diff --git a/2023_DLrecruits_Workshops/Workshop1_FastAI_Tutorial.ipynb b/2023_DLrecruits_Workshops/Workshop1_FastAI_Tutorial.ipynb new file mode 100644 index 0000000..a240778 --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop1_FastAI_Tutorial.ipynb @@ -0,0 +1,671 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "accelerator": "GPU", + "gpuClass": "standard" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "1gror2KGz0rT" + }, + "source": [ + "# PLEASE READ BEFORE STARTING\n", + "1. **Don't edit this file, make a copy first:**\n", + " * Click on File -> Save a copy in Drive\n", + "\n", + "2. Also do the following:\n", + " * Click on Runtime -> Change runtime type -> Make sure hardware accelerator is set to GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z7R1gCAtxvbh" + }, + "source": [ + "# FastAI Tutorial\n", + "Neural networks can be used for a whole host of tasks.\n", + "The most basic of these though is image classification.\n", + "Hence, today we'll go through using supervised learning to predict an images class/label from the CIFAR-10 dataset.\n", + "\n", + "We'll be using a library called FastAI which is built ontop of PyTorch to abstract away all the nitty-gritty details.\n", + "Instead we can focus on comparing, contrarsting and understanding the different concepts we are able to use (like pretraining).\n", + "By the end of this you should be roughly familiar with all the major concepts and theory behind basic neural nets!\n", + "\n", + "There's a lot going on here, so let's get going!" + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Image Classification" + ], + "metadata": { + "id": "nDj2AgEfVB1F" + } + }, + { + "cell_type": "markdown", + "metadata": { + "id": "5GyVVCkCltvy" + }, + "source": [ + "## Importing Libraries\n", + "Before we dive into the image classification task for this workshop, it is important the relevant libraries.\n", + "\n", + "\n", + "Note here that we're importing everything from fastai's vision package, which is useful here but not in practice." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hzeDl-JllrlT" + }, + "source": [ + "!pip install fastai --upgrade --quiet" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "EdC8-DnKWYS7" + }, + "source": [ + "from fastai.vision.all import *" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3oISzwhy5r5P" + }, + "source": [ + "# Dataset Setup\n", + "The first step for anything data related is to read a dataset and split the data into seperate training and validation partitions.\n", + "In this tutorial we will be using the CIFAR-10 dataset, which includes 60,000 images, each belonging to one of ten categories.\n", + "The dataset was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. More information on the dataset can be found here: https://www.cs.toronto.edu/~kriz/cifar.html\n", + "\n", + "## Initialising the Data into a pipeline\n", + "To start, we will download the CIFAR-10 dataset and extract all the images from it." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "830MtputmzmJ" + }, + "source": [ + "path = untar_data(URLs.CIFAR) # Downloads url and unzips to folder destination" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8GohbK51G8bR" + }, + "source": [ + "Next we will initialise an instance of the 'DataBlock' class from the FastAI library. Which is a generic container that allows us to build a \"smart\" dataset called a dataloader, that contains the images seperated into training and validation sets, with their class attached to them. The purpose of the dataloader is that it specified a pipeline in which the model will receive data.\n", + "\n", + "We use the function ImageDataLoaders() to establish the dataloader by passing the path to the dataset, and the proportion of the dataset we wish to dedicate to the validation set.\n", + "\n", + "The validation dataset provides a way to check whether we are actually learning how to classify images or just overfitting the data (i.e. the model has just memorised which image belongs to which class).\n", + "To do this we can create a validation dataset which the model doesn't train with, but insntead is used to \"test\" how well it does \"out-of-sample\".\n", + "\n", + "In this case we've used 20% of CIFAR-10's 60,000 images for validation (but you can change this)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TtnJSZFUG4Mz" + }, + "source": [ + "data = ImageDataLoaders.from_folder(path=path, valid_pct=0.2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "1J-ClqMdIcpn" + }, + "source": [ + "## Validating the dataset\n", + "Before we proceed, it is important to validate the data to ensure that we do not have an incomplete dataset, a dateset with incorrect preprocessing, and more importantly to understand the data we are working in before we begin training.\n", + "\n", + "We know that CIFAR-10 has 60,000 images, lets start by verifying that we correctly set 20% of the dataset to the validation set." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IUEHSaDmB9jq" + }, + "source": [ + "print(\"Training Set Size = \" + str(len(data.train_ds)))\n", + "print(\"Validiation Set Size = \" + str(len(data.valid_ds)))\n", + "print(\"Total Dataset Size = \" + str(len(data.train_ds) + len(data.valid_ds)))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "IMqzc8zBQxp6" + }, + "source": [ + "We can also validate the dataset by checking a random batch to view the images with their respective labels.\n", + "\n", + "Notice that the images are blurry, this is because the CIFAR dataset is used to train neural nets to identify far away objects that are often pixilated. The images have been taken while the camera setting was zoomed in to maximum.\n", + "\n", + "Hence, this image classification is a real life example of where AI can be applied." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "agHpaXAHTidU" + }, + "source": [ + "data.show_batch(figsize=(10,10))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yUxBN2oIV0Bz" + }, + "source": [ + "# Model Setup\n", + "## Transfer Learning\n", + "Transfer learning allows us to repurpose a model trained for one task to many others.\n", + "This means our research and practical testing of image classification techniques using neural nets on CIFAR-10 can also be used for other datasets (and even other broad domains like Natural Language Processing).\n", + "\n", + "This is why transfer learning is one of the most fundemental aspect of deep learning.\n", + "We can find several examples of this, including:\n", + "- A model trained for the ImageNet competition can be repurposed to recognise between different dog breeds\n", + "- A model trained on ImageNet can be repurposed to help us classify whether an image is a 'plane' or a 'dog'\n", + "- A language model trained on Spanish could be adapted and repurposed for French/Italian\n", + "\n", + "The primary benefit of transfer learning is that we can use the same \"toolkit\" or \"basic techniques\" for a very wide variety of complex problems.\n", + "Hence, we have solid foundations which we can build up upon.\n", + "This cuts down on training time *substantially*.\n", + "\n", + "\n", + "In this example we're using a pretrained *ResNet-34* (more advanced model) as our base architecture, then retraining it to adapt it to classify our data." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Kn9d0OJSCSf1" + }, + "source": [ + "learn = cnn_learner(data, resnet34, metrics=accuracy)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Nc2P_9DCWhDX" + }, + "source": [ + "# Training\n", + "## Determining the Learning Rate\n", + "An important thing to figgure out is what learning rate to use.\n", + "It's a hard problem to solve, but we can nudge ourselves in the right direction by finding out how changing our learning rate effects the loss initially.\n", + "\n", + "We see this with FastAI's `lr_find()`.\n", + "It provides the minimum learning rate (divided by 10) and the point of steepest descent." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "70TmnJcbX7EK" + }, + "source": [ + "learn.lr_find()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_6FGnoo0YqLx" + }, + "source": [ + "You can try and pick the best learning rate and put it into the training method below (in the next section) to see how it changes your results.\n", + "If you try a few different values, you'll soon see that your choice greatly effects the results!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "KWSjg9LRZH1s" + }, + "source": [ + "## Training Time!\n", + "We are now ready to begin training!\n", + "\n", + "To start, we establish our base learning rate which we can choose from our previous graph. For this experiment, you will need to assign the variable `base_lr` with a learning rate of your choice.\n", + "\n", + "After selecting your learning rate, execute the code block below before you continue reading as it may take some time to train the model.\n", + "\n", + "After the learning rate is defined, we freeze the lower layers by calling the `freeze()` method. This concept is taken from Transfer Learning, as we have previously spoken about, and it will allow us to 'custom fit' the *ResNet-34* to the dataset, as the network is already pretrained on a similar problem. The actual 'freezing' occurs by preventing any weights in the lower layers from being modified until we unfreeze, allowing us to change the final fully connected layers.\n", + "\n", + "Now we can train our fully connected layers using the `fit_one_cycle()` method. This method takes in how many epochs we want to train and the learning rate at which we want to train our network at. We have also inputted an optional argument which is slightly out of scope for this workshop.\n", + "\n", + "After one epoch, we unfreeze the lower layers so that ALL layers can now have their weights updated according to the loss function. We can then train for another 3 epochs and evaluate the results.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TjkBCV_8CxrO" + }, + "source": [ + "base_lr = #\n", + "learn.freeze()\n", + "learn.fit_one_cycle(1, base_lr, pct_start=0.99)\n", + "base_lr /= 2\n", + "learn.unfreeze()\n", + "learn.fit_one_cycle(3, base_lr, pct_start=0.3)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lH2PyEYkgZwa" + }, + "source": [ + "While it is training, you may hopefully notice that the training loss and validation loss get lower over time, and the accuracy may increase as the model learns.\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "oiNSeJQNW7TC" + }, + "source": [ + "## Saving Model Weights\n", + "Once training has completed, we should save our models weights (so we can use or pretrain from it later).\n", + "\n", + "This could also allow:\n", + "- Reverting to a previous model\n", + "- Tracking a models progress through special version-control\n", + "\n", + "Model weights can be simply reloaded with `learner.load('some-name')`" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Yl-nenHGW-4S" + }, + "source": [ + "learn.save('trained-lr-default')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xlQZlz1QjXNF" + }, + "source": [ + "# Evaluation\n", + "Once the training has been completed, we need to gauge how good/bad our model is.\n", + "\n", + "## Sample Predictions\n", + "We can view some predictions with our newly trained model with the `show_results()` method. Activate the block below to see how it went!\n", + "\n", + "The text at the top indicates the actual class of the image, the bottom text indicates the predicted class, if they're green, our model successfully predicted correctly." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0xowfQ20h-H6" + }, + "source": [ + "learn.show_results()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mV94t__pjvM9" + }, + "source": [ + "Viewing a sample batch of predictions is visually appealing, however there are more appropriate metrics to validate the model.\n", + "\n", + "## Model Validation\n", + "It is important to validate our models with appropriate metrics to determine how well the model is performing, and whether or not further investigation is required. Today we will be doing using a Confusion Matrix and viewing our top losses. We will start by creating an instance of the `ClassificationInterpretation` class from our model in order to begin." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8Erc2_QflSQE" + }, + "source": [ + "interp = ClassificationInterpretation.from_learner(learn)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "lX90aRSwk4ra" + }, + "source": [ + "### Confusion Matrix\n", + "A Confusion Matrix can be used to determine where our model is producing false positives or false negatives. Click below to see what happened!" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hrtX8zCUjnKh" + }, + "source": [ + "interp.plot_confusion_matrix()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nbcCG7-xlim1" + }, + "source": [ + "### Top Losses\n", + "We can also produce a set of images that fastai considers 'top losses' with the `plot_top_losses()` method. The images are considered 'top losses' based on the probability that the prediction was correct. The images with a probability of 1 technically don't have a probability of 1, it's an softmax bug within FastAI." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "okqooChcmgkH" + }, + "source": [ + "interp.plot_top_losses(8, figsize=(15,11))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xMeA5AForEKk" + }, + "source": [ + "So how did your model perform? Did it increase in accuracy over time? Maybe have a play around with the base learning rate a little more and see what you can come up with. Maybe try thinking about using the lowest point on the learning rate curve or the point of steepest descent and compare the results.\n", + "\n", + "## Group Evaluation\n", + "As a breakout room, discuss how your models performed.\n", + "Try and consider areas you believed they performed well in, along with where you think they could improve.\n", + "Think about why there may be some common classes where confusion occurs between the actual and predicted classes while you wait for the results of your new training.\n", + "Feel free to additionally discuss the effect of learning rates once again." + ] + }, + { + "cell_type": "markdown", + "source": [ + "## Segmentation\n", + "Segmentation is a problem where we have to predict a category for each pixel of the image and segment parts of the image based on respective categories. For this task, we will use the [Camvid](https://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) dataset, a dataset of screenshots from cameras in cars. Each pixel of the image has a label such as \"road\", \"car\" or \"pedestrian\"." + ], + "metadata": { + "id": "4lakdZRaQ2AI" + } + }, + { + "cell_type": "code", + "source": [ + "path = untar_data(URLs.CAMVID_TINY) # Downloads url and unzips to folder destination\n", + "path.ls()" + ], + "metadata": { + "id": "ZTd1me3ERMK9" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The images folder contains the images, and the corresponding segmentation masks of labels are in the labels folder. The codes file contains the corresponding integer to class (the masks have an int value for each pixel)." + ], + "metadata": { + "id": "WEpjR5GJSBtP" + } + }, + { + "cell_type": "code", + "source": [ + "codes = np.loadtxt(path/'codes.txt', dtype=str)\n", + "codes" + ], + "metadata": { + "id": "JjJJG4w7SCWV" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "The get_image_files function is an in-built function from FastAI that helps us grab all the image filenames:" + ], + "metadata": { + "id": "sMB2q93lS4Rl" + } + }, + { + "cell_type": "code", + "source": [ + "fnames = get_image_files(path/\"images\")\n", + "fnames[0]" + ], + "metadata": { + "id": "SG-6kukKS85q" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "Let's have a look in the labels folder:" + ], + "metadata": { + "id": "KJmNTKJNTRdV" + } + }, + { + "cell_type": "code", + "source": [ + "(path/\"labels\").ls()[0]" + ], + "metadata": { + "id": "22aSwXckTWpn" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "It seems the segmentation masks have the same base names as the images but with an extra _P, so we can define a label function:" + ], + "metadata": { + "id": "04pq4WdvTcva" + } + }, + { + "cell_type": "code", + "source": [ + "def label_func(fn): return path/\"labels\"/f\"{fn.stem}_P{fn.suffix}\"" + ], + "metadata": { + "id": "DnHz_VA2ThMf" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We can then gather our data using SegmentationDataLoaders from FastAI:" + ], + "metadata": { + "id": "def1i0ABTkiD" + } + }, + { + "cell_type": "code", + "source": [ + "dls = SegmentationDataLoaders.from_label_func(\n", + " path, bs=8, fnames = fnames, label_func = label_func, codes = codes\n", + ")" + ], + "metadata": { + "id": "Pid1wQ1MTrEr" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We do not need to pass item_tfms to resize our images here because they already are all of the same size.\n", + "\n", + "As usual, we can have a look at our data with the show_batch method. In this instance, the fastai library is superimposing the masks with one specific color per pixel:\n" + ], + "metadata": { + "id": "f-d9dlbRTucP" + } + }, + { + "cell_type": "code", + "source": [ + "dls.show_batch(max_n=6)" + ], + "metadata": { + "id": "c-GcDYOjTySI" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "A traditional CNN won't work for segmentation, we have to use a special kind of model called a UNet, so we use unet_learner to define our Learner:\n" + ], + "metadata": { + "id": "z2Gms_cvT2rt" + } + }, + { + "cell_type": "code", + "source": [ + "learn = unet_learner(dls, resnet34)\n", + "learn.fine_tune(6)" + ], + "metadata": { + "id": "BifcqVMIT50D" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We can use show_results to get target vs prediction within the image itself" + ], + "metadata": { + "id": "oPo9n7TGT9ja" + } + }, + { + "cell_type": "code", + "source": [ + "learn.show_results(max_n=4, figsize=(10,8))" + ], + "metadata": { + "id": "q2r1113xUI60" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "\n", + "\n", + "We can also sort the model's errors on the validation set using the SegmentationInterpretation class and then plot the instances with the k highest contributions to the validation loss.\n" + ], + "metadata": { + "id": "rvS46DtSUOm6" + } + }, + { + "cell_type": "code", + "source": [ + "interp = SegmentationInterpretation.from_learner(learn)\n", + "interp.plot_top_losses(k=3)" + ], + "metadata": { + "id": "R2GibRihUQqx" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "## Group Evaluation\n", + "As a breakout room, discuss how segmentation can be useful along with why do we need them in the first place.\n", + "Feel free to discuss this with other members too." + ], + "metadata": { + "id": "YzFWyq2I4YUs" + } + } + ] +} \ No newline at end of file diff --git a/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial.ipynb b/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial.ipynb new file mode 100644 index 0000000..382d85f --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial.ipynb @@ -0,0 +1,553 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "provenance": [] + }, + "kernelspec": { + "name": "python3", + "display_name": "Python 3" + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "pGTnInyEharX" + }, + "source": [ + "# Before you start\n", + "1. **Don't edit this file, make a copy first:**\n", + " * Click on File -> Save a copy in Drive\n", + "\n", + "2. Also do the following:\n", + " * Click on Runtime -> Change runtime type -> Make sure hardware accelerator is set to GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cknZbgNdR6IM" + }, + "source": [ + "# An Overview Before We Begin\n", + "Here's a couple of important concepts to note down before we start:\n", + "- There is no one particular methodology that works best in all scenarios. This includes everything from model architecture, learning rate, loss function, and optimizer. \n", + "- Like any other engineering project, validation of what we have built is just as important as building it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZueWb5ZRDeyT" + }, + "source": [ + "# Library Imports" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DlJ_aMz20rxE" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch import optim\n", + "from torchvision import datasets, transforms, models\n", + "\n", + "from tqdm.notebook import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hepIxB_VKggb" + }, + "source": [ + "# Defining Path Variables" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "hO3kSpVxKiqC" + }, + "source": [ + "train_path = 'data/train'\n", + "valid_path = 'data/valid'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zszDeZ7CJ0Z3" + }, + "source": [ + "# Creating DataLoader (DataBunch in FastAI)\n", + "- Main difference between FastAI and PyTorch here is that there are 2 steps to creating a DataLoader in PyTorch.\n", + " 1. Creating a Dataset. This bundles the data in a way that the model can understand.\n", + " 2. Creating a DataLoader. This tells the model how to receive the images. Including batch_size, num_workers, shuffle configurations, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R1LivjuALGLi" + }, + "source": [ + "## [Instantiating Transforms](https://https://pytorch.org/docs/stable/torchvision/transforms.html)\n", + "- Transforms are the same in FastAI .transform() function\n", + " - the transforms.Normalize([...]) function basically changes the data slightly according to the average RGB weights in the ImageNet dataset. \n", + " - This may seem a bit strange to you, why do we do this? Turns out, normalizing the data before training results in noticeable performance gains and reduction in training time. \n", + " - So why does normalizing data have such performance boosts in training? That's because by itself, the RGB values of the raw data have differing ranges. the blue pixel may have a range of of 0->125 while the red pixel may have a range of 120->245. This different range often causes headaches for the optimizer and it takes the gradient descent to converge much slowly as it has to cater to the differing conditions of both the red and blue pixel.\n", + " - What batch normalization does is that it makes the RGB ranges somewhat similar, so the optimizer doesn't have such a hard time trying to cater for all the different ranges, and thus gradient descent covnerges faster. \n", + " - More information here https://medium.com/@urvashilluniya/why-data-normalization-is-necessary-for-machine-learning-models-681b65a05029\n", + "\n", + "Now, in the below block of code, come up with a set of tranforms for the training and validation datasets that you think might be suitable. Try using transforms such as rotation, flipping, normalizing, etc.\n", + "\n", + "Find the documentation for the transforms here : https://pytorch.org/vision/stable/transforms.html" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "mXzCC_fKJzVT" + }, + "source": [ + "# Define transforms for the training and validation set\n", + "training_transforms = transforms.Compose([# Insert random rotation, 30 degrees\n", + " # Insert random horizontal flip\n", + " # Convert to tensor\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])\n", + "\n", + "validation_transforms = transforms.Compose([# Convert to tensor\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qQ9qAGv1LgLC" + }, + "source": [ + "## [Torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html)\n", + "There are a number of ways to create a dataset, for example: \n", + "- Use an available Torchvision dataset\n", + " - We're using one below called CIFAR10 which we used in the first training session\n", + "- Use ImageFolder to create a dataset from folders\n", + "- Write your own dataset as a subclass of torch.utils.data.Dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "l_7ENLBwKYKO" + }, + "source": [ + "training_dataset = datasets.CIFAR10(train_path, train=True, transform=training_transforms, download=True)\n", + "validation_dataset = datasets.CIFAR10(valid_path, train=False, transform=validation_transforms, download=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "njZjCupaUJQN" + }, + "source": [ + "## [Instantiating DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)\n", + "- This tells the model *how* how to receive the data; with the dataset as an input, along with batch size and shuffle as args \n", + "\n", + "Now code up data loaders for the training and validation datasets as per the followig specs : \n", + "\n", + "- In the training_loader, we're telling it batch_size = 32, and we want to shuffle the dataloader after each epoch. \n", + "- In the validation_loader, we also take batch_size = 32 but we DON'T want to shuffle the dataloader. This is because we want to be testing on the data in the same order to make sure the model really is improving and didn't hit a fluke ordering of the dataset." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "NFxxs8XJULmT" + }, + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "training_loader = \n", + "validation_loader = " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3t0Ga6RYLOA4" + }, + "source": [ + "# Check what classes are in our dataset\n", + "\n", + "training_dataset.classes, validation_dataset.classes" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3NwqQ0DMFlBC" + }, + "source": [ + "# Instantiating ResNet18\n", + "- Unlike FastAI, when a model is downloaded, you need to reconfigure the 'classification' layer as the pretrained model was trained for ImageNet, hence it comes ready to classify for many classes (we only need it to classify 10 classes)\n", + "- In addition, downloaded models from PyTorch come unfrozen, which means we need to 'freeze' the entire network except for the classification layer so we can perform the first batch of training." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "VXc2SykWNm8J" + }, + "source": [ + "model = models.resnet18(pretrained=True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iMqHGCoyGb0d" + }, + "source": [ + "## Freezing The Model\n", + "- *%%capture* is colab syntax, it essentially stops the cell from printing out any logs. This is purely for *aesthetic* purposes.\n", + "- We're looping through all the parameters in the model, and setting requires_grad = False. Which 'freezes' the entire model.\n", + " - requires_grad stands for 'requires gradient'. When requires_grad is False, it's weights does not get updated and hence it is 'frozen'." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "oy-Sty6TXRWt" + }, + "source": [ + "%%capture\n", + "for param in model.parameters():\n", + " param.requires_grad = False" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SiySi5PyGzKF" + }, + "source": [ + "## Reconfiguring The Classification Layer\n", + "- model.fc = the last layer of the network\n", + "- model.fc.in_features = the features going into the last layer\n", + "- But we know that the pretrained Resnet18 is designed for dozens of classes, but we only need it to classify 10 classes, so we're going to have to replace the last layer. \n", + " - To do so, we use nn.Linear(out_ftrs, 10). \n", + " - This way, we keep the same numnber of features going into the last layer, but only change the number of features going out, which in this case is 10. \n", + " - We then reinsert it to the model using model.fc = nn.Linear(out_ftrs, 10)" + ] + }, + { + "cell_type": "code", + "source": [ + "# Print last layer of the model\n", + "model.fc" + ], + "metadata": { + "id": "pw-6SGpx0vKJ" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "R-X4os5mPCMj" + }, + "source": [ + "# Redefine final linear layer, such that output 10 classes\n", + "\n", + "out_ftrs = # Number of features going INTO the last layer\n", + "model.fc = nn.Linear() # Redefine last linear layer of network to output 10 classes" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_jKFQfhkhGf1" + }, + "source": [ + "# The Training Function\n", + "- Just like the fit() function in fast.ai we're going to need a function that trains out model\n", + "
\n", + "
\n", + "\n", + "Everytime we run through a 'batch' of data we need to do a few things\n", + "1. Clear the gradients from the previous loop \n", + "2. Perform a forward pass (put the input through the model once)\n", + "3. Calculate the loss\n", + "4. Back propogate the loss\n", + "5. Update the parameter weights by taking a step with the optimiser" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "gwvXsvTnhEKd" + }, + "source": [ + "# Function for the training \n", + "\n", + "def train(model, train_loader, loss_fn, optimizer, device):\n", + " model.train() # puts the model in training mode\n", + " running_loss = 0\n", + " with tqdm(total=len(train_loader)) as pbar:\n", + " for i, data in enumerate(train_loader, 0): # loops through training data\n", + " inputs, labels = data # separate inputs and labels (outputs)\n", + " inputs, labels = inputs.to(device), labels.to(device) # puts the data on the GPU\n", + "\n", + " # forward + backward + optimize \n", + " optimizer.zero_grad() # clear the gradients in model parameters\n", + " outputs = model(inputs) # forward pass and get predictions\n", + " loss = loss_fn(outputs, labels) # calculate loss\n", + " loss.backward() # calculates gradient w.r.t to loss for all parameters in model that have requires_grad=True\n", + " optimizer.step() # iterate over all parameters in the model with requires_grad=True and update their weights.\n", + "\n", + " running_loss += loss.item() # sum total loss in current epoch for print later\n", + "\n", + " pbar.update(1) #increment our progress bar\n", + "\n", + " return running_loss/len(train_loader) # returns the total training loss for the epoch" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZPD3ezm9OZ8t" + }, + "source": [ + "# The Validation Function\n", + "- A validation function is essential in any model training, because it helps you validate how well your model is performing on the validation dataset. \n", + "\n", + "Note: the validation function validates the model performance by passing the entire validation set through the model ONCE. Also note that we cacluate the loss but don't propogate it back or update any weights!" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "h2QY58AvqJDI" + }, + "source": [ + "# Function for the validation pass\n", + "\n", + "def validation(model, val_loader, loss_fn, device):\n", + " model.eval() # puts the model in validation mode\n", + " running_loss = 0\n", + " total = 0\n", + " correct = 0\n", + " \n", + " with torch.no_grad(): # save memory by not saving gradients which we don't need \n", + " with tqdm(total=len(val_loader)) as pbar:\n", + " for images, labels in iter(val_loader):\n", + " images, labels = images.to(device), labels.to(device) # put the data on the GPU\n", + " outputs = model(images) # passes image to the model, and gets a ouput which is the class probability prediction\n", + "\n", + " val_loss = loss_fn(outputs, labels) # calculates val_loss from model predictions and true labels\n", + " running_loss += val_loss.item()\n", + " _, predicted = torch.max(outputs, 1) # turns class probability predictions to class labels\n", + " total += labels.size(0) # sums the number of predictions\n", + " correct += (predicted == labels).sum().item() # sums the number of correct predictions\n", + " \n", + " pbar.update(1)\n", + "\n", + " return running_loss/len(val_loader), correct/total # return loss value, accuracy" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cb8hexwc-8ES" + }, + "source": [ + "#Things to note about our training and validation functions\n", + "\n", + "## What's the difference between `model.train()` and `model.eval()`? \n", + "These two are extremely important to your training and validation loops. `model.eval()` takes away some layers that should only be used during training such as dropout and batch normalisation. It's important to always use `model.train()` when training and `model.eval()` when evaluating. \n", + "\n", + "## Why do we need torch.no_grad()?\n", + "Running `with torch.no_grad()` means that we don't want gradients which is what happens during validation or testing, we don't need to update any gradients so we don't need to record them. Running this means that we optimize our code to not do things it doesn't need to. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jLUTOwwH4-9T" + }, + "source": [ + "# Setting Up Training\n", + "- When training models, it is substantially faster to train on NVIDIA GPU's, beacuse they offer a parallel computing platform called [cuda](https://developer.nvidia.com/cuda-zone) (cudnn is the API package to interface with cuda) that speeds up these computations exponentially. \n", + "- So here we check if cuda is available with cuda.is_available().\n", + " - Following which, we send the model to the cuda device so the computation can be done on the GPU." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "On6Xdb9TqNOF" + }, + "source": [ + "%%capture\n", + "import torch.backends.cudnn as cudnn\n", + "torch.cuda.empty_cache()\n", + "cudnn.benchmark = True # Optimise for hardware\n", + "\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device) # send model to GPU" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q4pZVkP5QiwM" + }, + "source": [ + "# Loss Function & Optimizers\n", + "- The **Loss Function** calculates how 'far' the model's class probability predictions are to the actual labels.\n", + " - Notice how I'm saying \"how far the model's class prob predictions are to the actual labels\" instead of \"how innacurate the model is\", that's because accurate/inaccurate is the percentage of correctly or incorrectly predicted labels. This may sound the same to you but just keep this in mind, it will all make sense in due time.\n", + " - CrossEntropyLoss is a way of calculating the loss of a model, other loss functions include Kullback Leibler Divergence Loss, Sparse Multiclass Cross-Entropy Loss, and much more. \n", + "\n", + "- **The Optimizer** is a way of updating the weights of the model to minimize loss. In other words, the optimizer is the part of deep learning that helps a model 'learn'.\n", + "- In this case we're using the Adam optimizer, this is purely by random choice as no particular optimizer can be said to be superior to the other. There's an important concept in deep learning called \"no free lunch\", which means there isn't a particular methodology that will achieve the best outcome for all scenarios, what it comes down to is experimentation. \n", + " - a lr of 0.001 is also chosen, this is usually a good learning rate start from with the Adam optimizer, however to get a more optimum learning rate, experimentation would need to be done. (The Pytorch documentation includes defaults for each different optimizer)\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IoIZ_7nsqKbt" + }, + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xedFUaJCMNMR" + }, + "source": [ + "# Let The Training Begin! Part 1\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lL8voSz-4fKi" + }, + "source": [ + "total_epoch = 10\n", + "for epoch in range(total_epoch): # loops through number of epochs\n", + " train_loss = train(model, training_loader, loss_fn, optimizer, device) # train the model for one epoch\n", + " val_loss, accuracy = validation(model, validation_loader, loss_fn, device) # after training for one epoch, run the validation() function to see how the model is doing on the validation dataset\n", + " print(\"Epoch: {}/{}, Training Loss: {}, Val Loss: {}, Val Accuracy: {}\".format(epoch+1, total_epoch, train_loss, val_loss, accuracy))\n", + " print('-' * 20)\n", + "\n", + "print(\"Finished Training\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "9-J2_a9Gbggo" + }, + "source": [ + "# as we do in FastAI, we save the model so we can come back later to it if need be\n", + "torch.save(model.state_dict(), 'stage-1')" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "kbNvgW6rOGLg" + }, + "source": [ + "# Let The Training Begin! Part 2\n", + "- Remember in FastAI we called the 'unfreeze()' function after the first batch of training? The below cell does the exact same thing, what it does is it allows the rest of the model to be optimized for this specific task.\n", + "- The cell after is exactly the same as the training of the model in 'let The Training Begin! Part 1', just that we're retraining for 2 epochs." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "H7rN2kYE2zLW" + }, + "source": [ + "%%capture\n", + "for param in model.parameters():\n", + " param.requires_grad = True" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Hwo7c3hz22lr" + }, + "source": [ + "total_epoch = 10\n", + "for epoch in range(total_epoch): # loops through number of epochs\n", + " train_loss = train(model, training_loader, loss_fn, optimizer, device) # train the model for one epoch\n", + " val_loss, accuracy = validation(model, validation_loader, loss_fn, device) # after training for one epoch, run the validation() function to see how the model is doing on the validation dataset\n", + " print(\"Epoch: {}/{}, Training Loss: {}, Val Loss: {}, Val Accuracy: {}\".format(epoch+1, total_epoch, train_loss, val_loss, accuracy))\n", + " print('-' * 20)\n", + "\n", + "print(\"Finished Training\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZikgH7ANmcXA" + }, + "source": [], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial_solution.ipynb b/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial_solution.ipynb new file mode 100644 index 0000000..42c78b7 --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop2_Classical_PyTorch_Tutorial_solution.ipynb @@ -0,0 +1,2889 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "pGTnInyEharX" + }, + "source": [ + "# Before you start\n", + "1. **Don't edit this file, make a copy first:**\n", + " * Click on File -> Save a copy in Drive\n", + "\n", + "2. Also do the following:\n", + " * Click on Runtime -> Change runtime type -> Make sure hardware accelerator is set to GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cknZbgNdR6IM" + }, + "source": [ + "# An Overview Before We Begin\n", + "Here's a couple of important concepts to note down before we start:\n", + "- There is no one particular methodology that works best in all scenarios. This includes everything from model architecture, learning rate, loss function, and optimizer. \n", + "- Like any other engineering project, validation of what we have built is just as important as building it." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZueWb5ZRDeyT" + }, + "source": [ + "# Library Imports" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "DlJ_aMz20rxE" + }, + "outputs": [], + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch import optim\n", + "from torchvision import datasets, transforms, models\n", + "\n", + "from tqdm.notebook import tqdm" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hepIxB_VKggb" + }, + "source": [ + "# Defining Path Variables" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "hO3kSpVxKiqC" + }, + "outputs": [], + "source": [ + "train_path = 'data/train'\n", + "valid_path = 'data/valid'" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zszDeZ7CJ0Z3" + }, + "source": [ + "# Creating DataLoader (DataBunch in FastAI)\n", + "- Main difference between FastAI and PyTorch here is that there are 2 steps to creating a DataLoader in PyTorch.\n", + " 1. Creating a Dataset. This bundles the data in a way that the model can understand.\n", + " 2. Creating a DataLoader. This tells the model how to receive the images. Including batch_size, num_workers, shuffle configurations, etc." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "R1LivjuALGLi" + }, + "source": [ + "## [Instantiating Transforms](https://https://pytorch.org/docs/stable/torchvision/transforms.html)\n", + "- Transforms are the same in FastAI .transform() function\n", + " - the transforms.Normalize([...]) function basically changes the data slightly according to the average RGB weights in the ImageNet dataset. \n", + " - This may seem a bit strange to you, why do we do this? Turns out, normalizing the data before training results in noticeable performance gains and reduction in training time. \n", + " - So why does normalizing data have such performance boosts in training? That's because by itself, the RGB values of the raw data have differing ranges. the blue pixel may have a range of of 0->125 while the red pixel may have a range of 120->245. This different range often causes headaches for the optimizer and it takes the gradient descent to converge much slowly as it has to cater to the differing conditions of both the red and blue pixel.\n", + " - What batch normalization does is that it makes the RGB ranges somewhat similar, so the optimizer doesn't have such a hard time trying to cater for all the different ranges, and thus gradient descent covnerges faster. \n", + " - More information here https://medium.com/@urvashilluniya/why-data-normalization-is-necessary-for-machine-learning-models-681b65a05029\n", + "\n", + "Now, in the below block of code, come up with a set of tranforms for the training and validation datasets that you think might be suitable. Try using transforms such as rotation, flipping, normalizing, etc.\n", + "\n", + "Find the documentation for the transforms here : https://pytorch.org/vision/stable/transforms.html" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "mXzCC_fKJzVT" + }, + "outputs": [], + "source": [ + "from torch.nn.modules import normalization\n", + "# Define transforms for the training and validation set\n", + "training_transforms = transforms.Compose([ transforms.RandomRotation(30),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.ToTensor(),\n", + " # Insert random rotation, 30 degrees\n", + " # Insert random horizontal flip\n", + " # Convert to tensor\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])\n", + "\n", + "validation_transforms = transforms.Compose([# Convert to tensor\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "qQ9qAGv1LgLC" + }, + "source": [ + "## [Torchvision datasets](https://pytorch.org/docs/stable/torchvision/datasets.html)\n", + "There are a number of ways to create a dataset, for example: \n", + "- Use an available Torchvision dataset\n", + " - We're using one below called CIFAR10 which we used in the first training session\n", + "- Use ImageFolder to create a dataset from folders\n", + "- Write your own dataset as a subclass of torch.utils.data.Dataset" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "l_7ENLBwKYKO", + "outputId": "c5db542c-db5c-4b10-d03e-c2bc09ff67a9" + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Files already downloaded and verified\n", + "Files already downloaded and verified\n" + ] + } + ], + "source": [ + "training_dataset = datasets.CIFAR10(train_path, train=True, transform=training_transforms, download=True)\n", + "validation_dataset = datasets.CIFAR10(valid_path, train=False, transform=validation_transforms, download=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "njZjCupaUJQN" + }, + "source": [ + "## [Instantiating DataLoader](https://pytorch.org/tutorials/beginner/basics/data_tutorial.html)\n", + "- This tells the model *how* how to receive the data; with the dataset as an input, along with batch size and shuffle as args \n", + "\n", + "Now code up data loaders for the training and validation datasets as per the followig specs : \n", + "\n", + "- In the training_loader, we're telling it batch_size = 32, and we want to shuffle the dataloader after each epoch. \n", + "- In the validation_loader, we also take batch_size = 32 but we DON'T want to shuffle the dataloader. This is because we want to be testing on the data in the same order to make sure the model really is improving and didn't hit a fluke ordering of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "NFxxs8XJULmT" + }, + "outputs": [], + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "training_loader = DataLoader(training_dataset, batch_size=32, shuffle=True)\n", + "validation_loader= DataLoader(validation_dataset, batch_size=32, shuffle=True)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "3t0Ga6RYLOA4", + "outputId": "d46e8472-63e7-48fb-ca75-ad4c16d0c01f" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(['airplane',\n", + " 'automobile',\n", + " 'bird',\n", + " 'cat',\n", + " 'deer',\n", + " 'dog',\n", + " 'frog',\n", + " 'horse',\n", + " 'ship',\n", + " 'truck'],\n", + " ['airplane',\n", + " 'automobile',\n", + " 'bird',\n", + " 'cat',\n", + " 'deer',\n", + " 'dog',\n", + " 'frog',\n", + " 'horse',\n", + " 'ship',\n", + " 'truck'])" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Check what classes are in our dataset\n", + "\n", + "training_dataset.classes, validation_dataset.classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3NwqQ0DMFlBC" + }, + "source": [ + "# Instantiating ResNet18\n", + "- Unlike FastAI, when a model is downloaded, you need to reconfigure the 'classification' layer as the pretrained model was trained for ImageNet, hence it comes ready to classify for many classes (we only need it to classify 10 classes)\n", + "- In addition, downloaded models from PyTorch come unfrozen, which means we need to 'freeze' the entire network except for the classification layer so we can perform the first batch of training." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "VXc2SykWNm8J", + "outputId": "299c1531-cd3a-4a56-bc9a-e3edb0b67d7f" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and may be removed in the future, please use 'weights' instead.\n", + " warnings.warn(\n", + "/usr/local/lib/python3.9/dist-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and may be removed in the future. The current behavior is equivalent to passing `weights=ResNet18_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet18_Weights.DEFAULT` to get the most up-to-date weights.\n", + " warnings.warn(msg)\n" + ] + } + ], + "source": [ + "model = models.resnet18(pretrained=True)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "iMqHGCoyGb0d" + }, + "source": [ + "## Freezing The Model\n", + "- *%%capture* is colab syntax, it essentially stops the cell from printing out any logs. This is purely for *aesthetic* purposes.\n", + "- We're looping through all the parameters in the model, and setting requires_grad = False. Which 'freezes' the entire model.\n", + " - requires_grad stands for 'requires gradient'. When requires_grad is False, it's weights does not get updated and hence it is 'frozen'." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "oy-Sty6TXRWt" + }, + "outputs": [], + "source": [ + "%%capture\n", + "for param in model.parameters():\n", + " param.requires_grad = False" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "SiySi5PyGzKF" + }, + "source": [ + "## Reconfiguring The Classification Layer\n", + "- model.fc = the last layer of the network\n", + "- model.fc.in_features = the features going into the last layer\n", + "- But we know that the pretrained Resnet18 is designed for dozens of classes, but we only need it to classify 10 classes, so we're going to have to replace the last layer. \n", + " - To do so, we use nn.Linear(out_ftrs, 10). \n", + " - This way, we keep the same numnber of features going into the last layer, but only change the number of features going out, which in this case is 10. \n", + " - We then reinsert it to the model using model.fc = nn.Linear(out_ftrs, 10)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "pw-6SGpx0vKJ", + "outputId": "b5b17bc2-07a5-4f5f-f4dc-f12f1c6e0d62" + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Linear(in_features=512, out_features=1000, bias=True)" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Print last layer of the model\n", + "model.fc" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "R-X4os5mPCMj" + }, + "outputs": [], + "source": [ + "# Redefine final linear layer, such that output 10 classes\n", + "\n", + "out_ftrs = 512 # Number of features going INTO the last layer\n", + "model.fc = nn.Linear(out_ftrs,10) # Redefine last linear layer of network to output 10 classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "_jKFQfhkhGf1" + }, + "source": [ + "# The Training Function\n", + "- Just like the fit() function in fast.ai we're going to need a function that trains out model\n", + "
\n", + "
\n", + "\n", + "Everytime we run through a 'batch' of data we need to do a few things\n", + "1. Clear the gradients from the previous loop \n", + "2. Perform a forward pass (put the input through the model once)\n", + "3. Calculate the loss\n", + "4. Back propogate the loss\n", + "5. Update the parameter weights by taking a step with the optimiser" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "gwvXsvTnhEKd" + }, + "outputs": [], + "source": [ + "# Function for the training \n", + "\n", + "def train(model, train_loader, loss_fn, optimizer, device):\n", + " model.train() # puts the model in training mode\n", + " running_loss = 0\n", + " with tqdm(total=len(train_loader)) as pbar:\n", + " for i, data in enumerate(train_loader, 0): # loops through training data\n", + " inputs, labels = data # separate inputs and labels (outputs)\n", + " inputs, labels = inputs.to(device), labels.to(device) # puts the data on the GPU\n", + "\n", + " # forward + backward + optimize \n", + " optimizer.zero_grad() # clear the gradients in model parameters\n", + " outputs = model(inputs) # forward pass and get predictions\n", + " loss = loss_fn(outputs, labels) # calculate loss\n", + " loss.backward() # calculates gradient w.r.t to loss for all parameters in model that have requires_grad=True\n", + " optimizer.step() # iterate over all parameters in the model with requires_grad=True and update their weights.\n", + "\n", + " running_loss += loss.item() # sum total loss in current epoch for print later\n", + "\n", + " pbar.update(1) #increment our progress bar\n", + "\n", + " return running_loss/len(train_loader) # returns the total training loss for the epoch" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ZPD3ezm9OZ8t" + }, + "source": [ + "# The Validation Function\n", + "- A validation function is essential in any model training, because it helps you validate how well your model is performing on the validation dataset. \n", + "\n", + "Note: the validation function validates the model performance by passing the entire validation set through the model ONCE. Also note that we cacluate the loss but don't propogate it back or update any weights!" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "h2QY58AvqJDI" + }, + "outputs": [], + "source": [ + "# Function for the validation pass\n", + "\n", + "def validation(model, val_loader, loss_fn, device):\n", + " model.eval() # puts the model in validation mode\n", + " running_loss = 0\n", + " total = 0\n", + " correct = 0\n", + " \n", + " with torch.no_grad(): # save memory by not saving gradients which we don't need \n", + " with tqdm(total=len(val_loader)) as pbar:\n", + " for images, labels in iter(val_loader):\n", + " images, labels = images.to(device), labels.to(device) # put the data on the GPU\n", + " outputs = model(images) # passes image to the model, and gets a ouput which is the class probability prediction\n", + "\n", + " val_loss = loss_fn(outputs, labels) # calculates val_loss from model predictions and true labels\n", + " running_loss += val_loss.item()\n", + " _, predicted = torch.max(outputs, 1) # turns class probability predictions to class labels\n", + " total += labels.size(0) # sums the number of predictions\n", + " correct += (predicted == labels).sum().item() # sums the number of correct predictions\n", + " \n", + " pbar.update(1)\n", + "\n", + " return running_loss/len(val_loader), correct/total # return loss value, accuracy" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "cb8hexwc-8ES" + }, + "source": [ + "#Things to note about our training and validation functions\n", + "\n", + "## What's the difference between `model.train()` and `model.eval()`? \n", + "These two are extremely important to your training and validation loops. `model.eval()` takes away some layers that should only be used during training such as dropout and batch normalisation. It's important to always use `model.train()` when training and `model.eval()` when evaluating. \n", + "\n", + "## Why do we need torch.no_grad()?\n", + "Running `with torch.no_grad()` means that we don't want gradients which is what happens during validation or testing, we don't need to update any gradients so we don't need to record them. Running this means that we optimize our code to not do things it doesn't need to. \n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jLUTOwwH4-9T" + }, + "source": [ + "# Setting Up Training\n", + "- When training models, it is substantially faster to train on NVIDIA GPU's, beacuse they offer a parallel computing platform called [cuda](https://developer.nvidia.com/cuda-zone) (cudnn is the API package to interface with cuda) that speeds up these computations exponentially. \n", + "- So here we check if cuda is available with cuda.is_available().\n", + " - Following which, we send the model to the cuda device so the computation can be done on the GPU." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "On6Xdb9TqNOF" + }, + "outputs": [], + "source": [ + "%%capture\n", + "import torch.backends.cudnn as cudnn\n", + "torch.cuda.empty_cache()\n", + "cudnn.benchmark = True # Optimise for hardware\n", + "\n", + "device = torch.device(\"cuda:0\" if torch.cuda.is_available() else \"cpu\")\n", + "model.to(device) # send model to GPU" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "q4pZVkP5QiwM" + }, + "source": [ + "# Loss Function & Optimizers\n", + "- The **Loss Function** calculates how 'far' the model's class probability predictions are to the actual labels.\n", + " - Notice how I'm saying \"how far the model's class prob predictions are to the actual labels\" instead of \"how innacurate the model is\", that's because accurate/inaccurate is the percentage of correctly or incorrectly predicted labels. This may sound the same to you but just keep this in mind, it will all make sense in due time.\n", + " - CrossEntropyLoss is a way of calculating the loss of a model, other loss functions include Kullback Leibler Divergence Loss, Sparse Multiclass Cross-Entropy Loss, and much more. \n", + "\n", + "- **The Optimizer** is a way of updating the weights of the model to minimize loss. In other words, the optimizer is the part of deep learning that helps a model 'learn'.\n", + "- In this case we're using the Adam optimizer, this is purely by random choice as no particular optimizer can be said to be superior to the other. There's an important concept in deep learning called \"no free lunch\", which means there isn't a particular methodology that will achieve the best outcome for all scenarios, what it comes down to is experimentation. \n", + " - a lr of 0.001 is also chosen, this is usually a good learning rate start from with the Adam optimizer, however to get a more optimum learning rate, experimentation would need to be done. (The Pytorch documentation includes defaults for each different optimizer)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "id": "IoIZ_7nsqKbt" + }, + "outputs": [], + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.001)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "xedFUaJCMNMR" + }, + "source": [ + "# Let The Training Begin! Part 1\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "colab": { + "background_save": true, + "base_uri": "https://localhost:8080/", + "height": 280, + "referenced_widgets": [ + "4d55d02aacb747f8a131db879f30d72b", + "a3b69ea8be5d4426aaeac34359fa02e5", + "fd248ff975d3450ba37b6517661d323a", + "510ef19dcd4b4309883f1b04a666dc7f", + "c2f5c14a8064434cb774bf88c26e5089", + "d688c591fd7945dd9bd66f64ba04faf4", + "22a8a9a2bb8649c9b4afcabe7fdde092", + "216804b1f5bf442292262b0dea3648ea", + "51413bac20a648f2acdfd5ce2fc75d3e", + "55f95f0aceeb40cea5c7e7178a5d16e4", + "2333dc159f1547189a2c39aff2d2d35a", + "46f98c269d184b5390399212e4dfc154", + "823dc65216f84a5e93df8f5340d40740", + "75f776729790409a8ee76e4e5758cfd5", + "67a3899194fa4dae81cb170e2a5a6769", + "c9b9f074b7f4414bb9cde626f44c97b3", + "91cb865767394880888c1185a609d056", + "a000febb8d86426582054d466c0f0451", + "cc2f6bd2fe4541689bb29ccb9054e47a", + "06f7ec8c6a4a42c9a4fb30d7f91ad4d7", + "1dcd803c65e8438f8100eaf7e5bc275a", + "f9472809d8be40e0939120e645966d93", + "7bc09bbf02ab4f2bb9263a010cea4ea8", + "d9bbb9b5316d415cb9368ed3d086f880", + "b3b14f8a9ae04fc7b39be404b7ca9a0f", + "556f5c1ffe7a4e538ab24d06ac27755d", + "8a50a79e9e22410492d4f1ba065f58eb", + "51aad4d7df764eb980343505ade9170e", + "b81d1e5d3eba405f947a8a6b67a5b21b", + "17b3793a96d049b483f1d2c8ff26ce50", + "bbd9f564105d463b8fecd8fab504dd25", + "b3ae8a30d4b945acae918de4b9a4bcab", + "1fc3f51e0e684499a7e8e2e1ae470ee5", + "5d9da0b893394d26a8205fbcd89fb90a", + "8018e9e362934188901ce488307762f1", + "81d74ad8a8ed469d926438e1cd1bbc5a", + "dd80c4c154724539bd73eba4291aeadc", + "57d9dd6c9579476daa8c49c3ef48aec4", + "45f9e608732041258fbac42ea92e738b", + "bb87dc128d7e4849ad1da4db78b16252", + "3049b4613f0146fd8b0ab8daafdd7b11", + "645736a92d1645ca8892d1bc0e09a2c9", + "fce5f820d2ec4a4196696607c77bbdb1", + "044c3eb0a3b842b79b1b14363ed30abe", + "42ef4cd3895e436e91567b3dd5350963", + "a64ac482b3de45058c7fa0b947134335", + "afa7e9a07ec9491b8c4f5fbc8d206576", + "d8f4cef0e8ca4db6be8f7bb47a1551af", + "cd13193f29624591a54839b70268e521", + "6e3781eac6bf41d8a7433616649a1f1a", + "5b33e842128b4d2d8ccdb61265abd707", + "5ebbcbf3a72f4eef86624b175b296356", + "67caedcb570c4c758851f44dd294d158", + "6fab9bea6d7347e5ae257d73df74e28c", + "b16f28cac0984ad4b4a70bd1cff89a34", + "d8cd2a4c663d4539a67099a27d52a602", + "107a90ba8a574dcba22e9f1cd50da275", + "6f62012e724941e09c4f8abecccf0166", + "7d2469379b6441288298fd40159d4c98", + "1b2b208a25524ebc8605299c950581f8", + "a26185b4c6444117a1de9cf23a097a9f", + "31c8affdd269471e836555b09fe646f2", + "6607b4d25a7744b7a00f81afc07c91c6", + "c349568386224ce7b95e90e69d0fa2cf", + "158608639a2445499aceaf4999e370c6", + "53800dce194044b6bf769c32f00f18d4", + "71b868610894406a84910071223cdacc" + ] + }, + "id": "lL8voSz-4fKi", + "outputId": "39e74f69-3bac-4ce7-f701-57c55e14a759" + }, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4d55d02aacb747f8a131db879f30d72b", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + " 0%| | 0/1563 [00:00\n", + "\n", + "\n", + "\n", + "```\n", + "heart_disease = w1 * biking + w2 * smoking + b1\n", + "```\n", + "\n" + ], + "metadata": { + "id": "Q8FArjjQiGep" + } + }, + { + "cell_type": "markdown", + "source": [ + "# What is our aim? \n", + "\n", + "Our aim is to find values for these weights w1 and w2 such that we can predict the persons likelihood of heart disease given the number of hours ridden and smoked.\n", + "\n", + "If we think about this problem having only one factor altering the likelihood of heart disease say, bike riding, and we had several tuples of data (hrs_ridden,likelihood). If we were approximating a line to these points we would want to adjust the gradient of \n", + "\n", + "*y= mx + b* \n", + "\n", + "such that it nicely reflects the trend of the plotted data. \n", + "\n", + "\n", + "\n", + "\n", + "We are essentially doing the same thing when we're training our model, we will adjust the weights w1, w2 and b1 fractionally each several times untill our model makes better predictions" + ], + "metadata": { + "id": "BTMxrLXadtmH" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Data for training" + ], + "metadata": { + "id": "DRg9lPG_p8eJ" + } + }, + { + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import torch" + ], + "metadata": { + "id": "3GLZ9PATnG6M" + }, + "execution_count": 7, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We will use numpy to create numpy arrays which we will convert later into tensors. As often you will be dealing with data in formats such as CSV, where you will be using numpy or pandas to process it.\n", + "\n", + "(A tensor is a number, vector, matrix, basically an n-dimensional array)\n", + "\n", + "** They can hold one single type ie. All floating point numbers or all integers\n", + "\n", + "```\n", + "# To create a tensor in PyTorch\n", + " tensor = torch.tensor([1.,2,3,4,5])\n", + "\n", + " # As element 0, 1. is a floating point number the rest of the number will become floating point numbers!!\n", + "```\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "CaVUVfmZqHnj" + } + }, + { + "cell_type": "markdown", + "source": [ + "##Importing Data" + ], + "metadata": { + "id": "1Sw31abF4399" + } + }, + { + "cell_type": "code", + "source": [ + "from numpy import genfromtxt\n", + "from google.colab import drive\n", + "import os \n", + "try:\n", + " drive.mount('/content/drive')\n", + " os.chdir('/content/drive/Shareddrives/DeepNeuron Team/Training/Deep Learning/2023')\n", + " heart_data = genfromtxt('heart.data.csv', delimiter=',',skip_header = 1)\n", + "\n", + "except:\n", + " heart_data = genfromtxt('/Data/heart.data.csv', delimiter=',',skip_header = 1)\n", + "\n" + ], + "metadata": { + "id": "iZvhrjsD1xSw", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "aa682c03-0945-43c1-bc64-67539e0eb517" + }, + "execution_count": 9, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "## What do we need to do?\n", + "\n", + "We can represent the training data using two matrices: inputs and targets" + ], + "metadata": { + "id": "aYqhCRCn4sPA" + } + }, + { + "cell_type": "code", + "source": [ + "# Creating Inputs Matrix (biking,smoking)\n", + "inputs = heart_data[:,[1,2]]\n", + "print(inputs.dtype)\n", + "inputs = inputs.astype(np.float32)" + ], + "metadata": { + "id": "T0qS8fei5Nab", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c5e5000f-0ba4-4e9e-e3be-55a04dc7822d" + }, + "execution_count": 10, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "float64\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "*The default element datatype for torch.tensor() is a float32 and the deault for np.array() is a float64" + ], + "metadata": { + "id": "0T-1lFap_dmx" + } + }, + { + "cell_type": "code", + "source": [ + "# Creating Targets Matrix (heart disease)\n", + "targets = heart_data[:,[3]]\n", + "targets = targets.astype(np.float32)" + ], + "metadata": { + "id": "8zE-hve05qN0" + }, + "execution_count": 11, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "We will convert these NumPy arrays to tensors such that we can process them with PyTorch" + ], + "metadata": { + "id": "-uwx5YX06OY3" + } + }, + { + "cell_type": "code", + "source": [ + "# Convert inputs and targets to tensors\n", + "inputs = torch.from_numpy(inputs)\n", + "targets = torch.from_numpy(targets)\n", + "print(inputs[0:10])\n", + "print(targets[0:10])" + ], + "metadata": { + "id": "1f-KM9vb6cB6", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "d82f60a1-3b65-4191-e8b3-16686bccd647" + }, + "execution_count": 12, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[30.8012, 10.8966],\n", + " [65.1292, 2.2196],\n", + " [ 1.9597, 17.5883],\n", + " [44.8002, 2.8026],\n", + " [69.4285, 15.9745],\n", + " [54.4036, 29.3332],\n", + " [49.0562, 9.0608],\n", + " [ 4.7846, 12.8350],\n", + " [65.7308, 11.9913],\n", + " [35.2575, 23.2777]])\n", + "tensor([[11.7694],\n", + " [ 2.8541],\n", + " [17.1778],\n", + " [ 6.8166],\n", + " [ 4.0622],\n", + " [ 9.5500],\n", + " [ 7.6245],\n", + " [15.8547],\n", + " [ 3.0675],\n", + " [12.0985]])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Creating Our Model\n", + "\n", + "PyTorch has several in-built functions to help us built in functions to help us create and train models, with fewer lines of code. However, for the purposes of understanding we will not be using them till later." + ], + "metadata": { + "id": "rDW-ZDVBD22K" + } + }, + { + "cell_type": "markdown", + "source": [ + "We will just initialise the weights and biases as random numbers, and will learn the weights and biases. This is because gradient descent is a heuristic method, meaning it does not really matter where we start." + ], + "metadata": { + "id": "uvzXjza25v4p" + } + }, + { + "cell_type": "code", + "source": [ + "# Weights and biases\n", + "w = torch.randn(1, 2, requires_grad=True)\n", + "b = torch.randn(1, requires_grad=True)\n", + "print(w)\n", + "print(b)" + ], + "metadata": { + "id": "XsWSiR_25vOA", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "9a2118b5-d779-4091-8996-956045c07d5b" + }, + "execution_count": 13, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[ 0.8026, -1.5907]], requires_grad=True)\n", + "tensor([1.1427], requires_grad=True)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "def model(x):\n", + " return (x @ w.t() + b)" + ], + "metadata": { + "id": "I8xaB5rgD2rM" + }, + "execution_count": 14, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + " # Generate predictions\n", + "preds = model(inputs)\n", + "print(preds[0:10])" + ], + "metadata": { + "id": "RSHXpSgk6Zdt", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5f194e9c-d60d-411d-91d8-26896d7eff91" + }, + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[ 8.5295],\n", + " [ 49.8828],\n", + " [-25.2626],\n", + " [ 32.6400],\n", + " [ 31.4530],\n", + " [ -1.8553],\n", + " [ 26.1005],\n", + " [-15.4342],\n", + " [ 34.8216],\n", + " [ -7.5889]], grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(targets[0:10])" + ], + "metadata": { + "id": "WMQ6gpi8910o", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c994f8ab-5ef2-4e4d-949a-1ac6feb72103" + }, + "execution_count": 16, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[11.7694],\n", + " [ 2.8541],\n", + " [17.1778],\n", + " [ 6.8166],\n", + " [ 4.0622],\n", + " [ 9.5500],\n", + " [ 7.6245],\n", + " [15.8547],\n", + " [ 3.0675],\n", + " [12.0985]])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "There is obviously a large descrepency between the current predicitons and the actual targets so we need a way of evaluating how good the model is actually performing, which is where the loss function comes into play." + ], + "metadata": { + "id": "K0yKGNO63wZN" + } + }, + { + "cell_type": "markdown", + "source": [ + "#Loss Function\n", + "\n", + "The loss function measures how well our model is performing. This is calculated via computing the distance between our output (preds) and our expected output (targets). We will be using the Mean Squared Error loss function as this is a regression problem. MSE tell us how close a regression line is close to a set of data points.\n", + "\n", + "[Other Loss Functions](https://medium.com/udacity-pytorch-challengers/a-brief-overview-of-loss-functions-in-pytorch-c0ddb78068f7)\n" + ], + "metadata": { + "id": "V8wBUUeL_-qK" + } + }, + { + "cell_type": "code", + "source": [ + "def MSE(predictions,targets):\n", + "\n", + "# Calculate the distance between output and expected output (preds and targets).\n", + " difference = predictions - targets\n", + "# Square to get rid off negative values.\n", + " diff_squared = difference ** 2\n", + "# Calculate the average of the elements \n", + " average = torch.sum(diff_squared) / difference.numel()\n", + "# This average is the (MSE).\n", + "\n", + " return average" + ], + "metadata": { + "id": "D3SwMxKVJohV" + }, + "execution_count": 17, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "loss = MSE(preds,targets)\n", + "print(loss)" + ], + "metadata": { + "id": "stPnCFL_R5v1", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "296076a5-008c-487b-db3c-716d4497c315" + }, + "execution_count": 18, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(679.1526, grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "###What does this actually mean?\n", + "\n", + "\n", + "\n", + "Essentially what this loss value tells us is that from the red line - which represents the models predicitons - the actual target - one of the blue points - differs on average by a distance of sqrt(loss). \n", + "\n", + "This sets up the fundamental objective of machine learning, which is to reduce the loss so that our model is better at making predictions." + ], + "metadata": { + "id": "4hofo74wSICk" + } + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "9DS5WFV3k8fl" + }, + "execution_count": 18, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "#Gradients\n", + "\n", + "PyTorch makes computing gradients really easy as it has a feature called *autograd*, meaning that for tensors with the property of `requires_grad` set to true, its derivative will be automatically computed. Tensors store these gradients in the `.grad` property." + ], + "metadata": { + "id": "fkdTxBExWWel" + } + }, + { + "cell_type": "code", + "source": [ + "# Compute gradients\n", + "loss.backward()" + ], + "metadata": { + "id": "83vng5jtWWEK" + }, + "execution_count": 19, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Adjusting Weights and Biases\n", + "\n", + "The main objective is to find values for our wights and biases to reduce the loss function. \n", + "\n", + "\n", + "\n", + "If a gradient element is **positive**:\n", + "\n", + "* **increasing** the weight element's value slightly will **increase** the loss\n", + "* **decreasing** the weight element's value slightly will **decrease** the loss\n", + "\n", + "If a gradient element is **negative**:\n", + "\n", + "* **increasing** the weight element's value slightly will **decrease** the loss\n", + "* **decreasing** the weight element's value slightly will **increase** the loss\n", + "\n", + "We will essentially be altering the values of the initial weights several times until we reach the minima. " + ], + "metadata": { + "id": "IwPrfNv1Xmel" + } + }, + { + "cell_type": "code", + "source": [ + "# Train for 100 epochs\n", + "lr = 1e-5\n", + "\n", + "for i in range(300):\n", + " preds = model(inputs)\n", + " loss = MSE(preds, targets)\n", + " loss.backward()\n", + " with torch.no_grad():\n", + " w -= w.grad * lr\n", + " b -= b.grad * lr\n", + " w.grad.zero_() \n", + " b.grad.zero_()" + ], + "metadata": { + "id": "ypPb9EeSgNZI" + }, + "execution_count": 20, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Calculate loss\n", + "preds = model(inputs)\n", + "loss = MSE(preds, targets)\n", + "print(loss)" + ], + "metadata": { + "id": "gx8Y5hvqgab-", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "134fa219-bbc5-4be9-9cac-a8dfe17a09b7" + }, + "execution_count": 21, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor(183.6610, grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(preds[0:10])" + ], + "metadata": { + "id": "c0XO_BTyPJNX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1a78aae4-e75b-44ad-c80f-4b9d17cfe934" + }, + "execution_count": 22, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[ 5.2010],\n", + " [20.7581],\n", + " [-7.5514],\n", + " [13.9685],\n", + " [14.8115],\n", + " [ 2.9165],\n", + " [11.9959],\n", + " [-4.1225],\n", + " [15.7518],\n", + " [ 0.0352]], grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(targets[0:10])" + ], + "metadata": { + "id": "dAVv5oJlPTKB", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "e6230e6d-3f9c-460d-8057-e168bc6d4aeb" + }, + "execution_count": 23, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[11.7694],\n", + " [ 2.8541],\n", + " [17.1778],\n", + " [ 6.8166],\n", + " [ 4.0622],\n", + " [ 9.5500],\n", + " [ 7.6245],\n", + " [15.8547],\n", + " [ 3.0675],\n", + " [12.0985]])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "#Logistic Regression with PyTorch \n", + "\n", + "As we have solidified some of the concepts, we will move onto a more PyTorch intensive example." + ], + "metadata": { + "id": "z2ICnZT2y600" + } + }, + { + "cell_type": "code", + "source": [ + "import torch\n", + "import torchvision\n", + "import matplotlib.pyplot as plt\n", + "from torchvision.datasets import MNIST\n", + "%matplotlib inline" + ], + "metadata": { + "id": "UHt1kH5HdIHs" + }, + "execution_count": 24, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "dataset = MNIST(root = 'data/',download=True)" + ], + "metadata": { + "id": "4CPblmaadRCv", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "4a62a539-98ec-466f-ebd5-da76248472dc" + }, + "execution_count": 25, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/raw/train-images-idx3-ubyte.gz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 9912422/9912422 [00:00<00:00, 109925258.04it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz to data/MNIST/raw/train-labels-idx1-ubyte.gz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 28881/28881 [00:00<00:00, 13351228.24it/s]\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/raw/t10k-images-idx3-ubyte.gz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "100%|โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1648877/1648877 [00:00<00:00, 112207210.13it/s]" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Extracting data/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + "\n" + ] + }, + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ] + }, + { + "output_type": "stream", + "name": "stderr", + "text": [ + 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+ }, + "outputId": "21d8a401-b95c-4afc-c127-cb53e96af305" + }, + "execution_count": 27, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Number: 3\n" + ] + }, + { + "output_type": "display_data", + "data": { + "text/plain": [ + "
" + ], + "image/png": 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\n" + }, + "metadata": {} + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Splitting Our Data\n", + "\n", + "\n", + "\n", + "We split our data into three different sets:\n", + "\n", + "1. **Training set** - used to train the model, i.e., compute the loss and adjust the model's weights using gradient descent.\n", + "2. **Validation set** - used to evaluate the model during training, adjust hyperparameters (learning rate, etc.), and pick the best version of the model.\n", + "3. **Test set** - used to compare different models or approaches and report the model's final accuracy.\n", + "\n", + "The MNIST dataset comes with 60,000 images in the training set and 10,000 testing images for the evaluation of our model." + ], + "metadata": { + "id": "1J2engqjfpgT" + } + }, + { + "cell_type": "code", + "source": [ + "import torchvision.transforms as transforms" + ], + "metadata": { + "id": "Y9vvI1r9rUwg" + }, + "execution_count": 28, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "testing_ds = MNIST(root = 'data/',\n", + " train = False,\n", + " transform =transforms.ToTensor())\n", + "dataset = MNIST(root='data/', \n", + " train=True,\n", + " transform=transforms.ToTensor())" + ], + "metadata": { + "id": "oOR-VAu8wFYg" + }, + "execution_count": 29, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "len(testing_ds), len(dataset)" + ], + "metadata": { + "id": "FXcDMJ66reBN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7f1dc256-cf30-468b-fbcc-23294f381898" + }, + "execution_count": 30, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(10000, 60000)" + ] + }, + "metadata": {}, + "execution_count": 30 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "** As the 60,000 dataset segemenent does not include a segment for validation and training data, we will need to manually split it up.\n", + "\n", + "We do this using the `random_spilt` function in PyTorch in the data utils module. This is just so that we can have a good representation of the whole data set in our split. As when you have a dataset the way its sorted may mean when we select for example 50,000 images for our training set we may not have any number 9's and our 10,000 images for our validation set may be just 9's. " + ], + "metadata": { + "id": "YKHMFo3JrdxW" + } + }, + { + "cell_type": "code", + "source": [ + "from torch.utils.data import random_split\n", + "training_ds, validation_ds = random_split(dataset,[50000,10000])" + ], + "metadata": { + "id": "R7OR5swRDG47" + }, + "execution_count": 31, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "len(training_ds), len(validation_ds)" + ], + "metadata": { + "id": "WeOCwICaLx_H", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c1526ed8-0a76-4cb5-8850-2867d4705a10" + }, + "execution_count": 32, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "(50000, 10000)" + ] + }, + "metadata": {}, + "execution_count": 32 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Data Loader" + ], + "metadata": { + "id": "VsCrwSH7iUtM" + } + }, + { + "cell_type": "code", + "source": [ + "from torch.utils.data import DataLoader\n", + "\n", + "training_loader = DataLoader(training_ds, batch_size = 64, shuffle = True)\n", + "validation_loader = DataLoader(validation_ds, batch_size = 64, shuffle = False)" + ], + "metadata": { + "id": "YR4Ez0vrCfOI" + }, + "execution_count": 33, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "For the training loadee `shuffle = True`, which just shuffles the data, to make sure the batches in each epoch are different. You want to shuffle your data after each epoch because you will always have the risk to create batches that are not representative of the overall dataset, and therefore, your estimate of the gradient will be off. " + ], + "metadata": { + "id": "NFzcJ-PP_Ig7" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Creating our Model " + ], + "metadata": { + "id": "YUhlsS-7MVRC" + } + }, + { + "cell_type": "markdown", + "source": [ + "Logistic Regression model is relatively similar to a linear regression model, where the predicted output is obtained via the following:\n", + "\n", + "```\n", + "p = x @ w.t() + b\n", + "```\n", + "However, this time we will use the `nn.Linear` to create the model. Which applies a linear transformation to the incoming data: `y = xA^T + b`\n" + ], + "metadata": { + "id": "1_9qQzx4M8Rx" + } + }, + { + "cell_type": "code", + "source": [ + "image,label = training_ds[0]\n", + "print(f\"Dimensions: {image.shape}\")" + ], + "metadata": { + "id": "vUZvkPFpMVAX", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c551c891-14fa-4cd8-9742-3c18475fa33d" + }, + "execution_count": 34, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Dimensions: torch.Size([1, 28, 28])\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "We know we have 10 images classes, as any given image is a number between 0 - 9, and our vector provided to `nn.Linear` will be of size (784) so we will reshape our image into 2D using the `.reshape` method." + ], + "metadata": { + "id": "HkaneikrN-l7" + } + }, + { + "cell_type": "markdown", + "source": [ + "All PyTorch models are created by extending the `nn.Module` class" + ], + "metadata": { + "id": "Kq_nffzEZjGi" + } + }, + { + "cell_type": "code", + "source": [ + "import torch.nn as nn" + ], + "metadata": { + "id": "0RWo7OK7ZZcD" + }, + "execution_count": 35, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "class LrModel(nn.Module):\n", + " def __init__(self,input_size,output_size): # Sub Class \n", + " super().__init__() \n", + " self.linear = nn.Linear(input_size, output_size)\n", + " \n", + " def forward(self, x):\n", + " x = x.reshape(-1, 784)\n", + " x = self.linear(x)\n", + " return x" + ], + "metadata": { + "id": "f_EXtIG_Meog" + }, + "execution_count": 36, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "model = LrModel(784,10)" + ], + "metadata": { + "id": "EWt0oLE1eLCr" + }, + "execution_count": 37, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "# Training and Validation Loops\n", + "\n", + "- This is over one epoch" + ], + "metadata": { + "id": "E3BkdOLLcsbF" + } + }, + { + "cell_type": "code", + "source": [ + "from tqdm.notebook import tqdm" + ], + "metadata": { + "id": "XsMDbVahfQj0" + }, + "execution_count": 38, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def train(model, train_loader, loss_fn, optimizer):\n", + " model.train() # puts the model in training mode\n", + " running_loss = 0\n", + " with tqdm(total=len(train_loader)) as pbar:\n", + " for i, data in enumerate(train_loader, 0): # batch in training loader\n", + " inputs, labels = data # separate inputs and labels (outputs)\n", + " \n", + " # forward + backward + optimize \n", + " outputs = model(inputs) # forward pass and get predictions\n", + " loss = loss_fn(outputs, labels) # calculate loss (how well the model is fitting training data)\n", + " loss.backward() # calculates gradient w.r.t to loss for all parameters in model that have requires_grad=True\n", + " optimizer.step() # iterate over all parameters in the model with requires_grad=True and update their weights.\n", + " optimizer.zero_grad() # clear the gradients in model parameters\n", + "\n", + " running_loss += loss.item() # sum total loss in current epoch for print later\n", + "\n", + " pbar.update(1) #increment our progress bar\n", + "\n", + " return running_loss/len(train_loader) # returns the total training loss for the epoch" + ], + "metadata": { + "id": "8ssuYlAdes-b" + }, + "execution_count": 39, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "# Function for the validation pass\n", + "def validation(model, val_loader, loss_fn):\n", + " model.eval() # puts the model in validation mode\n", + " running_loss = 0\n", + " total = 0\n", + " correct = 0\n", + " \n", + " with torch.no_grad(): # save memory by not saving gradients which we don't need \n", + " with tqdm(total=len(val_loader)) as pbar:\n", + " for images, labels in iter(val_loader): #batch in val loader\n", + " \n", + " outputs = model(images) # passes image to the model, and gets a ouput which is the class probability prediction\n", + "\n", + " val_loss = loss_fn(outputs, labels) # calculates val_loss from model predictions and true labels (ie. How well model is fitting new data)\n", + " running_loss += val_loss.item()\n", + "\n", + "\n", + " # Essentially measuring accuracy by checking the percantage of labels that have been correctly predicted\n", + " _, predicted = torch.max(outputs, 1) # turns class probability predictions to class labels\n", + " total += labels.size(0) # sums the number of predictions\n", + " correct += (predicted == labels).sum().item() # sums the number of correct predictions\n", + " \n", + " pbar.update(1)\n", + "\n", + " return running_loss/len(val_loader), correct/total # return loss value, accuracy" + ], + "metadata": { + "id": "l8Di5AWZfScY" + }, + "execution_count": 40, + "outputs": [] + }, + { + "cell_type": "markdown", + "source": [ + "You might be asking how does our model output probabilities for us to take the max and say, we are 60% confident the image inputted is a 8. With our Cross Entropy Loss function PyTorch will convert these weight sums to probablities using the softmax fucntion.\n", + "\n", + "How does it work?\n", + "\n", + "\n", + "\n" + ], + "metadata": { + "id": "v79GLEdQk2Rc" + } + }, + { + "cell_type": "code", + "source": [ + "from torch.nn.functional import softmax" + ], + "metadata": { + "id": "VmS6cJI7rwN4" + }, + "execution_count": 41, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "for images,labels in training_loader:\n", + " outputs = model(images)\n", + " break\n", + "\n", + "print(outputs[0:5])" + ], + "metadata": { + "id": "EwOhjWPeub4S", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "1edcf813-db34-4f5c-d252-4e7174376ac7" + }, + "execution_count": 42, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[-0.0096, 0.0993, 0.4343, -0.1909, 0.0017, 0.0887, 0.2848, 0.1860,\n", + " 0.0179, -0.0623],\n", + " [-0.0809, -0.1281, 0.0726, 0.0736, 0.2048, 0.0336, 0.0577, 0.2804,\n", + " 0.0253, 0.0367],\n", + " [-0.2171, -0.0942, -0.0144, -0.1221, 0.3720, 0.2191, 0.3760, 0.2270,\n", + " 0.2073, -0.1302],\n", + " [ 0.0660, -0.2365, 0.0357, -0.3930, 0.2850, 0.3594, 0.0892, 0.4412,\n", + " 0.1613, -0.0077],\n", + " [-0.0948, -0.1093, -0.0738, -0.3057, 0.3966, 0.4229, 0.4185, 0.4388,\n", + " 0.1337, -0.0939]], grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "probability = softmax(outputs,1)\n", + "print(probability[0:5])" + ], + "metadata": { + "id": "GCB7Zmdvu26G", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7047a572-1156-4369-85f4-73be7a40d140" + }, + "execution_count": 43, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "tensor([[0.0896, 0.0999, 0.1397, 0.0748, 0.0906, 0.0989, 0.1203, 0.1090, 0.0921,\n", + " 0.0850],\n", + " [0.0865, 0.0825, 0.1009, 0.1010, 0.1151, 0.0970, 0.0994, 0.1242, 0.0962,\n", + " 0.0973],\n", + " [0.0725, 0.0820, 0.0888, 0.0797, 0.1307, 0.1121, 0.1312, 0.1130, 0.1108,\n", + " 0.0791],\n", + " [0.0958, 0.0708, 0.0930, 0.0606, 0.1193, 0.1285, 0.0981, 0.1395, 0.1054,\n", + " 0.0890],\n", + " [0.0784, 0.0772, 0.0800, 0.0635, 0.1281, 0.1315, 0.1309, 0.1336, 0.0985,\n", + " 0.0784]], grad_fn=)\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "print(torch.max(probability[0:5],1))" + ], + "metadata": { + "id": "dJTrZAEazVnH", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5969a517-a785-4e07-c8ae-08719afedeae" + }, + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "torch.return_types.max(\n", + "values=tensor([0.1397, 0.1242, 0.1312, 0.1395, 0.1336], grad_fn=),\n", + "indices=tensor([2, 7, 6, 7, 7]))\n" + ] + } + ] + }, + { + "cell_type": "code", + "source": [ + "labels[0:5]" + ], + "metadata": { + "id": "Dq8hnAyrzcD8", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "8d782258-f0b8-4ce2-9b37-18d98bb86edf" + }, + "execution_count": 45, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor([3, 4, 2, 7, 2])" + ] + }, + "metadata": {}, + "execution_count": 45 + } + ] + }, + { + "cell_type": "markdown", + "source": [ + "# Loss Functions\n", + "Cross Entropy Loss function is often used for classification problems, it works by taking the predicted probability for the correct label, so from above in the first row it would be its max value. It then logs this number so high probs (0.89etc) will have a value loss value closer to 0 and lower probs a loss value closer to 1.\n", + "\n", + "The average of all the losses is the overall loss for the batch.\n", + "\n", + "Access loss functions from `torch.nn`" + ], + "metadata": { + "id": "hUExLGhizr7A" + } + }, + { + "cell_type": "markdown", + "source": [ + "# Training" + ], + "metadata": { + "id": "HYuAkKIc5nL2" + } + }, + { + "cell_type": "code", + "source": [ + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = torch.optim.SGD(model.parameters(), lr = 0.001)" + ], + "metadata": { + "id": "mjk8R3X46H6Y" + }, + "execution_count": 46, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "def fit(total_epoch, model, training_loader, loss_fn, optimizer, validation_loader):\n", + " for epoch in range(total_epoch): # loops through number of epochs\n", + " train_loss = train(model, training_loader, loss_fn, optimizer) # train the model for one epoch\n", + " val_loss, accuracy = validation(model, validation_loader, loss_fn) # after training for one epoch, run the validation() function to see how the model is doing on the validation dataset\n", + " print(\"Epoch: {}/{}, Training Loss: {}, Val Loss: {}, Val Accuracy: {}\".format(epoch+1, total_epoch, train_loss, val_loss, accuracy))\n", + " print('-' * 20)\n", + "\n", + " print(\"Finished Training\")" + ], + "metadata": { + "id": "Co3763ec7JfK" + }, + "execution_count": 47, + "outputs": [] + }, + { + "cell_type": "code", + "source": [ + "\n", + "fit(5, model, training_loader, loss_fn, optimizer, validation_loader)" + ], + "metadata": { + "id": "TVcomU5Tzrq-", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 0, + "referenced_widgets": [ + "be6ae3d324194f2c858ab7aa21f17622", + "da5034a4cc2646d99c667374cd4b6f11", + "a730a1a43b134eca8f8de929fd4dfd44", + "705179273dfe429b96b782031e3d7c67", + "f3da76b32e3b4b29b11fc0003dd9006b", + "4e610828bac24ea893f00c1d75f973e2", + "d2cc894794aa4dc2ba4af3ef3aaa8e0b", + "8a633e949a734107bfe656c2079302fa", + "8f744f7559134f6d83aa9b3ebd748b0f", + "43be27f3c24a48208146120fb9a75b3f", + "383ccd3dbd664d73816739827e358616", + "2fb0cdd044ea4f75b43089a7980efbd1", + "2c620316abec42a090ef81c70a01cb88", + "5739b19195c24565ae2f2a64689d42e0", + "74362ed5c3f7400d9f8c919cdeeefefc", + "fa893f5804084f9c91703dcf98220127", + "6947b2b23c874b279620dfea6d58819e", + "a597373d3e174de9abebf9ff339386c6", + "a9676c0abf344b92be9a9a7b1f784103", + "b151a06717e14e49ab46cfa6b8d86eca", + "3ef76f203afc4791aea8daeb5a58aa12", + "0184b3daa53c4894b4815a83788a62e0", + "67604d69aeed4560804d869fc634a39b", + "746d6398ba9942dda5129b2b069924e7", + "f31d12e7bd1846b6b454334cf5811b4f", + "e6a89bc7f62742d386e723e3ddae190b", + "b116f22ce6dc4c45931b2a648c6881fe", + "05837aa699884f6f8dc7f0da6397014a", + "896c085bed6c402187c0c7da2d861b10", + "be30a4d3bafc4bc8b1ce137543ea0969", + "d42fedd0313e4112ba4f53dd92111a6a", + "8668e37210b1444a990cf8e23e082b4d", + "e7983b4dc76f4ab286ada76e179ccec7" + ] + }, + "outputId": "e7f07eec-78bc-495d-f46b-224feb837f9d" + }, + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "text/plain": [ + " 0%| | 0/782 [00:00 Save a copy in Drive\n", + "\n", + "2. Also do the following:\n", + " * Click on Runtime -> Change runtime type -> Make sure hardware accelerator is set to GPU\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8A5h_HSa1ntg" + }, + "source": [ + "## Imports\n", + "Do all your imports here" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "EyssqirK0o_i" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "from torch import optim\n", + "from torchvision import datasets, transforms\n", + "\n", + "from tqdm.notebook import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "YuT7EHsP0w6V" + }, + "source": [ + "## Model Creation\n", + "Create your model here. The model stub has already been created, you will need to define an `__init__` and a `forward()` method for your class.\n", + "\n", + "Your model will need:\n", + "1. Convolutional Layer (3 input channels, 7 output channels, kernel size = 5)\n", + "2. Max Pool (kernel size = 2, stride = 2)\n", + "3. Convolutional Layer (7 input channels, 14 output channels, kernel size = 5)\n", + "4. Fully Connected Layer (Figure out the input size, output size is 64)\n", + "5. Fully Connected Layer (input size is 64, output size is the number of classes)\n", + "\n", + "Then, in your forward pass, the input should flow like so:\n", + "1. Convolutional Layer 1\n", + "2. ReLU\n", + "3. Max Pool\n", + "4. Convolutional Layer 2\n", + "5. ReLU\n", + "6. Max Pool (use the same max pool layer)\n", + "7. Flatten the input, so it can be passed through the fully connected layers\n", + "8. Fully Connected Layer 1\n", + "9. ReLU\n", + "10. Fully Connected Layer 2\n", + "\n", + "### Hints\n", + "\n", + "All layers are found in nn:\n", + "* Fully Connected Layer: `Linear(num inputs, num outputs)`\n", + "* Max Pooling: `MaxPool2d(kernel size, stride)`\n", + "* Convolutional Layer: `Conv2d(input channels, output channels, kernel size, stride)`\n", + "* Define your layers in the `__init__` method of your model\n", + "* Flatten a PyTorch tensor using `.flatten()`\n", + "\n", + "The ReLU function can be found in `nn.functional.relu()`\n", + "\n", + "Make sure to set the flatten start dimension to 1 (to not flatten out the batch boundaries too): `.flatten(start_dim=1)`\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "0PEhHODf0hyp" + }, + "source": [ + "class MyCNN(nn.Module):\n", + " def __init__(self, output_size):\n", + " super(model, self).__init__()\n", + " self.conv1 = nn.Conv2d(in_channels = 3, out_channels = 7,kernel_size = 5)\n", + " self.pool = nn.Maxpool2d(kernel_size = 2, stride = 2)\n", + " self.conv2 = nn.Conv2d(in_channels = 7, out_channels = 14,kernel_size = 5) \n", + "\n", + " def forward(self, x):\n", + " x = torch.nn.functional.relu(self.pool(self.conv1(x)))\n", + " x = torch.nn.functional.relu(self.conv2(x))\n", + " x = x.flatten(start_dim=1)\n", + " x = nn.Linear()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "9R5Ys2SU1lIP" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "c4Af1zPc11cQ" + }, + "source": [ + "## Training Using Your Model\n", + "It's time to train your model!\n", + "\n", + "We use a basic PyTorch training loop, with standard built-in datasets, dataloaders and training loops" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kKikAPOX8OUP" + }, + "source": [ + "# Function for the training \n", + "\n", + "def train(model, train_loader, loss_fn, optimizer, device):\n", + " model.train() # puts the model in training mode\n", + " running_loss = 0\n", + " with tqdm(total=len(train_loader)) as pbar:\n", + " for i, data in enumerate(train_loader, 0): # loops through training data\n", + " inputs, labels = data # separate inputs and labels (outputs)\n", + " inputs, labels = inputs.to(device), labels.to(device) # puts the data on the GPU\n", + "\n", + " # forward + backward + optimize \n", + " optimizer.zero_grad() # clear the gradients in model parameters\n", + " outputs = model(inputs) # forward pass and get predictions\n", + " loss = loss_fn(outputs, labels) # calculate loss\n", + " loss.backward() # calculates gradient w.r.t to loss for all parameters in model that have requires_grad=True\n", + " optimizer.step() # iterate over all parameters in the model with requires_grad=True and update their weights.\n", + "\n", + " running_loss += loss.item() # sum total loss in current epoch for print later\n", + "\n", + " pbar.update(1) #increment our progress bar\n", + "\n", + " return running_loss/len(train_loader) # returns the total training loss for the epoch" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "Z0aOkgss8Q9L" + }, + "source": [ + "# Function for the validation pass\n", + "\n", + "def validation(model, val_loader, loss_fn, device):\n", + " model.eval() # puts the model in validation mode\n", + " running_loss = 0\n", + " total = 0\n", + " correct = 0\n", + " \n", + " with torch.no_grad(): # save memory by not saving gradients which we don't need \n", + " with tqdm(total=len(val_loader)) as pbar:\n", + " for images, labels in iter(val_loader):\n", + " images, labels = images.to(device), labels.to(device) # put the data on the GPU\n", + " outputs = model(images) # passes image to the model, and gets a ouput which is the class probability prediction\n", + "\n", + " val_loss = loss_fn(outputs, labels) # calculates val_loss from model predictions and true labels\n", + " running_loss += val_loss.item()\n", + " _, predicted = torch.max(outputs, 1) # turns class probability predictions to class labels\n", + " total += labels.size(0) # sums the number of predictions\n", + " correct += (predicted == labels).sum().item() # sums the number of correct predictions\n", + " \n", + " pbar.update(1)\n", + "\n", + " return running_loss/len(val_loader), correct/total # return loss value, accuracy" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "4SqDdINODxlI" + }, + "source": [ + "## Dataset\n", + "Set a path for the dataset downloads" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "XSU0v-izbdSw" + }, + "source": [ + "train_path = 'data/train'\n", + "valid_path = 'data/valid'" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "source": [], + "metadata": { + "id": "d8Z0HYdg6n6p" + }, + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "tC050ZXb7xST" + }, + "source": [ + "# Define transforms for the training and validation set\n", + "training_transforms = transforms.Compose([transforms.RandomRotation(30),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])\n", + "\n", + "validation_transforms = transforms.Compose([transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], \n", + " [0.229, 0.224, 0.225])])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "sbt08-r472CL", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 154, + "referenced_widgets": [ + "00620dcae97f425b92c5557b8906873a", + "f51646a6bbbe4e68ac059e20d749f868", + "f0c5d13d2eeb4dd4aa36ef18d7f97bcf", + "e5ae86cda6534254af832a2689dbc7ef", + "fdd8a309ac1540b589fd568317a199e5", + "c92c1d25914c4ad08410e805f36f3d73", + "ee8bbc5474104ab3a1fe36d3e16503ad", + "93b16c0a2c6744188848121cb0f3c462", + "88624776f5e941d998c462d79648d16d", + "3441c576d36049ca87a134787fb7386b", + "d5231b7925ad4dbab4d30099c315f9ce", + "69aa06f435584411aabf8f61890b031b", + "c4241a5288ed4768b7816d38376179c1", + "b5702eacc941456f863880cca94e3295", + "ea6489afeb0d4575a99ef0fb6d7bc8c8", + "d528e8a9c8d447a98ff1b8ed0c6d2ec3", + "28ba374706b34682a1be3c8eb09725e5", + "d7c9a7b5a7db42eba8be49970fcadfa2", + "6d5a7e3120ae46e694d84c14591ae3d7", + "efbdc63c40704fa580dc69fa3558b9a0", + "e607ba93c87641099fba6ffc4b560e56", + "be12d0697d6e41899d72612d3c048f91" + ] + }, + "outputId": "a390562d-6937-433c-fcb8-0e02231a8fc9" + }, + "source": [ + "training_dataset = datasets.CIFAR10(train_path, train=True, transform=training_transforms, download=True)\n", + "validation_dataset = datasets.CIFAR10(valid_path, train=False, transform=validation_transforms, download=True)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to data/train/cifar-10-python.tar.gz\n" + ] + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "00620dcae97f425b92c5557b8906873a", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/170498071 [00:00" + ] + }, + "metadata": {} + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TzKcOpI-avvJ", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "c8a83db7-3c1f-4832-af99-cc103ff541dd" + }, + "source": [ + "# If you haven't seen python path objects, they're just a simpler way to work with addresses.\n", + "print(\"Type:\", type(path))\n", + "print(\"Path\", path)\n", + "print(path/\"some\"/\"directory\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stdout", + "text": [ + "Type: \n", + "Path /root/.fastai/data/imagewoof2\n", + "/root/.fastai/data/imagewoof2/some/directory\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "I6aTQKa3avvL", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 166 + }, + "outputId": "31a70256-4918-4c2b-9dcf-989240ce3871" + }, + "source": [ + "# Plot the data to get an idea of our samples\n", + "size = 3\n", + "num = 10\n", + "f, axs = plt.subplots(1, num, figsize=(size * num, size))\n", + "\n", + "# Get all classnames\n", + "classnames = os.listdir(path/\"train\")\n", + "\n", + "\n", + "for i, classname in enumerate(classnames):\n", + " # Get image path\n", + " filename = os.listdir(path/\"train\"/classname)[0]\n", + " image_path = path/\"train\"/classname/filename\n", + " \n", + " # Plot image\n", + " plt.subplot(1, num, i + 1)\n", + " img = Image.open(image_path)\n", + " plt.axis('off')\n", + " plt.imshow(img)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Af7a-cwlavvM" + }, + "source": [ + "The dataset is composed of coloured images, all with different sizes. We will need to use transforms to standardise the input size for our model. Similarly, we will also want to convert our inputs to a PyTorch tensor and to normalise it, so that our model can better handle the inputs. We can also use augmentations to stretch our data further, changing the inputs as we load them from memory.\n", + "\n", + "The list of PyTorch transforms is available on their [documentation](https://pytorch.org/docs/stable/torchvision/transforms.html)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4epEx7hVfthb", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "01d0d9ac-8a74-44c3-8d8e-649b48bba0d2" + }, + "source": [ + "# Define transforms for the training set\n", + "\n", + "# Resize the image to a 256x216 image\n", + "# Add a random horizontal flip\n", + "# Randomly crop the data to make it square\n", + "# Transform the image to a tensor\n", + "# Normalise the data with imagenet statistics (means: 0.485, 0.456, 0.406, std: 0.229, 0.224, 0.225)\n", + "train_trans = transforms.Compose([transforms.Resize(256, interpolation=2),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.RandomRotation(20),\n", + " transforms.RandomCrop(256, pad_if_needed=True, padding_mode=\"reflect\"),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", + " ])\n", + "\n", + "# Copy the training transforms, but only include the transforms that aren't augmentations\n", + "valid_trans = transforms.Compose([transforms.Resize(256, interpolation=2),\n", + " transforms.CenterCrop(256),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])\n", + " ])" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torchvision/transforms/transforms.py:281: UserWarning: Argument interpolation should be of type InterpolationMode instead of int. Please, use InterpolationMode enum.\n", + " \"Argument interpolation should be of type InterpolationMode instead of int. \"\n" + ] + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "u_ZnYcOUc9kV" + }, + "source": [ + "# Create the train and validation datasets\n", + "# The train / validation split is predetermined in this dataset and divided by folder\n", + "train_ds = datasets.ImageFolder(path/\"train\", transform=train_trans)\n", + "valid_ds = datasets.ImageFolder(path/\"val\", transform=valid_trans)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "1Yznn9Rfd84v", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "5ccd48ee-9737-4a96-bb91-8d1b08737b39" + }, + "source": [ + "# Make dataloaders\n", + "bs = 16\n", + "nw = 16\n", + "train_loader = DataLoader(train_ds, batch_size=bs, shuffle=True, num_workers=nw)\n", + "valid_loader = DataLoader(valid_ds, batch_size=bs, shuffle=False, num_workers=nw)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "name": "stderr", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:481: UserWarning: This DataLoader will create 16 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n", + " cpuset_checked))\n" + ] + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "8kDN7g4LavvP" + }, + "source": [ + "# Training loops\n", + "### Exercise: Complete the training and validation loop in the functions below. Look at the previous workshop if you need a hint." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zt6NnLFGje97" + }, + "source": [ + "def train(model, train_loader, loss_fn, optimizer, device):\n", + " # Prepare for training\n", + " model.train()\n", + " running_loss = 0\n", + " \n", + " with tqdm(total=len(train_loader)) as pbar:\n", + " for inputs, labels in train_loader:\n", + " # Put images and labels on GPU\n", + " inputs, labels = inputs.to(device), labels.to(device)\n", + "\n", + " # Run through model and update\n", + " optimizer.zero_grad()\n", + " outputs = model(inputs)\n", + " loss = loss_fn(outputs, labels)\n", + " loss.backward()\n", + " optimizer.step() \n", + "\n", + " # Track loss and update progress\n", + " running_loss += loss.item()\n", + " pbar.update(1)\n", + "\n", + " return running_loss / len(train_loader)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "3tGoXkGRk8dz" + }, + "source": [ + "# Function for the validation pass\n", + "def validation(model, val_loader, loss_fn, device):\n", + " # Prepare for validating\n", + " model.eval()\n", + " val_loss = 0\n", + " correct = 0\n", + " total = 0\n", + "\n", + " with torch.no_grad():\n", + " with tqdm(total=len(val_loader)) as pbar:\n", + " for images, labels in iter(val_loader):\n", + " # Put images and labels on GPU\n", + " images, labels = images.to(device), labels.to(device)\n", + "\n", + " # Run through model\n", + " outputs = model(images)\n", + "\n", + " # Track loss and update progress\n", + " val_loss += loss_fn(outputs, labels).item()\n", + "\n", + " # Update accuracy\n", + " _, predicted = torch.max(outputs, 1)\n", + " total += labels.size(0)\n", + " correct += (predicted == labels).sum().item()\n", + "\n", + " pbar.update(1)\n", + " \n", + " return val_loss / len(val_loader), correct / total" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VHyzrQVPavvQ" + }, + "source": [ + "# Writing a ResNet\n", + "Rather than writing one giant class that handles every thing, we can write more readable code that is easier to maintain by breaking the model up into several smaller classes" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nBZzu-CravvR" + }, + "source": [ + "Every time that we use a convolution, we usually want to activate it with `BatchNorm` and `ReLU`. A ResNet is also composed mostly of 3x3 convolutions that maintain the input dimensions (`ks=3`, `stride=1` and `pad=1`).\n", + "\n", + "### Exercise: Complete the `Conv3x3` class to run a convolution, then optionally `BatchNorm` and `ReLU`." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "AFx4Blu2k9e-" + }, + "source": [ + "# Convolution, batch norm, ReLu with typical parameters defaulted\n", + "class Conv(nn.Module):\n", + " def __init__(self, in_planes, out_planes, ks=3, stride=1, pad=1, bn=True, activ=True):\n", + " super().__init__()\n", + " # The main convolution\n", + " # Use nn.Conv2d to define a convolution layer with the parameters specified\n", + " self.conv = nn.Conv2d(in_planes, out_planes, ks, stride, pad)\n", + "\n", + " # Optionally include activations\n", + " # If BatchNorm is True, use nn.BatchNorm2d to include a Batch Normalisation\n", + " if bn:\n", + " self.bn = nn.BatchNorm2d(out_planes)\n", + " else:\n", + " self.bn = None\n", + " # If activ is True, use nn.ReLU to include a ReLU\n", + "\n", + " if activ:\n", + " self.activ = nn.ReLU()\n", + " else:\n", + " self.activ = None\n", + " \n", + "\n", + " def forward(self, x):\n", + " # Run through convolution then BatchNorm then ReLU\n", + " \n", + " h = self.conv(x)\n", + " if self.bn is not None:\n", + " h = self.bn(h)\n", + " \n", + " if self.activ is not None:\n", + " h = self.activ(h)\n", + " \n", + " return h\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "mTInR0wCavvS" + }, + "source": [ + "![alt text](https://mohitjainweb.files.wordpress.com/2018/06/bottleneck-layer-resnet1.png?w=700)\n", + "\n", + "A ResNet uses one of two types of repeating blocks, the `Basic Block` and the `Bottleneck Block`. The Basic Block is simpler and simply has two 3x3 convolutions and a skip connection, it is used in ResNet 18 and ResNet 34. The Bottleneck Block is uses a 1x1 convolution .\n", + "\n", + "These blocks can also be used to downsample by increasing the stride to 2 on the first 3x3 convolution and adding a single 3x3 convolution to 'fix' the dimensions on the skip connection.\n", + "\n", + "### Exercise: Using the `Conv` class we've just made, complete the `Basic Block` class to run a double convolution with a skip connection. You can use the Bottleneck class as an example if you need to." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "STJ-5QBHlbM2" + }, + "source": [ + "# ResNet Basic Block\n", + "class BasicBlock(nn.Module):\n", + " def __init__(self, in_planes, out_planes, downsample=False):\n", + " super(BasicBlock, self).__init__()\n", + "\n", + " # If downsample is True, we will downsample with a stride 2 convolution\n", + " self.downsample = downsample\n", + " if self.downsample:\n", + " # Use the Conv class to downsample, changing the number of filters to out_planes\n", + " self.conv1 = Conv(in_planes, out_planes, stride=2, pad=2)\n", + "\n", + " # We will also need a convolution to downsample on the skip connection to 'fix' the dimensions\n", + " # Use the Conv class to downsample without ReLU\n", + " self.conv_skip1 = Conv(in_planes, out_planes, activ=False, stride=2, pad=2)\n", + "\n", + " # If downsample is False, we can just use the default settings of our Conv class\n", + " else:\n", + " # Use the Conv class, changing the number of filters to out_planes\n", + "\n", + " self.conv1 = Conv(in_planes, out_planes)\n", + "\n", + " \n", + "\n", + " # The second convolution doesn't use ReLU since this is applied after the skip connection\n", + " # Use the Conv class without ReLU\n", + "\n", + " self.conv2 = Conv(out_planes, out_planes, activ=False)\n", + "\n", + " # Define a ReLU activation\n", + "\n", + " self.relu = nn.ReLU()\n", + "\n", + " def forward(self, x):\n", + " # Save original or downsample\n", + " if self.downsample:\n", + " original_x = self.conv_skip1(x)\n", + " else:\n", + " original_x = x\n", + "\n", + " # Double convolution\n", + " out = self.conv1(x)\n", + " out = self.conv2(out)\n", + "\n", + " # Apply skip connection and final activation\n", + " out = out + original_x\n", + " out = self.relu(out)\n", + "\n", + " return out\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "VDOmB_Zxn7vy" + }, + "source": [ + "# ResNet Bottleneck Block\n", + "class Bottleneck(nn.Module):\n", + " def __init__(self, in_planes, out_planes, downsample=False):\n", + " super(Bottleneck, self).__init__()\n", + " \n", + " # Reduce planes in the centre of the bottleneck by a factor of 4\n", + " reduced_planes = out_planes // 4\n", + " \n", + " # Reduce planes with a 1x1 convolution\n", + " self.conv1 = Conv(in_planes, reduced_planes, ks=1, pad=0)\n", + " \n", + " # Downsampling uses a stride 2 conv\n", + " self.downsample = downsample\n", + " \n", + " # If downsample is True, we will downsample with a stride 2 convolution\n", + " if self.downsample:\n", + " self.conv2 = Conv(reduced_planes, reduced_planes, stride=2, pad=1)\n", + " \n", + " # We will also need a convolution to downsample on the skip connection\n", + " self.downsample = Conv(in_planes, out_planes, ks=1, stride=2, activ=None)\n", + " \n", + " # If downsample is False, we can just use the default settings of our Conv class\n", + " else:\n", + " self.conv2 = Conv(reduced_planes, reduced_planes)\n", + " \n", + " # Increase planes with a 1x1 convolution\n", + " self.conv3 = Conv(reduced_planes, out_planes, ks=1, pad=0, activ=None)\n", + "\n", + " \n", + " self.relu = nn.ReLU(inplace=True)\n", + "\n", + " def forward(self, x):\n", + " # Save original or downsample\n", + " identity = self.downsample(x) if self.downsample else x\n", + "\n", + " # Triple convolution\n", + " out = self.conv1(x)\n", + " out = self.conv2(out)\n", + " out = self.conv3(out)\n", + " \n", + " # Apply skip connection and final activation\n", + " out += identity\n", + " out = self.relu(out)\n", + "\n", + " return out" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rAspAAZDavvV" + }, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q8Lx6zJMavvW" + }, + "source": [ + "A `ResNet` can be decomposed into its input transition, the middle layers and its output transition:\n", + "- The input transition uses a 7x7 convolution and a 3x3 max pool with a stride of 2. \n", + "- The four 'middle layers' use repeating blocks with the first convolution downsampling. Though, since the input transition downsamples, the first middle layer will not downsample.\n", + "- The output transition uses an average pool to take the average of each filter, then flattens the collected features to remove excess dimensions and passes this into a fully connected network\n", + "\n", + "### Exercise: Complete the `ResNet` class below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kNJ6kI2llbss" + }, + "source": [ + "# A ResNet Model that \n", + "class ResNet(nn.Module):\n", + " def __init__(self, block, depths, num_classes):\n", + " super(ResNet, self).__init__()\n", + " self.block = block\n", + " self.depths = depths\n", + "\n", + " # Input transition\n", + " self.in1 = Conv(3, 64, ks=7, stride=2, pad=3)\n", + " self.in2 = nn.MaxPool2d(2)\n", + " \n", + " # Downsample path\n", + " self.down1 = self._make_layer(64, 64, depths[0], downsample=False)\n", + " self.down2 = self._make_layer(64, 128, depths[1])\n", + " self.down3 = self._make_layer(128, 256, depths[2])\n", + " self.down4 = self._make_layer(256, 512, depths[3])\n", + "\n", + " # Output transition\n", + " self.avg_pool = nn.AdaptiveAvgPool2d(1)\n", + " self.flatten = nn.Flatten(start_dim=1)\n", + " self.fc = nn.Linear(in_features=512, out_features=num_classes)\n", + "\n", + " # Create a middle layer\n", + " def _make_layer(self, in_channels, out_channels, depth, downsample=True):\n", + " # Increase the number of filters\n", + " layers = [self.block(in_channels, out_channels, downsample=downsample)]\n", + "\n", + " # Add a repeat the block depth - 1 times\n", + " for _ in range(depth - 1):\n", + " layers.append(self.block(out_channels, out_channels))\n", + "\n", + " # Convert the layers list into a Sequential\n", + " return nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " # Input\n", + "\n", + " h = self.in1(x)\n", + " h = self.in2(h)\n", + "\n", + " # Downsample\n", + "\n", + " h = self.down1(h)\n", + " h = self.down2(h)\n", + " h = self.down3(h)\n", + " h = self.down4(h)\n", + " \n", + " # Output\n", + " h = self.avg_pool(h)\n", + " h = self.flatten(h)\n", + " h = self.fc(h)\n", + " \n", + " return h" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4vyJ7D6-sMKQ" + }, + "source": [ + "# Define a series of functions to make each ResNet\n", + "def resnet18(num_classes): return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)\n", + "def resnet34(num_classes): return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)\n", + "def resnet50(num_classes): return ResNet(Bottleneck, [3, 4, 6, 3], num_classes)\n", + "def resnet101(num_classes): return ResNet(Bottleneck, [3, 4, 23, 3], num_classes)\n", + "def resnet152(num_classes): return ResNet(Bottleneck, [3, 8, 36, 3], num_classes)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yD_5HFjkavvY" + }, + "source": [ + "Compare our ResNet with PyTorch's ResNet. We've broken down the class differently and used different layer names, but the model's structure should be the same." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DvgnYZijavvY", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "7f81dc55-5343-4319-f76c-1399efc14229" + }, + "source": [ + "resnet18(1000)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ResNet(\n", + " (in1): Conv(\n", + " (conv): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3))\n", + " (bn): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (activ): ReLU()\n", + " )\n", + " (in2): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n", + " (down1): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv(\n", + " (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n", + " (bn): BatchNorm2d(64, eps=1e-05, 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ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer4): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=512, out_features=1000, bias=True)\n", + ")" + ] + }, + "metadata": {}, + "execution_count": 17 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JFxZNGyCavva" + }, + "source": [ + "# Testing the Model\n", + "Finally let's train our model to see how it goes. Feel free to experiment with the other ResNets to see how they differ in performance. If everything has worked as intended, it should approach the performance of a ResNet without pretraining." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fkb50ehaxCIF" + }, + "source": [ + "model = resnet34(10).to(device)\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.0003)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HJden8fZti7l", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000, + "referenced_widgets": [ + "c7c23ea4c87b4447b7e9cd130bf9f8a9", + "6c0406d0f7854ff98b04fe759c358bbe", + "e0ce0e50440249b9b839de8746638e42", + "69a3bf8496694e29b061b12e60a19147", + "f5eab14d5a684a3f9f087913c8fb3f30", + "98209f94b16f427289e17d768511fc34", + "15347e934a2c4ef9b3e24d36d46c5a3c", + "33a0fde85e6041a69d4251cadcc89472", + "bcb237cbcfb94c2e9ea18e859fb234cb", + "804e0bd55d31404091eaa17bd9e44d6a", + "b7fd01f31dff41e18a707889df9225c2", + 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total_epoch, train_loss, val_loss, accuracy))\n", + " print('-' * 20)\n", + "\n", + "print(\"Finished Training\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c7c23ea4c87b4447b7e9cd130bf9f8a9", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + " 0%| | 0/565 [00:00" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q8Lx6zJMavvW" + }, + "source": [ + "A `ResNet` can be decomposed into its input transition, the middle layers and its output transition:\n", + "- The input transition uses a 7x7 convolution and a 3x3 max pool with a stride of 2. \n", + "- The four 'middle layers' use repeating blocks with the first convolution downsampling. Though, since the input transition downsamples, the first middle layer will not downsample.\n", + "- The output transition uses an average pool to take the average of each filter, then flattens the collected features to remove excess dimensions and passes this into a fully connected network\n", + "\n", + "### Exercise: Complete the `ResNet` class below." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "kNJ6kI2llbss" + }, + "source": [ + "class ResNet(nn.Module):\n", + " def __init__(self, block, depths, num_classes):\n", + " super(ResNet, self).__init__()\n", + " self.block = block\n", + " self.depths = depths\n", + "\n", + " # Input transition\n", + "\n", + " # Downsample path\n", + " self.down1 = self._make_layer(64, 64, depths[0], downsample=False)\n", + " self.down2 = self._make_layer(64, 128, depths[1])\n", + " self.down3 = self._make_layer(128, 256, depths[2])\n", + " self.down4 = self._make_layer(256, 512, depths[3])\n", + "\n", + " # Output transition\n", + " self.avg_pool = nn.AdaptiveAvgPool2d(1)\n", + " self.flatten = nn.Flatten(1)\n", + " self.fc = nn.Linear(in_features=512, out_features=num_classes)\n", + "\n", + " # Create a middle layer\n", + " def _make_layer(self, in_channels, out_channels, depth, downsample=True):\n", + " # Increase the number of filters\n", + " layers = [# Change the filters and downsample with the first block]\n", + " \n", + " # Add a repeat the block depth - 1 times\n", + " for _ in range(depth - 1):\n", + " layers.append(# Repeat the block without changing the filters)\n", + " \n", + " # Convert the layers list into a Sequential\n", + " return nn.Sequential(*layers)\n", + "\n", + " def forward(self, x):\n", + " # Input\n", + "\n", + " # Downsample\n", + "\n", + " # Output\n", + "\n", + " return x" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "4vyJ7D6-sMKQ" + }, + "source": [ + "# Define a series of functions to make each ResNet\n", + "def resnet18(num_classes): return ResNet(BasicBlock, [2, 2, 2, 2], num_classes)\n", + "def resnet34(num_classes): return ResNet(BasicBlock, [3, 4, 6, 3], num_classes)\n", + "def resnet50(num_classes): return ResNet(Bottleneck, [3, 4, 6, 3], num_classes)\n", + "def resnet101(num_classes): return ResNet(Bottleneck, [3, 4, 23, 3], num_classes)\n", + "def resnet152(num_classes): return ResNet(Bottleneck, [3, 8, 36, 3], num_classes)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yD_5HFjkavvY" + }, + "source": [ + "Compare our ResNet with PyTorch's ResNet. We've broken down the class differently and used different layer names, but the model's structure should be the same." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "DvgnYZijavvY" + }, + "source": [ + "resnet18(1000)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "scrolled": true, + "id": "HVdallaAavvZ" + }, + "source": [ + "models.resnet18()" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JFxZNGyCavva" + }, + "source": [ + "# Testing the Model\n", + "Finally let's train our model to see how it goes. Feel free to experiment with the other ResNets to see how they differ in performance. If everything has worked as intended, it should approach the performance of a ResNet without pretraining." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Fkb50ehaxCIF" + }, + "source": [ + "model = resnet34(10).to(device)\n", + "loss_fn = nn.CrossEntropyLoss()\n", + "optimizer = optim.Adam(model.parameters(), lr=0.0003)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "HJden8fZti7l" + }, + "source": [ + "total_epoch = 20\n", + "\n", + "for epoch in range(total_epoch):\n", + " # Train the model for one epoch\n", + " train_loss = train(model, train_loader, loss_fn, optimizer, device)\n", + " # Calculate validation metrics for one epoch\n", + " val_loss, accuracy = validation(model, valid_loader, loss_fn, device)\n", + " print(\"Epoch: {}/{}, Training Loss: {:.4f}, Val Loss: {:.4f}, Val Accuracy: {:.4f}\".format(epoch+1, total_epoch, train_loss, val_loss, accuracy))\n", + " print('-' * 20)\n", + "\n", + "print(\"Finished Training\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "l_uMoxxDavvb" + }, + "source": [], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "fV3O7SJxavvc" + }, + "source": [], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/2023_DLrecruits_Workshops/Workshop5_GAN.ipynb b/2023_DLrecruits_Workshops/Workshop5_GAN.ipynb new file mode 100644 index 0000000..4c9f667 --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop5_GAN.ipynb @@ -0,0 +1,5920 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "GAN", + "provenance": [], + "collapsed_sections": [ + "Lt4xRKJn5_Gs" + ] + }, + "kernelspec": { + "display_name": "Python 3", + "name": "python3" + }, + "language_info": { + "name": "python" + }, + "accelerator": "GPU" + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "jyx4YDdI5ri5" + }, + "source": [ + "# Generating Images with Generative Adversarial Networks\n", + "This notebook will work you through a full example of how to use a GAN to generate realistic images.\n", + "Please note that this is an example running on Colab (in a limited time frame), so the size and resolution of the output images will not be the maximum possible.\n", + "\n", + "\n", + "There are additionally a small variety of optional add ons that you can experiment with which are in the [original repositiory this code came from](https://github.com/KamWithK/Comic-Character-Generation).\n", + "The main ones are spectral normalisation which enhances a GANs stability, self attention (which finds global instead of local information) and improved loss functions (i.e. relativistic).\n", + "\n", + "**Note: Ignore any warnings about missing temp files in this notebook**\n", + "\n", + "**LINK TO THE DATA - https://drive.google.com/drive/folders/0B7EVK8r0v71pTUZsaXdaSnZBZzg** - Right click \"img_align_celeba.zip\" and click \"Make a copy\"\n", + "Note that this may not work with your uni Google Drive, so make sure to use a personal account..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zsLgtNc753Ra" + }, + "source": [ + "## Imports\n", + "We'll need to import a variety of necessary functions and classes to help us out\n", + "Sample API Key - `c513e5b74941c479613253312bde834151bea3a2`" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "eLTYuuAC5Hx6", + "outputId": "6f3c66ef-8c8f-4e43-ded9-28a5f2bd8d3f" + }, + "source": [ + "!pip install wandb torchsummary --quiet\n", + "!wandb login --anonymously" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 1.8MB 34.0MB/s \n", + "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 102kB 12.0MB/s \n", + "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 174kB 53.5MB/s \n", + "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 133kB 51.9MB/s \n", + "\u001b[K |โ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆโ–ˆ| 71kB 10.2MB/s \n", + "\u001b[?25h Building wheel for pathtools (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + " Building wheel for subprocess32 (setup.py) ... \u001b[?25l\u001b[?25hdone\n", + "\u001b[34m\u001b[1mwandb\u001b[0m: Appending key for api.wandb.ai to your netrc file: /root/.netrc\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "MtQHbWFf1X6P" + }, + "source": [ + "import torch\n", + "\n", + "import torch.nn as nn\n", + "\n", + "from torch.nn.utils import spectral_norm\n", + "from itertools import starmap" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "oYymYT6d5id5" + }, + "source": [ + "import os\n", + "import imghdr\n", + "\n", + "from torch.utils.data import Dataset\n", + "from PIL import Image\n", + "from os import listdir, walk\n", + "from os.path import join\n", + "from torch import randn" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "akA0_x3Q5FqF" + }, + "source": [ + "import wandb\n", + "\n", + "import torch.optim as optim\n", + "\n", + "from tqdm import tqdm, trange\n", + "from torchvision import transforms, models\n", + "from torch.utils.data import DataLoader\n", + "from torchsummary import summary" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "hGNwWz9goUvL" + }, + "source": [ + "The following code may ask you for an API key - if so continue through to link and copy/paste the generated API key in here\n", + "\n", + "Comment this out if Google Drive isn't working for you" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0zxYPfKIN4v4", + "outputId": "a4b5fb62-ba06-4c19-e09a-77618c0bcedf" + }, + "source": [ + "from google.colab import drive\n", + "drive.mount(\"/content/drive\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Mounted at /content/drive\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "VVWkxCK9AK2o" + }, + "source": [ + "## Parameters\n", + "GANs have a lot of parameters, and can be quite volatile.\n", + "This means the values we choose for these parameters have a very large impact on how well our generator and discriminator fair.\n", + "\n", + "We'll set these up here ahead of time.\n", + "\n", + "\n", + "The results of the run will be displayed on this page, remember that you have to log into Weights and Biases from your browser and open the authentication like which previouslly showed up." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "KWuekWgQALNv" + }, + "source": [ + "DEVICE = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "\n", + "SIZE, CHANNELS = 64, 3\n", + "NOISE_SIZE, LATENT_DIMS = 1, 128\n", + "BATCH_SIZE = 128\n", + "\n", + "EPOCHS = 300\n", + "MAX_IMAGES, LOG_IMAGES_EVERY = None, 100\n", + "GENERATOR_HIDDEN_DIMS = [800, 400, 200, 100, CHANNELS]\n", + "DISCRIMINATOR_HIDDEN_DIMS = [64, 128, 256, 512, NOISE_SIZE]\n", + "\n", + "GENERATOR_LR, DISCRIMINATOR_LR = 0.0002, 0.0002" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "3BqAlkMDpy5a" + }, + "source": [ + "The progress of the GAN can be seen through the dashboard\n", + "Below the loss metrics you should see the images the GAN is creating (watch them as they improve)" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 602 + }, + "id": "a5EKtVaqHvOY", + "outputId": "f9c41a23-f1bb-4413-e835-28682c2d8308" + }, + "source": [ + "# mode can be set to \"online\" or \"disabled\"\n", + "wandb.init(project=\"gan-demo\", name=f\"CelebA {SIZE} Standard Test\", mode=\"online\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[34m\u001b[1mwandb\u001b[0m: Currently logged in as: \u001b[33manony-moose-102698\u001b[0m (use `wandb login --relogin` to force relogin)\n" + ], + "name": "stderr" + }, + { + "output_type": "display_data", + "data": { + "text/html": [ + "\n", + " Tracking run with wandb version 0.10.30
\n", + " Syncing run CelebA 64 Standard Test to Weights & Biases (Documentation).
\n", + " Project page: https://wandb.ai/anony-moose-102698/gan-demo?apiKey=129062315548ca1f89015a09df3fd24414f776b4
\n", + " Run page: https://wandb.ai/anony-moose-102698/gan-demo/runs/icjuyana?apiKey=129062315548ca1f89015a09df3fd24414f776b4
\n", + " Run data is saved locally in /content/wandb/run-20210521_021157-icjuyana

\n", + " " + ], + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "

Run(icjuyana)

" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "FgQHf09Zd--s" + }, + "source": [ + "wandb.config.update({\n", + " \"size\": SIZE, \"channels\": CHANNELS,\n", + " \"batch_size\": BATCH_SIZE,\n", + " \"epochs\": EPOCHS,\n", + " \"max_images\": MAX_IMAGES,\n", + " \"generator_hidden_dims\": GENERATOR_HIDDEN_DIMS,\n", + " \"discriminator_hidden_dims\": DISCRIMINATOR_HIDDEN_DIMS,\n", + " \"noise_size\": NOISE_SIZE, \"latent_dims\": LATENT_DIMS,\n", + " \"generator_lr\": GENERATOR_LR, \"discriminator_lr\": DISCRIMINATOR_LR\n", + "})" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Lt4xRKJn5_Gs" + }, + "source": [ + "## Utilities\n", + "To make our life easier, we'll go ahead and create a number of helper functions and classes which we can use later on:\n", + "* Dataset - handles opening images and generating random indices\n", + "* Weight Initialisation - sets up initial random weights\n", + "* Self Attention - advanced layer type for finding global structure\n", + "\n", + "These classes aren't unique, they'll just help abstract away some of the finer details later on (don't worry if you're confused, relatively minor)." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "8MDyfYb25od6" + }, + "source": [ + "class ImageDataset(Dataset):\n", + " def __init__(self, path, max_images=None, transform=None, size=None, latent_vector=None):\n", + " self.all_paths = [join(folder, image_path) for folder, _, fn in walk(path) for image_path in fn if imghdr.what(join(folder, image_path)) != None]\n", + " self.transform = transform\n", + "\n", + " self.paths = self.all_paths if max_images == None else self.all_paths[:max_images]\n", + " \n", + " self.size, self.latent_vector = size, latent_vector\n", + " \n", + " def __len__(self):\n", + " return len(self.paths)\n", + " \n", + " def __getitem__(self, index):\n", + " img = Image.open(self.paths[index]).convert(\"RGB\")\n", + " img = self.transform(img)# if self.transform != None else transforms.ToTensor()(img)\n", + " \n", + " if None in [self.size, self.latent_vector]:\n", + " return img\n", + " else:\n", + " return randn(self.latent_vector, self.size, self.size), img" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "J5S5A_Hb-m0c" + }, + "source": [ + "def weights_init(m):\n", + " classname = m.__class__.__name__\n", + " if classname.find(\"Conv\") != -1:\n", + " nn.init.normal_(m.weight.data, 0.0, 0.02)\n", + " elif classname.find(\"BatchNorm\") != -1:\n", + " nn.init.normal_(m.weight.data, 1.0, 0.02)\n", + " nn.init.constant_(m.bias.data, 0)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HQzox2cbADQj" + }, + "source": [ + "## Data\n", + "We'll use a few transforms to increase the amount of data we have available" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "rFYTmW6NAFDv" + }, + "source": [ + "transform = transforms.Compose([\n", + " transforms.Resize(SIZE),\n", + " transforms.CenterCrop(SIZE),\n", + " transforms.ToTensor(),\n", + " transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)) # ImageNet Values\n", + "])" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "WSQ0uiRKOTgu", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "270f927b-e277-404c-fd77-213bd5a0d65b" + }, + "source": [ + "if not os.path.exists(\"img_align_celeba\"):\n", + " # !wget https://deepneuron-gan-workshop.s3-ap-southeast-2.amazonaws.com/img_align_celeba.zip # uncomment if drive doesn't work\n", + " !unzip \"drive/MyDrive/Copy of img_align_celeba.zip\"" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n", + " extracting: img_align_celeba/197600.jpg \n", + " extracting: img_align_celeba/197601.jpg \n", + " extracting: img_align_celeba/197602.jpg \n", + " extracting: img_align_celeba/197603.jpg \n", + " extracting: img_align_celeba/197604.jpg \n", + " extracting: img_align_celeba/197605.jpg \n", + " extracting: img_align_celeba/197606.jpg \n", + " extracting: img_align_celeba/197607.jpg \n", + " extracting: img_align_celeba/197608.jpg \n", + " extracting: img_align_celeba/197609.jpg \n", + " extracting: img_align_celeba/197610.jpg \n", + " extracting: img_align_celeba/197611.jpg \n", + " extracting: img_align_celeba/197612.jpg \n", + " extracting: img_align_celeba/197613.jpg \n", + " extracting: img_align_celeba/197614.jpg \n", + " extracting: img_align_celeba/197615.jpg \n", + " extracting: img_align_celeba/197616.jpg \n", + " extracting: img_align_celeba/197617.jpg \n", + " extracting: img_align_celeba/197618.jpg \n", + " extracting: img_align_celeba/197619.jpg \n", + " extracting: img_align_celeba/197620.jpg \n", + " extracting: img_align_celeba/197621.jpg \n", + " extracting: img_align_celeba/197622.jpg \n", + " extracting: img_align_celeba/197623.jpg \n", + " extracting: img_align_celeba/197624.jpg \n", + " extracting: img_align_celeba/197625.jpg \n", + " extracting: img_align_celeba/197626.jpg \n", + " extracting: img_align_celeba/197627.jpg \n", + " extracting: img_align_celeba/197628.jpg \n", + " extracting: img_align_celeba/197629.jpg \n", + " extracting: img_align_celeba/197630.jpg \n", + " extracting: img_align_celeba/197631.jpg \n", + " extracting: img_align_celeba/197632.jpg \n", + " extracting: img_align_celeba/197633.jpg \n", + " extracting: img_align_celeba/197634.jpg \n", + " extracting: img_align_celeba/197635.jpg \n", + " extracting: img_align_celeba/197636.jpg \n", + " extracting: img_align_celeba/197637.jpg \n", + " extracting: img_align_celeba/197638.jpg \n", + " extracting: img_align_celeba/197639.jpg \n", + " extracting: img_align_celeba/197640.jpg \n", + " extracting: img_align_celeba/197641.jpg \n", + " extracting: img_align_celeba/197642.jpg \n", + " extracting: img_align_celeba/197643.jpg \n", + " extracting: img_align_celeba/197644.jpg \n", + " extracting: img_align_celeba/197645.jpg \n", + " extracting: img_align_celeba/197646.jpg \n", + " extracting: img_align_celeba/197647.jpg \n", + " extracting: img_align_celeba/197648.jpg \n", + " extracting: img_align_celeba/197649.jpg \n", + " extracting: img_align_celeba/197650.jpg \n", + " extracting: img_align_celeba/197651.jpg \n", + " extracting: img_align_celeba/197652.jpg \n", + " extracting: img_align_celeba/197653.jpg \n", + " extracting: img_align_celeba/197654.jpg \n", + " extracting: img_align_celeba/197655.jpg \n", + " extracting: img_align_celeba/197656.jpg \n", + " extracting: img_align_celeba/197657.jpg \n", + " extracting: img_align_celeba/197658.jpg \n", + " extracting: img_align_celeba/197659.jpg \n", + " extracting: img_align_celeba/197660.jpg \n", + " extracting: img_align_celeba/197661.jpg \n", + " extracting: img_align_celeba/197662.jpg \n", + " extracting: img_align_celeba/197663.jpg \n", + " extracting: img_align_celeba/197664.jpg \n", + " extracting: img_align_celeba/197665.jpg \n", + " extracting: img_align_celeba/197666.jpg \n", + " extracting: img_align_celeba/197667.jpg \n", + " extracting: img_align_celeba/197668.jpg \n", + " extracting: img_align_celeba/197669.jpg \n", + " extracting: img_align_celeba/197670.jpg \n", + " extracting: img_align_celeba/197671.jpg \n", + " extracting: img_align_celeba/197672.jpg \n", + " extracting: img_align_celeba/197673.jpg \n", + " extracting: img_align_celeba/197674.jpg \n", + " extracting: img_align_celeba/197675.jpg \n", + " extracting: img_align_celeba/197676.jpg \n", + " extracting: img_align_celeba/197677.jpg \n", + " extracting: img_align_celeba/197678.jpg \n", + " extracting: img_align_celeba/197679.jpg \n", + " extracting: img_align_celeba/197680.jpg \n", + " extracting: img_align_celeba/197681.jpg \n", + " extracting: img_align_celeba/197682.jpg \n", + " extracting: img_align_celeba/197683.jpg \n", + " extracting: img_align_celeba/197684.jpg \n", + " extracting: img_align_celeba/197685.jpg \n", + " extracting: img_align_celeba/197686.jpg \n", + " extracting: img_align_celeba/197687.jpg \n", + " extracting: img_align_celeba/197688.jpg \n", + " extracting: img_align_celeba/197689.jpg \n", + " extracting: img_align_celeba/197690.jpg \n", + " extracting: img_align_celeba/197691.jpg \n", + " extracting: img_align_celeba/197692.jpg \n", + " extracting: img_align_celeba/197693.jpg \n", + " extracting: img_align_celeba/197694.jpg \n", + " extracting: img_align_celeba/197695.jpg \n", + " extracting: img_align_celeba/197696.jpg \n", + " extracting: img_align_celeba/197697.jpg \n", + " extracting: img_align_celeba/197698.jpg \n", + " extracting: img_align_celeba/197699.jpg \n", + " extracting: img_align_celeba/197700.jpg \n", + " extracting: img_align_celeba/197701.jpg \n", + " extracting: img_align_celeba/197702.jpg \n", + " extracting: img_align_celeba/197703.jpg \n", + " extracting: img_align_celeba/197704.jpg \n", + " extracting: img_align_celeba/197705.jpg \n", + " extracting: img_align_celeba/197706.jpg \n", + " extracting: img_align_celeba/197707.jpg \n", + " extracting: img_align_celeba/197708.jpg \n", + " extracting: img_align_celeba/197709.jpg \n", + " extracting: img_align_celeba/197710.jpg \n", + " extracting: img_align_celeba/197711.jpg \n", + " extracting: img_align_celeba/197712.jpg \n", + " extracting: img_align_celeba/197713.jpg \n", + " extracting: img_align_celeba/197714.jpg \n", + " extracting: img_align_celeba/197715.jpg \n", + " extracting: img_align_celeba/197716.jpg \n", + " extracting: img_align_celeba/197717.jpg \n", + " extracting: img_align_celeba/197718.jpg \n", + " extracting: img_align_celeba/197719.jpg \n", + " extracting: img_align_celeba/197720.jpg \n", + " extracting: img_align_celeba/197721.jpg \n", + " extracting: img_align_celeba/197722.jpg \n", + " extracting: img_align_celeba/197723.jpg \n", + " extracting: img_align_celeba/197724.jpg \n", + " extracting: img_align_celeba/197725.jpg \n", + " extracting: img_align_celeba/197726.jpg \n", + " extracting: img_align_celeba/197727.jpg \n", + " extracting: img_align_celeba/197728.jpg \n", + " extracting: img_align_celeba/197729.jpg \n", + " extracting: img_align_celeba/197730.jpg \n", + " extracting: img_align_celeba/197731.jpg \n", + " extracting: img_align_celeba/197732.jpg \n", + " extracting: img_align_celeba/197733.jpg \n", + " extracting: img_align_celeba/197734.jpg \n", + " extracting: img_align_celeba/197735.jpg \n", + " extracting: img_align_celeba/197736.jpg \n", + " extracting: img_align_celeba/197737.jpg \n", + " extracting: img_align_celeba/197738.jpg \n", + " extracting: img_align_celeba/197739.jpg \n", + " extracting: img_align_celeba/197740.jpg \n", + " extracting: img_align_celeba/197741.jpg \n", + " extracting: img_align_celeba/197742.jpg \n", + " extracting: img_align_celeba/197743.jpg \n", + " extracting: img_align_celeba/197744.jpg \n", + " extracting: img_align_celeba/197745.jpg \n", + " extracting: img_align_celeba/197746.jpg \n", + " extracting: img_align_celeba/197747.jpg \n", + " extracting: img_align_celeba/197748.jpg \n", + " extracting: img_align_celeba/197749.jpg \n", + " extracting: img_align_celeba/197750.jpg \n", + " extracting: img_align_celeba/197751.jpg \n", + " extracting: img_align_celeba/197752.jpg \n", + " extracting: img_align_celeba/197753.jpg \n", + " extracting: img_align_celeba/197754.jpg \n", + " extracting: img_align_celeba/197755.jpg \n", + " extracting: img_align_celeba/197756.jpg \n", + " extracting: img_align_celeba/197757.jpg \n", + " extracting: img_align_celeba/197758.jpg \n", + " extracting: img_align_celeba/197759.jpg \n", + " extracting: img_align_celeba/197760.jpg \n", + " extracting: img_align_celeba/197761.jpg \n", + " extracting: img_align_celeba/197762.jpg \n", + " extracting: img_align_celeba/197763.jpg \n", + " extracting: img_align_celeba/197764.jpg \n", + " extracting: img_align_celeba/197765.jpg \n", + " extracting: img_align_celeba/197766.jpg \n", + " extracting: img_align_celeba/197767.jpg \n", + " extracting: img_align_celeba/197768.jpg \n", + " extracting: img_align_celeba/197769.jpg \n", + " extracting: img_align_celeba/197770.jpg \n", + " extracting: img_align_celeba/197771.jpg \n", + " extracting: img_align_celeba/197772.jpg \n", + " extracting: img_align_celeba/197773.jpg \n", + " extracting: img_align_celeba/197774.jpg \n", + " extracting: img_align_celeba/197775.jpg \n", + " extracting: img_align_celeba/197776.jpg \n", + " extracting: img_align_celeba/197777.jpg \n", + " extracting: img_align_celeba/197778.jpg \n", + " extracting: img_align_celeba/197779.jpg \n", + " extracting: img_align_celeba/197780.jpg \n", + " extracting: img_align_celeba/197781.jpg \n", + " extracting: img_align_celeba/197782.jpg \n", + " extracting: img_align_celeba/197783.jpg \n", + " extracting: img_align_celeba/197784.jpg \n", + " extracting: img_align_celeba/197785.jpg \n", + " extracting: img_align_celeba/197786.jpg \n", + " extracting: img_align_celeba/197787.jpg \n", + " extracting: img_align_celeba/197788.jpg \n", + " extracting: img_align_celeba/197789.jpg \n", + " extracting: img_align_celeba/197790.jpg \n", + " extracting: img_align_celeba/197791.jpg \n", + " extracting: img_align_celeba/197792.jpg \n", + " extracting: img_align_celeba/197793.jpg \n", + " extracting: img_align_celeba/197794.jpg \n", + " extracting: img_align_celeba/197795.jpg \n", + " extracting: img_align_celeba/197796.jpg \n", + " extracting: img_align_celeba/197797.jpg \n", + " extracting: img_align_celeba/197798.jpg \n", + " extracting: img_align_celeba/197799.jpg \n", + " extracting: img_align_celeba/197800.jpg \n", + " extracting: img_align_celeba/197801.jpg \n", + " extracting: img_align_celeba/197802.jpg \n", + " extracting: img_align_celeba/197803.jpg \n", + " extracting: img_align_celeba/197804.jpg \n", + " extracting: img_align_celeba/197805.jpg \n", + " extracting: img_align_celeba/197806.jpg \n", + " extracting: img_align_celeba/197807.jpg \n", + " extracting: img_align_celeba/197808.jpg \n", + " extracting: img_align_celeba/197809.jpg \n", + " extracting: img_align_celeba/197810.jpg \n", + " extracting: img_align_celeba/197811.jpg \n", + " extracting: img_align_celeba/197812.jpg \n", + " extracting: img_align_celeba/197813.jpg \n", + " extracting: img_align_celeba/197814.jpg \n", + " extracting: img_align_celeba/197815.jpg \n", + " extracting: img_align_celeba/197816.jpg \n", + " extracting: img_align_celeba/197817.jpg \n", + " extracting: img_align_celeba/197818.jpg \n", + " extracting: img_align_celeba/197819.jpg \n", + " extracting: img_align_celeba/197820.jpg \n", + " extracting: img_align_celeba/197821.jpg \n", + " extracting: img_align_celeba/197822.jpg \n", + " extracting: img_align_celeba/197823.jpg \n", + " extracting: img_align_celeba/197824.jpg \n", + " extracting: img_align_celeba/197825.jpg \n", + " extracting: img_align_celeba/197826.jpg \n", + " extracting: img_align_celeba/197827.jpg \n", + " extracting: img_align_celeba/197828.jpg \n", + " extracting: img_align_celeba/197829.jpg \n", + " extracting: img_align_celeba/197830.jpg \n", + " extracting: img_align_celeba/197831.jpg \n", + " extracting: img_align_celeba/197832.jpg \n", + " extracting: img_align_celeba/197833.jpg \n", + " extracting: img_align_celeba/197834.jpg \n", + " extracting: img_align_celeba/197835.jpg \n", + " extracting: img_align_celeba/197836.jpg \n", + " extracting: img_align_celeba/197837.jpg \n", + " extracting: img_align_celeba/197838.jpg \n", + " extracting: img_align_celeba/197839.jpg \n", + " extracting: img_align_celeba/197840.jpg \n", + " extracting: img_align_celeba/197841.jpg \n", + " extracting: img_align_celeba/197842.jpg \n", + " extracting: img_align_celeba/197843.jpg \n", + " extracting: img_align_celeba/197844.jpg \n", + " extracting: img_align_celeba/197845.jpg \n", + " extracting: img_align_celeba/197846.jpg \n", + " extracting: img_align_celeba/197847.jpg \n", + " extracting: img_align_celeba/197848.jpg \n", + " extracting: img_align_celeba/197849.jpg \n", + " extracting: img_align_celeba/197850.jpg \n", + " extracting: img_align_celeba/197851.jpg \n", + " extracting: img_align_celeba/197852.jpg \n", + " extracting: img_align_celeba/197853.jpg \n", + " extracting: img_align_celeba/197854.jpg \n", + " extracting: img_align_celeba/197855.jpg \n", + " extracting: img_align_celeba/197856.jpg \n", + " extracting: img_align_celeba/197857.jpg \n", + " extracting: img_align_celeba/197858.jpg \n", + " extracting: img_align_celeba/197859.jpg \n", + " extracting: 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img_align_celeba/197880.jpg \n", + " extracting: img_align_celeba/197881.jpg \n", + " extracting: img_align_celeba/197882.jpg \n", + " extracting: img_align_celeba/197883.jpg \n", + " extracting: img_align_celeba/197884.jpg \n", + " extracting: img_align_celeba/197885.jpg \n", + " extracting: img_align_celeba/197886.jpg \n", + " extracting: img_align_celeba/197887.jpg \n", + " extracting: img_align_celeba/197888.jpg \n", + " extracting: img_align_celeba/197889.jpg \n", + " extracting: img_align_celeba/197890.jpg \n", + " extracting: img_align_celeba/197891.jpg \n", + " extracting: img_align_celeba/197892.jpg \n", + " extracting: img_align_celeba/197893.jpg \n", + " extracting: img_align_celeba/197894.jpg \n", + " extracting: img_align_celeba/197895.jpg \n", + " extracting: img_align_celeba/197896.jpg \n", + " extracting: img_align_celeba/197897.jpg \n", + " extracting: img_align_celeba/197898.jpg \n", + " extracting: img_align_celeba/197899.jpg \n", + " extracting: img_align_celeba/197900.jpg \n", + " extracting: img_align_celeba/197901.jpg \n", + " extracting: img_align_celeba/197902.jpg \n", + " extracting: img_align_celeba/197903.jpg \n", + " extracting: img_align_celeba/197904.jpg \n", + " extracting: img_align_celeba/197905.jpg \n", + " extracting: img_align_celeba/197906.jpg \n", + " extracting: img_align_celeba/197907.jpg \n", + " extracting: img_align_celeba/197908.jpg \n", + " extracting: img_align_celeba/197909.jpg \n", + " extracting: img_align_celeba/197910.jpg \n", + " extracting: img_align_celeba/197911.jpg \n", + " extracting: img_align_celeba/197912.jpg \n", + " extracting: img_align_celeba/197913.jpg \n", + " extracting: img_align_celeba/197914.jpg \n", + " extracting: img_align_celeba/197915.jpg \n", + " extracting: img_align_celeba/197916.jpg \n", + " extracting: img_align_celeba/197917.jpg \n", + " extracting: img_align_celeba/197918.jpg \n", + " extracting: img_align_celeba/197919.jpg \n", + " extracting: img_align_celeba/197920.jpg \n", + " extracting: img_align_celeba/197921.jpg \n", + " extracting: img_align_celeba/197922.jpg \n", + " extracting: img_align_celeba/197923.jpg \n", + " extracting: img_align_celeba/197924.jpg \n", + " extracting: img_align_celeba/197925.jpg \n", + " extracting: img_align_celeba/197926.jpg \n", + " extracting: img_align_celeba/197927.jpg \n", + " extracting: img_align_celeba/197928.jpg \n", + " extracting: img_align_celeba/197929.jpg \n", + " extracting: img_align_celeba/197930.jpg \n", + " extracting: img_align_celeba/197931.jpg \n", + " extracting: img_align_celeba/197932.jpg \n", + " extracting: img_align_celeba/197933.jpg \n", + " extracting: img_align_celeba/197934.jpg \n", + " extracting: img_align_celeba/197935.jpg \n", + " extracting: img_align_celeba/197936.jpg \n", + " extracting: img_align_celeba/197937.jpg \n", + " extracting: img_align_celeba/197938.jpg \n", + " extracting: img_align_celeba/197939.jpg \n", + " extracting: img_align_celeba/197940.jpg \n", + " extracting: img_align_celeba/197941.jpg \n", + " extracting: img_align_celeba/197942.jpg \n", + " extracting: img_align_celeba/197943.jpg \n", + " extracting: img_align_celeba/197944.jpg \n", + " extracting: img_align_celeba/197945.jpg \n", + " extracting: img_align_celeba/197946.jpg \n", + " extracting: img_align_celeba/197947.jpg \n", + " extracting: img_align_celeba/197948.jpg \n", + " extracting: img_align_celeba/197949.jpg \n", + " extracting: img_align_celeba/197950.jpg \n", + " extracting: img_align_celeba/197951.jpg \n", + " extracting: img_align_celeba/197952.jpg \n", + " extracting: img_align_celeba/197953.jpg \n", + " extracting: img_align_celeba/197954.jpg \n", + " extracting: img_align_celeba/197955.jpg \n", + " extracting: img_align_celeba/197956.jpg \n", + " extracting: img_align_celeba/197957.jpg \n", + " extracting: img_align_celeba/197958.jpg \n", + " extracting: img_align_celeba/197959.jpg \n", + " extracting: img_align_celeba/197960.jpg \n", + " extracting: img_align_celeba/197961.jpg \n", + " extracting: img_align_celeba/197962.jpg \n", + " extracting: img_align_celeba/197963.jpg \n", + " extracting: img_align_celeba/197964.jpg \n", + " extracting: img_align_celeba/197965.jpg \n", + " extracting: img_align_celeba/197966.jpg \n", + " extracting: img_align_celeba/197967.jpg \n", + " extracting: img_align_celeba/197968.jpg \n", + " extracting: img_align_celeba/197969.jpg \n", + " extracting: img_align_celeba/197970.jpg \n", + " extracting: img_align_celeba/197971.jpg \n", + " extracting: img_align_celeba/197972.jpg \n", + " extracting: img_align_celeba/197973.jpg \n", + " extracting: img_align_celeba/197974.jpg \n", + " extracting: img_align_celeba/197975.jpg \n", + " extracting: img_align_celeba/197976.jpg \n", + " extracting: img_align_celeba/197977.jpg \n", + " extracting: img_align_celeba/197978.jpg \n", + " extracting: img_align_celeba/197979.jpg \n", + " extracting: img_align_celeba/197980.jpg \n", + " extracting: img_align_celeba/197981.jpg \n", + " extracting: img_align_celeba/197982.jpg \n", + " extracting: img_align_celeba/197983.jpg \n", + " extracting: img_align_celeba/197984.jpg \n", + " extracting: img_align_celeba/197985.jpg \n", + " extracting: img_align_celeba/197986.jpg \n", + " extracting: img_align_celeba/197987.jpg \n", + " extracting: img_align_celeba/197988.jpg \n", + " extracting: img_align_celeba/197989.jpg \n", + " extracting: img_align_celeba/197990.jpg \n", + " extracting: img_align_celeba/197991.jpg \n", + " extracting: img_align_celeba/197992.jpg \n", + " extracting: img_align_celeba/197993.jpg \n", + " extracting: img_align_celeba/197994.jpg \n", + " extracting: img_align_celeba/197995.jpg \n", + " extracting: img_align_celeba/197996.jpg \n", + " extracting: img_align_celeba/197997.jpg \n", + " extracting: img_align_celeba/197998.jpg \n", + " extracting: img_align_celeba/197999.jpg \n", + " extracting: img_align_celeba/198000.jpg \n", + " extracting: img_align_celeba/198001.jpg \n", + " extracting: img_align_celeba/198002.jpg \n", + " extracting: img_align_celeba/198003.jpg \n", + " extracting: img_align_celeba/198004.jpg \n", + " extracting: img_align_celeba/198005.jpg \n", + " extracting: img_align_celeba/198006.jpg \n", + " extracting: img_align_celeba/198007.jpg \n", + " extracting: img_align_celeba/198008.jpg \n", + " extracting: img_align_celeba/198009.jpg \n", + " extracting: img_align_celeba/198010.jpg \n", + " extracting: img_align_celeba/198011.jpg \n", + " extracting: img_align_celeba/198012.jpg \n", + " extracting: img_align_celeba/198013.jpg \n", + " extracting: img_align_celeba/198014.jpg \n", + " extracting: img_align_celeba/198015.jpg \n", + " extracting: img_align_celeba/198016.jpg \n", + " extracting: img_align_celeba/198017.jpg \n", + " extracting: img_align_celeba/198018.jpg \n", + " extracting: img_align_celeba/198019.jpg \n", + " extracting: img_align_celeba/198020.jpg \n", + " extracting: img_align_celeba/198021.jpg \n", + " extracting: img_align_celeba/198022.jpg \n", + " extracting: img_align_celeba/198023.jpg \n", + " extracting: img_align_celeba/198024.jpg \n", + " extracting: img_align_celeba/198025.jpg \n", + " extracting: img_align_celeba/198026.jpg \n", + " extracting: img_align_celeba/198027.jpg \n", + " extracting: img_align_celeba/198028.jpg \n", + " extracting: img_align_celeba/198029.jpg \n", + " extracting: img_align_celeba/198030.jpg \n", + " extracting: img_align_celeba/198031.jpg \n", + " extracting: img_align_celeba/198032.jpg \n", + " extracting: img_align_celeba/198033.jpg \n", + " extracting: img_align_celeba/198034.jpg \n", + " extracting: img_align_celeba/198035.jpg \n", + " extracting: img_align_celeba/198036.jpg \n", + " extracting: img_align_celeba/198037.jpg \n", + " extracting: img_align_celeba/198038.jpg \n", + " extracting: img_align_celeba/198039.jpg \n", + " extracting: img_align_celeba/198040.jpg \n", + " extracting: img_align_celeba/198041.jpg \n", + " extracting: img_align_celeba/198042.jpg \n", + " extracting: img_align_celeba/198043.jpg \n", + " extracting: img_align_celeba/198044.jpg \n", + " extracting: img_align_celeba/198045.jpg \n", + " extracting: img_align_celeba/198046.jpg \n", + " extracting: img_align_celeba/198047.jpg \n", + " extracting: img_align_celeba/198048.jpg \n", + " extracting: img_align_celeba/198049.jpg \n", + " extracting: img_align_celeba/198050.jpg \n", + " extracting: img_align_celeba/198051.jpg \n", + " extracting: img_align_celeba/198052.jpg \n", + " extracting: img_align_celeba/198053.jpg \n", + " extracting: img_align_celeba/198054.jpg \n", + " extracting: img_align_celeba/198055.jpg \n", + " extracting: img_align_celeba/198056.jpg \n", + " extracting: img_align_celeba/198057.jpg \n", + " extracting: img_align_celeba/198058.jpg \n", + " extracting: img_align_celeba/198059.jpg \n", + " extracting: img_align_celeba/198060.jpg \n", + " extracting: img_align_celeba/198061.jpg \n", + " extracting: img_align_celeba/198062.jpg \n", + " extracting: img_align_celeba/198063.jpg \n", + " extracting: img_align_celeba/198064.jpg \n", + " extracting: img_align_celeba/198065.jpg \n", + " extracting: img_align_celeba/198066.jpg \n", + " extracting: img_align_celeba/198067.jpg \n", + " extracting: img_align_celeba/198068.jpg \n", + " extracting: img_align_celeba/198069.jpg \n", + " extracting: img_align_celeba/198070.jpg \n", + " extracting: img_align_celeba/198071.jpg \n", + " extracting: img_align_celeba/198072.jpg \n", + " extracting: img_align_celeba/198073.jpg \n", + " extracting: img_align_celeba/198074.jpg \n", + " extracting: img_align_celeba/198075.jpg \n", + " extracting: img_align_celeba/198076.jpg \n", + " extracting: img_align_celeba/198077.jpg \n", + " extracting: img_align_celeba/198078.jpg \n", + " extracting: img_align_celeba/198079.jpg \n", + " extracting: 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img_align_celeba/198800.jpg \n", + " extracting: img_align_celeba/198801.jpg \n", + " extracting: img_align_celeba/198802.jpg \n", + " extracting: img_align_celeba/198803.jpg \n", + " extracting: img_align_celeba/198804.jpg \n", + " extracting: img_align_celeba/198805.jpg \n", + " extracting: img_align_celeba/198806.jpg \n", + " extracting: img_align_celeba/198807.jpg \n", + " extracting: img_align_celeba/198808.jpg \n", + " extracting: img_align_celeba/198809.jpg \n", + " extracting: img_align_celeba/198810.jpg \n", + " extracting: img_align_celeba/198811.jpg \n", + " extracting: img_align_celeba/198812.jpg \n", + " extracting: img_align_celeba/198813.jpg \n", + " extracting: img_align_celeba/198814.jpg \n", + " extracting: img_align_celeba/198815.jpg \n", + " extracting: img_align_celeba/198816.jpg \n", + " extracting: img_align_celeba/198817.jpg \n", + " extracting: img_align_celeba/198818.jpg \n", + " extracting: img_align_celeba/198819.jpg \n", + " extracting: img_align_celeba/198820.jpg \n", + " extracting: img_align_celeba/198821.jpg \n", + " extracting: img_align_celeba/198822.jpg \n", + " extracting: img_align_celeba/198823.jpg \n", + " extracting: img_align_celeba/198824.jpg \n", + " extracting: img_align_celeba/198825.jpg \n", + " extracting: img_align_celeba/198826.jpg \n", + " extracting: img_align_celeba/198827.jpg \n", + " extracting: img_align_celeba/198828.jpg \n", + " extracting: img_align_celeba/198829.jpg \n", + " extracting: img_align_celeba/198830.jpg \n", + " extracting: img_align_celeba/198831.jpg \n", + " extracting: img_align_celeba/198832.jpg \n", + " extracting: img_align_celeba/198833.jpg \n", + " extracting: img_align_celeba/198834.jpg \n", + " extracting: img_align_celeba/198835.jpg \n", + " extracting: img_align_celeba/198836.jpg \n", + " extracting: img_align_celeba/198837.jpg \n", + " extracting: img_align_celeba/198838.jpg \n", + " extracting: img_align_celeba/198839.jpg \n", + " extracting: img_align_celeba/198840.jpg \n", + " extracting: img_align_celeba/198841.jpg \n", + " extracting: img_align_celeba/198842.jpg \n", + " extracting: img_align_celeba/198843.jpg \n", + " extracting: img_align_celeba/198844.jpg \n", + " extracting: img_align_celeba/198845.jpg \n", + " extracting: img_align_celeba/198846.jpg \n", + " extracting: img_align_celeba/198847.jpg \n", + " extracting: img_align_celeba/198848.jpg \n", + " extracting: img_align_celeba/198849.jpg \n", + " extracting: img_align_celeba/198850.jpg \n", + " extracting: img_align_celeba/198851.jpg \n", + " extracting: img_align_celeba/198852.jpg \n", + " extracting: img_align_celeba/198853.jpg \n", + " extracting: img_align_celeba/198854.jpg \n", + " extracting: img_align_celeba/198855.jpg \n", + " extracting: img_align_celeba/198856.jpg \n", + " extracting: img_align_celeba/198857.jpg \n", + " extracting: img_align_celeba/198858.jpg \n", + " extracting: img_align_celeba/198859.jpg \n", + " extracting: 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img_align_celeba/200000.jpg \n", + " extracting: img_align_celeba/200001.jpg \n", + " extracting: img_align_celeba/200002.jpg \n", + " extracting: img_align_celeba/200003.jpg \n", + " extracting: img_align_celeba/200004.jpg \n", + " extracting: img_align_celeba/200005.jpg \n", + " extracting: img_align_celeba/200006.jpg \n", + " extracting: img_align_celeba/200007.jpg \n", + " extracting: img_align_celeba/200008.jpg \n", + " extracting: img_align_celeba/200009.jpg \n", + " extracting: img_align_celeba/200010.jpg \n", + " extracting: img_align_celeba/200011.jpg \n", + " extracting: img_align_celeba/200012.jpg \n", + " extracting: img_align_celeba/200013.jpg \n", + " extracting: img_align_celeba/200014.jpg \n", + " extracting: img_align_celeba/200015.jpg \n", + " extracting: img_align_celeba/200016.jpg \n", + " extracting: img_align_celeba/200017.jpg \n", + " extracting: img_align_celeba/200018.jpg \n", + " extracting: img_align_celeba/200019.jpg \n", + " extracting: 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img_align_celeba/200480.jpg \n", + " extracting: img_align_celeba/200481.jpg \n", + " extracting: img_align_celeba/200482.jpg \n", + " extracting: img_align_celeba/200483.jpg \n", + " extracting: img_align_celeba/200484.jpg \n", + " extracting: img_align_celeba/200485.jpg \n", + " extracting: img_align_celeba/200486.jpg \n", + " extracting: img_align_celeba/200487.jpg \n", + " extracting: img_align_celeba/200488.jpg \n", + " extracting: img_align_celeba/200489.jpg \n", + " extracting: img_align_celeba/200490.jpg \n", + " extracting: img_align_celeba/200491.jpg \n", + " extracting: img_align_celeba/200492.jpg \n", + " extracting: img_align_celeba/200493.jpg \n", + " extracting: img_align_celeba/200494.jpg \n", + " extracting: img_align_celeba/200495.jpg \n", + " extracting: img_align_celeba/200496.jpg \n", + " extracting: img_align_celeba/200497.jpg \n", + " extracting: img_align_celeba/200498.jpg \n", + " extracting: img_align_celeba/200499.jpg \n", + " extracting: 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img_align_celeba/200540.jpg \n", + " extracting: img_align_celeba/200541.jpg \n", + " extracting: img_align_celeba/200542.jpg \n", + " extracting: img_align_celeba/200543.jpg \n", + " extracting: img_align_celeba/200544.jpg \n", + " extracting: img_align_celeba/200545.jpg \n", + " extracting: img_align_celeba/200546.jpg \n", + " extracting: img_align_celeba/200547.jpg \n", + " extracting: img_align_celeba/200548.jpg \n", + " extracting: img_align_celeba/200549.jpg \n", + " extracting: img_align_celeba/200550.jpg \n", + " extracting: img_align_celeba/200551.jpg \n", + " extracting: img_align_celeba/200552.jpg \n", + " extracting: img_align_celeba/200553.jpg \n", + " extracting: img_align_celeba/200554.jpg \n", + " extracting: img_align_celeba/200555.jpg \n", + " extracting: img_align_celeba/200556.jpg \n", + " extracting: img_align_celeba/200557.jpg \n", + " extracting: img_align_celeba/200558.jpg \n", + " extracting: img_align_celeba/200559.jpg \n", + " extracting: 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img_align_celeba/200700.jpg \n", + " extracting: img_align_celeba/200701.jpg \n", + " extracting: img_align_celeba/200702.jpg \n", + " extracting: img_align_celeba/200703.jpg \n", + " extracting: img_align_celeba/200704.jpg \n", + " extracting: img_align_celeba/200705.jpg \n", + " extracting: img_align_celeba/200706.jpg \n", + " extracting: img_align_celeba/200707.jpg \n", + " extracting: img_align_celeba/200708.jpg \n", + " extracting: img_align_celeba/200709.jpg \n", + " extracting: img_align_celeba/200710.jpg \n", + " extracting: img_align_celeba/200711.jpg \n", + " extracting: img_align_celeba/200712.jpg \n", + " extracting: img_align_celeba/200713.jpg \n", + " extracting: img_align_celeba/200714.jpg \n", + " extracting: img_align_celeba/200715.jpg \n", + " extracting: img_align_celeba/200716.jpg \n", + " extracting: img_align_celeba/200717.jpg \n", + " extracting: img_align_celeba/200718.jpg \n", + " extracting: img_align_celeba/200719.jpg \n", + " extracting: img_align_celeba/200720.jpg \n", + " extracting: img_align_celeba/200721.jpg \n", + " extracting: img_align_celeba/200722.jpg \n", + " extracting: img_align_celeba/200723.jpg \n", + " extracting: img_align_celeba/200724.jpg \n", + " extracting: img_align_celeba/200725.jpg \n", + " extracting: img_align_celeba/200726.jpg \n", + " extracting: img_align_celeba/200727.jpg \n", + " extracting: img_align_celeba/200728.jpg \n", + " extracting: img_align_celeba/200729.jpg \n", + " extracting: img_align_celeba/200730.jpg \n", + " extracting: img_align_celeba/200731.jpg \n", + " extracting: img_align_celeba/200732.jpg \n", + " extracting: img_align_celeba/200733.jpg \n", + " extracting: img_align_celeba/200734.jpg \n", + " extracting: img_align_celeba/200735.jpg \n", + " extracting: img_align_celeba/200736.jpg \n", + " extracting: img_align_celeba/200737.jpg \n", + " extracting: img_align_celeba/200738.jpg \n", + " extracting: img_align_celeba/200739.jpg \n", + " extracting: img_align_celeba/200740.jpg \n", + " extracting: img_align_celeba/200741.jpg \n", + " extracting: img_align_celeba/200742.jpg \n", + " extracting: img_align_celeba/200743.jpg \n", + " extracting: img_align_celeba/200744.jpg \n", + " extracting: img_align_celeba/200745.jpg \n", + " extracting: img_align_celeba/200746.jpg \n", + " extracting: img_align_celeba/200747.jpg \n", + " extracting: img_align_celeba/200748.jpg \n", + " extracting: img_align_celeba/200749.jpg \n", + " extracting: img_align_celeba/200750.jpg \n", + " extracting: img_align_celeba/200751.jpg \n", + " extracting: img_align_celeba/200752.jpg \n", + " extracting: img_align_celeba/200753.jpg \n", + " extracting: img_align_celeba/200754.jpg \n", + " extracting: img_align_celeba/200755.jpg \n", + " extracting: img_align_celeba/200756.jpg \n", + " extracting: img_align_celeba/200757.jpg \n", + " extracting: img_align_celeba/200758.jpg \n", + " extracting: img_align_celeba/200759.jpg \n", + " extracting: img_align_celeba/200760.jpg \n", + " extracting: img_align_celeba/200761.jpg \n", + " extracting: img_align_celeba/200762.jpg \n", + " extracting: img_align_celeba/200763.jpg \n", + " extracting: img_align_celeba/200764.jpg \n", + " extracting: img_align_celeba/200765.jpg \n", + " extracting: img_align_celeba/200766.jpg \n", + " extracting: img_align_celeba/200767.jpg \n", + " extracting: img_align_celeba/200768.jpg \n", + " extracting: img_align_celeba/200769.jpg \n", + " extracting: img_align_celeba/200770.jpg \n", + " extracting: img_align_celeba/200771.jpg \n", + " extracting: img_align_celeba/200772.jpg \n", + " extracting: img_align_celeba/200773.jpg \n", + " extracting: img_align_celeba/200774.jpg \n", + " extracting: img_align_celeba/200775.jpg \n", + " extracting: img_align_celeba/200776.jpg \n", + " extracting: img_align_celeba/200777.jpg \n", + " extracting: img_align_celeba/200778.jpg \n", + " extracting: img_align_celeba/200779.jpg \n", + " extracting: img_align_celeba/200780.jpg \n", + " extracting: img_align_celeba/200781.jpg \n", + " extracting: img_align_celeba/200782.jpg \n", + " extracting: img_align_celeba/200783.jpg \n", + " extracting: img_align_celeba/200784.jpg \n", + " extracting: img_align_celeba/200785.jpg \n", + " extracting: img_align_celeba/200786.jpg \n", + " extracting: img_align_celeba/200787.jpg \n", + " extracting: img_align_celeba/200788.jpg \n", + " extracting: img_align_celeba/200789.jpg \n", + " extracting: img_align_celeba/200790.jpg \n", + " extracting: img_align_celeba/200791.jpg \n", + " extracting: img_align_celeba/200792.jpg \n", + " extracting: img_align_celeba/200793.jpg \n", + " extracting: img_align_celeba/200794.jpg \n", + " extracting: img_align_celeba/200795.jpg \n", + " extracting: img_align_celeba/200796.jpg \n", + " extracting: img_align_celeba/200797.jpg \n", + " extracting: img_align_celeba/200798.jpg \n", + " extracting: img_align_celeba/200799.jpg \n", + " extracting: img_align_celeba/200800.jpg \n", + " extracting: img_align_celeba/200801.jpg \n", + " extracting: img_align_celeba/200802.jpg \n", + " extracting: img_align_celeba/200803.jpg \n", + " extracting: img_align_celeba/200804.jpg \n", + " extracting: img_align_celeba/200805.jpg \n", + " extracting: img_align_celeba/200806.jpg \n", + " extracting: img_align_celeba/200807.jpg \n", + " extracting: img_align_celeba/200808.jpg \n", + " extracting: img_align_celeba/200809.jpg \n", + " extracting: img_align_celeba/200810.jpg \n", + " extracting: img_align_celeba/200811.jpg \n", + " extracting: img_align_celeba/200812.jpg \n", + " extracting: img_align_celeba/200813.jpg \n", + " extracting: img_align_celeba/200814.jpg \n", + " extracting: img_align_celeba/200815.jpg \n", + " extracting: img_align_celeba/200816.jpg \n", + " extracting: img_align_celeba/200817.jpg \n", + " extracting: img_align_celeba/200818.jpg \n", + " extracting: img_align_celeba/200819.jpg \n", + " extracting: img_align_celeba/200820.jpg \n", + " extracting: img_align_celeba/200821.jpg \n", + " extracting: img_align_celeba/200822.jpg \n", + " extracting: img_align_celeba/200823.jpg \n", + " extracting: img_align_celeba/200824.jpg \n", + " extracting: img_align_celeba/200825.jpg \n", + " extracting: img_align_celeba/200826.jpg \n", + " extracting: img_align_celeba/200827.jpg \n", + " extracting: img_align_celeba/200828.jpg \n", + " extracting: img_align_celeba/200829.jpg \n", + " extracting: img_align_celeba/200830.jpg \n", + " extracting: img_align_celeba/200831.jpg \n", + " extracting: img_align_celeba/200832.jpg \n", + " extracting: img_align_celeba/200833.jpg \n", + " extracting: img_align_celeba/200834.jpg \n", + " extracting: img_align_celeba/200835.jpg \n", + " extracting: img_align_celeba/200836.jpg \n", + " extracting: img_align_celeba/200837.jpg \n", + " extracting: img_align_celeba/200838.jpg \n", + " extracting: img_align_celeba/200839.jpg \n", + " extracting: img_align_celeba/200840.jpg \n", + " extracting: img_align_celeba/200841.jpg \n", + " extracting: img_align_celeba/200842.jpg \n", + " extracting: img_align_celeba/200843.jpg \n", + " extracting: img_align_celeba/200844.jpg \n", + " extracting: img_align_celeba/200845.jpg \n", + " extracting: img_align_celeba/200846.jpg \n", + " extracting: img_align_celeba/200847.jpg \n", + " extracting: img_align_celeba/200848.jpg \n", + " extracting: img_align_celeba/200849.jpg \n", + " extracting: img_align_celeba/200850.jpg \n", + " extracting: img_align_celeba/200851.jpg \n", + " extracting: img_align_celeba/200852.jpg \n", + " extracting: img_align_celeba/200853.jpg \n", + " extracting: img_align_celeba/200854.jpg \n", + " extracting: img_align_celeba/200855.jpg \n", + " extracting: img_align_celeba/200856.jpg \n", + " extracting: img_align_celeba/200857.jpg \n", + " extracting: img_align_celeba/200858.jpg \n", + " extracting: img_align_celeba/200859.jpg \n", + " extracting: 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img_align_celeba/201600.jpg \n", + " extracting: img_align_celeba/201601.jpg \n", + " extracting: img_align_celeba/201602.jpg \n", + " extracting: img_align_celeba/201603.jpg \n", + " extracting: img_align_celeba/201604.jpg \n", + " extracting: img_align_celeba/201605.jpg \n", + " extracting: img_align_celeba/201606.jpg \n", + " extracting: img_align_celeba/201607.jpg \n", + " extracting: img_align_celeba/201608.jpg \n", + " extracting: img_align_celeba/201609.jpg \n", + " extracting: img_align_celeba/201610.jpg \n", + " extracting: img_align_celeba/201611.jpg \n", + " extracting: img_align_celeba/201612.jpg \n", + " extracting: img_align_celeba/201613.jpg \n", + " extracting: img_align_celeba/201614.jpg \n", + " extracting: img_align_celeba/201615.jpg \n", + " extracting: img_align_celeba/201616.jpg \n", + " extracting: img_align_celeba/201617.jpg \n", + " extracting: img_align_celeba/201618.jpg \n", + " extracting: img_align_celeba/201619.jpg \n", + " extracting: 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img_align_celeba/201920.jpg \n", + " extracting: img_align_celeba/201921.jpg \n", + " extracting: img_align_celeba/201922.jpg \n", + " extracting: img_align_celeba/201923.jpg \n", + " extracting: img_align_celeba/201924.jpg \n", + " extracting: img_align_celeba/201925.jpg \n", + " extracting: img_align_celeba/201926.jpg \n", + " extracting: img_align_celeba/201927.jpg \n", + " extracting: img_align_celeba/201928.jpg \n", + " extracting: img_align_celeba/201929.jpg \n", + " extracting: img_align_celeba/201930.jpg \n", + " extracting: img_align_celeba/201931.jpg \n", + " extracting: img_align_celeba/201932.jpg \n", + " extracting: img_align_celeba/201933.jpg \n", + " extracting: img_align_celeba/201934.jpg \n", + " extracting: img_align_celeba/201935.jpg \n", + " extracting: img_align_celeba/201936.jpg \n", + " extracting: img_align_celeba/201937.jpg \n", + " extracting: img_align_celeba/201938.jpg \n", + " extracting: img_align_celeba/201939.jpg \n", + " extracting: 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img_align_celeba/202020.jpg \n", + " extracting: img_align_celeba/202021.jpg \n", + " extracting: img_align_celeba/202022.jpg \n", + " extracting: img_align_celeba/202023.jpg \n", + " extracting: img_align_celeba/202024.jpg \n", + " extracting: img_align_celeba/202025.jpg \n", + " extracting: img_align_celeba/202026.jpg \n", + " extracting: img_align_celeba/202027.jpg \n", + " extracting: img_align_celeba/202028.jpg \n", + " extracting: img_align_celeba/202029.jpg \n", + " extracting: img_align_celeba/202030.jpg \n", + " extracting: img_align_celeba/202031.jpg \n", + " extracting: img_align_celeba/202032.jpg \n", + " extracting: img_align_celeba/202033.jpg \n", + " extracting: img_align_celeba/202034.jpg \n", + " extracting: img_align_celeba/202035.jpg \n", + " extracting: img_align_celeba/202036.jpg \n", + " extracting: img_align_celeba/202037.jpg \n", + " extracting: img_align_celeba/202038.jpg \n", + " extracting: img_align_celeba/202039.jpg \n", + " extracting: 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img_align_celeba/202420.jpg \n", + " extracting: img_align_celeba/202421.jpg \n", + " extracting: img_align_celeba/202422.jpg \n", + " extracting: img_align_celeba/202423.jpg \n", + " extracting: img_align_celeba/202424.jpg \n", + " extracting: img_align_celeba/202425.jpg \n", + " extracting: img_align_celeba/202426.jpg \n", + " extracting: img_align_celeba/202427.jpg \n", + " extracting: img_align_celeba/202428.jpg \n", + " extracting: img_align_celeba/202429.jpg \n", + " extracting: img_align_celeba/202430.jpg \n", + " extracting: img_align_celeba/202431.jpg \n", + " extracting: img_align_celeba/202432.jpg \n", + " extracting: img_align_celeba/202433.jpg \n", + " extracting: img_align_celeba/202434.jpg \n", + " extracting: img_align_celeba/202435.jpg \n", + " extracting: img_align_celeba/202436.jpg \n", + " extracting: img_align_celeba/202437.jpg \n", + " extracting: img_align_celeba/202438.jpg \n", + " extracting: img_align_celeba/202439.jpg \n", + " extracting: 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+ { + "cell_type": "code", + "metadata": { + "id": "CtMmYO4MAHkQ" + }, + "source": [ + "dataset = ImageDataset(\"img_align_celeba\", MAX_IMAGES, transform, NOISE_SIZE, LATENT_DIMS)\n", + "dataloader = DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, num_workers=2)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Fgm-V9uz6lbS" + }, + "source": [ + "## Models\n", + "We'll need to set up models for our Generator (creates images) and Discriminator (distinguishes between real and fake)\n", + "\n", + "To make our lives easier:\n", + "* The input and output sizes are specified\n", + "* Latent (noise) dimentions can be manually set\n", + "* The number of layers and their structure can be set through a list of hidden dimentions\n", + " * Each number represents the number of output layers for each convolution\n", + "* Ensures the first convolution is always non-strided and last has small/no padding\n", + " * Otherwise network will struggle to learn\n", + "\n", + "Remember that the structure of a generator is a decoder (increasing size) and discriminantor an encoder (decreasing size)" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "2LNa1cua1ue0" + }, + "source": [ + "class Generator(nn.Module):\n", + " def __init__(self, latent_dims=128, hidden_dims=[512, 256, 128, 64, 32]):\n", + " super().__init__()\n", + "\n", + " in_dims = [latent_dims] + hidden_dims[:-1]\n", + " self.main = nn.Sequential(*starmap(self.block, zip(in_dims, hidden_dims)))\n", + " \n", + " self.final_layer = nn.Tanh()\n", + " \n", + " def block(self, in_channels, out_channels):\n", + " if in_channels == 128:\n", + " return nn.Sequential(\n", + " nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, bias=False),\n", + " nn.BatchNorm2d(out_channels),\n", + " nn.ReLU(),\n", + " )\n", + " elif out_channels == 3:\n", + " return nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False)\n", + " else:\n", + " return nn.Sequential(\n", + " nn.ConvTranspose2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False),\n", + " nn.BatchNorm2d(out_channels),\n", + " nn.ReLU(),\n", + " )\n", + "\n", + " def forward(self, input):\n", + " return self.final_layer(self.main(input))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "s6BUNHkG14w1" + }, + "source": [ + "class Discriminator(nn.Module):\n", + " def __init__(self, channels=3, hidden_dims=[32, 64, 128, 256, 512]):\n", + " super().__init__()\n", + " \n", + " in_dims = [channels] + hidden_dims[:-1]\n", + " self.main = nn.Sequential(*starmap(self.block, zip(in_dims, hidden_dims)))\n", + " \n", + " def block(self, in_channels, out_channels):\n", + " # Custom final layer values for better performance\n", + " if out_channels != 1:\n", + " return nn.Sequential(\n", + " nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1, bias=False),\n", + " nn.BatchNorm2d(out_channels),\n", + " nn.LeakyReLU(0.2)\n", + " )\n", + " else:\n", + " return nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=1, padding=0, bias=False)\n", + "\n", + " def forward(self, input):\n", + " return self.main(input)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z47ACv2q-zmB" + }, + "source": [ + "**OUR MODELS**:\n", + "\n", + "Here we create a generator and discriminator and view its architecture.\n", + "\n", + "Take note of three things:\n", + "* The increasing output sizes of the generator and decreasing of discriminator\n", + "* Initial input vs final output sizes\n", + "* We initialise weights in a specific way\n", + "\n", + "![image.png](https://miro.medium.com/max/450/1*q_YbvPUH42qGHQZC371YvQ.jpeg)\n", + "\n", + "*Meme Source: https://medium.com/nybles/understanding-machine-learning-through-memes-4580b67527bf*\n", + "\n", + "Try to think about how these would change if you were to:\n", + "* Increase/decrease the input images sizes (resolution)\n", + "* Increase/decrease the number of input layers" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "pY12nzEu-xxR" + }, + "source": [ + "generator = Generator(LATENT_DIMS, GENERATOR_HIDDEN_DIMS).to(DEVICE)\n", + "discriminator = Discriminator(CHANNELS, DISCRIMINATOR_HIDDEN_DIMS).to(DEVICE)\n", + "\n", + "generator = generator.apply(weights_init)\n", + "discriminator = discriminator.apply(weights_init) # comment out for ResNet" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "uVWvAjHSGVFK", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "01ea47ae-337b-412d-a260-bc56bee8ea73" + }, + "source": [ + "generator" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Generator(\n", + " (main): Sequential(\n", + " (0): Sequential(\n", + " (0): ConvTranspose2d(128, 800, kernel_size=(4, 4), stride=(1, 1), bias=False)\n", + " (1): BatchNorm2d(800, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " )\n", + " (1): Sequential(\n", + " (0): ConvTranspose2d(800, 400, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(400, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " )\n", + " (2): Sequential(\n", + " (0): ConvTranspose2d(400, 200, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(200, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " )\n", + " (3): Sequential(\n", + " (0): ConvTranspose2d(200, 100, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(100, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): ReLU()\n", + " )\n", + " (4): ConvTranspose2d(100, 3, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " )\n", + " (final_layer): Tanh()\n", + ")" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "3niwJxJWFtl7", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3153264a-c9c7-4cff-f7a2-6579c750e20b" + }, + "source": [ + "summary(generator, (LATENT_DIMS, NOISE_SIZE, NOISE_SIZE))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " ConvTranspose2d-1 [-1, 800, 4, 4] 1,638,400\n", + " BatchNorm2d-2 [-1, 800, 4, 4] 1,600\n", + " ReLU-3 [-1, 800, 4, 4] 0\n", + " ConvTranspose2d-4 [-1, 400, 8, 8] 5,120,000\n", + " BatchNorm2d-5 [-1, 400, 8, 8] 800\n", + " ReLU-6 [-1, 400, 8, 8] 0\n", + " ConvTranspose2d-7 [-1, 200, 16, 16] 1,280,000\n", + " BatchNorm2d-8 [-1, 200, 16, 16] 400\n", + " ReLU-9 [-1, 200, 16, 16] 0\n", + " ConvTranspose2d-10 [-1, 100, 32, 32] 320,000\n", + " BatchNorm2d-11 [-1, 100, 32, 32] 200\n", + " ReLU-12 [-1, 100, 32, 32] 0\n", + " ConvTranspose2d-13 [-1, 3, 64, 64] 4,800\n", + " Tanh-14 [-1, 3, 64, 64] 0\n", + "================================================================\n", + "Total params: 8,366,200\n", + "Trainable params: 8,366,200\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.00\n", + "Forward/backward pass size (MB): 4.58\n", + "Params size (MB): 31.91\n", + "Estimated Total Size (MB): 36.50\n", + "----------------------------------------------------------------\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wi3cTr2QGeRb", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "80c7ea9e-d419-47be-ef0f-04a3e9fd084c" + }, + "source": [ + "discriminator" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Discriminator(\n", + " (main): Sequential(\n", + " (0): Sequential(\n", + " (0): Conv2d(3, 64, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2)\n", + " )\n", + " (1): Sequential(\n", + " (0): Conv2d(64, 128, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2)\n", + " )\n", + " (2): Sequential(\n", + " (0): Conv2d(128, 256, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2)\n", + " )\n", + " (3): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (2): LeakyReLU(negative_slope=0.2)\n", + " )\n", + " (4): Conv2d(512, 1, kernel_size=(4, 4), stride=(1, 1), bias=False)\n", + " )\n", + ")" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9MjlSVzqFvBl", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "f85dd314-7873-407d-be9f-66ccb2f91044" + }, + "source": [ + "summary(discriminator, (CHANNELS, SIZE, SIZE))" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 64, 32, 32] 3,072\n", + " BatchNorm2d-2 [-1, 64, 32, 32] 128\n", + " LeakyReLU-3 [-1, 64, 32, 32] 0\n", + " Conv2d-4 [-1, 128, 16, 16] 131,072\n", + " BatchNorm2d-5 [-1, 128, 16, 16] 256\n", + " LeakyReLU-6 [-1, 128, 16, 16] 0\n", + " Conv2d-7 [-1, 256, 8, 8] 524,288\n", + " BatchNorm2d-8 [-1, 256, 8, 8] 512\n", + " LeakyReLU-9 [-1, 256, 8, 8] 0\n", + " Conv2d-10 [-1, 512, 4, 4] 2,097,152\n", + " BatchNorm2d-11 [-1, 512, 4, 4] 1,024\n", + " LeakyReLU-12 [-1, 512, 4, 4] 0\n", + " Conv2d-13 [-1, 1, 1, 1] 8,192\n", + "================================================================\n", + "Total params: 2,765,696\n", + "Trainable params: 2,765,696\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.05\n", + "Forward/backward pass size (MB): 2.81\n", + "Params size (MB): 10.55\n", + "Estimated Total Size (MB): 13.41\n", + "----------------------------------------------------------------\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "CRCLrsNN8Yi7" + }, + "source": [ + "## Training\n", + "Next we'll need to train the networks.\n", + "Remember that the discriminator's aim is to tell apart real from fake images whilst the generators to create counterfit images which pass of as real.\n", + "\n", + "\n", + "**Note: GANs take a long, long time to train - take a look at the results above whilst you're waiting and consider what it really means!**\n", + "\n", + "\n", + "**Loss functions show the following:**\n", + "\n", + "**Generator**:\n", + "* Adverserial Loss - probability fake images are real\n", + "\n", + "**Discriminator**:\n", + "* Real Loss - probability real images are real\n", + "* Fake Loss - probability fake images are fake\n", + "\n", + "The trainer functions are grouped into a single class" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "Xdv88Wlm1n7P" + }, + "source": [ + "class GANTrainer():\n", + " def __init__(self, generator, discriminator, generator_optimiser, discriminator_optimiser):\n", + " self.generator, self.discriminator = generator, discriminator\n", + " self.generator_optimiser, self.discriminator_optimiser = generator_optimiser, discriminator_optimiser\n", + "\n", + " self.criterion = nn.BCEWithLogitsLoss()\n", + "\n", + " def train_generator(self, noise, imgs, real_label, fake_label):\n", + " self.generator.train()\n", + " self.generator_optimiser.zero_grad()\n", + "\n", + " fake_preds = self.discriminator(self.generator(noise)).view(-1)\n", + " generator_loss = self.criterion(fake_preds, real_label) # pass off fake generations as real\n", + "\n", + " generator_loss.backward()\n", + " self.generator_optimiser.step()\n", + "\n", + " return generator_loss\n", + "\n", + " def train_discriminator(self, noise, imgs, real_label, fake_label):\n", + " self.discriminator.train()\n", + " self.discriminator_optimiser.zero_grad()\n", + "\n", + " real_preds, fake_preds = self.discriminator(imgs).view(-1), self.discriminator(self.generator(noise).detach()).view(-1)\n", + " real_loss, fake_loss = self.criterion(real_preds, real_label), self.criterion(fake_preds, fake_label)\n", + "\n", + " (real_loss + fake_loss).backward()\n", + " self.discriminator_optimiser.step()\n", + "\n", + " return real_loss, fake_loss" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "9lmZgcpS_1_4" + }, + "source": [ + "We now need to create our model and its optimisers" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "PUAYMcDK-dC7" + }, + "source": [ + "generator_optimiser = optim.Adam(generator.parameters(), lr=GENERATOR_LR, betas=(0.5, 0.999))\n", + "discriminator_optimiser = optim.Adam(discriminator.parameters(), lr=DISCRIMINATOR_LR, betas=(0.5, 0.999))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rtHkL3_T_faY" + }, + "source": [ + "gan_trainer = GANTrainer(generator, discriminator, generator_optimiser, discriminator_optimiser)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "mEEFsupwVNqQ" + }, + "source": [ + "denormalise = transforms.Compose([\n", + " transforms.Normalize(mean=[0., 0., 0.], std=[1/0.229, 1/0.224, 1/0.225]),\n", + " transforms.Normalize(mean=[-0.485, -0.456, -0.406], std=[1., 1., 1.])\n", + "])\n", + "generations = lambda num_samples : denormalise(gan_trainer.generator(torch.randn(num_samples, *dataset[0][0].shape, device=DEVICE)))\n", + "wandb_images = lambda images : {\"generations\": [wandb.Image(image) for image in images]}" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "MsrWsYEdF7OS" + }, + "source": [ + "# LOG SAMPLE IMAGES BEFORE TRAINING STARTS\n", + "wandb.log(wandb_images(generations(5)))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "yxcN0noE_zgA" + }, + "source": [ + "for epoch in trange(EPOCHS):\n", + " for index, (noise, imgs) in enumerate(dataloader):\n", + " # Labels\n", + " real_label = torch.full((imgs.size(0),), 1., dtype=torch.float, device=DEVICE)\n", + " fake_label = torch.full((imgs.size(0),), 0., dtype=torch.float, device=DEVICE)\n", + "\n", + " noise, imgs = noise.to(DEVICE), imgs.to(DEVICE)\n", + " \n", + " real_loss, fake_loss = gan_trainer.train_discriminator(noise, imgs, real_label, fake_label)\n", + " generator_loss = gan_trainer.train_generator(noise, imgs, real_label, fake_label)\n", + " \n", + " # Log Stats\n", + " wandb.log({\n", + " \"real_loss\": real_loss, \"fake_loss\": fake_loss,\n", + " \"generator_loss\": generator_loss\n", + " })\n", + "\n", + " # LOG SAMPLE IMAGES AFTER LOG_IMAGES_EVERY STEPS\n", + " if index % LOG_IMAGES_EVERY == 0: wandb.log(wandb_images(generations(5)))\n", + " \n", + " # LOG SAMPLE IMAGES AFTER EACH EPOCH\n", + " wandb.log(wandb_images(generations(5)))" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "GWEtZNtjsxOE" + }, + "source": [], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/2023_DLrecruits_Workshops/Workshop5_GAN_fastAI.ipynb b/2023_DLrecruits_Workshops/Workshop5_GAN_fastAI.ipynb new file mode 100644 index 0000000..198cace --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop5_GAN_fastAI.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["# TO DO\n","## Download file from https://drive.google.com/drive/folders/1ryQu-VBk5Hw77f_Ep7QH0exbVw4p7xtr?usp=share_link\n","## Upload to your drive \n","\n"],"metadata":{"id":"ei5yxl5OVKcR"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xOpaKMaA3h3R","outputId":"c426b28c-a67a-43c6-8603-81a75c844a51"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m55.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["%matplotlib inline\n","!pip install jmd_imagescraper --quiet\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ua_X_4_o3h3T"},"outputs":[],"source":["from fastai.vision.all import *\n","from fastai.vision.gan import *"]},{"cell_type":"code","source":["from google.colab import drive\n","import os\n","drive.mount('/content/drive')\n","os.getcwd()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":72},"id":"dIK4eEAhQc_-","outputId":"e243323a-971f-4cd3-dfcd-1d2ca5f938ae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]},{"output_type":"execute_result","data":{"text/plain":["'/content'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["os.chdir('/content/drive/MyDrive')\n"],"metadata":{"id":"kUfPmmFnSarm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ytmfvOJN3h3U"},"source":["## data"]},{"cell_type":"markdown","metadata":{"id":"7ga8Jz6i3h3U"},"source":["For this lesson, we'll be using the bedrooms from the [LSUN dataset](http://lsun.cs.princeton.edu/2017/). The full dataset is a bit too large so we'll use a sample from [kaggle](https://www.kaggle.com/jhoward/lsun_bedroom).\n","Alternatively you can create your own data or use a different dataset PET"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qC_5gT-E3h3V","outputId":"c8d2988a-b8bf-48e9-c481-70b1ca6c93d8"},"outputs":[{"output_type":"stream","name":"stdout","text":["using ROOM\n"]}],"source":["option = 'default' # chose between default - room or custom or PET\n","prompt = \"lions\" # for custom only - specify what to search for \n","if option == 'custom':\n"," num_imgs = 10\n"," folder_name = \"folder_name\"\n"," from jmd_imagescraper.core import * # dont't worry, it's designed to work with import *\n"," from pathlib import Path\n"," root = Path().cwd()/\"images\"/folder_name\n"," duckduckgo_search(root, folder_name, prompt , max_results=num_imgs)\n"," path = root # use custom data\n"," print('using CUSTOM')\n","elif option == 'pet':\n"," print('using PET')\n"," path = untar_data(URLs.PETS)/'images'\n","else:\n"," path = untar_data(URLs.LSUN_BEDROOMS)\n"," print('using ROOM')\n"]},{"cell_type":"markdown","metadata":{"id":"-fkStgH-3h3V"},"source":["We then grab all the images in the folder with the data block API. We don't create a validation set here for reasons we'll explain later. It consists of random noise of size 100 by default (can be changed if you replace `generate_noise` by `partial(generate_noise, size=...)`) as inputs and the images of bedrooms as targets."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D7XuKyZw3h3V"},"outputs":[],"source":["def get_dls(bs, size):\n"," dblock = DataBlock(blocks = (TransformBlock, ImageBlock),\n"," get_x = generate_noise,\n"," get_items = get_image_files,\n"," splitter = IndexSplitter([]),\n"," item_tfms=Resize(size, method=ResizeMethod.Crop),\n"," batch_tfms = Normalize.from_stats(torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])))\n"," return dblock.dataloaders(path, path=path, bs=bs)"]},{"cell_type":"markdown","metadata":{"id":"VPJ4pJsh3h3W"},"source":["We'll begin with a small size since GANs take a lot of time to train."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v0ER9pGH3h3W"},"outputs":[],"source":["dls = get_dls(16, 64)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"RqijCiXH3h3W","outputId":"91b54603-d286-4318-9493-578bac56e3ca"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["dls.show_batch(max_n=16)"]},{"cell_type":"markdown","metadata":{"id":"paPGfB7R3h3X"},"source":["## Models"]},{"cell_type":"markdown","metadata":{"id":"1cHF6cUf3h3X"},"source":["GAN stands for [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) and were invented by Ian Goodfellow. The concept is that we will train two models at the same time: a generator and a critic. The generator will try to make new images similar to the ones in our dataset, and the critic will try to classify real images from the ones the generator does. The generator returns images, the critic a single number (usually 0. for fake images and 1. for real ones).\n","\n","We train them against each other in the sense that at each step (more or less), we:\n","1. Freeze the generator and train the critic for one step by:\n"," - getting one batch of true images (let's call that `real`)\n"," - generating one batch of fake images (let's call that `fake`)\n"," - have the critic evaluate each batch and compute a loss function from that; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the critic with the gradients of this loss\n"," \n"," \n","2. Freeze the critic and train the generator for one step by:\n"," - generating one batch of fake images\n"," - evaluate the critic on it\n"," - return a loss that rewards positively the critic thinking those are real images; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the generator with the gradients of this loss\n"," \n","Here, we'll use the [Wassertein GAN](https://arxiv.org/pdf/1701.07875.pdf)."]},{"cell_type":"markdown","metadata":{"id":"J7WYvl_73h3X"},"source":["We create a generator and a critic that we pass to `gan_learner`. The noise_size is the size of the random vector from which our generator creates images."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uyK-JRTY3h3X"},"outputs":[],"source":["generator = basic_generator(64, n_channels=3, n_extra_layers=1)\n","critic = basic_critic (64, n_channels=3, n_extra_layers=1, act_cls=partial(nn.LeakyReLU, negative_slope=0.2))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QiaRFdQz3h3X","outputId":"965ad1a4-bd01-4e8f-be29-59c71d57522e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":54}],"source":["learn = GANLearner.wgan(dls, generator, critic, opt_func = partial(Adam, mom=0.))\n","try:\n"," learn.load(os.getcwd()+'/save_folder/roomGAN')\n"," learn.gan_trainer.switch(gen_mode=True)\n"," learn.show_results(max_n=16, figsize=(8,8), ds_idx=0)\n","except:\n"," print('Could not load - have you upload data to your drive ?')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iIspfJMH3h3Y"},"outputs":[],"source":["learn.recorder.train_metrics=True\n","learn.recorder.valid_metrics=False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M-WsewJG3h3Y"},"outputs":[],"source":["# Start training - take quite long though so feel free to test it at home\n","learn.fit(3, 2e-4, wd=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":590},"id":"N96ku9n83h3Y","outputId":"2468d68a-09c7-41b1-ea55-a559c6db9c7a"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (gen_mode) that exists in the learner. Use `self.learn.gen_mode` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n","/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (generator) that exists in the learner. Use `self.learn.generator` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n","/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (critic) that exists in the learner. Use `self.learn.critic` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["learn.gan_trainer.switch(gen_mode=True)\n","learn.show_results(max_n=16, figsize=(8,8), ds_idx=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PMZe7oFv3h3Y","outputId":"e76eb528-6490-400a-d024-7598994eaa22"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Path('/content/roomGAN.pth')"]},"metadata":{},"execution_count":36}],"source":["learn.save(os.getcwd()+'/save_folder/roomGAN')\n"]},{"cell_type":"code","source":[],"metadata":{"id":"1orIPvQKUyv_"},"execution_count":null,"outputs":[]}],"metadata":{"jupytext":{"split_at_heading":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"colab":{"provenance":[]},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/2023_DLrecruits_Workshops/Workshop5_stable_diffusion_huggingface.ipynb b/2023_DLrecruits_Workshops/Workshop5_stable_diffusion_huggingface.ipynb new file mode 100644 index 0000000..0088c10 --- /dev/null +++ b/2023_DLrecruits_Workshops/Workshop5_stable_diffusion_huggingface.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"DfjV7Y-h-2lg"},"source":["# Stable Diffusion with ๐Ÿค— Diffusers"]},{"cell_type":"markdown","metadata":{"id":"4oTwQWHI-2li"},"source":["**Pedro Cuenca, Patrick von Platen, Suraj Patil, Jeremy Howard**\n","\n","Chances are you'll have seen examples in Twitter (and elsewhere) of images generated by typing a short description of the scene you want to create. This is the culmination of years of work in generative models. This notebook introduces Stable Diffusion, the highest-quality open source text to image model as of now. It's also small enough to run in consumer GPUs rather than in a datacenter. We use the ๐Ÿค— Hugging Face [๐Ÿงจ Diffusers library](https://github.com/huggingface/diffusers), which is currently our recommended library for using diffusion models.\n","\n","As we'll see during the course, understanding state-of-the-art generative models requires a deep understanding of many of the fundamental blocks in modern machine learning models. This notebook shows what Stable Diffusion can do and a glimpse of its main components.\n","\n","_If you open this notebook in Colab, or if you get type errors when generating your first image, please uncomment and run the following cell._"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iqGCdykh-2ln","executionInfo":{"status":"ok","timestamp":1677419412832,"user_tz":-660,"elapsed":23401,"user":{"displayName":"Jason Toskov","userId":"06095288519589060777"}},"outputId":"c1dbfe3b-5d4f-4229-d5c6-f77aedc6d788"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m716.4/716.4 KB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m55.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["!pip install -Uq diffusers transformers fastcore"]},{"cell_type":"markdown","metadata":{"id":"CSu50C8P-2lo"},"source":["## Using Stable Diffusion"]},{"cell_type":"markdown","metadata":{"id":"orO6_tmE-2lo"},"source":["To run Stable Diffusion on your computer you have to accept the model license. It's an open CreativeML OpenRail-M license that claims no rights on the outputs you generate and prohibits you from deliberately producing illegal or harmful content. The [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) provides more details. If you do accept the license, you need to be a registered user in ๐Ÿค— Hugging Face Hub and use an access token for the code to work. You have two options to provide your access token:\n","\n","* Use the `huggingface-cli login` command-line tool in your terminal and paste your token when prompted. It will be saved in a file in your computer.\n","* Or use `notebook_login()` in a notebook, which does the same thing."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C96Iea9l-2lo"},"outputs":[],"source":["import logging\n","from pathlib import Path\n","\n","import matplotlib.pyplot as plt\n","import torch\n","from diffusers import StableDiffusionPipeline\n","from fastcore.all import concat\n","from huggingface_hub import notebook_login\n","from PIL import Image\n","\n","logging.disable(logging.WARNING)\n","\n","torch.manual_seed(1)\n","if not (Path.home()/'.huggingface'/'token').exists(): notebook_login()"]},{"cell_type":"markdown","metadata":{"id":"sueEewRD-2lo"},"source":["### Stable Diffusion Pipeline"]},{"cell_type":"markdown","metadata":{"id":"gcJwt1I8-2lp"},"source":["[`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline) is an end-to-end [diffusion inference pipeline](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion) that allows you to start generating images with just a few lines of code. Many Hugging Face libraries (along with other libraries such as scikit-learn) use the concept of a \"pipeline\" to indicate a sequence of steps that when combined complete some task. We'll look at the individual steps of the pipeline later -- for now though, let's just use it to see what it can do.\n","\n","When we say \"inference\" we're referring to using an existing model to generate samples (in this case, images), as opposed to \"training\" (or fine-tuning) models using new data.\n","\n","We use [`from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) to create the pipeline and download the pretrained weights. We indicate that we want to use the `fp16` (half-precision) version of the weights, and we tell `diffusers` to expect the weights in that format. This allows us to perform much faster inference with almost no discernible difference in quality. The string passed to `from_pretrained` in this case (`CompVis/stable-diffusion-v1-4`) is the repo id of a pretrained pipeline hosted on [Hugging Face Hub](https://huggingface.co/models); it can also be a path to a directory containing pipeline weights. The weights for all the models in the pipeline will be downloaded and cached the first time you run this cell."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fBHSM2PX-2lp"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", revision=\"fp16\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"ZJ0FOrGp-2lq"},"source":["The weights are cached in your home directory by default."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fKRh9yIt-2lq"},"outputs":[],"source":["!ls ~/.cache/huggingface/diffusers/"]},{"cell_type":"markdown","metadata":{"id":"yqpUDcrx-2lr"},"source":["We are now ready to use the pipeline to start creating images."]},{"cell_type":"markdown","metadata":{"id":"zFpQZHxd-2lr"},"source":["If your GPU is not big enough to use `pipe`, run `pipe.enable_attention_slicing()` \n","As described in the docs: \n","> When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MRZBK5Fn-2lr"},"outputs":[],"source":["#pipe.enable_attention_slicing()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_ZavhNiP-2lr"},"outputs":[],"source":["prompt = \"a photograph of an astronaut riding a horse\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oEHaovk5-2ls"},"outputs":[],"source":["pipe(prompt).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6FQjlD2w-2ls"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt).images[0]"]},{"cell_type":"markdown","metadata":{"id":"nlQOUWQn-2ls"},"source":["You will have noticed that running the pipeline shows a progress bar with a certain number of steps. This is because Stable Diffusion is based on a progressive denoising algorithm that is able to create a convincing image starting from pure random noise. Models in this family are known as _diffusion models_. Here's an example of the process (from random noise at top to progressively improved images towards the bottom) of a model drawing handwritten digits, which we'll build from scratch ourselves later in the course."]},{"cell_type":"markdown","metadata":{"id":"9lD_G4uu-2ls"},"source":["![image.png](attachment:image.png)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CXhiiqgR-2ls"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt, num_inference_steps=3).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yv5BTAwq-2lt"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt, num_inference_steps=16).images[0]"]},{"cell_type":"markdown","metadata":{"id":"Hd68HoT7-2lt"},"source":["### Classifier-Free Guidance"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bYOW1Dvr-2lt"},"outputs":[],"source":["def image_grid(imgs, rows, cols):\n"," w,h = imgs[0].size\n"," grid = Image.new('RGB', size=(cols*w, rows*h))\n"," for i, img in enumerate(imgs): grid.paste(img, box=(i%cols*w, i//cols*h))\n"," return grid"]},{"cell_type":"markdown","metadata":{"id":"fGiauPct-2lt"},"source":["_Classifier-Free Guidance_ is a method to increase the adherence of the output to the conditioning signal we used (the text).\n","\n","Roughly speaking, the larger the guidance the more the model tries to represent the text prompt. However, large values tend to produce less diversity. The default is `7.5`, which represents a good compromise between variety and fidelity. This [blog post](https://benanne.github.io/2022/05/26/guidance.html) goes into deeper details on how it works.\n","\n","We can generate multiple images for the same prompt by simply passing a list of prompts instead of a string."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lu9vqctw-2lt"},"outputs":[],"source":["num_rows,num_cols = 4,4\n","prompts = [prompt] * num_cols"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"heuqecm3-2lu"},"outputs":[],"source":["images = concat(pipe(prompts, guidance_scale=g).images for g in [1.1,3,7,14])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-rL5bT51-2lu"},"outputs":[],"source":["image_grid(images, rows=num_rows, cols=num_cols)"]},{"cell_type":"markdown","metadata":{"id":"Cy4D8-aN-2lu"},"source":["### Negative prompts"]},{"cell_type":"markdown","metadata":{"id":"sikhSpRD-2lu"},"source":["_Negative prompting_ refers to the use of another prompt (instead of a completely unconditioned generation), and scaling the difference between generations of that prompt and the conditioned generation."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MhT8-2SH-2ly"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Labrador in the style of Vermeer\"\n","pipe(prompt).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V0RMD3h0-2lz"},"outputs":[],"source":["torch.manual_seed(1000)\n","pipe(prompt, negative_prompt=\"blue\").images[0]"]},{"cell_type":"markdown","metadata":{"id":"wCKwoIwe-2lz"},"source":["By using the negative prompt we move more towards the direction of the positive prompt, effectively reducing the importance of the negative prompt in our composition."]},{"cell_type":"markdown","metadata":{"id":"0adEmVZx-2lz"},"source":["### Image to Image"]},{"cell_type":"markdown","metadata":{"id":"0o2ix3R0-2l0"},"source":["Even though Stable Diffusion was trained to generate images, and optionally drive the generation using text conditioning, we can use the raw image diffusion process for other tasks.\n","\n","For example, instead of starting from pure noise, we can start from an image an add a certain amount of noise to it. We are replacing the initial steps of the denoising and pretending our image is what the algorithm came up with. Then we continue the diffusion process from that state as usual.\n","\n","This usually preserves the composition although details may change a lot. It's great for sketches!"]},{"cell_type":"markdown","metadata":{"id":"pFXfHiY5-2l0"},"source":["These operations (provide an initial image, add some noise to it and run diffusion from there) can be automatically performed by a special image to image pipeline: `StableDiffusionImg2ImgPipeline`. This is the source code for its [`__call__` method](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L124)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MO-N6nwq-2l0"},"outputs":[],"source":["from diffusers import StableDiffusionImg2ImgPipeline\n","from fastdownload import FastDownload"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KkJXDISU-2l0"},"outputs":[],"source":["pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n"," \"CompVis/stable-diffusion-v1-4\",\n"," revision=\"fp16\",\n"," torch_dtype=torch.float16,\n",").to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"xtiRYn5m-2l1"},"source":["We'll use as an example the following sketch created by [user VigilanteRogue81](https://huggingface.co/spaces/huggingface-projects/diffuse-the-rest/discussions/204)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9f7IZQDo-2l1"},"outputs":[],"source":["p = FastDownload().download('https://s3.amazonaws.com/moonup/production/uploads/1664665907257-noauth.png')\n","init_image = Image.open(p).convert(\"RGB\")\n","init_image"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aCQ_lWbi-2l1"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Wolf howling at the moon, photorealistic 4K\"\n","images = pipe(prompt=prompt, num_images_per_prompt=3, init_image=init_image, strength=0.8, num_inference_steps=50).images\n","image_grid(images, rows=1, cols=3)"]},{"cell_type":"markdown","metadata":{"id":"quljDW8w-2l1"},"source":["When we get a composition we like we can use it as the next seed for another prompt and further change the results. For example, let's take the third image above and try to use it to generate something in the style of Van Gogh."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GOmmR-2X-2l2"},"outputs":[],"source":["init_image = images[2]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gd_UCTEm-2l2"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Oil painting of wolf howling at the moon by Van Gogh\"\n","images = pipe(prompt=prompt, num_images_per_prompt=3, init_image=init_image, strength=1, num_inference_steps=70).images\n","image_grid(images, rows=1, cols=3)"]},{"cell_type":"markdown","metadata":{"id":"VzPjopxD-2l2"},"source":["Creative people use different tools in a process of iterative refinement to come up with the ideas they have in mind. Here's a [list with some suggestions](https://github.com/fastai/diffusion-nbs/blob/43a090286e5742f807d4ff58524c02a1888b3004/suggested_tools.md) to get started. "]},{"cell_type":"markdown","metadata":{"id":"bcBRmlzO-2l2"},"source":["### Fine-tuning"]},{"cell_type":"markdown","metadata":{"id":"NqN5LylJ-2l2"},"source":["[How we made the text-to-pokemon model at Lambda](https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/)"]},{"cell_type":"markdown","metadata":{"id":"hc_-Mtcc-2l2"},"source":["![](https://lambdalabs.com/blog/content/images/2022/09/image.png)\n","\n","Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty"]},{"cell_type":"markdown","metadata":{"id":"PwrC3obP-2l3"},"source":["### Textual Inversion"]},{"cell_type":"markdown","metadata":{"id":"Wbb_reim-2l3"},"source":["Textual inversion is a process where you can quickly \"teach\" a new word to the text model and train its embeddings close to some visual representation. This is achieved by adding a new token to the vocabulary, freezing the weights of all the models (except the text encoder), and train with a few representative images.\n","\n","This is a schematic representation of the process by the [authors of the paper](https://textual-inversion.github.io).\n","\n","![Textual Inversion diagram](https://textual-inversion.github.io/static/images/training/training.JPG)"]},{"cell_type":"markdown","metadata":{"id":"Ij4XFfAi-2l3"},"source":["---"]},{"cell_type":"markdown","metadata":{"id":"0fEgcCcj-2l3"},"source":["You can train your own tokens with photos you provide using [this training script](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [Google Colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). There's also a [Colab notebook for inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb), but we'll show below the steps we have to follow to add a trained token to the vocabulary and make it work the pre-trained Stable Diffusion model.\n","\n","We'll try an example using embeddings trained for [this style](https://huggingface.co/sd-concepts-library/indian-watercolor-portraits)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nl1pfoja-2l3"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", revision=\"fp16\", torch_dtype=torch.float16) \n","pipe = pipe.to(\"cuda\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sUv6gQU2-2l4"},"outputs":[],"source":["embeds_url = \"https://huggingface.co/sd-concepts-library/indian-watercolor-portraits/resolve/main/learned_embeds.bin\"\n","embeds_path = FastDownload().download(embeds_url)\n","embeds_dict = torch.load(str(embeds_path), map_location=\"cpu\")"]},{"cell_type":"markdown","metadata":{"id":"cfGN-oUv-2l4"},"source":["The embeddings for the new token are stored in a small PyTorch pickled dictionary, whose key is the new text token that was trained. Since the encoder of our pipeline does not know about this term, we need to manually append it."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q_1ScINr-2l4"},"outputs":[],"source":["tokenizer = pipe.tokenizer\n","text_encoder = pipe.text_encoder\n","new_token, embeds = next(iter(embeds_dict.items()))\n","embeds = embeds.to(text_encoder.dtype)\n","new_token"]},{"cell_type":"markdown","metadata":{"id":"vk3sKAHw-2l4"},"source":["We add the new token to the tokenizer and the trained embeddings to the embeddings table."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1kzrqc-Q-2l4"},"outputs":[],"source":["assert tokenizer.add_tokens(new_token) == 1, \"The token already exists!\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kXeDj7-z-2l5"},"outputs":[],"source":["text_encoder.resize_token_embeddings(len(tokenizer))\n","new_token_id = tokenizer.convert_tokens_to_ids(new_token)\n","text_encoder.get_input_embeddings().weight.data[new_token_id] = embeds"]},{"cell_type":"markdown","metadata":{"id":"cfmZke9d-2l5"},"source":["We can now run inference and refer to the style as if it was another word in the dictionary."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P0XoNAA4-2l5"},"outputs":[],"source":["torch.manual_seed(1000)\n","image = pipe(\"Woman reading in the style of \").images[0]\n","image"]},{"cell_type":"markdown","metadata":{"id":"0LJbZ5Kn-2l5"},"source":["### Dreambooth"]},{"cell_type":"markdown","metadata":{"id":"UoAcSgWP-2l5"},"source":["[Dreambooth](https://dreambooth.github.io) is a kind of fine-tuning that attempts to introduce new subjects by providing just a few images of the new subject. The goal is similar to that of [Textual Inversion](#Textual-Inversion), but the process is different. Instead of creating a new token as Textual Inversion does, we select an existing token in the vocabulary (usually a rarely used one), and fine-tune the model for a few hundred steps to bring that token close to the images we provide. This is a regular fine-tuning process in which all modules are unfrozen."]},{"cell_type":"markdown","metadata":{"id":"cWQvmCuH-2l5"},"source":["For example, we fine-tuned a model with a prompt like `\"photo of a sks person\"`, using the rare `sks` token to qualify the term `person`, and using photos of Jeremy as the targets. Let's see how it works."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-yKu9QNK-2l6"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"pcuenq/jh_dreambooth_1000\", torch_dtype=torch.float16)\n","pipe = pipe.to(\"cuda\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0a-Wno29-2l6"},"outputs":[],"source":["torch.manual_seed(1000)\n","\n","prompt = \"Painting of sks person in the style of Paul Signac\"\n","images = pipe(prompt, num_images_per_prompt=4).images\n","image_grid(images, 1, 4)"]},{"cell_type":"markdown","metadata":{"id":"IKE5zpYv-2l6"},"source":["Fine-tuning with Dreambooth is finicky and sensitive to hyperparameters, as we are essentially asking the model to overfit the prompt to the supplied images. In some situations we observe problems such as catastrophic forgetting of the associated term (`\"person\"` in this case). The authors applied a technique called \"prior preservation\" to try to avoid it by performing a special regularization using other examples of the term, in addition to the ones we provided. The cool thing about this idea is that those examples can be easily generated by the pre-trained Stable Diffusion model itself! We did not use that method in the model we trained for the previous example.\n","\n","Other ideas that may work include: use EMA so that the final weights preserve some of the previous knowledge, use progressive learning rates for fine-tuning, or combine the best of Textual Inversion with Dreambooth. These could make for some interesting projects to try out!\n","\n","If you want to train your own Dreambooth model, you can use [this script](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) or [this Colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). "]},{"cell_type":"markdown","metadata":{"id":"BuGZCO_1-2l6"},"source":["### What is Stable Diffusion"]},{"cell_type":"markdown","metadata":{"id":"nOh7Sjbk-2l_"},"source":["There are three main components in latent diffusion.\n","\n","1. An autoencoder (VAE).\n","2. A [U-Net](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb#scrollTo=wW8o1Wp0zRkq).\n","3. A text-encoder, *e.g.* [CLIP's Text Encoder](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).\n","\n","The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm. Many different scheduler algorithms can be used for this computation, each having its pros and cons. For Stable Diffusion, we recommend using one of:\n","\n","- [PNDM scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) (used by default)\n","- [DDIM scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)\n","- [K-LMS scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py)"]},{"cell_type":"markdown","metadata":{"id":"KPjpwIas-2l_"},"source":["### Latents and callbacks"]},{"cell_type":"markdown","metadata":{"id":"_3cEPdAg-2mA"},"source":["Stable Diffusion is based on a particular type of diffusion model called **Latent Diffusion**, proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752).\n","\n","General diffusion models are machine learning systems that are trained to *denoise* random gaussian noise step by step, to get to a sample of interest, such as an *image*. For a more detailed overview of how they work, check [this colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb).\n","\n","Diffusion models have shown to achieve state-of-the-art results for generating image data. But one downside of diffusion models is that the reverse denoising process is slow. In addition, these models consume a lot of memory because they operate in pixel space, which becomes unreasonably expensive when generating high-resolution images. Therefore, it is challenging to train these models and also use them for inference.\n","\n","Latent diffusion can reduce the memory and compute complexity by applying the diffusion process over a lower dimensional _latent_ space, instead of using the actual pixel space. This is the key difference between standard diffusion and latent diffusion models: **in latent diffusion the model is trained to generate latent (compressed) representations of the images.** \n","\n","The Stable Diffusion pipeline can send intermediate latents to a callback function we provide. By running these latents through the image decoder (the `vae` component of the pipeline), we can see how the denoising process progresses and the image unfolds."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o152JUgO-2mA"},"outputs":[],"source":["vae = pipe.vae\n","images = []\n","\n","def latents_callback(i, t, latents):\n"," latents = 1 / 0.18215 * latents\n"," image = vae.decode(latents).sample[0]\n"," image = (image / 2 + 0.5).clamp(0, 1)\n"," image = image.cpu().permute(1, 2, 0).numpy()\n"," images.extend(pipe.numpy_to_pil(image))\n","\n","prompt = \"Portrait painting of Jeremy Howard looking happy.\"\n","torch.manual_seed(9000)\n","final_image = pipe(prompt, callback=latents_callback, callback_steps=12).images[0]\n","images.append(final_image)\n","image_grid(images, rows=1, cols=len(images))"]},{"cell_type":"markdown","metadata":{"id":"IC7lG919-2mA"},"source":["**Why is latent diffusion fast and efficient?**\n","\n","Since the U-Net of latent diffusion models operates on a low dimensional space, it greatly reduces the memory and compute requirements compared to pixel-space diffusion models. For example, the autoencoder used in Stable Diffusion has a reduction factor of 8 but uses 4 channels instead of 3. This means that an image of shape `(3, 512, 512)` becomes `(4, 64, 64)` in latent space, which requires `8 ร— 8 ร— 3/4 = 48` times less memory.\n","\n","This is why it's possible to generate `512 ร— 512` images so quickly, even on 16GB Colab GPUs!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zFwipDzV-2mB"},"outputs":[],"source":["del pipe"]},{"cell_type":"markdown","metadata":{"id":"uBy4eD6C-2mB"},"source":["## Looking inside the pipeline"]},{"cell_type":"markdown","metadata":{"id":"aryhtvro-2mB"},"source":["The inference pipeline is just a small piece of code that plugs the components together and performs the inference loop. [This is all there it to is](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L204).\n","\n","We'll go through the process of loading and plugging the pieces to see how we could have written it ourselves. We'll start by loading all the modules that we need from their pretrained weights.\n","\n","First, we need the text encoder and the tokenizer. These come from the text portion of a standard CLIP model, so we'll use the weights released by Open AI."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y9UFcWi5-2mB"},"outputs":[],"source":["from transformers import CLIPTextModel, CLIPTokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sWkJdW8m-2mB"},"outputs":[],"source":["tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16)\n","text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"3QqP_-5Y-2mB"},"source":["Next we'll load the `vae` and the `unet`. These are distinct models whose weights are stored inside folders of the Stable Diffusion repository. We can use the `subfolder` argument to refer to [these locations](https://huggingface.co/CompVis/stable-diffusion-v1-4/tree/main)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9WpSJLCn-2mC"},"outputs":[],"source":["from diffusers import AutoencoderKL, UNet2DConditionModel"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yiqHV103-2mC"},"outputs":[],"source":["# Here we use a different VAE to the original release, which has been fine-tuned for more steps\n","vae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-ema\", torch_dtype=torch.float16).to(\"cuda\")\n","unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"BzzbP91I-2mC"},"source":["To make things a bit different, we'll use another scheduler. The standard pipeline uses the [PNDM Scheduler](https://arxiv.org/abs/2202.09778), but we'll use [Katherine Crowson's](https://github.com/crowsonkb) excellent K-LMS scheduler.\n","\n","We need to be careful to use the same noising schedule that was used during training. The schedule is defined by the number of noising steps and the amount of noise added at each step, which is derived from the _beta_ parameters.\n","\n","In the case of the k-LMS scheduler, this is how the betas evolve during the 1000 steps of the noising process used during training:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jSNTyByp-2mC"},"outputs":[],"source":["beta_start,beta_end = 0.00085,0.012\n","plt.plot(torch.linspace(beta_start**0.5, beta_end**0.5, 1000) ** 2)\n","plt.xlabel('Timestep')\n","plt.ylabel('ฮฒ');"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pYpktBtd-2mD"},"outputs":[],"source":["from diffusers import LMSDiscreteScheduler"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jU6YfZwj-2mD"},"outputs":[],"source":["scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\", num_train_timesteps=1000)"]},{"cell_type":"markdown","metadata":{"id":"40KrUHd0-2mE"},"source":["We now define the parameters we'll use for generation.\n","\n","In contrast with the previous examples, we set `num_inference_steps` to 70 to get an even more defined image."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PldH5EW2-2mE"},"outputs":[],"source":["prompt = [\"a photograph of an astronaut riding a horse\"]\n","\n","height = 512\n","width = 512\n","num_inference_steps = 70\n","guidance_scale = 7.5\n","batch_size = 1"]},{"cell_type":"markdown","metadata":{"id":"oF8sdjg1-2mE"},"source":["We tokenize the prompt. The model requires the same number of tokens for every prompt, so padding is used to ensure we meet the required length."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VeWknRpZ-2mE"},"outputs":[],"source":["text_input = tokenizer(prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n","text_input['input_ids']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I3ejTToK-2mF"},"outputs":[],"source":["tokenizer.decode(49407)"]},{"cell_type":"markdown","metadata":{"id":"VXSV3YgV-2mF"},"source":["The attention mask uses zero to represent tokens we are not interested in. These are all of the padding tokens."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RQs6WjsR-2mF"},"outputs":[],"source":["text_input['attention_mask']"]},{"cell_type":"markdown","metadata":{"id":"3syCUMx_-2mF"},"source":["The text encoder gives us the embeddings for the text prompt we used."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Awjd6tFQ-2mG"},"outputs":[],"source":["text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0].half()\n","text_embeddings.shape"]},{"cell_type":"markdown","metadata":{"id":"cw-2y6Qu-2mG"},"source":["We also get the embeddings required to perform unconditional generation, which is achieved with an empty string: the model is free to go in whichever direction it wants as long as it results in a reasonably-looking image. These embeddings will be applied to apply classifier-free guidance."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0FEJT1SC-2mG"},"outputs":[],"source":["max_length = text_input.input_ids.shape[-1]\n","uncond_input = tokenizer(\n"," [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n",")\n","uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0].half()\n","uncond_embeddings.shape"]},{"cell_type":"markdown","metadata":{"id":"yTjESaPB-2mH"},"source":["For classifier-free guidance, we need to do two forward passes. One with the conditioned input (`text_embeddings`), and another with the unconditional embeddings (`uncond_embeddings`). In practice, we can concatenate both into a single batch to avoid doing two forward passes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DyLpGORT-2mH"},"outputs":[],"source":["text_embeddings = torch.cat([uncond_embeddings, text_embeddings])"]},{"cell_type":"markdown","metadata":{"id":"41a3lUxx-2mH"},"source":["To start the denoising process, we start from pure Gaussian (normal) noise. These are our initial latents."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5Fke6S2T-2mH"},"outputs":[],"source":["torch.manual_seed(100)\n","latents = torch.randn((batch_size, unet.in_channels, height // 8, width // 8))\n","latents = latents.to(\"cuda\").half()\n","latents.shape"]},{"cell_type":"markdown","metadata":{"id":"yP9Wl7F4-2mH"},"source":["`4ร—64ร—64` is the input shape. The decoder will later transform this latent representation into a `3ร—512ร—512` image after the denoising process is complete.\n","\n","Next, we initialize the scheduler with our chosen `num_inference_steps`. This will prepare the internal state to be used during denoising."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7fjN_UQ0-2mH"},"outputs":[],"source":["scheduler.set_timesteps(num_inference_steps)"]},{"cell_type":"markdown","metadata":{"id":"PklW6gjb-2mH"},"source":["We scale the initial noise by the standard deviation required by the scheduler. This value will depend on the particular scheduler we use."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rbmdx1e5-2mL"},"outputs":[],"source":["latents = latents * scheduler.init_noise_sigma"]},{"cell_type":"markdown","metadata":{"id":"OsC6prhu-2mL"},"source":["We are ready to write the denoising loop. The timesteps go from `999` to `0` (1000 steps that were used during training) following a particular schedule."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x4RAFcrY-2mM"},"outputs":[],"source":["scheduler.timesteps"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ChotNTa_-2mM"},"outputs":[],"source":["scheduler.sigmas"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c8lbxO_r-2mM"},"outputs":[],"source":["plt.plot(scheduler.timesteps, scheduler.sigmas[:-1]);"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"exg731GE-2mM"},"outputs":[],"source":["from tqdm.auto import tqdm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rqcJhMXO-2mM"},"outputs":[],"source":["for i, t in enumerate(tqdm(scheduler.timesteps)):\n"," input = torch.cat([latents] * 2)\n"," input = scheduler.scale_model_input(input, t)\n","\n"," # predict the noise residual\n"," with torch.no_grad(): pred = unet(input, t, encoder_hidden_states=text_embeddings).sample\n","\n"," # perform guidance\n"," pred_uncond, pred_text = pred.chunk(2)\n"," pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)\n","\n"," # compute the \"previous\" noisy sample\n"," latents = scheduler.step(pred, t, latents).prev_sample"]},{"cell_type":"markdown","metadata":{"id":"ORZhfNsk-2mN"},"source":["After this process complets our `latents` contain the denoised representation of the image. We use the `vae` decoder to convert it back to pixel space."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uJ4JOqQN-2mN"},"outputs":[],"source":["with torch.no_grad(): image = vae.decode(1 / 0.18215 * latents).sample"]},{"cell_type":"markdown","metadata":{"id":"83S1kJ5y-2mN"},"source":["And finally, let's convert the image to PIL so we can display it."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IJJZdA6d-2mN"},"outputs":[],"source":["image = (image / 2 + 0.5).clamp(0, 1)\n","image = image[0].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"markdown","metadata":{"id":"fMDVhOOI-2mN"},"source":["### Just the code"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CN4R5o65-2mO"},"outputs":[],"source":["prompts = [\n"," 'a photograph of an astronaut riding a horse',\n"," 'an oil painting of an astronaut riding a horse in the style of grant wood'\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ndi0F8Lu-2mO"},"outputs":[],"source":["text_input = tokenizer(prompts, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n","text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0].half()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pa_b326E-2mO"},"outputs":[],"source":["max_length = text_input.input_ids.shape[-1]\n","uncond_input = tokenizer([\"\"] * len(prompts), padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n","uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0].half()\n","emb = torch.cat([uncond_embeddings, text_embeddings])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FyNM2YG7-2mO"},"outputs":[],"source":["torch.manual_seed(100)\n","g = guidance_scale"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vg5JgmVV-2mO"},"outputs":[],"source":["latents = torch.randn((len(prompts), unet.in_channels, height//8, width//8))\n","scheduler.set_timesteps(num_inference_steps)\n","latents = latents.to(\"cuda\").half() * scheduler.init_noise_sigma"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WNItr4xS-2mO"},"outputs":[],"source":["for i,ts in enumerate(tqdm(scheduler.timesteps)):\n"," inp = scheduler.scale_model_input(torch.cat([latents] * 2), ts)\n"," with torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)\n"," pred = u + g*(t-u)\n"," latents = scheduler.step(pred, ts, latents).prev_sample"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E_vFKMdU-2mP"},"outputs":[],"source":["with torch.no_grad(): image = vae.decode(1 / 0.18215 * latents).sample\n","res = (image / 2 + 0.5).clamp(0, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kK4lVJLe-2mP"},"outputs":[],"source":["image = res[0].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ZHbaZ4W-2mP"},"outputs":[],"source":["image = res[1].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"markdown","metadata":{"id":"W_VV19Xf-2mP"},"source":["### Put it in functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X_YbEE-C-2mP"},"outputs":[],"source":["def text_enc(prompts, maxlen=None):\n"," if maxlen is None: maxlen = tokenizer.model_max_length\n"," inp = tokenizer(prompts, padding=\"max_length\", max_length=maxlen, truncation=True, return_tensors=\"pt\")\n"," return text_encoder(inp.input_ids.to(\"cuda\"))[0].half()\n","\n","def mk_img(t):\n"," image = (t/2+0.5).clamp(0,1).detach().cpu().permute(1, 2, 0).numpy()\n"," return Image.fromarray((image*255).round().astype(\"uint8\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hF4FQUWb-2mQ"},"outputs":[],"source":["def mk_samples(prompts, g=7.5, seed=100, steps=70):\n"," bs = len(prompts)\n"," text = text_enc(prompts)\n"," uncond = text_enc([\"\"] * bs, text.shape[1])\n"," emb = torch.cat([uncond, text])\n"," if seed: torch.manual_seed(seed)\n","\n"," latents = torch.randn((bs, unet.in_channels, height//8, width//8))\n"," scheduler.set_timesteps(steps)\n"," latents = latents.to(\"cuda\").half() * scheduler.init_noise_sigma\n","\n"," for i,ts in enumerate(tqdm(scheduler.timesteps)):\n"," inp = scheduler.scale_model_input(torch.cat([latents] * 2), ts)\n"," with torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)\n"," pred = u + g*(t-u)\n"," latents = scheduler.step(pred, ts, latents).prev_sample\n","\n"," with torch.no_grad(): return vae.decode(1 / 0.18215 * latents).sample"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZAl09AqT-2mQ"},"outputs":[],"source":["images = mk_samples(prompts)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qWuqZ-YI-2mQ"},"outputs":[],"source":["from IPython.display import display"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mjjC8au_-2mQ"},"outputs":[],"source":["for img in images: display(mk_img(img))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CvqjSQTR-2mQ"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.10.4"},"colab":{"provenance":[]},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/MACxMDN_Workshops/Copy of MACxMDN_workshop1 FastAI Tutorial.ipynb b/MACxMDN_Workshops/Copy of MACxMDN_workshop1 FastAI Tutorial.ipynb new file mode 100644 index 0000000..a73a8bf --- /dev/null +++ b/MACxMDN_Workshops/Copy of MACxMDN_workshop1 FastAI Tutorial.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1khwZW0mAjdm0zM1No1IU4hTsZ9hqUV4_","timestamp":1676014520473},{"file_id":"14X2UmiT3rG0GNMiug_OzKzN6lfZBdcjG","timestamp":1607252308549}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"accelerator":"GPU","gpuClass":"standard"},"cells":[]} \ No newline at end of file diff --git a/MACxMDN_Workshops/FastAI_GAN_training.ipynb b/MACxMDN_Workshops/FastAI_GAN_training.ipynb new file mode 100644 index 0000000..1187fb2 --- /dev/null +++ b/MACxMDN_Workshops/FastAI_GAN_training.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"2EYR3jo75kCX","executionInfo":{"status":"ok","timestamp":1677517149702,"user_tz":-660,"elapsed":601,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["%reload_ext autoreload\n","%autoreload 2\n","%matplotlib inline"]},{"cell_type":"code","source":["!pip install fastai --upgrade --quiet"],"metadata":{"id":"5U6Y4haa6XaK","executionInfo":{"status":"ok","timestamp":1677517153970,"user_tz":-660,"elapsed":4270,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"execution_count":2,"outputs":[]},{"cell_type":"code","execution_count":3,"metadata":{"id":"WCTPDEYN5kCY","executionInfo":{"status":"ok","timestamp":1677517156436,"user_tz":-660,"elapsed":2473,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["from fastai.vision.all import *\n","from fastai.vision.gan import *\n","import fastai.vision.gan as fgan\n","from fastai.callback.all import *"]},{"cell_type":"code","source":["# dir(fgan)"],"metadata":{"id":"kVGJKzLO94FI","executionInfo":{"status":"ok","timestamp":1677517156437,"user_tz":-660,"elapsed":8,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"execution_count":4,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"KdLz2tJA5kCZ"},"source":["## LSun bedroom data"]},{"cell_type":"markdown","metadata":{"id":"1EJ2FpkY5kCf"},"source":["For this lesson, we'll be using the bedrooms from the [LSUN dataset](http://lsun.cs.princeton.edu/2017/). The full dataset is a bit too large so we'll use a sample from [kaggle](https://www.kaggle.com/jhoward/lsun_bedroom)."]},{"cell_type":"code","execution_count":5,"metadata":{"id":"UjwgqiJX5kCf","executionInfo":{"status":"ok","timestamp":1677517156437,"user_tz":-660,"elapsed":6,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["# path = untar_data(URLs.LSUN_BEDROOMS)\n","path = untar_data(URLs.PETS)"]},{"cell_type":"markdown","metadata":{"id":"Un_LK4km5kCf"},"source":["We then grab all the images in the folder with the data block API. We don't create a validation set here for reasons we'll explain later. It consists of random noise of size 100 by default (can be changed below) as inputs and the images of bedrooms as targets. We apply transform to the targets\n"]},{"cell_type":"code","execution_count":22,"metadata":{"id":"4gldtUjI5kCf","executionInfo":{"status":"ok","timestamp":1677520843399,"user_tz":-660,"elapsed":401,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["bs = 8\n","size = 64\n","\n","dblock = DataBlock(blocks = (TransformBlock, ImageBlock),\n"," get_x = generate_noise,\n"," get_items = get_image_files,\n"," splitter = IndexSplitter([]),\n"," item_tfms=Resize(size, method=ResizeMethod.Crop), \n"," # batch_tfms = Normalize.from_stats(torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5]))\n"," )\n","# dblock = DataBlock(blocks=(ImageBlock, ImageBlock),\n","# get_items=get_image_files,\n","# splitter=RandomSplitter(),\n","# item_tfms=Resize(size),\n","# batch_tfms=[*aug_transforms(max_zoom=2.),\n","# Normalize.from_stats(*imagenet_stats)])\n","\n","dls = dblock.dataloaders(path, path=path, bs=bs)"]},{"cell_type":"markdown","metadata":{"id":"icr06WDg5kCg"},"source":["We'll begin with a small side and use gradual resizing."]},{"cell_type":"code","execution_count":23,"metadata":{"id":"S-WtsXiB5kCg","executionInfo":{"status":"ok","timestamp":1677520847226,"user_tz":-660,"elapsed":2019,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}},"colab":{"base_uri":"https://localhost:8080/","height":326},"outputId":"21411302-5ec7-4e1d-894b-f61397f3de0e"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["dls.show_batch(max_n=8)\n"]},{"cell_type":"markdown","metadata":{"id":"x6Kq-l4J5kCh"},"source":["## Models"]},{"cell_type":"markdown","metadata":{"id":"TGvStEWv5kCh"},"source":["GAN stands for [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) and were invented by Ian Goodfellow. The concept is that we will train two models at the same time: a generator and a critic. The generator will try to make new images similar to the ones in our dataset, and the critic will try to classify real images from the ones the generator does. The generator returns images, the critic a single number (usually 0. for fake images and 1. for real ones).\n","\n","We train them against each other in the sense that at each step (more or less), we:\n","1. Freeze the generator and train the critic for one step by:\n"," - getting one batch of true images (let's call that `real`)\n"," - generating one batch of fake images (let's call that `fake`)\n"," - have the critic evaluate each batch and compute a loss function from that; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the critic with the gradients of this loss\n"," \n"," \n","2. Freeze the critic and train the generator for one step by:\n"," - generating one batch of fake images\n"," - evaluate the critic on it\n"," - return a loss that rewards posisitivly the critic thinking those are real images; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the generator with the gradients of this loss\n"," \n","Here, we'll use the [Wassertein GAN](https://arxiv.org/pdf/1701.07875.pdf)."]},{"cell_type":"markdown","metadata":{"id":"0Q2ZQgu35kCh"},"source":["We create a generator and a critic that we pass to `gan_learner`. The noise_size is the size of the random vector from which our generator creates images."]},{"cell_type":"code","execution_count":8,"metadata":{"id":"wSb1N3k-5kCi","executionInfo":{"status":"ok","timestamp":1677517159877,"user_tz":-660,"elapsed":22,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["# generator = basic_generator(in_size=64, n_channels=3, n_extra_layers=1)\n","# critic = basic_critic (in_size=64, n_channels=3, n_extra_layers=1)\n","# learn = GANLearner.wgan(data, generator, critic, switch_eval=False,\n","# opt_func = partial(optim.Adam, betas = (0.,0.99)), wd=0.)\n","# learn.fit(30,2e-4)\n","# learn.gan_trainer.switch(gen_mode=True)\n","# learn.show_results(ds_type=DatasetType.Train, rows=16, figsize=(8,8))"]},{"cell_type":"code","execution_count":9,"metadata":{"id":"0TpzUWSO5kCi","executionInfo":{"status":"ok","timestamp":1677517159878,"user_tz":-660,"elapsed":22,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}}},"outputs":[],"source":["from fastai.callback.all import *\n","\n","generator = basic_generator(64, n_channels=3, n_extra_layers=1)\n","critic = basic_critic (64, n_channels=3, n_extra_layers=1, act_cls=partial(nn.LeakyReLU, negative_slope=0.2))\n","\n","learn = GANLearner.wgan(dls, generator, critic, opt_func = RMSProp)\n","\n","learn.recorder.train_metrics=True\n","learn.recorder.valid_metrics=False"]},{"cell_type":"code","source":["learn.fit(10,2e-4)\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":363},"id":"4tAuoGEiwmOo","executionInfo":{"status":"ok","timestamp":1677519917489,"user_tz":-660,"elapsed":732507,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}},"outputId":"76ee6bcf-7dbd-4cfa-ac4e-8ac7069ce77e"},"execution_count":14,"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n"," \n","
epochtrain_lossgen_losscrit_losstime
0-0.4967490.307215-0.64486101:13
1-0.3921070.282997-0.58203501:13
2-0.4047470.234076-0.52889401:12
3-0.3922830.229931-0.52004101:12
4-0.3710920.209457-0.48708401:13
5-0.3630480.260184-0.50728501:14
6-0.3263500.256338-0.47203601:12
7-0.3784100.244381-0.46914301:13
8-0.3195580.212293-0.45408701:11
9-0.3227860.251039-0.46855101:13
"]},"metadata":{}}]},{"cell_type":"code","source":["learn.gan_trainer.switch(gen_mode=True)\n","learn.show_results(max_n=9, ds_idx=0)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":520},"id":"hPEOyisvRaaT","executionInfo":{"status":"ok","timestamp":1677519918776,"user_tz":-660,"elapsed":1296,"user":{"displayName":"Tin Nguyen","userId":"13589095193260818263"}},"outputId":"1e4c0316-6f67-4c01-bb41-2a9512929add"},"execution_count":15,"outputs":[{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}]},{"cell_type":"code","source":[],"metadata":{"id":"1Wr4rgbq3IAp"},"execution_count":null,"outputs":[]}],"metadata":{"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"colab":{"provenance":[]},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/MACxMDN_Workshops/GAN_MACxMDN.ipynb b/MACxMDN_Workshops/GAN_MACxMDN.ipynb new file mode 100644 index 0000000..198cace --- /dev/null +++ b/MACxMDN_Workshops/GAN_MACxMDN.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","source":["# TO DO\n","## Download file from https://drive.google.com/drive/folders/1ryQu-VBk5Hw77f_Ep7QH0exbVw4p7xtr?usp=share_link\n","## Upload to your drive \n","\n"],"metadata":{"id":"ei5yxl5OVKcR"}},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"xOpaKMaA3h3R","outputId":"c426b28c-a67a-43c6-8603-81a75c844a51"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[?25l \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m0.0/1.6 MB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m55.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["%matplotlib inline\n","!pip install jmd_imagescraper --quiet\n"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ua_X_4_o3h3T"},"outputs":[],"source":["from fastai.vision.all import *\n","from fastai.vision.gan import *"]},{"cell_type":"code","source":["from google.colab import drive\n","import os\n","drive.mount('/content/drive')\n","os.getcwd()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":72},"id":"dIK4eEAhQc_-","outputId":"e243323a-971f-4cd3-dfcd-1d2ca5f938ae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Drive already mounted at /content/drive; to attempt to forcibly remount, call drive.mount(\"/content/drive\", force_remount=True).\n"]},{"output_type":"execute_result","data":{"text/plain":["'/content'"],"application/vnd.google.colaboratory.intrinsic+json":{"type":"string"}},"metadata":{},"execution_count":39}]},{"cell_type":"code","source":["os.chdir('/content/drive/MyDrive')\n"],"metadata":{"id":"kUfPmmFnSarm"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"ytmfvOJN3h3U"},"source":["## data"]},{"cell_type":"markdown","metadata":{"id":"7ga8Jz6i3h3U"},"source":["For this lesson, we'll be using the bedrooms from the [LSUN dataset](http://lsun.cs.princeton.edu/2017/). The full dataset is a bit too large so we'll use a sample from [kaggle](https://www.kaggle.com/jhoward/lsun_bedroom).\n","Alternatively you can create your own data or use a different dataset PET"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"qC_5gT-E3h3V","outputId":"c8d2988a-b8bf-48e9-c481-70b1ca6c93d8"},"outputs":[{"output_type":"stream","name":"stdout","text":["using ROOM\n"]}],"source":["option = 'default' # chose between default - room or custom or PET\n","prompt = \"lions\" # for custom only - specify what to search for \n","if option == 'custom':\n"," num_imgs = 10\n"," folder_name = \"folder_name\"\n"," from jmd_imagescraper.core import * # dont't worry, it's designed to work with import *\n"," from pathlib import Path\n"," root = Path().cwd()/\"images\"/folder_name\n"," duckduckgo_search(root, folder_name, prompt , max_results=num_imgs)\n"," path = root # use custom data\n"," print('using CUSTOM')\n","elif option == 'pet':\n"," print('using PET')\n"," path = untar_data(URLs.PETS)/'images'\n","else:\n"," path = untar_data(URLs.LSUN_BEDROOMS)\n"," print('using ROOM')\n"]},{"cell_type":"markdown","metadata":{"id":"-fkStgH-3h3V"},"source":["We then grab all the images in the folder with the data block API. We don't create a validation set here for reasons we'll explain later. It consists of random noise of size 100 by default (can be changed if you replace `generate_noise` by `partial(generate_noise, size=...)`) as inputs and the images of bedrooms as targets."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"D7XuKyZw3h3V"},"outputs":[],"source":["def get_dls(bs, size):\n"," dblock = DataBlock(blocks = (TransformBlock, ImageBlock),\n"," get_x = generate_noise,\n"," get_items = get_image_files,\n"," splitter = IndexSplitter([]),\n"," item_tfms=Resize(size, method=ResizeMethod.Crop),\n"," batch_tfms = Normalize.from_stats(torch.tensor([0.5,0.5,0.5]), torch.tensor([0.5,0.5,0.5])))\n"," return dblock.dataloaders(path, path=path, bs=bs)"]},{"cell_type":"markdown","metadata":{"id":"VPJ4pJsh3h3W"},"source":["We'll begin with a small size since GANs take a lot of time to train."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"v0ER9pGH3h3W"},"outputs":[],"source":["dls = get_dls(16, 64)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":683},"id":"RqijCiXH3h3W","outputId":"91b54603-d286-4318-9493-578bac56e3ca"},"outputs":[{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["dls.show_batch(max_n=16)"]},{"cell_type":"markdown","metadata":{"id":"paPGfB7R3h3X"},"source":["## Models"]},{"cell_type":"markdown","metadata":{"id":"1cHF6cUf3h3X"},"source":["GAN stands for [Generative Adversarial Nets](https://arxiv.org/pdf/1406.2661.pdf) and were invented by Ian Goodfellow. The concept is that we will train two models at the same time: a generator and a critic. The generator will try to make new images similar to the ones in our dataset, and the critic will try to classify real images from the ones the generator does. The generator returns images, the critic a single number (usually 0. for fake images and 1. for real ones).\n","\n","We train them against each other in the sense that at each step (more or less), we:\n","1. Freeze the generator and train the critic for one step by:\n"," - getting one batch of true images (let's call that `real`)\n"," - generating one batch of fake images (let's call that `fake`)\n"," - have the critic evaluate each batch and compute a loss function from that; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the critic with the gradients of this loss\n"," \n"," \n","2. Freeze the critic and train the generator for one step by:\n"," - generating one batch of fake images\n"," - evaluate the critic on it\n"," - return a loss that rewards positively the critic thinking those are real images; the important part is that it rewards positively the detection of real images and penalizes the fake ones\n"," - update the weights of the generator with the gradients of this loss\n"," \n","Here, we'll use the [Wassertein GAN](https://arxiv.org/pdf/1701.07875.pdf)."]},{"cell_type":"markdown","metadata":{"id":"J7WYvl_73h3X"},"source":["We create a generator and a critic that we pass to `gan_learner`. The noise_size is the size of the random vector from which our generator creates images."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uyK-JRTY3h3X"},"outputs":[],"source":["generator = basic_generator(64, n_channels=3, n_extra_layers=1)\n","critic = basic_critic (64, n_channels=3, n_extra_layers=1, act_cls=partial(nn.LeakyReLU, negative_slope=0.2))"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"QiaRFdQz3h3X","outputId":"965ad1a4-bd01-4e8f-be29-59c71d57522e"},"outputs":[{"output_type":"execute_result","data":{"text/plain":[""]},"metadata":{},"execution_count":54}],"source":["learn = GANLearner.wgan(dls, generator, critic, opt_func = partial(Adam, mom=0.))\n","try:\n"," learn.load(os.getcwd()+'/save_folder/roomGAN')\n"," learn.gan_trainer.switch(gen_mode=True)\n"," learn.show_results(max_n=16, figsize=(8,8), ds_idx=0)\n","except:\n"," print('Could not load - have you upload data to your drive ?')"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"iIspfJMH3h3Y"},"outputs":[],"source":["learn.recorder.train_metrics=True\n","learn.recorder.valid_metrics=False"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"M-WsewJG3h3Y"},"outputs":[],"source":["# Start training - take quite long though so feel free to test it at home\n","learn.fit(3, 2e-4, wd=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":590},"id":"N96ku9n83h3Y","outputId":"2468d68a-09c7-41b1-ea55-a559c6db9c7a"},"outputs":[{"output_type":"stream","name":"stderr","text":["/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (gen_mode) that exists in the learner. Use `self.learn.gen_mode` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n","/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (generator) that exists in the learner. Use `self.learn.generator` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n","/usr/local/lib/python3.8/dist-packages/fastai/callback/core.py:69: UserWarning: You are shadowing an attribute (critic) that exists in the learner. Use `self.learn.critic` to avoid this\n"," warn(f\"You are shadowing an attribute ({name}) that exists in the learner. Use `self.learn.{name}` to avoid this\")\n"]},{"output_type":"display_data","data":{"text/plain":[""],"text/html":["\n","\n"]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":[""],"text/html":[]},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["
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\n"},"metadata":{"needs_background":"light"}}],"source":["learn.gan_trainer.switch(gen_mode=True)\n","learn.show_results(max_n=16, figsize=(8,8), ds_idx=0)"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PMZe7oFv3h3Y","outputId":"e76eb528-6490-400a-d024-7598994eaa22"},"outputs":[{"output_type":"execute_result","data":{"text/plain":["Path('/content/roomGAN.pth')"]},"metadata":{},"execution_count":36}],"source":["learn.save(os.getcwd()+'/save_folder/roomGAN')\n"]},{"cell_type":"code","source":[],"metadata":{"id":"1orIPvQKUyv_"},"execution_count":null,"outputs":[]}],"metadata":{"jupytext":{"split_at_heading":true},"kernelspec":{"display_name":"Python 3","language":"python","name":"python3"},"colab":{"provenance":[]},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/MACxMDN_Workshops/MACxMDN_workshop1 FastAI Tutorial.ipynb b/MACxMDN_Workshops/MACxMDN_workshop1 FastAI Tutorial.ipynb new file mode 100644 index 0000000..d662d85 --- /dev/null +++ b/MACxMDN_Workshops/MACxMDN_workshop1 FastAI Tutorial.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[{"file_id":"1khwZW0mAjdm0zM1No1IU4hTsZ9hqUV4_","timestamp":1676014520473},{"file_id":"14X2UmiT3rG0GNMiug_OzKzN6lfZBdcjG","timestamp":1607252308549}]},"kernelspec":{"display_name":"Python 3","name":"python3"},"accelerator":"GPU","gpuClass":"standard"},"cells":[{"cell_type":"markdown","metadata":{"id":"1gror2KGz0rT"},"source":["# PLEASE READ BEFORE STARTING\n","1. **Don't edit this file, make a copy first:**\n"," * Click on File -> Save a copy in Drive\n","\n","2. Also do the following:\n"," * Click on Runtime -> Change runtime type -> Make sure hardware accelerator is set to GPU"]},{"cell_type":"markdown","metadata":{"id":"z7R1gCAtxvbh"},"source":["# FastAI Tutorial\n","Neural networks can be used for a whole host of tasks.\n","The most basic of these though is image classification.\n","Hence, today we'll go through using supervised learning to predict an images class/label from the CIFAR-10 dataset.\n","\n","We'll be using a library called FastAI which is built ontop of PyTorch to abstract away all the nitty-gritty details.\n","Instead we can focus on comparing, contrarsting and understanding the different concepts we are able to use (like pretraining).\n","By the end of this you should be roughly familiar with all the major concepts and theory behind basic neural nets!\n","\n","There's a lot going on here, so let's get going!"]},{"cell_type":"markdown","source":["# Image Classification"],"metadata":{"id":"nDj2AgEfVB1F"}},{"cell_type":"markdown","metadata":{"id":"5GyVVCkCltvy"},"source":["## Importing Libraries\n","Before we dive into the image classification task for this workshop, it is important the relevant libraries.\n","\n","\n","Note here that we're importing everything from fastai's vision package, which is useful here but not in practice."]},{"cell_type":"code","metadata":{"id":"hzeDl-JllrlT"},"source":["!pip install fastai --upgrade --quiet"],"execution_count":null,"outputs":[]},{"cell_type":"code","metadata":{"id":"EdC8-DnKWYS7"},"source":["from fastai.vision.all import *"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"3oISzwhy5r5P"},"source":["# Dataset Setup\n","The first step for anything data related is to read a dataset and split the data into seperate training and validation partitions.\n","In this tutorial we will be using the CIFAR-10 dataset, which includes 60,000 images, each belonging to one of ten categories.\n","The dataset was collected by Alex Krizhevsky, Vinod Nair, and Geoffrey Hinton. More information on the dataset can be found here: https://www.cs.toronto.edu/~kriz/cifar.html\n","\n","## Initialising the Data into a pipeline\n","To start, we will download the CIFAR-10 dataset and extract all the images from it."]},{"cell_type":"code","metadata":{"id":"830MtputmzmJ"},"source":["path = untar_data(URLs.CIFAR) # Downloads url and unzips to folder destination"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"8GohbK51G8bR"},"source":["Next we will initialise an instance of the 'DataBlock' class from the FastAI library. Which is a generic container that allows us to build a \"smart\" dataset called a dataloader, that contains the images seperated into training and validation sets, with their class attached to them. The purpose of the dataloader is that it specified a pipeline in which the model will receive data.\n","\n","We use the function ImageDataLoaders() to establish the dataloader by passing the path to the dataset, and the proportion of the dataset we wish to dedicate to the validation set.\n","\n","The validation dataset provides a way to check whether we are actually learning how to classify images or just overfitting the data (i.e. the model has just memorised which image belongs to which class).\n","To do this we can create a validation dataset which the model doesn't train with, but insntead is used to \"test\" how well it does \"out-of-sample\".\n","\n","In this case we've used 20% of CIFAR-10's 60,000 images for validation (but you can change this)."]},{"cell_type":"code","metadata":{"id":"TtnJSZFUG4Mz"},"source":["data = ImageDataLoaders.from_folder(path=path, valid_pct=0.2)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"1J-ClqMdIcpn"},"source":["## Validating the dataset\n","Before we proceed, it is important to validate the data to ensure that we do not have an incomplete dataset, a dateset with incorrect preprocessing, and more importantly to understand the data we are working in before we begin training.\n","\n","We know that CIFAR-10 has 60,000 images, lets start by verifying that we correctly set 20% of the dataset to the validation set."]},{"cell_type":"code","metadata":{"id":"IUEHSaDmB9jq"},"source":["print(\"Training Set Size = \" + str(len(data.train_ds)))\n","print(\"Validiation Set Size = \" + str(len(data.valid_ds)))\n","print(\"Total Dataset Size = \" + str(len(data.train_ds) + len(data.valid_ds)))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"IMqzc8zBQxp6"},"source":["We can also validate the dataset by checking a random batch to view the images with their respective labels.\n","\n","Notice that the images are blurry, this is because the CIFAR dataset is used to train neural nets to identify far away objects that are often pixilated. The images have been taken while the camera setting was zoomed in to maximum.\n","\n","Hence, this image classification is a real life example of where AI can be applied."]},{"cell_type":"code","metadata":{"id":"agHpaXAHTidU"},"source":["data.show_batch(figsize=(10,10))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"yUxBN2oIV0Bz"},"source":["# Model Setup\n","## Transfer Learning\n","Transfer learning allows us to repurpose a model trained for one task to many others.\n","This means our research and practical testing of image classification techniques using neural nets on CIFAR-10 can also be used for other datasets (and even other broad domains like Natural Language Processing).\n","\n","This is why transfer learning is one of the most fundemental aspect of deep learning.\n","We can find several examples of this, including:\n","- A model trained for the ImageNet competition can be repurposed to recognise between different dog breeds\n","- A model trained on ImageNet can be repurposed to help us classify whether an image is a 'plane' or a 'dog'\n","- A language model trained on Spanish could be adapted and repurposed for French/Italian\n","\n","The primary benefit of transfer learning is that we can use the same \"toolkit\" or \"basic techniques\" for a very wide variety of complex problems.\n","Hence, we have solid foundations which we can build up upon.\n","This cuts down on training time *substantially*.\n","\n","\n","In this example we're using a pretrained *ResNet-34* (more advanced model) as our base architecture, then retraining it to adapt it to classify our data."]},{"cell_type":"code","metadata":{"id":"Kn9d0OJSCSf1"},"source":["learn = cnn_learner(data, resnet34, metrics=accuracy)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"Nc2P_9DCWhDX"},"source":["# Training\n","## Determining the Learning Rate\n","An important thing to figgure out is what learning rate to use.\n","It's a hard problem to solve, but we can nudge ourselves in the right direction by finding out how changing our learning rate effects the loss initially.\n","\n","We see this with FastAI's `lr_find()`.\n","It provides the minimum learning rate (divided by 10) and the point of steepest descent."]},{"cell_type":"code","metadata":{"id":"70TmnJcbX7EK"},"source":["learn.lr_find()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"_6FGnoo0YqLx"},"source":["You can try and pick the best learning rate and put it into the training method below (in the next section) to see how it changes your results.\n","If you try a few different values, you'll soon see that your choice greatly effects the results!"]},{"cell_type":"markdown","metadata":{"id":"KWSjg9LRZH1s"},"source":["## Training Time!\n","We are now ready to begin training!\n","\n","To start, we establish our base learning rate which we can choose from our previous graph. For this experiment, you will need to assign the variable `base_lr` with a learning rate of your choice.\n","\n","After selecting your learning rate, execute the code block below before you continue reading as it may take some time to train the model.\n","\n","After the learning rate is defined, we freeze the lower layers by calling the `freeze()` method. This concept is taken from Transfer Learning, as we have previously spoken about, and it will allow us to 'custom fit' the *ResNet-34* to the dataset, as the network is already pretrained on a similar problem. The actual 'freezing' occurs by preventing any weights in the lower layers from being modified until we unfreeze, allowing us to change the final fully connected layers.\n","\n","Now we can train our fully connected layers using the `fit_one_cycle()` method. This method takes in how many epochs we want to train and the learning rate at which we want to train our network at. We have also inputted an optional argument which is slightly out of scope for this workshop.\n","\n","After one epoch, we unfreeze the lower layers so that ALL layers can now have their weights updated according to the loss function. We can then train for another 3 epochs and evaluate the results.\n"]},{"cell_type":"code","metadata":{"id":"TjkBCV_8CxrO"},"source":["base_lr = #\n","learn.freeze()\n","learn.fit_one_cycle(1, base_lr, pct_start=0.99)\n","base_lr /= 2\n","learn.unfreeze()\n","learn.fit_one_cycle(3, base_lr, pct_start=0.3)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lH2PyEYkgZwa"},"source":["While it is training, you may hopefully notice that the training loss and validation loss get lower over time, and the accuracy may increase as the model learns.\n","\n"]},{"cell_type":"markdown","metadata":{"id":"oiNSeJQNW7TC"},"source":["## Saving Model Weights\n","Once training has completed, we should save our models weights (so we can use or pretrain from it later).\n","\n","This could also allow:\n","- Reverting to a previous model\n","- Tracking a models progress through special version-control\n","\n","Model weights can be simply reloaded with `learner.load('some-name')`"]},{"cell_type":"code","metadata":{"id":"Yl-nenHGW-4S"},"source":["learn.save('trained-lr-default')"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xlQZlz1QjXNF"},"source":["# Evaluation\n","Once the training has been completed, we need to gauge how good/bad our model is.\n","\n","## Sample Predictions\n","We can view some predictions with our newly trained model with the `show_results()` method. Activate the block below to see how it went!\n","\n","The text at the top indicates the actual class of the image, the bottom text indicates the predicted class, if they're green, our model successfully predicted correctly."]},{"cell_type":"code","metadata":{"id":"0xowfQ20h-H6"},"source":["learn.show_results()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"mV94t__pjvM9"},"source":["Viewing a sample batch of predictions is visually appealing, however there are more appropriate metrics to validate the model.\n","\n","## Model Validation\n","It is important to validate our models with appropriate metrics to determine how well the model is performing, and whether or not further investigation is required. Today we will be doing using a Confusion Matrix and viewing our top losses. We will start by creating an instance of the `ClassificationInterpretation` class from our model in order to begin."]},{"cell_type":"code","metadata":{"id":"8Erc2_QflSQE"},"source":["interp = ClassificationInterpretation.from_learner(learn)"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"lX90aRSwk4ra"},"source":["### Confusion Matrix\n","A Confusion Matrix can be used to determine where our model is producing false positives or false negatives. Click below to see what happened!"]},{"cell_type":"code","metadata":{"id":"hrtX8zCUjnKh"},"source":["interp.plot_confusion_matrix()"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"nbcCG7-xlim1"},"source":["### Top Losses\n","We can also produce a set of images that fastai considers 'top losses' with the `plot_top_losses()` method. The images are considered 'top losses' based on the probability that the prediction was correct. The images with a probability of 1 technically don't have a probability of 1, it's an softmax bug within FastAI."]},{"cell_type":"code","metadata":{"id":"okqooChcmgkH"},"source":["interp.plot_top_losses(8, figsize=(15,11))"],"execution_count":null,"outputs":[]},{"cell_type":"markdown","metadata":{"id":"xMeA5AForEKk"},"source":["So how did your model perform? Did it increase in accuracy over time? Maybe have a play around with the base learning rate a little more and see what you can come up with. Maybe try thinking about using the lowest point on the learning rate curve or the point of steepest descent and compare the results.\n","\n","## Group Evaluation\n","As a breakout room, discuss how your models performed.\n","Try and consider areas you believed they performed well in, along with where you think they could improve.\n","Think about why there may be some common classes where confusion occurs between the actual and predicted classes while you wait for the results of your new training.\n","Feel free to additionally discuss the effect of learning rates once again."]},{"cell_type":"markdown","source":["## Segmentation\n","Segmentation is a problem where we have to predict a category for each pixel of the image and segment parts of the image based on respective categories. For this task, we will use the [Camvid](https://mi.eng.cam.ac.uk/research/projects/VideoRec/CamVid/) dataset, a dataset of screenshots from cameras in cars. Each pixel of the image has a label such as \"road\", \"car\" or \"pedestrian\"."],"metadata":{"id":"4lakdZRaQ2AI"}},{"cell_type":"code","source":["path = untar_data(URLs.CAMVID_TINY) # Downloads url and unzips to folder destination\n","path.ls()"],"metadata":{"id":"ZTd1me3ERMK9"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The images folder contains the images, and the corresponding segmentation masks of labels are in the labels folder. The codes file contains the corresponding integer to class (the masks have an int value for each pixel)."],"metadata":{"id":"WEpjR5GJSBtP"}},{"cell_type":"code","source":["codes = np.loadtxt(path/'codes.txt', dtype=str)\n","codes"],"metadata":{"id":"JjJJG4w7SCWV"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["The get_image_files function is an in-built function from FastAI that helps us grab all the image filenames:"],"metadata":{"id":"sMB2q93lS4Rl"}},{"cell_type":"code","source":["fnames = get_image_files(path/\"images\")\n","fnames[0]"],"metadata":{"id":"SG-6kukKS85q"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Let's have a look in the labels folder:"],"metadata":{"id":"KJmNTKJNTRdV"}},{"cell_type":"code","source":["(path/\"labels\").ls()[0]"],"metadata":{"id":"22aSwXckTWpn"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["It seems the segmentation masks have the same base names as the images but with an extra _P, so we can define a label function:"],"metadata":{"id":"04pq4WdvTcva"}},{"cell_type":"code","source":["def label_func(fn): return path/\"labels\"/f\"{fn.stem}_P{fn.suffix}\""],"metadata":{"id":"DnHz_VA2ThMf"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We can then gather our data using SegmentationDataLoaders from FastAI:"],"metadata":{"id":"def1i0ABTkiD"}},{"cell_type":"code","source":["dls = SegmentationDataLoaders.from_label_func(\n"," path, bs=8, fnames = fnames, label_func = label_func, codes = codes\n",")"],"metadata":{"id":"Pid1wQ1MTrEr"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We do not need to pass item_tfms to resize our images here because they already are all of the same size.\n","\n","As usual, we can have a look at our data with the show_batch method. In this instance, the fastai library is superimposing the masks with one specific color per pixel:\n"],"metadata":{"id":"f-d9dlbRTucP"}},{"cell_type":"code","source":["dls.show_batch(max_n=6)"],"metadata":{"id":"c-GcDYOjTySI"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["\n","\n","A traditional CNN won't work for segmentation, we have to use a special kind of model called a UNet, so we use unet_learner to define our Learner:\n"],"metadata":{"id":"z2Gms_cvT2rt"}},{"cell_type":"code","source":["learn = unet_learner(dls, resnet34)\n","learn.fine_tune(6)"],"metadata":{"id":"BifcqVMIT50D"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["We can use show_results to get target vs prediction within the image itself"],"metadata":{"id":"oPo9n7TGT9ja"}},{"cell_type":"code","source":["learn.show_results(max_n=4, figsize=(10,8))"],"metadata":{"id":"q2r1113xUI60"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["\n","\n","We can also sort the model's errors on the validation set using the SegmentationInterpretation class and then plot the instances with the k highest contributions to the validation loss.\n"],"metadata":{"id":"rvS46DtSUOm6"}},{"cell_type":"code","source":["interp = SegmentationInterpretation.from_learner(learn)\n","interp.plot_top_losses(k=3)"],"metadata":{"id":"R2GibRihUQqx"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["## Group Evaluation\n","As a breakout room, discuss how segmentation can be useful along with why do we need them in the first place.\n","Feel free to discuss this with other members too."],"metadata":{"id":"YzFWyq2I4YUs"}}]} \ No newline at end of file diff --git a/MACxMDN_Workshops/stable_diffusion.ipynb b/MACxMDN_Workshops/stable_diffusion.ipynb new file mode 100644 index 0000000..0088c10 --- /dev/null +++ b/MACxMDN_Workshops/stable_diffusion.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"markdown","metadata":{"id":"DfjV7Y-h-2lg"},"source":["# Stable Diffusion with ๐Ÿค— Diffusers"]},{"cell_type":"markdown","metadata":{"id":"4oTwQWHI-2li"},"source":["**Pedro Cuenca, Patrick von Platen, Suraj Patil, Jeremy Howard**\n","\n","Chances are you'll have seen examples in Twitter (and elsewhere) of images generated by typing a short description of the scene you want to create. This is the culmination of years of work in generative models. This notebook introduces Stable Diffusion, the highest-quality open source text to image model as of now. It's also small enough to run in consumer GPUs rather than in a datacenter. We use the ๐Ÿค— Hugging Face [๐Ÿงจ Diffusers library](https://github.com/huggingface/diffusers), which is currently our recommended library for using diffusion models.\n","\n","As we'll see during the course, understanding state-of-the-art generative models requires a deep understanding of many of the fundamental blocks in modern machine learning models. This notebook shows what Stable Diffusion can do and a glimpse of its main components.\n","\n","_If you open this notebook in Colab, or if you get type errors when generating your first image, please uncomment and run the following cell._"]},{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"iqGCdykh-2ln","executionInfo":{"status":"ok","timestamp":1677419412832,"user_tz":-660,"elapsed":23401,"user":{"displayName":"Jason Toskov","userId":"06095288519589060777"}},"outputId":"c1dbfe3b-5d4f-4229-d5c6-f77aedc6d788"},"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m716.4/716.4 KB\u001b[0m \u001b[31m18.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m6.3/6.3 MB\u001b[0m \u001b[31m45.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m190.3/190.3 KB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[2K \u001b[90mโ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”โ”\u001b[0m \u001b[32m7.6/7.6 MB\u001b[0m \u001b[31m55.4 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n","\u001b[?25h"]}],"source":["!pip install -Uq diffusers transformers fastcore"]},{"cell_type":"markdown","metadata":{"id":"CSu50C8P-2lo"},"source":["## Using Stable Diffusion"]},{"cell_type":"markdown","metadata":{"id":"orO6_tmE-2lo"},"source":["To run Stable Diffusion on your computer you have to accept the model license. It's an open CreativeML OpenRail-M license that claims no rights on the outputs you generate and prohibits you from deliberately producing illegal or harmful content. The [model card](https://huggingface.co/CompVis/stable-diffusion-v1-4) provides more details. If you do accept the license, you need to be a registered user in ๐Ÿค— Hugging Face Hub and use an access token for the code to work. You have two options to provide your access token:\n","\n","* Use the `huggingface-cli login` command-line tool in your terminal and paste your token when prompted. It will be saved in a file in your computer.\n","* Or use `notebook_login()` in a notebook, which does the same thing."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"C96Iea9l-2lo"},"outputs":[],"source":["import logging\n","from pathlib import Path\n","\n","import matplotlib.pyplot as plt\n","import torch\n","from diffusers import StableDiffusionPipeline\n","from fastcore.all import concat\n","from huggingface_hub import notebook_login\n","from PIL import Image\n","\n","logging.disable(logging.WARNING)\n","\n","torch.manual_seed(1)\n","if not (Path.home()/'.huggingface'/'token').exists(): notebook_login()"]},{"cell_type":"markdown","metadata":{"id":"sueEewRD-2lo"},"source":["### Stable Diffusion Pipeline"]},{"cell_type":"markdown","metadata":{"id":"gcJwt1I8-2lp"},"source":["[`StableDiffusionPipeline`](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion#diffusers.StableDiffusionPipeline) is an end-to-end [diffusion inference pipeline](https://huggingface.co/docs/diffusers/main/en/api/pipelines/stable_diffusion) that allows you to start generating images with just a few lines of code. Many Hugging Face libraries (along with other libraries such as scikit-learn) use the concept of a \"pipeline\" to indicate a sequence of steps that when combined complete some task. We'll look at the individual steps of the pipeline later -- for now though, let's just use it to see what it can do.\n","\n","When we say \"inference\" we're referring to using an existing model to generate samples (in this case, images), as opposed to \"training\" (or fine-tuning) models using new data.\n","\n","We use [`from_pretrained`](https://huggingface.co/docs/diffusers/main/en/api/diffusion_pipeline#diffusers.DiffusionPipeline.from_pretrained) to create the pipeline and download the pretrained weights. We indicate that we want to use the `fp16` (half-precision) version of the weights, and we tell `diffusers` to expect the weights in that format. This allows us to perform much faster inference with almost no discernible difference in quality. The string passed to `from_pretrained` in this case (`CompVis/stable-diffusion-v1-4`) is the repo id of a pretrained pipeline hosted on [Hugging Face Hub](https://huggingface.co/models); it can also be a path to a directory containing pipeline weights. The weights for all the models in the pipeline will be downloaded and cached the first time you run this cell."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fBHSM2PX-2lp"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", revision=\"fp16\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"ZJ0FOrGp-2lq"},"source":["The weights are cached in your home directory by default."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"fKRh9yIt-2lq"},"outputs":[],"source":["!ls ~/.cache/huggingface/diffusers/"]},{"cell_type":"markdown","metadata":{"id":"yqpUDcrx-2lr"},"source":["We are now ready to use the pipeline to start creating images."]},{"cell_type":"markdown","metadata":{"id":"zFpQZHxd-2lr"},"source":["If your GPU is not big enough to use `pipe`, run `pipe.enable_attention_slicing()` \n","As described in the docs: \n","> When this option is enabled, the attention module will split the input tensor in slices, to compute attention in several steps. This is useful to save some memory in exchange for a small speed decrease."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MRZBK5Fn-2lr"},"outputs":[],"source":["#pipe.enable_attention_slicing()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"_ZavhNiP-2lr"},"outputs":[],"source":["prompt = \"a photograph of an astronaut riding a horse\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"oEHaovk5-2ls"},"outputs":[],"source":["pipe(prompt).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"6FQjlD2w-2ls"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt).images[0]"]},{"cell_type":"markdown","metadata":{"id":"nlQOUWQn-2ls"},"source":["You will have noticed that running the pipeline shows a progress bar with a certain number of steps. This is because Stable Diffusion is based on a progressive denoising algorithm that is able to create a convincing image starting from pure random noise. Models in this family are known as _diffusion models_. Here's an example of the process (from random noise at top to progressively improved images towards the bottom) of a model drawing handwritten digits, which we'll build from scratch ourselves later in the course."]},{"cell_type":"markdown","metadata":{"id":"9lD_G4uu-2ls"},"source":["![image.png](attachment:image.png)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CXhiiqgR-2ls"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt, num_inference_steps=3).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Yv5BTAwq-2lt"},"outputs":[],"source":["torch.manual_seed(1024)\n","pipe(prompt, num_inference_steps=16).images[0]"]},{"cell_type":"markdown","metadata":{"id":"Hd68HoT7-2lt"},"source":["### Classifier-Free Guidance"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"bYOW1Dvr-2lt"},"outputs":[],"source":["def image_grid(imgs, rows, cols):\n"," w,h = imgs[0].size\n"," grid = Image.new('RGB', size=(cols*w, rows*h))\n"," for i, img in enumerate(imgs): grid.paste(img, box=(i%cols*w, i//cols*h))\n"," return grid"]},{"cell_type":"markdown","metadata":{"id":"fGiauPct-2lt"},"source":["_Classifier-Free Guidance_ is a method to increase the adherence of the output to the conditioning signal we used (the text).\n","\n","Roughly speaking, the larger the guidance the more the model tries to represent the text prompt. However, large values tend to produce less diversity. The default is `7.5`, which represents a good compromise between variety and fidelity. This [blog post](https://benanne.github.io/2022/05/26/guidance.html) goes into deeper details on how it works.\n","\n","We can generate multiple images for the same prompt by simply passing a list of prompts instead of a string."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"lu9vqctw-2lt"},"outputs":[],"source":["num_rows,num_cols = 4,4\n","prompts = [prompt] * num_cols"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"heuqecm3-2lu"},"outputs":[],"source":["images = concat(pipe(prompts, guidance_scale=g).images for g in [1.1,3,7,14])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-rL5bT51-2lu"},"outputs":[],"source":["image_grid(images, rows=num_rows, cols=num_cols)"]},{"cell_type":"markdown","metadata":{"id":"Cy4D8-aN-2lu"},"source":["### Negative prompts"]},{"cell_type":"markdown","metadata":{"id":"sikhSpRD-2lu"},"source":["_Negative prompting_ refers to the use of another prompt (instead of a completely unconditioned generation), and scaling the difference between generations of that prompt and the conditioned generation."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MhT8-2SH-2ly"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Labrador in the style of Vermeer\"\n","pipe(prompt).images[0]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"V0RMD3h0-2lz"},"outputs":[],"source":["torch.manual_seed(1000)\n","pipe(prompt, negative_prompt=\"blue\").images[0]"]},{"cell_type":"markdown","metadata":{"id":"wCKwoIwe-2lz"},"source":["By using the negative prompt we move more towards the direction of the positive prompt, effectively reducing the importance of the negative prompt in our composition."]},{"cell_type":"markdown","metadata":{"id":"0adEmVZx-2lz"},"source":["### Image to Image"]},{"cell_type":"markdown","metadata":{"id":"0o2ix3R0-2l0"},"source":["Even though Stable Diffusion was trained to generate images, and optionally drive the generation using text conditioning, we can use the raw image diffusion process for other tasks.\n","\n","For example, instead of starting from pure noise, we can start from an image an add a certain amount of noise to it. We are replacing the initial steps of the denoising and pretending our image is what the algorithm came up with. Then we continue the diffusion process from that state as usual.\n","\n","This usually preserves the composition although details may change a lot. It's great for sketches!"]},{"cell_type":"markdown","metadata":{"id":"pFXfHiY5-2l0"},"source":["These operations (provide an initial image, add some noise to it and run diffusion from there) can be automatically performed by a special image to image pipeline: `StableDiffusionImg2ImgPipeline`. This is the source code for its [`__call__` method](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion_img2img.py#L124)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"MO-N6nwq-2l0"},"outputs":[],"source":["from diffusers import StableDiffusionImg2ImgPipeline\n","from fastdownload import FastDownload"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"KkJXDISU-2l0"},"outputs":[],"source":["pipe = StableDiffusionImg2ImgPipeline.from_pretrained(\n"," \"CompVis/stable-diffusion-v1-4\",\n"," revision=\"fp16\",\n"," torch_dtype=torch.float16,\n",").to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"xtiRYn5m-2l1"},"source":["We'll use as an example the following sketch created by [user VigilanteRogue81](https://huggingface.co/spaces/huggingface-projects/diffuse-the-rest/discussions/204)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9f7IZQDo-2l1"},"outputs":[],"source":["p = FastDownload().download('https://s3.amazonaws.com/moonup/production/uploads/1664665907257-noauth.png')\n","init_image = Image.open(p).convert(\"RGB\")\n","init_image"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"aCQ_lWbi-2l1"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Wolf howling at the moon, photorealistic 4K\"\n","images = pipe(prompt=prompt, num_images_per_prompt=3, init_image=init_image, strength=0.8, num_inference_steps=50).images\n","image_grid(images, rows=1, cols=3)"]},{"cell_type":"markdown","metadata":{"id":"quljDW8w-2l1"},"source":["When we get a composition we like we can use it as the next seed for another prompt and further change the results. For example, let's take the third image above and try to use it to generate something in the style of Van Gogh."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"GOmmR-2X-2l2"},"outputs":[],"source":["init_image = images[2]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"gd_UCTEm-2l2"},"outputs":[],"source":["torch.manual_seed(1000)\n","prompt = \"Oil painting of wolf howling at the moon by Van Gogh\"\n","images = pipe(prompt=prompt, num_images_per_prompt=3, init_image=init_image, strength=1, num_inference_steps=70).images\n","image_grid(images, rows=1, cols=3)"]},{"cell_type":"markdown","metadata":{"id":"VzPjopxD-2l2"},"source":["Creative people use different tools in a process of iterative refinement to come up with the ideas they have in mind. Here's a [list with some suggestions](https://github.com/fastai/diffusion-nbs/blob/43a090286e5742f807d4ff58524c02a1888b3004/suggested_tools.md) to get started. "]},{"cell_type":"markdown","metadata":{"id":"bcBRmlzO-2l2"},"source":["### Fine-tuning"]},{"cell_type":"markdown","metadata":{"id":"NqN5LylJ-2l2"},"source":["[How we made the text-to-pokemon model at Lambda](https://lambdalabs.com/blog/how-to-fine-tune-stable-diffusion-how-we-made-the-text-to-pokemon-model-at-lambda/)"]},{"cell_type":"markdown","metadata":{"id":"hc_-Mtcc-2l2"},"source":["![](https://lambdalabs.com/blog/content/images/2022/09/image.png)\n","\n","Girl with a pearl earring, Cute Obama creature, Donald Trump, Boris Johnson, Totoro, Hello Kitty"]},{"cell_type":"markdown","metadata":{"id":"PwrC3obP-2l3"},"source":["### Textual Inversion"]},{"cell_type":"markdown","metadata":{"id":"Wbb_reim-2l3"},"source":["Textual inversion is a process where you can quickly \"teach\" a new word to the text model and train its embeddings close to some visual representation. This is achieved by adding a new token to the vocabulary, freezing the weights of all the models (except the text encoder), and train with a few representative images.\n","\n","This is a schematic representation of the process by the [authors of the paper](https://textual-inversion.github.io).\n","\n","![Textual Inversion diagram](https://textual-inversion.github.io/static/images/training/training.JPG)"]},{"cell_type":"markdown","metadata":{"id":"Ij4XFfAi-2l3"},"source":["---"]},{"cell_type":"markdown","metadata":{"id":"0fEgcCcj-2l3"},"source":["You can train your own tokens with photos you provide using [this training script](https://github.com/huggingface/diffusers/tree/main/examples/textual_inversion) or [Google Colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_textual_inversion_training.ipynb). There's also a [Colab notebook for inference](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/stable_conceptualizer_inference.ipynb), but we'll show below the steps we have to follow to add a trained token to the vocabulary and make it work the pre-trained Stable Diffusion model.\n","\n","We'll try an example using embeddings trained for [this style](https://huggingface.co/sd-concepts-library/indian-watercolor-portraits)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Nl1pfoja-2l3"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"CompVis/stable-diffusion-v1-4\", revision=\"fp16\", torch_dtype=torch.float16) \n","pipe = pipe.to(\"cuda\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sUv6gQU2-2l4"},"outputs":[],"source":["embeds_url = \"https://huggingface.co/sd-concepts-library/indian-watercolor-portraits/resolve/main/learned_embeds.bin\"\n","embeds_path = FastDownload().download(embeds_url)\n","embeds_dict = torch.load(str(embeds_path), map_location=\"cpu\")"]},{"cell_type":"markdown","metadata":{"id":"cfGN-oUv-2l4"},"source":["The embeddings for the new token are stored in a small PyTorch pickled dictionary, whose key is the new text token that was trained. Since the encoder of our pipeline does not know about this term, we need to manually append it."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"q_1ScINr-2l4"},"outputs":[],"source":["tokenizer = pipe.tokenizer\n","text_encoder = pipe.text_encoder\n","new_token, embeds = next(iter(embeds_dict.items()))\n","embeds = embeds.to(text_encoder.dtype)\n","new_token"]},{"cell_type":"markdown","metadata":{"id":"vk3sKAHw-2l4"},"source":["We add the new token to the tokenizer and the trained embeddings to the embeddings table."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1kzrqc-Q-2l4"},"outputs":[],"source":["assert tokenizer.add_tokens(new_token) == 1, \"The token already exists!\""]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kXeDj7-z-2l5"},"outputs":[],"source":["text_encoder.resize_token_embeddings(len(tokenizer))\n","new_token_id = tokenizer.convert_tokens_to_ids(new_token)\n","text_encoder.get_input_embeddings().weight.data[new_token_id] = embeds"]},{"cell_type":"markdown","metadata":{"id":"cfmZke9d-2l5"},"source":["We can now run inference and refer to the style as if it was another word in the dictionary."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"P0XoNAA4-2l5"},"outputs":[],"source":["torch.manual_seed(1000)\n","image = pipe(\"Woman reading in the style of \").images[0]\n","image"]},{"cell_type":"markdown","metadata":{"id":"0LJbZ5Kn-2l5"},"source":["### Dreambooth"]},{"cell_type":"markdown","metadata":{"id":"UoAcSgWP-2l5"},"source":["[Dreambooth](https://dreambooth.github.io) is a kind of fine-tuning that attempts to introduce new subjects by providing just a few images of the new subject. The goal is similar to that of [Textual Inversion](#Textual-Inversion), but the process is different. Instead of creating a new token as Textual Inversion does, we select an existing token in the vocabulary (usually a rarely used one), and fine-tune the model for a few hundred steps to bring that token close to the images we provide. This is a regular fine-tuning process in which all modules are unfrozen."]},{"cell_type":"markdown","metadata":{"id":"cWQvmCuH-2l5"},"source":["For example, we fine-tuned a model with a prompt like `\"photo of a sks person\"`, using the rare `sks` token to qualify the term `person`, and using photos of Jeremy as the targets. Let's see how it works."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"-yKu9QNK-2l6"},"outputs":[],"source":["pipe = StableDiffusionPipeline.from_pretrained(\"pcuenq/jh_dreambooth_1000\", torch_dtype=torch.float16)\n","pipe = pipe.to(\"cuda\")"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0a-Wno29-2l6"},"outputs":[],"source":["torch.manual_seed(1000)\n","\n","prompt = \"Painting of sks person in the style of Paul Signac\"\n","images = pipe(prompt, num_images_per_prompt=4).images\n","image_grid(images, 1, 4)"]},{"cell_type":"markdown","metadata":{"id":"IKE5zpYv-2l6"},"source":["Fine-tuning with Dreambooth is finicky and sensitive to hyperparameters, as we are essentially asking the model to overfit the prompt to the supplied images. In some situations we observe problems such as catastrophic forgetting of the associated term (`\"person\"` in this case). The authors applied a technique called \"prior preservation\" to try to avoid it by performing a special regularization using other examples of the term, in addition to the ones we provided. The cool thing about this idea is that those examples can be easily generated by the pre-trained Stable Diffusion model itself! We did not use that method in the model we trained for the previous example.\n","\n","Other ideas that may work include: use EMA so that the final weights preserve some of the previous knowledge, use progressive learning rates for fine-tuning, or combine the best of Textual Inversion with Dreambooth. These could make for some interesting projects to try out!\n","\n","If you want to train your own Dreambooth model, you can use [this script](https://github.com/huggingface/diffusers/tree/main/examples/dreambooth) or [this Colab notebook](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/sd_dreambooth_training.ipynb). "]},{"cell_type":"markdown","metadata":{"id":"BuGZCO_1-2l6"},"source":["### What is Stable Diffusion"]},{"cell_type":"markdown","metadata":{"id":"nOh7Sjbk-2l_"},"source":["There are three main components in latent diffusion.\n","\n","1. An autoencoder (VAE).\n","2. A [U-Net](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb#scrollTo=wW8o1Wp0zRkq).\n","3. A text-encoder, *e.g.* [CLIP's Text Encoder](https://huggingface.co/docs/transformers/model_doc/clip#transformers.CLIPTextModel).\n","\n","The output of the U-Net, being the noise residual, is used to compute a denoised latent image representation via a scheduler algorithm. Many different scheduler algorithms can be used for this computation, each having its pros and cons. For Stable Diffusion, we recommend using one of:\n","\n","- [PNDM scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_pndm.py) (used by default)\n","- [DDIM scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_ddim.py)\n","- [K-LMS scheduler](https://github.com/huggingface/diffusers/blob/main/src/diffusers/schedulers/scheduling_lms_discrete.py)"]},{"cell_type":"markdown","metadata":{"id":"KPjpwIas-2l_"},"source":["### Latents and callbacks"]},{"cell_type":"markdown","metadata":{"id":"_3cEPdAg-2mA"},"source":["Stable Diffusion is based on a particular type of diffusion model called **Latent Diffusion**, proposed in [High-Resolution Image Synthesis with Latent Diffusion Models](https://arxiv.org/abs/2112.10752).\n","\n","General diffusion models are machine learning systems that are trained to *denoise* random gaussian noise step by step, to get to a sample of interest, such as an *image*. For a more detailed overview of how they work, check [this colab](https://colab.research.google.com/github/huggingface/notebooks/blob/main/diffusers/diffusers_intro.ipynb).\n","\n","Diffusion models have shown to achieve state-of-the-art results for generating image data. But one downside of diffusion models is that the reverse denoising process is slow. In addition, these models consume a lot of memory because they operate in pixel space, which becomes unreasonably expensive when generating high-resolution images. Therefore, it is challenging to train these models and also use them for inference.\n","\n","Latent diffusion can reduce the memory and compute complexity by applying the diffusion process over a lower dimensional _latent_ space, instead of using the actual pixel space. This is the key difference between standard diffusion and latent diffusion models: **in latent diffusion the model is trained to generate latent (compressed) representations of the images.** \n","\n","The Stable Diffusion pipeline can send intermediate latents to a callback function we provide. By running these latents through the image decoder (the `vae` component of the pipeline), we can see how the denoising process progresses and the image unfolds."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"o152JUgO-2mA"},"outputs":[],"source":["vae = pipe.vae\n","images = []\n","\n","def latents_callback(i, t, latents):\n"," latents = 1 / 0.18215 * latents\n"," image = vae.decode(latents).sample[0]\n"," image = (image / 2 + 0.5).clamp(0, 1)\n"," image = image.cpu().permute(1, 2, 0).numpy()\n"," images.extend(pipe.numpy_to_pil(image))\n","\n","prompt = \"Portrait painting of Jeremy Howard looking happy.\"\n","torch.manual_seed(9000)\n","final_image = pipe(prompt, callback=latents_callback, callback_steps=12).images[0]\n","images.append(final_image)\n","image_grid(images, rows=1, cols=len(images))"]},{"cell_type":"markdown","metadata":{"id":"IC7lG919-2mA"},"source":["**Why is latent diffusion fast and efficient?**\n","\n","Since the U-Net of latent diffusion models operates on a low dimensional space, it greatly reduces the memory and compute requirements compared to pixel-space diffusion models. For example, the autoencoder used in Stable Diffusion has a reduction factor of 8 but uses 4 channels instead of 3. This means that an image of shape `(3, 512, 512)` becomes `(4, 64, 64)` in latent space, which requires `8 ร— 8 ร— 3/4 = 48` times less memory.\n","\n","This is why it's possible to generate `512 ร— 512` images so quickly, even on 16GB Colab GPUs!"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"zFwipDzV-2mB"},"outputs":[],"source":["del pipe"]},{"cell_type":"markdown","metadata":{"id":"uBy4eD6C-2mB"},"source":["## Looking inside the pipeline"]},{"cell_type":"markdown","metadata":{"id":"aryhtvro-2mB"},"source":["The inference pipeline is just a small piece of code that plugs the components together and performs the inference loop. [This is all there it to is](https://github.com/huggingface/diffusers/blob/main/src/diffusers/pipelines/stable_diffusion/pipeline_stable_diffusion.py#L204).\n","\n","We'll go through the process of loading and plugging the pieces to see how we could have written it ourselves. We'll start by loading all the modules that we need from their pretrained weights.\n","\n","First, we need the text encoder and the tokenizer. These come from the text portion of a standard CLIP model, so we'll use the weights released by Open AI."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"y9UFcWi5-2mB"},"outputs":[],"source":["from transformers import CLIPTextModel, CLIPTokenizer"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"sWkJdW8m-2mB"},"outputs":[],"source":["tokenizer = CLIPTokenizer.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16)\n","text_encoder = CLIPTextModel.from_pretrained(\"openai/clip-vit-large-patch14\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"3QqP_-5Y-2mB"},"source":["Next we'll load the `vae` and the `unet`. These are distinct models whose weights are stored inside folders of the Stable Diffusion repository. We can use the `subfolder` argument to refer to [these locations](https://huggingface.co/CompVis/stable-diffusion-v1-4/tree/main)."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"9WpSJLCn-2mC"},"outputs":[],"source":["from diffusers import AutoencoderKL, UNet2DConditionModel"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"yiqHV103-2mC"},"outputs":[],"source":["# Here we use a different VAE to the original release, which has been fine-tuned for more steps\n","vae = AutoencoderKL.from_pretrained(\"stabilityai/sd-vae-ft-ema\", torch_dtype=torch.float16).to(\"cuda\")\n","unet = UNet2DConditionModel.from_pretrained(\"CompVis/stable-diffusion-v1-4\", subfolder=\"unet\", torch_dtype=torch.float16).to(\"cuda\")"]},{"cell_type":"markdown","metadata":{"id":"BzzbP91I-2mC"},"source":["To make things a bit different, we'll use another scheduler. The standard pipeline uses the [PNDM Scheduler](https://arxiv.org/abs/2202.09778), but we'll use [Katherine Crowson's](https://github.com/crowsonkb) excellent K-LMS scheduler.\n","\n","We need to be careful to use the same noising schedule that was used during training. The schedule is defined by the number of noising steps and the amount of noise added at each step, which is derived from the _beta_ parameters.\n","\n","In the case of the k-LMS scheduler, this is how the betas evolve during the 1000 steps of the noising process used during training:"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jSNTyByp-2mC"},"outputs":[],"source":["beta_start,beta_end = 0.00085,0.012\n","plt.plot(torch.linspace(beta_start**0.5, beta_end**0.5, 1000) ** 2)\n","plt.xlabel('Timestep')\n","plt.ylabel('ฮฒ');"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"pYpktBtd-2mD"},"outputs":[],"source":["from diffusers import LMSDiscreteScheduler"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"jU6YfZwj-2mD"},"outputs":[],"source":["scheduler = LMSDiscreteScheduler(beta_start=beta_start, beta_end=beta_end, beta_schedule=\"scaled_linear\", num_train_timesteps=1000)"]},{"cell_type":"markdown","metadata":{"id":"40KrUHd0-2mE"},"source":["We now define the parameters we'll use for generation.\n","\n","In contrast with the previous examples, we set `num_inference_steps` to 70 to get an even more defined image."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"PldH5EW2-2mE"},"outputs":[],"source":["prompt = [\"a photograph of an astronaut riding a horse\"]\n","\n","height = 512\n","width = 512\n","num_inference_steps = 70\n","guidance_scale = 7.5\n","batch_size = 1"]},{"cell_type":"markdown","metadata":{"id":"oF8sdjg1-2mE"},"source":["We tokenize the prompt. The model requires the same number of tokens for every prompt, so padding is used to ensure we meet the required length."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"VeWknRpZ-2mE"},"outputs":[],"source":["text_input = tokenizer(prompt, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n","text_input['input_ids']"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"I3ejTToK-2mF"},"outputs":[],"source":["tokenizer.decode(49407)"]},{"cell_type":"markdown","metadata":{"id":"VXSV3YgV-2mF"},"source":["The attention mask uses zero to represent tokens we are not interested in. These are all of the padding tokens."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"RQs6WjsR-2mF"},"outputs":[],"source":["text_input['attention_mask']"]},{"cell_type":"markdown","metadata":{"id":"3syCUMx_-2mF"},"source":["The text encoder gives us the embeddings for the text prompt we used."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Awjd6tFQ-2mG"},"outputs":[],"source":["text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0].half()\n","text_embeddings.shape"]},{"cell_type":"markdown","metadata":{"id":"cw-2y6Qu-2mG"},"source":["We also get the embeddings required to perform unconditional generation, which is achieved with an empty string: the model is free to go in whichever direction it wants as long as it results in a reasonably-looking image. These embeddings will be applied to apply classifier-free guidance."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"0FEJT1SC-2mG"},"outputs":[],"source":["max_length = text_input.input_ids.shape[-1]\n","uncond_input = tokenizer(\n"," [\"\"] * batch_size, padding=\"max_length\", max_length=max_length, return_tensors=\"pt\"\n",")\n","uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0].half()\n","uncond_embeddings.shape"]},{"cell_type":"markdown","metadata":{"id":"yTjESaPB-2mH"},"source":["For classifier-free guidance, we need to do two forward passes. One with the conditioned input (`text_embeddings`), and another with the unconditional embeddings (`uncond_embeddings`). In practice, we can concatenate both into a single batch to avoid doing two forward passes."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"DyLpGORT-2mH"},"outputs":[],"source":["text_embeddings = torch.cat([uncond_embeddings, text_embeddings])"]},{"cell_type":"markdown","metadata":{"id":"41a3lUxx-2mH"},"source":["To start the denoising process, we start from pure Gaussian (normal) noise. These are our initial latents."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"5Fke6S2T-2mH"},"outputs":[],"source":["torch.manual_seed(100)\n","latents = torch.randn((batch_size, unet.in_channels, height // 8, width // 8))\n","latents = latents.to(\"cuda\").half()\n","latents.shape"]},{"cell_type":"markdown","metadata":{"id":"yP9Wl7F4-2mH"},"source":["`4ร—64ร—64` is the input shape. The decoder will later transform this latent representation into a `3ร—512ร—512` image after the denoising process is complete.\n","\n","Next, we initialize the scheduler with our chosen `num_inference_steps`. This will prepare the internal state to be used during denoising."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"7fjN_UQ0-2mH"},"outputs":[],"source":["scheduler.set_timesteps(num_inference_steps)"]},{"cell_type":"markdown","metadata":{"id":"PklW6gjb-2mH"},"source":["We scale the initial noise by the standard deviation required by the scheduler. This value will depend on the particular scheduler we use."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Rbmdx1e5-2mL"},"outputs":[],"source":["latents = latents * scheduler.init_noise_sigma"]},{"cell_type":"markdown","metadata":{"id":"OsC6prhu-2mL"},"source":["We are ready to write the denoising loop. The timesteps go from `999` to `0` (1000 steps that were used during training) following a particular schedule."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"x4RAFcrY-2mM"},"outputs":[],"source":["scheduler.timesteps"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ChotNTa_-2mM"},"outputs":[],"source":["scheduler.sigmas"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"c8lbxO_r-2mM"},"outputs":[],"source":["plt.plot(scheduler.timesteps, scheduler.sigmas[:-1]);"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"exg731GE-2mM"},"outputs":[],"source":["from tqdm.auto import tqdm"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"rqcJhMXO-2mM"},"outputs":[],"source":["for i, t in enumerate(tqdm(scheduler.timesteps)):\n"," input = torch.cat([latents] * 2)\n"," input = scheduler.scale_model_input(input, t)\n","\n"," # predict the noise residual\n"," with torch.no_grad(): pred = unet(input, t, encoder_hidden_states=text_embeddings).sample\n","\n"," # perform guidance\n"," pred_uncond, pred_text = pred.chunk(2)\n"," pred = pred_uncond + guidance_scale * (pred_text - pred_uncond)\n","\n"," # compute the \"previous\" noisy sample\n"," latents = scheduler.step(pred, t, latents).prev_sample"]},{"cell_type":"markdown","metadata":{"id":"ORZhfNsk-2mN"},"source":["After this process complets our `latents` contain the denoised representation of the image. We use the `vae` decoder to convert it back to pixel space."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"uJ4JOqQN-2mN"},"outputs":[],"source":["with torch.no_grad(): image = vae.decode(1 / 0.18215 * latents).sample"]},{"cell_type":"markdown","metadata":{"id":"83S1kJ5y-2mN"},"source":["And finally, let's convert the image to PIL so we can display it."]},{"cell_type":"code","execution_count":null,"metadata":{"id":"IJJZdA6d-2mN"},"outputs":[],"source":["image = (image / 2 + 0.5).clamp(0, 1)\n","image = image[0].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"markdown","metadata":{"id":"fMDVhOOI-2mN"},"source":["### Just the code"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CN4R5o65-2mO"},"outputs":[],"source":["prompts = [\n"," 'a photograph of an astronaut riding a horse',\n"," 'an oil painting of an astronaut riding a horse in the style of grant wood'\n","]"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Ndi0F8Lu-2mO"},"outputs":[],"source":["text_input = tokenizer(prompts, padding=\"max_length\", max_length=tokenizer.model_max_length, truncation=True, return_tensors=\"pt\")\n","text_embeddings = text_encoder(text_input.input_ids.to(\"cuda\"))[0].half()"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"Pa_b326E-2mO"},"outputs":[],"source":["max_length = text_input.input_ids.shape[-1]\n","uncond_input = tokenizer([\"\"] * len(prompts), padding=\"max_length\", max_length=max_length, return_tensors=\"pt\")\n","uncond_embeddings = text_encoder(uncond_input.input_ids.to(\"cuda\"))[0].half()\n","emb = torch.cat([uncond_embeddings, text_embeddings])"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"FyNM2YG7-2mO"},"outputs":[],"source":["torch.manual_seed(100)\n","g = guidance_scale"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"vg5JgmVV-2mO"},"outputs":[],"source":["latents = torch.randn((len(prompts), unet.in_channels, height//8, width//8))\n","scheduler.set_timesteps(num_inference_steps)\n","latents = latents.to(\"cuda\").half() * scheduler.init_noise_sigma"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"WNItr4xS-2mO"},"outputs":[],"source":["for i,ts in enumerate(tqdm(scheduler.timesteps)):\n"," inp = scheduler.scale_model_input(torch.cat([latents] * 2), ts)\n"," with torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)\n"," pred = u + g*(t-u)\n"," latents = scheduler.step(pred, ts, latents).prev_sample"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"E_vFKMdU-2mP"},"outputs":[],"source":["with torch.no_grad(): image = vae.decode(1 / 0.18215 * latents).sample\n","res = (image / 2 + 0.5).clamp(0, 1)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"kK4lVJLe-2mP"},"outputs":[],"source":["image = res[0].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1ZHbaZ4W-2mP"},"outputs":[],"source":["image = res[1].detach().cpu().permute(1, 2, 0).numpy()\n","image = (image * 255).round().astype(\"uint8\")\n","Image.fromarray(image)"]},{"cell_type":"markdown","metadata":{"id":"W_VV19Xf-2mP"},"source":["### Put it in functions"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"X_YbEE-C-2mP"},"outputs":[],"source":["def text_enc(prompts, maxlen=None):\n"," if maxlen is None: maxlen = tokenizer.model_max_length\n"," inp = tokenizer(prompts, padding=\"max_length\", max_length=maxlen, truncation=True, return_tensors=\"pt\")\n"," return text_encoder(inp.input_ids.to(\"cuda\"))[0].half()\n","\n","def mk_img(t):\n"," image = (t/2+0.5).clamp(0,1).detach().cpu().permute(1, 2, 0).numpy()\n"," return Image.fromarray((image*255).round().astype(\"uint8\"))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"hF4FQUWb-2mQ"},"outputs":[],"source":["def mk_samples(prompts, g=7.5, seed=100, steps=70):\n"," bs = len(prompts)\n"," text = text_enc(prompts)\n"," uncond = text_enc([\"\"] * bs, text.shape[1])\n"," emb = torch.cat([uncond, text])\n"," if seed: torch.manual_seed(seed)\n","\n"," latents = torch.randn((bs, unet.in_channels, height//8, width//8))\n"," scheduler.set_timesteps(steps)\n"," latents = latents.to(\"cuda\").half() * scheduler.init_noise_sigma\n","\n"," for i,ts in enumerate(tqdm(scheduler.timesteps)):\n"," inp = scheduler.scale_model_input(torch.cat([latents] * 2), ts)\n"," with torch.no_grad(): u,t = unet(inp, ts, encoder_hidden_states=emb).sample.chunk(2)\n"," pred = u + g*(t-u)\n"," latents = scheduler.step(pred, ts, latents).prev_sample\n","\n"," with torch.no_grad(): return vae.decode(1 / 0.18215 * latents).sample"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"ZAl09AqT-2mQ"},"outputs":[],"source":["images = mk_samples(prompts)"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"qWuqZ-YI-2mQ"},"outputs":[],"source":["from IPython.display import display"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"mjjC8au_-2mQ"},"outputs":[],"source":["for img in images: display(mk_img(img))"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"CvqjSQTR-2mQ"},"outputs":[],"source":[]}],"metadata":{"kernelspec":{"display_name":"Python 3 (ipykernel)","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":"3.10.4"},"colab":{"provenance":[]},"accelerator":"GPU","gpuClass":"standard"},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git 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