Upsample2D¶
Description¶
Increases the dimension of two-dimensional maps by a specified number of times, filling new cells with values according to the selected mode (see parameters).
In contrast to deconvolution this layer is not trainable.
Initializing¶
def __init__(self, scale=2, mode="nearest", name=None):
Parameters
| Parameter | Allowed types | Description | Default |
|---|---|---|---|
| scale | Union[int, tuple] | Scale: a number by which the tensor dimensions will be multiplied | 2 |
| mode | str | New cells filling mode | "nearest" |
| name | str | Layer name | None |
Explanations
scale - possible to specify either a single scale value in height or width, and a tuple of the form (scale_h, scale_w), where scale_h - scale value for the map height, and scale_w - for the width;
mode - possible options: "nearest" (copies the value of the nearest cell), "linear" (uses linear interpolation according to the values of nearby cells).
Examples¶
Necessary imports:
>>> import numpy as np
>>> from PuzzleLib.Backend import gpuarray
>>> from PuzzleLib.Modules import Upsample2D
Info
gpuarray is required to properly place the tensor in the GPU
>>> batchsize, maps, h, w = 1, 1, 3, 3
>>> data = gpuarray.to_gpu(np.random.randint(0, 10, (batchsize, maps, h, w)).astype(np.float32))
>>> print(data)
[[[[9. 8. 4.]
[2. 7. 5.]
[3. 8. 3.]]]]
>>> upsample = Upsample2D(scale=2, mode="nearest")
>>> upsample(data)
[[[[9. 9. 8. 8. 4. 4.]
[9. 9. 8. 8. 4. 4.]
[2. 2. 7. 7. 5. 5.]
[2. 2. 7. 7. 5. 5.]
[3. 3. 8. 8. 3. 3.]
[3. 3. 8. 8. 3. 3.]]]]
As mentioned above, the scale can be set different for height and width (in this case, the length of the scale tuple should correspond to the number of map dimensions):
>>> upsample = Upsample2D(scale=(2, 1), mode="nearest")
>>> upsample(data)
[[[[9. 8. 4.]
[9. 8. 4.]
[2. 7. 5.]
[2. 7. 5.]
[3. 8. 3.]
[3. 8. 3.]]]]
With linear interpolation, the results will be different:
>>> upsample = Upsample2D(scale=2, mode="linear")
>>> upsample(data)
[[[[9. 8.6 8.2 7.2 5.6 4. ]
[6.2000003 6.76 7.32 6.96 5.68 4.4 ]
[3.3999999 4.92 6.44 6.7200003 5.7599998 4.8 ]
[2.2 4.2 6.2 6.68 5.64 4.6 ]
[2.6 4.6 6.6000004 6.84 5.3199997 3.8 ]
[3. 5. 7. 7. 5. 3. ]]]]