# Function Differences with torch.nn.Upsample

<a href="https://gitee.com/mindspore/docs/blob/r2.0.0-alpha/docs/mindspore/source_en/note/api_mapping/pytorch_diff/ResizeBilinear.md" target="_blank"><img src="https://mindspore-website.obs.cn-north-4.myhuaweicloud.com/website-images/r2.0.0-alpha/resource/_static/logo_source_en.png"></a>

## torch.nn.Upsample

```python
torch.nn.Upsample(
    size=None,
    scale_factor=None,
    mode='nearest',
    align_corners=None
)(input)
```

For more information, see [torch.nn.Upsample](https://pytorch.org/docs/1.5.0/nn.html#torch.nn.Upsample).

## mindspore.nn.ResizeBilinear

```python
class mindspore.nn.ResizeBilinear(half_pixel_centers=False)(x, size=None, scale_factor=None, align_corners=False)
```

For more information, see [mindspore.nn.ResizeBilinear](https://mindspore.cn/docs/en/r2.0.0-alpha/api_python/nn/mindspore.nn.ResizeBilinear.html#mindspore.nn.ResizeBilinear).

## Differences

PyTorch: Multiple modes can be chosen when upsampling data.

MindSpore：Currently only supports `bilinear` mode to sample data. `half_pixel_centers` defaults to False, to achieve the same result as PyTorch, it should be set to True.

## Code Example

```python
import mindspore as ms
import mindspore.nn as nn
import torch
import numpy as np

# In MindSpore, it is predetermined to use bilinear to resize the input image.
x = np.random.randn(1, 2, 3, 4).astype(np.float32)
resize = nn.ResizeBilinear(half_pixel_centers=True)
tensor = ms.Tensor(x)
output = resize(tensor, (5, 5))
print(output.shape)
# Out：
# (1, 2, 5, 5)

# In torch, parameter mode should be passed to determine which method to apply for resizing input image.
x = np.random.randn(1, 2, 3, 4).astype(np.float32)
resize = torch.nn.Upsample(size=(5, 5), mode='bilinear')
tensor = torch.tensor(x)
output = resize(tensor)
print(output.detach().numpy().shape)
# Out：
# (1, 2, 5, 5)
```