planet.py

from fastai.imports import *
from fastai.transforms import *
from fastai.dataset import *
from sklearn.metrics import fbeta_score
import warnings

def f2(preds, targs, start=0.17, end=0.24, step=0.01):
    with warnings.catch_warnings():
        warnings.simplefilter("ignore")
        return max([fbeta_score(targs, (preds>th), 2, average='samples')
                    for th in np.arange(start,end,step)])

def opt_th(preds, targs, start=0.17, end=0.24, step=0.01):
    ths = np.arange(start,end,step)
    idx = np.argmax([fbeta_score(targs, (preds>th), 2, average='samples')
                for th in ths])
    return ths[idx]

def get_data(path, tfms,bs,  n, cv_idx):
    val_idxs = get_cv_idxs(n, cv_idx)
    return ImageClassifierData.from_csv(path, 'train-jpg', f'{path}train_v2.csv', bs, tfms,
                                 suffix='.jpg', val_idxs=val_idxs, test_name='test-jpg')

def get_data_zoom(f_model, path, sz, bs, n, cv_idx):
    tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_top_down, max_zoom=1.05)
    return get_data(path, tfms, bs, n, cv_idx)

def get_data_pad(f_model, path, sz, bs, n, cv_idx):
    transforms_pt = [RandomRotateZoom(9, 0.18, 0.1), RandomLighting(0.05, 0.1), RandomDihedral()]
    tfms = tfms_from_model(f_model, sz, aug_tfms=transforms_pt, pad=sz//12)
    return get_data(path, tfms, bs, n, cv_idx)