Train an Autoencoder in Fastai, in 5 steps

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2020-10-04 17:45:00 +0530

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This is a quick post to understand the coding philosophy of `fastai v2.0`. I wanted to train an autoencoder on a set of images using `fastai` library and here's how it went. (It is easy, only if you know what you are looking at)

1. Get the imports and load the path that contains all the data
```python
import warnings; warnings.filterwarnings("ignore") # I have some RGBA images that keep warning me
from fastai.vision.all import *
from torch_snippets import * # This is my library, I wrapped a lot of functions into simple names

PATH = '/folder/of/images/'
```

2. One of the flexible things of fastai is the `DataBlock` api. Create a data-bunch template as follows -
```python
shoes_db =  DataBlock(
    blocks=(ImageBlock, ImageBlock),                 # Our input and output are both images
    get_items=get_image_files,                       # I'm yet to explore this, but all we are saying is "get paths of all images from this folder"
    splitter=RandomSplitter(valid_pct=0.2, seed=42), # self explanatory
    get_y=lambda x: read(x,1),                       # The target as a function of image path - read the image in color mode (torch_snippets function)
    item_tfms=RandomResizedCrop(128, min_scale=1),   # Resize every image like so, while loading
    batch_tfms=aug_transforms(mult=2.0,              # Augmentation functions.
                                do_flip=True,
                                flip_vert=True,
                                max_rotate=0,
                                max_warp=0,
                                xtra_tfms=[Normalize.from_stats(*imagenet_stats)]),
)

shoe_dls = shoes_db.dataloaders(path, bs=128)
shoe_dls.show_batch()
```

3. Get your autoencoder model in place here
```python
class AutoEncoder(nn.Module):
    ... # You can find any number of decent models online

model = AutoEncoder()
```

4. Create a learner out of the components now
```python
learn = Learner(shoe_dl, AutoEncoder().to(device), loss_func=nn.MSELoss())
```

5. And that's it! We can now `lr_find` and `fit_one_cycle` as we want like the standard tutorials tell
```python
learn.lr_find()
```

```python
learn.fit_one_cycle(10, 1e-2)
```

Ain't that easy? You have taken advantage of a plethora of library utilities like `fit_one_cycle`, `lr_find`, `kornia`'s augmentations, and more techniques in theory such as `progressive-resizing`, `half-precision` and more!

This is a quick post to understand the coding philosophy of fastai v2.0. I wanted to train an autoencoder on a set of images using fastai library and here's how it went. (It is easy, only if you know what you are looking at)

Get the imports and load the path that contains all the data

import warnings; warnings.filterwarnings("ignore") # I have some RGBA images that keep warning me
from fastai.vision.all import *
from torch_snippets import * # This is my library, I wrapped a lot of functions into simple names

PATH = '/folder/of/images/'

One of the flexible things of fastai is the DataBlock api. Create a data-bunch template as follows -

shoes_db =  DataBlock(
    blocks=(ImageBlock, ImageBlock),                 # Our input and output are both images
    get_items=get_image_files,                       # I'm yet to explore this, but all we are saying is "get paths of all images from this folder"
    splitter=RandomSplitter(valid_pct=0.2, seed=42), # self explanatory
    get_y=lambda x: read(x,1),                       # The target as a function of image path - read the image in color mode (torch_snippets function)
    item_tfms=RandomResizedCrop(128, min_scale=1),   # Resize every image like so, while loading
    batch_tfms=aug_transforms(mult=2.0,              # Augmentation functions.
                                do_flip=True,
                                flip_vert=True,
                                max_rotate=0,
                                max_warp=0,
                                xtra_tfms=[Normalize.from_stats(*imagenet_stats)]),
)

shoe_dls = shoes_db.dataloaders(path, bs=128)
shoe_dls.show_batch()

Get your autoencoder model in place here

class AutoEncoder(nn.Module):
    ... # You can find any number of decent models online

model = AutoEncoder()

Create a learner out of the components now

learn = Learner(shoe_dl, AutoEncoder().to(device), loss_func=nn.MSELoss())

And that's it! We can now lr_find and fit_one_cycle as we want like the standard tutorials tell

learn.lr_find()

learn.fit_one_cycle(10, 1e-2)

Ain't that easy? You have taken advantage of a plethora of library utilities like fit_one_cycle, lr_find, kornia's augmentations, and more techniques in theory such as progressive-resizing, half-precision and more!

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