torch.argmax(label_count, dim=1)
时间: 2023-10-10 09:05:34 浏览: 33
This code will return the index of the maximum value in each row of the tensor "label_count".
For example, if "label_count" is a tensor of shape (3, 5), where each row represents the count of a label in a dataset of 5 classes, the code will return a tensor of shape (3,) containing the index of the most frequent label in each row.
Note that "dim=1" specifies that the operation is performed along the second axis (columns) of the tensor.
相关问题
torch.argmax(pred, dim=1)
这个问题可以回答。torch.argmax(pred, dim=1)是一个PyTorch函数,用于在给定维度上返回张量中最大值的索引。具体来说,它会返回一个张量,其中每个元素都是在给定维度上最大值的索引。
def test(): with torch.no_grad(): for data in valid_loader: val_label, val_input = data val_input = val_input.to(torch.float32) val_input, val_label = val_input.to(device), val_label.to(device) val_pred = model.forward(x=val_input) _, predicted = torch.max(val_pred, dim=1) # 找到出现次数最多的元素的索引 most_common_index = torch.argmax(torch.bincount(val_pred)) # 获取出现次数最多的值 most_common_value = val_pred[most_common_index] return most_common_value.item() #return predicted if __name__ == '__main__': #startTime = time.time() model.load_state_dict(torch.load('model.pth')) recognize = test() #endTime = time.time() #print("GPU耗时: ", endTime - startTime) print(recognize) 出现“"bincount_cpu" not implemented for 'Float'” 怎么修改
`"bincount_cpu" not implemented for 'Float'`错误是因为`torch.bincount()`函数只能用于整数类型的张量,而不能用于浮点数类型的张量。在你的代码中,`val_pred`是一个浮点数类型的张量。
要解决这个问题,你可以将`val_pred`张量转换为整数类型。你可以使用`torch.argmax()`函数找到`val_pred`中出现次数最多的元素的索引,然后使用该索引从`val_pred`中获取对应的值。下面是修改后的代码:
```python
import torch
def test():
with torch.no_grad():
for data in valid_loader:
val_label, val_input = data
val_input = val_input.to(torch.float32)
val_input, val_label = val_input.to(device), val_label.to(device)
val_pred = model.forward(x=val_input)
_, predicted = torch.max(val_pred, dim=1)
# 找到出现次数最多的元素的索引
most_common_index = torch.argmax(torch.bincount(predicted.int()))
# 获取出现次数最多的值
most_common_value = predicted[most_common_index]
return most_common_value.item()
if __name__ == '__main__':
model.load_state_dict(torch.load('model.pth'))
recognize = test()
print(recognize)
```
在修改后的代码中,我们将`predicted`张量转换为整数类型(`predicted.int()`),然后使用它来计算出现次数最多的元素。这样,你就可以避免`"bincount_cpu" not implemented for 'Float'`错误,并成功获取出现次数最多的值。
请注意,如果`predicted`张量包含浮点数,你可能需要在使用`torch.argmax()`之前将其转换为整数类型。