def __init__(self): super().__init__() # stores the current probability of an image being augmented self.probability = tf.Variable(0.0) # the corresponding augmentation names from the paper are shown above each layer # the authors show (see figure 4), that the blitting and geometric augmentations # are the most helpful in the low-data regime self.augmenter = keras.Sequential( [ layers.InputLayer(input_shape=(image_size, image_size, 3)), # blitting/x-flip: layers.RandomFlip("horizontal"), # blitting/integer translation: layers.RandomTranslation( height_factor=max_translation, width_factor=max_translation, interpolation="nearest", ), # geometric/rotation: layers.RandomRotation(factor=max_rotation), # geometric/isotropic and anisotropic scaling: layers.RandomZoom(height_factor=(-max_zoom, 0.0), width_factor=(-max_zoom, 0.0)), ], name="adaptive_augmenter", )
def build(self, hp, inputs=None): input_node = nest.flatten(inputs)[0] output_node = input_node # Translate translation_factor = utils.add_to_hp(self.translation_factor, hp) if translation_factor not in [0, (0, 0)]: height_factor, width_factor = self._get_fraction_value( translation_factor ) output_node = layers.RandomTranslation(height_factor, width_factor)( output_node ) # Flip horizontal_flip = self.horizontal_flip if horizontal_flip is None: horizontal_flip = hp.Boolean("horizontal_flip", default=True) vertical_flip = self.vertical_flip if self.vertical_flip is None: vertical_flip = hp.Boolean("vertical_flip", default=True) if not horizontal_flip and not vertical_flip: flip_mode = "" elif horizontal_flip and vertical_flip: flip_mode = "horizontal_and_vertical" elif horizontal_flip and not vertical_flip: flip_mode = "horizontal" elif not horizontal_flip and vertical_flip: flip_mode = "vertical" if flip_mode != "": output_node = layers.RandomFlip(mode=flip_mode)(output_node) # Rotate rotation_factor = utils.add_to_hp(self.rotation_factor, hp) if rotation_factor != 0: output_node = layers.RandomRotation(rotation_factor)(output_node) # Zoom zoom_factor = utils.add_to_hp(self.zoom_factor, hp) if zoom_factor not in [0, (0, 0)]: height_factor, width_factor = self._get_fraction_value(zoom_factor) # TODO: Add back RandomZoom when it is ready. # output_node = layers.RandomZoom( # height_factor, width_factor)(output_node) # Contrast contrast_factor = utils.add_to_hp(self.contrast_factor, hp) if contrast_factor not in [0, (0, 0)]: output_node = layers.RandomContrast(contrast_factor)(output_node) return output_node
def build_model_augment() -> keras.Sequential: # build a sequential model model = keras.Sequential() # add a data augmentation layer data_augmentation = keras.Sequential([ # random flip horizontally layers.RandomFlip("horizontal"), # random flip vertically layers.RandomFlip("vertical"), # random rotation at most 0.2 degrees layers.RandomRotation(0.2), # random zoom at most 0.2 times difference layers.RandomZoom(0.2), # random contrast at most 0.1 difference layers.RandomContrast(0.1) ]) model.add(data_augmentation) # add the first convolutional layer, with ReLU as activation funtion and same padding model.add( keras.layers.Conv2D(32, (3, 3), activation="relu", padding="same", input_shape=(32, 32, 3))) # [32,32, 32] # add a maxpooling layer to reduce the dimension model.add(keras.layers.MaxPooling2D(2, 2)) # [16, 16, 32] # add the second convolutional layer, with ReLU as activation funtion and same padding model.add( keras.layers.Conv2D(64, (3, 3), activation="relu", padding="same")) # [16, 16, 64] # add a maxpooling layer to reduce the dimension model.add(keras.layers.MaxPooling2D(2, 2)) # [8, 8, 64] # add the third convolutional layer, with ReLU as activation funtion and same padding model.add( keras.layers.Conv2D(128, (3, 3), activation="relu", padding="same")) # [8, 8, 128] # add a maxpooling layer to reduce the dimension model.add(keras.layers.MaxPooling2D(2, 2)) # [4, 4, 128] # add a flatten layer to make the data into a 1-dimensional array model.add(keras.layers.Flatten()) # [1024, ] # add a fully connected layer, with ReLU as activation function model.add(keras.layers.Dense(128, activation="relu")) # add a fully connected layer, the output size is the same as the number of classes and # softmax as activation function to do multiclass classification model.add(keras.layers.Dense(10, activation="softmax")) # compile the model, use SDG as the optimizer and categorical crossentropy as loss function model.compile(optimizer=optimizers.SGD(learning_rate=1e-3, momentum=0.9), loss="categorical_crossentropy", metrics=["accuracy"]) # return the model return model
ax = plt.subplot(3, 3, i + 1) plt.imshow(image.numpy().astype("uint8")) plt.title("{}".format(format_label(label))) plt.axis("off") """ ### Data augmentation We can use the preprocessing layers APIs for image augmentation. """ from tensorflow.keras.models import Sequential from tensorflow.keras import layers img_augmentation = Sequential( [ layers.RandomRotation(factor=0.15), layers.RandomTranslation(height_factor=0.1, width_factor=0.1), layers.RandomFlip(), layers.RandomContrast(factor=0.1), ], name="img_augmentation", ) """ This `Sequential` model object can be used both as a part of the model we later build, and as a function to preprocess data before feeding into the model. Using them as function makes it easy to visualize the augmented images. Here we plot 9 examples of augmentation result of a given figure. """ for image, label in ds_train.take(1):
plt.imshow(images[i].numpy().astype("uint8")) plt.title(int(labels[i])) plt.axis("off") """ ## Using image data augmentation When you don't have a large image dataset, it's a good practice to artificially introduce sample diversity by applying random yet realistic transformations to the training images, such as random horizontal flipping or small random rotations. This helps expose the model to different aspects of the training data while slowing down overfitting. """ data_augmentation = keras.Sequential( [layers.RandomFlip("horizontal"), layers.RandomRotation(0.1),] ) """ Let's visualize what the augmented samples look like, by applying `data_augmentation` repeatedly to the first image in the dataset: """ plt.figure(figsize=(10, 10)) for images, _ in train_ds.take(1): for i in range(9): augmented_images = data_augmentation(images) ax = plt.subplot(3, 3, i + 1) plt.imshow(augmented_images[0].numpy().astype("uint8")) plt.axis("off")
) # have a look at some of the images: import matplotlib.pyplot as plt plt.figure(figsize=(10, 10)) for images, labels in train_ds.take(1): for i in range(16): ax = plt.subplot(4, 4, i + 1) plt.imshow(images[i].numpy().astype("uint8")) #plt.title(int(labels[i])) plt.title(int(np.where(labels[i] == 1)[0])) plt.axis("off") data_augmentation = keras.Sequential([ layers.RandomFlip("horizontal"), layers.RandomRotation(0.1), ]) # have a look at what the image augmentation is doing: plt.figure(figsize=(10, 10)) for images, _ in train_ds.take(1): for i in range(9): augmented_images = data_augmentation(images) ax = plt.subplot(3, 3, i + 1) plt.imshow(augmented_images[0].numpy().astype("uint8")) plt.axis("off") # Configure the dataset for performance # Let's make sure to use buffered prefetching so we can yield data from disk without having I/O becoming blocking: #train_ds = train_ds.prefetch( buffer_size=32 ) #val_ds = val_ds.prefetch( buffer_size=32 )
strip_chars = "!\"#$%&'()*+,-./:;<=>?@[\]^_`{|}~" strip_chars = strip_chars.replace("<", "") strip_chars = strip_chars.replace(">", "") vectorization = TextVectorization( max_tokens=VOCAB_SIZE, output_mode="int", output_sequence_length=SEQ_LENGTH, standardize=custom_standardization, ) vectorization.adapt(text_data) # Data augmentation for image data image_augmentation = keras.Sequential([ layers.RandomFlip("horizontal"), layers.RandomRotation(0.2), layers.RandomContrast(0.3), ]) """ ## Building a `tf.data.Dataset` pipeline for training We will generate pairs of images and corresponding captions using a `tf.data.Dataset` object. The pipeline consists of two steps: 1. Read the image from the disk 2. Tokenize all the five captions corresponding to the image """ def decode_and_resize(img_path, size=IMAGE_SIZE): img = tf.io.read_file(img_path)
projection_dim * 2, projection_dim, ] # Size of the transformer layers transformer_layers = 8 # Size of the dense layers of the final classifier mlp_head_units = [2048, 1024] """ ## Use data augmentation """ data_augmentation = keras.Sequential( [ layers.Normalization(), layers.Resizing(image_size, image_size), layers.RandomFlip("horizontal"), layers.RandomRotation(factor=0.02), layers.RandomZoom(height_factor=0.2, width_factor=0.2), ], name="data_augmentation", ) # Compute the mean and the variance of the training data for normalization. data_augmentation.layers[0].adapt(x_train) """ ## Implement multilayer perceptron (MLP) """ 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)
print(f"Image shape: {metadata.features['image'].shape}") print(f"Training images: {metadata.splits['train'].num_examples}") print(f"Test images: {metadata.splits['test'].num_examples}") """ ## Use Data Augmentation We will rescale the data to `[0, 1]` and perform simple augmentations to our data. """ rescale = layers.Rescaling(1.0 / 255) data_augmentation = tf.keras.Sequential( [ layers.RandomFlip("horizontal_and_vertical"), layers.RandomRotation(0.3), layers.RandomZoom(0.2), ] ) def prepare(ds, shuffle=False, augment=False): # Rescale dataset ds = ds.map(lambda x, y: (rescale(x), y), num_parallel_calls=AUTOTUNE) if shuffle: ds = ds.shuffle(1024) # Batch dataset ds = ds.batch(batch_size)
def main(): # Download the dataset # Using the Flickr8K dataset for this tutorial. This dataset # comprises over 8,000 images, that are each paired with five # different captions. #!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_Dataset.zip #!wget -q https://github.com/jbrownlee/Datasets/releases/download/Flickr8k/Flickr8k_text.zip #!unzip -qq Flickr8k_Dataset.zip #!unzip -qq Flickr8k_text.zip #!rm Flickr8k_Dataset.zip Flickr8k_text.zip # Path to images. IMAGES_PATH = "Flicker8k_Dataset" # Desired image dimensions. IMAGE_SIZE = (299, 299) # Vocabulary size. VOCAB_SIZE = 10000 # Fixed length allowed for any sequence. SEQ_LENGTH = 25 # Dimension for the image embeddings and token embeddings. EMBED_DIM = 512 # Pre-layer units in the feed-forward network. FF_DIM = 512 # Other training parameters. BATCH_SIZE = 64 EPOCHS = 30 AUTOTUNE = tf.data.AUTOTUNE # Preparing the dataset. def load_captions_data(filename): # Load captions (text) data and maps them to corresponding # images. # @param: filename, path to the text file containing caption # data. # @return: caption_mapping, dictionary mapping image names and # the corresponding captions. # @return: text_data, list containing all the available # captions. with open(filename) as caption_file: caption_data = caption_file.readlines() caption_mapping = {} text_data = [] images_to_skip = set() for line in caption_data: line = line.rstrip("\n") # IMage name and captions are separated using a tab. img_name, caption = line.split("\t") # Each image is repeated five times for the five # different captions. Each image name has a suffix # '#(caption_number)'. img_name = img_name.split("#")[0] img_name = os.path.join(IMAGES_PATH, img_name.strip()) # Remove captions that are either too short or too # long. tokens = caption.strip().split() if len(tokens) < 5 or len(tokens) > SEQ_LENGTH: images_to_skip.add(img_name) continue if img_name.endswith("jpg") and img_name not in images_to_skip: # Add a start and end token to each caption. caption = "<start>" + caption.strip() + "<end>" text_data.append(caption) if img_name in caption_mapping: caption_mapping[img_name].append(caption) else: caption_mapping[img_name] = [caption] for img_name in images_to_skip: if img_name in caption_mapping: del caption_mapping[img_name] return caption_mapping, text_data def train_val_split(caption_data, train_size=0.8, shuffle=True): # Split the captioning dataset into train and validation sets. # @param: caption_data (dict), dictionary containing the mapped # data. # @param: train_size (float), fraction of all the full dataset # to use as training data. # @param: shuffle (bool), whether to shuffle the dataset before # splitting. # @return: training and validation datasets as two separated # dicts. # 1) Get the list of all image names. all_images = list(caption_data.keys()) # 2) Shuffle if necessary. if shuffle: np.random.shuffle(all_images) # 3) Split into training and validation sets. train_size = int(len(caption_data) * train_size) training_data = { img_name: caption_data[img_name] for img_name in all_images[:train_size] } validation_data = { img_name: caption_data[img_name] for img_name in all_images[train_size:] } # 4) Return the splits. return training_data, validation_data # Load the dataset. captions_mapping, text_data = load_captions_data("Flickr8k.token.txt") # Split the dataset into training and validation sets. train_data, valid_data = train_val_split(captions_mapping) print("Number of training samples: ", len(train_data)) print("Number of validation samples: ", len(valid_data)) # Vectorizing the text data # Use the TextVectorization layer to vectorize the text data, that # is to say, to turn the original strings into integer sequences # where each integer represents the index of a word in a # vocabulary. Use a custom string standardization scheme (in this # case, strip punctuation characters except < and >) and the # default splitting scheme (split on whitespace). def custom_standardization(input_string): lowercase = tf.strings.lower(input_string) return tf.strings.regex_replace(lowercase, "[%s]" % re.escape(strip_chars), "") strip_chars = "!\"#$%&'()*+,-./;<=>?@[\]^_`{|}~" strip_chars = strip_chars.replace("<", "") strip_chars = strip_chars.replace(">", "") vectorization = TextVectorization( max_tokens=VOCAB_SIZE, output_mode="int", output_sequence_length=SEQ_LENGTH, standardize=custom_standardization, ) vectorization.adapt(text_data) # Data augmentation for image data. image_augmentation = keras.Sequential([ layers.RandomFlip("horizontal"), layers.RandomRotation(0.2), layers.RandomContrast(0.3), ]) # Building a tf.data.Dataset pipeline for training # Generate pairs of images and corresponding captions using a # tf.data.Dataset object. The pipeline consists of two steps: # 1) Read the image from the disk. # 2) Tokenize all the five captions corresponding to the image. def decode_and_resize(img_path): img = tf.io.read_file(img_path) img = tf.image.decode_jpeg(img, channels=3) img = tf.image.resize(img, IMAGE_SIZE) img = tf.image.convert_image_dtype(img, tf.float32) return img def process_input(img_path, captions): return decode_and_resize(img_path), vectorization(captions) def make_dataset(images, captions): ''' if split == "train": img_dataset = tf.data.Dataset.from_tensor_slices(images).map( read_train_image, num_parallel_calls=AUTOTUNE ) else: img_dataset = tf.data.Dataset.from_tensor_slices(images).map( read_valid_image, num_parallel_calls=AUTOTUNE ) cap_dataset = tf.data.Dataset.from_tensor_slices(captions).map( vectorization, num_parallel_calls=AUTOTUNE ) dataset = tf.data.Dataset.zip((img_dataset, cap_dataset)) dataset = dataset.batch(BATCH_SIZE).shuffle(256).prefetch(AUTOTUNE) return dataset ''' dataset = tf.data.Dataset.from_tensor_slices((images, captions)) dataset = dataset.shuffle(len(images)) dataset = dataset.map(process_input, num_parallel_calls=AUTOTUNE) dataset = dataset.batch(BATCH_SIZE).prefetch(AUTOTUNE) return dataset # Pass the list of images and the list of corresponding captions. train_dataset = make_dataset(list(train_data.keys()), list(train_data.values())) valid_dataset = make_dataset(list(valid_data.keys()), list(valid_data.values())) # Building the model # The image captioning architecture consists of three models: # 1) A CNN: Used to extract the image features. # 2) A TransformerEncoder: The extracted image features are then # passed to a Transformer based encoder that generates a new # representation of the inputs. # 3) A TransformerDecoder: This model takes the encoder output and # the text data (sequences) as inputs and tries to learn to # generate the caption. def get_cnn_model(): base_model = efficientnet.EfficientNetB0( input_shape=(*IMAGE_SIZE, 3), include_top=False, weights="imagenet", ) # Freeze the feature extractor. base_model.trainable = False base_model_out = base_model.output base_model_out = layers.Reshape( (-1, base_model_out.shape[-1]))(base_model_out) cnn_model = keras.models.Model(base_model.input, base_model_out) return cnn_model class TransformerEncoderBlock(layers.Layer): def __init__(self, embed_dim, dense_dim, num_heads, **kwargs): super().__init__(**kwargs) self.embed_dim = embed_dim self.dense_dim = dense_dim self.num_heads = num_heads self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, dropout=0.0) self.layernorm1 = layers.LayerNormalization() self.layernorm2 = layers.LayerNormalization() self.dense_1 = layers.Dense(embed_dim, activation="relu") def call(self, inputs, training, mask=None): inputs = self.layernorm1(inputs) inputs = self.dense_1(inputs) attention_output_1 = self.attention_1( query=inputs, value=inputs, key=inputs, attention_mask=None, training=training, ) out_1 = self.layernorm2(inputs + attention_output_1) return out_1 class PositionalEmbedding(layers.Layer): def __init__(self, sequence_length, vocab_size, embed_dim, **kwargs): super().__init__(**kwargs) self.token_embeddings = layers.Embedding(input_dim=vocab_size, output_dim=embed_dim) self.position_embeddings = layers.Embedding( input_dim=sequence_length, output_dim=embed_dim) self.sequence_length = sequence_length self.vocab_size = vocab_size self.embed_dim = embed_dim self.embed_scale = tf.math.sqrt(tf.cast(embed_dim, tf.float32)) def call(self, inputs): length = tf.shape(inputs)[-1] positions = tf.range(start=0, limit=length, delta=1) embedded_tokens = self.token_embeddings(inputs) embedded_tokens = embedded_tokens * self.embed_scale embedded_positions = self.position_embeddings(positions) return embedded_tokens + embedded_positions def compute_mask(self, inputs, mask=None): return tf.math.not_equal(inputs, 0) class TransformerDecoderBlock(layers.Layer): def __init__(self, embed_dim, ff_dim, num_heads, **kwargs): super().__init__(**kwargs) self.embed_dim = embed_dim self.ff_dim = ff_dim self.num_heads = num_heads self.attention_1 = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, dropout=0.1) self.attention_2 = layers.MultiHeadAttention(num_heads=num_heads, key_dim=embed_dim, dropout=0.1) self.ffn_layer_1 = layers.Dense(ff_dim, activation="relu") self.ffn_layer_2 = layers.Dense(embed_dim) self.layernorm_1 = layers.LayerNormalization() self.layernorm_2 = layers.LayerNormalization() self.layernorm_3 = layers.LayerNormalization() self.embedding = PositionalEmbedding(embed_dim=EMBED_DIM, sequence_length=SEQ_LENGTH, vocab_size=VOCAB_SIZE) self.out = layers.Dense(VOCAB_SIZE, activation="softmax") self.dropout_1 = layers.Dropout(0.3) self.dropout_2 = layers.Dropout(0.5) self.supports_masking = True def call(self, inputs, encoder_outputs, training, mask=None): inputs = self.embedding(inputs) causal_mask = self.get_causal_attention_mask(inputs) if mask is not None: padding_mask = tf.cast(mask[:, :, tf.newaxis], dtype=tf.int32) combined_mask = tf.cast(mask[:, tf.newaxis, :], dtype=tf.int32) combined_mask = tf.minimum(combined_mask, causal_mask) attention_output_1 = self.attention_1( query=inputs, value=inputs, key=inputs, attention_mask=combined_mask, training=training, ) out_1 = self.layernorm_1(inputs + attention_output_1) attention_output_2 = self.attention_2( query=out_1, value=encoder_outputs, key=encoder_outputs, attention_mask=padding_mask, training=training, ) out_2 = self.layernorm_2(out_1 + attention_output_2) ffn_out = self.ffn_layer_1(out_2) ffn_out = self.dropout_1(ffn_out, training=training) ffn_out = self.ffn_layer_2(ffn_out) ffn_out = self.layernorm_3(ffn_out + out_2, training=training) ffn_out = self.dropout_2(ffn_out, training=training) preds = self.out(ffn_out) return preds def get_causal_attention_mask(self, inputs): input_shape = tf.shape(inputs) batch_size, sequence_length = input_shape[0], input_shape[1] i = tf.range(sequence_length)[:, tf.newaxis] j = tf.range(sequence_length) mask = tf.cast(i >= j, dtype="int32") mask = tf.reshape(mask, (1, input_shape[1], input_shape[1])) mult = tf.concat( [ tf.expand_dims(batch_size, -1), tf.constant([1, 1], dtype=tf.int32) ], axis=0, ) return tf.tile(mask, mult) class ImageCaptioningModel(keras.Model): def __init__(self, cnn_model, encoder, decoder, num_captions_per_image=5, image_aug=None): super().__init__() self.cnn_model = cnn_model self.encoder = encoder self.decoder = decoder self.loss_tracker = keras.metrics.Mean(name="loss") self.acc_tracker = keras.metrics.Mean(name="accuracy") self.num_captions_per_image = num_captions_per_image self.image_aug = image_aug def calculate_loss(self, y_true, y_pred, mask): loss = self.loss(y_true, y_pred) mask = tf.cast(mask, dtype=loss.dtype) loss *= mask return tf.reduce_sum(loss) / tf.reduce_sum(mask) def calculate_accuracy(self, y_true, y_pred, mask): accuracy = tf.equal(y_true, tf.argmax(y_pred, axis=2)) accuracy = tf.math.logical_and(mask, accuracy) accuracy = tf.cast(accuracy, dtype=tf.float32) mask = tf.cast(mask, dtype=tf.float32) return tf.reduce_sum(accuracy) / tf.reduce_sum(mask) def _compute_caption_loss_and_acc(self, img_embed, batch_seq, training=True): encoder_out = self.encoder(img_embed, training=training) batch_seq_inp = batch_seq[:, :-1] batch_seq_true = batch_seq[:, 1:] mask = tf.math.not_equal(batch_seq_true, 0) batch_seq_pred = self.decoder(batch_seq_inp, encoder_out, training=training, mask=mask) loss = self.calculate_loss(batch_seq_true, batch_seq_pred, mask) acc = self.calculate_accuracy(batch_seq_true, batch_seq_pred, mask) return loss, acc def train_step(self, batch_data): batch_img, batch_seq = batch_data batch_loss = 0 batch_acc = 0 if self.image_aug: batch_img = self.image_aug(batch_img) # 1) Get image embeddings. img_embed = self.cnn_model(batch_img) # 2) Pass each of the five captions one by one to the # decoder along with the encoder outputs and compute the # loss as well as accuracy for each caption. for i in range(self.num_captions_per_image): with tf.GradientTape() as tape: loss, acc = self._compute_caption_loss_and_acc( img_embed, batch_seq[:, i, :], training=True) # 3) Update loss and accuracy. batch_loss += loss batch_acc += acc # 4) Get the list of all the trainable weights. train_vars = (self.encoder.trainable_variables + self.decoder.trainable_variables) # 5) Get the gradients. grads = tape.gradient(loss, train_vars) # 6) Update the trainable weights. self.optimizer.apply_gradients(zip(grads, train_vars)) # 7) Update the trackers. batch_acc /= float(self.num_captions_per_image) self.loss_tracker.update_state(batch_loss) self.acc_tracker.update_state(batch_acc) # 8) Return the loss and accuracy values. return { "loss": self.loss_tracker.result(), "acc": self.acc_tracker.result() } def test_step(self, batch_data): batch_img, batch_seq = batch_data batch_loss = 0 batch_acc = 0 # 1) Get image embeddings. img_embed = self.cnn_model(batch_img) # 2) Pass each of the five captions one by one to the # decoder along with the encoder outputs and compute the # loss as well as accuracy for each caption. for i in range(self.num_captions_per_image): loss, acc = self._compute_caption_loss_and_acc(img_embed, batch_seq[:, i, :], training=False) # 3) Update loss and accuracy. batch_loss += loss batch_acc += acc batch_acc /= float(self.num_captions_per_image) # 4) Update the trackers. self.loss_tracker.update_state(batch_loss) self.acc_tracker.update_state(batch_acc) # 8) Return the loss and accuracy values. return { "loss": self.loss_tracker.result(), "acc": self.acc_tracker.result() } @property def metrics(self): # List the metrics here so the 'reset_states()' can be # called automatically. return [self.loss_tracker, self.acc_tracker] cnn_model = get_cnn_model() encoder = TransformerEncoderBlock(embed_dim=EMBED_DIM, dense_dim=FF_DIM, num_heads=1) decoder = TransformerDecoderBlock(embed_dim=EMBED_DIM, ff_dim=FF_DIM, num_heads=2) caption_model = ImageCaptioningModel( cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=image_augmentation, ) # Model training # Define the loss function. cross_entropy = keras.losses.SparseCategoricalCrossentropy( from_logits=False, reduction="none") # Early stopping criteria. early_stopping = keras.callbacks.EarlyStopping(patience=3, restore_best_weights=True) # Learning Rate Scheduler for the optimizer. class LRSchedule(keras.optimizers.schedules.LearningRateSchedule): def __init__(self, post_warmup_learning_rate, warmup_steps): super().__init__() self.post_warmup_learning_rate = post_warmup_learning_rate self.warmup_steps = warmup_steps def __call__(self, step): global_step = tf.cast(step, tf.float32) warmup_steps = tf.cast(self.warmup_steps, tf.float32) warmup_progress = global_step / warmup_steps warmup_learning_rate = self.post_warmup_learning_rate * warmup_progress return tf.cond( global_step < warmup_steps, lambda: warmup_learning_rate, lambda: self.post_warmup_learning_rate, ) # Create a learning rate schedule. num_train_steps = len(train_dataset) * EPOCHS num_warmup_steps = num_train_steps // 15 lr_schedule = LRSchedule(post_warmup_learning_rate=1e-4, warmup_steps=num_warmup_steps) # Compile the model. caption_model.compile(optimizer=keras.optimizers.Adam(lr_schedule), loss=cross_entropy) # Fit the model. caption_model.fit( train_dataset, epochs=EPOCHS, validation_data=valid_dataset, callbacks=[early_stopping], ) # Check sample predictions ''' vocab = vectorization.get_vocabulary() index_lookup = dict(zip(range(len(vocab)), vocab)) max_decoded_sentence_length = SEQ_LENGTH - 1 valid_images = list(valid_data.keys()) def generate_caption(): # Select a random image from the validation dataset. sample_img = np.random.choice(valid_images) # Read the image from the disk. sample_img = decode_and_resize(sample_img) img = sample_img.numpy().clip(0, 255).astype(np.uint8) plt.imshow(img) plt.show() # Pass the image to the CNN. img = tf.expand_dims(sample_img, 0) img = caption_model.cnn_model(img) # Pass the image features to the Transformer encoder. encoded_img = caption_model.encoder(img, training=False) # Generate the caption using the Transformer decoder. decoded_caption = "<start>" for i in range(max_decoded_sentence_length): tokenized_caption = vectorization([decoded_caption])[:, :-1] mask = tf.math.not_equal(tokenized_caption, 0) predictions = caption_model.decoder( tokenized_caption, encoded_img, training=False, mask=mask ) sampled_token_index = np.argmax(predictions[0, i, :]) sampled_token = index_lookup[sampled_token_index] if sampled_token == " <end>": break decoded_caption += " " + sampled_token decoded_caption = decoded_caption.replace("<start>", "") decoded_caption = decoded_caption.replace("<end>", "").strip() print("Predicted Caption: ", decoded_caption) # Check predictions for a few samples. generate_caption() generate_caption() generate_caption() ''' # End Notes # Notice that the model starts to generate reasonable captions # after a few epochs. To keep this example easily runnable, it has # been trained with a few constraints, like a minimal number of # attention heads. To improve predictions, try changing these # training settings and find a good model for your use case. # Exit the program. exit(0)
data_preprocessing.layers[-1].adapt(x_data) """ ## Data augmentation Unlike simCLR, which randomly picks a single data augmentation function to apply to an input image, we apply a set of data augmentation functions randomly to the input image. (You can experiment with other image augmentation techniques by following the [data augmentation tutorial](https://www.tensorflow.org/tutorials/images/data_augmentation).) """ data_augmentation = keras.Sequential([ layers.RandomTranslation(height_factor=(-0.2, 0.2), width_factor=(-0.2, 0.2), fill_mode="nearest"), layers.RandomFlip(mode="horizontal"), layers.RandomRotation(factor=0.15, fill_mode="nearest"), layers.RandomZoom(height_factor=(-0.3, 0.1), width_factor=(-0.3, 0.1), fill_mode="nearest"), ]) """ Display a random image """ image_idx = np.random.choice(range(x_data.shape[0])) image = x_data[image_idx] image_class = classes[y_data[image_idx][0]] plt.figure(figsize=(3, 3)) plt.imshow(x_data[image_idx].astype("uint8")) plt.title(image_class) _ = plt.axis("off")