bbox_offset = torch.stack(batch_offset) bbox_mask = torch.stack(batch_mask) class_labels = torch.stack(batch_class_labels)如何在这上面更改呢
时间: 2024-09-27 07:06:51 浏览: 13
如果你想对这个操作进行一些改变,你可以考虑以下几个选项:
1. **转换维度**:如果你希望堆叠后的结果有不同的形状或维度,可以在`stack`函数之前或之后添加其他操作,如`permute()`、`reshape()`等,调整batch元素的排列。
```python
# 先按某一维度合并,例如沿批次数维度堆叠
bbox_offset = batch_offset.permute(0, -1).stack()
bbox_mask = batch_mask.permute(0, -1).stack()
class_labels = batch_class_labels.permute(0, -1).stack()
# 或者先合并所有元素再调整形状
offset_stacked = torch.cat(batch_offset, dim=0)
mask_stacked = torch.cat(batch_mask, dim=0)
labels_stacked = torch.cat(batch_class_labels, dim=0)
offset_stacked = offset_stacked.reshape(-1, new_shape_for_offset)
mask_stacked = mask_stacked.reshape(-1, new_shape_for_mask)
labels_stacked = labels_stacked.reshape(-1, new_shape_for_labels)
```
2. **条件堆叠**:如果你只想针对满足特定条件的batch元素堆叠,可以添加一个条件判断或者使用`torch.where()`或`torch.masked_select()`。
```python
valid_idx = (batch_offset != some_value) & (batch_mask == True) # 示例条件
bbox_offset_valid = bbox_offset[valid_idx]
class_labels_valid = class_labels[valid_idx]
```
3. **使用循环**:如果每个batch元素需要独立的操作,可以用for循环遍历而不是一次性堆叠。
```python
new_offset_list = []
new_mask_list = []
new_labels_list = []
for i, (offset, mask, label) in enumerate(zip(batch_offset, batch_mask, batch_class_labels)):
new_offset_list.append(offset)
new_mask_list.append(mask)
new_labels_list.append(label)
bbox_offset = torch.stack(new_offset_list)
bbox_mask = torch.stack(new_mask_list)
class_labels = torch.stack(new_labels_list)
```