From 53b24c09b88a59169de026d729081b3cd2d38e3e Mon Sep 17 00:00:00 2001 From: Konstantine Tsafatinos Date: Tue, 16 Jul 2024 14:46:34 -0400 Subject: [PATCH] update projects for 2024 --- projects/ComputerVision/em_synapses.ipynb | 2542 +++++++---------- projects/ReinforcementLearning/human_rl.ipynb | 543 +--- .../ReinforcementLearning/lunar_lander.ipynb | 2514 ++++++++-------- requirements.txt | 10 +- 4 files changed, 2472 insertions(+), 3137 deletions(-) diff --git a/projects/ComputerVision/em_synapses.ipynb b/projects/ComputerVision/em_synapses.ipynb index 32441053b..3914df0e3 100644 --- a/projects/ComputerVision/em_synapses.ipynb +++ b/projects/ComputerVision/em_synapses.ipynb @@ -1,1487 +1,1107 @@ { - "cells": [ - { - "cell_type": "markdown", - "id": "2d9f0b20", - "metadata": { - "execution": {}, - "id": "2d9f0b20" - }, - "source": [ - "\"Open   \"Open" - ] - }, - { - "cell_type": "markdown", - "id": "renayVUI7b9x", - "metadata": { - "execution": {}, - "id": "renayVUI7b9x" - }, - "source": [ - "# Knowledge Extraction from a Convolutional Neural Network\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Jan Funke\n", - "\n", - "__Production editors:__ Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "id": "U6wofKujWp6X", - "metadata": { - "execution": {}, - "id": "U6wofKujWp6X" - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", - "\n", - "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", - "\n", - "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." - ] - }, - { - "cell_type": "markdown", - "id": "zO4YN6W8W0Cp", - "metadata": { - "execution": {}, - "id": "zO4YN6W8W0Cp" - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "fO1IZwvkW9Me", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "fO1IZwvkW9Me" - }, - "outputs": [], - "source": [ - "# @title Install dependencies\n", - "!pip install scikit-image --quiet\n", - "!pip install pillow --quiet\n", - "!pip install scikit-image --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "id": "gKkHjjTGWzUk", - "metadata": { - "execution": {}, - "id": "gKkHjjTGWzUk" - }, - "outputs": [], - "source": [ - "import glob\n", - "import json\n", - "import torch\n", - "import numpy as np\n", - "\n", - "import matplotlib.pyplot as plt\n", - "from tqdm import tqdm\n", - "\n", - "from skimage.io import imread\n", - "from torchvision.datasets import ImageFolder\n", - "from torch.utils.data import DataLoader, random_split\n", - "from torch.utils.data.sampler import WeightedRandomSampler\n", - "\n", - "%matplotlib inline" - ] - }, - { - "cell_type": "markdown", - "id": "bd7d427d", - "metadata": { - "execution": {}, - "id": "bd7d427d" - }, - "source": [ - "---\n", - "# Project Ideas\n", - "\n", - "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n", - " * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n", - " * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n", - " * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n", - " * (medium) Other networks: Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n", - " * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n", - " \n", - " \n", - "2. Explore the CycleGAN.\n", - " * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n", - " * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n", - " \n", - "\n", - "3. Try on your own data!\n", - " * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n", - "\n", - "### Acknowledgments\n", - "\n", - "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n" - ] - }, - { - "cell_type": "markdown", - "id": "5642d709", - "metadata": { - "execution": {}, - "id": "5642d709" - }, - "source": [ - "---\n", - "# Train an Image Classifier\n", - "\n", - "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine." - ] - }, - { - "cell_type": "markdown", - "id": "c61a11c6", - "metadata": { - "execution": {}, - "id": "c61a11c6" - }, - "source": [ - "## Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 12, - "id": "821dc497", - "metadata": { - "execution": {}, - "colab": { - "base_uri": "https://localhost:8080/" - }, - "id": "821dc497", - "outputId": "9871a113-d169-4d6a-b927-b00706fabd22" - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Data downloading...\n", - "Downloaded: ./part1\n", - "Downloaded: ./part2\n", - "Downloaded: ./part3\n", - "Download is completed.\n", - "Reassembling Files...\n", - "Downloaded files have been removed!\n", - "Reassembled 3 parts into resources.zip\n", - "Extracting all the files now...\n", - "Done!\n", - "\n", - "Archive: data.zip\n", - "\tExtracting data...\n", - "\n", - "Archive: checkpoints.zip\n", - "\tExtracting data...\n", - "Done!\n" - ] - } - ], - "source": [ - "# @title Download the data\n", - "import requests, os\n", - "from zipfile import ZipFile\n", - "\n", - "def download_file_parts(urls, output_directory='.'):\n", - " \"\"\"\n", - " Download file parts from given URLs and save them in the specified directory.\n", - "\n", - " :param urls: List of URLs to download\n", - " :param output_directory: Directory to save the downloaded parts (default is current directory)\n", - " :return: List of downloaded file paths\n", - " \"\"\"\n", - " if not os.path.exists(output_directory):\n", - " os.makedirs(output_directory)\n", - "\n", - " downloaded_files = []\n", - "\n", - " for i, url in enumerate(urls, 1):\n", - " try:\n", - " response = requests.get(url, stream=True)\n", - " response.raise_for_status() # Raises an HTTPError for bad requests\n", - "\n", - " file_name = f\"part{i}\"\n", - " file_path = os.path.join(output_directory, file_name)\n", - "\n", - " with open(file_path, 'wb') as file:\n", - " for chunk in response.iter_content(chunk_size=32768):\n", - " file.write(chunk)\n", - "\n", - " downloaded_files.append(file_path)\n", - " print(f\"Downloaded: {file_path}\")\n", - "\n", - "\n", - " except requests.RequestException as e:\n", - " print(f\"Error downloading {url}: {e}\")\n", - "\n", - " return downloaded_files\n", - "\n", - "def reassemble_file(output_file):\n", - " chunk_number = 1\n", - " with open(output_file, 'wb') as outfile:\n", - " while True:\n", - " chunk_name = f'part{chunk_number}'\n", - " if not os.path.exists(chunk_name):\n", - " break\n", - " with open(chunk_name, 'rb') as infile:\n", - " outfile.write(infile.read())\n", - " chunk_number += 1\n", - " for i in ['part1', 'part2', 'part3']:\n", - " if os.path.exists(i):\n", - " os.remove(i)\n", - " print(f\"Downloaded files have been removed!\")\n", - " print(f\"Reassembled {chunk_number-1} parts into {output_file}\")\n", - "\n", - "\n", - "\n", - "# @markdown Download the resources for this tutorial (one zip file)\n", - "fname = 'resources.zip'\n", - "urls = [\n", - " \"https://osf.io/download/4x7p3/\",\n", - " \"https://osf.io/download/fzwea/\",\n", - " \"https://osf.io/download/qpbcv/\"\n", - "]\n", - "\n", - "if not os.path.exists('data/'):\n", - " print('Data downloading...')\n", - " output_dir = \".\"\n", - " downloaded_parts = download_file_parts(urls, output_dir)\n", - " print('Download is completed.')\n", - "\n", - " print('Reassembling Files...')\n", - " base_name = ''\n", - " reassemble_file(fname)\n", - "\n", - " # @markdown Unzip the file\n", - " with ZipFile(fname, 'r') as zf:\n", - " # extracting all the files\n", - " print('Extracting all the files now...')\n", - " zf.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # # @markdown Extract the data\n", - " fnames = ['data.zip', 'checkpoints.zip']\n", - "\n", - " for fname in fnames:\n", - " with ZipFile(fname, 'r') as zh:\n", - " # extracting all the files\n", - " print(f\"\\nArchive: {fname}\")\n", - " print(f\"\\tExtracting data...\")\n", - " zh.extractall(path='.')\n", - " print('Done!')\n", - "\n", - " # @markdown Make sure the order of classes matches the pretrained model\n", - " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", - " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", - " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", - " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", - " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", - " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", - "\n", - " # @markdown Remove the archives\n", - " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", - " if os.path.exists(i):\n", - " os.remove(i)\n", - "\n", - "else:\n", - " print('Data are already downloaded.')" - ] - }, - { - "cell_type": "markdown", - "id": "0b84ec7b", - "metadata": { - "execution": {}, - "id": "0b84ec7b" - }, - "source": [ - "## Classifier Training" - ] - }, - { - "cell_type": "markdown", - "id": "a79ab567", - "metadata": { - "execution": {}, - "id": "a79ab567" - }, - "source": [ - "### Create and Inspect Datasets\n", - "\n", - "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training." - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "id": "ae50b16a", - "metadata": { - "execution": {}, - "id": "ae50b16a", - "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Number of images per class:\n", - "\t0_gaba:\tn=15855\tweight=6.30715862503942e-05\n", - "\t1_acetylcholine:\tn=4911\tweight=0.00020362451639177357\n", - "\t2_glutamate:\tn=3550\tweight=0.00028169014084507044\n", - "\t3_serotonin:\tn=2297\tweight=0.00043535045711797995\n", - "\t4_octopamine:\tn=951\tweight=0.0010515247108307045\n", - "\t5_dopamine:\tn=4649\tweight=0.00021510002151000216\n" - ] - } - ], - "source": [ - "def load_image(filename):\n", - "\n", - " image = imread(filename)\n", - "\n", - " # images are grescale, we only need one of the RGB channels\n", - " image = image[:, :, 0]\n", - " # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n", - " image = image.astype(np.float32)/255.0\n", - " image = (image - 0.5)/0.5\n", - "\n", - " return image\n", - "\n", - "\n", - "# create a dataset for all images of all classes\n", - "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n", - "\n", - "# randomly split the dataset into train, validation, and test\n", - "num_images = len(full_dataset)\n", - "# ~70% for training\n", - "num_training = int(0.7 * num_images)\n", - "# ~15% for validation\n", - "num_validation = int(0.15 * num_images)\n", - "# ~15% for testing\n", - "num_test = num_images - (num_training + num_validation)\n", - "# split the data randomly (but with a fixed random seed)\n", - "train_dataset, validation_dataset, test_dataset = random_split(\n", - " full_dataset,\n", - " [num_training, num_validation, num_test],\n", - " generator=torch.Generator().manual_seed(23061912))\n", - "\n", - "# compute class weights in training dataset for uniform sampling\n", - "ys = np.array([y for _, y in train_dataset])\n", - "counts = np.bincount(ys)\n", - "label_weights = 1.0 / counts\n", - "weights = label_weights[ys]\n", - "\n", - "print(\"Number of images per class:\")\n", - "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n", - " print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n", - "\n", - "# create a data loader with uniform sampling\n", - "sampler = WeightedRandomSampler(weights, len(weights))\n", - "# this data loader will serve 8 images in a \"mini-batch\" at a time\n", - "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)" - ] - }, - { - "cell_type": "markdown", - "id": "e9010bdc", - "metadata": { - "execution": {}, - "id": "e9010bdc" - }, - "source": [ - "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "id": "3d8c6f3a", - "metadata": { - "execution": {}, - "id": "3d8c6f3a", - "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 117 - } - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
" - ], - "image/png": 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\n" - }, - "metadata": {} - } - ], - "source": [ - "def show_batch(x, y):\n", - " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", - " for i in range(x.shape[0]):\n", - " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", - " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", - " plt.show()\n", - "\n", - "# show a random batch from the data loader\n", - "# (run this cell repeatedly to see different batches)\n", - "for x, y in dataloader:\n", - " show_batch(x, y)\n", - " break" - ] - }, - { - "cell_type": "markdown", - "id": "f882416f", - "metadata": { - "execution": {}, - "id": "f882416f" - }, - "source": [ - "### Create a Model, Loss, and Optimizer" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "id": "54f177cc", - "metadata": { - "execution": {}, - "id": "54f177cc" - }, - "outputs": [], - "source": [ - "class Vgg2D(torch.nn.Module):\n", - "\n", - " def __init__(\n", - " self,\n", - " input_size,\n", - " fmaps=12,\n", - " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", - " output_classes=6):\n", - "\n", - " super(Vgg2D, self).__init__()\n", - "\n", - " self.input_size = input_size\n", - "\n", - " current_fmaps = 1\n", - " current_size = tuple(input_size)\n", - "\n", - " features = []\n", - " for i in range(len(downsample_factors)):\n", - "\n", - " features += [\n", - " torch.nn.Conv2d(\n", - " current_fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Conv2d(\n", - " fmaps,\n", - " fmaps,\n", - " kernel_size=3,\n", - " padding=1),\n", - " torch.nn.BatchNorm2d(fmaps),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.MaxPool2d(downsample_factors[i])\n", - " ]\n", - "\n", - " current_fmaps = fmaps\n", - " fmaps *= 2\n", - "\n", - " size = tuple(\n", - " int(c/d)\n", - " for c, d in zip(current_size, downsample_factors[i]))\n", - " check = (\n", - " s*d == c\n", - " for s, d, c in zip(size, downsample_factors[i], current_size))\n", - " assert all(check), \\\n", - " \"Can not downsample %s by chosen downsample factor\" % \\\n", - " (current_size,)\n", - " current_size = size\n", - "\n", - " self.features = torch.nn.Sequential(*features)\n", - "\n", - " classifier = [\n", - " torch.nn.Linear(\n", - " current_size[0] *\n", - " current_size[1] *\n", - " current_fmaps,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " 4096),\n", - " torch.nn.ReLU(inplace=True),\n", - " torch.nn.Dropout(),\n", - " torch.nn.Linear(\n", - " 4096,\n", - " output_classes)\n", - " ]\n", - "\n", - " self.classifier = torch.nn.Sequential(*classifier)\n", - "\n", - " def forward(self, raw):\n", - "\n", - " # add a channel dimension to raw\n", - " shape = tuple(raw.shape)\n", - " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", - "\n", - " # compute features\n", - " f = self.features(raw)\n", - " f = f.view(f.size(0), -1)\n", - "\n", - " # classify\n", - " y = self.classifier(f)\n", - "\n", - " return y" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "id": "5da43245", - "metadata": { - "execution": {}, - "id": "5da43245" - }, - "outputs": [], - "source": [ - "# get the size of our images\n", - "for x, y in train_dataset:\n", - " input_size = x.shape\n", - " break\n", - "\n", - "# create the model to train\n", - "model = Vgg2D(input_size)\n", - "\n", - "# create a loss\n", - "loss = torch.nn.CrossEntropyLoss()\n", - "\n", - "# create an optimzer\n", - "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" - ] - }, - { - "cell_type": "markdown", - "id": "01688095", - "metadata": { - "execution": {}, - "id": "01688095" - }, - "source": [ - "### Train the Model" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "id": "fa65090d", - "metadata": { - "execution": {}, - "id": "fa65090d", - "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Will use device cuda for training\n" - ] - } - ], - "source": [ - "# use a GPU, if it is available\n", - "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", - "model.to(device)\n", - "print(f\"Will use device {device} for training\")" - ] - }, - { - "cell_type": "markdown", - "id": "ecbab4f7", - "metadata": { - "execution": {}, - "id": "ecbab4f7" - }, - "source": [ - "The next cell merely defines some convenience functions for training, validation, and testing:" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "id": "1a8c7fe9", - "metadata": { - "execution": {}, - "id": "1a8c7fe9" - }, - "outputs": [], - "source": [ - "def train(dataloader, optimizer, loss, device):\n", - " '''Train the model for one epoch.'''\n", - "\n", - " # set the model into train mode\n", - " model.train()\n", - "\n", - " epoch_loss, num_batches = 0, 0\n", - " for x, y in tqdm(dataloader, 'train'):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - " optimizer.zero_grad()\n", - "\n", - " y_pred = model(x)\n", - " l = loss(y_pred, y)\n", - " l.backward()\n", - "\n", - " optimizer.step()\n", - "\n", - " epoch_loss += l\n", - " num_batches += 1\n", - "\n", - " return epoch_loss/num_batches\n", - "\n", - "\n", - "def evaluate(dataloader, name, device):\n", - "\n", - " correct = 0\n", - " total = 0\n", - " for x, y in tqdm(dataloader, name):\n", - "\n", - " x, y = x.to(device), y.to(device)\n", - "\n", - " logits = model(x)\n", - " probs = torch.nn.Softmax(dim=1)(logits)\n", - " predictions = torch.argmax(probs, dim=1)\n", - "\n", - " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", - " total += len(y)\n", - "\n", - " accuracy = correct/total\n", - "\n", - " return accuracy\n", - "\n", - "\n", - "def validate(validation_dataset, device):\n", - " '''Evaluate prediction accuracy on the validation dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(validation_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'validate', device)\n", - "\n", - "\n", - "def test(test_dataset, device):\n", - " '''Evaluate prediction accuracy on the test dataset.'''\n", - "\n", - " model.eval()\n", - " dataloader = DataLoader(test_dataset, batch_size=32)\n", - "\n", - " return evaluate(dataloader, 'test', device)" - ] - }, - { - "cell_type": "markdown", - "id": "68bcfbbf", - "metadata": { - "execution": {}, - "id": "68bcfbbf" - }, - "source": [ - "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." - ] - }, - { - "cell_type": "code", - "execution_count": 19, - "id": "d0af7638", - "metadata": { - "execution": {}, - "id": "d0af7638" - }, - "outputs": [], - "source": [ - "def train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device):\n", - "\n", - " for epoch in range(num_epochs):\n", - " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", - " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", - "\n", - " accuracy = validate(validation_dataset, device=device)\n", - " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "id": "45e31b87", - "metadata": { - "execution": {}, - "id": "45e31b87" - }, - "source": [ - "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", - "\n", - "Unceck the box below and run the cell to train a model." - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "id": "W5KA7zDIa3Lw", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "W5KA7zDIa3Lw" - }, - "outputs": [], - "source": [ - "# @markdown\n", - "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "id": "53fb8dda", - "metadata": { - "execution": {}, - "id": "53fb8dda" - }, - "outputs": [], - "source": [ - "# Load a pretrained model or train the model from scratch\n", - "\n", - "# set this to True and run this cell if you want a shortcut\n", - "\n", - "if yes_I_want_the_pretrained_model:\n", - " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", - " map_location=device)\n", - " model.load_state_dict(checkpoint['model_state_dict'])\n", - "else:\n", - " train_from_scratch(dataloader, validation_dataset,\n", - " optimizer, loss,\n", - " num_epochs=100, device=device)" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "id": "4f6e3663", - "metadata": { - "execution": {}, - "id": "4f6e3663", - "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "test: 100%|██████████| 216/216 [00:12<00:00, 17.76it/s]" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "final test accuracy: 0.8054750869061413\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "\n" - ] - } - ], - "source": [ - "accuracy = test(test_dataset, device=device)\n", - "print(f\"final test accuracy: {accuracy}\")" - ] - }, - { - "cell_type": "markdown", - "id": "3f43bba5", - "metadata": { - "execution": {}, - "id": "3f43bba5" - }, - "source": [ - "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", - "\n", - "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n", - "\n", - "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types." - ] - }, + "cells": [ + { + "cell_type": "markdown", + "id": "2d9f0b20", + "metadata": { + "execution": {}, + "id": "2d9f0b20" + }, + "source": [ + "\"Open   \"Open" + ] + }, + { + "cell_type": "markdown", + "id": "renayVUI7b9x", + "metadata": { + "execution": {}, + "id": "renayVUI7b9x" + }, + "source": [ + "# Knowledge Extraction from a Convolutional Neural Network\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Jan Funke\n", + "\n", + "__Production editors:__ Spiros Chavlis, Konstantine Tsafatinos" + ] + }, + { + "cell_type": "markdown", + "id": "U6wofKujWp6X", + "metadata": { + "execution": {}, + "id": "U6wofKujWp6X" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "Train a convolutional neural network to classify images and a CycleGAN to translate between images of different types.\n", + "\n", + "This notebook contains everything to train a VGG network on labelled images and to train a CycleGAN to translate between images.\n", + "\n", + "We will use electron microscopy images of Drosophila synapses for this project. Those images can be classified according to the neurotransmitter type they release." + ] + }, + { + "cell_type": "markdown", + "id": "zO4YN6W8W0Cp", + "metadata": { + "execution": {}, + "id": "zO4YN6W8W0Cp" + }, + "source": [ + "---\n", + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "fO1IZwvkW9Me", + "metadata": { + "cellView": "form", + "id": "fO1IZwvkW9Me" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "72b5240c", - "metadata": { - "execution": {}, - "id": "72b5240c" - }, - "source": [ - "---\n", - "# Train a GAN to Translate Images\n", - "\n", - "We will train a so-called CycleGAN to translate images from one class to another." - ] - }, + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install scikit-image --quiet\n", + "!pip install pillow --quiet\n", + "!pip install scikit-image --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "gKkHjjTGWzUk", + "metadata": { + "id": "gKkHjjTGWzUk" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 23, - "id": "41c9e63b", - "metadata": { - "cellView": "form", - "execution": {}, - "id": "41c9e63b" - }, - "outputs": [], - "source": [ - "# @title Get the CycleGAN code and dependencies\n", - "\n", - "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n", - "\n", - "import requests, zipfile, io\n", - "\n", - "url = 'https://osf.io/vutn5/download'\n", - "r = requests.get(url)\n", - "z = zipfile.ZipFile(io.BytesIO(r.content))\n", - "z.extractall()\n", - "\n", - "!pip install dominate --quiet" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/torch/cuda/__init__.py:619: UserWarning: Can't initialize NVML\n", + " warnings.warn(\"Can't initialize NVML\")\n" + ] + } + ], + "source": [ + "import glob\n", + "import json\n", + "import torch\n", + "import numpy as np\n", + "\n", + "import matplotlib.pyplot as plt\n", + "from tqdm import tqdm\n", + "\n", + "from skimage.io import imread\n", + "from torchvision.datasets import ImageFolder\n", + "from torch.utils.data import DataLoader, random_split\n", + "from torch.utils.data.sampler import WeightedRandomSampler\n", + "\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "id": "bd7d427d", + "metadata": { + "execution": {}, + "id": "bd7d427d" + }, + "source": [ + "---\n", + "# Project Ideas\n", + "\n", + "1. Improve the classifier. This code uses a VGG network for the classification. On the synapse dataset, we will get a validation accuracy of around 80%. Try to see if you can improve the classifier accuracy.\n", + " * (easy) Data augmentation: The training code for the classifier is quite simple in this example. Enlarge the amount of available training data by adding augmentations (transpose and mirror the images, add noise, change the intensity, etc.).\n", + " * (easy) Network architecture: The VGG network has a few parameters that one can tune. Try a few to see what difference it makes.\n", + " * (easy) Inspect the classifier predictions: Take random samples from the test dataset and classify them. Show the images together with their predicted and actual labels.\n", + " * (medium) Other networks: Try different architectures (e.g., a ResNet) and see if the accuracy can be improved.\n", + " * (medium) Inspect errors made by the classifier. Which classes are most accurately predicted? Which classes are confused with each other?\n", + " \n", + " \n", + "2. Explore the CycleGAN.\n", + " * (easy) The example code below shows how to translate between GABA and glutamate. Try different combinations, and also in the reverse direction. Can you start to see differences between some pairs of classes? Which are the ones where the differences are the most or the least obvious?\n", + " * (hard) Watching the CycleGAN train can be a bit boring. Find a way to show (periodically) the current image and its translation to see how the network is improving over time. Hint: The `cycle_gan` module has a `Visualizer`, which might be helpful.\n", + " \n", + "\n", + "3. Try on your own data!\n", + " * Have a look at how the synapse images are organized in `data/raw/synapses`. Copy the directory structure and use your own images. Depending on your data, you might have to adjust the image size (128x128 for the synapses) and number of channels in the VGG network and CycleGAN code.\n", + "\n", + "### Acknowledgments\n", + "\n", + "This notebook was written by Jan Funke, using code from Nils Eckstein and a modified version of the [CycleGAN](https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix) implementation.\n" + ] + }, + { + "cell_type": "markdown", + "id": "5642d709", + "metadata": { + "execution": {}, + "id": "5642d709" + }, + "source": [ + "---\n", + "# Train an Image Classifier\n", + "\n", + "In this section, we will implement and train a VGG classifier to classify images of synapses into one of six classes, corresponding to the neurotransmitter type that is released at the synapse: GABA, acethylcholine, glutamate, octopamine, serotonin, and dopamine." + ] + }, + { + "cell_type": "markdown", + "id": "c61a11c6", + "metadata": { + "execution": {}, + "id": "c61a11c6" + }, + "source": [ + "## Data Preparation" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "821dc497", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "821dc497", + "outputId": "9871a113-d169-4d6a-b927-b00706fabd22" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "e5da5c01", - "metadata": { - "execution": {}, - "id": "e5da5c01" - }, - "source": [ - "In this example, we will translate between GABAergic and glutamatergic synapses.\n", - "\n", - "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Data are already downloaded.\n" + ] + } + ], + "source": [ + "# @title Download the data\n", + "import requests, os\n", + "from zipfile import ZipFile\n", + "\n", + "def download_file_parts(urls, output_directory='.'):\n", + " \"\"\"\n", + " Download file parts from given URLs and save them in the specified directory.\n", + "\n", + " :param urls: List of URLs to download\n", + " :param output_directory: Directory to save the downloaded parts (default is current directory)\n", + " :return: List of downloaded file paths\n", + " \"\"\"\n", + " if not os.path.exists(output_directory):\n", + " os.makedirs(output_directory)\n", + "\n", + " downloaded_files = []\n", + "\n", + " for i, url in enumerate(urls, 1):\n", + " try:\n", + " response = requests.get(url, stream=True)\n", + " response.raise_for_status() # Raises an HTTPError for bad requests\n", + "\n", + " file_name = f\"part{i}\"\n", + " file_path = os.path.join(output_directory, file_name)\n", + "\n", + " with open(file_path, 'wb') as file:\n", + " for chunk in response.iter_content(chunk_size=32768):\n", + " file.write(chunk)\n", + "\n", + " downloaded_files.append(file_path)\n", + " print(f\"Downloaded: {file_path}\")\n", + "\n", + "\n", + " except requests.RequestException as e:\n", + " print(f\"Error downloading {url}: {e}\")\n", + "\n", + " return downloaded_files\n", + "\n", + "def reassemble_file(output_file):\n", + " chunk_number = 1\n", + " with open(output_file, 'wb') as outfile:\n", + " while True:\n", + " chunk_name = f'part{chunk_number}'\n", + " if not os.path.exists(chunk_name):\n", + " break\n", + " with open(chunk_name, 'rb') as infile:\n", + " outfile.write(infile.read())\n", + " chunk_number += 1\n", + " for i in ['part1', 'part2', 'part3']:\n", + " if os.path.exists(i):\n", + " os.remove(i)\n", + " print(f\"Downloaded files have been removed!\")\n", + " print(f\"Reassembled {chunk_number-1} parts into {output_file}\")\n", + "\n", + "\n", + "\n", + "# @markdown Download the resources for this tutorial (one zip file)\n", + "fname = 'resources.zip'\n", + "urls = [\n", + " \"https://osf.io/download/4x7p3/\",\n", + " \"https://osf.io/download/fzwea/\",\n", + " \"https://osf.io/download/qpbcv/\"\n", + "]\n", + "\n", + "if not os.path.exists('data/'):\n", + " print('Data downloading...')\n", + " output_dir = \".\"\n", + " downloaded_parts = download_file_parts(urls, output_dir)\n", + " print('Download is completed.')\n", + "\n", + " print('Reassembling Files...')\n", + " base_name = ''\n", + " reassemble_file(fname)\n", + "\n", + " # @markdown Unzip the file\n", + " with ZipFile(fname, 'r') as zf:\n", + " # extracting all the files\n", + " print('Extracting all the files now...')\n", + " zf.extractall(path='.')\n", + " print('Done!')\n", + "\n", + " # # @markdown Extract the data\n", + " fnames = ['data.zip', 'checkpoints.zip']\n", + "\n", + " for fname in fnames:\n", + " with ZipFile(fname, 'r') as zh:\n", + " # extracting all the files\n", + " print(f\"\\nArchive: {fname}\")\n", + " print(f\"\\tExtracting data...\")\n", + " zh.extractall(path='.')\n", + " print('Done!')\n", + "\n", + " # @markdown Make sure the order of classes matches the pretrained model\n", + " os.rename('data/raw/synapses/gaba', 'data/raw/synapses/0_gaba')\n", + " os.rename('data/raw/synapses/acetylcholine', 'data/raw/synapses/1_acetylcholine')\n", + " os.rename('data/raw/synapses/glutamate', 'data/raw/synapses/2_glutamate')\n", + " os.rename('data/raw/synapses/serotonin', 'data/raw/synapses/3_serotonin')\n", + " os.rename('data/raw/synapses/octopamine', 'data/raw/synapses/4_octopamine')\n", + " os.rename('data/raw/synapses/dopamine', 'data/raw/synapses/5_dopamine')\n", + "\n", + " # @markdown Remove the archives\n", + " for i in ['checkpoints.zip', 'experiments.zip', 'data.zip', 'resources.zip']:\n", + " if os.path.exists(i):\n", + " os.remove(i)\n", + "\n", + "else:\n", + " print('Data are already downloaded.')" + ] + }, + { + "cell_type": "markdown", + "id": "0b84ec7b", + "metadata": { + "execution": {}, + "id": "0b84ec7b" + }, + "source": [ + "## Classifier Training" + ] + }, + { + "cell_type": "markdown", + "id": "a79ab567", + "metadata": { + "execution": {}, + "id": "a79ab567" + }, + "source": [ + "### Create and Inspect Datasets\n", + "\n", + "First, we create a `torch` data loaders for training, validation, and testing. We will use weighted sampling to account for the class imbalance during training." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ae50b16a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "ae50b16a", + "outputId": "e92b100a-6711-4369-c025-6a0e10fbbdb6" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 24, - "id": "2b2519c4", - "metadata": { - "execution": {}, - "id": "2b2519c4", - "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "100%|██████████| 22648/22648 [00:03<00:00, 6536.44it/s]\n", - "100%|██████████| 5085/5085 [00:01<00:00, 3398.25it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n", - "0it [00:00, ?it/s]\n" - ] - } - ], - "source": [ - "import cycle_gan\n", - "\n", - "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n", - "\n", - "## Uncomment if you want to enable the training procedure\n", - "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of images per class:\n", + "\t0_gaba:\tn=15945\tweight=6.271558482282847e-05\n", + "\t1_acetylcholine:\tn=4852\tweight=0.00020610057708161583\n", + "\t2_glutamate:\tn=3556\tweight=0.00028121484814398203\n", + "\t3_serotonin:\tn=2316\tweight=0.0004317789291882556\n", + "\t4_octopamine:\tn=934\tweight=0.0010706638115631692\n", + "\t5_dopamine:\tn=4640\tweight=0.00021551724137931034\n", + "\tcycle_gan:\tn=19383\tweight=5.159160088737554e-05\n" + ] + } + ], + "source": [ + "def load_image(filename):\n", + "\n", + " image = imread(filename)\n", + "\n", + " # images are grescale, we only need one of the RGB channels\n", + " image = image[:, :, 0]\n", + " # img is uint8 in [0, 255], but we want float32 in [-1, 1]\n", + " image = image.astype(np.float32)/255.0\n", + " image = (image - 0.5)/0.5\n", + "\n", + " return image\n", + "\n", + "\n", + "# create a dataset for all images of all classes\n", + "full_dataset = ImageFolder(root='data/raw/synapses', loader=load_image)\n", + "\n", + "# randomly split the dataset into train, validation, and test\n", + "num_images = len(full_dataset)\n", + "# ~70% for training\n", + "num_training = int(0.7 * num_images)\n", + "# ~15% for validation\n", + "num_validation = int(0.15 * num_images)\n", + "# ~15% for testing\n", + "num_test = num_images - (num_training + num_validation)\n", + "# split the data randomly (but with a fixed random seed)\n", + "train_dataset, validation_dataset, test_dataset = random_split(\n", + " full_dataset,\n", + " [num_training, num_validation, num_test],\n", + " generator=torch.Generator().manual_seed(23061912))\n", + "\n", + "# compute class weights in training dataset for uniform sampling\n", + "ys = np.array([y for _, y in train_dataset])\n", + "counts = np.bincount(ys)\n", + "label_weights = 1.0 / counts\n", + "weights = label_weights[ys]\n", + "\n", + "print(\"Number of images per class:\")\n", + "for c, n, w in zip(full_dataset.classes, counts, label_weights):\n", + " print(f\"\\t{c}:\\tn={n}\\tweight={w}\")\n", + "\n", + "# create a data loader with uniform sampling\n", + "sampler = WeightedRandomSampler(weights, len(weights))\n", + "# this data loader will serve 8 images in a \"mini-batch\" at a time\n", + "dataloader = DataLoader(train_dataset, batch_size=8, drop_last=True, sampler=sampler)" + ] + }, + { + "cell_type": "markdown", + "id": "e9010bdc", + "metadata": { + "execution": {}, + "id": "e9010bdc" + }, + "source": [ + "The cell below visualizes a single, randomly chosen batch from the training data loader. Feel free to execute this cell multiple times to get a feeling for the dataset. See if you can tell the difference between synapses of different types!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d8c6f3a", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 117 }, + "id": "3d8c6f3a", + "outputId": "5d4a01f7-aa52-4e35-b6a6-a7950779dd3d" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "0d328904", - "metadata": { - "execution": {}, - "id": "0d328904" - }, - "source": [ - "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", - "\n", - "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset." + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "def show_batch(x, y):\n", + " fig, axs = plt.subplots(1, x.shape[0], figsize=(14, 14), sharey=True)\n", + " for i in range(x.shape[0]):\n", + " axs[i].imshow(np.squeeze(x[i]), cmap='gray')\n", + " axs[i].set_title(train_dataset.dataset.classes[y[i].item()])\n", + " plt.show()\n", + "\n", + "# show a random batch from the data loader\n", + "# (run this cell repeatedly to see different batches)\n", + "for x, y in dataloader:\n", + " show_batch(x, y)\n", + " break" + ] + }, + { + "cell_type": "markdown", + "id": "f882416f", + "metadata": { + "execution": {}, + "id": "f882416f" + }, + "source": [ + "### Create a Model, Loss, and Optimizer" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "54f177cc", + "metadata": { + "id": "54f177cc" + }, + "outputs": [], + "source": [ + "class Vgg2D(torch.nn.Module):\n", + "\n", + " def __init__(\n", + " self,\n", + " input_size,\n", + " fmaps=12,\n", + " downsample_factors=[(2, 2), (2, 2), (2, 2), (2, 2)],\n", + " output_classes=6):\n", + "\n", + " super(Vgg2D, self).__init__()\n", + "\n", + " self.input_size = input_size\n", + "\n", + " current_fmaps = 1\n", + " current_size = tuple(input_size)\n", + "\n", + " features = []\n", + " for i in range(len(downsample_factors)):\n", + "\n", + " features += [\n", + " torch.nn.Conv2d(\n", + " current_fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Conv2d(\n", + " fmaps,\n", + " fmaps,\n", + " kernel_size=3,\n", + " padding=1),\n", + " torch.nn.BatchNorm2d(fmaps),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.MaxPool2d(downsample_factors[i])\n", + " ]\n", + "\n", + " current_fmaps = fmaps\n", + " fmaps *= 2\n", + "\n", + " size = tuple(\n", + " int(c/d)\n", + " for c, d in zip(current_size, downsample_factors[i]))\n", + " check = (\n", + " s*d == c\n", + " for s, d, c in zip(size, downsample_factors[i], current_size))\n", + " assert all(check), \\\n", + " \"Can not downsample %s by chosen downsample factor\" % \\\n", + " (current_size,)\n", + " current_size = size\n", + "\n", + " self.features = torch.nn.Sequential(*features)\n", + "\n", + " classifier = [\n", + " torch.nn.Linear(\n", + " current_size[0] *\n", + " current_size[1] *\n", + " current_fmaps,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " 4096),\n", + " torch.nn.ReLU(inplace=True),\n", + " torch.nn.Dropout(),\n", + " torch.nn.Linear(\n", + " 4096,\n", + " output_classes)\n", + " ]\n", + "\n", + " self.classifier = torch.nn.Sequential(*classifier)\n", + "\n", + " def forward(self, raw):\n", + "\n", + " # add a channel dimension to raw\n", + " shape = tuple(raw.shape)\n", + " raw = raw.reshape(shape[0], 1, shape[1], shape[2])\n", + "\n", + " # compute features\n", + " f = self.features(raw)\n", + " f = f.view(f.size(0), -1)\n", + "\n", + " # classify\n", + " y = self.classifier(f)\n", + "\n", + " return y" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "5da43245", + "metadata": { + "id": "5da43245" + }, + "outputs": [], + "source": [ + "# get the size of our images\n", + "for x, y in train_dataset:\n", + " input_size = x.shape\n", + " break\n", + "\n", + "# create the model to train\n", + "model = Vgg2D(input_size)\n", + "\n", + "# create a loss\n", + "loss = torch.nn.CrossEntropyLoss()\n", + "\n", + "# create an optimzer\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)" + ] + }, + { + "cell_type": "markdown", + "id": "01688095", + "metadata": { + "execution": {}, + "id": "01688095" + }, + "source": [ + "### Train the Model" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "fa65090d", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "fa65090d", + "outputId": "98fb7896-a799-438e-ba02-fd0fe24b15c1" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 25, - "id": "a182c3bc", - "metadata": { - "execution": {}, - "id": "a182c3bc", - "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "----------------- Options ---------------\n", - " aspect_ratio: 1.0 \n", - " aux_checkpoint: checkpoints/synapses/classifier/vgg_checkpoint\t[default: None]\n", - " aux_downsample_factors: [(2, 2), (2, 2), (2, 2), (2, 2)]\n", - " aux_input_nc: 1 \n", - " aux_input_size: 128 \n", - " aux_net: vgg2d \n", - " aux_output_classes: 6 \n", - " batch_size: 1 \n", - " checkpoints_dir: checkpoints/synapses/cycle_gan/gaba_glutamate\t[default: ./checkpoints]\n", - " crop_size: 128 \n", - " dataroot: data/raw/synapses/cycle_gan/0_gaba_2_glutamate\t[default: None]\n", - " dataset_mode: single \n", - " direction: AtoB \n", - " display_winsize: 256 \n", - " epoch: latest \n", - " eval: False \n", - " gpu_ids: 0 \n", - " init_gain: 0.02 \n", - " init_type: normal \n", - " input_nc: 1 \n", - " isTrain: False \t[default: None]\n", - " load_iter: 0 \t[default: 0]\n", - " load_size: 128 \n", - " max_dataset_size: inf \n", - " model: test \n", - " model_suffix: _A \t[default: ]\n", - " n_layers_D: 3 \n", - " name: \t[default: experiment_name]\n", - " ndf: 64 \n", - " netD: basic \n", - " netG: resnet_9blocks \n", - " ngf: 64 \n", - " no_dropout: True \t[default: False]\n", - " no_flip: True \n", - " norm: instance \n", - " ntest: inf \n", - " num_test: 500 \t[default: 50]\n", - " num_threads: 1 \t[default: 4]\n", - " output_nc: 1 \n", - " phase: test \n", - " preprocess: none \n", - " results_dir: data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results\t[default: ./results/]\n", - " serial_batches: False \n", - " suffix: \n", - " verbose: True \t[default: False]\n", - "----------------- End -------------------\n", - "dataset [SingleDataset] was created\n", - "initialize network with normal\n", - "model [TestModel] was created\n", - "loading the model from checkpoints/synapses/cycle_gan/gaba_glutamate/latest_net_G_A.pth\n", - "---------- Networks initialized -------------\n", - "DataParallel(\n", - " (module): ResnetGenerator(\n", - " (model): Sequential(\n", - " (0): ReflectionPad2d((3, 3, 3, 3))\n", - " (1): Conv2d(1, 64, kernel_size=(7, 7), stride=(1, 1))\n", - " (2): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", - " (5): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (6): ReLU(inplace=True)\n", - " (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))\n", - " (8): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (9): ReLU(inplace=True)\n", - " (10): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (11): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (12): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (13): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (14): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (15): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (16): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (17): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (18): ResnetBlock(\n", - " (conv_block): Sequential(\n", - " (0): ReflectionPad2d((1, 1, 1, 1))\n", - " (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (2): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (3): ReLU(inplace=True)\n", - " (4): ReflectionPad2d((1, 1, 1, 1))\n", - " (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1))\n", - " (6): InstanceNorm2d(256, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " )\n", - " )\n", - " (19): ConvTranspose2d(256, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", - " (20): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (21): ReLU(inplace=True)\n", - " (22): ConvTranspose2d(128, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), output_padding=(1, 1))\n", - " (23): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False, track_running_stats=False)\n", - " (24): ReLU(inplace=True)\n", - " (25): ReflectionPad2d((3, 3, 3, 3))\n", - " (26): Conv2d(64, 1, kernel_size=(7, 7), stride=(1, 1))\n", - " (27): Tanh()\n", - " )\n", - " )\n", - ")\n", - "[Network G_A] Total number of parameters : 11.366 M\n", - "-----------------------------------------------\n", - "creating web directory data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest\n", - "processing (0000)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/0_train.png']\n", - "processing (0005)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10004_train.png']\n", - "processing (0010)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10009_train.png']\n", - "processing (0015)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10013_train.png']\n", - "processing (0020)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10018_train.png']\n", - "processing (0025)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10022_train.png']\n", - "processing (0030)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10027_train.png']\n", - "processing (0035)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10031_train.png']\n", - "processing (0040)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10036_train.png']\n", - "processing (0045)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10040_train.png']\n", - "processing (0050)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10045_train.png']\n", - "processing (0055)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1004_train.png']\n", - "processing (0060)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10054_train.png']\n", - "processing (0065)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10059_train.png']\n", - "processing (0070)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10063_train.png']\n", - "processing (0075)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10068_train.png']\n", - "processing (0080)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10072_train.png']\n", - "processing (0085)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10077_train.png']\n", - "processing (0090)-th image... 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['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10365_train.png']\n", - "processing (0410)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1036_train.png']\n", - "processing (0415)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10374_train.png']\n", - "processing (0420)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10379_train.png']\n", - "processing (0425)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10383_train.png']\n", - "processing (0430)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10388_train.png']\n", - "processing (0435)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10392_train.png']\n", - "processing (0440)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10397_train.png']\n", - "processing (0445)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10400_train.png']\n", - "processing (0450)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10405_train.png']\n", - "processing (0455)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/1040_train.png']\n", - "processing (0460)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10414_train.png']\n", - "processing (0465)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10419_train.png']\n", - "processing (0470)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10423_train.png']\n", - "processing (0475)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10428_train.png']\n", - "processing (0480)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10432_train.png']\n", - "processing (0485)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10437_train.png']\n", - "processing (0490)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10441_train.png']\n", - "processing (0495)-th image... ['data/raw/synapses/cycle_gan/0_gaba_2_glutamate/trainA/10446_train.png']\n" - ] - } - ], - "source": [ - "# translate images from class A to B, and classify each with the VGG network trained above\n", - "cycle_gan.test(\n", - " data_dir='data/raw/synapses/',\n", - " class_A='0_gaba',\n", - " class_B='2_glutamate',\n", - " img_size=128,\n", - " checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n", - " vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n", - ")" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Will use device cpu for training\n" + ] + } + ], + "source": [ + "# use a GPU, if it is available\n", + "device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')\n", + "model.to(device)\n", + "print(f\"Will use device {device} for training\")" + ] + }, + { + "cell_type": "markdown", + "id": "ecbab4f7", + "metadata": { + "execution": {}, + "id": "ecbab4f7" + }, + "source": [ + "The next cell merely defines some convenience functions for training, validation, and testing:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "1a8c7fe9", + "metadata": { + "id": "1a8c7fe9" + }, + "outputs": [], + "source": [ + "def train(dataloader, optimizer, loss, device):\n", + " '''Train the model for one epoch.'''\n", + "\n", + " # set the model into train mode\n", + " model.train()\n", + "\n", + " epoch_loss, num_batches = 0, 0\n", + " for x, y in tqdm(dataloader, 'train'):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + " optimizer.zero_grad()\n", + "\n", + " y_pred = model(x)\n", + " l = loss(y_pred, y)\n", + " l.backward()\n", + "\n", + " optimizer.step()\n", + "\n", + " epoch_loss += l\n", + " num_batches += 1\n", + "\n", + " return epoch_loss/num_batches\n", + "\n", + "\n", + "def evaluate(dataloader, name, device):\n", + "\n", + " correct = 0\n", + " total = 0\n", + " for x, y in tqdm(dataloader, name):\n", + "\n", + " x, y = x.to(device), y.to(device)\n", + "\n", + " logits = model(x)\n", + " probs = torch.nn.Softmax(dim=1)(logits)\n", + " predictions = torch.argmax(probs, dim=1)\n", + "\n", + " correct += int(torch.sum(predictions == y).cpu().detach().numpy())\n", + " total += len(y)\n", + "\n", + " accuracy = correct/total\n", + "\n", + " return accuracy\n", + "\n", + "\n", + "def validate(validation_dataset, device):\n", + " '''Evaluate prediction accuracy on the validation dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(validation_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'validate', device)\n", + "\n", + "\n", + "def test(test_dataset, device):\n", + " '''Evaluate prediction accuracy on the test dataset.'''\n", + "\n", + " model.eval()\n", + " dataloader = DataLoader(test_dataset, batch_size=32)\n", + "\n", + " return evaluate(dataloader, 'test', device)" + ] + }, + { + "cell_type": "markdown", + "id": "68bcfbbf", + "metadata": { + "execution": {}, + "id": "68bcfbbf" + }, + "source": [ + "We are ready to train. After each epoch (roughly going through each training image once), we report the training loss and the validation accuracy." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "d0af7638", + "metadata": { + "id": "d0af7638" + }, + "outputs": [], + "source": [ + "def train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device):\n", + "\n", + " for epoch in range(num_epochs):\n", + " epoch_loss = train(dataloader, optimizer, loss, device=device)\n", + " print(f\"epoch {epoch}, training loss={epoch_loss}\")\n", + "\n", + " accuracy = validate(validation_dataset, device=device)\n", + " print(f\"epoch {epoch}, validation accuracy={accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "45e31b87", + "metadata": { + "execution": {}, + "id": "45e31b87" + }, + "source": [ + "`yes_I_want_the_pretrained_model = True` will load a checkpoint that we already prepared, whereas setting it to `False` will train the model from scratch.\n", + "\n", + "Unceck the box below and run the cell to train a model." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "W5KA7zDIa3Lw", + "metadata": { + "cellView": "form", + "id": "W5KA7zDIa3Lw" + }, + "outputs": [], + "source": [ + "# @markdown\n", + "yes_I_want_the_pretrained_model = True # @param {type:\"boolean\"}" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "53fb8dda", + "metadata": { + "id": "53fb8dda" + }, + "outputs": [], + "source": [ + "# Load a pretrained model or train the model from scratch\n", + "\n", + "# set this to True and run this cell if you want a shortcut\n", + "\n", + "if yes_I_want_the_pretrained_model:\n", + " checkpoint = torch.load('checkpoints/synapses/classifier/vgg_checkpoint',\n", + " map_location=device)\n", + " model.load_state_dict(checkpoint['model_state_dict'])\n", + "else:\n", + " train_from_scratch(dataloader, validation_dataset,\n", + " optimizer, loss,\n", + " num_epochs=100, device=device)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "4f6e3663", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "4f6e3663", + "outputId": "ed82b421-2aea-41df-b67c-a4db884be19b" + }, + "outputs": [ { - "cell_type": "markdown", - "id": "17fc1703", - "metadata": { - "execution": {}, - "id": "17fc1703" - }, - "source": [ - "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "test: 100%|████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 346/346 [00:39<00:00, 8.72it/s]" + ] }, { - "cell_type": "code", - "execution_count": 26, - "id": "2a582ba6", - "metadata": { - "execution": {}, - "id": "2a582ba6" - }, - "outputs": [], - "source": [ - "class_A_index = 0\n", - "class_B_index = 2\n", - "\n", - "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n", - "classification_results = []\n", - "for f in glob.glob(result_dir + '/*.json'):\n", - " result = json.load(open(f))\n", - " result['basename'] = f.replace('_aux.json', '')\n", - " classification_results.append(result)\n", - "classification_results.sort(\n", - " key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n", - " reverse=True)" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "final test accuracy: 0.49737888647866957\n" + ] }, { - "cell_type": "markdown", - "id": "2cc0d486", - "metadata": { - "execution": {}, - "id": "2cc0d486" - }, - "source": [ - "Show the top real and fake images that make the classifier change its mind:" - ] - }, + "name": "stderr", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "accuracy = test(test_dataset, device=device)\n", + "print(f\"final test accuracy: {accuracy}\")" + ] + }, + { + "cell_type": "markdown", + "id": "3f43bba5", + "metadata": { + "execution": {}, + "id": "3f43bba5" + }, + "source": [ + "This concludes the first section. We now have a classifier that can discriminate between images of different types.\n", + "\n", + "If you used the images we provided, the classifier is not perfect (you should get an accuracy of around 80%), but pretty good considering that there are six different types of images. Furthermore, it is not so clear for humans how the classifier does it. Feel free to explore the data a bit more and see for yourself if you can tell the difference betwee, say, GABAergic and glutamatergic synapses.\n", + "\n", + "So this is an interesting situation: The VGG network knows something we don't quite know. In the next section, we will see how we can visualize the relevant differences between images of different types." + ] + }, + { + "cell_type": "markdown", + "id": "72b5240c", + "metadata": { + "execution": {}, + "id": "72b5240c" + }, + "source": [ + "---\n", + "# Train a GAN to Translate Images\n", + "\n", + "We will train a so-called CycleGAN to translate images from one class to another." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "41c9e63b", + "metadata": { + "cellView": "form", + "id": "41c9e63b" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 27, - "id": "1567b00e", - "metadata": { - "execution": {}, - "id": "1567b00e", - "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 1000 - } - }, - "outputs": [ - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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pdbz77rvj8MMPX2G+FZV58803x/bbbx+f//znY8GCBdHV1RVrrbVWXHjhhSst09l9992jp6cnjj322Lj77rvjgQceiEsuuSS+/e1vx2mnnTaZ77/+679iaGgoNtpoozj44IOjvb092tra4lWvepXs397e3jj11FPjwx/+sH35kCnzpptuis022yx+8IMfxDrrrBNdXV2x2mqrxcc+9rEVjs8yjz32WEQ8e/mZDT/4wQ9iyZIlpZdXd955Z3R0dMTLXvayKenL5uKdd945Jf1Xv/pVXHbZZfG5z33uOV80AAAAXmgv1N3mubr88sujpaUlXvOa18Tll18el19+eRx77LERkb/fnHjiiXHvvffG6aefHvvvv3986Utfio997GPxxje+McbHx+PMM8+MV7/61XHOOeeU/txRlbvZiur6+OOPx0477RQ33XRTnHDCCXH++efHRhttFMccc0x87nOfK9V1u+22i7vuuit6enpW2D+jo6OV72IrO2tnz7/LW9FdrL29PR599NHJc3z2PjJdX19f9PX1VboPqLvDTTfdFBHP/uOqV77yldHR0RHt7e1x2GGHxTPPPDPl53ffffe44YYb4oILLoj7778/7rnnnjj++ONj6dKlcfLJJ5c+75RTTonXvva18YY3vGGldVvG3UdmInNnrerJJ5+MG2+8Md70pjdN+TNhG264Yayzzjrx7//+73H99dfHww8/HL/61a/iuOOOiw022ED+Cbaenp546qmn4p577okPf/jDcdddd5X+9HTEsy9eb7vttvjMZz6z0vq9/e1vj+7u7mhtbY3Xvva18Zvf/GZmDQZq1Yv8GysA/pf6xCc+UURE6Vdm3/Oe9xQRUfz+978viqIoRkdHi7q6uuL9739/qYyXv/zlxTrrrFP09vZOpv3kJz8pIqJYf/31p+Sd/mu9IyMjxZZbblnsscceU9IjooiI4je/+c1k2gMPPFC0trYWb37zm215RfHsn3SKiOI//uM/Sv/tzDPPLCKiePzxx0v/bXmLFi2arMPK/rcyW2yxRal9RVEU//3f/11ERHHJJZfYn33yySeLurq64phjjpmSfs8990x+/lNPPSV/9umnny6am5uLt7zlLSut4/vf//4iIoo//elPK8znynzmmWeKiChWW221orOzszjnnHOKq6++uthnn31W2sYV/arz2NhYccIJJxRNTU2T7W1oaCi+8IUvTMl37rnnTn7+DjvsUFxxxRXFxRdfXKy55prFvHnzikcffXRK/g984APFBhtsUAwNDRVFUcg/pZUps7u7u5g3b17R0tJSfOxjHyu+9a1vFYcffngREcWHPvShlfbpGmusUbzmNa+xebJ/Suuggw4qWlpaisWLF09J33fffYt/+Id/KOXv7+8v1XViYqLYYYcdire+9a1FUTz7K+/Bn9ICAAD/i73Qd5uY9meAjjrqqFKe5eu1PHcGrnq/WXZf2Xvvvaf8SaSdd965qKurK4477rjJtLGxsWKdddYpdttttxV+lrububoec8wxxVprrVW6jxx22GHFnDlzSuVfeeWVRUQUv/zlL0tlLW/ZnxGq8r9lf5bJyZx/pxsfHy/mzp1b7LnnnlPSn3rqqaKjo2PKfTV7H5lu2Z+Iuvnmm1eYb3h4uNh8882LDTbYoBgdHZ1M33///Sc//4gjjii+9a1vFR/72MeKxsbGYpdddpkyRx5//PFizz33nNKPq6++enHbbbeVPu973/te0djYWPz3f/93URTPzvEqf0rL3UeWl/1TWlXvrJk/pXXBBRcUEVH84Ac/KP23X/7yl8WGG244pZ+22267yT93PN3ee+89ma+5ubk49thji8HBwSl5BgYGivXWW6847bTTiqLwfzLr5z//eXHQQQcVX/3qV4vrrruuOOuss4rVVlutaG1tLX77299WahuA/4fg6wBW6Pjjj5/y/5944olx8cUXxw9+8IPYaqut4plnnomiKGLevHlT8j366KPxxz/+MT784Q9HZ2fnZPpuu+0WL3/5y0v/Gmj5f22zePHiGB8fj9e85jXx9a9/vVSnnXfeObbbbrvJ/3+99daLAw44IK6//voYHx+PhoaGKeWNjo5GT09PbLTRRjF37tz47W9/G//0T/80pcxl9X/qqadijTXWsP2x9957x4033mj/e8bg4GC0tLSU0pcF/B4cHLQ/u/rqq8db3vKWuOyyy+JlL3tZvPnNb45HHnkkTjzxxGhqaorR0VH789/61rdiZGRkpf9KZ2JiIq666qrYZpttSv+SqmqZy/5s1tNPPx1XXXVVHHrooRERcfDBB8fLX/7y+PSnPz35L8syGhoaYsMNN4y99947DjnkkGhtbY2vf/3rceKJJ8bChQsnA0Au+/y6urq4+eabJ+fiNttsEzvvvHNcdNFF8elPfzoing2Od/7558fXv/51OS7T21SlzL6+vpiYmIizzz47Tj311IiIOOigg+KZZ56J888/Pz784Q9HV1dX6TMmJibiiCOOiCVLlsQFF1yQ7h+lp6cnvv/978cb3vCG0p8gy8zFSy+9NP74xz+u8E/dAQAA/G/0Qt1tng/Z+80xxxwz5Td7d9xxx7j99tvjmGOOmUxraGiIV77ylaXfesnczaYriiKuueaaeMtb3hJFUUz5Tfq99947rrrqqvjtb38br3rVqybTl7+LrcjWW29d+S62sj89O5O7WH19fRx77LHxb//2b3HaaafFO97xjujp6YkPfvCDMTIyMuXnM3eH6X7605/G6aefHm95y1tijz32WGF7TjjhhPjTn/4U3//+96f8Watln7/99tvH1772tYh49j7S3t4ep512Wtx8883xute9LiKe/W2XTTfdNNZZZ53Yb7/9ore3N84777w48MAD42c/+9lkcO+RkZF473vfG8cdd1xsvvnmK6zX8lZ0H3muMnfWjCuvvDIWLFgQr3/960v/bd68efGKV7wiDjnkkNhpp53ir3/9a5x11llxyCGHxI033jg5h5Y5++yz4/3vf3889NBDcdlll8XIyEjpT2CdffbZMTo6Gh/+8IdXWK9ddtllyp8L23///ePggw+OrbbaKk477bS44YYbZtBqoPbwYgTACm288cZT/v8NN9ww6uvrS3/Ltpj2d2kfeOCBiIjJw9PyNtpoo/jtb387Je173/tefPrTn47f/e53U/7mqvozPdPrFBGxySabxMDAQDz55JOxcOHCGBwcjLPOOisWLVoUjzzyyJT6LV26tPTzy/77yv4s0FprrTX590Nnqq2tTf592WV/53hlf5/0i1/8YgwODsYHPvCB+MAHPhAREUceeWRsuOGG8e1vf3vKpW15V1xxRcyfPz/+8R//cYXl33rrrfHII4/Ee9/73pW2xZW5rA1NTU1x8MEHT6bX19fHoYceGp/4xCfiwQcfjPXWW2+ln7G8s88+O84///y49957J9v5lre8JV772tfG8ccfH/vtt180NjZOfv4b3/jGKf2x0047xQYbbDDlz6edfPLJscsuu8RBBx20ws/OlNnW1hb9/f3x1re+dUoZb33rW+OGG26IO++8M3bdddfSZ5x44olxww03xH/8x3/E1ltvXbVbVuiaa66JoaEh+UKs6lzs6emJ0047Lf7lX/4l1l133VmpFwAAwAvlhbrbPB+y95vp5+s5c+ZERJTOcHPmzInFixdPScvczaZ78sknY8mSJfGlL30pvvSlL8k8TzzxxJT/v+pdbN68eZNf4s/UTO9iZ5xxRjz11FPxmc98Js4+++yIiNhrr73imGOOiUsuuWTynpC5OyzvnnvuiTe/+c2x5ZZbxle+8pUV1uWcc86JL3/5y/GpT32q9Getln3+9PvI4YcfHqeddlrcdtttk316yCGHRGNjY1x//fWT+Q444IDYeOON4yMf+UhcffXVERFx3nnnxVNPPRWnn376Cus13YruI89V5s5a1X333Re33357nHDCCaXYKUuXLo3XvOY18S//8i/x/ve/fzL9la98Zey+++6xaNGi+Od//ucpP/OKV7xi8v8+8sgjY9ttt42jjz568h+a3X///XHOOefERRddZO/wK7LRRhvFAQccEN/+9rcn/6EogGp4MQIgZfphdf78+VFXV1c6TGf87Gc/i/333z923XXXuPjii2OttdaKpqamWLRoUVx55ZXPqcwTTzwxFi1aFKecckrsvPPOMWfOnKirq4vDDjtM/r3ZZfVf2d9uHRwclBcPZWX/SmmttdaKRx55pJT+97//PSIi1l577RX+/Jw5c+K6666LBx98MO6///5Yf/31Y/31149ddtllMsjidA8++GD87Gc/i3e/+93R1NS0wvKvuOKKqK+vLx2iM2XOnz9/Mkj69APast/MWbx4cfrFyMUXXxx77LFH6eC4//77x/ve9764//77Y6ONNprsw+nB8ZZ9/rJx//GPfxw33HBDfPvb355yMR4bG4vBwcG4//77Y/78+dHd3V25zIhnx/Dee+8t5V2+7dOdfvrpcfHFF8fZZ59d+pd/M3HFFVfEnDlzYr/99iv9t7XWWituueWWKIpiyhqfPhc/+9nPxsjISBx66KGT/fTwww9PtuX++++Ptddee0oQSgAAgP+tno+7TZXPWcYFwVay9xv35ahKX/4ly0zvZsvqcuSRR8ZRRx0l82y11VZT/v+qd7GRkZFSXAxnwYIFK/yCuOr512lubo6vfOUr8a//+q/xl7/8JdZcc83YZJNN4vDDD4/6+vrJl2iZu8MyDz30UOy1114xZ86c+MEPfiB/w3yZSy+9NE499dQ47rjj4qMf/Wjpv7vPn34fue++++KGG24ovcyaP39+vPrVr46f//znEfHsi4FPf/rT8Z73vCd6enomf1uqr68viqKI+++/P9rb2+VfYVjRfeS5qnpnzVg2z9ULnGuuuSYef/zx2H///aek77bbbtHd3R0///nPSy9Gltfc3Bz7779/nH322TE4OBhtbW3x8Y9/PF7ykpfE7rvvPnnHWhY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- }, - "metadata": {} - } - ], - "source": [ - "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", - " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", - " axs[0].imshow(a, cmap='gray')\n", - " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", - " axs[1].imshow(b, cmap='gray')\n", - " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", - " plt.show()\n", - "\n", - "\n", - "# show the top successful translations (according to our VGG classifier)\n", - "for i in range(10):\n", - " basename = classification_results[i]['basename']\n", - " score_A = classification_results[i]['aux_real'][class_A_index]\n", - " score_B = classification_results[i]['aux_fake'][class_B_index]\n", - " real_A = imread(basename + '_real.png')\n", - " fake_B = imread(basename + '_fake.png')\n", - " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Get the CycleGAN code and dependencies\n", + "\n", + "# @markdown GitHub repo: https://github.com/funkey/neuromatch_xai\n", + "\n", + "import requests, zipfile, io\n", + "\n", + "url = 'https://osf.io/vutn5/download'\n", + "r = requests.get(url)\n", + "z = zipfile.ZipFile(io.BytesIO(r.content))\n", + "z.extractall()\n", + "\n", + "!pip install dominate --quiet" + ] + }, + { + "cell_type": "markdown", + "id": "e5da5c01", + "metadata": { + "execution": {}, + "id": "e5da5c01" + }, + "source": [ + "In this example, we will translate between GABAergic and glutamatergic synapses.\n", + "\n", + "First, we have to copy images of either type into a format that the CycleGAN library is happy with. Afterwards, we can start training on those images." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "2b2519c4", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "2b2519c4", + "outputId": "d4a64912-7066-4c08-ec2d-878ffaf9506c" + }, + "outputs": [ { - "cell_type": "code", - "source": [], - "metadata": { - "id": "m7XsnW_R7wBN" - }, - "id": "m7XsnW_R7wBN", - "execution_count": 27, - "outputs": [] + "name": "stderr", + "output_type": "stream", + "text": [ + "100%|███████████████████████████████████████████████████████████████████████████████████████████████████████████████| 22648/22648 [00:01<00:00, 20146.29it/s]\n", + "100%|█████████████████████████████████████████████████████████████████████████████████████████████████████████████████| 5085/5085 [00:00<00:00, 20709.24it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n", + "0it [00:00, ?it/s]\n" + ] } - ], - "metadata": { + ], + "source": [ + "import cycle_gan\n", + "\n", + "cycle_gan.prepare_dataset('data/raw/synapses/', ['0_gaba', '2_glutamate'])\n", + "\n", + "## Uncomment if you want to enable the training procedure\n", + "# cycle_gan.train('data/raw/synapses/', '0_gaba', '2_glutamate', 128)" + ] + }, + { + "cell_type": "markdown", + "id": "0d328904", + "metadata": { + "execution": {}, + "id": "0d328904" + }, + "source": [ + "Training the CycleGAN takes a lot longer than the VGG we trained above (on the synapse dataset, this will be around 7 days...).\n", + "\n", + "To continue, interrupt the kernel and continue with the next one, which will just use one of the pretrained CycleGAN models for the synapse dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "a182c3bc", + "metadata": { "colab": { - "provenance": [], - "gpuType": "T4" - }, - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "base_uri": "https://localhost:8080/" }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.10" + "id": "a182c3bc", + "outputId": "d24e2055-7fcf-40fc-cd3e-ea8a163e7129" + }, + "outputs": [], + "source": [ + "# translate images from class A to B, and classify each with the VGG network trained above\n", + "# Note: cycle_gan requires CUDA devices\n", + "if device == \"cuda\":\n", + " cycle_gan.test(\n", + " data_dir='data/raw/synapses/',\n", + " class_A='0_gaba',\n", + " class_B='2_glutamate',\n", + " img_size=128,\n", + " checkpoints_dir='checkpoints/synapses/cycle_gan/gaba_glutamate/',\n", + " vgg_checkpoint='checkpoints/synapses/classifier/vgg_checkpoint'\n", + " )" + ] + }, + { + "cell_type": "markdown", + "id": "17fc1703", + "metadata": { + "execution": {}, + "id": "17fc1703" + }, + "source": [ + "Read all translated images and sort them by how much the translation \"fools\" the VGG classifier trained above:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "2a582ba6", + "metadata": { + "id": "2a582ba6" + }, + "outputs": [], + "source": [ + "class_A_index = 0\n", + "class_B_index = 2\n", + "\n", + "result_dir = 'data/raw/synapses/cycle_gan/0_gaba_2_glutamate/results/test_latest/images/'\n", + "classification_results = []\n", + "for f in glob.glob(result_dir + '/*.json'):\n", + " result = json.load(open(f))\n", + " result['basename'] = f.replace('_aux.json', '')\n", + " classification_results.append(result)\n", + "classification_results.sort(\n", + " key=lambda c: c['aux_real'][class_A_index] * c['aux_fake'][class_B_index],\n", + " reverse=True)" + ] + }, + { + "cell_type": "markdown", + "id": "2cc0d486", + "metadata": { + "execution": {}, + "id": "2cc0d486" + }, + "source": [ + "Show the top real and fake images that make the classifier change its mind:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "1567b00e", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1000 }, - "accelerator": "GPU" + "id": "1567b00e", + "outputId": "90762e50-afe8-4b03-970d-c9088935cc0c" + }, + "outputs": [], + "source": [ + "def show_pair(a, b, score_a, score_b, class_a, class_b):\n", + " fig, axs = plt.subplots(1, 2, figsize=(20, 20), sharey=True)\n", + " axs[0].imshow(a, cmap='gray')\n", + " axs[0].set_title(f\"p({class_a}) = \" + str(score_a))\n", + " axs[1].imshow(b, cmap='gray')\n", + " axs[1].set_title(f\"p({class_b}) = \" + str(score_b))\n", + " plt.show()\n", + "\n", + "\n", + "# show the top successful translations (according to our VGG classifier)\n", + "# Note: only run if cycle_gan ran successfully\n", + "if classification_results:\n", + " for i in range(10):\n", + " basename = classification_results[i]['basename']\n", + " score_A = classification_results[i]['aux_real'][class_A_index]\n", + " score_B = classification_results[i]['aux_fake'][class_B_index]\n", + " real_A = imread(basename + '_real.png')\n", + " fake_B = imread(basename + '_fake.png')\n", + " show_pair(real_A, fake_B, score_A, score_B, 'gaba', 'glutamate')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "m7XsnW_R7wBN", + "metadata": { + "id": "m7XsnW_R7wBN" + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "gpuType": "T4", + "provenance": [] + }, + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 5 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/projects/ReinforcementLearning/human_rl.ipynb b/projects/ReinforcementLearning/human_rl.ipynb index e72d4367f..954a4bbba 100644 --- a/projects/ReinforcementLearning/human_rl.ipynb +++ b/projects/ReinforcementLearning/human_rl.ipynb @@ -54,21 +54,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "numba 0.56.4 requires numpy<1.24,>=1.18, but you have numpy 1.25.1 which is incompatible.\u001B[0m\u001B[31m\n", - "\u001B[0m\u001B[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n", - "chex 0.1.81 requires numpy>=1.25.0, but you have numpy 1.23.3 which is incompatible.\u001B[0m\u001B[31m\n", - "\u001B[0m" + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\u001b[33mWARNING: Skipping seaborn as it is not installed.\u001b[0m\u001b[33m\n", + "\u001b[0m" ] } ], @@ -76,29 +75,25 @@ "# @title Install dependencies\n", "!pip install jedi --quiet --root-user-action=ignore\n", "!pip install --upgrade pip setuptools wheel --quiet --root-user-action=ignore\n", - "!pip install dm-acme[jax] --quiet --root-user-action=ignore\n", + "!pip install 'dm-acme[jax]' --quiet --root-user-action=ignore\n", "!pip install dm-sonnet --quiet --root-user-action=ignore\n", "!pip install trfl --quiet --root-user-action=ignore\n", - "!pip install numpy==1.23.3 --quiet --ignore-installed --root-user-action=ignore\n", + "!pip install numpy==1.24.1 --quiet --ignore-installed --root-user-action=ignore\n", "!pip uninstall seaborn -y --quiet --root-user-action=ignore\n", "!pip install seaborn --quiet --root-user-action=ignore" ] }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 2, + "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ - "/usr/local/lib/python3.10/dist-packages/reverb/platform/default/ensure_tf_install.py:53: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " if (distutils.version.LooseVersion(version) <\n", - "/usr/local/lib/python3.10/dist-packages/tensorflow_probability/python/__init__.py:57: DeprecationWarning: distutils Version classes are deprecated. Use packaging.version instead.\n", - " if (distutils.version.LooseVersion(tf.__version__) <\n" + "2024-07-16 14:41:51.924400: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libcudart.so.11.0'; dlerror: libcudart.so.11.0: cannot open shared object file: No such file or directory; LD_LIBRARY_PATH: :/usr/local/cuda-11.0/lib64/:/usr/local/cuda-11.0/lib64/:/usr/local/cuda-11.0/lib64/\n", + "2024-07-16 14:41:51.924418: I tensorflow/stream_executor/cuda/cudart_stub.cc:29] Ignore above cudart dlerror if you do not have a GPU set up on your machine.\n" ] } ], @@ -123,21 +118,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { - "cellView": "form", - "execution": {} + "cellView": "form" }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], + "outputs": [], "source": [ "# @title Figure settings\n", "from IPython.display import clear_output, display, HTML\n", @@ -235,14 +220,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 4, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", + "image/png": 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" ], "text/plain": [ " participant_id trial_index stimulus response response_time \\\n", "0 sub-1 1 A None NaN \n", - "1 sub-1 2 D None NaN \n", - "2 sub-1 3 A non-match 1.697356 \n", - "3 sub-1 4 F non-match 0.149110 \n", - "4 sub-1 5 D non-match 0.277760 \n", + "1 sub-1 2 C None NaN \n", + "2 sub-1 3 C non-match 0.317768 \n", + "3 sub-1 4 C non-match 1.096391 \n", + "4 sub-1 5 F non-match 0.843995 \n", "\n", " expected_response is_correct \n", "0 None 1 \n", "1 None 1 \n", - "2 match 0 \n", - "3 non-match 1 \n", + "2 non-match 1 \n", + "3 match 0 \n", "4 non-match 1 " ] }, @@ -604,20 +441,9 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], + "execution_count": 5, + "metadata": {}, + "outputs": [], "source": [ "class NBack(dm_env.Environment):\n", "\n", @@ -776,10 +602,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ "class RandomAgent(acme.Actor):\n", @@ -817,10 +641,8 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 7, + "metadata": {}, "outputs": [ { "name": "stdout", @@ -855,14 +677,12 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": { - "execution": {} - }, + "execution_count": 8, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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" ], "text/plain": [ " episode_length episode_return steps_per_second episodes steps\n", - "995 32 -10.0 329.379165 996 31872\n", - "996 32 -11.0 326.324034 997 31904\n", - "997 32 -9.0 373.017676 998 31936\n", - "998 32 -11.0 309.737031 999 31968\n", - "999 32 -9.0 405.329983 1000 32000" + "995 32 -13.0 1049.248175 996 31872\n", + "996 32 -13.0 1113.221097 997 31904\n", + "997 32 -14.0 1053.158887 998 31936\n", + "998 32 -19.0 1213.915019 999 31968\n", + "999 32 -11.0 1209.724540 1000 32000" ] }, "execution_count": 11, @@ -1283,11 +978,23 @@ "name": "python3" }, "kernelspec": { - "display_name": "Python 3", + "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": "3.9.16" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 4 } diff --git a/projects/ReinforcementLearning/lunar_lander.ipynb b/projects/ReinforcementLearning/lunar_lander.ipynb index 80b6eda54..adf6b71b6 100644 --- a/projects/ReinforcementLearning/lunar_lander.ipynb +++ b/projects/ReinforcementLearning/lunar_lander.ipynb @@ -1,1300 +1,1306 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "sgIXqXYCDBuR" - }, - "source": [ - "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", - "\n", - "**By Neuromatch Academy**\n", - "\n", - "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", - "\n", - "__Content editors:__ Shaonan Wang, Spiros Chavlis" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "AKlrTCmFDBuS" - }, - "source": [ - "---\n", - "# Objective\n", - "\n", - "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "x5XKBDyYDBuS" - }, - "source": [ - "---\n", - "# Setup" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "cellView": "form", - "execution": {}, - "id": "WsHayfTHDBuS" - }, - "outputs": [], - "source": [ - "# @title Update/Upgrade the system and install libs\n", - "!apt-get update > /dev/null 2>&1\n", - "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", - "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", - "!apt-get install x11-utils > /dev/null 2>&1\n", - "!apt-get install xvfb > /dev/null 2>&1" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": { - "execution": {}, - "id": "6fooEJQSDBuT", - "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Requirement already satisfied: swig in /usr/local/lib/python3.10/dist-packages (4.2.1)\n", - "\u001b[2K \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.0/14.0 MB\u001b[0m \u001b[31m61.0 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n", - "\u001b[?25h" - ] - } - ], - "source": [ - "# @title Install dependencies\n", - "!pip install rarfile --quiet\n", - "!pip install stable-baselines3[extra] --quiet\n", - "!pip install ale-py --quiet\n", - "!pip install swig\n", - "!pip install gym[box2d] --quiet\n", - "!pip install pyvirtualdisplay --quiet\n", - "!pip install pyglet --quiet\n", - "!pip install pygame --quiet\n", - "!pip install minigrid --quiet\n", - "!pip install -q swig --quiet\n", - "!pip install -q gymnasium[box2d] --quiet\n", - "!pip install 'minigrid<=2.1.1' --quiet\n", - "!pip3 install box2d-py --quiet" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "execution": {}, - "id": "nA2Y9HGUDBuT" - }, - "outputs": [], - "source": [ - "# Imports\n", - "import io\n", - "import os\n", - "import glob\n", - "import torch\n", - "import base64\n", - "\n", - "import numpy as np\n", - "import matplotlib.pyplot as plt\n", - "\n", - "import sys\n", - "import gymnasium\n", - "sys.modules[\"gym\"] = gymnasium\n", - "\n", - "import stable_baselines3\n", - "from stable_baselines3 import DQN\n", - "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", - "from stable_baselines3.common.callbacks import EvalCallback\n", - "from stable_baselines3.common.env_util import make_atari_env\n", - "\n", - "import gymnasium as gym\n", - "from gym import spaces\n", - "from gym.envs.box2d.lunar_lander import *\n", - "from gym.wrappers.monitoring.video_recorder import VideoRecorder" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "cellView": "form", - "execution": {}, - "id": "_M-76WwDDBuT", - "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "# @title Play Video function\n", - "from IPython.display import HTML\n", - "from base64 import b64encode\n", - "from pyvirtualdisplay import Display\n", - "\n", - "# create the directory to store the video(s)\n", - "os.makedirs(\"./video\", exist_ok=True)\n", - "\n", - "display = Display(visible=False, size=(1400, 900))\n", - "_ = display.start()\n", - "\n", - "\"\"\"\n", - "Utility functions to enable video recording of gym environment\n", - "and displaying it.\n", - "To enable video, just do \"env = wrap_env(env)\"\"\n", - "\"\"\"\n", - "def render_mp4(videopath: str) -> str:\n", - " \"\"\"\n", - " Gets a string containing a b4-encoded version of the MP4 video\n", - " at the specified path.\n", - " \"\"\"\n", - " mp4 = open(videopath, 'rb').read()\n", - " base64_encoded_mp4 = b64encode(mp4).decode()\n", - " return f''" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "UJnBSc5KDBuU" - }, - "source": [ - "---\n", - "# Introduction\n", - "\n", - "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", - "\n", - "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", - "\n", - "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", - "\n", - "In this project, you should investigate the following question:\n", - "\n", - "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", - "\n", - "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", - "\n", - "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", - "\n", - "The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "The actions of the agents are:\n", - " 1. Do Nothing\n", - " 2. Fire Main Engine\n", - " 3. Fire Left Engine\n", - " 4. Fire Right Engine\n", - "\n", - "\n", - "" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "XuSpVWuGDBuU" - }, - "source": [ - "---\n", - "# Basic DQN Implementation\n", - "\n", - "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "dYwJRvx-DBuV" - }, - "source": [ - "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "execution": {}, - "id": "MvWRAJiSDBuV", - "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", - " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", - "\n", - "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", - " # Tip: Use smaller step-sizes for larger networks." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "EHLx2d5xDBuV" - }, - "source": [ - "Now, let us setup our model and the DQN algorithm." - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "execution": {}, - "id": "4PzeDS2dDBuV" - }, - "outputs": [], - "source": [ - "log_dir = \"/tmp/gym/\"\n", - "os.makedirs(log_dir, exist_ok=True)\n", - "\n", - "# Create environment\n", - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", - "# Refer to https://gym.openai.com/docs/ for descriptions.\n", - "\n", - "# For example, if you would like to load Cartpole,\n", - "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", - "\n", - "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", - "\n", - "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", - "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", - " net_arch=nn_layers)\n", - "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", - " learning_rate=learning_rate,\n", - " batch_size=1, # for simplicity, we are not doing batch update.\n", - " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", - " learning_starts=1, # learning starts immediately!\n", - " gamma=0.99, # discount facto. range is between 0 and 1.\n", - " tau = 1, # the soft update coefficient for updating the target network\n", - " target_update_interval=1, # update the target network immediately.\n", - " train_freq=(1,\"step\"), # train the network at every step.\n", - " max_grad_norm = 10, # the maximum value for the gradient clipping\n", - " exploration_initial_eps = 1, # initial value of random action probability\n", - " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", - " gradient_steps = 1, # number of gradient steps\n", - " seed = 1, # seed for the pseudo random generators\n", - " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", - "\n", - "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", - "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", - "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "1FIshtazDBuW" - }, - "source": [ - "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", - "\n", - "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "execution": {}, - "id": "SyD6VwDhDBuW", - "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "State shape: (8,)\n", - "Number of actions: 4\n" - ] - }, - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } - ], - "source": [ - "env_name = 'LunarLander-v2'\n", - "env = gym.make(env_name)\n", - "print('State shape: ', env.observation_space.shape)\n", - "print('Number of actions: ', env.action_space.n)" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "execution": {}, - "id": "68D-3iePDBuW", - "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n", - "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = _posixsubprocess.fork_exec(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Total reward: -449.2162305654916\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", - "HTML(html)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "fhtq8GDLDBuW" - }, - "source": [ - "From the video above, we see that the lander has crashed!\n", - "It is now the time for training!\n" - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "execution": {}, - "id": "Xhl3ojMwDBuW", - "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "Eval num_timesteps=10000, episode_reward=-420.98 +/- 27.22\n", - "Episode length: 151.80 +/- 30.46\n", - "New best mean reward!\n", - "Eval num_timesteps=20000, episode_reward=-561.62 +/- 27.61\n", - "Episode length: 878.80 +/- 72.71\n", - "Eval num_timesteps=30000, episode_reward=-249.88 +/- 48.31\n", - "Episode length: 240.00 +/- 51.61\n", - "New best mean reward!\n", - "Eval num_timesteps=40000, episode_reward=-161.24 +/- 24.32\n", - "Episode length: 338.20 +/- 107.08\n", - "New best mean reward!\n", - "Eval num_timesteps=50000, episode_reward=160.32 +/- 108.81\n", - "Episode length: 241.20 +/- 55.82\n", - "New best mean reward!\n", - "Eval num_timesteps=60000, episode_reward=190.88 +/- 14.49\n", - "Episode length: 646.80 +/- 65.03\n", - "New best mean reward!\n", - "Eval num_timesteps=70000, episode_reward=67.05 +/- 92.04\n", - "Episode length: 139.80 +/- 35.46\n", - "Eval num_timesteps=80000, episode_reward=267.52 +/- 20.00\n", - "Episode length: 321.60 +/- 31.12\n", - "New best mean reward!\n", - "Eval num_timesteps=90000, episode_reward=67.08 +/- 126.76\n", - "Episode length: 536.00 +/- 257.21\n", - "Eval num_timesteps=100000, episode_reward=259.59 +/- 13.39\n", - "Episode length: 339.80 +/- 19.18\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ] - }, - "metadata": {}, - "execution_count": 9 - } - ], - "source": [ - "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", - "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", - "# view the performance at every training episode." - ] + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "sgIXqXYCDBuR" + }, + "source": [ + "# Performance Analysis of DQN Algorithm on the Lunar Lander task\n", + "\n", + "**By Neuromatch Academy**\n", + "\n", + "__Content creators:__ Raghuram Bharadwaj Diddigi, Geraud Nangue Tasse, Yamil Vidal, Sanjukta Krishnagopal, Sara Rajaee\n", + "\n", + "__Content editors:__ Shaonan Wang, Spiros Chavlis" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "AKlrTCmFDBuS" + }, + "source": [ + "---\n", + "# Objective\n", + "\n", + "In this project, the objective is to analyze the performance of the Deep Q-Learning algorithm on an exciting task- Lunar Lander. Before we describe the task, let us focus on two keywords here - analysis and performance. What exactly do we mean by these keywords in the context of Reinforcement Learning (RL)?" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "x5XKBDyYDBuS" + }, + "source": [ + "---\n", + "# Setup" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "cellView": "form", + "id": "WsHayfTHDBuS" + }, + "outputs": [], + "source": [ + "# @title Update/Upgrade the system and install libs\n", + "!apt-get update > /dev/null 2>&1\n", + "!apt-get install -y xvfb python-opengl ffmpeg > /dev/null 2>&1\n", + "!apt-get install -y swig build-essential python-dev python3-dev > /dev/null 2>&1\n", + "!apt-get install x11-utils > /dev/null 2>&1\n", + "!apt-get install xvfb > /dev/null 2>&1" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "6fooEJQSDBuT", + "outputId": "73371ac6-9d7e-42e4-acee-5a0636eec589" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "IYynM83tDBuX" - }, - "source": [ - "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", - "\n", - "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", - "\n", - "```\n", - "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", - "Episode length: 259.80 +/- 47.47\n", - "```" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "Requirement already satisfied: swig in /home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages (4.2.1)\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n", + "\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m A new release of pip is available: \u001b[0m\u001b[31;49m24.0\u001b[0m\u001b[39;49m -> \u001b[0m\u001b[32;49m24.1.2\u001b[0m\n", + "\u001b[1m[\u001b[0m\u001b[34;49mnotice\u001b[0m\u001b[1;39;49m]\u001b[0m\u001b[39;49m To update, run: \u001b[0m\u001b[32;49mpip install --upgrade pip\u001b[0m\n" + ] + } + ], + "source": [ + "# @title Install dependencies\n", + "!pip install rarfile --quiet\n", + "!pip install 'stable-baselines3[extra]' --quiet\n", + "!pip install ale-py --quiet\n", + "!pip install swig\n", + "!pip install gym --quiet\n", + "!pip install pyvirtualdisplay --quiet\n", + "!pip install pyglet --quiet\n", + "!pip install pygame --quiet\n", + "!pip install minigrid --quiet\n", + "!pip install -q swig --quiet\n", + "!pip install -q gymnasium --quiet\n", + "!pip install 'minigrid<=2.1.1' --quiet\n", + "!pip3 install box2d-py --quiet" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "id": "nA2Y9HGUDBuT" + }, + "outputs": [], + "source": [ + "# Imports\n", + "import io\n", + "import os\n", + "import glob\n", + "import torch\n", + "import base64\n", + "\n", + "import numpy as np\n", + "import matplotlib.pyplot as plt\n", + "\n", + "import sys\n", + "import gymnasium\n", + "sys.modules[\"gym\"] = gymnasium\n", + "\n", + "import stable_baselines3\n", + "from stable_baselines3 import DQN\n", + "from stable_baselines3.common.results_plotter import ts2xy, load_results\n", + "from stable_baselines3.common.callbacks import EvalCallback\n", + "from stable_baselines3.common.env_util import make_atari_env\n", + "\n", + "import gymnasium as gym\n", + "from gym import spaces\n", + "from gym.envs.box2d.lunar_lander import *\n", + "from gym.wrappers.monitoring.video_recorder import VideoRecorder" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "cellView": "form", + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "UFNQVKokDBuX" - }, - "source": [ - "Now, let us look at the visual performance of the lander.\n", - "\n", - "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" - ] + "id": "_M-76WwDDBuT", + "outputId": "74dde974-1a97-4be7-e7ce-6a2964b602e2" + }, + "outputs": [], + "source": [ + "# @title Play Video function\n", + "from IPython.display import HTML\n", + "from base64 import b64encode\n", + "from pyvirtualdisplay import Display\n", + "\n", + "# create the directory to store the video(s)\n", + "os.makedirs(\"./video\", exist_ok=True)\n", + "\n", + "display = Display(visible=False, size=(1400, 900))\n", + "_ = display.start()\n", + "\n", + "\"\"\"\n", + "Utility functions to enable video recording of gym environment\n", + "and displaying it.\n", + "To enable video, just do \"env = wrap_env(env)\"\"\n", + "\"\"\"\n", + "def render_mp4(videopath: str) -> str:\n", + " \"\"\"\n", + " Gets a string containing a b4-encoded version of the MP4 video\n", + " at the specified path.\n", + " \"\"\"\n", + " mp4 = open(videopath, 'rb').read()\n", + " base64_encoded_mp4 = b64encode(mp4).decode()\n", + " return f''" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UJnBSc5KDBuU" + }, + "source": [ + "---\n", + "# Introduction\n", + "\n", + "In a standard RL setting, an agent learns optimal behavior from an environment through a feedback mechanism to maximize a given objective. Many algorithms have been proposed in the RL literature that an agent can apply to learn the optimal behavior. One such popular algorithm is the Deep Q-Network (DQN). This algorithm makes use of deep neural networks to compute optimal actions. In this project, your goal is to understand the effect of the number of neural network layers on the algorithm's performance. The performance of the algorithm can be evaluated through two metrics - Speed and Stability.\n", + "\n", + "**Speed:** How fast the algorithm reaches the maximum possible reward.\n", + "\n", + "**Stability** In some applications (especially when online learning is involved), along with speed, stability of the algorithm, i.e., minimal fluctuations in performance, is equally important.\n", + "\n", + "In this project, you should investigate the following question:\n", + "\n", + "**What is the impact of number of neural network layers on speed and stability of the algorithm?**\n", + "\n", + "You do not have to write the DQN code from scratch. We have provided a basic implementation of the DQN algorithm. You only have to tune the hyperparameters (neural network size, learning rate, etc), observe the performance, and analyze. More details on this are provided below.\n", + "\n", + "Now, let us discuss the RL task we have chosen, i.e., Lunar Lander. This task consists of the lander and a landing pad marked by two flags. The episode starts with the lander moving downwards due to gravity. The objective is to land safely using different engines available on the lander with zero speed on the landing pad as quickly and fuel efficient as possible. Reward for moving from the top of the screen and landing on landing pad with zero speed is between 100 to 140 points. Each leg ground contact yields a reward of 10 points. Firing main engine leads to a reward of -0.3 points in each frame. Firing the side engine leads to a reward of -0.03 points in each frame. An additional reward of -100 or +100 points is received if the lander crashes or comes to rest respectively which also leads to end of the episode.\n", + "\n", + "The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "The actions of the agents are:\n", + " 1. Do Nothing\n", + " 2. Fire Main Engine\n", + " 3. Fire Left Engine\n", + " 4. Fire Right Engine\n", + "\n", + "\n", + "" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "XuSpVWuGDBuU" + }, + "source": [ + "---\n", + "# Basic DQN Implementation\n", + "\n", + "We will now implement the DQN algorithm using the existing code base. We encourage you to understand this example and re-use it in an application/project of your choice!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "dYwJRvx-DBuV" + }, + "source": [ + "Now, let us set some hyperparameters for our algorithm. This is the only part you would play around with, to solve the first part of the project." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": { - "execution": {}, - "id": "hc0xXn5aDBuX", - "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 412 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gym/wrappers/monitoring/video_recorder.py:101: DeprecationWarning: \u001b[33mWARN: is marked as deprecated and will be removed in the future.\u001b[0m\n", - " logger.deprecation(\n", - "/usr/lib/python3.10/subprocess.py:1796: RuntimeWarning: os.fork() was called. os.fork() is incompatible with multithreaded code, and JAX is multithreaded, so this will likely lead to a deadlock.\n", - " self.pid = _posixsubprocess.fork_exec(\n" - ] - }, - { - "output_type": "stream", - "name": "stdout", - "text": [ - "\n", - "Total reward: 249.47508187060953\n" - ] - }, - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "" - ] - }, - "metadata": {}, - "execution_count": 10 - } - ], - "source": [ - "env = gym.make(env_name, render_mode=\"rgb_array\")\n", - "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", - "observation = env.reset()[0]\n", - "\n", - "total_reward = 0\n", - "done = False\n", - "while not done:\n", - " frame = env.render()\n", - " vid.capture_frame()\n", - " action, states = model.predict(observation, deterministic=True)\n", - " observation, reward, done, info, _ = env.step(action)\n", - " total_reward += reward\n", - "vid.close()\n", - "env.close()\n", - "print(f\"\\nTotal reward: {total_reward}\")\n", - "\n", - "# show video\n", - "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", - "HTML(html)" - ] + "id": "MvWRAJiSDBuV", + "outputId": "23422e4b-fa32-4edd-d283-31b62668d30e" + }, + "outputs": [], + "source": [ + "nn_layers = [64, 64] # This is the configuration of your neural network. Currently, we have two layers, each consisting of 64 neurons.\n", + " # If you want three layers with 64 neurons each, set the value to [64,64,64] and so on.\n", + "\n", + "learning_rate = 0.001 # This is the step-size with which the gradient descent is carried out.\n", + " # Tip: Use smaller step-sizes for larger networks." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EHLx2d5xDBuV" + }, + "source": [ + "Now, let us setup our model and the DQN algorithm." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "id": "4PzeDS2dDBuV" + }, + "outputs": [], + "source": [ + "log_dir = \"/tmp/gym/\"\n", + "os.makedirs(log_dir, exist_ok=True)\n", + "\n", + "# Create environment\n", + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "# You can also load other environments like cartpole, MountainCar, Acrobot.\n", + "# Refer to https://gym.openai.com/docs/ for descriptions.\n", + "\n", + "# For example, if you would like to load Cartpole,\n", + "# just replace the above statement with \"env = gym.make('CartPole-v1')\".\n", + "\n", + "env = stable_baselines3.common.monitor.Monitor(env, log_dir )\n", + "\n", + "callback = EvalCallback(env, log_path=log_dir, deterministic=True) # For evaluating the performance of the agent periodically and logging the results.\n", + "policy_kwargs = dict(activation_fn=torch.nn.ReLU,\n", + " net_arch=nn_layers)\n", + "model = DQN(\"MlpPolicy\", env,policy_kwargs = policy_kwargs,\n", + " learning_rate=learning_rate,\n", + " batch_size=1, # for simplicity, we are not doing batch update.\n", + " buffer_size=1, # size of experience of replay buffer. Set to 1 as batch update is not done\n", + " learning_starts=1, # learning starts immediately!\n", + " gamma=0.99, # discount facto. range is between 0 and 1.\n", + " tau = 1, # the soft update coefficient for updating the target network\n", + " target_update_interval=1, # update the target network immediately.\n", + " train_freq=(1,\"step\"), # train the network at every step.\n", + " max_grad_norm = 10, # the maximum value for the gradient clipping\n", + " exploration_initial_eps = 1, # initial value of random action probability\n", + " exploration_fraction = 0.5, # fraction of entire training period over which the exploration rate is reduced\n", + " gradient_steps = 1, # number of gradient steps\n", + " seed = 1, # seed for the pseudo random generators\n", + " verbose=0) # Set verbose to 1 to observe training logs. We encourage you to set the verbose to 1.\n", + "\n", + "# You can also experiment with other RL algorithms like A2C, PPO, DDPG etc.\n", + "# Refer to https://stable-baselines3.readthedocs.io/en/master/guide/examples.html\n", + "# for documentation. For example, if you would like to run DDPG, just replace \"DQN\" above with \"DDPG\"." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1FIshtazDBuW" + }, + "source": [ + "Before we train the model, let us look at an instance of Lunar Lander **before training**. \n", + "\n", + "**Note:** The following code for rendering the video is taken from [here](https://colab.research.google.com/github/jeffheaton/t81_558_deep_learning/blob/master/t81_558_class_12_01_ai_gym.ipynb#scrollTo=T9RpF49oOsZj)." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "SyD6VwDhDBuW", + "outputId": "1689b33b-720e-4d7f-d8e8-56cabfb398f1" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "cVCcx8GUDBuX" - }, - "source": [ - "The lander has landed safely!!\n", - "\n", - "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "State shape: (8,)\n", + "Number of actions: 4\n" + ] + } + ], + "source": [ + "env_name = 'LunarLander-v2'\n", + "env = gym.make(env_name)\n", + "print('State shape: ', env.observation_space.shape)\n", + "print('Number of actions: ', env.action_space.n)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 }, + "id": "68D-3iePDBuW", + "outputId": "3a259ce6-a11c-4027-86b8-be30f9b0d622" + }, + "outputs": [ { - "cell_type": "code", - "execution_count": 11, - "metadata": { - "execution": {}, - "id": "_8ibUiTmDBuX", - "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 510 - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - }, - { - "output_type": "display_data", - "data": { - "text/plain": [ - "
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\n" - }, - "metadata": {} - } - ], - "source": [ - "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", - "plt.plot(x, y)\n", - "plt.ylim([-300, 300])\n", - "plt.xlabel('Timesteps')\n", - "plt.ylabel('Episode Rewards')\n", - "plt.show()" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Building video video/LunarLander-v2_pretraining.mp4.\n", + "Moviepy - Writing video video/LunarLander-v2_pretraining.mp4\n", + "\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "2Zo8kpDUDBuX" - }, - "source": [ - "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", - "\n", - "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " " + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "D7JAEDEzDBuX" - }, - "source": [ - "---\n", - "# Additional Project Ideas" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Done !\n", + "Moviepy - video ready video/LunarLander-v2_pretraining.mp4\n", + "\n", + "Total reward: -723.5946813053503\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "1m6YBf5nDBuX" - }, - "source": [ - "## 1 Play with exploration-exploitation trade-off\n", - "\n", - "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] }, { - "cell_type": "code", - "execution_count": 12, - "metadata": { - "execution": {}, - "id": "tnbb16KUDBuY", - "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/ipykernel/ipkernel.py:283: DeprecationWarning: `should_run_async` will not call `transform_cell` automatically in the future. Please pass the result to `transformed_cell` argument and any exception that happen during thetransform in `preprocessing_exc_tuple` in IPython 7.17 and above.\n", - " and should_run_async(code)\n" - ] - } + "data": { + "text/html": [ + "" ], - "source": [ - "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", - "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", - "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "794AyGDPDBuY" - }, - "source": [ - "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "Ljf9XG5BDBuY" - }, - "source": [ - "## 2 Reward Shaping\n", - "\n", - "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": { - "execution": {}, - "id": "zAAhdiflDBuY" - }, - "outputs": [], - "source": [ - "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", - "class CustomEnv(gym.Env):\n", - " \"\"\"Custom Environment that follows gym interface\"\"\"\n", - " metadata = {'render.modes': ['human']}\n", - "\n", - " def __init__(self, arg1, arg2):\n", - " super(CustomEnv, self).__init__()\n", - " # Define action and observation space\n", - " # They must be gym.spaces objects\n", - " # Example when using discrete actions:\n", - " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", - " # Example for using image as input (channel-first; channel-last also works):\n", - " self.observation_space = spaces.Box(low=0, high=255,\n", - " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", - "\n", - " def step(self, action):\n", - " ...\n", - " return observation, reward, done, info\n", - " def reset(self):\n", - " ...\n", - " return observation # reward, done, info can't be included\n", - " def render(self, mode='human'):\n", - " ...\n", - " def close (self):\n", - " ..." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "n7u1oEO2DBuY" - }, - "source": [ - "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": { - "execution": {}, - "id": "463GUtbuDBuY" - }, - "outputs": [], - "source": [ - "class Custom_LunarLander(LunarLander):\n", - "\n", - " def step(self, action):\n", - " assert self.lander is not None\n", - "\n", - " # Update wind\n", - " assert self.lander is not None, \"You forgot to call reset()\"\n", - " if self.enable_wind and not (\n", - " self.legs[0].ground_contact or self.legs[1].ground_contact\n", - " ):\n", - " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " wind_mag = (\n", - " math.tanh(\n", - " math.sin(0.02 * self.wind_idx)\n", - " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", - " )\n", - " * self.wind_power\n", - " )\n", - " self.wind_idx += 1\n", - " self.lander.ApplyForceToCenter(\n", - " (wind_mag, 0.0),\n", - " True,\n", - " )\n", - "\n", - " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", - " # which is proven to never be periodic, k = 0.01\n", - " torque_mag = math.tanh(\n", - " math.sin(0.02 * self.torque_idx)\n", - " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", - " ) * (self.turbulence_power)\n", - " self.torque_idx += 1\n", - " self.lander.ApplyTorque(\n", - " (torque_mag),\n", - " True,\n", - " )\n", - "\n", - " if self.continuous:\n", - " action = np.clip(action, -1, +1).astype(np.float32)\n", - " else:\n", - " assert self.action_space.contains(\n", - " action\n", - " ), f\"{action!r} ({type(action)}) invalid \"\n", - "\n", - " # Engines\n", - " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", - " side = (-tip[1], tip[0])\n", - " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", - "\n", - " m_power = 0.0\n", - " if (self.continuous and action[0] > 0.0) or (\n", - " not self.continuous and action == 2\n", - " ):\n", - " # Main engine\n", - " if self.continuous:\n", - " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", - " assert m_power >= 0.5 and m_power <= 1.0\n", - " else:\n", - " m_power = 1.0\n", - " # 4 is move a bit downwards, +-2 for randomness\n", - " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", - " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", - " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", - " p = self._create_particle(\n", - " 3.5, # 3.5 is here to make particle speed adequate\n", - " impulse_pos[0],\n", - " impulse_pos[1],\n", - " m_power,\n", - " ) # particles are just a decoration\n", - " p.ApplyLinearImpulse(\n", - " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " s_power = 0.0\n", - " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", - " not self.continuous and action in [1, 3]\n", - " ):\n", - " # Orientation engines\n", - " if self.continuous:\n", - " direction = np.sign(action[1])\n", - " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", - " assert s_power >= 0.5 and s_power <= 1.0\n", - " else:\n", - " direction = action - 2\n", - " s_power = 1.0\n", - " ox = tip[0] * dispersion[0] + side[0] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " oy = -tip[1] * dispersion[0] - side[1] * (\n", - " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", - " )\n", - " impulse_pos = (\n", - " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", - " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", - " )\n", - " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", - " p.ApplyLinearImpulse(\n", - " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - " self.lander.ApplyLinearImpulse(\n", - " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", - " impulse_pos,\n", - " True,\n", - " )\n", - "\n", - " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", - "\n", - " pos = self.lander.position\n", - " vel = self.lander.linearVelocity\n", - " state = [\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]\n", - " assert len(state) == 8\n", - "\n", - " # Compare with / without shaping, referring the state description below\n", - " '''\n", - " state[0]: the horizontal coordinate\n", - " state[1]: the vertical coordinate\n", - " state[2]: the horizontal speed\n", - " state[3]: the vertical speed\n", - " state[4]: the angle\n", - " state[5]: the angular speed\n", - " state[6]: first leg contact\n", - " state[7]: second leg contact\n", - " '''\n", - " reward = 0\n", - " shaping = (\n", - " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", - " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", - " - 100 * abs(state[4])\n", - " + 10 * state[6]\n", - " + 10 * state[7]\n", - " ) # And ten points for legs contact, the idea is if you\n", - " # lose contact again after landing, you get negative reward\n", - " if self.prev_shaping is not None:\n", - " reward = shaping - self.prev_shaping\n", - " self.prev_shaping = shaping\n", - "\n", - " reward -= (\n", - " m_power * 0.30\n", - " ) # less fuel spent is better, about -30 for heuristic landing\n", - " reward -= s_power * 0.03\n", - "\n", - " terminated = False\n", - " if self.game_over or abs(state[0]) >= 1.0:\n", - " terminated = True\n", - " reward = -100\n", - " if not self.lander.awake:\n", - " terminated = True\n", - " reward = +100\n", - "\n", - " if self.render_mode == \"human\":\n", - " self.render()\n", - " return np.array(state, dtype=np.float32), reward, terminated, False, {}" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "V-qZ4WYxDBuZ" - }, - "source": [ - "Once you have cutomized your own environment, you can execute that environment by just calling:" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": { - "execution": {}, - "id": "yq4902DQDBuZ" - }, - "outputs": [], - "source": [ - "## Enter the name of the custome environment you created and uncomment the line below.\n", - "# env = Custom_LunarLander()" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "EAP-DUd6DBuZ" - }, - "source": [ - "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "QTq0hmHCDBuZ" - }, - "source": [ - "## 3 Identify the state information crucial to its performance.\n", - "\n", - "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", - "\n", - " 1. Horizontal Position\n", - " 2. Vertical Position\n", - " 3. Horizontal Velocity\n", - " 4. Vertical Velocity\n", - " 5. Angle\n", - " 6. Angular Velocity\n", - " 7. Left Leg Contact\n", - " 8. Right Leg Contact\n", - "\n", - "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": { - "execution": {}, - "id": "sz45kgEaDBuZ" - }, - "outputs": [], - "source": [ - "def step(self, actions):\n", - " ...\n", - " ...\n", - " ...\n", - " state = [ # Remove one component at a time to investigate the effect on performance!\n", - " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", - " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", - " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", - " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", - " self.lander.angle,\n", - " 20.0 * self.lander.angularVelocity / FPS,\n", - " 1.0 if self.legs[0].ground_contact else 0.0,\n", - " 1.0 if self.legs[1].ground_contact else 0.0,\n", - " ]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "DXy9s2ymDBuZ" - }, - "source": [ - "## 4 Extension to Atari Games\n", - "\n", - "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": { - "execution": {}, - "id": "4RjAt0W-DBuZ" - }, - "outputs": [], - "source": [ - "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", - "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", - "\n", - "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", - "# env = VecFrameStack(env, n_stack=4)\n", - "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", - "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" + "text/plain": [ + "" ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_pretraining.mp4\")\n", + "\n", + "observation = env.reset()[0]\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_pretraining.mp4\")\n", + "HTML(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "fhtq8GDLDBuW" + }, + "source": [ + "From the video above, we see that the lander has crashed!\n", + "It is now the time for training!\n" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "Xhl3ojMwDBuW", + "outputId": "c22a910b-0983-438b-dfb6-3cc20d07992e" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "E6_fFAYhDBue" - }, - "source": [ - "## 5 Obstacle Avoidance and Transfer Learning\n", - "\n", - "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", - "\n", - "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", - "\n", - "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Eval num_timesteps=10000, episode_reward=-283.46 +/- 58.80\n", + "Episode length: 187.00 +/- 38.92\n", + "New best mean reward!\n", + "Eval num_timesteps=20000, episode_reward=-406.35 +/- 59.73\n", + "Episode length: 201.40 +/- 71.19\n", + "Eval num_timesteps=30000, episode_reward=-323.96 +/- 57.66\n", + "Episode length: 774.80 +/- 166.26\n", + "Eval num_timesteps=40000, episode_reward=69.12 +/- 117.61\n", + "Episode length: 418.00 +/- 31.63\n", + "New best mean reward!\n", + "Eval num_timesteps=50000, episode_reward=-170.94 +/- 98.78\n", + "Episode length: 145.40 +/- 73.48\n", + "Eval num_timesteps=60000, episode_reward=147.63 +/- 90.91\n", + "Episode length: 543.60 +/- 90.69\n", + "New best mean reward!\n", + "Eval num_timesteps=70000, episode_reward=-30.95 +/- 269.95\n", + "Episode length: 421.40 +/- 225.90\n", + "Eval num_timesteps=80000, episode_reward=117.20 +/- 86.79\n", + "Episode length: 766.40 +/- 125.80\n", + "Eval num_timesteps=90000, episode_reward=-26.99 +/- 20.29\n", + "Episode length: 639.80 +/- 441.17\n", + "Eval num_timesteps=100000, episode_reward=101.46 +/- 61.07\n", + "Episode length: 893.80 +/- 130.44\n" + ] }, { - "cell_type": "code", - "execution_count": 18, - "metadata": { - "execution": {}, - "id": "h6knZ3U8DBue" - }, - "outputs": [], - "source": [ - "## Specify the load path and uncomment below:\n", - "\n", - "# model = load(load_path,\n", - "# env=gym.make('LunarLander-v2'),\n", - "# custom_objects=None, **kwargs)" + "data": { + "text/plain": [ + "" ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "model.learn(total_timesteps=100000, log_interval=10, callback=callback)\n", + "# The performance of the training will be printed every 10 episodes. Change it to 1, if you wish to\n", + "# view the performance at every training episode." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "IYynM83tDBuX" + }, + "source": [ + "The training takes time. We encourage you to analyze the output logs (set verbose to 1 to print the output logs). The main component of the logs that you should track is \"ep_rew_mean\" (mean of episode rewards). As the training proceeds, the value of \"ep_rew_mean\" should increase. The improvement need not be monotonic, but the trend should be upwards!\n", + "\n", + "Along with training, we are also periodically evaluating the performance of the current model during the training. This was reported in logs as follows:\n", + "\n", + "```\n", + "Eval num_timesteps=100000, episode_reward=63.41 +/- 130.02\n", + "Episode length: 259.80 +/- 47.47\n", + "```" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "UFNQVKokDBuX" + }, + "source": [ + "Now, let us look at the visual performance of the lander.\n", + "\n", + "**Note:** The performance varies across different seeds and runs. This code is not optimized to be stable across all runs and seeds. We hope you will be able to find an optimal configuration!" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 412 }, + "id": "hc0xXn5aDBuX", + "outputId": "2bf3c03e-00b6-4d5f-a5a0-0d9077d30537" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "GXBIbO25DBue" - }, - "source": [ - "Following are some of the resources on transfer learning that you would want to start with.\n", - "\n", - "**Research Papers**\n", - "\n", - "Surveys:\n", - "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", - " - Long, Old, Highly cited\n", - "\n", - "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", - " - Medium, Old, Good for a quick read\n", - "\n", - "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", - " - Medium, Recent, Good for a quick read\n", - "\n", - "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", - " - Specific example" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Building video video/LunarLander-v2_learned.mp4.\n", + "Moviepy - Writing video video/LunarLander-v2_learned.mp4\n", + "\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "MlOksW3ODBue" - }, - "source": [ - "## 5(b) Transfer Learning in minigrid environment\n", - "\n", - "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." - ] + "name": "stderr", + "output_type": "stream", + "text": [ + " " + ] }, { - "cell_type": "code", - "execution_count": 19, - "metadata": { - "execution": {}, - "id": "v7rB2JQlDBue" - }, - "outputs": [], - "source": [ - "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" - ] + "name": "stdout", + "output_type": "stream", + "text": [ + "Moviepy - Done !\n", + "Moviepy - video ready video/LunarLander-v2_learned.mp4\n", + "\n", + "Total reward: 211.6755132885056\n" + ] }, { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "OPcds7ZtDBue" - }, - "source": [ - "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "\r" + ] }, { - "cell_type": "code", - "execution_count": 20, - "metadata": { - "execution": {}, - "id": "d0NiSkyeDBue", - "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "stream", - "name": "stderr", - "text": [ - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n", - "/usr/local/lib/python3.10/dist-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", - " logger.warn(\n" - ] - } + "data": { + "text/html": [ + "" ], - "source": [ - "import minigrid\n", - "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "7J92iMqqDBue" - }, - "source": [ - "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", - "\n", - "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" + "text/plain": [ + "" ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "env = gym.make(env_name, render_mode=\"rgb_array\")\n", + "vid = VideoRecorder(env, path=f\"video/{env_name}_learned.mp4\")\n", + "observation = env.reset()[0]\n", + "\n", + "total_reward = 0\n", + "done = False\n", + "while not done:\n", + " frame = env.render()\n", + " vid.capture_frame()\n", + " action, states = model.predict(observation, deterministic=True)\n", + " observation, reward, done, info, _ = env.step(action)\n", + " total_reward += reward\n", + "vid.close()\n", + "env.close()\n", + "print(f\"\\nTotal reward: {total_reward}\")\n", + "\n", + "# show video\n", + "html = render_mp4(f\"video/{env_name}_learned.mp4\")\n", + "HTML(html)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "cVCcx8GUDBuX" + }, + "source": [ + "The lander has landed safely!!\n", + "\n", + "Let us analyze its performance (speed and stability). For this purpose, we plot the number of time steps on the x-axis and the episodic reward given by the trained model on the y-axis." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 510 }, + "id": "_8ibUiTmDBuX", + "outputId": "25fbda7f-4dc6-47e6-c1e9-0d765db9e6b6" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "HL0W5M4uDBue" - }, - "source": [ - "## 6 Preference-Based RL (PBRL)\n", - "\n", - "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", - "\n", - "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", - "\n", - "**Research papers:**\n", - "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", - "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", - "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", - "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", - "\n", - "**Code Bases:**\n", - "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", - "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" + "data": { + "image/png": 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", + "text/plain": [ + "
" ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x, y = ts2xy(load_results(log_dir), 'timesteps') # Organising the logged results in to a clean format for plotting.\n", + "plt.plot(x, y)\n", + "plt.ylim([-300, 300])\n", + "plt.xlabel('Timesteps')\n", + "plt.ylabel('Episode Rewards')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "2Zo8kpDUDBuX" + }, + "source": [ + "From the above plot, we observe that, although the maximum reward is achieved quickly. Achieving an episodic reward of > 200 is good. We see that the agent has achieved it in less than 50000 timesteps (speed is good!). However, there are a lot of fluctuations in the performance (stability is not good!).\n", + "\n", + "Your objective now is to modify the model parameters (nn_layers, learning_rate in the code cell #2 above), run all the cells following it and investigate the stability and speed of the chosen configuration. \n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "D7JAEDEzDBuX" + }, + "source": [ + "---\n", + "# Additional Project Ideas" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "1m6YBf5nDBuX" + }, + "source": [ + "## 1 Play with exploration-exploitation trade-off\n", + "\n", + "Exploration (selecting random actions) and exploitation (selecting greedy action) is a crucial component of the DQN algorithm. Explore random actions for a long time will slow down the training process. At the same time, if all actions are not explored enough, it might lead to a sub-optimal performance. In the DQN code above, we have used the following parameters:" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" }, + "id": "tnbb16KUDBuY", + "outputId": "2d275702-253e-4f5d-8139-2b7796c8d66f" + }, + "outputs": [], + "source": [ + "exploration_initial_eps = 1 # initial value of random action probability. Range is between 0 and 1.\n", + "exploration_fraction = 0.5 # fraction of entire training period over which the exploration rate is reduced. Range is between 0 and 1.\n", + "exploration_final_eps = 0.05 # (set by defualt) final value of random action probability. Range is between 0 and 1." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "794AyGDPDBuY" + }, + "source": [ + "Your objective is to play around with these parameters and analyze their performance (speed and stability). You can modify these parameters and set them as arguments in DQN(...,exploration_initial_eps = 1, exploration_fraction = 0.5, exploration_final_eps = 0.05,...)." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "Ljf9XG5BDBuY" + }, + "source": [ + "## 2 Reward Shaping\n", + "\n", + "Your objective here is to construct a modified reward function that improves the performance of the Lunar Lander. To this end, you would have to create your own custom environment. An example of a custom environment is given below:" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "id": "zAAhdiflDBuY" + }, + "outputs": [], + "source": [ + "# Taken from https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html\n", + "class CustomEnv(gym.Env):\n", + " \"\"\"Custom Environment that follows gym interface\"\"\"\n", + " metadata = {'render.modes': ['human']}\n", + "\n", + " def __init__(self, arg1, arg2):\n", + " super(CustomEnv, self).__init__()\n", + " # Define action and observation space\n", + " # They must be gym.spaces objects\n", + " # Example when using discrete actions:\n", + " self.action_space = spaces.Discrete(N_DISCRETE_ACTIONS)\n", + " # Example for using image as input (channel-first; channel-last also works):\n", + " self.observation_space = spaces.Box(low=0, high=255,\n", + " shape=(N_CHANNELS, HEIGHT, WIDTH), dtype=np.uint8)\n", + "\n", + " def step(self, action):\n", + " ...\n", + " return observation, reward, done, info\n", + " def reset(self):\n", + " ...\n", + " return observation # reward, done, info can't be included\n", + " def render(self, mode='human'):\n", + " ...\n", + " def close (self):\n", + " ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "n7u1oEO2DBuY" + }, + "source": [ + "As you are only changing the reward structure, you can inherit the [original Lunar Lander environment](https://github.com/openai/gym/blob/master/gym/envs/box2d/lunar_lander.py) and modify just the \"step\" function. Focus on modifying the following part of the code in the \"step\" function." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "id": "463GUtbuDBuY" + }, + "outputs": [], + "source": [ + "class Custom_LunarLander(LunarLander):\n", + "\n", + " def step(self, action):\n", + " assert self.lander is not None\n", + "\n", + " # Update wind\n", + " assert self.lander is not None, \"You forgot to call reset()\"\n", + " if self.enable_wind and not (\n", + " self.legs[0].ground_contact or self.legs[1].ground_contact\n", + " ):\n", + " # the function used for wind is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " wind_mag = (\n", + " math.tanh(\n", + " math.sin(0.02 * self.wind_idx)\n", + " + (math.sin(math.pi * 0.01 * self.wind_idx))\n", + " )\n", + " * self.wind_power\n", + " )\n", + " self.wind_idx += 1\n", + " self.lander.ApplyForceToCenter(\n", + " (wind_mag, 0.0),\n", + " True,\n", + " )\n", + "\n", + " # the function used for torque is tanh(sin(2 k x) + sin(pi k x)),\n", + " # which is proven to never be periodic, k = 0.01\n", + " torque_mag = math.tanh(\n", + " math.sin(0.02 * self.torque_idx)\n", + " + (math.sin(math.pi * 0.01 * self.torque_idx))\n", + " ) * (self.turbulence_power)\n", + " self.torque_idx += 1\n", + " self.lander.ApplyTorque(\n", + " (torque_mag),\n", + " True,\n", + " )\n", + "\n", + " if self.continuous:\n", + " action = np.clip(action, -1, +1).astype(np.float32)\n", + " else:\n", + " assert self.action_space.contains(\n", + " action\n", + " ), f\"{action!r} ({type(action)}) invalid \"\n", + "\n", + " # Engines\n", + " tip = (math.sin(self.lander.angle), math.cos(self.lander.angle))\n", + " side = (-tip[1], tip[0])\n", + " dispersion = [self.np_random.uniform(-1.0, +1.0) / SCALE for _ in range(2)]\n", + "\n", + " m_power = 0.0\n", + " if (self.continuous and action[0] > 0.0) or (\n", + " not self.continuous and action == 2\n", + " ):\n", + " # Main engine\n", + " if self.continuous:\n", + " m_power = (np.clip(action[0], 0.0, 1.0) + 1.0) * 0.5 # 0.5..1.0\n", + " assert m_power >= 0.5 and m_power <= 1.0\n", + " else:\n", + " m_power = 1.0\n", + " # 4 is move a bit downwards, +-2 for randomness\n", + " ox = tip[0] * (4 / SCALE + 2 * dispersion[0]) + side[0] * dispersion[1]\n", + " oy = -tip[1] * (4 / SCALE + 2 * dispersion[0]) - side[1] * dispersion[1]\n", + " impulse_pos = (self.lander.position[0] + ox, self.lander.position[1] + oy)\n", + " p = self._create_particle(\n", + " 3.5, # 3.5 is here to make particle speed adequate\n", + " impulse_pos[0],\n", + " impulse_pos[1],\n", + " m_power,\n", + " ) # particles are just a decoration\n", + " p.ApplyLinearImpulse(\n", + " (ox * MAIN_ENGINE_POWER * m_power, oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * MAIN_ENGINE_POWER * m_power, -oy * MAIN_ENGINE_POWER * m_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " s_power = 0.0\n", + " if (self.continuous and np.abs(action[1]) > 0.5) or (\n", + " not self.continuous and action in [1, 3]\n", + " ):\n", + " # Orientation engines\n", + " if self.continuous:\n", + " direction = np.sign(action[1])\n", + " s_power = np.clip(np.abs(action[1]), 0.5, 1.0)\n", + " assert s_power >= 0.5 and s_power <= 1.0\n", + " else:\n", + " direction = action - 2\n", + " s_power = 1.0\n", + " ox = tip[0] * dispersion[0] + side[0] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " oy = -tip[1] * dispersion[0] - side[1] * (\n", + " 3 * dispersion[1] + direction * SIDE_ENGINE_AWAY / SCALE\n", + " )\n", + " impulse_pos = (\n", + " self.lander.position[0] + ox - tip[0] * 17 / SCALE,\n", + " self.lander.position[1] + oy + tip[1] * SIDE_ENGINE_HEIGHT / SCALE,\n", + " )\n", + " p = self._create_particle(0.7, impulse_pos[0], impulse_pos[1], s_power)\n", + " p.ApplyLinearImpulse(\n", + " (ox * SIDE_ENGINE_POWER * s_power, oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + " self.lander.ApplyLinearImpulse(\n", + " (-ox * SIDE_ENGINE_POWER * s_power, -oy * SIDE_ENGINE_POWER * s_power),\n", + " impulse_pos,\n", + " True,\n", + " )\n", + "\n", + " self.world.Step(1.0 / FPS, 6 * 30, 2 * 30)\n", + "\n", + " pos = self.lander.position\n", + " vel = self.lander.linearVelocity\n", + " state = [\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]\n", + " assert len(state) == 8\n", + "\n", + " # Compare with / without shaping, referring the state description below\n", + " '''\n", + " state[0]: the horizontal coordinate\n", + " state[1]: the vertical coordinate\n", + " state[2]: the horizontal speed\n", + " state[3]: the vertical speed\n", + " state[4]: the angle\n", + " state[5]: the angular speed\n", + " state[6]: first leg contact\n", + " state[7]: second leg contact\n", + " '''\n", + " reward = 0\n", + " shaping = (\n", + " -100 * np.sqrt(state[0] * state[0] + state[1] * state[1])\n", + " - 100 * np.sqrt(state[2] * state[2] + state[3] * state[3])\n", + " - 100 * abs(state[4])\n", + " + 10 * state[6]\n", + " + 10 * state[7]\n", + " ) # And ten points for legs contact, the idea is if you\n", + " # lose contact again after landing, you get negative reward\n", + " if self.prev_shaping is not None:\n", + " reward = shaping - self.prev_shaping\n", + " self.prev_shaping = shaping\n", + "\n", + " reward -= (\n", + " m_power * 0.30\n", + " ) # less fuel spent is better, about -30 for heuristic landing\n", + " reward -= s_power * 0.03\n", + "\n", + " terminated = False\n", + " if self.game_over or abs(state[0]) >= 1.0:\n", + " terminated = True\n", + " reward = -100\n", + " if not self.lander.awake:\n", + " terminated = True\n", + " reward = +100\n", + "\n", + " if self.render_mode == \"human\":\n", + " self.render()\n", + " return np.array(state, dtype=np.float32), reward, terminated, False, {}" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "V-qZ4WYxDBuZ" + }, + "source": [ + "Once you have cutomized your own environment, you can execute that environment by just calling:" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "id": "yq4902DQDBuZ" + }, + "outputs": [], + "source": [ + "## Enter the name of the custome environment you created and uncomment the line below.\n", + "# env = Custom_LunarLander()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "EAP-DUd6DBuZ" + }, + "source": [ + "**Note:** Refer to [this page](https://stable-baselines3.readthedocs.io/en/master/guide/custom_env.html), if you would like to create more complex environments." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QTq0hmHCDBuZ" + }, + "source": [ + "## 3 Identify the state information crucial to its performance.\n", + "\n", + "Your objective here is to alter the input state information and analyze the performance. The input state of the Lunar Lander consists of following components:\n", + "\n", + " 1. Horizontal Position\n", + " 2. Vertical Position\n", + " 3. Horizontal Velocity\n", + " 4. Vertical Velocity\n", + " 5. Angle\n", + " 6. Angular Velocity\n", + " 7. Left Leg Contact\n", + " 8. Right Leg Contact\n", + "\n", + "You can train the algorithm by masking one of the eight components at a time and understand how that affects the performance of the algorithm. Similar to the reward shaping task, you would have to create a custom environment and modify the state space. Again, you can inherit all the necessary functions and modify the following portion of the \"Step\" function:" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "id": "sz45kgEaDBuZ" + }, + "outputs": [], + "source": [ + "def step(self, actions):\n", + " ...\n", + " ...\n", + " ...\n", + " state = [ # Remove one component at a time to investigate the effect on performance!\n", + " (pos.x - VIEWPORT_W / SCALE / 2) / (VIEWPORT_W / SCALE / 2),\n", + " (pos.y - (self.helipad_y + LEG_DOWN / SCALE)) / (VIEWPORT_H / SCALE / 2),\n", + " vel.x * (VIEWPORT_W / SCALE / 2) / FPS,\n", + " vel.y * (VIEWPORT_H / SCALE / 2) / FPS,\n", + " self.lander.angle,\n", + " 20.0 * self.lander.angularVelocity / FPS,\n", + " 1.0 if self.legs[0].ground_contact else 0.0,\n", + " 1.0 if self.legs[1].ground_contact else 0.0,\n", + " ]" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "DXy9s2ymDBuZ" + }, + "source": [ + "## 4 Extension to Atari Games\n", + "\n", + "In the Lunar Lander task, the input to the algorithm is a vector of state information. Deep RL algorithms can also be applied when the input to the training is image frames, which is the case in the Atari games. For example, consider an Atari game - Pong. In this environment, the observation is an RGB image of the screen, which is an array of shape (210, 160, 3). To train the Pong game, you can start with the following sample code:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "id": "4RjAt0W-DBuZ" + }, + "outputs": [ { - "cell_type": "markdown", - "metadata": { - "execution": {}, - "id": "QdW-XKCMDBue" - }, - "source": [ - "---\n", - "# References\n", - "\n", - "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", - "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", - "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", - "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" - ] + "name": "stderr", + "output_type": "stream", + "text": [ + "A.L.E: Arcade Learning Environment (version 0.8.1+53f58b7)\n", + "[Powered by Stella]\n" + ] } - ], - "metadata": { - "accelerator": "GPU", + ], + "source": [ + "## Taken from: https://colab.research.google.com/github/Stable-Baselines-Team/rl-colab-notebooks/blob/sb3/atari_games.ipynb#scrollTo=f3K4rMXwimBO\n", + "env = make_atari_env('PongNoFrameskip-v4', n_envs=4, seed=0)\n", + "\n", + "## Atari Games take a lot of memory. Following commands crash on Coalb. Run the following code on Colab Pro or your local Jupyter notebook!\n", + "# env = VecFrameStack(env, n_stack=4)\n", + "# model = DQN('CnnPolicy', env, verbose=1) # Note the difference here! We use 'CnnPolicy\" here instead of 'MlpPolicy' as the input is frames.\n", + "# model.learn(total_timesteps=1) #change the number of timesteps as desired and run this command!" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "E6_fFAYhDBue" + }, + "source": [ + "## 5 Obstacle Avoidance and Transfer Learning\n", + "\n", + "Your obstacle here is to add an obstacle in the path of the lunar lander (by creating a custom environment as described in point 2 above) and train the model such that the lander lands safely, avoiding collisions.\n", + "\n", + "You would first want to devise a mechansim for adding obstacles. For example, you could have an imaginary obstacle at some horizantal and vertical position cooridnates and modify the reward function such that a penalty is levied if the lander comes close to it.\n", + "\n", + "An interesting approach to solve this problem is to apply the techniques of transfer learning. For example, you could initialise the neural network model with the weights of the trained model on the original problem to improve the sample effeciency. This can be done using the following code:" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "id": "h6knZ3U8DBue" + }, + "outputs": [], + "source": [ + "## Specify the load path and uncomment below:\n", + "\n", + "# model = load(load_path,\n", + "# env=gym.make('LunarLander-v2'),\n", + "# custom_objects=None, **kwargs)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "GXBIbO25DBue" + }, + "source": [ + "Following are some of the resources on transfer learning that you would want to start with.\n", + "\n", + "**Research Papers**\n", + "\n", + "Surveys:\n", + "1. Taylor, M. E., et al. (2009). Transfer learning for reinforcement learning domains. url: [www.jmlr.org/papers/volume10/taylor09a/taylor09a](https://www.jmlr.org/papers/volume10/taylor09a/taylor09a.pdf)\n", + " - Long, Old, Highly cited\n", + "\n", + "2. Lazaric, A. (2012). Transfer in reinforcement learning: a framework and a survey. url: [hal.inria.fr/docs/00/77/26/26/PDF/transfer](https://hal.inria.fr/docs/00/77/26/26/PDF/transfer.pdf)\n", + " - Medium, Old, Good for a quick read\n", + "\n", + "3. Zhu, Z., Lin, K., & Zhou, J. (2020). Transfer learning in deep reinforcement learning. [arxiv:2009.07888](https://arxiv.org/pdf/2009.07888.pdf)\n", + " - Medium, Recent, Good for a quick read\n", + "\n", + "4. Barreto, A., et al. (2016). Successor features for transfer in reinforcement learning. [arxiv:1606.05312](https://arxiv.org/pdf/1606.05312)\n", + " - Specific example" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "MlOksW3ODBue" + }, + "source": [ + "## 5(b) Transfer Learning in minigrid environment\n", + "\n", + "These are some simple gridworld gym environments designed to be particularly simple, lightweight and fast. Refer to [this repo](https://github.com/maximecb/gym-minigrid) for a description of the environments. An example to load a minigrid environment is given below." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "id": "v7rB2JQlDBue" + }, + "outputs": [], + "source": [ + "env = gym.make('MiniGrid-Empty-5x5-v0', render_mode='rgb_array')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "OPcds7ZtDBue" + }, + "source": [ + "You can train a standard DQN agent in this env by wrapping the env with full image observation wrappers:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { "colab": { - "name": "lunar_lander", - "provenance": [], - "toc_visible": true + "base_uri": "https://localhost:8080/" }, - "gpuClass": "standard", - "kernel": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "kernelspec": { - "display_name": "Python 3", - "name": "python3" - }, - "language_info": { - "name": "python" + "id": "d0NiSkyeDBue", + "outputId": "ae937a7d-d815-46ac-c29c-44c650f50c22" + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.width to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.width` for environment variables or `env.get_wrapper_attr('width')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n", + "/home/yuda/code/neuromatch/course-content-dl/venv/lib/python3.9/site-packages/gymnasium/core.py:311: UserWarning: \u001b[33mWARN: env.height to get variables from other wrappers is deprecated and will be removed in v1.0, to get this variable you can do `env.unwrapped.height` for environment variables or `env.get_wrapper_attr('height')` that will search the reminding wrappers.\u001b[0m\n", + " logger.warn(\n" + ] } + ], + "source": [ + "import minigrid\n", + "env = minigrid.wrappers.ImgObsWrapper(minigrid.wrappers.RGBImgObsWrapper(env))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "7J92iMqqDBue" + }, + "source": [ + "Note that with full image observations, the shape of the image observations may differ between envs. For e.g., MiniGrid-Empty-5x5-v0 is (40,40,3) while MiniGrid-Empty-8x8-v0 is (64,64,3). So you may need to resize the observations for transfer learning to work with the same DQN architecture.\n", + "\n", + "Now try training a DQN (or another method) in one (or multiple) minigrid env(s),and see if that knowledge transfers to another (or multiple other) minigrid env(s).\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "HL0W5M4uDBue" + }, + "source": [ + "## 6 Preference-Based RL (PBRL)\n", + "\n", + "PBRL is an exciting sub-area in RL where the traditional reward structure is replaced with human preferences. This setting is very useful in applications where it is difficult to construct a reward function.\n", + "\n", + "In the earlier section, we have successfully trained the lunar lander to land safely. Here, the path that the lander follows to land safely can be arbitrary. In this project, using the techniques of PBRL, you will solve the lunar lander problem with an additional requirement that the lander should follow a specially curated path (for example, a straight line path). Following are some of the resources that will help you to get started with this project.\n", + "\n", + "**Research papers:**\n", + "1. [Deep Reinforcement Learning from Human Preferences](https://papers.nips.cc/paper/2017/file/d5e2c0adad503c91f91df240d0cd4e49-Paper.pdf)\n", + "2. [Deep Q-learning from Demonstrations](https://arxiv.org/pdf/1704.03732.pdf)\n", + "3. [Reward learning from human preferences](https://arxiv.org/pdf/1811.06521.pdf)\n", + "4. [T-REX](https://arxiv.org/pdf/1904.06387.pdf)\n", + "\n", + "**Code Bases:**\n", + "1. [rl-teacher](https://github.com/nottombrown/rl-teacher)\n", + "2. [ICML2019-TREX](https://github.com/hiwonjoon/ICML2019-TREX)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "execution": {}, + "id": "QdW-XKCMDBue" + }, + "source": [ + "---\n", + "# References\n", + "\n", + "1. [Stable Baselines Framework](https://stable-baselines3.readthedocs.io/en/master/guide/examples.html)\n", + "2. [Lunar Lander Environment](https://gym.openai.com/envs/LunarLander-v2/)\n", + "3. [OpenAI gym environments](https://gym.openai.com/docs/)\n", + "4. [A good reference for introduction to RL](http://incompleteideas.net/book/the-book-2nd.html)\n" + ] + } + ], + "metadata": { + "accelerator": "GPU", + "colab": { + "name": "lunar_lander", + "provenance": [], + "toc_visible": true + }, + "gpuClass": "standard", + "kernel": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" }, - "nbformat": 4, - "nbformat_minor": 0 -} \ No newline at end of file + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/requirements.txt b/requirements.txt index b8d8390a9..356436466 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,15 +1,17 @@ requests -numpy +numpy==1.26.4 scipy matplotlib scikit-learn -torch +torch==1.13.0 +torchvision==0.14.0 ipywidgets tqdm torchvision pathlib xkcd -decorator==5.0.9 +decorator==4.0.2 pyvirtualdisplay tensorboard -moviepy +moviepy==1.0.3 +imageio_ffmpeg