Flatten a batch `pixels` and return the features as an `OrderedDict`.
时间: 2023-11-21 10:11:02 浏览: 39
好的,对于您的问题,我能给您提供以下代码:
```python
import torch
from collections import OrderedDict
def flatten_batch(pixels):
"""
Flatten a batch of pixels and return the features as an OrderedDict
"""
num_pixels = pixels.shape[0]
num_channels = pixels.shape[1]
height = pixels.shape[2]
width = pixels.shape[3]
flattened_pixels = pixels.view(num_pixels, -1)
features = OrderedDict()
for channel in range(num_channels):
channel_name = f'channel{channel+1}'
channel_features = flattened_pixels[:, channel*height*width:(channel+1)*height*width]
features[channel_name] = channel_features
return features
```
该函数会接受一个形状为 [batch_size, num_channels, height, width] 的输入 `pixels` , 并将它展平为形状为 [batch_size, num_channels x height x width] 的张量,最后将每个通道分别存储在一个名为 `channel1`, `channel2`, ... 的有序字典中返回。希望这能帮到您!
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