Datasets:
HenryAI
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KerasCodeExamples.txt

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\"\"\"
def _pp(image, label, train):
if train:
channels = image.shape[-1]
begin, size, _ = tf.image.sample_distorted_bounding_box(
tf.shape(image),
tf.zeros([0, 0, 4], tf.float32),
area_range=(0.05, 1.0),
min_object_covered=0,
use_image_if_no_bounding_boxes=True,
)
image = tf.slice(image, begin, size)
image.set_shape([None, None, channels])
image = tf.image.resize(image, [image_size, image_size])
image = tf.image.random_flip_left_right(image)
else:
image = tf.image.resize(image, [image_size, image_size])
return image, label
return _pp
def preprocess_finetune(image, label, train):
\"\"\"Preprocessing function for fine-tuning on a higher resolution.
For training, resize to a bigger resolution to maintain the ratio ->
random_horizontal_flip -> center_crop.
For validation, do the same without any horizontal flipping.
No color-jittering has been used.
\"\"\"
image = tf.image.resize(image, [size_for_resizing, size_for_resizing])
if train:
image = tf.image.random_flip_left_right(image)
image = central_crop_layer(image[None, ...])[0]
return image, label
def make_dataset(
dataset: tf.data.Dataset,
train: bool,
image_size: int = smaller_size,
fixres: bool = True,
num_parallel_calls=auto,
):
if image_size not in [smaller_size, bigger_size]:
raise ValueError(f\"{image_size} resolution is not supported.\")
# Determine which preprocessing function we are using.
if image_size == smaller_size:
preprocess_func = preprocess_initial(train, image_size)
elif not fixres and image_size == bigger_size:
preprocess_func = preprocess_initial(train, image_size)
else:
preprocess_func = preprocess_finetune
if train:
dataset = dataset.shuffle(batch_size * 10)
return (
dataset.map(
lambda x, y: preprocess_func(x, y, train),
num_parallel_calls=num_parallel_calls,
)
.batch(batch_size)
.prefetch(num_parallel_calls)
)
Notice how the augmentation transforms vary for the kind of dataset we are preparing.
Prepare datasets
initial_train_dataset = make_dataset(train_dataset, train=True, image_size=smaller_size)
initial_val_dataset = make_dataset(val_dataset, train=False, image_size=smaller_size)
finetune_train_dataset = make_dataset(train_dataset, train=True, image_size=bigger_size)
finetune_val_dataset = make_dataset(val_dataset, train=False, image_size=bigger_size)
vanilla_train_dataset = make_dataset(
train_dataset, train=True, image_size=bigger_size, fixres=False
)
vanilla_val_dataset = make_dataset(
val_dataset, train=False, image_size=bigger_size, fixres=False
)
Visualize the datasets
def visualize_dataset(batch_images):
plt.figure(figsize=(10, 10))
for n in range(25):
ax = plt.subplot(5, 5, n + 1)
plt.imshow(batch_images[n].numpy().astype(\"int\"))
plt.axis(\"off\")
plt.show()
print(f\"Batch shape: {batch_images.shape}.\")
# Smaller resolution.
initial_sample_images, _ = next(iter(initial_train_dataset))
visualize_dataset(initial_sample_images)