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How to upsample a given multi-channel temporal, spatial or volumetric data in PyTorch?
A temporal data can be represented as a 1D tensor, and spatial data as 2D tensor while a volumetric data can be represented as a 3D tensor. The Upsample class provided by torch.nn module supports these types of data to be upsampled. But these data must be in the form N ☓ C ☓ D (optional) ☓ H (optional) ☓ W (optional),Where N is the minibatch size, C is the numberchannels, D, H and W are depth, height and width of the data, respectively. Hence, to upsample a temporal data (1D), we need it to be in 3D in the form of N ☓ C ☓ W, spatial data (2D) need to be in 4D in the form of N ☓ C ☓ H ☓ W, whereas volumetric data (3D) need to be in 5D in the form of N ☓ C ☓ D ☓ H ☓ W.
It supports different scale factors and modes. On a 3D(temporal) tensor, we can apply mode='linear' and 'nearest'. On a 4D (Spatial) tensor we can apply mode='nearest', 'bilinear' and 'bicubic'. On a 5D (Volumetric) tensor, we can apply mode='nearest' and 'trilinear'.
Syntax
torch.nn.Upsample()
Steps
You could use the following steps to upsample temporal, spatial or volumetric data
Import the required library. In all the following examples, the required Python library is torch. Make sure you have already installed it.
import torch
Define temporal (3D), spatial (4D) or volumetric (5D) tensors and print them.
input = torch.tensor([[1., 2.],[3., 4.]]).view(1,2,2)
print(input.size())
print("Input Tensor:\n", input)Create an instance of Upsample with scale_fator and mode to upsample a given multichannel data.
upsample = torch.nn.Upsample(scale_factor=3, mode='nearest')
Upsample the above-defined temporal, spatial or volumetric tensor using the created instance.
output = upsample(input)
Print the above upsampled tensor.
print("Upsample by a scale_factor=3 with mode='nearest':\n",output)Example 1
In this program, we upsample a temporal with different scale_factor and modes.
# Python program to upsample a 3D (Temporal) tensor
# 3D tensor we can apply mode='linear' and 'nearest'
import torch
# define a tensor and view as a 3D tensor
input = torch.tensor([[1., 2.],[3., 4.]]).view(1,2,2)
print(input.size())
print("Input Tensor:\n", input)
# create an instance of Upsample with scale_factor and mode
upsample1 = torch.nn.Upsample(scale_factor=2)
output1 = upsample1(input)
print("Upsample by a scale_factor=2\n", output1)
# define upsample with scale_factor and mode
upsample2 = torch.nn.Upsample(scale_factor=3)
output2 = upsample2(input)
print("Upsample by a scale_factor=3 with default mode:\n", output2)
upsample2 = torch.nn.Upsample(scale_factor=3, mode='nearest')
output2 = upsample2(input)
print("Upsample by a scale_factor=3 mode='nearest':\n", output2)
upsample_linear = torch.nn.Upsample(scale_factor=3, mode='linear')
output_linear = upsample_linear(input)
print("Upsample by a scale_factor=3, mode='linear':\n", output_linear)Output
torch.Size([1, 2, 2]) Input Tensor: tensor([[[1., 2.],[3., 4.]]]) Upsample by a scale_factor=2 tensor([[[1., 1., 2., 2.],[3., 3., 4., 4.]]]) Upsample by a scale_factor=3 with default mode: tensor([[[1., 1., 1., 2., 2., 2.],[3., 3., 3., 4., 4., 4.]]]) Upsample by a scale_factor=3 mode='nearest': tensor([[[1., 1., 1., 2., 2., 2.],[3., 3., 3., 4., 4., 4.]]]) Upsample by a scale_factor=3, mode='linear': tensor([[[1.0000, 1.0000, 1.3333, 1.6667, 2.0000, 2.0000],[3.0000, 3.0000, 3.3333, 3.6667, 4.0000, 4.0000]]])
Note the differences between output tensors with different scale_factors and modes.
Example 2
In the following Python program, we upsample a 4D(Spatial) tensor with different scale_factors and modes..
# Python program to upsample a 4D (Spatial) tensor
# on 4D(Spatial) tensor we can apply mode='nearest', 'bilinear' and 'bicubic'
import torch
# define a tensor and view as a 4D tensor
input = torch.tensor([[1., 2.],[3., 4.]]).view(1,1,2,2)
print(input.size())
print("Input Tensor:\n", input)
# upsample using mode='nearest'
upsample_nearest = torch.nn.Upsample(scale_factor=3,
mode='nearest')
output_nearest = upsample_nearest(input)
# upsample using mode='bilinear'
upsample_bilinear = torch.nn.Upsample(scale_factor=3,
mode='bilinear')
output_bilinear = upsample_bilinear(input)
# upsample using mode='bicubic'
upsample_bicubic = torch.nn.Upsample(scale_factor=3, mode='bicubic')
output_bicubic = upsample_bicubic(input)
# display the outputs
print("Upsample by a scale_factor=3, mode='nearest':\n", output_nearest)
print("Upsample by a scale_factor=3, mode='bilinear':\n",
output_bilinear)
print("Upsample by a scale_factor=3, mode='bicubic':\n", output_bicubic)Output
torch.Size([1, 1, 2, 2]) Input Tensor: tensor([[[[1., 2.],[3., 4.]]]]) Upsample by a scale_factor=3, mode='nearest': tensor([[[[1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.]]]]) Upsample by a scale_factor=3, mode='bilinear': tensor([[[[1.0000, 1.0000, 1.3333, 1.6667, 2.0000, 2.0000], [1.0000, 1.0000, 1.3333, 1.6667, 2.0000, 2.0000], [1.6667, 1.6667, 2.0000, 2.3333, 2.6667, 2.6667], [2.3333, 2.3333, 2.6667, 3.0000, 3.3333, 3.3333], [3.0000, 3.0000, 3.3333, 3.6667, 4.0000, 4.0000], [3.0000, 3.0000, 3.3333, 3.6667, 4.0000, 4.0000]]]]) Upsample by a scale_factor=3, mode='bicubic': tensor([[[[0.6667, 0.7778, 1.0926, 1.4630, 1.7778, 1.8889], [0.8889, 1.0000, 1.3148, 1.6852, 2.0000, 2.1111], [1.5185, 1.6296, 1.9444, 2.3148, 2.6296, 2.7407], [2.2593, 2.3704, 2.6852, 3.0556, 3.3704, 3.4815], [2.8889, 3.0000, 3.3148, 3.6852, 4.0000, 4.1111], [3.1111, 3.2222, 3.5370, 3.9074, 4.2222, 4.3333]]]])
Note the differences between output tensors with different scale_factors and modes.
Example 3
In this program, we upsample a 5D (Volumetric) tensor with different scale_factors and modes.
# Python program to upsample a 5D (Volumetric) tensor
# on 5D (Volumetric) tensor we can apply mode='nearest' and 'trilinear'
import torch
# define a tensor and view as a 5D tensor
input = torch.tensor([[1., 2.],[3., 4.]]).view(1,1,1,2,2)
print(input.size())
print("Input Tensor:\n", input)
# use mode='nearest', factor=2
upsample_nearest = torch.nn.Upsample(scale_factor=2, mode='nearest')
output_nearest = upsample_nearest(input)
print("Upsample by a scale_factor=2, mode='nearest'\n",
output_nearest)
# use mode='nearest', factor=3
upsample_nearest = torch.nn.Upsample(scale_factor=3,
mode='nearest')
output_nearest = upsample_nearest(input)
print("Upsample by a scale_factor=3, mode='nearest'\n",
output_nearest)
# use mode='trilinear'
upsample_trilinear = torch.nn.Upsample(scale_factor=2,
mode='trilinear')
output_trilinear = upsample_trilinear(input)
print("Upsample by a scale_factor=2, mode='trilinear':\n",
output_trilinear)Output
torch.Size([1, 1, 1, 2, 2]) Input Tensor: tensor([[[[[1., 2.],[3., 4.]]]]]) Upsample by a scale_factor=2, mode='nearest' tensor([[[[[1., 1., 2., 2.], [1., 1., 2., 2.], [3., 3., 4., 4.], [3., 3., 4., 4.]], [[1., 1., 2., 2.], [1., 1., 2., 2.], [3., 3., 4., 4.], [3., 3., 4., 4.]]]]]) Upsample by a scale_factor=3, mode='nearest' tensor([[[[[1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.]], [[1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.]], [[1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [1., 1., 1., 2., 2., 2.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.], [3., 3., 3., 4., 4., 4.]]]]]) Upsample by a scale_factor=2, mode='trilinear': tensor([[[[[1.0000, 1.2500, 1.7500, 2.0000], [1.5000, 1.7500, 2.2500, 2.5000], [2.5000, 2.7500, 3.2500, 3.5000], [3.0000, 3.2500, 3.7500, 4.0000]], [[1.0000, 1.2500, 1.7500, 2.0000], [1.5000, 1.7500, 2.2500, 2.5000], [2.5000, 2.7500, 3.2500, 3.5000], [3.0000, 3.2500, 3.7500, 4.0000]]]]])
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