Function Differences with torch.nn.Upsample
torch.nn.Upsample
torch.nn.Upsample(
size=None,
scale_factor=None,
mode='nearest',
align_corners=None
)(input)
For more information, see torch.nn.Upsample.
mindspore.nn.ResizeBilinear
class mindspore.nn.ResizeBilinear()(x, size=None, scale_factor=None, align_corners=False)
For more information, see mindspore.nn.ResizeBilinear.
Differences
PyTorch: Multiple modes can be chosen when upsampling data.
MindSpore:Only supports bilinear
mode to sample data.
Code Example
from mindspore import Tensor
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()
tensor = 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.shape)
# Out:
# torch.Size([1, 2, 5, 5])