def test_shuffle(dataset: ILSVRCDataset) -> None:
"""Tests that the shuffling flag works as expected. Also tests that
filenames and labels are still properly mapped.
:param dataset: the dataset.
"""
dataset.trim_dataset(DATASET_FRACTION)
x_train_filenames = dataset.partition[TRAIN_KEY]
y_train = dataset.get_labels(x_train_filenames, True, NUM_CLASSES)
train_sequence = ImageDatasetSequence(x_train_filenames,
y=y_train,
batch_size=BATCH_SIZE,
image_target_size=IMAGE_TARGET_SIZE,
batch_augment_fn=None,
batch_format_fn=None,
overfit_single_batch=False,
shuffle_on_epoch_end=True)
img_to_label_before = {}
for batch in train_sequence:
x_batch, y_batch = batch
for i in range(x_batch.shape[0]):
img_data = tuple(x_batch[i].flatten())
label = tuple(y_batch[i])
img_to_label_before[img_data] = label
# Test shuffle.
first_batch_before = train_sequence.__getitem__(0)
train_sequence.on_epoch_end()
first_batch_after = train_sequence.__getitem__(0)
assert (first_batch_before[0] != first_batch_after[0]).any()
# Test filename/label mappings.
for batch in train_sequence:
x_batch, y_batch = batch
for i in range(x_batch.shape[0]):
img_data = tuple(x_batch[i].flatten())
label = tuple(y_batch[i])
assert img_to_label_before[img_data] == label
def test_images(dataset: ILSVRCDataset) -> None:
"""Tests that the sequence output images meet expected standards.
:param dataset: the dataset.
"""
dataset.trim_dataset(DATASET_FRACTION)
x_train_filenames = dataset.partition[TRAIN_KEY]
y_train = dataset.get_labels(x_train_filenames, True, NUM_CLASSES)
train_sequence = ImageDatasetSequence(x_train_filenames,
y=y_train,
batch_size=BATCH_SIZE,
image_target_size=IMAGE_TARGET_SIZE,
batch_augment_fn=None,
batch_format_fn=None,
overfit_single_batch=False,
shuffle_on_epoch_end=True)
# Test that only the last batch is not of length BATCH_SIZE.
# Also test that there are the correct number of batches.
on_last_batch = False
num_batches_seen = 0
for batch in train_sequence:
assert not on_last_batch
x_batch, y_batch = batch
# Take the first image/label pair and check that it meets standards.
# Check that the image is of the right size.
assert x_batch[0].shape == IMAGE_TARGET_SIZE + (3, )
# Check that the image is of the right datatype.
assert x_batch.dtype == np.float32
# Check that the image is normalized.
assert (0.0 <= x_batch.flatten()).all()
assert (x_batch.flatten() <= 1.0).all()
# Check that the label is categorical and of the right dimension.
assert y_batch.shape[1] == NUM_CLASSES
# Check that the label is of the right datatype.
assert y_batch.dtype == np.float32
# Check that the label is one-hot.
for label in y_batch:
assert sum(label) == 1
on_last_batch = not (x_batch.shape[0] == BATCH_SIZE
and y_batch.shape[0] == BATCH_SIZE)
num_batches_seen += 1
assert num_batches_seen == len(train_sequence)
def test_overfit_single_batch(dataset: ILSVRCDataset) -> None:
"""Tests that the same batch of images is always presented to the
model if overfitting on a single batch.
:param dataset: the dataset.
"""
dataset.trim_dataset(DATASET_FRACTION)
x_train_filenames = dataset.partition[TRAIN_KEY]
y_train = dataset.get_labels(x_train_filenames, True, NUM_CLASSES)
# Test that you can't set overfit and shuffle flags together.
train_sequence = ImageDatasetSequence(x_train_filenames,
y=y_train,
batch_size=BATCH_SIZE,
image_target_size=IMAGE_TARGET_SIZE,
batch_augment_fn=None,
batch_format_fn=None,
overfit_single_batch=True,
shuffle_on_epoch_end=True)
with pytest.raises(ValueError):
for _ in train_sequence:
pass
# Test that you always get the same batch, even after multiple epochs.
train_sequence = ImageDatasetSequence(x_train_filenames,
y=y_train,
batch_size=BATCH_SIZE,
image_target_size=IMAGE_TARGET_SIZE,
batch_augment_fn=None,
batch_format_fn=None,
overfit_single_batch=True,
shuffle_on_epoch_end=False)
num_batches_epoch_1 = 0
for batch in train_sequence:
assert (batch[0] == train_sequence.__getitem__(0)[0]).all()
num_batches_epoch_1 += 1
train_sequence.on_epoch_end()
num_batches_epoch_2 = 0
for batch in train_sequence:
assert (batch[0] == train_sequence.__getitem__(0)[0]).all()
num_batches_epoch_2 += 1
assert num_batches_epoch_1 == num_batches_epoch_2