def images_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))
return (trn, val, preproc)
def images_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),
)
return (trn, val, preproc)
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
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_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'])