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test_imageclassification.py
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test_imageclassification.py
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#!/usr/bin/env python3
"""
Tests of ktrain image classification flows
"""
import testenv
from unittest import TestCase, main, skip
import numpy as np
import ktrain
from ktrain import vision as vis
import ktrain.utils as U
from ktrain.imports import ACC_NAME, VAL_ACC_NAME
#def classify_from_csv():
#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))
#print(vars(trn))
#model = vis.image_classifier('pretrained_resnet50', trn, val)
#learner = ktrain.get_learner(model, train_data=trn, val_data=val, batch_size=1)
#learner.freeze()
#hist = learner.autofit(1e-3, 10)
#return hist
class TestImageClassification(TestCase):
#@skip('temporarily disabled')
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'])
@skip('temporarily disabled')
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'])
#@skip('temporarily disabled')
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')
#@skip('temporarily disabled')
def test_array_regression(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)
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])
if __name__ == "__main__":
main()