torch_moving_mnist

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torch_moving_mnist

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A simple Dataset generator for Moving Mnist

torch_moving_mnist 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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torch_moving_mnist
================

## Install

``` sh
pip install -e .
```

## How to use

``` python
from types import SimpleNamespace
from torch_moving_mnist.data import MovingMNIST
from torch_moving_mnist.utils import show_images
```

``` python
affine_params = SimpleNamespace(
    angle=(-5, 5), # rotation in degrees (min and max values)
    translate=((-5, 5), (-5, 5)), # translation in pixels x and y
    scale=(.9, 1.1), # scaling in percentage (1.0 = no scaling)
    shear=(-2, 2), # deformation on the z-plane
)
```

Create a MovingMNIST dataset with `affine_params`, with 10 frames and
may include up to 3 digitis. Image size is 64.

``` python
from nbdev.showdoc import show_doc
```

------------------------------------------------------------------------

<a
href="https://github.com/tcapelle/torch_moving_mnist/blob/main/torch_moving_mnist/data.py#LNone"
target="_blank" style="float:right; font-size:smaller">source</a>

### MovingMNIST

>      MovingMNIST (path='.', affine_params:dict=namespace(angle=(-4, 4),
>                   translate=((-5, 5), (-5, 5)), scale=(0.8, 1.2), shear=(-3,
>                   3)), num_digits:list[int]=[1, 2], num_frames:int=4,
>                   img_size=64, concat=True, normalize=False)

Initialize self. See help(type(self)) for accurate signature.

|               | **Type** | **Default**                                                                             | **Details**                                                                                                                                         |
|---------------|----------|-----------------------------------------------------------------------------------------|-----------------------------------------------------------------------------------------------------------------------------------------------------|
| path          | str      | .                                                                                       | path to store the MNIST dataset                                                                                                                     |
| affine_params | dict     | namespace(angle=(-4, 4), translate=((-5, 5), (-5, 5)), scale=(0.8, 1.2), shear=(-3, 3)) | affine transform parameters, refer to torchvision.transforms.functional.affine                                                                      |
| num_digits    | list     | \[1, 2\]                                                                                | how many digits to move, random choice between the value provided                                                                                   |
| num_frames    | int      | 4                                                                                       | how many frames to create                                                                                                                           |
| img_size      | int      | 64                                                                                      | the canvas size, the actual digits are always 28x28                                                                                                 |
| concat        | bool     | True                                                                                    | if we concat the final results (frames, 1, 28, 28) or a list of frames.                                                                             |
| normalize     | bool     | False                                                                                   | scale images in \[0,1\] and normalize them with MNIST stats. Applied at batch level. Have to take care of the canvas size that messes up the stats! |

``` python
ds = MovingMNIST(affine_params=affine_params, num_frames=10, num_digits=[1,2,3], img_size=64)
```

when you index the dataset, it generates a random set of MNIST digits
and trajectories. You could basically only call `ds[0]`

``` python
sequence = ds[0]
```

``` python
show_images(sequence, figsize=(20,10))
```

![](https://raw.githubusercontent.com/tcapelle/torch_moving_mnist/main/index_files/figure-gfm/cell-8-output-1.png)

``` python
t = sequence
type(t), t.shape
```

(torch.Tensor, torch.Size([10, 1, 64, 64]))

## Dataloader

This dataset is randomly creating sequences on the fly, so the
dataloader is just going to generate a batch…

``` python
batch = ds.get_batch(bs=128)
```

``` python
batch.shape
```

torch.Size([128, 10, 1, 64, 64])

``` python
show_images(batch[0])
```

![](https://raw.githubusercontent.com/tcapelle/torch_moving_mnist/main/index_files/figure-gfm/cell-12-output-1.png)

the dataloader also is normalizing the inputs for you, after
constructing the batch.

``` python
ds.batch_tfms
```

Compose(
        ConvertImageDtype()
    )

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