from fastai.basics import *
from fastai.gen_doc.nbdoc import *

jekyll_note("""As usual, this page is generated from a notebook that you can find in the docs_src folder of the
<a href="https://github.com/fastai/fastai">fastai repo</a>. The examples are all designed to run fast, which is why we use
samples of the dataset, a resnet18 as a backbone and don't train for very long. You can change all of those parameters
to run your own experiments!
""")

from fastai.vision import *

mnist = untar_data(URLs.MNIST_TINY)
tfms = get_transforms(do_flip=False)

data = (ImageList.from_folder(mnist)
        .split_by_folder()          
        .label_from_folder()
        .transform(tfms, size=32)
        .databunch()
        .normalize(imagenet_stats))

data.show_batch()

data.show_batch(rows=3, figsize=(4,4))

learn = cnn_learner(data, models.resnet18, metrics=accuracy)
learn.fit_one_cycle(1,1e-2)
learn.save('mini_train')

learn.show_results()

learn.show_results(ds_type=DatasetType.Train, rows=4, figsize=(8,10))

planet = untar_data(URLs.PLANET_TINY)
planet_tfms = get_transforms(flip_vert=True, max_lighting=0.1, max_zoom=1.05, max_warp=0.)

data = (ImageList.from_csv(planet, 'labels.csv', folder='train', suffix='.jpg')
        .split_by_rand_pct()
        .label_from_df(label_delim=' ')
        .transform(planet_tfms, size=128)
        .databunch()
        .normalize(imagenet_stats))

data.show_batch(rows=2, figsize=(9,7))

learn = cnn_learner(data, models.resnet18)
learn.fit_one_cycle(5,1e-2)
learn.save('mini_train')

learn.show_results(rows=3, figsize=(12,15))

biwi = untar_data(URLs.BIWI_SAMPLE)
fn2ctr = pickle.load(open(biwi/'centers.pkl', 'rb'))

data = (PointsItemList.from_folder(biwi)
        .split_by_rand_pct(seed=42)
        .label_from_func(lambda o:fn2ctr[o.name])
        .transform(get_transforms(), tfm_y=True, size=(120,160))
        .databunch()
        .normalize(imagenet_stats))

data.show_batch(rows=3, figsize=(9,6))

learn = cnn_learner(data, models.resnet18, lin_ftrs=[100], ps=0.05)
learn.fit_one_cycle(5, 5e-2)
learn.save('mini_train')

learn.show_results(rows=3)

camvid = untar_data(URLs.CAMVID_TINY)
path_lbl = camvid/'labels'
path_img = camvid/'images'

codes = np.loadtxt(camvid/'codes.txt', dtype=str)
get_y_fn = lambda x: path_lbl/f'{x.stem}_P{x.suffix}'

data = (SegmentationItemList.from_folder(path_img)
        .split_by_rand_pct()
        .label_from_func(get_y_fn, classes=codes)
        .transform(get_transforms(), tfm_y=True, size=128)
        .databunch(bs=16, path=camvid)
        .normalize(imagenet_stats))

data.show_batch(rows=2, figsize=(7,5))

jekyll_warn("This training is fairly unstable, you should use more epochs and the full dataset to get better results.")

learn = unet_learner(data, models.resnet18)
learn.fit_one_cycle(3,1e-2)
learn.save('mini_train')

learn.show_results()

from fastai.text import *

imdb = untar_data(URLs.IMDB_SAMPLE)

data_lm = (TextList.from_csv(imdb, 'texts.csv', cols='text')
                   .split_by_rand_pct()
                   .label_for_lm()
                   .databunch())
data_lm.save()

data_lm.show_batch()

learn = language_model_learner(data_lm, AWD_LSTM)
learn.fit_one_cycle(2, 1e-2)
learn.save('mini_train_lm')
learn.save_encoder('mini_train_encoder')

learn.show_results()

data_clas = (TextList.from_csv(imdb, 'texts.csv', cols='text', vocab=data_lm.vocab)
                   .split_from_df(col='is_valid')
                   .label_from_df(cols='label')
                   .databunch(bs=42))

data_clas.show_batch()

learn = text_classifier_learner(data_clas, AWD_LSTM)
learn.load_encoder('mini_train_encoder')
learn.fit_one_cycle(2, slice(1e-3,1e-2))
learn.save('mini_train_clas')

learn.show_results()

from fastai.tabular import *

adult = untar_data(URLs.ADULT_SAMPLE)
df = pd.read_csv(adult/'adult.csv')
dep_var = 'salary'
cat_names = ['workclass', 'education', 'marital-status', 'occupation', 'relationship', 'race', 'sex', 'native-country']
cont_names = ['education-num', 'hours-per-week', 'age', 'capital-loss', 'fnlwgt', 'capital-gain']
procs = [FillMissing, Categorify, Normalize]

data = (TabularList.from_df(df, path=adult, cat_names=cat_names, cont_names=cont_names, procs=procs)
                           .split_by_idx(valid_idx=range(800,1000))
                           .label_from_df(cols=dep_var)
                           .databunch())

data.show_batch()

learn = tabular_learner(data, layers=[200,100], metrics=accuracy)
learn.fit(5, 1e-2)
learn.save('mini_train')

learn.show_results()