%reload_ext autoreload
%autoreload 2

from fastai import *
from fastai.vision import *

folder = 'black'
file = 'urls_black.txt'

folder = 'teddys'
file = 'urls_teddys.txt'

folder = 'grizzly'
file = 'urls_grizzly.txt'

path = Path('data/bears')
dest = path/folder
dest.mkdir(parents=True, exist_ok=True)

classes = ['teddys','grizzly','black']

download_images(path/file, dest, max_pics=200)

# If you have problems download, try with `max_workers=0` to see exceptions:
# download_images(path/file, dest, max_pics=20, max_workers=0)

for c in classes:
    print(c)
    verify_images(path/c, delete=True)

np.random.seed(42)
data = ImageDataBunch.from_folder(path, train=".", valid_pct=0.2,
        ds_tfms=get_transforms(), size=224, num_workers=4).normalize(imagenet_stats)

data.classes

data.show_batch(rows=3, figsize=(7,8))

data.classes, data.c, len(data.train_ds), len(data.valid_ds)

learn = create_cnn(data, models.resnet34, metrics=error_rate)

learn.fit_one_cycle(4)

learn.save('stage-1')

learn.unfreeze()

learn.lr_find()

learn.recorder.plot()

learn.fit_one_cycle(2, max_lr=slice(3e-5,3e-4))

learn.save('stage-2')

learn.load('stage-2');

interp = ClassificationInterpretation.from_learner(learn)

interp.plot_confusion_matrix()

from fastai.widgets import *

ds, idxs = DatasetFormatter().from_toplosses(learn, ds_type=DatasetType.Valid)
fd = ImageCleaner(ds, idxs)

ds, idxs = DatasetFormatter().from_similars(learn, ds_type=DatasetType.Valid)

ImageCleaner(ds, idxs, duplicates=True)

import fastai
fastai.defaults.device = torch.device('cpu')

img = open_image(path/'black'/'00000021.jpg')
img

classes = ['black', 'grizzly', 'teddys']

data2 = ImageDataBunch.single_from_classes(path, classes, tfms=get_transforms(), size=224).normalize(imagenet_stats)

learn = create_cnn(data2, models.resnet34).load('stage-2')

pred_class,pred_idx,outputs = learn.predict(img)
pred_class

learn = create_cnn(data, models.resnet34, metrics=error_rate)

learn.fit_one_cycle(1, max_lr=0.5)

learn = create_cnn(data, models.resnet34, metrics=error_rate)

learn.fit_one_cycle(5, max_lr=1e-5)

learn.recorder.plot_losses()

learn = create_cnn(data, models.resnet34, metrics=error_rate, pretrained=False)

learn.fit_one_cycle(1)

np.random.seed(42)
data = ImageDataBunch.from_folder(path, train=".", valid_pct=0.9, bs=32, 
        ds_tfms=get_transforms(do_flip=False, max_rotate=0, max_zoom=1, max_lighting=0, max_warp=0
                              ),size=224, num_workers=4).normalize(imagenet_stats)

learn = create_cnn(data, models.resnet50, metrics=error_rate, ps=0, wd=0)
learn.unfreeze()

learn.fit_one_cycle(40, slice(1e-6,1e-4))