import sys
import logging
import numpy as np
from sklearn.cross_validation import ShuffleSplit
# from sklearn.cross_validation import StratifiedShuffleSplit
from sklearn.cross_validation import StratifiedKFold
class TrainSplit(object):
def __init__(self, eval_size, stratify=True, random_state=None):
self.eval_size = eval_size
self.stratify = stratify
self.random_state = random_state
def __call__(self, X, y, net):
if self.eval_size is not None:
if net.regression or not self.stratify:
# test_size = self.eval_size
# kf = ShuffleSplit(
# y.shape[0], test_size=test_size,
# random_state=self.random_state
# )
# train_indices, valid_indices = next(iter(kf))
# valid_indices = shuffle(valid_indices)
test_size = 1 - self.eval_size
kf = ShuffleSplit(
y.shape[0], test_size=test_size,
random_state=self.random_state
)
valid_indices, train_indices = next(iter(kf))
else:
n_folds = int(round(1 / self.eval_size))
kf = StratifiedKFold(y, n_folds=n_folds, random_state=self.random_state)
train_indices, valid_indices = next(iter(kf))
X_train, y_train = X[train_indices], y[train_indices]
X_valid, y_valid = X[valid_indices], y[valid_indices]
else:
X_train, y_train = X, y
X_valid, y_valid = X[len(X):], y[len(y):]
return X_train, X_valid, y_train, y_valid
def add_padding_to_bbox(x, y, w, h, pad, max_x, max_y, format='ltwh'):
l, t = x, y
r, b = l + w, t + h
pad_x = int(round(w * pad / 2))
pad_y = int(round(h * pad / 2))
new_l = min(max(l - pad_x, 0), max_x)
new_r = min(max(r + pad_x, 0), max_x)
new_t = min(max(t - pad_y, 0), max_y)
new_b = min(max(b + pad_y, 0), max_y)
new_w = new_r - new_l
new_h = new_b - new_t
if format == 'ltwh':
return int(new_l), int(new_t), int(new_w), int(new_h)
elif format == 'ltrb':
return int(new_l), int(new_t), int(new_r), int(new_b)
else:
raise ValueError('Format is not recongized: %s' % format)
class LogFile(object):
"""
File-like object to log text using the `logging` module.
http://stackoverflow.com/questions/616645/how-do-i-duplicate-sys-stdout-to-a-log-file-in-python/2216517#2216517
"""
def __init__(self, name=None, stdout=None):
self.logger = logging.getLogger(name)
self.stdout = stdout
def write(self, msg, level=logging.DEBUG):
self.logger.log(level, msg)
if self.stdout is not None:
self.stdout.write(msg)
def flush(self):
# for handler in self.logger.handlers:
# handler.flush()
if self.stdout is not None:
self.stdout.flush()
def mirror_to_log(fname):
logging.basicConfig(level=logging.DEBUG, filename=fname)
# Redirect stdout
sys.stdout = LogFile(fname, sys.stdout)
def float32(k):
return np.cast['float32'](k)
