%reload_ext autoreload
%autoreload 2
%matplotlib inline

from fast_gen import *
from learner import *
from pt_models import *
from dataset_pt import *
from sgdr_pt import *

from planet import *

bs=64; f_model = resnet34
path = "/data/jhoward/fast/planet/"
torch.cuda.set_device(1)

n=len(list(open(f'{path}train_v2.csv')))-1

data=get_data_pad(f_model, path, 256, 64, n, 0)

learn = Learner.pretrained_convnet(f_model, data, metrics=[f2])

learn.fit(0.2, 1, cycle_len=1)

learn.sched.plot_lr()

learn.unfreeze()

learn.fit([0.01,0.05,0.2], 12, cycle_len=4)

learn.fit([1e-4,1e-3,0.01], 4)

name = '170809'

def load_cycle_cv(cv, cycle):
    data=get_data_zoom(f_model, path, 256, 64, n, cv)
    learn.set_data(data)
    learn.load_cycle(f'{name}_{cv}', cycle)
    return data

data = load_cycle_cv(0,1)

val = learn.predict()

f2(val,data.val_y)

f2(learn.TTA(),data.val_y)

f2(val,data.val_y)

f2(learn.TTA(),data.val_y)

def get_labels(a): return [data.classes[o] for o in a.nonzero()[0]]

lbls = test>0.2
idx=9
print(get_labels(lbls[idx]))
PIL.Image.open(path+data.test_dl.dataset.fnames[idx]).convert('RGB')

res = [get_labels(o) for o in lbls]
data.test_dl.dataset.fnames[:5]

outp = pd.DataFrame({'image_name': [f[9:-4] for f in data.test_dl.dataset.fnames],
        'tags': [' '.join(l) for l in res]})
outp.head()

outp.to_csv('tmp/subm.gz', compression='gzip', index=None)

from IPython.display import FileLink

FileLink('tmp/subm.gz')

def cycle_preds(name, cycle, n_tta=4, is_test=False):
    learn.load_cycle(name, cycle)
    return learn.TTA(n_tta, is_test=is_test)

def cycle_cv_preds(cv, n_tta=4, is_test=False):
    data=get_data_pad(f_model, path, 256, 64, n, cv)
    learn.set_data(data)
    return [cycle_preds(f'{name}_{cv}',i, is_test=is_test) for i in range(5)]

%%time
preds_arr=[]
for i in range(5):
    print(i)
    preds_arr.append(cycle_cv_preds(i, is_test=True))

def all_cycle_cv_preds(end_cycle, start_cycle=0, n_tta=4, is_test=False):
    return [cycle_cv_preds(i, is_test=is_test) for i in range(start_cycle, end_cycle)]

np.savez_compressed(f'{path}tmp/test_preds', preds_arr)

preds_avg = [np.mean(o,0) for o in preds_arr]
test = np.mean(preds_avg,0)

%time preds_arr = all_cycle_cv_preds(5)

[f2(preds_arr[0][o],data.val_y) for o in range(5)]

preds_avg = [np.mean(o,0) for o in preds_arr]

ys = [get_data_zoom(f_model, path, 256, 64, n, cv).val_y for cv in range(5)]

f2s = [f2(o,y) for o,y in zip(preds_avg,ys)]; f2s

ots = [opt_th(o,y) for o,y in zip(preds_avg,ys)]; ots

np.mean(ots)

np.mean(f2s,0)