def get_train_augmentation_model():
model = keras.Sequential(
[
layers.Rescaling(1 / 255.0),
layers.Resizing(INPUT_SHAPE[0] + 20, INPUT_SHAPE[0] + 20),
layers.RandomCrop(IMAGE_SIZE, IMAGE_SIZE),
layers.RandomFlip("horizontal"),
],
name="train_data_augmentation",
)
return model
# number of training examples, we'll apply random augmentation # transformations (crop and horizontal flip) to them each time we are # looping over them. This way, we "augment" our training dataset to # contain more data. # # The augmentation transformations are implemented as preprocessing # layers in Keras. There are various such layers readily available, # see https://keras.io/guides/preprocessing_layers/ for more # information. # # ### Initialization inputs = keras.Input(shape=[256, 256, 3]) x = layers.Rescaling(scale=1. / 255)(inputs) x = layers.RandomCrop(160, 160)(x) x = layers.RandomFlip(mode="horizontal")(x) x = layers.Conv2D(32, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Conv2D(32, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Conv2D(64, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Flatten()(x) x = layers.Dense(64, activation='relu')(x) x = layers.Dropout(0.5)(x) outputs = layers.Dense(37, activation='softmax')(x)
x1 = x[:, 1::2, 0::2, :]
x2 = x[:, 0::2, 1::2, :]
x3 = x[:, 1::2, 1::2, :]
x = tf.concat((x0, x1, x2, x3), axis=-1)
x = tf.reshape(x, shape=(-1, (height // 2) * (width // 2), 4 * C))
return self.linear_trans(x)
"""
### Build the model
We put together the Swin Transformer model.
"""
input = layers.Input(input_shape)
x = layers.RandomCrop(image_dimension, image_dimension)(input)
x = layers.RandomFlip("horizontal")(x)
x = PatchExtract(patch_size)(x)
x = PatchEmbedding(num_patch_x * num_patch_y, embed_dim)(x)
x = SwinTransformer(
dim=embed_dim,
num_patch=(num_patch_x, num_patch_y),
num_heads=num_heads,
window_size=window_size,
shift_size=0,
num_mlp=num_mlp,
qkv_bias=qkv_bias,
dropout_rate=dropout_rate,
)(x)
x = SwinTransformer(
dim=embed_dim,
def main():
# Hyperparameters and constraints
positional_emb = True
conv_layers = 2
projection_dim = 128
num_heads = 2
transformer_units = [
projection_dim,
projection_dim,
]
transformer_layers = 2
stochastic_depth_rate = 0.1
learning_rate = 0.001
weight_decay = 0.0001
batch_size = 128
num_epochs = 30
image_size = 32
# Load CIFAR-10 dataset
num_classes = 10
input_shape = (32, 32, 3)
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data()
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
print(f"x_train shape: {x_train.shape} - y_train shape: {y_train.shape}")
print(f"x_test shape: {x_test.shape} - y_test shape: {y_test.shape}")
# The CCT tokenizer
# The first recipe introduced by the CCT authors is the tokenizer
# for processing the images. In a standard ViT, images are
# organized into uniform non-overlapping patches. This eliminates
# the boundary-level information present in between different
# patches. This is important for a neural network to effectively
# exploit the locality information. The figure below presents an
# illustration of how images are organized into patches.
# We already know that convolutions are quite good at exploiting
# locality information. So, based on this, the authors introduced
# an all-convolutional mini-network to produce image patches.
class CCTTokenizer(layers.Layer):
def __init__(self,
kernel_size=3,
stride=1,
padding=1,
pooling_kernel_size=3,
pooling_stride=2,
num_conv_layers=conv_layers,
num_output_channels=[64, 128],
positional_emb=positional_emb,
**kwargs):
super(CCTTokenizer, self).__init__(**kwargs)
# This is the tokenizer.
self.conv_model = keras.Sequential()
for i in range(num_conv_layers):
self.conv_model.add(
layers.Conv2D(
num_output_channels[i],
kernel_size,
stride,
padding="valid",
use_bias=False,
activation="relu",
kernel_initializer="he_normal",
))
self.conv_model.add(layers.ZeroPadding2D(padding))
self.conv_model.add(
layers.MaxPooling2D(pooling_kernel_size, pooling_stride,
"same"))
self.positional_emb = positional_emb
def call(self, images):
outputs = self.conv_model(images)
# After passing the images through the mini-network the
# spatial dimensions are flattened to form sequences.
reshaped = tf.reshape(
outputs, (-1, tf.shape(outputs)[1] * tf.shape(outputs)[2],
tf.shape(outputs)[-1]))
return reshaped
def positional_embedding(self, image_size):
# Positional embeddings are optional in CCT. Here, we
# calculate the number of sequences and initialize an
# 'Embedding' layer to computer the positional embeddings
# later.
if self.positional_emb:
dummy_inputs = tf.ones((1, image_size, image_size, 3))
dummy_outputs = self.call(dummy_inputs)
sequence_length = tf.shape(dummy_outputs)[1]
projection_dim = tf.shape(dummy_outputs)[-1]
embed_layer = layers.Embedding(input_dim=sequence_length,
output_dim=projection_dim)
return embed_layer, sequence_length
else:
return None
# Stochastic depth for regularization
# Stochastic depth is a regularization technique that randomly
# drops a set of layers. During inference, the layers are kept as
# they are. It is very much similar to Dropout but only that it
# operates on a block of layers rather than individual nodes
# present inside a layer. In CCT, stochastic depth is used just
# before the residual blocks of a transformer encoder.
# Referred from: github.com:rwightman/pytorch-image-models.
class StochasticDepth(layers.Layer):
def __init__(self, drop_prop, **kwargs):
super(StochasticDepth, self).__init__(**kwargs)
self.drop_prob = drop_prop
def call(self, x, training=None):
if training:
keep_prob = 1 - self.drop_prob
shape = (tf.shape(x)[0], ) + (1, ) * (len(tf.shape(x)) - 1)
random_tensor = keep_prob + tf.random.uniform(shape, 0, 1)
random_tensor = tf.floor(random_tensor)
return (x / keep_prob) * random_tensor
return x
# MLP for the transformer encoder
def mlp(x, hidden_units, dropout_rate):
for units in hidden_units:
x = layers.Dense(units, activation=tf.nn.gelu)(x)
x = layers.Dropout(dropout_rate)(x)
return x
# Data augmentation
# In the original paper, the authors use AutoAugment to induce
# stronger regularization. For this example, use the standard
# geometric augmentations like random cropping and flipping.
# Note the rescaling layer. These layers have pre-defined inference
# behavior.
data_augmentation = keras.Sequential(
[
layers.Rescaling(scale=1.0 / 255),
layers.RandomCrop(image_size, image_size),
layers.RandomFlip('horizontal'),
],
name="data_augmentation",
)
# The final CCT model
# Another recipe introduced in CCT is attention pooling or sequence
# pooling. In ViT, only the feature map corresponding to the class
# token is pooled and is then used for the subsequent
# classification task (or any other downstream task). In CCT,
# outputs from the transformers encoder are weighted and then
# passed on to the final task specific layer (in this example, we
# do classification).
def create_cct_model(image_size=image_size,
input_shape=input_shape,
num_heads=num_heads,
projection_dim=projection_dim,
transformer_units=transformer_units):
inputs = layers.Input(input_shape)
# Augment data.
augmented = data_augmentation(inputs)
# Encode patches.
cct_tokenizer = CCTTokenizer()
encoded_patches = cct_tokenizer(augmented)
# Apply positional embedding.
if positional_emb:
pos_embed, seq_length = cct_tokenizer.positional_embedding(
image_size)
positions = tf.range(start=0, limit=seq_length, delta=1)
position_embeddings = pos_embed(positions)
encoded_patches += position_embeddings
# Calculate Stochastic Depth probabilities.
dpr = [
x
for x in np.linspace(0, stochastic_depth_rate, transformer_layers)
]
# Create multiple layers of the transformer block.
for i in range(transformer_layers):
# Layer normalization 1.
x1 = layers.LayerNormalization(epsilon=1e-5)(encoded_patches)
# Create a multi-head attention layer.
attention_output = layers.MultiHeadAttention(
num_heads=num_heads, key_dim=projection_dim, dropout=0.1)(x1,
x1)
# Skip connection 1.
attention_output = StochasticDepth(dpr[i])(attention_output)
x2 = layers.Add()([attention_output, encoded_patches])
# Layer normalization 2.
x3 = layers.LayerNormalization(epsilon=1e-5)(x2)
# MLP.
x3 = mlp(x3, hidden_units=transformer_units, dropout_rate=0.1)
# Skip connection 2.
x3 = StochasticDepth(dpr[i])(x3)
encoded_patches = layers.Add()([x3, x2])
# Apply sequence pooling.
representation = layers.LayerNormalization(
epsilon=1e-5)(encoded_patches)
attention_weights = tf.nn.softmax(layers.Dense(1)(representation),
axis=1)
weighted_representation = tf.matmul(attention_weights,
representation,
transpose_a=True)
weighted_representation = tf.squeeze(weighted_representation, -2)
# Classify outputs.
logits = layers.Dense(num_classes)(weighted_representation)
# Create the keras model.
model = keras.Model(inputs=inputs, outputs=logits)
return model
# Model training and evaluation
def run_experiment(model):
optimizer = tfa.optimizers.AdamW(learning_rate=0.001,
weight_decay=0.0001)
model.compile(
optimizer=optimizer,
loss=keras.losses.CategoricalCrossentropy(from_logits=True,
label_smoothing=0.1),
metrics=[
keras.metrics.CategoricalAccuracy(name="accuracy"),
keras.metrics.TopKCategoricalAccuracy(5, name="top-5-accuracy")
],
)
checkpoint_filepath = "./tmp/checkpoint"
checkpoint_callback = keras.callbacks.ModelCheckpoint(
checkpoint_filepath,
monitor="val_accuracy",
save_best_only=True,
save_weights_only=True,
)
history = model.fit(
x=x_train,
y=y_train,
batch_size=batch_size,
epochs=num_epochs,
validation_split=0.1,
callbacks=[checkpoint_callback],
)
model.load_weights(checkpoint_filepath)
_, accuracy, top_5_accuracy = model.evaluate(x_test, y_test)
print(f"Test accuracy: {round(accuracy * 100, 2)}%")
print(f"Test top 5 accuracy: {round(top_5_accuracy * 100, 2)}%")
return history
cct_model = create_cct_model()
history = run_experiment(cct_model)
# Now visualize the training progress of the model.
'''
plt.plot(history.history["loss"], label="train_loss")
plt.plot(history.history["val_loss"], label="val_loss")
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.title("Train and Validation Losses Over Epochs", fontsize=14)
plt.legend()
plt.grid()
plt.show()
'''
# The CCT model trained above has just 0.4 million parameters, and
# it gets to ~78% top-1 accuracy within 30 epochs. The plot above
# shows no signs of overfitting as well. This means we can train
# this network for longer (perhaps with a bit more regularization)
# and may obtain even better performance. This performance can
# further be improved by additional recipes like cosine decay
# learning rate schedule, other data augmentation techniques like
# AutoAugment, MixUp or Cutmix. With these modifications, the
# authors present 95.1% top-1 accuracy on the CIFAR-10 dataset. THe
# authors also present a number of experiments to study how the
# number of convolution blocks, transformer layers, etc. affect the
# final performance of CCTs.
# For a comparison, a ViT model takes about 4.7 million parameters
# and 100 epochs of training to reach top-1 accuracy of 78.22% on
# the CIFAR-10 dataset. You can refer to this notebook to know
# about the experimental setup.
# The authors also demonstrate the performance of Compact
# Convolutional Transformers on NLP tasks and they report
# competitive results there.
# Exit the program.
exit(0)
"""
## Data augmentation
In the [original paper](https://arxiv.org/abs/2104.05704), the authors use
[AutoAugment](https://arxiv.org/abs/1805.09501) to induce stronger regularization. For
this example, we will be using the standard geometric augmentations like random cropping
and flipping.
"""
# Note the rescaling layer. These layers have pre-defined inference behavior.
data_augmentation = keras.Sequential(
[
layers.Rescaling(scale=1.0 / 255),
layers.RandomCrop(image_size, image_size),
layers.RandomFlip("horizontal"),
],
name="data_augmentation",
)
"""
## The final CCT model
Another recipe introduced in CCT is attention pooling or sequence pooling. In ViT, only
the feature map corresponding to the class token is pooled and is then used for the
subsequent classification task (or any other downstream task). In CCT, outputs from the
Transformers encoder are weighted and then passed on to the final task-specific layer (in
this example, we do classification).
"""
# number of training examples, we'll apply random augmentation # transformations (small random crop and contrast adjustment) to them # each time we are looping over them. This way, we "augment" our # training dataset to contain more data. # # The augmentation transformations are implemented as preprocessing # layers in Keras. There are various such layers readily available, # see https://keras.io/guides/preprocessing_layers/ for more # information. # # ### Initialization inputs = keras.Input(shape=INPUT_IMAGE_SIZE + [3]) x = layers.Rescaling(scale=1. / 255)(inputs) x = layers.RandomCrop(75, 75)(x) x = layers.RandomContrast(0.1)(x) x = layers.Conv2D(32, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Conv2D(32, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Conv2D(64, (3, 3), activation='relu')(x) x = layers.MaxPooling2D(pool_size=(2, 2))(x) x = layers.Flatten()(x) x = layers.Dense(128, activation='relu')(x) x = layers.Dropout(0.5)(x) outputs = layers.Dense(43, activation='softmax')(x)
"""
## Data augmentation
The augmentation pipeline consists of:
- Rescaling
- Resizing
- Random cropping (fixed-sized or random sized)
- Random horizontal flipping
"""
data_augmentation = keras.Sequential(
[
layers.Rescaling(1 / 255.0),
layers.Resizing(INPUT_SHAPE[0] + 20, INPUT_SHAPE[0] + 20),
layers.RandomCrop(IMAGE_SIZE, IMAGE_SIZE),
layers.RandomFlip("horizontal"),
],
name="data_augmentation",
)
"""
Note that image data augmentation layers do not apply data transformations at inference time.
This means that when these layers are called with `training=False` they behave differently. Refer
[to the documentation](https://keras.io/api/layers/preprocessing_layers/image_augmentation/) for more
details.
"""
"""
## Positional embedding module
A [Transformer](https://arxiv.org/abs/1706.03762) architecture consists of **multi-head
self attention** layers and **fully-connected feed forward** networks (MLP) as the main
