def test_mixup_batch_fail4():
"""
Test MixUpBatch Fail 2
We expect this to fail because alpha is zero
"""
logger.info("test_mixup_batch_fail4")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5)
images_original = np.array([])
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
with pytest.raises(ValueError) as error:
vision.MixUpBatch(0.0)
error_message = "Input is not within the required interval"
assert error_message in str(error.value)
def test_mixup_batch_fail3():
"""
Test MixUpBatch op
We expect this to fail because label column is not passed to mixup_batch
"""
logger.info("test_mixup_batch_fail3")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5, drop_remainder=True)
images_original = None
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch()
data1 = data1.batch(5, drop_remainder=True)
data1 = data1.map(input_columns=["image"], operations=mixup_batch_op)
with pytest.raises(RuntimeError) as error:
images_mixup = np.array([])
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
error_message = "Both images and labels columns are required"
assert error_message in str(error.value)
def test_mixup_batch_fail1():
"""
Test MixUpBatch Fail 1
We expect this to fail because the images and labels are not batched
"""
logger.info("test_mixup_batch_fail1")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5)
images_original = np.array([])
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch(0.1)
with pytest.raises(RuntimeError) as error:
data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
error_message = "You must make sure images are HWC or CHW and batch"
assert error_message in str(error.value)
def test_mixup_batch_fail5():
"""
Test MixUpBatch Fail 5
We expect this to fail because labels are not OntHot encoded
"""
logger.info("test_mixup_batch_fail5")
# Original Images
ds_original = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
ds_original = ds_original.batch(5)
images_original = np.array([])
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
mixup_batch_op = vision.MixUpBatch()
data1 = data1.batch(5, drop_remainder=True)
data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
with pytest.raises(RuntimeError) as error:
images_mixup = np.array([])
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
error_message = "MixUpBatch: Wrong labels shape. The second column (labels) must have a shape of NC or NLC"
assert error_message in str(error.value)
def test_mixup_batch_success4(plot=False):
"""
Test MixUpBatch op on a dataset where OneHot returns a 2D vector.
Alpha parameter will be selected by default in this case
"""
logger.info("test_mixup_batch_success4")
# Original Images
ds_original = ds.CelebADataset(DATA_DIR3, shuffle=False)
decode_op = vision.Decode()
ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
ds_original = ds_original.batch(2, drop_remainder=True)
images_original = None
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.CelebADataset(DATA_DIR3, shuffle=False)
decode_op = vision.Decode()
data1 = data1.map(input_columns=["image"], operations=[decode_op])
one_hot_op = data_trans.OneHot(num_classes=100)
data1 = data1.map(input_columns=["attr"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch()
data1 = data1.batch(2, drop_remainder=True)
data1 = data1.map(input_columns=["image", "attr"], operations=mixup_batch_op)
images_mixup = np.array([])
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
if plot:
visualize_list(images_original, images_mixup)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_mixup[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_mixup_batch_success2(plot=False):
"""
Test MixUpBatch op with specified alpha parameter on ImageFolderDatasetV2
"""
logger.info("test_mixup_batch_success2")
# Original Images
ds_original = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
ds_original = ds_original.map(input_columns=["image"], operations=[decode_op])
ds_original = ds_original.batch(4, pad_info={}, drop_remainder=True)
images_original = None
for idx, (image, _) in enumerate(ds_original):
if idx == 0:
images_original = image
else:
images_original = np.append(images_original, image, axis=0)
# MixUp Images
data1 = ds.ImageFolderDatasetV2(dataset_dir=DATA_DIR2, shuffle=False)
decode_op = vision.Decode()
data1 = data1.map(input_columns=["image"], operations=[decode_op])
one_hot_op = data_trans.OneHot(num_classes=10)
data1 = data1.map(input_columns=["label"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch(2.0)
data1 = data1.batch(4, pad_info={}, drop_remainder=True)
data1 = data1.map(input_columns=["image", "label"], operations=mixup_batch_op)
images_mixup = None
for idx, (image, _) in enumerate(data1):
if idx == 0:
images_mixup = image
else:
images_mixup = np.append(images_mixup, image, axis=0)
if plot:
visualize_list(images_original, images_mixup)
num_samples = images_original.shape[0]
mse = np.zeros(num_samples)
for i in range(num_samples):
mse[i] = diff_mse(images_mixup[i], images_original[i])
logger.info("MSE= {}".format(str(np.mean(mse))))
def test_mixup_batch_md5():
"""
Test MixUpBatch with MD5:
"""
logger.info("test_mixup_batch_md5")
original_seed = config_get_set_seed(0)
original_num_parallel_workers = config_get_set_num_parallel_workers(1)
# MixUp Images
data = ds.Cifar10Dataset(DATA_DIR, num_samples=10, shuffle=False)
one_hot_op = data_trans.OneHot(num_classes=10)
data = data.map(input_columns=["label"], operations=one_hot_op)
mixup_batch_op = vision.MixUpBatch()
data = data.batch(5, drop_remainder=True)
data = data.map(input_columns=["image", "label"], operations=mixup_batch_op)
filename = "mixup_batch_c_result.npz"
save_and_check_md5(data, filename, generate_golden=GENERATE_GOLDEN)
# Restore config setting
ds.config.set_seed(original_seed)
ds.config.set_num_parallel_workers(original_num_parallel_workers)
