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model.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:697c158f2ac305a5c5b83585f217b720e9bf330b379e0c651b6db1a7d0ab01d8
+size 221999608

model.py

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+import tensorflow as tf
+import os
+import json
+import pandas as pd
+import re
+import numpy as np
+import time
+import matplotlib.pyplot as plt
+import collections
+import random
+import pickle
+
+import requests
+import json
+from math import sqrt
+from PIL import Image
+from tqdm.auto import tqdm
+
+MAX_LENGTH = 40
+VOCABULARY_SIZE = 17000
+BATCH_SIZE = 64
+BUFFER_SIZE = 1000
+EMBEDDING_DIM = 512
+UNITS = 512
+EPOCHS = 8
+
+vocab = pickle.load(open('vocab_coco.file', 'rb'))
+
+tokenizer = tf.keras.layers.TextVectorization(
+    # max_tokens=VOCABULARY_SIZE,
+    standardize=None,
+    output_sequence_length=MAX_LENGTH,
+    vocabulary=vocab
+)
+
+idx2word = tf.keras.layers.StringLookup(
+    mask_token="",
+    vocabulary=tokenizer.get_vocabulary(),
+    invert=True
+)
+
+def CNN_Encoder():
+    inception_v3 = tf.keras.applications.InceptionV3(
+        include_top=False,           #we are not doing classification on image net so we have to drop last dense layers
+        weights='imagenet'
+    )
+
+    output = inception_v3.output
+    print(output.shape)
+    output = tf.keras.layers.Reshape(
+        (-1, output.shape[-1]))(output)
+    print(output.shape)
+    cnn_model = tf.keras.models.Model(inception_v3.input, output)
+    return cnn_model
+
+class TransformerEncoderLayer(tf.keras.layers.Layer):
+
+    def __init__(self, embed_dim, num_heads):
+        super().__init__()
+        self.layer_norm_1 = tf.keras.layers.LayerNormalization()
+        self.layer_norm_2 = tf.keras.layers.LayerNormalization()
+        self.attention = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim)
+        self.dense = tf.keras.layers.Dense(embed_dim, activation="relu")
+    
+
+    def call(self, x, training):
+        x = self.layer_norm_1(x)
+        x = self.dense(x)
+
+        attn_output = self.attention(
+            query=x,
+            value=x,
+            key=x,
+            attention_mask=None,
+            training=training
+        )
+
+        x = self.layer_norm_2(x + attn_output)   #skip connection
+        return x
+    
+# combines token embeddings and position embeddings
+
+class Embeddings(tf.keras.layers.Layer):
+
+    def __init__(self, vocab_size, embed_dim, max_len):
+        super().__init__()
+        self.token_embeddings = tf.keras.layers.Embedding(
+            vocab_size, embed_dim)
+        self.position_embeddings = tf.keras.layers.Embedding(
+            max_len, embed_dim, input_shape=(None, max_len))
+    
+
+    def call(self, input_ids):
+        length = tf.shape(input_ids)[-1]
+        #calculate the total length of input sequence so that it would be used to calculate positional id
+        position_ids = tf.range(start=0, limit=length, delta=1)
+        #give id positional id to each word in caption
+        position_ids = tf.expand_dims(position_ids, axis=0)
+        #This is done to match the shape of the input tensor when performing element-wise addition in the next step.
+        token_embeddings = self.token_embeddings(input_ids) 
+        #so we are creating token embedding for input ids
+        position_embeddings = self.position_embeddings(position_ids)
+        #but we are creating postion embedding with position_ids only
+        return token_embeddings + position_embeddings
+    
+class TransformerDecoderLayer(tf.keras.layers.Layer):
+
+    def __init__(self, embed_dim, units, num_heads):
+        super().__init__()
+        self.embedding = Embeddings(
+            tokenizer.vocabulary_size(), embed_dim, MAX_LENGTH)
+            #embedding from 
+        self.attention_1 = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
+        )
+        self.attention_2 = tf.keras.layers.MultiHeadAttention(
+            num_heads=num_heads, key_dim=embed_dim, dropout=0.1
+        )
+
+        self.layernorm_1 = tf.keras.layers.LayerNormalization()
+        self.layernorm_2 = tf.keras.layers.LayerNormalization()
+        self.layernorm_3 = tf.keras.layers.LayerNormalization()
+
+        self.ffn_layer_1 = tf.keras.layers.Dense(units, activation="relu")
+        self.ffn_layer_2 = tf.keras.layers.Dense(embed_dim)
+
+        self.out = tf.keras.layers.Dense(tokenizer.vocabulary_size(), activation="softmax")
+
+        self.dropout_1 = tf.keras.layers.Dropout(0.3)
+        self.dropout_2 = tf.keras.layers.Dropout(0.5)
+    
+
+    def call(self, input_ids, encoder_output, training, mask=None):
+        embeddings = self.embedding(input_ids)
+
+        combined_mask = None
+        padding_mask = None
+        
+        if mask is not None:
+            causal_mask = self.get_causal_attention_mask(embeddings)
+            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)
+            
+            
+            #this layer contain masked self attention layer 
+        attn_output_1 = self.attention_1(
+            query=embeddings,
+            value=embeddings,
+            key=embeddings,
+            attention_mask=combined_mask,
+            training=training
+        )
+            #this layer contain cross attention 
+            #which is taking query vector from the previous masked attention and key and value vector from encoder so that some information of input is there
+            #Expalin:
+        out_1 = self.layernorm_1(embeddings + attn_output_1)
+
+        attn_output_2 = self.attention_2(
+            query=out_1,             #query vector from deocder 
+            value=encoder_output,   #key and value vector from encoder
+            key=encoder_output,
+            attention_mask=padding_mask,    #no masking is there 
+            training=training
+        )
+
+        out_2 = self.layernorm_2(out_1 + attn_output_2)   #skip connection 
+
+        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)
+        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(tf.keras.Model):
+
+    def __init__(self, cnn_model, encoder, decoder, image_aug=None):
+        super().__init__()
+        self.cnn_model = cnn_model
+        self.encoder = encoder
+        self.decoder = decoder
+        self.image_aug = image_aug
+        self.loss_tracker = tf.keras.metrics.Mean(name="loss")
+        self.acc_tracker = tf.keras.metrics.Mean(name="accuracy")
+
+
+    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_loss_and_acc(self, img_embed, captions, training=True):
+        encoder_output = self.encoder(img_embed, training=True)
+        y_input = captions[:, :-1]
+        y_true = captions[:, 1:]
+        mask = (y_true != 0)
+        y_pred = self.decoder(
+            y_input, encoder_output, training=True, mask=mask
+        )
+        loss = self.calculate_loss(y_true, y_pred, mask)
+        acc = self.calculate_accuracy(y_true, y_pred, mask)
+        return loss, acc
+
+    
+    def train_step(self, batch):
+        imgs, captions = batch
+
+        if self.image_aug:
+            imgs = self.image_aug(imgs)
+        
+        img_embed = self.cnn_model(imgs)
+
+        with tf.GradientTape() as tape:
+            loss, acc = self.compute_loss_and_acc(
+                img_embed, captions
+            )
+    
+        train_vars = (
+            self.encoder.trainable_variables + self.decoder.trainable_variables
+        )
+        grads = tape.gradient(loss, train_vars)
+        self.optimizer.apply_gradients(zip(grads, train_vars))
+        self.loss_tracker.update_state(loss)
+        self.acc_tracker.update_state(acc)
+
+        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
+    
+
+    def test_step(self, batch):
+        imgs, captions = batch
+
+        img_embed = self.cnn_model(imgs)
+
+        loss, acc = self.compute_loss_and_acc(
+            img_embed, captions, training=False
+        )
+
+        self.loss_tracker.update_state(loss)
+        self.acc_tracker.update_state(acc)
+
+        return {"loss": self.loss_tracker.result(), "acc": self.acc_tracker.result()}
+
+    @property
+    def metrics(self):
+        return [self.loss_tracker, self.acc_tracker]
+    
+
+def load_image_from_path(img_path):
+    img = tf.io.read_file(img_path)
+    img = tf.io.decode_jpeg(img, channels=3)
+    img = tf.keras.layers.Resizing(299, 299)(img)
+    img = tf.keras.applications.inception_v3.preprocess_input(img)
+    return img
+
+
+def generate_caption(img_path, add_noise=False):
+    img = load_image_from_path(img_path)
+    
+    if add_noise:
+        noise = tf.random.normal(img.shape)*0.1
+        img = img + noise
+        img = (img - tf.reduce_min(img))/(tf.reduce_max(img) - tf.reduce_min(img))
+    
+    img = tf.expand_dims(img, axis=0)
+    img_embed = model.cnn_model(img)
+    img_encoded = model.encoder(img_embed, training=False)
+
+    y_inp = '[start]'
+    for i in range(MAX_LENGTH-1):
+        tokenized = tokenizer([y_inp])[:, :-1]
+        mask = tf.cast(tokenized != 0, tf.int32)
+        pred = model.decoder(
+            tokenized, img_encoded, training=False, mask=mask)
+        
+        pred_idx = np.argmax(pred[0, i, :])
+        pred_idx = tf.convert_to_tensor(pred_idx)
+        pred_word = idx2word(pred_idx).numpy().decode('utf-8')
+        if pred_word == '[end]':
+            break
+        
+        y_inp += ' ' + pred_word
+    
+    y_inp = y_inp.replace('[start] ', '')
+    return y_inp
+
+def get_caption_model():
+    encoder = TransformerEncoderLayer(EMBEDDING_DIM, 1)
+    decoder = TransformerDecoderLayer(EMBEDDING_DIM, UNITS, 8)
+
+    cnn_model = CNN_Encoder()
+
+    caption_mode = ImageCaptioningModel(
+        cnn_model=cnn_model, encoder=encoder, decoder=decoder, image_aug=None,
+    )
+
+    def call_fn(batch, training):
+        return batch
+
+    caption_mode.call = call_fn
+    sample_x, sample_y = tf.random.normal((1, 299, 299, 3)), tf.zeros((1, 40))
+
+    caption_mode((sample_x, sample_y))
+
+    sample_img_embed = caption_mode.cnn_model(sample_x)
+    sample_enc_out = caption_mode.encoder(sample_img_embed, training=False)
+    caption_mode.decoder(sample_y, sample_enc_out, training=False)
+
+    caption_mode.load_weights('model.h5')
+
+    return caption_mode
+