask[:,:,:, :self.config.kv_cache_max]...