%reload_ext autoreload
%autoreload 2
%matplotlib inline

from fastai.conv_learner import *

PATH = 'data/planet/'

# Data preparation steps if you are using Crestle:

os.makedirs('data/planet/models', exist_ok=True)
os.makedirs('/cache/planet/tmp', exist_ok=True)

!ln -s /datasets/kaggle/planet-understanding-the-amazon-from-space/train-jpg {PATH}
!ln -s /datasets/kaggle/planet-understanding-the-amazon-from-space/test-jpg {PATH}
!ln -s /datasets/kaggle/planet-understanding-the-amazon-from-space/train_v2.csv {PATH}
!ln -s /cache/planet/tmp {PATH}

ls {PATH}

from fastai.plots import *

def get_1st(path): return glob(f'{path}/*.*')[0]

dc_path = "data/dogscats/valid/"
list_paths = [get_1st(f"{dc_path}cats"), get_1st(f"{dc_path}dogs")]
plots_from_files(list_paths, titles=["cat", "dog"], maintitle="Single-label classification")

list_paths = [f"{PATH}train-jpg/train_0.jpg", f"{PATH}train-jpg/train_1.jpg"]
titles=["haze primary", "agriculture clear primary water"]
plots_from_files(list_paths, titles=titles, maintitle="Multi-label classification")

from planet import f2

metrics=[f2]
f_model = resnet34

label_csv = f'{PATH}train_v2.csv'
n = len(list(open(label_csv)))-1
val_idxs = get_cv_idxs(n)

def get_data(sz):
    tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_top_down, max_zoom=1.05)
    return ImageClassifierData.from_csv(PATH, 'train-jpg', label_csv, tfms=tfms,
                    suffix='.jpg', val_idxs=val_idxs, test_name='test-jpg')

data = get_data(256)

x,y = next(iter(data.val_dl))

y

list(zip(data.classes, y[0]))

plt.imshow(data.val_ds.denorm(to_np(x))[0]*1.4);

sz=64

data = get_data(sz)

data = data.resize(int(sz*1.3), 'tmp')

learn = ConvLearner.pretrained(f_model, data, metrics=metrics)

lrf=learn.lr_find()
learn.sched.plot()

lr = 0.2

learn.fit(lr, 3, cycle_len=1, cycle_mult=2)

lrs = np.array([lr/9,lr/3,lr])

learn.unfreeze()
learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)

learn.save(f'{sz}')

learn.sched.plot_loss()

sz=128

learn.set_data(get_data(sz))
learn.freeze()
learn.fit(lr, 3, cycle_len=1, cycle_mult=2)

learn.unfreeze()
learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)
learn.save(f'{sz}')

sz=256

learn.set_data(get_data(sz))
learn.freeze()
learn.fit(lr, 3, cycle_len=1, cycle_mult=2)

learn.unfreeze()
learn.fit(lrs, 3, cycle_len=1, cycle_mult=2)
learn.save(f'{sz}')

multi_preds, y = learn.TTA()
preds = np.mean(multi_preds, 0)

f2(preds,y)