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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
"""
from unittest import TestCase, main, skip
import numpy as np
import testenv
import ktrain
import ktrain.utils as U
from ktrain import vision as vis
from ktrain.imports import ACC_NAME, VAL_ACC_NAME
# def classify_from_csv():
# train_fpath = './resources/image_data/train-vision.csv'
# val_fpath = './resources/image_data/valid-vision.csv'
# trn, val, preproc = vis.images_from_csv(
# train_fpath,
#'filename',
# directory='./resources/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="resources/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("resources/image_data/image_folder/train/")
print(r)
self.assertEqual(r[0][1], "cat")
r = p.predict_proba_folder("resources/image_data/image_folder/train/")
self.assertEqual(np.argmax(r[0][1]), 0)
r = p.predict_filename(
"resources/image_data/image_folder/train/cat/cat.11737.jpg"
)
self.assertEqual(r, ["cat"])
r = p.predict_proba_filename(
"resources/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(
"resources/image_data/image_folder/train/cat/cat.11737.jpg"
)
self.assertEqual(r, ["cat"])
@skip("temporarily disabled")
def test_csv(self):
train_fpath = "./resources/image_data/train-vision.csv"
val_fpath = "./resources/image_data/valid-vision.csv"
trn, val, preproc = vis.images_from_csv(
train_fpath,
"filename",
directory="./resources/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("resources/image_data/image_folder/train/")
print(r)
self.assertEqual(r[0][1], "cat")
r = p.predict_proba_folder("resources/image_data/image_folder/train/")
self.assertEqual(np.argmax(r[0][1]), 0)
r = p.predict_filename(
"resources/image_data/image_folder/train/cat/cat.11737.jpg"
)
self.assertEqual(r, ["cat"])
r = p.predict_proba_filename(
"resources/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(
"resources/image_data/image_folder/train/cat/cat.11737.jpg"
)
self.assertEqual(r, ["cat"])
# @skip('temporarily disabled')
def test_array(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)
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):
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])
if __name__ == "__main__":
main()