多模态蕴含

!pip install -q tensorflow_text

from sklearn.model_selection import train_test_split
import matplotlib.pyplot as plt
import pandas as pd
import numpy as np
import os

import tensorflow as tf
import tensorflow_hub as hub
import tensorflow_text as text
from tensorflow import keras

label_map = {"Contradictory": 0, "Implies": 1, "NoEntailment": 2}

image_base_path = keras.utils.get_file(
    "tweet_images",
    "https://github.com/sayakpaul/Multimodal-Entailment-Baseline/releases/download/v1.0.0/tweet_images.tar.gz",
    untar=True,
)

df = pd.read_csv(
    "https://github.com/sayakpaul/Multimodal-Entailment-Baseline/raw/main/csvs/tweets.csv"
)
df.sample(10)

images_one_paths = []
images_two_paths = []

for idx in range(len(df)):
    current_row = df.iloc[idx]
    id_1 = current_row["id_1"]
    id_2 = current_row["id_2"]
    extentsion_one = current_row["image_1"].split(".")[-1]
    extentsion_two = current_row["image_2"].split(".")[-1]

    image_one_path = os.path.join(image_base_path, str(id_1) + f".{extentsion_one}")
    image_two_path = os.path.join(image_base_path, str(id_2) + f".{extentsion_two}")

    images_one_paths.append(image_one_path)
    images_two_paths.append(image_two_path)

df["image_1_path"] = images_one_paths
df["image_2_path"] = images_two_paths

# Create another column containing the integer ids of
# the string labels.
df["label_idx"] = df["label"].apply(lambda x: label_map[x])

def visualize(idx):
    current_row = df.iloc[idx]
    image_1 = plt.imread(current_row["image_1_path"])
    image_2 = plt.imread(current_row["image_2_path"])
    text_1 = current_row["text_1"]
    text_2 = current_row["text_2"]
    label = current_row["label"]

    plt.subplot(1, 2, 1)
    plt.imshow(image_1)
    plt.axis("off")
    plt.title("Image One")
    plt.subplot(1, 2, 2)
    plt.imshow(image_1)
    plt.axis("off")
    plt.title("Image Two")
    plt.show()

    print(f"Text one: {text_1}")
    print(f"Text two: {text_2}")
    print(f"Label: {label}")


random_idx = np.random.choice(len(df))
visualize(random_idx)

random_idx = np.random.choice(len(df))
visualize(random_idx)

df["label"].value_counts()

# 10% for test
train_df, test_df = train_test_split(
    df, test_size=0.1, stratify=df["label"].values, random_state=42
)
# 5% for validation
train_df, val_df = train_test_split(
    train_df, test_size=0.05, stratify=train_df["label"].values, random_state=42
)

print(f"Total training examples: {len(train_df)}")
print(f"Total validation examples: {len(val_df)}")
print(f"Total test examples: {len(test_df)}")

# Define TF Hub paths to the BERT encoder and its preprocessor
bert_model_path = (
    "https://tfhub.dev/tensorflow/small_bert/bert_en_uncased_L-2_H-256_A-4/1"
)
bert_preprocess_path = "https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3"

def make_bert_preprocessing_model(sentence_features, seq_length=128):
    """Returns Model mapping string features to BERT inputs.

  Args:
    sentence_features: A list with the names of string-valued features.
    seq_length: An integer that defines the sequence length of BERT inputs.

  Returns:
    A Keras Model that can be called on a list or dict of string Tensors
    (with the order or names, resp., given by sentence_features) and
    returns a dict of tensors for input to BERT.
  """

    input_segments = [
        tf.keras.layers.Input(shape=(), dtype=tf.string, name=ft)
        for ft in sentence_features
    ]

    # Tokenize the text to word pieces.
    bert_preprocess = hub.load(bert_preprocess_path)
    tokenizer = hub.KerasLayer(bert_preprocess.tokenize, name="tokenizer")
    segments = [tokenizer(s) for s in input_segments]

    # Optional: Trim segments in a smart way to fit seq_length.
    # Simple cases (like this example) can skip this step and let
    # the next step apply a default truncation to approximately equal lengths.
    truncated_segments = segments

    # Pack inputs. The details (start/end token ids, dict of output tensors)
    # are model-dependent, so this gets loaded from the SavedModel.
    packer = hub.KerasLayer(
        bert_preprocess.bert_pack_inputs,
        arguments=dict(seq_length=seq_length),
        name="packer",
    )
    model_inputs = packer(truncated_segments)
    return keras.Model(input_segments, model_inputs)


bert_preprocess_model = make_bert_preprocessing_model(["text_1", "text_2"])
keras.utils.plot_model(bert_preprocess_model, show_shapes=True, show_dtype=True)

idx = np.random.choice(len(train_df))
row = train_df.iloc[idx]
sample_text_1, sample_text_2 = row["text_1"], row["text_2"]
print(f"Text 1: {sample_text_1}")
print(f"Text 2: {sample_text_2}")

test_text = [np.array([sample_text_1]), np.array([sample_text_2])]
text_preprocessed = bert_preprocess_model(test_text)

print("Keys           : ", list(text_preprocessed.keys()))
print("Shape Word Ids : ", text_preprocessed["input_word_ids"].shape)
print("Word Ids       : ", text_preprocessed["input_word_ids"][0, :16])
print("Shape Mask     : ", text_preprocessed["input_mask"].shape)
print("Input Mask     : ", text_preprocessed["input_mask"][0, :16])
print("Shape Type Ids : ", text_preprocessed["input_type_ids"].shape)
print("Type Ids       : ", text_preprocessed["input_type_ids"][0, :16])

def dataframe_to_dataset(dataframe):
    columns = ["image_1_path", "image_2_path", "text_1", "text_2", "label_idx"]
    dataframe = dataframe[columns].copy()
    labels = dataframe.pop("label_idx")
    ds = tf.data.Dataset.from_tensor_slices((dict(dataframe), labels))
    ds = ds.shuffle(buffer_size=len(dataframe))
    return ds

resize = (128, 128)
bert_input_features = ["input_word_ids", "input_type_ids", "input_mask"]


def preprocess_image(image_path):
    extension = tf.strings.split(image_path)[-1]

    image = tf.io.read_file(image_path)
    if extension == b"jpg":
        image = tf.image.decode_jpeg(image, 3)
    else:
        image = tf.image.decode_png(image, 3)
    image = tf.image.resize(image, resize)
    return image


def preprocess_text(text_1, text_2):
    text_1 = tf.convert_to_tensor([text_1])
    text_2 = tf.convert_to_tensor([text_2])
    output = bert_preprocess_model([text_1, text_2])
    output = {feature: tf.squeeze(output[feature]) for feature in bert_input_features}
    return output


def preprocess_text_and_image(sample):
    image_1 = preprocess_image(sample["image_1_path"])
    image_2 = preprocess_image(sample["image_2_path"])
    text = preprocess_text(sample["text_1"], sample["text_2"])
    return {"image_1": image_1, "image_2": image_2, "text": text}

batch_size = 32
auto = tf.data.AUTOTUNE


def prepare_dataset(dataframe, training=True):
    ds = dataframe_to_dataset(dataframe)
    if training:
        ds = ds.shuffle(len(train_df))
    ds = ds.map(lambda x, y: (preprocess_text_and_image(x), y)).cache()
    ds = ds.batch(batch_size).prefetch(auto)
    return ds


train_ds = prepare_dataset(train_df)
validation_ds = prepare_dataset(val_df, False)
test_ds = prepare_dataset(test_df, False)

def project_embeddings(
    embeddings, num_projection_layers, projection_dims, dropout_rate
):
    projected_embeddings = keras.layers.Dense(units=projection_dims)(embeddings)
    for _ in range(num_projection_layers):
        x = tf.nn.gelu(projected_embeddings)
        x = keras.layers.Dense(projection_dims)(x)
        x = keras.layers.Dropout(dropout_rate)(x)
        x = keras.layers.Add()([projected_embeddings, x])
        projected_embeddings = keras.layers.LayerNormalization()(x)
    return projected_embeddings

def create_vision_encoder(
    num_projection_layers, projection_dims, dropout_rate, trainable=False
):
    # Load the pre-trained ResNet50V2 model to be used as the base encoder.
    resnet_v2 = keras.applications.ResNet50V2(
        include_top=False, weights="imagenet", pooling="avg"
    )
    # Set the trainability of the base encoder.
    for layer in resnet_v2.layers:
        layer.trainable = trainable

    # Receive the images as inputs.
    image_1 = keras.Input(shape=(128, 128, 3), name="image_1")
    image_2 = keras.Input(shape=(128, 128, 3), name="image_2")

    # Preprocess the input image.
    preprocessed_1 = keras.applications.resnet_v2.preprocess_input(image_1)
    preprocessed_2 = keras.applications.resnet_v2.preprocess_input(image_2)

    # Generate the embeddings for the images using the resnet_v2 model
    # concatenate them.
    embeddings_1 = resnet_v2(preprocessed_1)
    embeddings_2 = resnet_v2(preprocessed_2)
    embeddings = keras.layers.Concatenate()([embeddings_1, embeddings_2])

    # Project the embeddings produced by the model.
    outputs = project_embeddings(
        embeddings, num_projection_layers, projection_dims, dropout_rate
    )
    # Create the vision encoder model.
    return keras.Model([image_1, image_2], outputs, name="vision_encoder")

def create_text_encoder(
    num_projection_layers, projection_dims, dropout_rate, trainable=False
):
    # Load the pre-trained BERT model to be used as the base encoder.
    bert = hub.KerasLayer(bert_model_path, name="bert",)
    # Set the trainability of the base encoder.
    bert.trainable = trainable

    # Receive the text as inputs.
    bert_input_features = ["input_type_ids", "input_mask", "input_word_ids"]
    inputs = {
        feature: keras.Input(shape=(128,), dtype=tf.int32, name=feature)
        for feature in bert_input_features
    }

    # Generate embeddings for the preprocessed text using the BERT model.
    embeddings = bert(inputs)["pooled_output"]

    # Project the embeddings produced by the model.
    outputs = project_embeddings(
        embeddings, num_projection_layers, projection_dims, dropout_rate
    )
    # Create the text encoder model.
    return keras.Model(inputs, outputs, name="text_encoder")

def create_multimodal_model(
    num_projection_layers=1,
    projection_dims=256,
    dropout_rate=0.1,
    vision_trainable=False,
    text_trainable=False,
):
    # Receive the images as inputs.
    image_1 = keras.Input(shape=(128, 128, 3), name="image_1")
    image_2 = keras.Input(shape=(128, 128, 3), name="image_2")

    # Receive the text as inputs.
    bert_input_features = ["input_type_ids", "input_mask", "input_word_ids"]
    text_inputs = {
        feature: keras.Input(shape=(128,), dtype=tf.int32, name=feature)
        for feature in bert_input_features
    }

    # Create the encoders.
    vision_encoder = create_vision_encoder(
        num_projection_layers, projection_dims, dropout_rate, vision_trainable
    )
    text_encoder = create_text_encoder(
        num_projection_layers, projection_dims, dropout_rate, text_trainable
    )

    # Fetch the embedding projections.
    vision_projections = vision_encoder([image_1, image_2])
    text_projections = text_encoder(text_inputs)

    # Concatenate the projections and pass through the classification layer.
    concatenated = keras.layers.Concatenate()([vision_projections, text_projections])
    outputs = keras.layers.Dense(3, activation="softmax")(concatenated)
    return keras.Model([image_1, image_2, text_inputs], outputs)


multimodal_model = create_multimodal_model()
keras.utils.plot_model(multimodal_model, show_shapes=True)

multimodal_model.compile(
    optimizer="adam", loss="sparse_categorical_crossentropy", metrics="accuracy"
)

history = multimodal_model.fit(train_ds, validation_data=validation_ds, epochs=10)

_, acc = multimodal_model.evaluate(test_ds)
print(f"Accuracy on the test set: {round(acc * 100, 2)}%.")

vision_projections = keras.layers.Dropout(rate)(vision_projections)
text_projections = keras.layers.Dropout(rate)(text_projections)
concatenated = keras.layers.Concatenate()([vision_projections, text_projections])

# Embeddings.
vision_projections = vision_encoder([image_1, image_2])
text_projections = text_encoder(text_inputs)

# Cross-attention (Luong-style).
query_value_attention_seq = keras.layers.Attention(use_scale=True, dropout=0.2)(
    [vision_projections, text_projections]
)
# Concatenate.
concatenated = keras.layers.Concatenate()([vision_projections, text_projections])
contextual = keras.layers.Concatenate()([concatenated, query_value_attention_seq])