{ "cells": [ { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "CQhd0TTQCMeh" }, "source": [ "## Dataloaders for Machine Learning (Tensorflow & PyTorch)\n", "\n", "This tutorial acts as a step by step guide for fetching, preprocessing, storing and loading the [MS-COCO](http://cocodataset.org/#home) dataset for image captioning using deep learning. We have chosen **image captioning** for this tutorial not by accident. For such an application, the dataset required will have both fixed shape (image) and variably shaped (caption because it's sequence of natural language) data. This diversity should help the user to get a mental model about how flexible and easy is to plug Hangar to the existing workflow.\n", "\n", "You will use the MS-COCO dataset to train our model. The dataset contains over 82,000 images, each of which has at least 5 different caption annotations.\n", "\n", "This tutorial assumes you have downloaded and extracted the [MS-COCO dataset](http://cocodataset.org/#home) in the current directory. If you haven't yet, shell commands below should help you do it (beware, it's about 14 GB data). If you are on Windows, please find the equivalent commands to get the dataset downloaded.\n", "\n", "\n", "```bash\n", "wget http://images.cocodataset.org/zips/train2014.zip\n", "unzip train2014.zip\n", "rm train2014.zip\n", "wget http://images.cocodataset.org/annotations/annotations_trainval2014.zip\n", "unzip annotations_trainval2014.zip\n", "rm annotations_trainval2014.zip\n", "```\n", "\n", "Let's install the required packages in our environment. We will be using Tensorflow 1.14 in this tutorial but it should work in all the Tensorflow versions starting from 1.12. But do let us know if you face any hiccups. Install below-given packages before continue. Apart from Tensorflow and Hangar, we use [SpaCy](https://spacy.io/) for pre-processing the captions. SpaCy is probably the most widely used natural language toolkit now.\n", "\n", "```bash\n", "tensorflow==1.14.0\n", "hangar\n", "spacy==2.1.8\n", "```\n", "\n", "One more thing before jumping into the tutorial: we need to download the SpaCy English model `en_core_web_md` which cannot be dynamically loaded. Which means that it must be downloaded with the below command outside this runtime and should reload this runtime.\n", "\n", "```bash\n", "python -m spacy download en_core_web_md\n", "```\n", "\n", "Once all the dependencies are installed and loaded, we can start building our hangar repository.\n", "\n", "\n", "### Hangar Repository creation and column init\n", "We will create a repository and initialize one column named `images` now for a quick demo of how Tensorflow dataloader work. Then we wipe the current repository and create new columns for later portions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "HGXOwLJ3IWPq" }, "outputs": [], "source": [ "repo_path = 'hangar_repo'\n", "username = 'hhsecond'\n", "email = 'sherin@tensorwerk.com'\n", "img_shape = (299, 299, 3)\n", "image_dir = '/content/drive/My Drive/train2014'\n", "annotation_file = ''\n", "import logging\n", "logging.getLogger(\"tensorflow\").setLevel(logging.ERROR)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "fHehOEhwCMej", "outputId": "210f9b87-9c59-49ea-fd31-92ba18d140b3" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hangar Repo initialized at: hangar_repo/.hangar\n" ] } ], "source": [ "import os\n", "from hangar import Repository\n", "import tensorflow as tf\n", "import numpy as np\n", "\n", "tf.compat.v1.enable_eager_execution()\n", "\n", "\n", "if not os.path.isdir(repo_path):\n", " os.mkdir(repo_path)\n", "\n", "repo = Repository(repo_path)\n", "repo.init(user_name=username, user_email=email, remove_old=True)\n", "co = repo.checkout(write=True)\n", "\n", "images_column = co.add_ndarray_column('images', shape=img_shape, dtype=np.uint8,)\n", "co.commit('column init')\n", "co.close()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "QENDY8LvGGhb" }, "source": [ "### Add sample images\n", "Here we add few images to the repository and show how we can load this data as Tensorflow dataloader. We use the idea we learn here in the later portions to build a fully fledged training loop." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "g61tY81hHr8c" }, "outputs": [], "source": [ "import os\n", "from PIL import Image\n", "\n", "\n", "co = repo.checkout(write=True)\n", "images_column = co.columns['images']\n", "try:\n", " for i, file in enumerate(os.listdir(image_dir)):\n", " pil_img = Image.open(os.path.join(image_dir, file))\n", " if pil_img.mode == 'L':\n", " pil_img = pil_img.convert('RGB')\n", " img = pil_img.resize(img_shape[:-1])\n", " img = np.array(img)\n", " images_column[i] = img\n", " if i != 0 and i % 2 == 0: # stopping at 2th image\n", " break\n", "except Exception as e:\n", " print('Exception', e)\n", " co.close()\n", " raise e\n", "co.commit('added image')\n", "co.close()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "dvFci5P8Lm7C" }, "source": [ "### Let's make a Tensorflow dataloader\n", "Hangar provides `make_tf_dataset` & `make_torch_dataset` for creating Tensorflow & PyTorch datasets from Hangar columns. You can read more about it in the [documentation](https://hangar-py.readthedocs.io/en/latest/api.html#ml-framework-dataloaders). Next we'll make a Tensorflow dataset and loop over it to make sure we have got a proper Tensorflow dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "Sc7XGXMVLuDO" }, "outputs": [], "source": [ "from hangar import make_tf_dataset" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 357 }, "colab_type": "code", "id": "tb5g_JrJVbqT", "outputId": "a8fe4e7d-243d-4dae-dc94-66364342a913" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * Checking out BRANCH: master with current HEAD: b769f6d49a7dbb3dcd4f7c6e1c2a32696fd4128f\n", "(repo_pth=hangar_repo/.hangar, aset_name=images, default_schema_hash=b6edf0320f20, isVar=False, varMaxShape=(299, 299, 3), varDtypeNum=2, mode=r)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/hangar/dataloaders/tfloader.py:88: UserWarning: Dataloaders are experimental in the current release.\n", " warnings.warn(\"Dataloaders are experimental in the current release.\", UserWarning)\n" ] }, { "data": { "image/png": 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dHV1cnJvBICqcO3uCgYEhxsZ28sPvfIuBkSH27r+OyekFApEYIwMdTM5Mky+W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cdHR0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\nBGqpmkQySbFRo7NvEKVOSzqdJlmIc/2VV5k4fo4bd66jbFb57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IDb7aBWq6NWqwmHwkwd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GYbbS5u9gdmaB69ev4HJaEGQ6NMoU9UYLpcXIwpVb3Hfqw4R20sjUdbwBGzSLDPaOIKOFTNZA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QaipFAuVbj/voe4fvMiCDnjiYduzd82vp9z7NgyRZ7SaNTJJIX+ZER37zJb169w/quPMXFd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"text/plain": [ "
" ] }, "metadata": { "tags": [] }, "output_type": "display_data" } ], "source": [ "from matplotlib.pyplot import imshow\n", "co = repo.checkout()\n", "image_column = co.columns['images']\n", "dataset = make_tf_dataset(image_column)\n", "for image in dataset:\n", " imshow(image[0].numpy())\n", " break" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "FTArZhtZfg7S" }, "source": [ "### New columns\n", "\n", "For our example, we would need two columns. One for the image and another one for captions. Let's wipe our existing repository (`remove_old` argument in `repo.init` does this) and create these columns" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "ISMdkXtYHg2c", "outputId": "0e18d9d3-1a4f-4f75-d388-c8c0c316f69b" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Hangar Repo initialized at: hangar_repo/.hangar\n" ] } ], "source": [ "repo = Repository(repo_path)\n", "repo.init(user_name=username, user_email=email, remove_old=True)\n", "co = repo.checkout(write=True)\n", "\n", "images_column = co.add_ndarray_column('images', shape=img_shape, dtype=np.uint8)\n", "captions_column = co.add_ndarray_column('captions', shape=(60,), dtype=np.float, variable_shape=True)\n", "co.commit('column init')\n", "co.close()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "z_fUUIpKCMen" }, "source": [ "### Store image and captions to Hangar repo\n", "Each image will be converted to RGB channels with dtype `uint8`. Each caption will be prepended with `START` token and ended with `END` token before converting them to floats. We have another preprocessing stage for images later.\n", "\n", "We'll start with loading the caption file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "VlX-su-gCMep" }, "outputs": [], "source": [ "import json\n", "annotation_file = 'annotations/captions_train2014.json'\n", "with open(annotation_file, 'r') as f:\n", " annotations = json.load(f)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "UMcYzkWgCMes" }, "outputs": [], "source": [ "import spacy\n", "# if you have installed spacy and the model in the same notebook session, you might need to restart the runtime to get it into the scope\n", "nlp = spacy.load('en_core_web_md')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "wxpbxEvmCMev" }, "outputs": [], "source": [ "def sent2index(sent):\n", " \"\"\"\n", " Convert sentence to an array of indices using SpaCy\n", " \"\"\"\n", " ids = []\n", " doc = nlp(sent)\n", " for token in doc:\n", " if token.has_vector:\n", " id = nlp.vocab.vectors.key2row[token.norm]\n", " else:\n", " id = sent2index('UNK')[0]\n", " ids.append(id)\n", " return ids" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "RIvqFIHUCMey" }, "source": [ "### Save the data to Hangar" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "__I8ntp3CMez", "outputId": "287685d1-2e7c-4d3f-94b7-87db73f966e3" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 414113/414113 [00:03<00:00, 122039.19it/s]\n" ] } ], "source": [ "import os\n", "from tqdm import tqdm\n", "\n", "all_captions = []\n", "all_img_name_vector = []\n", "limit = 100 # if you are not planning to save the whole dataset to Hangar. Zero means whole dataset\n", "\n", "co = repo.checkout(write=True)\n", "images_column = co.columns['images']\n", "captions_column = co.columns['captions']\n", "all_files = set(os.listdir(image_dir))\n", "i = 0\n", "with images_column, captions_column:\n", " for annot in tqdm(annotations['annotations']):\n", " if limit and i > limit:\n", " continue\n", " image_id = annot['image_id']\n", " assumed_image_paths = 'COCO_train2014_' + '%012d.jpg' % (image_id)\n", " if assumed_image_paths not in all_files:\n", " continue\n", " img_path = os.path.join(image_dir, assumed_image_paths)\n", " img = Image.open(img_path)\n", " if img.mode == 'L':\n", " img = img.convert('RGB')\n", " img = img.resize(img_shape[:-1])\n", " img = np.array(img)\n", " cap = sent2index('sos ' + annot['caption'] + ' eos')\n", " cap = np.array(cap, dtype=np.float)\n", " key = images_column.append(img)\n", " captions_column[key] = cap\n", " if i % 1000 == 0 and i != 0:\n", " if co.diff.status() == 'DIRTY':\n", " co.commit(f'Added batch {i}')\n", " i += 1\n", "co.commit('Added full data')\n", "co.close()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "gXvSa2iCCMe2" }, "source": [ "### Preprocess Images\n", "\n", "Our image captioning network requires a pre-processed input. We use transfer learning for this with a pretrained InceptionV3 network which is available in Keras. But we have a problem. Preprocessing is costly and we don't want to do it all the time. Since Hangar is flexible enough to create multiple columns and let you call the group of column as a `dataset`, it is quite easy to do make a new column for the processed image and we don't have to do the preprocessing online but keep a preprocessed image in the new column in the same repository with the same key. Which means, we have three columns in our repository (all three has different samples with the same name):\n", "- `images`\n", "- `captions`\n", "- `processed_images`\n", "\n", "Although we need only the `processed_images` for the network, we still keep the bare image in the repository in case we need to look into it later or if we decided to do some other preprocessing instead of InceptionV3 (it is always advised to keep the source truth with you).\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "QBGCS_ceCMe2" }, "outputs": [], "source": [ "import tensorflow as tf\n", "tf.compat.v1.enable_eager_execution()\n", "image_model = tf.keras.applications.InceptionV3(include_top=False, weights='imagenet')\n", "new_input = image_model.input\n", "hidden_layer = image_model.layers[-1].output\n", "image_features_extract_model = tf.keras.Model(new_input, hidden_layer)\n", "\n", "\n", "def process_image(img):\n", " img = tf.keras.applications.inception_v3.preprocess_input(img)\n", " img = np.expand_dims(img, axis=0)\n", " img = image_features_extract_model(img)\n", " return tf.reshape(img, (-1, img.shape[3]))\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "ANFPvYByCMe5" }, "outputs": [], "source": [ "from hangar import Repository\n", "import numpy as np\n", "\n", "repo_path = 'hangar_repo'\n", "\n", "repo = Repository(repo_path)\n", "co = repo.checkout(write=True)\n", "images = co.columns['images']\n", "sample_name = list(images.keys())[0]\n", "prototype = process_image(images[sample_name]).numpy()\n", "pimages = co.add_ndarray_column('processed_images', prototype=prototype)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "jWN6AxiHCMe7" }, "source": [ "#### Saving the pre-processed images to the new column" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "HdFxmi5ECMe8", "outputId": "38dddea0-64f8-47cf-fc9d-6b14a6140135" }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████| 101/101 [00:11<00:00, 8.44it/s]\n" ] } ], "source": [ "from tqdm import tqdm\n", "\n", "with pimages:\n", " for key in tqdm(images):\n", " pimages[key] = process_image(images[key]).numpy()\n", "\n", "co.commit('processed image saved')\n", "co.close()" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "zacZutpTCMe_" }, "source": [ "### Dataloaders for training\n", "We are using Tensorflow to build the network but how do we load this data from Hangar repository to Tensorflow?\n", "\n", "A naive option would be to run through the samples and load the numpy arrays and pass that to the `sess.run` of Tensorflow. But that would be quite inefficient. Tensorflow uses multiple threads to load the data in memory and its dataloaders can prefetch the data before-hand so that your training loop doesn't get blocked while loading the data. Also, Tensoflow dataloaders brings batching, shuffling, etc. to the table prebuilt. That's cool but how to load data from Hangar to Tensorflow using TF dataset? Well, we have `make_tf_dataset` which accepts the list of columns as a parameter and returns a TF dataset object." ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "gcKsE3d4CMfA", "outputId": "a42c5c84-e62f-4178-cc3a-175dac08aa7c" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " * Checking out BRANCH: master with current HEAD: 3cbb3fbe7eb0e056ff97e75f41d26303916ef686\n" ] } ], "source": [ "from hangar import make_tf_dataset\n", "co = repo.checkout() # we don't need write checkout here" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 105 }, "colab_type": "code", "id": "TybRGUGaCMfC", "outputId": "8e75b46d-f8da-4dd3-c607-1174b23a15a0" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(repo_pth=hangar_repo/.hangar, aset_name=processed_images, default_schema_hash=f230548212ab, isVar=False, varMaxShape=(64, 2048), varDtypeNum=11, mode=r)\n", "(repo_pth=hangar_repo/.hangar, aset_name=captions, default_schema_hash=4d60751421d5, isVar=True, varMaxShape=(60,), varDtypeNum=12, mode=r)\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "/usr/local/lib/python3.6/dist-packages/hangar/dataloaders/tfloader.py:88: UserWarning: Dataloaders are experimental in the current release.\n", " warnings.warn(\"Dataloaders are experimental in the current release.\", UserWarning)\n" ] } ], "source": [ "BATCH_SIZE = 1\n", "EPOCHS = 2\n", "embedding_dim = 256\n", "units = 512\n", "vocab_size = len(nlp.vocab.vectors.key2row)\n", "num_steps = 50\n", "\n", "\n", "captions_dset = co.columns['captions']\n", "pimages_dset = co.columns['processed_images']\n", "\n", "dataset = make_tf_dataset([pimages_dset, captions_dset], shuffle=True)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "27mQc673CMfF" }, "source": [ "### Padded Batching\n", "\n", "Batching needs a bit more explanation here since the dataset does not just consist of fixed shaped data. We have two dataset in which one is for captions. As you know captions are sequences which can be variably shaped. So instead of using `dataset.batch` we need to use `dataset.padded_batch` which takes care of padding the tensors with the longest value in each dimension for each batch. This `padded_batch` needs the shape by which the user needs the batch to be padded. Unless you need customization, you can use the shape stored in the `dataset` object by `make_tf_dataset` function." ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 34 }, "colab_type": "code", "id": "8tpHg3w2CMfF", "outputId": "e2145382-c73b-4acf-9076-40ff64554ade" }, "outputs": [ { "data": { "text/plain": [ "(TensorShape([Dimension(64), Dimension(2048)]), TensorShape([Dimension(None)]))" ] }, "execution_count": 9, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "output_shapes = tf.compat.v1.data.get_output_shapes(dataset)\n", "output_shapes" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "imMQrtn7CMfI" }, "outputs": [], "source": [ "dataset = dataset.padded_batch(BATCH_SIZE, padded_shapes=output_shapes)" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "tY6Z7y8TCMfO" }, "source": [ "### Build the network\n", "\n", "Since we have the dataloaders ready, we can now build the network for image captioning and start training. Rest of this tutorial is a copy of an official Tensorflow tutorial which is available at https://tensorflow.org/beta/tutorials/text/image_captioning. The content of Tensorflow tutorial page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License.\n", "Access date: Aug 20 2019\n", "\n", "\n", "In this example, you extract the features from the lower convolutional layer of InceptionV3 giving us a vector of shape (8, 8, 2048) and quash that to a shape of (64, 2048). We have stored the result of this already to our Hangar repo. This vector is then passed through the CNN Encoder (which consists of a single Fully connected layer). The RNN (here GRU) attends over the image to predict the next word." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "6Kc-yZ0iCMfO" }, "outputs": [], "source": [ "class BahdanauAttention(tf.keras.Model):\n", " def __init__(self, units):\n", " super(BahdanauAttention, self).__init__()\n", " self.W1 = tf.keras.layers.Dense(units)\n", " self.W2 = tf.keras.layers.Dense(units)\n", " self.V = tf.keras.layers.Dense(1)\n", "\n", " def call(self, features, hidden):\n", " # features(CNN_encoder output) shape == (batch_size, 64, embedding_dim)\n", " # hidden shape == (batch_size, hidden_size)\n", " # hidden_with_time_axis shape == (batch_size, 1, hidden_size)\n", " hidden_with_time_axis = tf.expand_dims(hidden, 1)\n", " # score shape == (batch_size, 64, hidden_size)\n", " score = tf.nn.tanh(self.W1(features) + self.W2(hidden_with_time_axis))\n", " # attention_weights shape == (batch_size, 64, 1)\n", " # you get 1 at the last axis because you are applying score to self.V\n", " attention_weights = tf.nn.softmax(self.V(score), axis=1)\n", " # context_vector shape after sum == (batch_size, hidden_size)\n", " context_vector = attention_weights * features\n", " context_vector = tf.reduce_sum(context_vector, axis=1)\n", "\n", " return context_vector, attention_weights" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "up0nVnIZO2_c" }, "outputs": [], "source": [ "class CNN_Encoder(tf.keras.Model):\n", " # Since you have already extracted the features and dumped it using pickle\n", " # This encoder passes those features through a Fully connected layer\n", " def __init__(self, embedding_dim):\n", " super(CNN_Encoder, self).__init__()\n", " # shape after fc == (batch_size, 64, embedding_dim)\n", " self.fc = tf.keras.layers.Dense(embedding_dim)\n", "\n", " def call(self, x):\n", " x = self.fc(x)\n", " x = tf.nn.relu(x)\n", " return x" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "4qAEbanRO77k" }, "outputs": [], "source": [ "class RNN_Decoder(tf.keras.Model):\n", " def __init__(self, embedding_dim, units, vocab_size):\n", " super(RNN_Decoder, self).__init__()\n", " self.units = units\n", " self.embedding = tf.keras.layers.Embedding(vocab_size, embedding_dim)\n", " self.gru = tf.keras.layers.GRU(self.units,\n", " return_sequences=True,\n", " return_state=True,\n", " recurrent_initializer='glorot_uniform')\n", " self.fc1 = tf.keras.layers.Dense(self.units)\n", " self.fc2 = tf.keras.layers.Dense(vocab_size)\n", " self.attention = BahdanauAttention(self.units)\n", "\n", " def call(self, x, features, hidden):\n", " # defining attention as a separate model\n", " context_vector, attention_weights = self.attention(features, hidden)\n", " # x shape after passing through embedding == (batch_size, 1, embedding_dim)\n", " x = self.embedding(x)\n", " # x shape after concatenation == (batch_size, 1, embedding_dim + hidden_size)\n", " x = tf.concat([tf.expand_dims(context_vector, 1), x], axis=-1)\n", " # passing the concatenated vector to the GRU\n", " output, state = self.gru(x)\n", " # shape == (batch_size, max_length, hidden_size)\n", " x = self.fc1(output)\n", " # x shape == (batch_size * max_length, hidden_size)\n", " x = tf.reshape(x, (-1, x.shape[2]))\n", " # output shape == (batch_size * max_length, vocab)\n", " x = self.fc2(x)\n", " return x, state, attention_weights\n", "\n", " def reset_state(self, batch_size):\n", " return tf.zeros((batch_size, self.units))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "9ZlfcS5VO_yA" }, "outputs": [], "source": [ "def loss_function(real, pred):\n", " mask = tf.math.logical_not(tf.math.equal(real, 0))\n", " loss_ = loss_object(real, pred)\n", " mask = tf.cast(mask, dtype=loss_.dtype)\n", " loss_ *= mask\n", " return tf.reduce_mean(loss_)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "s5kEPFlZCMfR" }, "outputs": [], "source": [ "@tf.function\n", "def train_step(img_tensor, target):\n", " loss = 0\n", " # initializing the hidden state for each batch\n", " # because the captions are not related from image to image\n", " hidden = decoder.reset_state(batch_size=target.shape[0])\n", " # TODO: do this dynamically: '' == 2\n", " dec_input = tf.expand_dims([2] * BATCH_SIZE, 1)\n", "\n", " with tf.GradientTape() as tape:\n", " features = encoder(img_tensor)\n", " for i in range(1, target.shape[1]):\n", " # passing the features through the decoder\n", " predictions, hidden, _ = decoder(dec_input, features, hidden)\n", " loss += loss_function(target[:, i], predictions)\n", " # using teacher forcing\n", " dec_input = tf.expand_dims(target[:, i], 1)\n", " total_loss = (loss / int(target.shape[1]))\n", " trainable_variables = encoder.trainable_variables + decoder.trainable_variables\n", "\n", " gradients = tape.gradient(loss, trainable_variables)\n", " optimizer.apply_gradients(zip(gradients, trainable_variables))\n", " return loss, total_loss" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "cQeg3v4KCMfU" }, "outputs": [], "source": [ "encoder = CNN_Encoder(embedding_dim)\n", "decoder = RNN_Decoder(embedding_dim, units, vocab_size)\n", "optimizer = tf.keras.optimizers.Adam()\n", "loss_object = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True, reduction='none')" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "pyYHHBHVCMfW" }, "source": [ "### Training\n", "\n", "Here we consume the dataset we have made before by looping over it. The dataset returns the image tensor and target tensor (captions) which we will pass to `train_step` for training the network.\n", "\n", "The encoder output, hidden state (initialized to 0) and the decoder input (which is the start token) is passed to the decoder. The decoder returns the predictions and the decoder hidden state. The decoder hidden state is then passed back into the model and the predictions are used to calculate the loss. Use teacher forcing to decide the next input to the decoder. Teacher forcing is the technique where the target word is passed as the next input to the decoder. The final step is to calculate the gradients and apply it to the optimizer and backpropagate." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "colab": {}, "colab_type": "code", "id": "l4gg61xSCMfX" }, "outputs": [], "source": [ "import time\n", "\n", "loss_plot = []\n", "\n", "for epoch in range(0, EPOCHS):\n", " start = time.time()\n", " total_loss = 0\n", " for (batch, (img_tensor, target)) in enumerate(dataset):\n", " batch_loss, t_loss = train_step(img_tensor, target)\n", " total_loss += t_loss\n", " if batch % 1 == 0:\n", " print('Epoch {} Batch {} Loss {:.4f}'.format(\n", " epoch + 1, batch, batch_loss.numpy() / int(target.shape[1])))\n", " # storing the epoch and loss value to plot later\n", " loss_plot.append(total_loss / num_steps)\n", "\n", " print('Epoch {} Loss {:.6f}'.format(epoch + 1,\n", " total_loss / num_steps))\n", " print('Time taken for 1 epoch {} sec\\n'.format(time.time() - start))\n" ] }, { "cell_type": "markdown", "metadata": { "colab_type": "text", "id": "J7JPiJjtCMfb" }, "source": [ "#### Visualize the loss" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "colab": { "base_uri": "https://localhost:8080/", "height": 295 }, "colab_type": "code", "id": "M0icezYgCMfd", "outputId": "5c2bf016-120c-4ca9-f7d2-cef69eb216a0" }, "outputs": [ { "data": { "image/png": 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OGcJ7x+fx3vF5zB6bS9aQlJArlZOlUBCR0+Lu7Kip5+Wgq+m1rTXUNzS3djWd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\nAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "execution_count": 23, "metadata": { "tags": [] }, "output_type": "execute_result" } ], "source": [ "import matplotlib.pyplot as plt\n", "# Below loss curve is not the actual loss image we have got\n", "# while training and kept it here only as a reference\n", "plt.plot(loss_plot)\n", "plt.xlabel('Epochs')\n", "plt.ylabel('Loss')\n", "plt.title('Loss Plot')\n", "plt.show()" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "dataloaders.ipynb", "provenance": [], "toc_visible": true, "version": "0.3.2" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }