{ "cells": [ { "cell_type": "markdown", "id": "a0e97051", "metadata": { "colab_type": "text", "execution": {}, "id": "view-in-github" }, "source": [ "\"Open   \"Open" ] }, { "cell_type": "markdown", "id": "ranging-burst", "metadata": { "execution": {} }, "source": [ "# Example Deep Learning Project\n", "\n", "**By Neuromatch Academy**\n", "\n", "__Content creators:__ Marius 't Hart, Megan Peters, Vladimir Haltakov, Paul Schrater, Gunnar Blohm\n", "\n", "__Production editor:__ Spiros Chavlis" ] }, { "cell_type": "markdown", "id": "lfthSM088QdJ", "metadata": { "execution": {} }, "source": [ "---\n", "# Objectives\n", "\n", "We're interested in automatically classifying movement. There is a great dataset (MoVi) with different modalities of movement recordings (videos, visual markers, accelerometers, skeletal motion reconstructions, etc). We will use a sub-set of this data, i.e. estimated skeletal motion, to perform a pilot study investigating whether we can classify different movements from the skeletal motion. And if so, which skeletal motions (if not all) are neccessary for good decoding performance?\n", "\n", "Please check out the different resources below to better understand the MoVi dataset and learn more about the movements.\n", "\n", "**Resources**:\n", "* [see MoVi paper here](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253157)\n", "* [GitHub page of MoVi](https://github.com/saeed1262/MoVi-Toolbox)\n", "* [MoVi website and description](https://www.biomotionlab.ca/movi/)\n", "* [full MoVi dataset (not needed for this demo)](https://dataverse.scholarsportal.info/dataset.xhtml?persistentId=doi:10.5683/SP2/JRHDRN)" ] }, { "cell_type": "markdown", "id": "YLvELJIDJiW8", "metadata": { "execution": {} }, "source": [ "---\n", "# Setup\n", "\n", "For your own project, you can put together a colab notebook by copy-pasting bits of code from the tutorials. We still recommend keeping the 4 setup cells at the top, like here; Imports, Figure Settings, Plotting functions, and Data retrieval." ] }, { "cell_type": "code", "execution_count": null, "id": "bPkNdonuJpIL", "metadata": { "execution": {} }, "outputs": [], "source": [ "# Imports\n", "# get some matrices and plotting:\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "\n", "# get some pytorch:\n", "import torch\n", "import torch.nn as nn\n", "from torch.nn import MaxPool1d\n", "from torch.utils.data import Dataset\n", "from torch.utils.data import DataLoader\n", "\n", "# confusion matrix from sklearn\n", "from sklearn.metrics import confusion_matrix\n", "\n", "# to get some idea of how long stuff will take to complete:\n", "import time\n", "\n", "# to see how unbalanced the data is:\n", "from collections import Counter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Figure settings\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Figure settings\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Figure settings\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Figure settings\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Figure settings\n" ] }, { "cell_type": "code", "execution_count": null, "id": "Fn81OPRAJxS1", "metadata": { "cellView": "form", "execution": {}, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# @title Figure settings\n", "import ipywidgets as widgets #interactive display\n", "\n", "%config InlineBackend.figure_format = 'retina'\n", "plt.style.use(\"https://raw.githubusercontent.com/NeuromatchAcademy/content-creation/main/nma.mplstyle\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Plotting functions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting functions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Plotting functions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Plotting functions\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Plotting functions\n" ] }, { "cell_type": "code", "execution_count": null, "id": "ieSP3d3Z8dOl", "metadata": { "cellView": "form", "execution": {}, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# @title Plotting functions\n", "\n", "def plotConfusionMatrix(real_labels, predicted_labels, label_names):\n", "\n", " # conver the labels to integers:\n", " real_labels = [int(x) for x in real_labels]\n", " predicted_labels = [int(x) for x in predicted_labels]\n", " tick_names = [a.replace(\"_\", \" \") for a in label_names]\n", "\n", " cm = confusion_matrix(real_labels, predicted_labels, normalize='true')\n", "\n", " fig = plt.figure(figsize=(8,6))\n", " plt.imshow(cm)\n", " plt.xticks(range(len(tick_names)),tick_names, rotation=90)\n", " plt.yticks(range(len(tick_names)),tick_names)\n", " plt.xlabel('predicted move')\n", " plt.ylabel('real move')\n", " plt.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Data retrieval\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Run this cell to download the data for this example project.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data retrieval\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Run this cell to download the data for this example project.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### Data retrieval\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Run this cell to download the data for this example project.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "##### Data retrieval\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Run this cell to download the data for this example project.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "###### Data retrieval\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Run this cell to download the data for this example project.\n" ] }, { "cell_type": "code", "execution_count": null, "id": "vN9AoEDiJ7jp", "metadata": { "cellView": "form", "execution": {}, "tags": [ "hide-input" ] }, "outputs": [], "source": [ "# @title Data retrieval\n", "# @markdown Run this cell to download the data for this example project.\n", "import io\n", "import requests\n", "r = requests.get('https://osf.io/mnqb7/download')\n", "if r.status_code != 200:\n", " print('Failed to download data')\n", "else:\n", " train_moves=np.load(io.BytesIO(r.content), allow_pickle=True)['train_moves']\n", " train_labels=np.load(io.BytesIO(r.content), allow_pickle=True)['train_labels']\n", " test_moves=np.load(io.BytesIO(r.content), allow_pickle=True)['test_moves']\n", " test_labels=np.load(io.BytesIO(r.content), allow_pickle=True)['test_labels']\n", " label_names=np.load(io.BytesIO(r.content), allow_pickle=True)['label_names']\n", " joint_names=np.load(io.BytesIO(r.content), allow_pickle=True)['joint_names']" ] }, { "cell_type": "markdown", "id": "auburn-demonstration", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 1: Question\n", "There are many different questions we could ask with the MoVi dataset. We will start with a simple question: **\"Can we classify movements from skeletal motion data, and if so, which body parts are the most informative ones?\"**\n", "\n", "Our goal is to perform a *pilot* study to see if this is possible in principle. We will therefore use \"ground truth\" skeletal motion data that has been computed using an inference algorithm (see [MoVi paper](https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0253157)). If this works out, then as a next step we might want to use the raw sensor data or even videos...\n", "\n", "The ultimate goal could for example be to figure out which body parts to record movements from (e.g. is just a wristband enough?) to classify movement." ] }, { "cell_type": "markdown", "id": "meaningful-oracle", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 2: literature review\n", "Most importantly, our literature review needs to address the following:\n", "* what modeling approaches make it possible to classify time series data?\n", "* how is human motion captured?\n", "* what exactly is in the MoVi dataset?\n", "* what is known regarding classification of human movement based on different measurements?\n", "\n", "What we learn from the literature review is too long to write out here... But we would like to point out that human motion classification has been done; we're not proposing a very novel project here. But that's ok for an NMA project!" ] }, { "cell_type": "markdown", "id": "capable-retirement", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 3: ingredients\n", "\n", "## Data ingredients\n", "\n", "After downloading the data, we should have 6 numpy arrays:\n", "\n", "- `train_moves`: the training set of 1032 movements\n", "- `train_labels`: the class labels for each of the 1032 training movements\n", "- `test_moves`: the test set of 172 movements\n", "- `test_labels`: the class labels for each of the 172 test movements\n", "- `label_names`: text labels for the values in the two arrays of class labels\n", "- `joint_names`: the names of the 24 joints used in each movement\n", "\n", "We'll take a closer look at the data below. *Note*: data is split into training and test sets. If you don't know what that means, NMA-DL will teach you!\n", "\n", "**Inputs**:\n", "\n", "For simplicity, we take the first 24 joints of the whole MoVi dataset including all major limbs. The data was in an exponential map format, which has 3 rotations/angles for each joint (pitch, yaw, roll). The advantage of this type of data is that it is (mostly) agnostic about body size or shape. And since we care about movements only, we choose this representation of the data (there are other representations in the full data set).\n", "\n", "Since the joints are simply points, the 3rd angle (i.e. roll) contained no information, and that is already dropped from the data that we pre-formatted for this demo project. That is, the movements of each joint are described by 2 angles, that change over time. Furthermore, we normalized all the angles/rotations to fall between 0 and 1 so they are good input for PyTorch.\n", "\n", "Finally, the movements originally took various amounts of time, but we need the same input for each movement, so we sub-sampled and (linearly) interpolated the data to have 75 timepoints.\n", "\n", "Our training data is supposed to have 1032 movements, 2 x 24 joints = 48 channels and 75 timepoints. Let's check and make sure:" ] }, { "cell_type": "code", "execution_count": null, "id": "mV3UL0-fNFsq", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(1032, 48, 75)\n" ] } ], "source": [ "print(train_moves.shape)" ] }, { "cell_type": "markdown", "id": "aGNWHN4gT8qW", "metadata": { "execution": {} }, "source": [ "Cool!\n", "\n", "**Joints**:\n", "\n", "For each movement we have 2 angles from 24 joints. Which joints are these?" ] }, { "cell_type": "code", "execution_count": null, "id": "XseMCJ_JUGpv", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0: Pelvis\n", "1: LeftHip\n", "2: RightHip\n", "3: Spine1\n", "4: LeftKnee\n", "5: RightKnee\n", "6: Spine2\n", "7: LeftAnkle\n", "8: RightAnkle\n", "9: Spine3\n", "10: LeftFoot\n", "11: RightFoot\n", "12: Neck\n", "13: LeftCollar\n", "14: RightCollar\n", "15: Head\n", "16: LeftShoulder\n", "17: RightShoulder\n", "18: LeftElbow\n", "19: RightElbow\n", "20: LeftWrist\n", "21: RightWrist\n", "22: LeftHand\n", "23: RightHand\n" ] } ], "source": [ "for joint_no in range(24):\n", " print(f\"{joint_no}: {joint_names[joint_no]}\")" ] }, { "cell_type": "markdown", "id": "73fNoLe3Nyui", "metadata": { "execution": {} }, "source": [ "**Labels**:\n", "\n", "Let's have a look at the `train_labels` array too:" ] }, { "cell_type": "code", "execution_count": null, "id": "wbfDO9YcN8IK", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 1 4 ... 6 2 11]\n", "(1032,)\n" ] } ], "source": [ "print(train_labels)\n", "print(train_labels.shape)" ] }, { "cell_type": "markdown", "id": "PuIGf6WdNHAn", "metadata": { "execution": {} }, "source": [ "The labels are numbers, and there are 1032 of them, so that matches the number of movements in the data set. There are text versions too in the array called `label_names`. Let's have a look. There are supposed to be 14 movement classes.\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "1z9OibQROznT", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13]\n", "0: crawling\n", "1: throw/catch\n", "2: walking\n", "3: running_in_spot\n", "4: cross_legged_sitting\n", "5: hand_clapping\n", "6: scratching_head\n", "7: kicking\n", "8: phone_talking\n", "9: sitting_down\n", "10: checking_watch\n", "11: pointing\n", "12: hand_waving\n", "13: taking_photo\n" ] } ], "source": [ "# let's check the values of the train_labels array:\n", "label_numbers = np.unique(train_labels)\n", "print(label_numbers)\n", "\n", "# and use them as indices into the label_names array:\n", "for label_no in label_numbers:\n", " print(f\"{label_no}: {label_names[label_no]}\")" ] }, { "cell_type": "markdown", "id": "z3fCrClWP85Z", "metadata": { "execution": {} }, "source": [ "The test data set has similar data, but fewer movements. That's ok. What's important is that both the training and test datasets have an even spread of movement types, i.e. we want them to be balanced. Let's see how balanced the data is:" ] }, { "cell_type": "code", "execution_count": null, "id": "barhwy9EQ5uI", "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "Counter({0: 74,\n", " 1: 74,\n", " 4: 73,\n", " 5: 73,\n", " 6: 74,\n", " 7: 74,\n", " 8: 74,\n", " 9: 74,\n", " 10: 74,\n", " 11: 74,\n", " 12: 74,\n", " 13: 74,\n", " 3: 73,\n", " 2: 73})" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(train_labels)" ] }, { "cell_type": "code", "execution_count": null, "id": "CCJystGFRO3H", "metadata": { "execution": {} }, "outputs": [ { "data": { "text/plain": [ "Counter({2: 13,\n", " 3: 13,\n", " 5: 13,\n", " 4: 13,\n", " 6: 12,\n", " 7: 12,\n", " 8: 12,\n", " 9: 12,\n", " 11: 12,\n", " 10: 12,\n", " 12: 12,\n", " 13: 12,\n", " 1: 12,\n", " 0: 12})" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "Counter(test_labels)" ] }, { "cell_type": "markdown", "id": "uGHra3KuRlBh", "metadata": { "execution": {} }, "source": [ "So that looks more or less OK. Movements 2, 3, 4 and 5 occur once more in the training data than the other movements, and one time fewer in the test data. Not perfect, but probably doesn't matter that much." ] }, { "cell_type": "markdown", "id": "rHpiIl1eOupO", "metadata": { "execution": {} }, "source": [ "## Model ingredients\n", "\n", "**\"Mechanisms\"**:\n", "\n", "* Feature engineering? --> Do we need anything else aside from angular time courses? For now we choose to only use the angular time courses (exponential maps), as our ultimate goal is to see how many joints we need for accurate movement classification so that we can decrease the number of measurements or devices for later work.\n", "\n", "* Feature selection? --> Which joint movements are most informative? These are related to our research questions and hypotheses, so this project will explicitly investigate which joints are most informative.\n", "\n", "* Feature grouping? --> Instead of trying all possible combinations of joints (very many) we could focus on limbs, by grouping joints. We could also try the model on individual joints.\n", "\n", "* Classifier? --> For our classifier we would like to keep it as simple as possible, but we will decide later.\n", "\n", "* Input? --> The training data (movements and labels) will be used to train the classifier.\n", "\n", "* Output? --> The test data will be used as input for the trained model and we will see if the predicted labels are the same as the actual labels." ] }, { "cell_type": "markdown", "id": "single-server", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 4: hypotheses\n", "Since humans can easily distinguish different movement types from video data and also more abstract \"stick figures\", a DL model should also be able to do so. Therefore, our hypotheses are more detailed with respect to parameters influencing model performance (and not just whether it will work or not).\n", "\n", "Remember, we're interested in seeing how many joints are needed for classification. So we could hypothezise (Hypothesis 1) that arm and leg motions are sufficient for classification (meaning: head and torso data is not needed).\n", "\n", "* Hypothesis 1: The performance of a model with four limbs plus torso and head is not higher than the performance of a model with only limbs.\n", "\n", "We could also hypothesize that data from only one side of the body is sufficient (Hypothesis 2), e.g. the right side, since our participants are right handed.\n", "\n", "* Hypothesis 2: A model using only joints in the right arm will outperform a model using only the joints in the left arm.\n", "\n", "Writing those in mathematical terms:\n", "* Hypothesis 1: $\\mathbb{E}(perf_{limbs})>\\mathbb{E}(perf_{torso})$\n", "* Hypothesis 2: $\\mathbb{E}(perf_{right arm})>\\mathbb{E}(perf_{left arm})$" ] }, { "cell_type": "markdown", "id": "fantastic-egypt", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 5: toolkit selection\n", "We need a toolkit that can deal with time-varying data as input (e.g. 1d convnet, LSTM, transformer...). We want to keep it as simple as possible to start with. So let's run with a 1d convnet. It allows us to answer our question, it will be able to speak to our hypotheses, and hopefully we can achieve our goal to see if automatic movement classification based on (sparse) body movement data is possible." ] }, { "cell_type": "markdown", "id": "parental-compensation", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 6: model drafting\n", "Here is our sketch of the model we wanted to build...\n", "\n", "

" ] }, { "cell_type": "markdown", "id": "animated-string", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 7: model implementation\n", "\n", "It's finally time to write some deep learning code... so here we go!" ] }, { "cell_type": "markdown", "id": "pharmaceutical-heater", "metadata": { "execution": {} }, "source": [ "The cell below creates an object class, and is based on https://pytorch.org/tutorials/beginner/basics/data_tutorial.html on the PyTorch website, adapted to work with our data.\n", "\n", "It is based on the `Dataset` object class in PyTorch and this is needed to set up a `Dataloader` object that will be used in the model.\n", "\n", "We can tell our dataset object to use the training or test data. We can also tell it which joints to return, so that we can build models that classify movements based on different sets of joints:" ] }, { "cell_type": "code", "execution_count": null, "id": "gDhgbCPKV31p", "metadata": { "execution": {} }, "outputs": [], "source": [ "class MoViJointDataset(Dataset):\n", " \"\"\"MoVi dataset.\"\"\"\n", "\n", " def __init__(self, train=True, joints=list(range(24))):\n", " \"\"\"\n", " Args:\n", " train (boolean): Use the training data, or otherwise the test data.\n", " joints (list): Indices of joints to return.\n", " \"\"\"\n", "\n", " # select the training or test data:\n", " if train:\n", " self.moves = train_moves\n", " self.labels = train_labels\n", " else:\n", " self.moves = test_moves\n", " self.labels = test_labels\n", "\n", " # convert joint indices to channel indices:\n", " joints = np.array(joints)\n", " self.channels = np.sort(list(joints*2)+ list((joints*2)+1)) # 2 channels per joint\n", "\n", " def __len__(self):\n", " return self.labels.size\n", "\n", " def __getitem__(self, idx):\n", " if torch.is_tensor(idx):\n", " idx = idx.tolist()\n", "\n", " sample = (np.float32(np.squeeze(self.moves[idx,self.channels,:])), self.labels[idx])\n", "\n", " return sample" ] }, { "cell_type": "markdown", "id": "u5yQ7DlWWWYw", "metadata": { "execution": {} }, "source": [ "We want to make sure that this object works the way we intended, so we try it out:" ] }, { "cell_type": "code", "execution_count": null, "id": "RO2gen1kW7Nk", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "TRAINING:\n", "1031\n", "(48, 75) pointing\n", "\n", "TESTING:\n", "171\n", "(6, 75) cross_legged_sitting\n" ] } ], "source": [ "# Create training and test datasets\n", "movi_train = MoViJointDataset(train=True)\n", "movi_test = MoViJointDataset(train=False, joints=[0,1,2])\n", "\n", "print('TRAINING:')\n", "for idx in range(len(movi_train)):\n", " pass\n", "print(idx)\n", "print(movi_train[idx][0].shape, label_names[movi_train[idx][1]])\n", "print('\\nTESTING:')\n", "for idx in range(len(movi_test)):\n", " pass\n", "print(idx)\n", "print(movi_test[idx][0].shape, label_names[movi_test[idx][1]])" ] }, { "cell_type": "markdown", "id": "DAjuDR1wY_mI", "metadata": { "execution": {} }, "source": [ "So we see the movement number (minus 1), the shape of the dataset (e.g. 48 channels and 75 time points for the training set), and the name of the movement." ] }, { "cell_type": "markdown", "id": "engaged-equality", "metadata": { "execution": {} }, "source": [ "## Build model\n", "pytorch expects as input not a single sample, but rather a minibatch of B samples stacked together along the \"minibatch dimension\".\n", "So a \"1D\" CNN in pytorch expects a 3D tensor as input: BxCxT\n", "\n", "- **B:** batch size (however many examples are used in batch training)\n", "- **C:** channels (up to 24 joints x 2 coordinates)\n", "- **T:** timepoints (75 in our case)\n", "\n", "We need `Dataloader` objects that use our `MoViJointDataset` objects to do this. For this we can simply use PyTorch `Dataloader` objects, but it also needs one of our hyperparameters (batch size) to be set:" ] }, { "cell_type": "code", "execution_count": null, "id": "-FhReTdFXzob", "metadata": { "execution": {} }, "outputs": [], "source": [ "# Hyperparameters\n", "num_epochs = 500\n", "num_classes = 14 # is this ever used?\n", "batch_size = 516\n", "learning_rate = 0.001\n", "\n", "# Create training and test datasets\n", "movi_train = MoViJointDataset(train = True)\n", "movi_test = MoViJointDataset(train = False)\n", "\n", "# Data loader\n", "train_loader = DataLoader(dataset=movi_train, batch_size=batch_size, shuffle=True)\n", "test_loader = DataLoader(dataset=movi_test, batch_size=batch_size, shuffle=False)" ] }, { "cell_type": "markdown", "id": "SyQERH4vXz5w", "metadata": { "execution": {} }, "source": [ "And we decided to use a simple 1D convnet. We want to specify the number of joints used and then use 2 input channels for every joint (2 dimensions of rotation). At the end of the convnet there are 14 probabilities, 1 for each class of movement, but we convert it to give the index of the highest probability." ] }, { "cell_type": "code", "execution_count": null, "id": "MxwaPvBtX0QZ", "metadata": { "execution": {} }, "outputs": [], "source": [ "class Mov1DCNN(nn.Module):\n", " def __init__(self, njoints=24):\n", "\n", " super(Mov1DCNN, self).__init__()\n", "\n", " self.layer1 = nn.Sequential(\n", " nn.Conv1d(in_channels=njoints*2, out_channels=56, kernel_size=5, stride=2),\n", " nn.ReLU(),\n", " nn.MaxPool1d(kernel_size=2, stride=2))\n", "\n", " self.layer2 = nn.Sequential(\n", " nn.Conv1d(in_channels=56, out_channels=14, kernel_size=1),\n", " nn.ReLU(),\n", " nn.MaxPool1d(kernel_size=2, stride=2))\n", "\n", " self.dropout1 = nn.Dropout(p=0.2)\n", " self.fc1 = nn.Linear(126, 2200) # fix dimensions\n", " self.nl = nn.ReLU()\n", " self.dropout2 = nn.Dropout(p=0.2)\n", " self.fc2 = nn.Linear(2200, 14)\n", "\n", " def forward(self, x):\n", " out = self.layer1(x)\n", " out = self.layer2(out)\n", "\n", " out = out.reshape(out.size(0), -1)\n", " out = self.dropout1(out)\n", " out = self.fc1(out)\n", " out = self.nl(out)\n", " out = self.dropout2(out)\n", " out = self.fc2(out)\n", " # pick the most likely class:\n", " out = nn.functional.log_softmax(out, dim=1)\n", "\n", " return out" ] }, { "cell_type": "markdown", "id": "EVbl_vZhZiNE", "metadata": { "execution": {} }, "source": [ "We can now instantiate the model object, with _all_ joints, and set a criterion and optimizer:" ] }, { "cell_type": "code", "execution_count": null, "id": "A9bjZkdFZm2c", "metadata": { "execution": {} }, "outputs": [], "source": [ "### ADDING GPU ###\n", "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", "\n", "# create the model object:\n", "model = Mov1DCNN(njoints=24).to(device)\n", "\n", "# loss and optimizer:\n", "criterion = nn.CrossEntropyLoss()\n", "optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)" ] }, { "cell_type": "markdown", "id": "OqNJJOcyaNUr", "metadata": { "execution": {} }, "source": [ "And now we are ready to train this model.\n", "\n", "**This takes up to ~20 seconds!**" ] }, { "cell_type": "code", "execution_count": null, "id": "icuFDR1naP_P", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Epoch [100/500], Step [1/2], Loss: 1.2020, Accuracy: 56.59%\n", "------------------------------------------\n", "Epoch [100/500], Step [2/2], Loss: 1.0936, Accuracy: 59.69%\n", "------------------------------------------\n", "Epoch [200/500], Step [1/2], Loss: 0.7598, Accuracy: 70.74%\n", "------------------------------------------\n", "Epoch [200/500], Step [2/2], Loss: 0.8129, Accuracy: 71.71%\n", "------------------------------------------\n", "Epoch [300/500], Step [1/2], Loss: 0.7019, Accuracy: 75.78%\n", "------------------------------------------\n", "Epoch [300/500], Step [2/2], Loss: 0.6576, Accuracy: 75.19%\n", "------------------------------------------\n", "Epoch [400/500], Step [1/2], Loss: 0.5963, Accuracy: 78.88%\n", "------------------------------------------\n", "Epoch [400/500], Step [2/2], Loss: 0.4709, Accuracy: 82.75%\n", "------------------------------------------\n", "Epoch [500/500], Step [1/2], Loss: 0.4971, Accuracy: 81.40%\n", "------------------------------------------\n", "Epoch [500/500], Step [2/2], Loss: 0.5040, Accuracy: 83.14%\n", "------------------------------------------\n" ] } ], "source": [ "# Train the model\n", "total_step = len(train_loader)\n", "loss_list = []\n", "acc_list = []\n", "for epoch in range(num_epochs):\n", " for i, (motions, labels) in enumerate(train_loader):\n", " motions, labels = motions.to(device), labels.to(device)\n", "\n", " # Run the forward pass\n", " outputs = model(motions)\n", " loss = criterion(outputs, labels)\n", " loss_list.append(loss.item())\n", "\n", " # Backprop and perform Adam optimisation\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # Track the accuracy\n", " total = labels.size(0)\n", " _, predicted = torch.max(outputs.data, 1)\n", " correct = (predicted == labels).sum().item()\n", " acc_list.append(correct / total)\n", "\n", " if (epoch + 1) % 100 == 0:\n", " print(f\"Epoch [{epoch+1}/{num_epochs}], Step [{i+1}/{total_step}], \"\n", " f\"Loss: {loss.item():.4f}, \"\n", " f\"Accuracy: {((correct / total) * 100):.2f}%\"\n", " f\"\\n------------------------------------------\")" ] }, { "cell_type": "markdown", "id": "Y2MNr2Egg1z_", "metadata": { "execution": {} }, "source": [ "The training accuracy usually starts out below $10\\%$, which makes sense as chance $100/14 \\approx 7\\%$. It usually quickly goes up to around $80\\%$. The model can get better if you run more epochs, but this is sufficient for now." ] }, { "cell_type": "markdown", "id": "tIejNowNa2H_", "metadata": { "execution": {} }, "source": [ " Now we want to see performance on the test data set:" ] }, { "cell_type": "code", "execution_count": null, "id": "NaIREOIeec-1", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Test Accuracy of the model on the 172 test moves: 75.581%\n" ] } ], "source": [ "# Test the model\n", "model.eval()\n", "real_labels, predicted_labels = [], []\n", "with torch.no_grad():\n", " correct = 0\n", " total = 0\n", " for motions, labels in test_loader:\n", " motions, labels = motions.to(device), labels.to(device)\n", " real_labels += list(labels)\n", " outputs = model(motions)\n", " _, predicted = torch.max(outputs.data, 1)\n", " predicted_labels += list(predicted)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", " print(f\"Test Accuracy of the model on the 172 test moves: {(correct / total)*100:.3f}%\")" ] }, { "cell_type": "markdown", "id": "wbu_cAU5emsh", "metadata": { "execution": {} }, "source": [ "Considering that we have a relatively small data set, and a fairly simple model that didn't really converge, this is decent performance (chance is ~7%). Let's look at where the errors are:" ] }, { "cell_type": "code", "execution_count": null, "id": "5Z-cA1FXe0Sl", "metadata": { "execution": {} }, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 575, "width": 574 } }, "output_type": "display_data" } ], "source": [ "# plt.style.use('dark_background') # uncomment if you're using dark mode...\n", "plotConfusionMatrix(real_labels, predicted_labels, label_names)" ] }, { "cell_type": "markdown", "id": "BHhhRjL2e5q_", "metadata": { "execution": {} }, "source": [ "The errors vary each time the model is run, but a common error seems to be that head scratching is predicted from some other movements that also involve arms a lot: throw/catch, hand clapping, phone talking, checking watch, hand waving, taking photo. If we train the model longer, these errors tend to go away as well. For some reason, crossed legged sitting is sometimes misclassified for crawling, but this doesn't always happen." ] }, { "cell_type": "markdown", "id": "compliant-session", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 8: Modeling completion\n", "\n", "Are we done yet? In order to answer our questions, reach our goals and evaluate our hypotheses we need to be able to get test performance from the model, and might want to investigate any errors that the model makes. We will first make a function that fits the model on a specified set of joints\n" ] }, { "cell_type": "code", "execution_count": null, "id": "L58_BD8pkJF5", "metadata": { "execution": {} }, "outputs": [], "source": [ "def testJointModel(joints=list(range(24)),\n", " num_epochs = 500,\n", " batch_size=516,\n", " learning_rate = 0.001):\n", "\n", " # Hyperparameters\n", " num_classes = 14\n", "\n", " # Create training and test datasets\n", " movi_train = MoViJointDataset(train = True, joints = joints)\n", " movi_test = MoViJointDataset(train = False, joints = joints)\n", "\n", " # Data loaders\n", " train_loader = DataLoader(dataset=movi_train, batch_size=batch_size, shuffle=True)\n", " test_loader = DataLoader(dataset=movi_test, batch_size=batch_size, shuffle=False)\n", "\n", " # create the model object:\n", " model = Mov1DCNN(njoints=len(joints)).to(device)\n", "\n", " # loss and optimizer:\n", " criterion = nn.CrossEntropyLoss()\n", " optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)\n", "\n", " # Train the model\n", " for epoch in range(num_epochs):\n", " for i, (motions, labels) in enumerate(train_loader):\n", " motions, labels = motions.to(device), labels.to(device)\n", "\n", " # Run the forward pass\n", " outputs = model(motions)\n", " loss = criterion(outputs, labels)\n", " loss_list.append(loss.item())\n", "\n", " # Backprop and perform Adam optimisation\n", " optimizer.zero_grad()\n", " loss.backward()\n", " optimizer.step()\n", "\n", " # Track the accuracy\n", " total = labels.size(0)\n", "\n", " # Test the model\n", " model.eval()\n", " real_labels, predicted_labels = [], []\n", " with torch.no_grad():\n", " correct = 0\n", " total = 0\n", " for motions, labels in test_loader:\n", " motions, labels = motions.to(device), labels.to(device)\n", " real_labels += list(labels)\n", " outputs = model(motions)\n", " _, predicted = torch.max(outputs.data, 1)\n", " predicted_labels += list(predicted)\n", " total += labels.size(0)\n", " correct += (predicted == labels).sum().item()\n", "\n", " performance = (correct / total) * 100\n", "\n", " return {'performance': performance,\n", " 'real_labels': real_labels,\n", " 'predicted_labels': predicted_labels}" ] }, { "cell_type": "markdown", "id": "wwuF1ioak3Ug", "metadata": { "execution": {} }, "source": [ "Let's test this on a few select joints:\n", "\n", "**This takes up to ~10 seconds:**" ] }, { "cell_type": "code", "execution_count": null, "id": "3gJpwYWIk9RM", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "70.34883720930233\n" ] }, { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": { "image/png": { "height": 575, "width": 574 } }, "output_type": "display_data" } ], "source": [ "cnn6j = testJointModel(joints=[0, 10, 11, 15, 22, 23])\n", "print(cnn6j['performance'])\n", "plotConfusionMatrix(real_labels = cnn6j['real_labels'], predicted_labels = cnn6j['predicted_labels'], label_names=label_names)" ] }, { "cell_type": "markdown", "id": "9XMG8T0Ogp0y", "metadata": { "execution": {} }, "source": [ "That is some pretty good performance based on only 6 / 24 joints!\n", "\n", "* Can we answer our question? --> YES, we can classify movement, and we can do so based on a sub-set of joints.\n", "* Have we reached our goals? --> YES, this pilot study shows that we can decode movement type based on skeletal joint motion data.\n", "* Can we evaluate our hypotheses? --> YES, we can now test the specific model performances and compare them.\n", "\n", "Good news, looks like we're done with a first iteration of modeling!" ] }, { "cell_type": "markdown", "id": "strategic-throw", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 9: Model evaluation\n", "\n", "We can now see how well our model actual does, by running it to test our hypotheses. To test our hypotheses, we will group the joints into limbs:\n" ] }, { "cell_type": "code", "execution_count": null, "id": "H3CG_lzdi0KB", "metadata": { "execution": {} }, "outputs": [], "source": [ "limb_joints = {'Left Leg': [1, 4, 7, 10],\n", " 'Right Leg': [2, 5, 8, 1],\n", " 'Left Arm': [13, 16, 18, 20, 22],\n", " 'Right Arm': [14, 17, 19, 21, 23],\n", " 'Torso': [0, 3, 6, 9],\n", " 'Head': [12, 15]}" ] }, { "cell_type": "markdown", "id": "EFddDEAmjN_z", "metadata": { "execution": {} }, "source": [ "Our second hypothesis was that since our participants are right handed, the right arm will give as better classification performance than the left arm. We will fit the model on each individual limb, and then we can compare performance on the left and right arm.\n", "\n", "**This should take up to ~1 minute!**" ] }, { "cell_type": "code", "execution_count": null, "id": "p4h9ABawjcEl", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "*** FITTING: Left Leg\n", "limb performance: 70.35%\n", "\n", "*** FITTING: Right Leg\n", "limb performance: 69.19%\n", "\n", "*** FITTING: Left Arm\n", "limb performance: 61.63%\n", "\n", "*** FITTING: Right Arm\n", "limb performance: 31.40%\n", "\n", "*** FITTING: Torso\n", "limb performance: 81.40%\n", "\n", "*** FITTING: Head\n", "limb performance: 46.51%\n" ] } ], "source": [ "limb_fits = {}\n", "for limb in limb_joints.keys():\n", " print(f\"\\n*** FITTING: {limb}\")\n", "\n", " joints = limb_joints[limb]\n", " limb_fit = testJointModel(joints=joints)\n", " limb_fits[limb] = limb_fit\n", " print(f\"limb performance: {limb_fit['performance']:.2f}%\")" ] }, { "cell_type": "markdown", "id": "Y3_XyAZap9oJ", "metadata": { "execution": {} }, "source": [ "Every time we run this, we get something along these lines:\n", "\n", "```\n", "*** FITTING: LeftLeg\n", "limb performance: 65.70%\n", "*** FITTING: RightLeg\n", "limb performance: 50.58%\n", "*** FITTING: LeftArm\n", "limb performance: 37.21%\n", "*** FITTING: RightArm\n", "limb performance: 22.09%\n", "*** FITTING: Torso\n", "limb performance: 73.84%\n", "*** FITTING: Head\n", "limb performance: 39.53%\n", "```\n", "\n", "\n", "\n", "For a formal test, you'd fit each model a number of times and let it converge by using many more epochs. We don't really have time for that here, but the pattern is fairly clear already. The head and arms are the worst, the legs are better, and the torso usually wins!\n", "\n", "The left arm seems to outperform the right arm in classifying movements. That was not what we expected. Maybe we should repeat this with left-handed participants to see if their right arm works better?" ] }, { "cell_type": "markdown", "id": "XnDAKFmpsT0U", "metadata": { "execution": {} }, "source": [ "We still want to test our first hypothesis, which we're not so certain about any more, given the performance above: the torso outperforms the other limbs. But that doesn't mean that a model with arms and legs only is necessarily worse than a model with arms, legs and head and torso as well.\n", "\n", "We will test each of these models six times, and take the median performance.\n", "\n", "**This takes up to ~4 minutes!** (About 2 minutes per kind of model.)" ] }, { "cell_type": "code", "execution_count": null, "id": "_7O-VJcavoOE", "metadata": { "execution": {} }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "*** FITTING: limbs only\n", "performance: 87.79%\n", "performance: 72.67%\n", "performance: 82.56%\n", "performance: 80.23%\n", "performance: 69.19%\n", "performance: 75.00%\n", "median performance: 77.62%\n", "\n", "*** FITTING: limbs+torso+head\n", "performance: 76.74%\n", "performance: 65.12%\n", "performance: 76.74%\n", "performance: 66.86%\n", "performance: 86.05%\n", "performance: 83.72%\n", "median performance: 76.74%\n" ] } ], "source": [ "limb_sets = {'limbs only':['Left Leg', 'Right Leg', 'Left Arm', 'Right Arm'],\n", " 'limbs+torso+head':['Left Leg', 'Right Leg', 'Left Arm',\n", " 'Right Arm', 'Torso', 'Head']}\n", "\n", "for limb_set in limb_sets.keys():\n", " print(f\"\\n*** FITTING: {limb_set}\")\n", "\n", " limbs = limb_sets[limb_set]\n", "\n", " joints = []\n", " for limb in limbs:\n", " joints += limb_joints[limb]\n", "\n", " performances = []\n", " for repeat in range(6):\n", " limb_set_fit = testJointModel(joints=joints)\n", " performances.append(limb_set_fit['performance'])\n", " print(f\"performance: {limb_set_fit['performance']:.2f}%\")\n", "\n", " print(f\"median performance: {(np.median(performances)):.2f}%\")" ] }, { "cell_type": "markdown", "id": "vhfVd16u_tar", "metadata": { "execution": {} }, "source": [ "The models are not converging, or perfect, but almost every time we run this cell the extra information from the torso and head do make the model perform a little better.\n", "\n", "It seems that our spine is pretty fundamental for movement!\n", "\n", "Maybe we should see how well we can do with a minimal number of joints measured. Can we go as low as 1 joint? For example, since we usually carry a phone in our pocket, the inertal motion units (IMU), i.e., accelerometers + gyroscopes, on a phone might be sufficient to get us some idea of movements people are making. We could test individual joints as well, or combinations of 2 or 3 joints.\n", "\n", "Of course, in real life people make many more types of movements, so we might need more joints or IMU's for decent classification. It will also be a problem to figure out when one movement type has ended and the next has begun." ] }, { "cell_type": "markdown", "id": "seven-single", "metadata": { "execution": {} }, "source": [ "---\n", "# Step 10: publication\n", "\n", "Let's write a simple abstract following the guidelines...\n", "\n", "**A. What is the phenomena**? Here summarize the part of the phenomena which your modeling addresses.\n", "\n", "_Movement is well characterized by angular joint information._\n", "\n", "**B. What is the key scientific question?**: Clearly articulate the question which your modeling tries to answer.\n", "\n", "_Here, we ask how many joints are needed to accurately classify movements, and which joints are the most informative for classification._\n", "\n", "**C. What was our hypothesis?**: Explain the key relationships which we relied on to simulate the phenomena.\n", "\n", "_We hypothesized that limb motion was more informative than torso motion; and we hypothesized that right side limbs carry more information about movement types than left side limbs._\n", "\n", "**D. How did your modeling work?** Give an overview of the model, it's main components, and how the modeling works. ''Here we ... ''\n", "\n", "_To investigate these hypotheses, we constructed a simple 1d convolutional neuroal network (CNN) and trained it on different subsets of the publicly available MoVi dataset._\n", "\n", "**E. What did you find? Did the modeling work?** Explain the key outcomes of your modeling evaluation.\n", "\n", "_Contrary to our expectations, we observed that the torso was more informative for classification then the rest of the joints. Furthermore the left limbs allowed for better classification than the right limbs._\n", "\n", "**F. What can you conclude?** Conclude as much as you can _with reference to the hypothesis_, within the limits of the modeling.\n", "\n", "_We conclude that while our model works to classify movements from subsets of joint rotations, the specific subsets of joints that were most informative were counter to our intuition._\n", "\n", "**G. What are the limitations and future directions?** What is left to be learned? Briefly argue the plausibility of the approach and/or what you think is essential that may have been left out.\n", "\n", "_Since our dataset contained limited number of movement types, generalization might be limited. Furthermore, our findings might be specific for our particular choice of model. Finally, classification of continuous movement presents an additional challenge since we used already segmented motion data here._\n", "\n", ">If we put this all in one paragraph, we have our final complete abstract. But, first, do not include the letters in _your_ abstract, and second, you might need to paraphrase the answers a little so they fit together.\n", "\n", "
\n", "\n", "**Abstract**\n", "\n", "(A) Movement is well characterized by angular joint information.\n", "(B) Here, we ask how many joints are needed to accurately classify movements, and which joints are the most informative for classification.\n", "(C) We hypothesized that limb motion was more informative than torso motion; and we hypothesized that right side limbs carry more information about movement types than left side limbs.\n", "(D) To investigate these hypotheses, we constructed a simple 1d convolutional neuroal network (CNN) and trained it on different subsets of the publicly available MoVi dataset.\n", "(E) Contrary to our expectations, we observed that the torso was more informative for classification then the rest of the joints. Furthermore the left limbs allowed for better classification than the right limbs.\n", "(F) We conclude that while our model works to classify movements from subsets of joint rotations, the specific subsets of joints that were most informative were counter to our intuition.\n", "(G) Since our dataset contained limited number of movement types, generalization might be limited. Furthermore, our findings might be specific for our particular choice of model. Finally, classification of continuous movement presents an additional challenge since we used already segmented motion data here." ] } ], "metadata": { "accelerator": "GPU", "colab": { "collapsed_sections": [], "include_colab_link": true, "name": "Example_Deep_Learning_Project", "provenance": [], "toc_visible": true }, "kernel": { "display_name": "Python 3", "language": "python", "name": "python3" }, "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.8" } }, "nbformat": 4, "nbformat_minor": 5 }