From Model to Production

%load_ext autoreload
%autoreload 2

Imports

from fastai.vision.all import *
from fastai.vision.widgets import *
from aiking.data.external import * #We need to import this after fastai modules
import warnings

warnings.filterwarnings("ignore")

Constructing Dataset

clstypes = ["Grizzly Bear", 'Black Bear', "Teddy Bear"]
dest = "Bears"
path = construct_image_dataset(clstypes, dest, key=os.getenv("BING_KEY"), loc=None, count=150, engine='bing'); path

Path('/Landmark2/pdo/aiking/data/Bears')

(path/"Teddy_Bear").ls()

(#146) [Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000086.png'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000057.png'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000146.JPG'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000076.jpg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000004.jpg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000129.jpeg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000078.jpg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000127.jpg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000003.jpg'),Path('/Landmark2/pdo/aiking/data/Bears/Teddy_Bear/00000036.jpg')...]

Define DataBlock

?DataBlock

Init signature:
DataBlock(
    blocks=None,
    dl_type=None,
    getters=None,
    n_inp=None,
    item_tfms=None,
    batch_tfms=None,
    *,
    get_items=None,
    splitter=None,
    get_y=None,
    get_x=None,
)
Docstring:      Generic container to quickly build `Datasets` and `DataLoaders`
File:           ~/anaconda3/envs/aiking/lib/python3.8/site-packages/fastai/data/block.py
Type:           type
Subclasses:     

bears = DataBlock(
    blocks = (ImageBlock, CategoryBlock),
    get_items=get_image_files,
    splitter=RandomSplitter(valid_pct=0.2, seed=42), 
    get_y=parent_label,
    item_tfms=Resize(128)
); bears

<fastai.data.block.DataBlock at 0x7f127cb77970>

dls = bears.dataloaders(path); dls

<fastai.data.core.DataLoaders at 0x7f127cb027c0>

dls.valid.show_batch(max_n=4, nrows=1)

bears = bears.new(item_tfms=Resize(128, ResizeMethod.Squish))
dls = bears.dataloaders(path)
dls.valid.show_batch(max_n=4, nrows=1)

bears = bears.new(item_tfms=Resize(128, ResizeMethod.Pad, pad_mode='zeros'))
dls = bears.dataloaders(path)
dls.valid.show_batch(max_n=4, nrows=1)

bears = bears.new(item_tfms=RandomResizedCrop(300, min_scale=0.1))
dls = bears.dataloaders(path)
dls.valid.show_batch(max_n=4, nrows=1, unique=True)

bears=bears.new(item_tfms=Resize(128),
                batch_tfms=aug_transforms(mult=2))
dls=bears.dataloaders(path)
dls.train.show_batch(max_n=8,nrows=2,unique=True)

Data Augmentation

bears=bears.new(item_tfms=RandomResizedCrop(224,min_scale=0.5),
                batch_tfms=aug_transforms())
dls=bears.dataloaders(path)
dls.train.show_batch(max_n=8,nrows=2,unique=True)

Training & Fine Tuning

learn = cnn_learner(dls, resnet18, metrics=accuracy); learn

<fastai.learner.Learner at 0x7f12d7830760>

learn.fine_tune(4)

epoch	train_loss	valid_loss	accuracy	time
0	1.376441	0.216977	0.921053	00:12

epoch	train_loss	valid_loss	accuracy	time
0	0.221669	0.076347	0.986842	00:11
1	0.164557	0.035963	0.986842	00:11
2	0.114788	0.023873	1.000000	00:12
3	0.102989	0.022870	1.000000	00:11

Interpretation

interp = ClassificationInterpretation.from_learner(learn); interp

<fastai.interpret.ClassificationInterpretation at 0x7f12ce3cd340>

interp.plot_confusion_matrix()

interp.plot_top_losses(5, nrows=5)

Data Cleaning [ML Driven]

cleaner = ImageClassifierCleaner(learn)
cleaner

for idx in cleaner.delete(): cleaner.fns[idx].unlink()

for idx,cat in cleaner.change():
    shutil.move(str(cleaner.fns[idx]), path/cat)

Retraining

bears=bears.new(item_tfms=RandomResizedCrop(224,min_scale=0.5),
                batch_tfms=aug_transforms())
dls=bears.dataloaders(path)
dls.train.show_batch(max_n=8,nrows=2,unique=True)

learn = cnn_learner(dls, resnet18, metrics=accuracy); learn
learn.fine_tune(4)

epoch	train_loss	valid_loss	accuracy	time
0	1.306404	0.231466	0.934211	00:11

epoch	train_loss	valid_loss	accuracy	time
0	0.241304	0.076639	0.973684	00:11
1	0.165573	0.045445	0.986842	00:11
2	0.141758	0.055787	0.986842	00:12
3	0.109832	0.060983	0.986842	00:12

interp = ClassificationInterpretation.from_learner(learn); interp
interp.plot_confusion_matrix()

interp.plot_top_losses(3, nrows=3)

Model Export

c = Config()
# c.model_path.mkdir()
(c.learner_path/'bears.pkl').unlink()
learn.export(c.learner_path/'bears.pkl')

c.model_path.ls()

(#1) [Path('/Landmark2/pdo/aiking/models/bears.pkl')]

Load Model and Run Inference

learn_inf=load_learner(c.learner_path/'bears.pkl')
learn_inf

<fastai.learner.Learner at 0x7f127c96d550>

uploader = widgets.FileUpload(); uploader

img = PILImage.create(uploader.data[0])
img

learn_inf.predict(img)

('Grizzly_Bear', tensor(1), tensor([9.8355e-09, 1.0000e+00, 1.5711e-08]))