{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Debiasing Facial Detection Systems\n", "\n", "> In this post, We will take a hands-on-lab of Debiasing Facial Detection Systems. This tutorial is the software lab, which is part of \"introduction to deep learning 2021\" offered from MIT 6.S191.\n", "\n", "- toc: true \n", "- badges: true\n", "- comments: true\n", "- author: Chanseok Kang\n", "- categories: [Python, Tensorflow, MIT]\n", "- image: images/ezgif-2-253dfd3f9097.gif" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Copyright Information\n", "\n", "Copyright 2021 MIT 6.S191 Introduction to Deep Learning. All Rights Reserved.\n", "\n", "Licensed under the MIT License. You may not use this file except in compliance\n", "with the License. Use and/or modification of this code outside of 6.S191 must\n", "reference:\n", "\n", "© MIT 6.S191: Introduction to Deep Learning\n", "http://introtodeeplearning.com" ] }, { "cell_type": "markdown", "metadata": { "id": "edLrpksDJfDg" }, "source": [ "## Debiasing Facial Detection Systems\n", "\n", "In this lab, we'll investigate [one recently published approach](http://introtodeeplearning.com/AAAI_MitigatingAlgorithmicBias.pdf) to addressing algorithmic bias. We'll build a facial detection model that learns the *latent variables* underlying face image datasets and uses this to adaptively re-sample the training data, thus mitigating any biases that may be present in order to train a *debiased* model." ] }, { "cell_type": "code", "execution_count": 38, "metadata": { "cellView": "form", "id": "dq6HedFjckGr" }, "outputs": [], "source": [ "import tensorflow as tf \n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import functools\n", "from IPython import display as ipythondisplay\n", "from tqdm import tqdm\n", "import time\n", "import os\n", "import sys\n", "import h5py\n", "import glob\n", "import cv2\n", "\n", "# Check that we are using a GPU, if not switch runtimes\n", "# using Runtime > Change Runtime Type > GPU\n", "assert len(tf.config.list_physical_devices('GPU')) > 0" ] }, { "cell_type": "markdown", "metadata": { "id": "ynLs1RCduz_O" }, "source": [ "## Dataset\n", "\n", "We'll be using three datasets in this lab. In order to train our facial detection models, we'll need a dataset of positive examples (i.e., of faces) and a dataset of negative examples (i.e., of things that are not faces). We'll use these data to train our models to classify images as either faces or not faces. Finally, we'll need a test dataset of face images. Since we're concerned about the potential *bias* of our learned models against certain demographics, it's important that the test dataset we use has equal representation across the demographics or features of interest. In this lab, we'll consider skin tone and gender. \n", "\n", "1. **Positive training data**: [CelebA Dataset](http://mmlab.ie.cuhk.edu.hk/projects/CelebA.html). A large-scale (over 200K images) of celebrity faces. \n", "2. **Negative training data**: [ImageNet](http://www.image-net.org/). Many images across many different categories. We'll take negative examples from a variety of non-human categories. \n", "[Fitzpatrick Scale](https://en.wikipedia.org/wiki/Fitzpatrick_scale) skin type classification system, with each image labeled as \"Lighter'' or \"Darker''.\n", "\n", "Let's begin by importing these datasets. We've written a class that does a bit of data pre-processing to import the training data in a usable format." ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [], "source": [ "class TrainingDatasetLoader(object):\n", " def __init__(self, data_path):\n", "\n", " print (\"Opening {}\".format(data_path))\n", " sys.stdout.flush()\n", "\n", " self.cache = h5py.File(data_path, 'r')\n", "\n", " print (\"Loading data into memory...\")\n", " sys.stdout.flush()\n", " self.images = self.cache['images'][:]\n", " self.labels = self.cache['labels'][:].astype(np.float32)\n", " self.image_dims = self.images.shape\n", " n_train_samples = self.image_dims[0]\n", "\n", " self.train_inds = np.random.permutation(np.arange(n_train_samples))\n", "\n", " self.pos_train_inds = self.train_inds[ self.labels[self.train_inds, 0] == 1.0 ]\n", " self.neg_train_inds = self.train_inds[ self.labels[self.train_inds, 0] != 1.0 ]\n", "\n", " def get_train_size(self):\n", " return self.train_inds.shape[0]\n", "\n", " def get_train_steps_per_epoch(self, batch_size, factor=10):\n", " return self.get_train_size()//factor//batch_size\n", "\n", " def get_batch(self, n, only_faces=False, p_pos=None, p_neg=None, return_inds=False):\n", " if only_faces:\n", " selected_inds = np.random.choice(self.pos_train_inds, size=n, replace=False, p=p_pos)\n", " else:\n", " selected_pos_inds = np.random.choice(self.pos_train_inds, size=n//2, replace=False, p=p_pos)\n", " selected_neg_inds = np.random.choice(self.neg_train_inds, size=n//2, replace=False, p=p_neg)\n", " selected_inds = np.concatenate((selected_pos_inds, selected_neg_inds))\n", "\n", " sorted_inds = np.sort(selected_inds)\n", " train_img = (self.images[sorted_inds,:,:,::-1]/255.).astype(np.float32)\n", " train_label = self.labels[sorted_inds,...]\n", " return (train_img, train_label, sorted_inds) if return_inds else (train_img, train_label)\n", "\n", " def get_n_most_prob_faces(self, prob, n):\n", " idx = np.argsort(prob)[::-1]\n", " most_prob_inds = self.pos_train_inds[idx[:10*n:10]]\n", " return (self.images[most_prob_inds,...]/255.).astype(np.float32)\n", "\n", " def get_all_train_faces(self):\n", " return self.images[ self.pos_train_inds ]" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Opening C:\\Users\\kcsgo\\.keras\\datasets\\train_face.h5\n", "Loading data into memory...\n" ] } ], "source": [ "# Get the training data: both images from CelebA and ImageNet\n", "path_to_training_data = tf.keras.utils.get_file('train_face.h5', 'https://www.dropbox.com/s/hlz8atheyozp1yx/train_face.h5?dl=1')\n", "# Instantiate a TrainingDatasetLoader using the downloaded dataset\n", "loader = TrainingDatasetLoader(path_to_training_data)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can look at the size of the training dataset and grab a batch of size 100:" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "number_of_training_examples = loader.get_train_size()\n", "(images, labels) = loader.get_batch(100)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Play around with displaying images to get a sense of what the training data actually looks like!" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "(100, 64, 64, 3)" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "images.shape" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "face_images = images[np.where(labels==1)[0]]\n", "not_face_images = images[np.where(labels==0)[0]]\n", "\n", "idx_face = np.random.choice(face_images.shape[0])\n", "idx_not_face = np.random.choice(not_face_images.shape[0])\n", "\n", "plt.figure(figsize=(8, 8))\n", "plt.subplot(1, 2, 1)\n", "plt.imshow(face_images[idx_face])\n", "plt.title(\"Face\")\n", "plt.grid(False)\n", "\n", "plt.subplot(1, 2, 2)\n", "plt.imshow(not_face_images[idx_not_face])\n", "plt.title(\"Not Face\")\n", "plt.grid(False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Thinking about bias\n", "\n", "Remember we'll be training our facial detection classifiers on the large, well-curated CelebA dataset (and ImageNet), and then evaluating their accuracy by testing them on an independent test dataset. Our goal is to build a model that trains on CelebA *and* achieves high classification accuracy on the the test dataset across all demographics, and to thus show that this model does not suffer from any hidden bias. \n", "\n", "What exactly do we mean when we say a classifier is biased? In order to formalize this, we'll need to think about [*latent variables*](https://en.wikipedia.org/wiki/Latent_variable), variables that define a dataset but are not strictly observed. As defined in the generative modeling lecture, we'll use the term *latent space* to refer to the probability distributions of the aforementioned latent variables. Putting these ideas together, we consider a classifier *biased* if its classification decision changes after it sees some additional latent features. This notion of bias may be helpful to keep in mind throughout the rest of the lab. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## CNN for facial detection \n", "\n", "First, we'll define and train a CNN on the facial classification task, and evaluate its accuracy. Later, we'll evaluate the performance of our debiased models against this baseline CNN. The CNN model has a relatively standard architecture consisting of a series of convolutional layers with batch normalization followed by two fully connected layers to flatten the convolution output and generate a class prediction. \n", "\n", "### Define and train the CNN model\n", "\n", "Like we did in the first part of the lab, we'll define our CNN model, and then train on the CelebA and ImageNet datasets using the `tf.GradientTape` class and the `tf.GradientTape.gradient` method." ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "n_filters = 12 # base number of convolutional filters\n", "\n", "'''Function to define a standard CNN model'''\n", "def make_standard_classifier(n_outputs=1):\n", " Conv2D = functools.partial(tf.keras.layers.Conv2D, padding='same', activation='relu')\n", " BatchNormalization = tf.keras.layers.BatchNormalization\n", " Flatten = tf.keras.layers.Flatten\n", " Dense = functools.partial(tf.keras.layers.Dense, activation='relu')\n", "\n", " model = tf.keras.Sequential([\n", " Conv2D(filters=1*n_filters, kernel_size=5, strides=2),\n", " BatchNormalization(),\n", " \n", " Conv2D(filters=2*n_filters, kernel_size=5, strides=2),\n", " BatchNormalization(),\n", "\n", " Conv2D(filters=4*n_filters, kernel_size=3, strides=2),\n", " BatchNormalization(),\n", "\n", " Conv2D(filters=6*n_filters, kernel_size=3, strides=2),\n", " BatchNormalization(),\n", "\n", " Flatten(),\n", " Dense(512),\n", " Dense(n_outputs, activation=None),\n", " ])\n", " return model\n", "\n", "standard_classifier = make_standard_classifier()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's train the standard CNN!" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [], "source": [ "class LossHistory:\n", " def __init__(self, smoothing_factor=0.0):\n", " self.alpha = smoothing_factor\n", " self.loss = []\n", " def append(self, value):\n", " self.loss.append( self.alpha*self.loss[-1] + (1-self.alpha)*value if len(self.loss)>0 else value )\n", " def get(self):\n", " return self.loss\n", "\n", "class PeriodicPlotter:\n", " def __init__(self, sec, xlabel='', ylabel='', scale=None):\n", "\n", " self.xlabel = xlabel\n", " self.ylabel = ylabel\n", " self.sec = sec\n", " self.scale = scale\n", "\n", " self.tic = time.time()\n", "\n", " def plot(self, data):\n", " if time.time() - self.tic > self.sec:\n", " plt.cla()\n", "\n", " if self.scale is None:\n", " plt.plot(data)\n", " elif self.scale == 'semilogx':\n", " plt.semilogx(data)\n", " elif self.scale == 'semilogy':\n", " plt.semilogy(data)\n", " elif self.scale == 'loglog':\n", " plt.loglog(data)\n", " else:\n", " raise ValueError(\"unrecognized parameter scale {}\".format(self.scale))\n", "\n", " plt.xlabel(self.xlabel); plt.ylabel(self.ylabel)\n", " ipythondisplay.clear_output(wait=True)\n", " ipythondisplay.display(plt.gcf())\n", "\n", " self.tic = time.time()" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "### Train the standard CNN ###\n", "\n", "# Training hyperparameters\n", "batch_size = 32\n", "num_epochs = 2 # keep small to run faster\n", "learning_rate = 5e-4\n", "\n", "optimizer = tf.keras.optimizers.Adam(learning_rate) # define our optimizer\n", "loss_history = LossHistory(smoothing_factor=0.99) # to record loss evolution\n", "plotter = PeriodicPlotter(sec=2, scale='semilogy')\n", "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n", "\n", "@tf.function\n", "def standard_train_step(x, y):\n", " with tf.GradientTape() as tape:\n", " # feed the images into the model\n", " logits = standard_classifier(x) \n", " # Compute the loss\n", " loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=y, logits=logits)\n", "\n", " # Backpropagation\n", " grads = tape.gradient(loss, standard_classifier.trainable_variables)\n", " optimizer.apply_gradients(zip(grads, standard_classifier.trainable_variables))\n", " return loss\n", "\n", "# The training loop!\n", "for epoch in range(num_epochs):\n", " for idx in tqdm(range(loader.get_train_size()//batch_size)):\n", " # Grab a batch of training data and propagate through the network\n", " x, y = loader.get_batch(batch_size)\n", " loss = standard_train_step(x, y)\n", "\n", " # Record the loss and plot the evolution of the loss as a function of training\n", " loss_history.append(loss.numpy().mean())\n", " plotter.plot(loss_history.get())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Evaluate performance of the standard CNN\n", "\n", "Next, let's evaluate the classification performance of our CelebA-trained standard CNN on the training dataset.\n" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Standard CNN accuracy on (potentially biased) training set: 0.9968\n" ] } ], "source": [ "### Evaluation of standard CNN ###\n", "\n", "# TRAINING DATA\n", "# Evaluate on a subset of CelebA+Imagenet\n", "(batch_x, batch_y) = loader.get_batch(5000)\n", "y_pred_standard = tf.round(tf.nn.sigmoid(standard_classifier.predict(batch_x)))\n", "acc_standard = tf.reduce_mean(tf.cast(tf.equal(batch_y, y_pred_standard), tf.float32))\n", "\n", "print(\"Standard CNN accuracy on (potentially biased) training set: {:.4f}\".format(acc_standard.numpy()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will also evaluate our networks on an independent test dataset containing faces that were not seen during training. For the test data, we'll look at the classification accuracy across four different demographics, based on the Fitzpatrick skin scale and sex-based labels: dark-skinned male, dark-skinned female, light-skinned male, and light-skinned female. \n", "\n", "Let's take a look at some sample faces in the test set. " ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [], "source": [ "def get_test_faces():\n", " cwd = os.path.dirname('./')\n", " images = {\n", " \"LF\": [],\n", " \"LM\": [],\n", " \"DF\": [],\n", " \"DM\": []\n", " }\n", " for key in images.keys():\n", " files = glob.glob(os.path.join(cwd, \"dataset\", \"faces\", key, \"*.png\"))\n", " for file in sorted(files):\n", " image = cv2.resize(cv2.imread(file), (64,64))[:,:,::-1]/255.\n", " images[key].append(image)\n", "\n", " return images[\"LF\"], images[\"LM\"], images[\"DF\"], images[\"DM\"]" ] }, { "cell_type": "code", "execution_count": 40, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "test_faces = get_test_faces()\n", "\n", "keys = [\"Light Female\", \"Light Male\", \"Dark Female\", \"Dark Male\"]\n", "for group, key in zip(test_faces,keys): \n", " plt.figure(figsize=(8, 8))\n", " plt.imshow(np.hstack(group))\n", " plt.title(key, fontsize=15)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's evaluated the probability of each of these face demographics being classified as a face using the standard CNN classifier we've just trained. " ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "standard_classifier_logits = [standard_classifier(np.array(x, dtype=np.float32)) for x in test_faces]\n", "standard_classifier_probs = tf.squeeze(tf.sigmoid(standard_classifier_logits))\n", "\n", "# Plot the prediction accuracies per demographic\n", "xx = range(len(keys))\n", "yy = standard_classifier_probs.numpy().mean(1)\n", "plt.bar(xx, yy)\n", "plt.xticks(xx, keys)\n", "plt.ylim(max(0,yy.min()-yy.ptp()/2.), yy.max()+yy.ptp()/2.)\n", "plt.title(\"Standard classifier predictions\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Take a look at the accuracies for this first model across these four groups. What do you observe? Would you consider this model biased or unbiased? What are some reasons why a trained model may have biased accuracies? " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Mitigating algorithmic bias\n", "\n", "Imbalances in the training data can result in unwanted algorithmic bias. For example, the majority of faces in CelebA (our training set) are those of light-skinned females. As a result, a classifier trained on CelebA will be better suited at recognizing and classifying faces with features similar to these, and will thus be biased.\n", "\n", "How could we overcome this? A naive solution -- and one that is being adopted by many companies and organizations -- would be to annotate different subclasses (i.e., light-skinned females, males with hats, etc.) within the training data, and then manually even out the data with respect to these groups.\n", "\n", "But this approach has two major disadvantages. First, it requires annotating massive amounts of data, which is not scalable. Second, it requires that we know what potential biases (e.g., race, gender, pose, occlusion, hats, glasses, etc.) to look for in the data. As a result, manual annotation may not capture all the different features that are imbalanced within the training data.\n", "\n", "Instead, let's actually **learn** these features in an unbiased, unsupervised manner, without the need for any annotation, and then train a classifier fairly with respect to these features. In the rest of this lab, we'll do exactly that." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Variational autoencoder (VAE) for learning latent structure\n", "\n", "As you saw, the accuracy of the CNN varies across the four demographics we looked at. To think about why this may be, consider the dataset the model was trained on, CelebA. If certain features, such as dark skin or hats, are *rare* in CelebA, the model may end up biased against these as a result of training with a biased dataset. That is to say, its classification accuracy will be worse on faces that have under-represented features, such as dark-skinned faces or faces with hats, relevative to faces with features well-represented in the training data! This is a problem. \n", "\n", "Our goal is to train a *debiased* version of this classifier -- one that accounts for potential disparities in feature representation within the training data. Specifically, to build a debiased facial classifier, we'll train a model that **learns a representation of the underlying latent space** to the face training data. The model then uses this information to mitigate unwanted biases by sampling faces with rare features, like dark skin or hats, *more frequently* during training. The key design requirement for our model is that it can learn an *encoding* of the latent features in the face data in an entirely *unsupervised* way. To achieve this, we'll turn to variational autoencoders (VAEs).\n", "\n", "![The concept of a VAE](image/vae.jpg)\n", "\n", "As shown in the schematic above and in Lecture 4, VAEs rely on an encoder-decoder structure to learn a latent representation of the input data. In the context of computer vision, the encoder network takes in input images, encodes them into a series of variables defined by a mean and standard deviation, and then draws from the distributions defined by these parameters to generate a set of sampled latent variables. The decoder network then \"decodes\" these variables to generate a reconstruction of the original image, which is used during training to help the model identify which latent variables are important to learn. \n", "\n", "Let's formalize two key aspects of the VAE model and define relevant functions for each.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding VAEs: loss function\n", "\n", "In practice, how can we train a VAE? In learning the latent space, we constrain the means and standard deviations to approximately follow a unit Gaussian. Recall that these are learned parameters, and therefore must factor into the loss computation, and that the decoder portion of the VAE is using these parameters to output a reconstruction that should closely match the input image, which also must factor into the loss. What this means is that we'll have two terms in our VAE loss function:\n", "\n", "1. **Latent loss ($L_{KL}$)**: measures how closely the learned latent variables match a unit Gaussian and is defined by the Kullback-Leibler (KL) divergence.\n", "2. **Reconstruction loss ($L_{x}{(x,\\hat{x})}$)**: measures how accurately the reconstructed outputs match the input and is given by the $L^1$ norm of the input image and its reconstructed output.\n", "\n", "The equation for the latent loss is provided by:\n", "\n", "$$L_{KL}(\\mu, \\sigma) = \\frac{1}{2}\\sum_{j=0}^{k-1} (\\sigma_j + \\mu_j^2 - 1 - \\log{\\sigma_j})$$\n", "\n", "The equation for the reconstruction loss is provided by:\n", "\n", "$$L_{x}{(x,\\hat{x})} = ||x-\\hat{x}||_1$$\n", "\n", "Thus for the VAE loss we have:\n", "\n", "$$L_{VAE} = c\\cdot L_{KL} + L_{x}{(x,\\hat{x})}$$\n", "\n", "where $c$ is a weighting coefficient used for regularization. Now we're ready to define our VAE loss function:" ] }, { "cell_type": "code", "execution_count": 42, "metadata": {}, "outputs": [], "source": [ "''' Function to calculate VAE loss given:\n", " an input x, \n", " reconstructed output x_recon, \n", " encoded means mu, \n", " encoded log of standard deviation logsigma, \n", " weight parameter for the latent loss kl_weight\n", "'''\n", "def vae_loss_function(x, x_recon, mu, logsigma, kl_weight=0.0005):\n", " # Define the latent loss. \n", " latent_loss = 0.5 * tf.reduce_sum(tf.exp(logsigma) + tf.square(mu) - 1.0 - logsigma, axis=1)\n", "\n", " # Define the reconstruction loss as the mean absolute pixel-wise \n", " # difference between the input and reconstruction. Hint: you'll need to \n", " # use tf.reduce_mean, and supply an axis argument which specifies which \n", " # dimensions to reduce over. For example, reconstruction loss needs to average \n", " # over the height, width, and channel image dimensions.\n", " # https://www.tensorflow.org/api_docs/python/tf/math/reduce_mean\n", " reconstruction_loss = tf.reduce_mean(tf.abs(x - x_recon), axis=(1, 2, 3))\n", "\n", " # Define the VAE loss. \n", " vae_loss = reconstruction_loss + kl_weight * latent_loss\n", " \n", " return vae_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Great! Now that we have a more concrete sense of how VAEs work, let's explore how we can leverage this network structure to train a debiased facial classifier." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Understanding VAEs: reparameterization \n", "\n", "As you may recall from lecture, VAEs use a \"reparameterization trick\" for sampling learned latent variables. Instead of the VAE encoder generating a single vector of real numbers for each latent variable, it generates a vector of means and a vector of standard deviations that are constrained to roughly follow Gaussian distributions. We then sample from the standard deviations and add back the mean to output this as our sampled latent vector. Formalizing this for a latent variable $z$ where we sample $\\epsilon \\sim \\mathcal{N}(0,(I))$ we have: \n", "\n", "$$z = \\mu + e^{\\left(\\frac{1}{2} \\cdot \\log{\\Sigma}\\right)}\\circ \\epsilon$$\n", "\n", "where $\\mu$ is the mean and $\\Sigma$ is the covariance matrix. This is useful because it will let us neatly define the loss function for the VAE, generate randomly sampled latent variables, achieve improved network generalization, **and** make our complete VAE network differentiable so that it can be trained via backpropagation. Quite powerful!\n", "\n", "Let's define a function to implement the VAE sampling operation:" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [], "source": [ "\"\"\"Reparameterization trick by sampling from an isotropic unit Gaussian.\n", "# Arguments\n", " z_mean, z_logsigma (tensor): mean and log of standard deviation of latent distribution (Q(z|X))\n", "# Returns\n", " z (tensor): sampled latent vector\n", "\"\"\"\n", "def sampling(z_mean, z_logsigma):\n", " # By default, random.normal is \"standard\" (ie. mean=0 and std=1.0)\n", " batch, latent_dim = z_mean.shape\n", " epsilon = tf.random.normal(shape=(batch, latent_dim))\n", "\n", " # Define the reparameterization computation!\n", " z = z_mean + tf.math.exp(0.5 * z_logsigma) * epsilon\n", " return z" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Debiasing variational autoencoder (DB-VAE)\n", "\n", "Now, we'll use the general idea behind the VAE architecture to build a model, termed a [*debiasing variational autoencoder*](https://lmrt.mit.edu/sites/default/files/AIES-19_paper_220.pdf) or DB-VAE, to mitigate (potentially) unknown biases present within the training idea. We'll train our DB-VAE model on the facial detection task, run the debiasing operation during training, evaluate on the PPB dataset, and compare its accuracy to our original, biased CNN model. \n", "\n", "### The DB-VAE model\n", "\n", "The key idea behind this debiasing approach is to use the latent variables learned via a VAE to adaptively re-sample the CelebA data during training. Specifically, we will alter the probability that a given image is used during training based on how often its latent features appear in the dataset. So, faces with rarer features (like dark skin, sunglasses, or hats) should become more likely to be sampled during training, while the sampling probability for faces with features that are over-represented in the training dataset should decrease (relative to uniform random sampling across the training data). \n", "\n", "A general schematic of the DB-VAE approach is shown here:\n", "\n", "![DB-VAE](image/DB-VAE.png)\n", "\n", "Recall that we want to apply our DB-VAE to a *supervised classification* problem -- the facial detection task. Importantly, note how the encoder portion in the DB-VAE architecture also outputs a single supervised variable, $z_o$, corresponding to the class prediction -- face or not face. Usually, VAEs are not trained to output any supervised variables (such as a class prediction)! This is another key distinction between the DB-VAE and a traditional VAE. \n", "\n", "Keep in mind that we only want to learn the latent representation of *faces*, as that's what we're ultimately debiasing against, even though we are training a model on a binary classification problem. We'll need to ensure that, **for faces**, our DB-VAE model both learns a representation of the unsupervised latent variables, captured by the distribution $q_\\phi(z|x)$, **and** outputs a supervised class prediction $z_o$, but that, **for negative examples**, it only outputs a class prediction $z_o$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining the DB-VAE loss function\n", "\n", "This means we'll need to be a bit clever about the loss function for the DB-VAE. The form of the loss will depend on whether it's a face image or a non-face image that's being considered. \n", "\n", "For **face images**, our loss function will have two components:\n", "\n", "\n", "1. **VAE loss ($L_{VAE}$)**: consists of the latent loss and the reconstruction loss.\n", "2. **Classification loss ($L_y(y,\\hat{y})$)**: standard cross-entropy loss for a binary classification problem. \n", "\n", "In contrast, for images of **non-faces**, our loss function is solely the classification loss. \n", "\n", "We can write a single expression for the loss by defining an indicator variable $\\mathcal{I}_f$which reflects which training data are images of faces ($\\mathcal{I}_f(y) = 1$ ) and which are images of non-faces ($\\mathcal{I}_f(y) = 0$). Using this, we obtain:\n", "\n", "$$L_{total} = L_y(y,\\hat{y}) + \\mathcal{I}_f(y)\\Big[L_{VAE}\\Big]$$\n", "\n", "Let's write a function to define the DB-VAE loss function:" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [], "source": [ "\"\"\"Loss function for DB-VAE.\n", "# Arguments\n", " x: true input x\n", " x_pred: reconstructed x\n", " y: true label (face or not face)\n", " y_logit: predicted labels\n", " mu: mean of latent distribution (Q(z|X))\n", " logsigma: log of standard deviation of latent distribution (Q(z|X))\n", "# Returns\n", " total_loss: DB-VAE total loss\n", " classification_loss = DB-VAE classification loss\n", "\"\"\"\n", "def debiasing_loss_function(x, x_pred, y, y_logit, mu, logsigma):\n", "\n", " # call the relevant function to obtain VAE loss\n", " vae_loss = vae_loss_function(x, x_pred, mu, logsigma) \n", "\n", " # define the classification loss using sigmoid_cross_entropy\n", " # https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits\n", " classification_loss = tf.nn.sigmoid_cross_entropy_with_logits(y, y_logit)\n", "\n", " # Use the training data labels to create variable face_indicator:\n", " # indicator that reflects which training data are images of faces\n", " face_indicator = tf.cast(tf.equal(y, 1), tf.float32)\n", "\n", " # define the DB-VAE total loss\n", " total_loss = tf.reduce_mean(classification_loss + face_indicator * vae_loss)\n", "\n", " return total_loss, classification_loss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### DB-VAE architecture\n", "\n", "Now we're ready to define the DB-VAE architecture. To build the DB-VAE, we will use the standard CNN classifier from above as our encoder, and then define a decoder network. We will create and initialize the two models, and then construct the end-to-end VAE. We will use a latent space with 100 latent variables.\n", "\n", "The decoder network will take as input the sampled latent variables, run them through a series of deconvolutional layers, and output a reconstruction of the original input image." ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [], "source": [ "n_filters = 12 # base number of convolutional filters, same as standard CNN\n", "latent_dim = 100 # number of latent variables\n", "\n", "def make_face_decoder_network():\n", " # Functionally define the different layer types we will use\n", " Conv2DTranspose = functools.partial(tf.keras.layers.Conv2DTranspose, padding='same', activation='relu')\n", " Flatten = tf.keras.layers.Flatten\n", " Dense = functools.partial(tf.keras.layers.Dense, activation='relu')\n", " Reshape = tf.keras.layers.Reshape\n", "\n", " # Build the decoder network using the Sequential API\n", " decoder = tf.keras.Sequential([\n", " # Transform to pre-convolutional generation\n", " Dense(units=4*4*6*n_filters), # 4x4 feature maps (with 6N occurances)\n", " Reshape(target_shape=(4, 4, 6*n_filters)),\n", "\n", " # Upscaling convolutions (inverse of encoder)\n", " Conv2DTranspose(filters=4*n_filters, kernel_size=3, strides=2),\n", " Conv2DTranspose(filters=2*n_filters, kernel_size=3, strides=2),\n", " Conv2DTranspose(filters=1*n_filters, kernel_size=5, strides=2),\n", " Conv2DTranspose(filters=3, kernel_size=5, strides=2),\n", " ])\n", "\n", " return decoder" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we will put this decoder together with the standard CNN classifier as our encoder to define the DB-VAE. Note that at this point, there is nothing special about how we put the model together that makes it a \"debiasing\" model -- that will come when we define the training operation. Here, we will define the core VAE architecture by sublassing the `Model` class; defining encoding, reparameterization, and decoding operations; and calling the network end-to-end." ] }, { "cell_type": "code", "execution_count": 46, "metadata": {}, "outputs": [], "source": [ "class DB_VAE(tf.keras.Model):\n", " def __init__(self, latent_dim):\n", " super(DB_VAE, self).__init__()\n", " self.latent_dim = latent_dim\n", "\n", " # Define the number of outputs for the encoder. Recall that we have \n", " # `latent_dim` latent variables, as well as a supervised output for the \n", " # classification.\n", " num_encoder_dims = 2*self.latent_dim + 1\n", "\n", " self.encoder = make_standard_classifier(num_encoder_dims)\n", " self.decoder = make_face_decoder_network()\n", "\n", " # function to feed images into encoder, encode the latent space, and output\n", " # classification probability \n", " def encode(self, x):\n", " # encoder output\n", " encoder_output = self.encoder(x)\n", "\n", " # classification prediction\n", " y_logit = tf.expand_dims(encoder_output[:, 0], -1)\n", " # latent variable distribution parameters\n", " z_mean = encoder_output[:, 1:self.latent_dim+1] \n", " z_logsigma = encoder_output[:, self.latent_dim+1:]\n", "\n", " return y_logit, z_mean, z_logsigma\n", "\n", " # VAE reparameterization: given a mean and logsigma, sample latent variables\n", " def reparameterize(self, z_mean, z_logsigma):\n", " # call the sampling function defined above\n", " z = sampling(z_mean, z_logsigma)\n", " return z\n", "\n", " # Decode the latent space and output reconstruction\n", " def decode(self, z):\n", " # use the decoder to output the reconstruction\n", " reconstruction = self.decoder(z)\n", " return reconstruction\n", "\n", " # The call function will be used to pass inputs x through the core VAE\n", " def call(self, x): \n", " # Encode input to a prediction and latent space\n", " y_logit, z_mean, z_logsigma = self.encode(x)\n", "\n", " # reparameterization\n", " z = self.reparameterize(z_mean, z_logsigma)\n", "\n", " # reconstruction\n", " recon = self.decode(z)\n", " return y_logit, z_mean, z_logsigma, recon\n", "\n", " # Predict face or not face logit for given input x\n", " def predict(self, x):\n", " y_logit, z_mean, z_logsigma = self.encode(x)\n", " return y_logit\n", "\n", "dbvae = DB_VAE(latent_dim)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As stated, the encoder architecture is identical to the CNN from earlier in this lab. Note the outputs of our constructed DB_VAE model in the `call` function: `y_logit, z_mean, z_logsigma, z`. Think carefully about why each of these are outputted and their significance to the problem at hand.\n", "\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Adaptive resampling for automated debiasing with DB-VAE\n", "\n", "So, how can we actually use DB-VAE to train a debiased facial detection classifier?\n", "\n", "Recall the DB-VAE architecture: as input images are fed through the network, the encoder learns an estimate $\\mathcal{Q}(z|X)$ of the latent space. We want to increase the relative frequency of rare data by increased sampling of under-represented regions of the latent space. We can approximate $\\mathcal{Q}(z|X)$ using the frequency distributions of each of the learned latent variables, and then define the probability distribution of selecting a given datapoint $x$ based on this approximation. These probability distributions will be used during training to re-sample the data.\n", "\n", "You'll write a function to execute this update of the sampling probabilities, and then call this function within the DB-VAE training loop to actually debias the model. \n", "\n", "First, we've defined a short helper function `get_latent_mu` that returns the latent variable means returned by the encoder after a batch of images is inputted to the network:" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [], "source": [ "# Function to return the means for an input image batch\n", "def get_latent_mu(images, dbvae, batch_size=1024):\n", " N = images.shape[0]\n", " mu = np.zeros((N, latent_dim))\n", " for start_ind in range(0, N, batch_size):\n", " end_ind = min(start_ind+batch_size, N+1)\n", " batch = (images[start_ind:end_ind]).astype(np.float32)/255.\n", " _, batch_mu, _ = dbvae.encode(batch)\n", " mu[start_ind:end_ind] = batch_mu\n", " return mu" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's define the actual resampling algorithm `get_training_sample_probabilities`. Importantly note the argument `smoothing_fac`. This parameter tunes the degree of debiasing: for `smoothing_fac=0`, the re-sampled training set will tend towards falling uniformly over the latent space, i.e., the most extreme debiasing." ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [], "source": [ "'''Function that recomputes the sampling probabilities for images within a batch\n", " based on how they distribute across the training data'''\n", "def get_training_sample_probabilities(images, dbvae, bins=10, smoothing_fac=0.001): \n", " print(\"Recomputing the sampling probabilities\")\n", " \n", " # run the input batch and get the latent variable means\n", " mu = get_latent_mu(images, dbvae)\n", "\n", " # sampling probabilities for the images\n", " training_sample_p = np.zeros(mu.shape[0])\n", " \n", " # consider the distribution for each latent variable \n", " for i in range(latent_dim):\n", " \n", " latent_distribution = mu[:,i]\n", " # generate a histogram of the latent distribution\n", " hist_density, bin_edges = np.histogram(latent_distribution, density=True, bins=bins)\n", "\n", " # find which latent bin every data sample falls in \n", " bin_edges[0] = -float('inf')\n", " bin_edges[-1] = float('inf')\n", " \n", " # call the digitize function to find which bins in the latent distribution \n", " # every data sample falls in to\n", " # https://docs.scipy.org/doc/numpy-1.13.0/reference/generated/numpy.digitize.html\n", " bin_idx = np.digitize(latent_distribution, bin_edges) \n", "\n", " # smooth the density function\n", " hist_smoothed_density = hist_density + smoothing_fac\n", " hist_smoothed_density = hist_smoothed_density / np.sum(hist_smoothed_density)\n", "\n", " # invert the density function \n", " p = 1.0/(hist_smoothed_density[bin_idx-1])\n", " \n", " # normalize all probabilities\n", " p = p / np.sum(p)\n", " \n", " # update sampling probabilities by considering whether the newly\n", " # computed p is greater than the existing sampling probabilities.\n", " training_sample_p = np.maximum(p, training_sample_p)\n", " \n", " # final normalization\n", " training_sample_p /= np.sum(training_sample_p)\n", "\n", " return training_sample_p" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've defined the resampling update, we can train our DB-VAE model on the CelebA/ImageNet training data, and run the above operation to re-weight the importance of particular data points as we train the model. Remember again that we only want to debias for features relevant to *faces*, not the set of negative examples. Complete the code block below to execute the training loop!" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [], "source": [ "def plot_sample(x,y,vae):\n", " plt.figure(figsize=(2,1))\n", " plt.subplot(1, 2, 1)\n", "\n", " idx = np.where(y==1)[0][0]\n", " plt.imshow(x[idx])\n", " plt.grid(False)\n", "\n", " plt.subplot(1, 2, 2)\n", " _, _, _, recon = vae(x)\n", " recon = np.clip(recon, 0, 1)\n", " plt.imshow(recon[idx])\n", " plt.grid(False)\n", "\n", " plt.show()" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Starting epoch 6/6\n", "Recomputing the sampling probabilities\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ " 0%| | 0/3434 [00:00" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ " 14%|███████████▎ | 492/3434 [00:06<00:36, 80.27it/s]" ] }, { "data": { "image/png": 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\n", 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\n", 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\n", 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\n", 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\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stderr", "output_type": "stream", "text": [ "100%|██████████████████████████████████████████████████████████████████████████████| 3434/3434 [00:46<00:00, 73.58it/s]\n" ] } ], "source": [ "# Hyperparameters\n", "batch_size = 32\n", "learning_rate = 5e-4\n", "latent_dim = 100\n", "\n", "# DB-VAE needs slightly more epochs to train since its more complex than \n", "# the standard classifier so we use 6 instead of 2\n", "num_epochs = 6 \n", "\n", "# instantiate a new DB-VAE model and optimizer\n", "dbvae = DB_VAE(100)\n", "optimizer = tf.keras.optimizers.Adam(learning_rate)\n", "\n", "# To define the training operation, we will use tf.function which is a powerful tool \n", "# that lets us turn a Python function into a TensorFlow computation graph.\n", "@tf.function\n", "def debiasing_train_step(x, y):\n", "\n", " with tf.GradientTape() as tape:\n", " # Feed input x into dbvae. Note that this is using the DB_VAE call function!\n", " y_logit, z_mean, z_logsigma, x_recon = dbvae(x)\n", "\n", " # call the DB_VAE loss function to compute the loss\n", " loss, class_loss = debiasing_loss_function(x, x_recon, y, y_logit, z_mean, z_logsigma)\n", " \n", " # use the GradientTape.gradient method to compute the gradients.\n", " # Hint: this is with respect to the trainable_variables of the dbvae.\n", " grads = tape.gradient(loss, dbvae.trainable_variables)\n", "\n", " # apply gradients to variables\n", " optimizer.apply_gradients(zip(grads, dbvae.trainable_variables))\n", " return loss\n", "\n", "# get training faces from data loader\n", "all_faces = loader.get_all_train_faces()\n", "\n", "if hasattr(tqdm, '_instances'): tqdm._instances.clear() # clear if it exists\n", "\n", "# The training loop -- outer loop iterates over the number of epochs\n", "for i in range(num_epochs):\n", "\n", " ipythondisplay.clear_output(wait=True)\n", " print(\"Starting epoch {}/{}\".format(i+1, num_epochs))\n", "\n", " # recompute the sampling probabilities for debiasing\n", " p_faces = get_training_sample_probabilities(all_faces, dbvae)\n", " \n", " # get a batch of training data and compute the training step\n", " for j in tqdm(range(loader.get_train_size() // batch_size)):\n", " # load a batch of data\n", " (x, y) = loader.get_batch(batch_size, p_pos=p_faces)\n", " # loss optimization\n", " loss = debiasing_train_step(x, y)\n", " \n", " # plot the progress every 200 steps\n", " if j % 500 == 0: \n", " plot_sample(x, y, dbvae)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Evaluation of DB-VAE on test dataset\n", "\n", "Finally let's test our DB-VAE model on the test dataset, looking specifically at its accuracy on each the \"Dark Male\", \"Dark Female\", \"Light Male\", and \"Light Female\" demographics. We will compare the performance of this debiased model against the (potentially biased) standard CNN from earlier in the lab." ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "image/png": 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PZWVlnH/++Ut/9rOfFUzwp5566srZs2d3mj17dsEvB8Vw8jczs1ajS5cu9bNnz669/vrrX6uoqKg77bTT9vnNb36zW6G6EyZM6FZdXd2/urq6+tVXX+0wa9asDg1lJ5988iqAIUOGvLdo0aL2AM8++2znM888cwXAyJEj1+yyyy4bGur/8pe/3GO//farHjRoUP+lS5e2nTNnTgeA8vJyTj/99FUAL7zwQodevXqtP/DAA9eXlZUxatSoFVu7n9/5zndWPP/88zvPmzevXX5ZmzZtOO+885aOHTv2U1vbvpO/mZm1Km3atOHYY49dd9111y359a9//fqf/vSnrvl15s2b1+7666/f4/HHH3/llVdeqT3yyCPXfPDBBx/nvA4dOkTaVtTV1X18X5qysk3T4pQpUzo//vjjnWtqaua9/PLLtf3793///fffLwNo165dfe55fqnQLW421r9///dramo6ba5O27ZtGTNmTKMJ/rvf/e7K6dOnd37ttdc2+XJQDCd/MzNrNWbNmtX+pZde+ni4e+bMmR179er1IcBOO+20Yc2aNWUAq1atKu/YsWN9t27dNixatKjNY4891qWptg899NB1t9xyy24AkyZN2mXt2rXlAKtXry7v0qXLhs6dO9fPnDmzw6xZs3YqtP7BBx/8weLFi9vNmTOnPcBdd93VrVC9iy++eNmkSZN2e+SRRz5u58Ybb+z2+uuvb3S14JgxY1Y8+eSTu6xcuXKTqwjbt28f3/3ud98aP35896b2q5BMr/Y3M7NPtmJ+mtec1q5dW37eeef1Wbt2bXl5eXlUVVWtnzBhwmsAp5122ttHH310v+7du380ffr0VwYMGPBev379DujTp8/6QYMGvdNU2+PGjVsyYsSIvaurq/sfdthh7/To0eNDgBEjRqy56aabKvbdd9/qffbZ54OBAwe+W2j9Tp06xW9/+9vXjj322L7dunWrGzp06Dtz587tmF+vd+/edbfddtuCSy65pNeKFSvalpWVxaGHHvrOt771rdW59Tp06BCjR49edvnll/fObwPg/PPPf/vaa6/dqgv/1Np+Vj948OCoqanZ3mGYWQn4JP3UT9JzETF4W9uZNWvWwoEDB77dHDFZtmbNmrX7wIEDqwqVedjfzMysxDj5m5mZlRgnfzMzsxLjC/7MzLJ2RZMXmhfZzprmacdKno/8zczMSoyTv5mZWYnxsL+ZmW29K7o06yN9uWJNk/cNKC8vH9SvX7/36+rqVF5eHieddNKKyy+//K3y8nKmTJnS+aSTTtqnsrLyw/r6enbfffe6e+65Z0FlZeVGj+W96KKLeq5fv1433HDDGw3Lnn766Y6nnHLK3gsWLJgD8NRTT3U8/PDDq++9995XR4wYsTZ/+w3zxx9//MqrrrpqafN0QMvwkb+ZmbUq7du3r583b17t/Pnz5zzyyCOv/PWvf+1y8cUXf/zUvsGDB78zb9682ldeeaX2kEMOeffqq6/e5C54p5122oo///nPG92B74477ug2YsSIlQ3zt99++26f/vSn35k4ceJG9Rq23/BqbYkfnPzNzKwVq6ysrLv55psX3nrrrd3r6+s3Kquvr2fdunXlXbt2rctfb+DAget32WWXutxb7E6ePLnbqaeeurJh3SlTpnS97bbbFj7xxBO7vPfee03ftL8VcfI3M7NWrbq6+sP6+nreeOONNgA1NTU777///tU9e/Y86Iknnug8ZsyYgnckHDFixMo777yzG8DDDz+806677lp34IEHrgd48MEHd+7du/f6Aw44YP3QoUPX3XPPPR//ZGP9+vVl+++/f3XD6/e///0mDxba0Tn5m5lZq5d7q/qGYf+lS5e+ePLJJ68YM2ZMr0LrnHbaaSsfeOCBrhs2bODOO+/sdsIJJ3w85H/HHXd8PD9y5MiVuQ/pyR/2P/vss1dluGuZyPSCP0lHAf8JlAM3R8S4vPLrgC+ls52A7hGxa5YxmZnZJ0ttbW278vJyKisr62bNmrVR2YgRI1afeOKJ+wAcfvjh/d5+++22AwcOfPfuu+9+rW/fvh9VVlaunzp1auepU6d2feqpp+YC1NXV8Ze//KXrgw8+uOu1117bIyJYvXp1m1WrVpV17dq1vkAIrU5myV9SOXAD8BVgMTBD0uSIqG2oExEX5tT/HnBIVvGYmdknz5IlS9qcffbZe55xxhnLyso2Hcx+9NFHd95zzz3XAzz55JOv5pefeOKJKy+55JLeffr0Wb/PPvt8BPDnP/95l/333/+93PrHH3981cSJE3c999xzV+a30RpleeQ/BJgfEQsAJN0FHAfUNlL/JOCnGcZjZmbNrYif5jW3hnPuDT/1++Y3v7nipz/96VsN5Q3n/COCzp07b7jlllsWNtbWqaeeuuqyyy7rfdVVVy1qWDZx4sRuw4cP3+jxuiNGjFg1fvz47ueee+7Khu03lB155JFrbrzxxjdoRbJM/pXAopz5xcDQQhUl7QnsBTzSSPloYDRAnz59mjdKMzNrVTZs2NDoF45jjz123bp1614otq2ePXvW1dXVPZ+77L777luYX2/UqFFrRo0ataap7bcWWV7wV+hnEVFgGcBI4N6I2FCoMCJuiojBETG4oqKi2QI0MzMrRVkm/8VA75z5XsCSRuqOBP6YYSxmZmaWyjL5zwD6SdpLUjuSBD85v5Kk/YCuwDMZxmJmZs2jvr6+/hN1w5tPovQ9avSXCZkl/4ioA8YA04C5wKSImCNprKThOVVPAu6K3B9pmpnZjmr28uXLu/gLwI6rvr5ey5cv7wLMbqxOpr/zj4ipwNS8ZT/Jm78iyxjMzKz51NXVnbV06dKbly5dOgDfKG5HVQ/MrqurO6uxCn6qn5mZFW3QoEHLgOFNVrQdmr+1mZmZlRgnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmJK6t7+VZc+0CztLBx3TLO0Y2Zmtj34yN/MzKzElNSRv9knhUexzGxb+MjfzMysxPjI35qdj0rNzHZsmR75SzpK0suS5ku6tJE635BUK2mOpIlZxmNmZmYZHvlLKgduAL4CLAZmSJocEbU5dfoBPwI+FxGrJHXPKh4zMzNLZDnsPwSYHxELACTdBRwH1ObUORu4ISJWAUTEsgzjsdbmii7N1M6a5mnHzOwTIsvkXwksyplfDAzNq7MvgKSngHLgioj4n/yGJI0GRgP06dMnk2C3iJOSfVL4s2xWkrI8568CyyJvvg3QDzgCOAm4WdKum6wUcVNEDI6IwRUVFc0eqJmZWSnJMvkvBnrnzPcClhSo8+eI+Cgi/gG8TPJlwMzMzDKSZfKfAfSTtJekdsBIYHJenT8BXwKQtDvJaYAFGcZkZmZW8jJL/hFRB4wBpgFzgUkRMUfSWEnD02rTgBWSaoFHgUsiYkVWMZmZmVnGN/mJiKnA1LxlP8mZDuCi9GVmZmYtwLf3NTMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMU7+ZmZmJcbJ38zMrMQ4+ZuZmZUYJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYlx8jczMysxmSZ/SUdJelnSfEmXFig/XdJySS+kr7OyjMfMzMygTVYNSyoHbgC+AiwGZkiaHBG1eVXvjogxWcVhZmZmG8vyyH8IMD8iFkTEh8BdwHEZbs/MzMyKUFTyl3SfpGMkbcmXhUpgUc784nRZvhGSXpR0r6TeW9C+mZmZbYVik/n/B5wMvCppnKT9i1hHBZZF3vz9QFVEHAQ8BEwo2JA0WlKNpJrly5cXGbKZmZkVUlTyj4iHImIU8GlgIfCgpKclnSGpbSOrLQZyj+R7AUvy2l0REevT2d8DgxrZ/k0RMTgiBldUVBQTspmZmTWi6GF8SbsBpwNnATOB/yT5MvBgI6vMAPpJ2ktSO2AkMDmvzR45s8OBuUVHbmZmZlulqKv9Jf03sD9wO/C1iHgzLbpbUk2hdSKiTtIYYBpQDtwSEXMkjQVqImIycJ6k4UAdsJLky4WZmZllqNif+t0cEVNzF0hqHxHrI2JwYyul60zNW/aTnOkfAT/agnjNzMxsGxU77H9lgWXPNGcgZmZm1jI2e+Qv6VMkP8/rKOkQ/nkF/y5Ap4xjMzMzsww0Nez/LyTn4XsB1+YsXwf8OKOYzMzMLEObTf4RMQGYIGlERNzXQjGZmZlZhpoa9j8lIu4AqiRdlF8eEdcWWM3MzMx2YE0N+++U/rtz1oGYmZlZy2hq2H98+u/PWiYcMzMzy1pTw/6/2Vx5RJzXvOGYmZlZ1poa9n+uRaIwMzOzFlPM1f5mZmb2CdLUsP9/RMQFku5n08fxEhHDM4vMzMzMMtHUsP/t6b9XZx2ImZmZtYymhv2fS/99PH0s7/4kIwAvR8SHLRCfmZmZNbNiH+l7DPA74O8k9/ffS9J3IuIvWQZnZmZmza/YR/peA3wpIuYDSNoHeABw8jczM2tlin2k77KGxJ9aACzLIB4zMzPLWFNX+x+fTs6RNBWYRHLO/0RgRsaxmZmZWQaaGvb/Ws70W8AX0+nlQNdMIjIzM7NMNXW1/xnb0riko4D/BMqBmyNiXCP1TgDuAT4TETXbsk0zMzPbvGKv9u8AfBs4AOjQsDwiztzMOuXADcBXgMXADEmTI6I2r15n4Dxg+hZHb2ZmZlus2Av+bgc+BfwL8DjQC1jXxDpDgPkRsSC9J8BdwHEF6v0c+BXwQZGxmJmZ2TYoNvn3jYjLgXfT+/0fAxzYxDqVwKKc+cXpso9JOgToHRFTNteQpNGSaiTVLF++vMiQzczMrJBik/9H6b+rJQ0AugBVTayjAss+fj6ApDLgOuD7TW08Im6KiMERMbiioqK4iM3MzKygYm/yc5OkrsDlwGRg53R6cxYDvXPmewFLcuY7AwOAxyRBclphsqThvujPzMwsO0Ul/4i4OZ18HNi7yLZnAP0k7QW8AYwETs5pcw2we8O8pMeAi534zczMslXUsL+k3ST9VtLzkp6T9B+SdtvcOhFRB4wBpgFzgUkRMUfSWEl+FLCZmdl2Uuyw/13A/wIj0vlRwN3AsM2tFBFTgal5y37SSN0jiozFzMzMtkGxyb9bRPw8Z/5KSV/PIiAzMzPLVrFX+z8qaaSksvT1DZKn+pmZmVkr09SDfdaR/DxPwEXAHWlRGfAO8NNMozMzM7Nm19S9/Tu3VCBmZmbWMoo95096hf4X0tnHmrorn5mZme2Yiv2p3zjgfKA2fZ2fLjMzM7NWptgj/38FDo6IegBJE4CZwKVZBWZmZmbZKPZqf4Bdc6a7NHcgZmZm1jKKPfL/BTBT0qMkV/5/AfhRZlGZmZlZZppM/kqeuvMkcCjwGZLk/8OIWJpxbGZmZpaBJpN/RISkP0XEIJIn+pmZmVkrVuw5/2clfSbTSMzMzKxFFHvO/0vAOZIWAu+SDP1HRByUVWBmZmaWjWKT/9GZRmFmZmYtpql7+3cAzgH6Ai8B/xURdS0RmJmZmWWjqXP+E4DBJIn/aOCazCMyMzOzTDU17F8dEQcCSPov4G/Zh2RmZmZZaurI/6OGia0Z7pd0lKSXJc2XtMmtgCWdI+klSS9IelJS9ZZuw8zMzLZMU0f+AyWtTacFdEznG67236WxFSWVAzcAXwEWAzMkTY6I2pxqEyPid2n94cC1wFFbtytmZmZWjM0m/4go34a2hwDzI2IBgKS7gONIngrY0P7anPo7AbEN2zMzM7MiFPtTv61RCSzKmV8MDM2vJOlc4CKgHXBkhvGYmZkZW/ZUvy2lAss2ObKPiBsiYh/gh8BlBRuSRkuqkVSzfPnyZg7TzMystGSZ/BcDvXPmewFLNlP/LuDrhQoi4qaIGBwRgysqKpoxRDMzs9KTZfKfAfSTtJekdsBI8h4MJKlfzuwxwKsZxmNmZmZkeM4/IuokjQGmAeXALRExR9JYoCYiJgNjJA0j+UnhKuC0rOIxMzOzRJYX/BERU4Gpect+kjN9fpbbNzMzs01lOexvZmZmOyAnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiSUSrCUAAAsJSURBVHHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMU7+ZmZmJcbJ38zMrMQ4+ZuZmZUYJ38zM7MSk2nyl3SUpJclzZd0aYHyiyTVSnpR0sOS9swyHjMzM8sw+UsqB24AjgaqgZMkVedVmwkMjoiDgHuBX2UVj5mZmSWyPPIfAsyPiAUR8SFwF3BcboWIeDQi3ktnnwV6ZRiPmZmZkW3yrwQW5cwvTpc15tvAXwoVSBotqUZSzfLly5sxRDMzs9KTZfJXgWVRsKJ0CjAY+HWh8oi4KSIGR8TgioqKZgzRzMys9LTJsO3FQO+c+V7AkvxKkoYB/wZ8MSLWZxiPmZmZke2R/wygn6S9JLUDRgKTcytIOgQYDwyPiGUZxmJmZmapzJJ/RNQBY4BpwFxgUkTMkTRW0vC02q+BnYF7JL0gaXIjzZmZmVkzyXLYn4iYCkzNW/aTnOlhWW7fzMzMNuU7/JmZmZUYJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYlx8jczMysxTv5mZmYlxsnfzMysxDj5m5mZlRgnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVmEyTv6SjJL0sab6kSwuUf0HS85LqJJ2QZSxmZmaWyCz5SyoHbgCOBqqBkyRV51V7HTgdmJhVHGZmZraxNhm2PQSYHxELACTdBRwH1DZUiIiFaVl9hnGYmZlZjiyH/SuBRTnzi9NlW0zSaEk1kmqWL1/eLMGZmZmVqiyTvwosi61pKCJuiojBETG4oqJiG8MyMzMrbVkm/8VA75z5XsCSDLdnZmZmRcgy+c8A+knaS1I7YCQwOcPtmZmZWREyS/4RUQeMAaYBc4FJETFH0lhJwwEkfUbSYuBEYLykOVnFY2ZmZoksr/YnIqYCU/OW/SRnegbJ6QAzMzNrIb7Dn5mZWYlx8jczMysxTv5mZmYlxsnfzMysxDj5m5mZlRgnfzMzsxLj5G9mZlZinPzNzMxKjJO/mZlZiXHyNzMzKzFO/mZmZiXGyd/MzKzEOPmbmZmVGCd/MzOzEuPkb2ZmVmKc/M3MzEqMk7+ZmVmJcfI3MzMrMZkmf0lHSXpZ0nxJlxYoby/p7rR8uqSqLOMxMzOzDJO/pHLgBuBooBo4SVJ1XrVvA6sioi9wHfDLrOIxMzOzRJZH/kOA+RGxICI+BO4CjsurcxwwIZ2+F/iyJGUYk5mZWclrk2HblcCinPnFwNDG6kREnaQ1wG7A27mVJI0GRqez70h6OZOIE7vnbz9fs307+VlJf89xP2fPfdwyWks/79lcYVjrl2XyL/Qpja2oQ0TcBNzUHEE1RVJNRAxuiW2VMvdz9tzHLcP9bK1RlsP+i4HeOfO9gCWN1ZHUBugCrMwwJjMzs5KXZfKfAfSTtJekdsBIYHJencnAaen0CcAjEbHJkb+ZmZk1n8yG/dNz+GOAaUA5cEtEzJE0FqiJiMnAfwG3S5pPcsQ/Mqt4tkCLnF4w93MLcB+3DPeztTrygbaZmVlp8R3+zMzMSoyTv5mZWYnZIZO/pHcKLDtH0qlNrHe6pOsbKfvxZtZbKOklSS+kr89uedTFkXSEpClZtd8ctlP/P5G37AVJs5vYXlVTdXYEkjak+zNH0ixJF0naov97xexrWuf9nM/xC+nFtpmQdIWki7Nqvzm0cN+HpJ/nLNtd0keN/Z/Iqdfo/xuzrGT5O/9mFRG/28YmfgxctZnyL0XEZm/UUcpaoP87S+odEYsk9d/Gbe1o3o+IgwEkdQcmkvys9afFrJzeKrtYf2/YlgEt2/cLgGOBy9P5E4E5W7C+WYvZIY/8C8k9ypD0GUkvSnpG0q/zvpX3lPQ/kl6V9Ku0/jigY3oEcOcWbPMSSTPSbf0sXVYlaZ6kmyXNlnSnpGGSnkq3OSStN0TS05Jmpv/uV6D9nSTdkm5jpqT82x/vMFqg/ycB30ynTwL+mLPtKklPSHo+fW0yMiOpPI2l4f36TnPsd3OLiGUkd6sco0TBfUtHiB6VNBF4KbcNSXunn5fPFLPNxj5n6RHnnyTdL+kfksakR8YzJT0rqVta7+x03VmS7pPUqcA29knf9+fS/dl/G7uq2bVA378PzJXUcMOfb5J8rhvW/ZqSB5jNlPSQpD3yG5BUkfbxjPT1uWbafbONRcQO9wLeKbDsCuDidHo28Nl0ehwwO50+neTbdxegA/Aa0LuxNnPaXkjyn/wFYHq67KskP+ERyZekKcAXgCqgDjgwXf4ccEta7zjgT+n6uwBt0ulhwH3p9BHAlHT6KuCUdHpX4BVgpxLt/32Bp9P5mSQPg2potxPQIZ3uR/JTUdL3oqHOaOCydLo9UAPstb37cjP9uQrYYzP7dgTwbsM+NOwrsF/aPwcXaLOKJAG9kL5u2NznLH2/5gOdgQpgDXBOWu864IJ0erecbVwJfK/AZ+JhoF86PZTknh2l1vezgeHA1SQ3NXs47ePr0zpd+ecvrM4Crsn5f9NQZyJweDrdB5i7vfvQr0/mq9UM+zeQtCvQOSKeThdNJBlqa/BwRKxJ69aS3M96EU3LH/b/avqamc7vTPIH4nXgHxHxUrqNOek2Q9JLJH8EIEmAEyT1I7llcdsC2/wqMFz/PG/agfQ/fBHxbhcZ9v9KYJWkkST7/15OWVvgekkHAxtIvijk+ypwkKQT0vkuJO/XP4rasZbXcGvrze3b3yIiN/4K4M/AiIhobDi50LB/Y58zgEcjYh2wTsmzNe5Pl78EHJROD5B0JckXh51J7t3xzx2RdgY+C9yjfz6Xq30j8e0Isup7gP8Bfg68BdydV9YLuFtSD6AdhT+bw4DqnH7cRVLn9D0yazatLvnT9DMy1udMb2Dr91HALyJi/EYLpaq8bdTnzNfnbO/nJH9Y/0+6zmONbGNERGT5oKLmlmX/303yGOjT85ZfSPLHdCDJaMsHjcT1vYiYVqBshyJpb5K+WUZy7rmxfXs3b9U1JF+kPseWnUsu+DmTNJTiPst/AL4eEbMknU5yZJyrDFhd4EvHDifrvo+IDyU9B3wfOAD4Wk7xb4FrI2KypCNIRk7ylQGHRcT7xe+V2ZZrNef8G0TEKpKjlEPTRcXeFfAjSYWOvhszDTgzPapBUqWSC4aK1QV4I50+fTPb+J7Sr/mSDtmC9reLjPv//wG/Iu/IkqQv34yIeuBbJHeMzDcN+G7DNiTtK2mnImNrMZIqgN+RDPMGxe1bgw+BrwOnSjp5Cza7rZ+zzsCbad+Oyi+MiLXAPySdmLYvSQO3cBuZa8G+vwb4YUSsyFue+zfhNAr7KzAmJ+Yd/guVtU47avLvJGlxzuuivPJvAzdJeobkqGZNEW3eBLyoIi/4i4i/kgxpP5MO599L8kewWL8CfiHpKRr/o/JzkqHHF5VcNPfzRuq1tO3S/xGxLiJ+GREf5hXdCJwm6VmSodn8ozKAm4Fa4Pm0L8ez44xsNVzsOAd4iOQP/M/SsmL27WMR8S7JaZYLVfwFotv6ObscmA48CMxrpM4o4NuSZpEcGe8oF6+2eN9HxJyImFCg6AqSUyNP0PgjgM8DBiu5aLUWOGdzMZltrVZ5e19JO0fEO+n0pUCPiDh/O4dVMtz/Zmat245yZLSljpH0I5L4X6PxYXXLhvvfzKwVa5VH/mZmZrb1dtRz/mZmZpYRJ38zM7MS4+RvZmZWYpz8zczMSoyTv5mZWYn5/wHtEDzZ3bEoLgAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dbvae_logits = [dbvae.predict(np.array(x, dtype=np.float32)) for x in test_faces]\n", "dbvae_probs = tf.squeeze(tf.sigmoid(dbvae_logits))\n", "\n", "xx = np.arange(len(keys))\n", "plt.bar(xx, standard_classifier_probs.numpy().mean(1), width=0.2, label=\"Standard CNN\")\n", "plt.bar(xx+0.2, dbvae_probs.numpy().mean(1), width=0.2, label=\"DB-VAE\")\n", "plt.xticks(xx, keys); \n", "plt.title(\"Network predictions on test dataset\")\n", "plt.ylabel(\"Probability\")\n", "plt.legend(bbox_to_anchor=(1.04,1), loc=\"upper left\")\n", "plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Conclusion and submission information\n", "\n", "We encourage you to think about and maybe even address some questions raised by the approach and results outlined here:\n", "\n", "* How does the accuracy of the DB-VAE across the four demographics compare to that of the standard CNN? Do you find this result surprising in any way?\n", "* How can the performance of the DB-VAE classifier be improved even further? We purposely did not optimize hyperparameters to leave this up to you!\n", "* In which applications (either related to facial detection or not!) would debiasing in this way be desired? Are there applications where you may not want to debias your model? \n", "* Do you think it should be necessary for companies to demonstrate that their models, particularly in the context of tasks like facial detection, are not biased? If so, do you have thoughts on how this could be standardized and implemented?\n", "* Do you have ideas for other ways to address issues of bias, particularly in terms of the training data?\n", "\n", "Try to optimize your model to achieve improved performance.\n", "\n", "Hopefully this lab has shed some light on a few concepts, from vision based tasks, to VAEs, to algorithmic bias. We like to think it has, but we're biased ;).\n", "\n", "" ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "name": "PyTorch_Tutorial.ipynb", "provenance": [] }, "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": "3.7.6" } }, "nbformat": 4, "nbformat_minor": 4 }