def test_folder(self):
(trn, val, preproc) = vis.images_from_folder(
datadir='image_data/image_folder',
data_aug=vis.get_data_aug(horizontal_flip=True),
classes=['cat', 'dog'],
train_test_names=['train', 'valid'])
model = vis.image_classifier('pretrained_resnet50', trn, val)
learner = ktrain.get_learner(model=model, train_data=trn, val_data=val, batch_size=1)
learner.freeze()
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# train
hist = learner.autofit(1e-3, monitor=VAL_ACC_NAME)
# test train
self.assertAlmostEqual(max(hist.history['lr']), 1e-3)
if max(hist.history[ACC_NAME]) == 0.5:
raise Exception('unlucky initialization: please run test again')
self.assertGreater(max(hist.history[ACC_NAME]), 0.8)
# test top_losses
obs = learner.top_losses(n=1, val_data=val)
print(obs)
if obs:
self.assertIn(obs[0][0], list(range(U.nsamples_from_data(val))))
else:
self.assertEqual(max(hist.history[VAL_ACC_NAME]), 1)
# test load and save model
learner.save_model('/tmp/test_model')
learner.load_model('/tmp/test_model')
# test validate
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc)
r = p.predict_folder('image_data/image_folder/train/')
print(r)
self.assertEqual(r[0][1], 'cat')
r = p.predict_proba_folder('image_data/image_folder/train/')
self.assertEqual(np.argmax(r[0][1]), 0)
r = p.predict_filename('image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(r, ['cat'])
r = p.predict_proba_filename('image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(np.argmax(r), 0)
p.save('/tmp/test_predictor')
p = ktrain.load_predictor('/tmp/test_predictor')
r = p.predict_filename('image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(r, ['cat'])
def __test_ner(self, trn, val, preproc):
self.assertTrue(U.is_iter(trn))
self.assertTrue(U.is_ner(data=trn))
self.assertFalse(U.is_multilabel(trn))
self.assertEqual(U.shape_from_data(trn), (14041, 47))
self.assertFalse(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), 14041)
self.assertEqual(U.nclasses_from_data(trn), 10)
self.assertEqual(len(U.y_from_data(trn)), 14041)
self.assertFalse(U.bert_data_tuple(trn))
self.assertEqual(
preproc.get_classes(),
[
"<pad>",
"O",
"B-LOC",
"B-PER",
"B-ORG",
"I-PER",
"I-ORG",
"B-MISC",
"I-LOC",
"I-MISC",
],
)
nerseq = preproc.preprocess(["hello world"])
self.assertEqual(len(nerseq), 1)
self.assertEqual(nerseq[0][0][0][0].tolist(), [21010, 100])
def __test_images(self, trn, val, preproc, nsamples=16):
self.assertTrue(U.is_iter(trn))
self.assertEqual(U.shape_from_data(trn), (224, 224, 3))
self.assertTrue(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), nsamples)
self.assertEqual(U.nclasses_from_data(trn), 2)
self.assertEqual(U.y_from_data(trn).shape, (nsamples,2))
self.assertFalse(U.bert_data_tuple(trn))
self.assertEqual(preproc.get_classes(), ['cat', 'dog'])
(gen, steps) = preproc.preprocess('./image_data/image_folder/all')
self.assertEqual(type(gen).__name__, 'DirectoryIterator')
self.assertEqual(steps, 1)
def test_images_from_fname_regression(self):
(trn, val, preproc) = images_from_fname_regression()
nsamples = 18
self.assertTrue(U.is_iter(trn))
self.assertEqual(U.shape_from_data(trn), (224, 224, 3))
self.assertTrue(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), nsamples)
#self.assertRaises(Exception, U.nclasses_from_data(trn))
self.assertEqual(U.y_from_data(trn).shape, (nsamples,))
self.assertFalse(U.bert_data_tuple(trn))
self.assertEqual(preproc.get_classes(), [])
(gen, steps) = preproc.preprocess('./image_data/image_folder/all')
self.assertEqual(type(gen).__name__, 'DirectoryIterator')
self.assertEqual(steps, 1)
def __test_texts_bert(self, trn, val, preproc):
self.assertFalse(U.is_iter(trn))
self.assertEqual(trn[0][0].shape, (4, 10))
self.assertEqual(trn[1].shape, (4, 2))
self.assertEqual(val[0][0].shape, (4, 10))
self.assertEqual(val[1].shape, (4, 2))
self.assertFalse(U.is_multilabel(trn))
self.assertEqual(U.shape_from_data(trn), (4, 10))
self.assertFalse(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), 4)
self.assertEqual(U.nclasses_from_data(trn), 2)
self.assertEqual(U.y_from_data(trn).shape, (4,2))
self.assertTrue(U.bert_data_tuple(trn))
self.assertEqual(preproc.get_classes(), preproc.get_classes())
self.assertEqual(preproc.preprocess(['hello book'])[0][0][0], 101)
self.assertEqual(preproc.preprocess(['hello book'])[0].shape, (1, 10))
self.assertEqual(preproc.undo(val[0][0][0]), '[CLS] the book is bad . [SEP]')
def __test_texts_standard(self, trn, val, preproc):
self.assertFalse(U.is_iter(trn))
self.assertEqual(trn[0].shape, (4, 10))
self.assertEqual(trn[1].shape, (4, 2))
self.assertEqual(val[0].shape, (4, 10))
self.assertEqual(val[1].shape, (4, 2))
self.assertFalse(U.is_multilabel(trn))
self.assertEqual(U.shape_from_data(trn), (4, 10))
self.assertFalse(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), 4)
self.assertEqual(U.nclasses_from_data(trn), 2)
self.assertEqual(U.y_from_data(trn).shape, (4,2))
self.assertFalse(U.bert_data_tuple(trn))
self.assertEqual(preproc.get_classes(), preproc.get_classes())
self.assertEqual(preproc.ngram_count(), 3)
self.assertEqual(preproc.preprocess(['hello book'])[0][-1], 1)
self.assertEqual(preproc.preprocess(['hello book']).shape, (1, 10))
self.assertEqual(preproc.undo(val[0][0]), 'the book is bad')
def __test_texts_tfidf(self, trn, val, preproc):
self.assertFalse(U.is_iter(trn))
self.assertEqual(trn[0].shape, (4, 100))
self.assertEqual(trn[1].shape, (4, 2))
self.assertEqual(val[0].shape, (4, 100))
self.assertEqual(val[1].shape, (4, 2))
self.assertFalse(U.is_multilabel(trn))
self.assertEqual(U.shape_from_data(trn), (4, 100))
self.assertFalse(U.ondisk(trn))
self.assertEqual(U.nsamples_from_data(trn), 4)
self.assertEqual(U.nclasses_from_data(trn), 2)
self.assertEqual(U.y_from_data(trn).shape, (4, 2))
self.assertFalse(U.bert_data_tuple(trn))
self.assertEqual(preproc.get_classes(), ['neg', 'pos'])
self.assertEqual(preproc.ngram_count(), 1)
self.assertEqual('%.4f' % (preproc.preprocess(['hello book'])[0][1]),
'0.5878')
self.assertEqual(preproc.preprocess(['hello book']).shape, (1, 100))
self.assertEqual(preproc.undo(val[0][0]), 'book is the bad')
def test_csv(self):
train_fpath = "./image_data/train-vision.csv"
val_fpath = "./image_data/valid-vision.csv"
trn, val, preproc = vis.images_from_csv(
train_fpath,
"filename",
directory="./image_data/image_folder/all",
val_filepath=val_fpath,
label_columns=["cat", "dog"],
data_aug=vis.get_data_aug(horizontal_flip=True),
)
lr = 1e-4
model = vis.image_classifier("pretrained_resnet50", trn, val)
learner = ktrain.get_learner(
model=model, train_data=trn, val_data=val, batch_size=4
)
learner.freeze()
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# train
hist = learner.fit_onecycle(lr, 3)
# test train
self.assertAlmostEqual(max(hist.history["lr"]), lr)
if max(hist.history[ACC_NAME]) == 0.5:
raise Exception("unlucky initialization: please run test again")
self.assertGreater(max(hist.history[ACC_NAME]), 0.8)
# test top_losses
obs = learner.top_losses(n=1, val_data=val)
print(obs)
if obs:
self.assertIn(obs[0][0], list(range(U.nsamples_from_data(val))))
else:
self.assertEqual(max(hist.history[VAL_ACC_NAME]), 1)
# test load and save model
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# test validate
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc)
r = p.predict_folder("image_data/image_folder/train/")
print(r)
self.assertEqual(r[0][1], "cat")
r = p.predict_proba_folder("image_data/image_folder/train/")
self.assertEqual(np.argmax(r[0][1]), 0)
r = p.predict_filename("image_data/image_folder/train/cat/cat.11737.jpg")
self.assertEqual(r, ["cat"])
r = p.predict_proba_filename("image_data/image_folder/train/cat/cat.11737.jpg")
self.assertEqual(np.argmax(r), 0)
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor")
r = p.predict_filename("image_data/image_folder/train/cat/cat.11737.jpg")
self.assertEqual(r, ["cat"])
def test_array_regression(self):
import numpy as np
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype("float32")
x_test = x_test.astype("float32")
x_train /= 255
x_test /= 255
x_train = np.expand_dims(x_train, axis=3)
x_test = np.expand_dims(x_test, axis=3)
classes = None
data_aug = vis.get_data_aug(
rotation_range=15,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1,
featurewise_center=False,
featurewise_std_normalization=False,
)
(trn, val, preproc) = vis.images_from_array(
x_train,
y_train,
validation_data=(x_test, y_test),
data_aug=data_aug,
is_regression=True,
class_names=classes,
)
model = vis.image_regression_model("default_cnn", trn, val)
learner = ktrain.get_learner(
model, train_data=trn, val_data=val, batch_size=128
)
hist = learner.fit_onecycle(1e-3, 1)
# test train
self.assertAlmostEqual(max(hist.history["lr"]), 1e-3)
self.assertLess(max(hist.history["val_mae"]), 1)
# test top_losses
obs = learner.top_losses(n=1, val_data=val)
print(obs)
if obs:
self.assertIn(obs[0][0], list(range(U.nsamples_from_data(val))))
else:
self.assertEqual(max(hist.history[VAL_ACC_NAME]), 1)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
learner.save_model("/tmp/test_model")
learner.load_model("/tmp/test_model")
# test validate
cm = learner.validate(val_data=val)
print(cm)
p = ktrain.get_predictor(learner.model, preproc)
r = p.predict(x_test[0:1])
print(r)
self.assertIn(round(r[0]), [6, 7, 8])
r = p.predict(x_test[0:1], return_proba=True)
self.assertIn(round(r[0]), [6, 7, 8])
p.save("/tmp/test_predictor")
p = ktrain.load_predictor("/tmp/test_predictor")
r = p.predict(x_test[0:1])
self.assertIn(round(r[0]), [6, 7, 8])
def test_array(self):
from tensorflow.keras.datasets import mnist
from tensorflow.keras.utils import to_categorical
import numpy as np
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
x_train = np.expand_dims(x_train, axis=3)
x_test = np.expand_dims(x_test, axis=3)
y_train = to_categorical(y_train)
y_test = to_categorical(y_test)
classes = [
'zero', 'one', 'two', 'three', 'four', 'five', 'six', 'seven',
'eight', 'nine'
]
data_aug = vis.get_data_aug(
rotation_range=15,
zoom_range=0.1,
width_shift_range=0.1,
height_shift_range=0.1,
featurewise_center=False,
featurewise_std_normalization=False,
)
(trn, val, preproc) = vis.images_from_array(x_train,
y_train,
validation_data=(x_test,
y_test),
data_aug=data_aug,
class_names=classes)
model = vis.image_classifier('default_cnn', trn, val)
learner = ktrain.get_learner(model,
train_data=trn,
val_data=val,
batch_size=128)
hist = learner.fit_onecycle(1e-3, 1)
# test train
self.assertAlmostEqual(max(hist.history['lr']), 1e-3)
self.assertGreater(max(hist.history[VAL_ACC_NAME]), 0.97)
# test top_losses
obs = learner.top_losses(n=1, val_data=val)
print(obs)
if obs:
self.assertIn(obs[0][0], list(range(U.nsamples_from_data(val))))
else:
self.assertEqual(max(hist.history[VAL_ACC_NAME]), 1)
# test weight decay
self.assertEqual(learner.get_weight_decay(), None)
learner.set_weight_decay(1e-2)
self.assertAlmostEqual(learner.get_weight_decay(), 1e-2)
# test load and save model
learner.save_model('/tmp/test_model')
learner.load_model('/tmp/test_model')
# test validate
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
p = ktrain.get_predictor(learner.model, preproc)
r = p.predict(x_test[0:1])
print(r)
self.assertEqual(r[0], 'seven')
r = p.predict(x_test[0:1], return_proba=True)
self.assertEqual(np.argmax(r[0]), 7)
p.save('/tmp/test_predictor')
p = ktrain.load_predictor('/tmp/test_predictor')
r = p.predict(x_test[0:1])
self.assertEqual(r[0], 'seven')
def test_csv(self):
train_fpath = './image_data/train-vision.csv'
val_fpath = './image_data/valid-vision.csv'
trn, val, preproc = vis.images_from_csv(
train_fpath,
'filename',
directory='./image_data/image_folder/all',
val_filepath=val_fpath,
label_columns=['cat', 'dog'],
data_aug=vis.get_data_aug(horizontal_flip=True))
lr = 1e-4
model = vis.image_classifier('pretrained_resnet50', trn, val)
learner = ktrain.get_learner(model=model,
train_data=trn,
val_data=val,
batch_size=4)
learner.freeze()
hist = learner.fit_onecycle(lr, 3)
# test train
self.assertAlmostEqual(max(hist.history['lr']), lr)
if max(hist.history[ACC_NAME]) == 0.5:
raise Exception('unlucky initialization: please run test again')
self.assertGreater(max(hist.history[ACC_NAME]), 0.8)
# test top_losses
obs = learner.top_losses(n=1, val_data=val)
print(obs)
if obs:
self.assertIn(obs[0][0], list(range(U.nsamples_from_data(val))))
else:
self.assertEqual(max(hist.history[VAL_ACC_NAME]), 1)
# test weight decay
self.assertEqual(len(learner.get_weight_decay()), 54)
self.assertEqual(learner.get_weight_decay()[0], None)
learner.set_weight_decay(1e-4)
self.assertAlmostEqual(learner.get_weight_decay()[0], 1e-4)
# test load and save model
learner.save_model('/tmp/test_model')
learner.load_model('/tmp/test_model')
# test validate
cm = learner.validate(val_data=val)
print(cm)
for i, row in enumerate(cm):
self.assertEqual(np.argmax(row), i)
# test predictor
p = ktrain.get_predictor(learner.model, preproc)
r = p.predict_folder('image_data/image_folder/train/')
print(r)
self.assertEqual(r[0][1], 'cat')
r = p.predict_proba_folder('image_data/image_folder/train/')
self.assertEqual(np.argmax(r[0][1]), 0)
r = p.predict_filename(
'image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(r, ['cat'])
r = p.predict_proba_filename(
'image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(np.argmax(r), 0)
p.save('/tmp/test_predictor')
p = ktrain.load_predictor('/tmp/test_predictor')
r = p.predict_filename(
'image_data/image_folder/train/cat/cat.11737.jpg')
self.assertEqual(r, ['cat'])
