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"grid_auto_flow": null, + "grid_area": null, + "grid_template_columns": null, + "flex": null, + "_model_name": "LayoutModel", + "justify_items": null, + "grid_row": null, + "max_height": null, + "align_content": null, + "visibility": null, + "align_self": null, + "height": null, + "min_height": null, + "padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "U2crZj4cw5UV" + }, + "source": [ + "### LifeLong Machine Learning\n", + "### TA's Slide\n", + "[Slide](https://docs.google.com/presentation/d/13JmcOZ9i_m5xJbRBKNMAKE1fIzGhyaeLck3frY0B2xY/edit?usp=sharing)\n", + "\n", + "### Definition\n", + "The detailed explanations and definitions of LifeLong Learning please refer to [LifeLong learning](https://youtu.be/7qT5P9KJnWo) \n", + "\n", + "\n", + "### Methods\n", + "Someone proposed a survey paper for LifeLong Learning at the end of 2019 to distinguish 2016-2019 LigeLong Learning methods into three families.\n", + "\n", + "We can distinguish LifeLong Learning methods into three families, based on how task\n", + "specific information is stored and used throughout the sequential learning process:\n", + "* Replay-based methods\n", + "* Regularization-based methods\n", + "* Parameter isolation methods\n", + "\n", + "\n", + "\n", + "In this assignment, we have to go through EWC, MAS, SI, Remanian Walk, SCP Methods in the prior-focused methods of the regularization-based methods. \n", + "\n", + "Source: [Continual Learning in Neural\n", + "Networks](https://arxiv.org/pdf/1910.02718.pdf)\n", + "\n", + "Please feel free to mail us if you have any questions.\n", + "\n", + "ntu-ml-2020spring-ta@googlegroups.com\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tuBc9bznXoo3" + }, + "source": [ + "### Table of Content\n", + "- Utils\n", + "- Visualization\n", + "- Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jwKUnZeByvhv" + }, + "source": [ + "### Utility\n", + "We utilize permuted MNIST as our training dataset.\n", + "\n", + "So, first we utilize 5 different permutations to generate 10 different permuted MNIST as different task." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "atDSdG4TUAAn" + }, + "source": [ + " #### - Pemutation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lsWYJnC7ymjz" + }, + "source": [ + "import torch.utils.data as data\n", + "import torch.utils.data.sampler as sampler\n", + "import torchvision\n", + "import os\n", + "import torch.nn.functional as F\n", + "from torchvision import datasets, transforms\n", + "\n", + "# Permute MNIST to generate 10 tasks\n", + "\n", + "def _permutate_image_pixels(image, permutation):\n", + " if permutation is None:\n", + " return image\n", + "\n", + " c, h, w = image.size()\n", + " image = image.view(-1, c)\n", + " image = image[permutation, :]\n", + " image.view(c, h, w)\n", + " return image\n", + "\n", + "def get_transform(permutation=None, normalize=True):\n", + " if normalize == True:\n", + " transform = transforms.Compose([transforms.ToTensor(),\n", + " Pad(28),\n", + " transforms.Normalize((0.1307,), (0.3081,)),\n", + " transforms.Lambda(lambda x: _permutate_image_pixels(x, permutation)),\n", + " ])\n", + " else:\n", + " transform = transforms.Compose([transforms.ToTensor(),\n", + " Pad(28),\n", + " transforms.Lambda(lambda x: _permutate_image_pixels(x, permutation)),\n", + " ])\n", + " return transform\n", + "\n", + "class Pad(object):\n", + " def __init__(self, size, fill=0, padding_mode='constant'):\n", + " self.size = size\n", + " self.fill = fill\n", + " self.padding_mode = padding_mode\n", + " \n", + " def __call__(self, img):\n", + " # If the H and W of img is not equal to desired size,\n", + " # then pad the channel of img to desired size.\n", + " img_size = img.size()[1]\n", + " assert ((self.size - img_size) % 2 == 0)\n", + " padding = (self.size - img_size) // 2\n", + " padding = (padding, padding, padding, padding)\n", + " return F.pad(img, padding, self.padding_mode, self.fill)\n", + "\n", + "class Data():\n", + " def __init__(self, path, train=True, permutation=None, normalize=True):\n", + "\n", + " transform = get_transform(permutation, normalize)\n", + " self.dataset = datasets.MNIST(root = os.path.join(path, \"MNIST\"),\n", + " transform=transform,\n", + " train = train,\n", + " download = True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X6JZjHmi6CGU" + }, + "source": [ + "#### - Dataloader and Argument\n", + "- Training Arguments\n", + "- Setup 5 different Permutation\n", + "- 5 Train DataLoader\n", + "- 5 Test DataLoader \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QQNEXhES4Zr9", + "colab": { + "base_uri": "https://localhost:8080/", + "referenced_widgets": [ + "c15dd9522b8249148461a81ef0e26d73", + "5e83b7cdf85947ff9e400b43cc446cef", + "917efdc2c9bc4c1eabb645367898711f", + "626117a83cec44db8abbe58b5a76da45", + "712e7f7ae0e94c0ebfb4096c9001e634", + "b7ad10b739bd445787585cd6c1ae8df7", + "9891764f51c84a1e9d5a3d84ca65a31d", + "f387feb29f8442c3b0ed1be2ffcbe20c", + "7717afd84e434d25b3068ff2ff733e1c", + "6dcc743d8e394905bde8004815269778", + "ef0a89c0c777497f8c50f15581ee7c71", + "a8d78a9649a1491b8939a03f07cc5acd", + "420a094c784f4b00bda227741fc0c737", + "a7dbef8db36c41b5861e00e1a4309a61", + "2566de9a6037414381f8ce9549dc9599", + "2b6d48829eb64962ba5f98b7a92aadaf", + "37ed32a2e0544b9886e0b63c96ea869c", + "84c42524a4d64ec992ae870762413dcb", + "0c38f88361f94cf68220c87fa6c46753", + "d1fda42d453a497d9c35b09c711cfe2b", + "f80a9d9f08c5401eac29e9be7c141ce2", + "6bf0a703aca1467b985bc1c23fdf9175", + "9cfae7e0cd5645f8a481f8f465b596bf", + "8f159e35751247b781020c5b3d5f2e79", + "79739e2492de4d0a9c4cb6ff7e32f14d", + "66678803c5674e6d9e9ba97bb0949651", + "c2f89e420b4b4161b4ae6e51b0dca61e", + "96854f96a7ff4c429aa886e40bde1e90", + "c84f35f2fc17434facff99bf495f3528", + "0b2d4905381e4bdb81e66bc90458f3e0", + "38b7710713484a4e9842e6f854c4f167", + "d01a9cb0685741d1a7fef71e5e7857ca" + ] + }, + "outputId": "90ee5fb1-6dc8-4e2a-da17-d6163d272cc2" + }, + "source": [ + "### Main Process\n", + "\n", + "import argparse\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import tqdm\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import trange\n", + "\n", + "class Args:\n", + " task_number = 5\n", + " epochs_per_task = 10\n", + " lr = 1.0e-4\n", + " batch_size = 128\n", + " test_size=8192\n", + " random_seed=0\n", + "\n", + "args=Args()\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "# generate permutations for the tasks.\n", + "np.random.seed(args.random_seed)\n", + "\n", + "#generate permuted MNIST data from 10 different permutation.\n", + "permutations = [\n", + " np.random.permutation(784) if index !=0 else np.arange(784) for index in range(args.task_number) ]\n", + "\n", + "# prepare permuted mnist datasets.\n", + "train_datasets = [\n", + " Data('data', permutation=permutations[index]) for index in range(len(permutations))\n", + "]\n", + "train_dataloaders = [\n", + " DataLoader(data.dataset, batch_size=args.batch_size, shuffle=True) for data in train_datasets\n", + "]\n", + "\n", + "\n", + "test_datasets = [\n", + " Data('data',train=False, permutation=permutations[index]) for index in range(len(permutations))\n", + "]\n", + "test_dataloaders = [\n", + " DataLoader(data.dataset, batch_size=args.test_size, shuffle=True) for data in test_datasets\n", + "]\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c15dd9522b8249148461a81ef0e26d73", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=9912422.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7717afd84e434d25b3068ff2ff733e1c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=28881.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "37ed32a2e0544b9886e0b63c96ea869c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1648877.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "79739e2492de4d0a9c4cb6ff7e32f14d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Processing...\n", + "Done!\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n", + " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yemZCVfzzLHK" + }, + "source": [ + "#### - Model\n", + "To fair comparison, \n", + "\n", + "We fix our model architecture to do this homework. \n", + "\n", + "The model architecture consist 4 layers fully-connected network." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wDfCEemDyTvN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb0cb76a-1587-4e9e-dbb0-0b5ecade6f2c" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "\n", + "class Model(nn.Module):\n", + " \"\"\"\n", + " Model architecture \n", + " 784 (input) → 1024 → 512 → 256 → 10\n", + " \"\"\"\n", + " def __init__(self):\n", + " super(Model, self).__init__()\n", + " self.fc1 = nn.Linear(784, 1024)\n", + " self.fc2 = nn.Linear(1024, 512)\n", + " self.fc3 = nn.Linear(512, 256)\n", + " self.fc4 = nn.Linear(256, 10)\n", + " self.relu = nn.ReLU()\n", + "\n", + " def forward(self, x):\n", + " x = x.view(-1, 1*28*28)\n", + " x = self.fc1(x)\n", + " x = self.relu(x)\n", + " x = self.fc2(x)\n", + " x = self.relu(x)\n", + " x = self.fc3(x)\n", + " x = self.relu(x)\n", + " x = self.fc4(x)\n", + " return x\n", + "\n", + "example = Model()\n", + "print(example)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model(\n", + " (fc1): Linear(in_features=784, out_features=1024, bias=True)\n", + " (fc2): Linear(in_features=1024, out_features=512, bias=True)\n", + " (fc3): Linear(in_features=512, out_features=256, bias=True)\n", + " (fc4): Linear(in_features=256, out_features=10, bias=True)\n", + " (relu): ReLU()\n", + ")\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vpi2lffuzuhv" + }, + "source": [ + "#### - Train\n", + "This is our function of training process.\n", + "\n", + "It can generally applied in different regularization-based lifelong learning algorithm in this homework." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zFmbMAc6rJ2Z" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import tqdm\n", + "import numpy as np\n", + "from tqdm import trange\n", + "\n", + "def train(model, optimizer, dataloader, epochs_per_task, lll_object, lll_lambda, test_dataloaders, evaluate, device, log_step=1):\n", + " model.train()\n", + " model.zero_grad()\n", + " objective = nn.CrossEntropyLoss()\n", + " acc_per_epoch = []\n", + " loss = 1.0\n", + " bar = tqdm.auto.trange(epochs_per_task, leave=False, desc=f\"Epoch 1, Loss: {loss:.7f}\")\n", + " for epoch in bar:\n", + " for imgs, labels in tqdm.auto.tqdm(dataloader, leave=False): \n", + " imgs, labels = imgs.to(device), labels.to(device)\n", + " outputs = model(imgs)\n", + " loss = objective(outputs, labels)\n", + " total_loss = loss\n", + " lll_loss = lll_object.penalty(model)\n", + " total_loss += lll_lambda * lll_loss \n", + " lll_object.update(model)\n", + " optimizer.zero_grad()\n", + " total_loss.backward()\n", + " optimizer.step()\n", + "\n", + " loss = total_loss.item()\n", + " bar.set_description_str(desc=f\"Epoch {epoch+1:2}, Loss: {loss:.7f}\", refresh=True)\n", + " acc_average = []\n", + " for test_dataloader in test_dataloaders: \n", + " acc_test = evaluate(model, test_dataloader, device)\n", + " acc_average.append(acc_test)\n", + " average=np.mean(np.array(acc_average))\n", + " acc_per_epoch.append(average*100.0)\n", + " bar.set_description_str(desc=f\"Epoch {epoch+2:2}, Loss: {loss:.7f}\", refresh=True)\n", + " \n", + " return model, optimizer, acc_per_epoch" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rjOiSSVD0GXB" + }, + "source": [ + "#### - Evaluate\n", + "This is our function of evaluation process.\n", + "\n", + "It can generally applied in different regularization-based lifelong learning algorithm in this homework." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BUh6J6LZrQVW" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "\n", + "def evaluate(model, test_dataloader, device):\n", + " model.eval()\n", + " correct_cnt = 0\n", + " total = 0\n", + " for imgs, labels in test_dataloader:\n", + " imgs, labels = imgs.to(device), labels.to(device)\n", + " outputs = model(imgs)\n", + " _, pred_label = torch.max(outputs.data, 1)\n", + "\n", + " correct_cnt += (pred_label == labels.data).sum().item()\n", + " total += torch.ones_like(labels.data).sum().item()\n", + " return correct_cnt / total" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gqAoSbiYzRiZ" + }, + "source": [ + "#### - Evaluation Metric\n", + "We utilize **Average Accuracy** as our evaluation metric, \n", + "\n", + "which average the accuracy from the all previous and current test set to measure the performance of lifelong learning . " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6JTAisD5uogo" + }, + "source": [ + "### Visualization\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ktQWQcVKTvnK" + }, + "source": [ + "#### - Permuted MNIST dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HXj1AIU3u7Hu", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + }, + "outputId": "a34fdda7-7370-426d-d504-9df4f0eb0d61" + }, + "source": [ + "### Visualize label 0-9 1 sample MNIST picture in first 3 task.\n", + "\n", + "sample = [\n", + " Data('data', permutation=permutations[index], normalize=False) for index in range(len(permutations))\n", + "]\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(30, 10))\n", + "for task in range(3):\n", + " labels = [list(sample[task].dataset.targets).index(l) for l in range(10)]\n", + " for idx, label in enumerate(labels):\n", + " plt.subplot(3, 10, (task)*10 + idx + 1)\n", + " curr_img = np.reshape(sample[task].dataset[label][0], (28, 28))\n", + " plt.matshow(curr_img, cmap=plt.get_cmap('gray'), fignum=False)\n", + " ax = plt.gca()\n", + " ax.axes.xaxis.set_ticks([])\n", + " ax.axes.yaxis.set_ticks([])\n", + " plt.title(\"task: \" + str(task+1) + \" \" + \"label: \" + str(idx), y=1)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f5iwmr4TWLFy" + }, + "source": [ + "### Methods\n", + "- Baseline\n", + "- EWC\n", + "- SI\n", + "- MAS\n", + "- RWalk\n", + "-SCP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wXN4EDl71sBu" + }, + "source": [ + "#### - Baseline\n", + "The baseline class will do nothing in regularization term." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZcyKKyrb0i2_" + }, + "source": [ + "# Baseline \n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "\n", + "class baseline(object):\n", + " \"\"\"\n", + " baseline technique: do nothing in regularization term [initialize and all weight is zero]\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + "\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #extract all parameters in models\n", + " self.p_old = {} # store current parameters\n", + " self._precision_matrices = self._calculate_importance() # generate weight matrix \n", + "\n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def _calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): # initialize weight matrix（fill zero）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ib50m1HZ5QAo" + }, + "source": [ + "Main process for baseline" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SgKgYHJz5HrW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141, + "referenced_widgets": [ + "ec2588ef879e419fa19b5b27e004c342", + "92bfa33413c54f4bb1df7a2aedb6c256", + "daac70865f1e4f5f8c96bfd846572352", + "00de71facd75413985fa5c3d7eb892ed", + "0f4dd12881e24eb888e21b9e5a3eb60f", + "c6ed98d9308e45679a7d117a5a100b5a", + "6c3447f5589f4167b39ef090dea13e08", + "29c741ce25534c8eaaf80b3fc01130e8", + "3d538393b0304acab0c9a67690c002b5", + "7d4e79d051ad49b6a94573b5e1d86fe9", + "48526f88886d4eb4ad59d7fc9600a74d", + "e5ba9e62395344a0879128f09ed423c9", + "a502c6e96efd46f081fa70ae7ed5f21e", + "98fdce97a2bd4c339d88c44beeafdf22", + "5442b88d556346a786fa6beada9ddc45", + "38ae0f4faf3e4846964055cc3cf04a31", + "17fd4fab27944c4a85d65bc96c02b857", + "2b75388b5af94af3a7d08d1103b8e225", + "b15bdd9c7f044b76bfad2d2de7e97549", + 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tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "\n", + "# iterate training on each task continually.\n", + "for train_indexes in task_bar:\n", + " # Train each task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + "\n", + " # get model weight to baseline class and do nothing!\n", + " lll_object=baseline(model=model, dataloaders=test_dataloaders[:train_indexes],device=device)\n", + "\n", + " # new a optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " baseline_acc.extend(acc_list)\n", + " \n", + " # display the information of the next task.\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(baseline_acc)\n", + 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"name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QIv2EUeu2lXv" + }, + "source": [ + "#### - EWC\n", + "\n", + "Elastic Weight Consolidation\n", + "\n", + "The ewc class applied EWC algorithm to calculate regularization term.\n", + "The central concept is included in Prof.Hung-yi's lectures. Here we will focus on the algorithm of EWC. \n", + "\n", + "In this assignment, we want to let our model learn 10 tasks successively.\n", + "Here we show a simple example that we let the model learn 2 tasks(task A, task B) successively.\n", + "\n", + "In EWC algorithm, the definition of loss function is shown below:\n", + " $$\\mathcal{L}_B = \\mathcal{L}(\\theta) + \\sum_{i} \\frac{\\lambda}{2} F_i (\\theta_{i} - \\theta_{A,i}^{*})^2 $$\n", + " \n", + "Assume we have a neural network with more than two parameters.\n", + "\n", + "$F_i$ correspond to the $i^{th}$ guard in Prof. Hung-yi's lecture. Please do not modify this parameters, because it's important to task A.\n", + "\n", + "The definition of $F$ is shown below.\n", + "$$ F = [ \\nabla \\log(p(y_n | x_n, \\theta_{A}^{*}) \\nabla \\log(p(y_n | x_n, \\theta_{A}^{*})^T ] $$ \n", + "\n", + "We only take the diagonal value of matrix to approximate each parameters' $F_i$.\n", + "\n", + "The detail infromation and derivation are shown in 2.4.1 and 2.4 of [Continual Learning in Neural\n", + "Networks](https://arxiv.org/pdf/1910.02718.pdf)\n", + "\n", + "For You Information: [Elastic Weight Consolidation](https://arxiv.org/pdf/1612.00796.pdf)\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4zf7sGSD2FID" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class ewc(object):\n", + " \"\"\"\n", + " @article{kirkpatrick2017overcoming,\n", + " title={Overcoming catastrophic forgetting in neural networks},\n", + " author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},\n", + " journal={Proceedings of the national academy of sciences},\n", + " year={2017},\n", + " url={https://arxiv.org/abs/1612.00796}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + "\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} # extract all parameters in models\n", + " self.p_old = {} # initialize parameters\n", + " self._precision_matrices = self._calculate_importance() # generate Fisher (F) matrix for EWC \n", + "\n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def _calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): \n", + " # initialize Fisher (F) matrix（all fill zero）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num=len(self.dataloaders)\n", + " number_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " # get image data\n", + " input = data[0].to(self.device)\n", + " # image data forward model\n", + " output = self.model(input)\n", + " # Simply use groud truth label of dataset. \n", + " label = data[1].to(self.device)\n", + " # print(output.shape, label.shape)\n", + " \n", + " ############################################################################\n", + " ##### generate Fisher(F) matrix for EWC #####\n", + " ############################################################################ \n", + " loss = F.nll_loss(F.log_softmax(output, dim=1), label) \n", + " loss.backward() \n", + " ############################################################################\n", + "\n", + " for n, p in self.model.named_parameters():\n", + " # get the gradient of each parameter and square it, then average it in all validation set. \n", + " precision_matrices[n].data += p.grad.data ** 2 / number_data \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + "\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " # generate the final regularization term by the ewc weight (self._precision_matrices[n]) and the square of weight difference ((p - self.p_old[n]) ** 2). \n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return \n" + ], + 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coefficient value of regularization term.\n", + "lll_lambda=100\n", + "ewc_acc= []\n", + "task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "\n", + "# iterate training on each task continually.\n", + "for train_indexes in task_bar:\n", + " # Train Each Task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=ewc(model=model, dataloaders=test_dataloaders[:train_indexes+1],device=device)\n", + "\n", + " # new a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # collect average accuracy in each epoch\n", + " ewc_acc.extend(acc_list)\n", + "\n", + " # Update tqdm displayer\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(ewc_acc)\n", + "print(\"==================================================================================================\")\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN EWC\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fdb56e98d0d43c4b7c3f4ff2dd3dc3e", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Task 1', max=5.0, style=ProgressStyle(description_width…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1ec628eb300c43ab84d9d08a816e1d8d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Epoch 1, Loss: 1.0000000', max=10.0, 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"==================================================================================================\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aPz7wBXo7rkr" + }, + "source": [ + "#### - MAS\n", + "Memory Aware Synapses\n", + "\n", + "The mas class applied MAS algorithm to calculate regularization term.\n", + "\n", + "The concept of MAS is similar to EWC, the only difference is the calculation of the important weight. \n", + "The details are mentioned in following blocks.\n", + "\n", + "MAS:\n", + "\n", + "In MAS, the Loss function is shown below, the model learn task A before it learned task B.\n", + "\n", + "$$\\mathcal{L}_B = \\mathcal{L}(\\theta) + \\sum_{i} \\frac{\\lambda}{2} \\Omega_i (\\theta_{i} - \\theta_{A,i}^{*})^2$$\n", + "\n", + "Compare with EWC, the $F_i$ in the loss function is replaced with $\\Omega_i$ in the following function.\n", + "\n", + "$$\\Omega_i = || \\frac{\\partial \\ell_2^2(M(x_k; \\theta))}{\\partial \\theta_i} || $$ \n", + "\n", + "$x_k$ is the sample data of the previous task. So the $\\Omega$ is obtained gradients of the squared L2-norm of the learned network output.\n", + "\n", + "The methods that proposed from paper is the local version by taking squared L2-norm outputs from the each layers of the model.\n", + "\n", + "Here we only implmented the global version by taking outputs from the last layer of the model. \n", + "\n", + "\n", + "For Your Information: \n", + "[Memory Aware Synapses](https://arxiv.org/pdf/1711.09601.pdf)\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nznnDrFP3UYf" + }, + "source": [ + "class mas(object):\n", + " \"\"\"\n", + " @article{aljundi2017memory,\n", + " title={Memory Aware Synapses: Learning what (not) to forget},\n", + " author={Aljundi, Rahaf and Babiloni, Francesca and Elhoseiny, Mohamed and Rohrbach, Marcus and Tuytelaars, Tinne},\n", + " booktitle={ECCV},\n", + " year={2018},\n", + " url={https://eccv2018.org/openaccess/content_ECCV_2018/papers/Rahaf_Aljundi_Memory_Aware_Synapses_ECCV_2018_paper.pdf}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model: nn.Module, dataloaders: list, device):\n", + " self.model = model \n", + " self.dataloaders = dataloaders\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #extract all parameters in models\n", + " self.p_old = {} # initialize parameters\n", + " self.device = device\n", + " self._precision_matrices = self.calculate_importance() # generate Omega(Ω) matrix for MAS\n", + " \n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items():\n", + " precision_matrices[n] = p.clone().detach().fill_(0) # initialize Omega(Ω) matrix（all filled zero）\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num = len(self.dataloaders)\n", + " num_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " output = self.model(data[0].to(self.device))\n", + "\n", + " ###########################################################################################################################################\n", + " ##### TODO BLOCK: generate Omega(Ω) matrix for MAS. (Hint: square of l2 norm of output vector, then backward and take its gradients #####\n", + " ###########################################################################################################################################\n", + " output.pow_(2) \n", + " loss = torch.sum(output,dim=1) \n", + " loss = loss.mean() \n", + " loss.backward() \n", + " ########################################################################################################################################### \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " precision_matrices[n].data += p.grad.abs() / num_data ## difference with EWC \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zfbD-yPG-LkN" + }, + "source": [ + "Main process for MAS" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HLoLPPmD-BHD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141, + "referenced_widgets": [ + "4ca31f1971c9488d872c47bad7f96889", + "9e915c9b2c664887b1d0d4937c97fe41", + "ccb22c8cee03438c8326c13a081c5fc7", + "09e1f35617a741bf800c6ac0b1488e4a", + "a5e656639fdb421090070d8a3a9f8afa", + "07ce24c5ee95478cbdbdb5c73ef74668", + "6ca43c3d9f044f96b00d72514dbb2c86", + "bf597639a51f462f87e985a2836d5a89", + "71f38e1d1c1545cd9eda838dfcf4bac2", + "cc1af4d131464c01964043964d730a6d", + "2282b2571aa440a4add872d652f9717a", + "060a9076eceb4cfbb6637f1e70562f06", + "f9982380a4974221943b2eab766956ed", + "b0fabb1a5b884970b8bbe2ee4d58061f", + "658d0ad8216642be82d59bbe2c7cbee1", + 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mas_acc= []\n", + " task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + " for train_indexes in task_bar:\n", + " # Train Each Task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=mas(model=model, dataloaders=test_dataloaders[:train_indexes+1],device=device)\n", + "\n", + " # New a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " mas_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + " \n", + " # average accuracy in each task per epoch! \n", + " print(mas_acc)\n", + " print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN MAS\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4ca31f1971c9488d872c47bad7f96889", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Task 1', max=5.0, style=ProgressStyle(description_width…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "71f38e1d1c1545cd9eda838dfcf4bac2", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Epoch 1, Loss: 1.0000000', max=10.0, style=ProgressStyle(…" + ] + }, + "metadata": { + "tags": [] + } + }, + { 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"metadata": { + "id": "OJ4LYprnRO73" + }, + "source": [ + "#### - SI\n", + "The si class applied SI (Synaptic Intelligence) algorithm to calculate regularization term." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vN4PS1nSRaEx" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "\n", + "class si(object):\n", + " \"\"\"\n", + " @article{kirkpatrick2017overcoming,\n", + " title={Overcoming catastrophic forgetting in neural networks},\n", + " author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},\n", + " journal={Proceedings of the national academy of sciences},\n", + " year={2017},\n", + " url={https://arxiv.org/abs/1612.00796}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, epsilon, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + " self.epsilon = epsilon\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #抓出模型的所有參數\n", + " self._n_p_prev, self._n_omega = self._calculate_importance() \n", + " self.W, self.p_old = self._init_()\n", + "\n", + " def _init_(self):\n", + " W = {}\n", + " p_old = {}\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " W[n] = p.data.clone().zero_()\n", + " p_old[n] = p.data.clone()\n", + " return W, p_old\n", + "\n", + " def _calculate_importance(self):\n", + " n_p_prev = {}\n", + " n_omega = {}\n", + "\n", + " if self.dataloaders[0] != None:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + "\n", + " # Find/calculate new values for quadratic penalty on parameters\n", + " p_prev = getattr(self.model, '{}_SI_prev_task'.format(n))\n", + " W = getattr(self.model, '{}_W'.format(n))\n", + " p_current = p.detach().clone()\n", + " p_change = p_current - p_prev\n", + " omega_add = W/(p_change**2 + self.epsilon)\n", + " try:\n", + " omega = getattr(self.model, '{}_SI_omega'.format(n))\n", + " except AttributeError:\n", + " omega = p.detach().clone().zero_()\n", + " omega_new = omega + omega_add\n", + " n_omega[n] = omega_new\n", + " n_p_prev[n] = p_current\n", + "\n", + "\n", + " # Store these new values in the model\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p_current)\n", + " self.model.register_buffer('{}_SI_omega'.format(n), omega_new)\n", + " else:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " n_p_prev[n] = p.detach().clone()\n", + " n_omega[n] = p.detach().clone().zero_()\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p.detach().clone())\n", + "\n", + "\n", + " return n_p_prev, n_omega\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0.0\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " prev_values = self._n_p_prev[n]\n", + " omega = self._n_omega[n]\n", + " _loss = omega * (p - prev_values) ** 2\n", + " loss += _loss.sum()\n", + " \n", + " return loss\n", + " \n", + " def update(self, model):\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " if p.grad is not None:\n", + " self.W[n].add_(-p.grad * (p.detach() - self.p_old[n]))\n", + " self.model.register_buffer('{}_W'.format(n), self.W[n])\n", + " self.p_old[n] = p.detach().clone()\n", + " return \n", + " \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z1yDmFFeRxi9" + }, + "source": [ + "Main Process for SI\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RDwVo5W9R5cx", + 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evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=si(model=model, dataloaders=test_dataloaders[:train_indexes+1], epsilon=0.1, device=device)\n", + "\n", + " # New a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " si_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + " # average accuracy in each task per epoch! \n", + " print(si_acc)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN SI\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "531cbe4dc8b14d4b9184c7c87fb511a3", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + 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95.90333333333334, 96.06333333333333, 95.71666666666665, 95.11666666666666, 95.73, 95.33333333333333, 95.39666666666666, 95.13666666666666, 95.28666666666666, 94.90333333333334, 93.1575, 92.99499999999999, 92.0825, 92.14750000000001, 91.7425, 91.6175, 92.4725, 92.015, 91.41999999999999, 92.58, 91.05000000000001, 90.41799999999999, 90.25399999999999, 90.43599999999999, 90.28399999999999, 89.524, 90.168, 90.66000000000001, 89.87399999999998, 90.428]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "64UECuLG66GM" + }, + "source": [ + "#### - RWalk\n", + "\n", + "#### Remanian Walk for Incremental Learning\n", + "\n", + "The rwalk class applied Remanian Walk algorithm to calculate regularization term.\n", + "\n", + "The details are mentioned in following blocks." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9hzivWHj8KFF" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class rwalk(object):\n", + " \"\"\"\n", + "\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, epsilon, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + " self.epsilon = epsilon\n", + " self.update_ewc_parameter = 0.4\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} # extract model parameters and store in dictionary\n", + " self._means = {} # initialize the guidance matrix\n", + " self._precision_matrices = self._calculate_importance_ewc() # Generate Fisher (F) Information Matrix \n", + " self._n_p_prev, self._n_omega = self._calculate_importance() \n", + " self.W, self.p_old = self._init_()\n", + "\n", + "\n", + " def _init_(self):\n", + " W = {}\n", + " p_old = {}\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " W[n] = p.data.clone().zero_()\n", + " p_old[n] = p.data.clone()\n", + " return W, p_old\n", + "\n", + " def _calculate_importance(self):\n", + " n_p_prev = {}\n", + " n_omega = {}\n", + "\n", + " if self.dataloaders[0] != None:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + "\n", + " # Find/calculate new values for quadratic penalty on parameters\n", + " p_prev = getattr(self.model, '{}_SI_prev_task'.format(n))\n", + " W = getattr(self.model, '{}_W'.format(n))\n", + " p_current = p.detach().clone()\n", + " p_change = p_current - p_prev\n", + " omega_add = W / (1.0 / 2.0*self._precision_matrices[n] *p_change**2 + self.epsilon)\n", + " try:\n", + " omega = getattr(self.model, '{}_SI_omega'.format(n))\n", + " except AttributeError:\n", + " omega = p.detach().clone().zero_()\n", + " omega_new = 0.5 * omega + 0.5 *omega_add\n", + " n_omega[n] = omega_new\n", + " n_p_prev[n] = p_current\n", + "\n", + "\n", + " # Store these new values in the model\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p_current)\n", + " self.model.register_buffer('{}_SI_omega'.format(n), omega_new)\n", + " else:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " n_p_prev[n] = p.detach().clone()\n", + " n_omega[n] = p.detach().clone().zero_()\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p.detach().clone())\n", + "\n", + "\n", + " return n_p_prev, n_omega\n", + " \n", + "\n", + " def _calculate_importance_ewc(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): \n", + " n = n.replace('.', '__') # 初始化 Fisher (F) 的矩陣（都補零）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num=len(self.dataloaders)\n", + " number_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for n, p in self.model.named_parameters(): \n", + " n = n.replace('.', '__')\n", + " precision_matrices[n].data *= (1 -self.update_ewc_parameter) \n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " input = data[0].to(self.device)\n", + " output = self.model(input)\n", + " label = data[1].to(self.device)\n", + "\n", + " \n", + " ############################################################################\n", + " ##### Generate Fisher Matrix #####\n", + " ############################################################################ \n", + " loss = F.nll_loss(F.log_softmax(output, dim=1), label) \n", + " loss.backward() \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " n = n.replace('.', '__')\n", + " precision_matrices[n].data += self.update_ewc_parameter*p.grad.data ** 2 / number_data \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + "\n", + " return precision_matrices\n", + "\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0.0\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " prev_values = self._n_p_prev[n]\n", + " omega = self._n_omega[n]\n", + "\n", + " #################################################################################\n", + " #### Generate regularization term _loss by omega and Fisher Matrix ####\n", + " #################################################################################\n", + " _loss = (omega + self._precision_matrices[n]) * (p - prev_values) ** 2\n", + " loss += _loss.sum()\n", + " \n", + " return loss\n", + " \n", + " def update(self, model):\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " if p.grad is not None:\n", + " self.W[n].add_(-p.grad * (p.detach() - self.p_old[n]))\n", + " self.model.register_buffer('{}_W'.format(n), self.W[n])\n", + " self.p_old[n] = p.detach().clone()\n", + " return \n", + " \n" + ], + 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tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "for train_indexes in task_bar:\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " lll_object=rwalk(model=model, dataloaders=test_dataloaders[:train_indexes+1], epsilon=0.1, device=device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + " rwalk_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(rwalk_acc)\n", + "print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN Rwalk\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + 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97.57000000000001, 97.75, 97.7, 98.00999999999999, 97.76, 97.89999999999999, 96.06, 97.03500000000001, 96.9, 97.275, 97.16, 97.21, 97.38, 97.255, 97.39999999999999, 97.32, 95.37666666666667, 96.08666666666666, 96.07333333333334, 96.30666666666666, 96.31333333333333, 96.22333333333333, 96.36333333333334, 96.08666666666666, 95.90666666666667, 96.07666666666667, 94.65, 95.075, 94.9325, 95.06500000000001, 94.80499999999999, 95.0975, 95.2225, 95.1975, 94.9925, 94.78, 93.96000000000001, 94.214, 93.836, 94.036, 94.152, 94.00800000000001, 94.17, 94.178, 93.89800000000001, 93.986]\n", + "==================================================================================================\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XicStkkOA_gg" + }, + "source": [ + "#### - SCP\n", + "Sliced Cramer Preservation\n", + "\n", + "\n", + "\n", + "Pseudo Code:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "72o_U5uxA8kX" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "import numpy as np\n", + "\n", + "\n", + "\n", + "def sample_spherical(npoints, ndim=3):\n", + " vec = np.random.randn(ndim, npoints)\n", + " vec /= np.linalg.norm(vec, axis=0)\n", + " return torch.from_numpy(vec)\n", + "\n", + "class scp(object):\n", + " \"\"\"\n", + " OPEN REVIEW VERSION:\n", + " https://openreview.net/forum?id=BJge3TNKwH\n", + " \"\"\"\n", + " def __init__(self, model: nn.Module, dataloaders: list, L: int, device):\n", + " self.model = model \n", + " self.dataloaders = dataloaders\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad}\n", + " self._state_parameters = {}\n", + " self.L= L\n", + " self.device = device\n", + " self._precision_matrices = self.calculate_importance()\n", + " \n", + " for n, p in self.params.items():\n", + " self._state_parameters[n] = p.clone().detach()\n", + " \n", + " def calculate_importance(self):\n", + "\n", + " precision_matrices = {}\n", + " for n, p in self.params.items():\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num = len(self.dataloaders)\n", + " num_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " output = self.model(data[0].to(self.device))\n", + " \n", + " ####################################################################################\n", + " ##### generate SCP's Gamma(Γ) matrix (like MAS's Omega(Ω) and EWC's Fisher(F)) #####\n", + " ####################################################################################\n", + " ##### 1.take average on a batch of Output vector to get vector φ(:,θ_A* )####\n", + " ####################################################################################\n", + " mean_vec = output.mean(dim=0)\n", + "\n", + " ####################################################################################\n", + " ##### 2. random sample L vectors ξ #（ Hint: sample_spherical() ） #####\n", + " ####################################################################################\n", + " L_vectors = sample_spherical(self.L, output.shape[-1])\n", + " L_vectors = L_vectors.transpose(1,0).to(self.device).float()\n", + "\n", + " ####################################################################################\n", + " ##### 3. 每一個 vector ξ 和 vector φ( :,θ_A* )內積得到 scalar ρ ####\n", + " ##### 對 scalar ρ 取 backward ， 每個參數得到各自的 gradient ∇ρ ####\n", + " ##### 每個參數的 gradient ∇ρ 取平方 取 L 平均 得到 各個參數的 Γ scalar #### \n", + " ##### 所有參數的 Γ scalar 組合而成其實就是 Γ 矩陣 ####\n", + " ####(hint: 記得 每次 backward 之後 要 zero_grad 去 清 gradient, 不然 gradient會累加 )#### \n", + " ####################################################################################\n", + " total_scalar = 0\n", + " for vec in L_vectors:\n", + " scalar=torch.matmul(vec, mean_vec)\n", + " total_scalar += scalar\n", + " total_scalar /= L_vectors.shape[0] \n", + " total_scalar.backward()\n", + " ################################################################################## \n", + " \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " precision_matrices[n].data += p.grad.abs() / num_data ## difference with EWC \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self._state_parameters[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6uBo7pzH-eQx" + }, + "source": [ + "Main process for 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"bcf36e5d2da943dd9ba1957aa79131e5", + "83007f139b2d485899e8afcb647bea89", + "5b9ae06c4547401193c5c892271e6c74", + "e275e141b65c45ac8a4972c53c77e364", + "cf88d6cc80e24e28be56c699398d39b6", + "ffa2a96da3374802b5ec2009c2ea8e29", + "2266a36f65134ab8806435850d7e2527", + "34928e149ac5468383ae967167778845", + "f0866b229a3944fc9151a1903658cba0", + "bcfbed0138214264b649fb289a6d8612", + "21e7b76309004f9d916d62880610b9e8" + ] + }, + "outputId": "1c409ff8-903a-4bf6-d752-0aba3fdc274c" + }, + "source": [ + "# SCP\n", + "print(\"RUN SLICE CRAMER PRESERVATION\")\n", + "model = Model()\n", + "model = model.to(device)\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + "lll_object=scp(model=model, dataloaders=[None], L=100, device=device)\n", + "lll_lambda=100\n", + "scp_acc= []\n", + "task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "for train_indexes in task_bar:\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " lll_object=scp(model=model, dataloaders=test_dataloaders[:train_indexes+1], L=100, device=device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + " scp_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(scp_acc)\n", + "print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN SLICE CRAMER PRESERVATION\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c4b9a5c6e5434bcbaa46720747035d9b", + "version_minor": 0, + "version_major": 2 + }, + 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null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "AeGB_b7IbKs1" + }, + "source": [ + "Homework 13 - Network Compression\n", + "===\n", + "\n", + "> Author: Arvin Liu (r09922071@ntu.edu.tw), this colab is modified from ML2021-HW3\n", + "\n", + "If you have any questions, feel free to ask: ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JY4wUHY3bNx1" + }, + "source": [ + "## **Intro**\n", + "\n", + "HW13 is about network compression\n", + "\n", + "There are many types of Network/Model Compression, here we introduce two:\n", + "* Knowledge Distillation\n", + "* Design Architecture\n", + "\n", + "\n", + "The process of this notebook is as follows:
\n", + "1. Introduce depthwise, pointwise and group convolution in MobileNet.\n", + "2. Design the model of this colab\n", + "3. Introduce Knowledge-Distillation\n", + "4. Set up TeacherNet and it would be helpful in training\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w3PoggZ91dRA" + }, + "source": [ + "## **About the Dataset** *(same as HW3)*\n", + "\n", + "The dataset used here is food-11, a collection of food images in 11 classes.\n", + "\n", + "For the requirement in the homework, TAs slightly modified the data.\n", + "Please DO NOT access the original fully-labeled training data or testing labels.\n", + "\n", + "Also, the modified dataset is for this course only, and any further distribution or commercial use is forbidden." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BiNaf9gu1pu2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "39da2215-344a-4955-8461-435c40bebf88" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Download the dataset\n", + "# You may choose where to download the data.\n", + "\n", + "# Google Drive\n", + "!gdown --id '1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy' --output food-11.zip\n", + "# If you cannot successfully gdown, you can change a link. (Backup link is provided at the bottom of this colab tutorial).\n", + "\n", + "# Dropbox\n", + "# !wget https://www.dropbox.com/s/m9q6273jl3djall/food-11.zip -O food-11.zip\n", + "\n", + "# MEGA\n", + "# !sudo apt install megatools\n", + "# !megadl \"https://mega.nz/#!zt1TTIhK!ZuMbg5ZjGWzWX1I6nEUbfjMZgCmAgeqJlwDkqdIryfg\"\n", + "\n", + "# Unzip the dataset.\n", + "# This may take some time.\n", + "!unzip -q food-11.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy\n", + "To: /content/food-11.zip\n", + "963MB [00:14, 67.3MB/s]\n", + "replace food-11/training/unlabeled/00/5176.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vg8nOEmK1saF" + }, + "source": [ + "## **Import Packages** *(same as HW3)*\n", + "\n", + "First, we need to import packages that will be used later.\n", + "\n", + "In this homework, we highly rely on **torchvision**, a library of PyTorch." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BAk_whhx1xuf" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Import necessary packages.\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch\n", + "import torchvision.transforms as transforms\n", + "import torchvision.models as models\n", + "\n", + "from PIL import Image\n", + "# \"ConcatDataset\" and \"Subset\" are possibly useful when doing semi-supervised learning.\n", + "from torch.utils.data import ConcatDataset, DataLoader, Subset\n", + "from torchvision.datasets import DatasetFolder\n", + "\n", + "# This is for the progress bar.\n", + "from tqdm.auto import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vPjS3Q9WRakn" + }, + "source": [ + "## **Dataset, Data Loader, and Transforms** *(similar to HW3)*\n", + "\n", + "Torchvision provides lots of useful utilities for image preprocessing, data wrapping as well as data augmentation.\n", + "\n", + "Here, since our data are stored in folders by class labels, we can directly apply **torchvision.datasets.DatasetFolder** for wrapping data without much effort.\n", + "\n", + "Please refer to [PyTorch official website](https://pytorch.org/vision/stable/transforms.html) for details about different transforms.\n", + "\n", + "---\n", + "**The only diffference with HW3 is that the transform functions are different.**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IImuu-Po11L-" + }, + "source": [ + "### This block is similar to HW3 ###\n", + "# It is important to do data augmentation in training.\n", + "# However, not every augmentation is useful.\n", + "# Please think about what kind of augmentation is helpful for food recognition.\n", + "\n", + "train_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 142)\n", + "\ttransforms.Resize((142, 142)),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.RandomRotation(15),\n", + "\ttransforms.RandomCrop(128),\n", + "\ttransforms.ToTensor(),\n", + "])\n", + "\n", + "# We don't need augmentations in testing and validation.\n", + "# All we need here is to resize the PIL image and transform it into Tensor.\n", + "test_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 142)\n", + " transforms.Resize((142, 142)),\n", + " transforms.CenterCrop(128),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rcU_xd_C12ce" + }, + "source": [ + "### This block is similar to HW3 ###\n", + "# Batch size for training, validation, and testing.\n", + "# A greater batch size usually gives a more stable gradient.\n", + "# But the GPU memory is limited, so please adjust it carefully.\n", + "batch_size = 64\n", + "\n", + "# Construct datasets.\n", + "# The argument \"loader\" tells how torchvision reads the data.\n", + "train_set = DatasetFolder(\"food-11/training/labeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "valid_set = DatasetFolder(\"food-11/validation\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "unlabeled_set = DatasetFolder(\"food-11/training/unlabeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "test_set = DatasetFolder(\"food-11/testing\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "\n", + "# Construct data loaders.\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zggTBkI6bPkn" + }, + "source": [ + "# **Architecture / Model Design**\n", + "The following are types of convolution layer design that has fewer parameters.\n", + "\n", + "## **Depthwise & Pointwise Convolution**\n", + "![](https://i.imgur.com/FBgcA0s.png)\n", + "> Blue: the connection between layers \\\n", + "> Green: the expansion of **receptive field** \\\n", + "> (reference: arxiv:1810.04231)\n", + "\n", + "(a) normal convolution layer: It is fully connected. The difference between fully connected layer and fully connected convolution layer is the operation. (multiply --> convolution)\n", + "\n", + "(b) Depthwise convolution layer(DW): You can consider each feature map pass through their own filter and then pass through pointwise convolution layer(PW) to combine the information of all pixels in feature maps.\n", + "\n", + "\n", + "(c) Group convolution layer(GC): Group the feature maps. Each group passes their filter then concate together. If group_size = input_feature_size, then GC becomes DC (channels are independent). If group_size = 1, then GC becomes fully connected.\n", + "\n", + "\n", + "\n", + "\n", + "## **Implementation details**\n", + "```python\n", + "# Regular Convolution, # of params = in_chs * out_chs * kernel_size^2\n", + "nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding)\n", + "\n", + "# Group Convolution, \"groups\" controls the connections between inputs and\n", + "# outputs. in_chs and out_chs must both be divisible by groups.\n", + "nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups)\n", + "\n", + "# Depthwise Convolution, out_chs=in_chs=groups, # of params = in_chs * kernel_size^2\n", + "nn.Conv2d(in_chs, out_chs=in_chs, kernel_size, stride, padding, groups=in_chs)\n", + "\n", + "# Pointwise Convolution, a.k.a 1 by 1 convolution, # of params = in_chs * out_chs\n", + "nn.Conv2d(in_chs, out_chs, 1)\n", + "\n", + "# Merge Depthwise and Pointwise Convolution (without )\n", + "def dwpw_conv(in_chs, out_chs, kernel_size, stride, padding):\n", + " return nn.Sequential(\n", + " nn.Conv2d(in_chs, in_chs, kernels, stride, padding, groups=in_chs),\n", + " nn.Conv2d(in_chs, out_chs, 1),\n", + " )\n", + "```\n", + "\n", + "## **Model**\n", + "\n", + "The basic model here is simply a stack of convolutional layers followed by some fully-connected layers. You can take advatage of depthwise & pointwise convolution to make your model deeper, but still follow the size constraint." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TRwpE9eebVXK" + }, + "source": [ + "class StudentNet(nn.Module):\n", + " def __init__(self):\n", + " super(StudentNet, self).__init__()\n", + "\n", + " # ---------- TODO ----------\n", + " # Modify your model architecture\n", + "\n", + " self.cnn = nn.Sequential(\n", + " nn.Conv2d(3, 32, 3), \n", + " nn.BatchNorm2d(32),\n", + " nn.ReLU(),\n", + " nn.Conv2d(32, 32, 3), \n", + " nn.BatchNorm2d(32),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0), \n", + "\n", + " nn.Conv2d(32, 64, 3), \n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0), \n", + "\n", + " nn.Conv2d(64, 100, 3), \n", + " nn.BatchNorm2d(100),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + " \n", + " # Here we adopt Global Average Pooling for various input size.\n", + " nn.AdaptiveAvgPool2d((1, 1)),\n", + " )\n", + " self.fc = nn.Sequential(\n", + " nn.Linear(100, 11),\n", + " )\n", + " \n", + " def forward(self, x):\n", + " out = self.cnn(x)\n", + " out = out.view(out.size()[0], -1)\n", + " return self.fc(out)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MvHiN7q1BCRs" + }, + "source": [ + "## **Model Analysis**\n", + "\n", + "Use `torchsummary` to get your model architecture (screenshot or pasting text are allowed.) and numbers of \n", + "parameters, these two information should be submit to your NTU Cool questions.\n", + "\n", + "Note that the number of parameters **should not greater than 100,000**, or you'll get penalty in this homework.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0gB2f5dO-ktA", + "outputId": "3def2925-be08-48e0-8b66-89cd032284d7" + }, + "source": [ + "from torchsummary import summary\n", + "\n", + "student_net = StudentNet()\n", + "summary(student_net, (3, 128, 128), device=\"cpu\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 32, 126, 126] 896\n", + " BatchNorm2d-2 [-1, 32, 126, 126] 64\n", + " ReLU-3 [-1, 32, 126, 126] 0\n", + " Conv2d-4 [-1, 32, 124, 124] 9,248\n", + " BatchNorm2d-5 [-1, 32, 124, 124] 64\n", + " ReLU-6 [-1, 32, 124, 124] 0\n", + " MaxPool2d-7 [-1, 32, 62, 62] 0\n", + " Conv2d-8 [-1, 64, 60, 60] 18,496\n", + " BatchNorm2d-9 [-1, 64, 60, 60] 128\n", + " ReLU-10 [-1, 64, 60, 60] 0\n", + " MaxPool2d-11 [-1, 64, 30, 30] 0\n", + " Conv2d-12 [-1, 100, 28, 28] 57,700\n", + " BatchNorm2d-13 [-1, 100, 28, 28] 200\n", + " ReLU-14 [-1, 100, 28, 28] 0\n", + " MaxPool2d-15 [-1, 100, 14, 14] 0\n", + "AdaptiveAvgPool2d-16 [-1, 100, 1, 1] 0\n", + " Linear-17 [-1, 11] 1,111\n", + "================================================================\n", + "Total params: 87,907\n", + "Trainable params: 87,907\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.19\n", + "Forward/backward pass size (MB): 31.49\n", + "Params size (MB): 0.34\n", + "Estimated Total Size (MB): 32.01\n", + "----------------------------------------------------------------\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NQXY4vLmR8ae" + }, + "source": [ + "## **Knowledge Distillation**\n", + "\n", + "\n", + "\n", + "Since we have a learned big model, let it teach the other small model. In implementation, let the training target be the prediction of big model instead of the ground truth.\n", + "\n", + "## **Why it works?**\n", + "* If the data is not clean, then the prediction of big model could ignore the noise of the data with wrong labeled.\n", + "* The labels might have some relations. Number 8 is more similar to 6, 9, 0 than 1, 7, for example.\n", + "\n", + "\n", + "## **How to implement?**\n", + "* $Loss = \\alpha T^2 \\times KL(\\frac{\\text{Teacher's Logits}}{T} || \\frac{\\text{Student's Logits}}{T}) + (1-\\alpha)(\\text{Original Loss})$\n", + "* Note that the logits here should have passed softmax." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6TMCyqrWTSsz" + }, + "source": [ + "def loss_fn_kd(outputs, labels, teacher_outputs, alpha=0.5):\n", + " hard_loss = F.cross_entropy(outputs, labels) * (1. - alpha) \n", + " # ---------- TODO ----------\n", + " # Complete soft loss in knowledge distillation\n", + " soft_loss = 0 \n", + " return hard_loss + soft_loss" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "97zXxEd3Thlb" + }, + "source": [ + "## **Teacher Model Setting**\n", + "We provide a well-trained teacher model to help you knowledge distillation to student model.\n", + "Note that if you want to change the transform function, you should consider if suitable for this well-trained teacher model.\n", + "* If you cannot successfully gdown, you can change a link. (Backup link is provided at the bottom of this colab tutorial).\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nR9J169XTjki", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3086910d-9a5e-4747-ce1d-8ff0a3609648" + }, + "source": [ + "# Download teacherNet\n", + "!gdown --id '1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m' --output teacher_net.ckpt\n", + "# Load teacherNet\n", + "teacher_net = torch.load('./teacher_net.ckpt')\n", + "teacher_net.eval()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m\n", + "To: /content/teacher_net.ckpt\n", + "44.8MB [00:00, 58.6MB/s]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ResNet(\n", + " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer2): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer3): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer4): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=512, out_features=11, bias=True)\n", + ")" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0DMKD-bnuIRF" + }, + "source": [ + "## **Generate Pseudo Labels in Unlabeled Data**\n", + "\n", + "Since we have a well-trained model, we can use this model to predict pseudo-labels and help the student network train well. Note that you \n", + "**CANNOT** use well-trained model to pseudo-label the test data. \n", + "\n", + "\n", + "---\n", + "\n", + "**AGAIN, DO NOT USE TEST DATA FOR PURPOSE OTHER THAN INFERENCING**\n", + "\n", + "* Because If you use teacher network to predict pseudo-labels of the test data, you can only use student network to overfit these pseudo-labels without train/unlabeled data. In this way, your kaggle accuracy will be as high as the teacher network, but the fact is that you just overfit the test data and your true testing accuracy is very low. \n", + "* These contradict the purpose of these assignment (network compression); therefore, you should not misuse the test data.\n", + "* If you have any concerns, you can email us.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "58608c3ebf3b4662b6208b0d12ed5082", + "ac9d4431b6034315b696e71756e718f6", + "a711cf06b5e84b18b05141116cc19639", + "322582864f8843ec8b3860ead19c7f92", + "d0aaa49ccc4f4b6c8244193118ad4217", + "c12fa6ff16644a638bf05d0c1c5110bb", + "bb0205c810d94c8f942515528b91fbc9", + "e14750af9cc447cf9f37edf4f7e312e6" + ] + }, + "id": "tyRc7MktuJQN", + "outputId": "b7641e78-7e27-4198-d5db-da5b72c5967f" + }, + "source": [ + "# \"cuda\" only when GPUs are available.\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "# Initialize a model, and put it on the device specified.\n", + "student_net = student_net.to(device)\n", + "teacher_net = teacher_net.to(device)\n", + "\n", + "# Whether to do pseudo label.\n", + "do_semi = True\n", + "\n", + "def get_pseudo_labels(dataset, model):\n", + " loader = DataLoader(dataset, batch_size=batch_size*3, shuffle=False, pin_memory=True)\n", + " pseudo_labels = []\n", + " for batch in tqdm(loader):\n", + " # A batch consists of image data and corresponding labels.\n", + " img, _ = batch\n", + "\n", + " # Forward the data\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = model(img.to(device))\n", + " pseudo_labels.append(logits.argmax(dim=-1).detach().cpu())\n", + " # Obtain the probability distributions by applying softmax on logits.\n", + " pseudo_labels = torch.cat(pseudo_labels)\n", + " # Update the labels by replacing with pseudo labels.\n", + " for idx, ((img, _), pseudo_label) in enumerate(zip(dataset.samples, pseudo_labels)):\n", + " dataset.samples[idx] = (img, pseudo_label.item())\n", + " return dataset\n", + "\n", + "if do_semi:\n", + " # Generate new trainloader with unlabeled set.\n", + " unlabeled_set = get_pseudo_labels(unlabeled_set, teacher_net)\n", + " concat_dataset = ConcatDataset([train_set, unlabeled_set])\n", + " train_loader = DataLoader(concat_dataset, batch_size=batch_size, shuffle=True, pin_memory=True, drop_last=True)\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "58608c3ebf3b4662b6208b0d12ed5082", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=36.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XeprOVBhT7cJ" + }, + "source": [ + "## **Training** *(similar to HW3)*\n", + "\n", + "You can finish supervised learning by simply running the provided code without any modification.\n", + "\n", + "The function \"get_pseudo_labels\" is used for semi-supervised learning.\n", + "It is expected to get better performance if you use unlabeled data for semi-supervised learning.\n", + "However, you have to implement the function on your own and need to adjust several hyperparameters manually.\n", + "\n", + "For more details about semi-supervised learning, please refer to [Prof. Lee's slides](https://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2016/Lecture/semi%20(v3).pdf).\n", + "\n", + "Again, please notice that utilizing external data (or pre-trained model) for training is **prohibited**.\n", + "\n", + "---\n", + "**The only diffference with HW3 is that you should use loss in knowledge distillation.**\n", + "\n", + "\n" + ] + }, + { + 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before training.\n", + " student_net.train()\n", + "\n", + " # These are used to record information in training.\n", + " train_loss = []\n", + " train_accs = []\n", + "\n", + " # Iterate the training set by batches.\n", + " for batch in tqdm(train_loader):\n", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # Forward the data. (Make sure data and model are on the same device.)\n", + " logits = student_net(imgs.to(device))\n", + " # Teacher net will not be updated. And we use torch.no_grad\n", + " # to tell torch do not retain the intermediate values\n", + " # (which are for backpropgation) and save the memory.\n", + " with torch.no_grad():\n", + " soft_labels = teacher_net(imgs.to(device))\n", + " \n", + " # Calculate the loss in knowledge distillation method.\n", + " loss = loss_fn_kd(logits, labels.to(device), soft_labels)\n", + "\n", + " # Gradients stored in the parameters in the previous step should be cleared out first.\n", + " optimizer.zero_grad()\n", + "\n", + " # Compute the gradients for parameters.\n", + " loss.backward()\n", + "\n", + " # Clip the gradient norms for stable training.\n", + " grad_norm = nn.utils.clip_grad_norm_(student_net.parameters(), max_norm=10)\n", + "\n", + " # Update the parameters with computed gradients.\n", + " optimizer.step()\n", + "\n", + " # Compute the accuracy for current batch.\n", + " acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n", + "\n", + " # Record the loss and accuracy.\n", + " train_loss.append(loss.item())\n", + " train_accs.append(acc)\n", + "\n", + " # The average loss and accuracy of the training set is the average of the recorded values.\n", + " train_loss = sum(train_loss) / len(train_loss)\n", + " train_acc = sum(train_accs) / len(train_accs)\n", + "\n", + " # Print the information.\n", + " print(f\"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\")\n", + "\n", + "\n", + " # ---------- Validation ----------\n", + " # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n", + " student_net.eval()\n", + "\n", + " # These are used to record information in validation.\n", + " valid_loss = []\n", + " valid_accs = []\n", + "\n", + " # Iterate the validation set by batches.\n", + " for batch in tqdm(valid_loader):\n", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # We don't need gradient in validation.\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = student_net(imgs.to(device))\n", + " soft_labels = teacher_net(imgs.to(device))\n", + " # We can still compute the loss (but not the gradient).\n", + " loss = loss_fn_kd(logits, labels.to(device), soft_labels)\n", + "\n", + " # Compute the accuracy for current batch.\n", + " acc = (logits.argmax(dim=-1) == labels.to(device)).float().detach().cpu().view(-1).numpy()\n", + "\n", + " # Record the loss and accuracy.\n", + " valid_loss.append(loss.item())\n", + " valid_accs += list(acc)\n", + "\n", + " # The average loss and accuracy for entire validation set is the average of the recorded values.\n", + " valid_loss = sum(valid_loss) / len(valid_loss)\n", + " valid_acc = sum(valid_accs) / len(valid_accs)\n", + "\n", + " # Print the information.\n", + " print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f757b82d83242ddac2e390dc9e48c65", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 001/080 ] loss = 0.98192, acc = 0.32518\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "28c0f9ba585b4f499e50d37cdcd97325", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 001/080 ] loss = 0.97429, acc = 0.29848\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8146f082f24b428886130d1325b4ce89", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 002/080 ] loss = 0.89496, acc = 0.39265\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2881e1484106480f9a82ae1515e30e39", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 002/080 ] loss = 0.97479, acc = 0.31818\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "23cc357e30374caca096994653215b13", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 003/080 ] loss = 0.85041, acc = 0.42390\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab3e94fd039f496ea5bb97cfc0db28e0", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 003/080 ] loss = 0.90649, acc = 0.35758\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": 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"HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 006/080 ] loss = 0.76561, acc = 0.48600\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2d2b05a5cee24d2e88fd150dc48debe6", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 006/080 ] loss = 0.95883, acc = 0.35455\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c789218e55874a548eebb6cb0d546728", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 007/080 ] loss = 0.74605, acc = 0.49493\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "384b3311e49049e28181aae8604fb1b1", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 007/080 ] loss = 0.86055, acc = 0.40758\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6e0421bfbffe43fabac166db506a7c84", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 008/080 ] loss = 0.72489, acc = 0.51461\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0f1b7f89bce14813acd86fa0714157d1", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 008/080 ] loss = 0.87083, acc = 0.38030\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "928ecc6bd7914f5a84af76390c3a4538", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 009/080 ] loss = 0.70889, acc = 0.52486\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7090625946c848198f5ed9b600c5a5fb", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 009/080 ] loss = 0.80854, acc = 0.40606\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "de35beb4cb9849aaa3ef02c9dbfcdd57", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 010/080 ] loss = 0.69723, acc = 0.53399\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "13171e9ef58c4e1da65492e4d1579b15", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 010/080 ] loss = 0.78312, acc = 0.45303\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "828a6471096c4846ab7e7541d3055804", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 011/080 ] loss = 0.68075, acc = 0.54221\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "5bd4ac6d26994418bc37ae33a42ebe77", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 011/080 ] loss = 0.84390, acc = 0.41364\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3482db3d76724748bfa203ce4a107d45", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 012/080 ] loss = 0.67010, acc = 0.55235\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "383a4ad2b35d4db989148222d811ca58", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 012/080 ] loss = 0.96750, acc = 0.34394\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9187a011f36243f98d9cab7227c21062", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 013/080 ] loss = 0.65844, acc = 0.55783\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "3eb74838b55c443ea6101609532f1844", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 013/080 ] loss = 0.82304, acc = 0.44545\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "86bb785172184d0b88488c6e9b178632", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 014/080 ] loss = 0.64468, acc = 0.57183\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "12231c068cd04355a35b7e6867003855", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 014/080 ] loss = 0.75373, acc = 0.46667\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "610fba66249044b5b5ad0188d8da273f", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 015/080 ] loss = 0.63711, acc = 0.57884\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e914ce2e09164b2f9ff6a0fcc74328d2", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 015/080 ] loss = 0.77423, acc = 0.44545\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "6332b1be7e584548943cdada1ef81f1d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 016/080 ] loss = 0.62796, acc = 0.58320\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6256c6d393047d6b5fef55bbd6d0b5c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + 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}, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 076/080 ] loss = 0.42248, acc = 0.71997\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "527652239e5645bd953f9221883a759b", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 076/080 ] loss = 0.52974, acc = 0.66061\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "feb9105ac2914035b18fe1f5fa62df35", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 077/080 ] loss = 0.41915, acc = 0.72545\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "166ae5b0cb834f05af3733b101f4209d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 077/080 ] loss = 0.59899, acc = 0.61818\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "03a5b8e74e1a400e8bf0f2415d51b157", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 078/080 ] loss = 0.41670, acc = 0.72534\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e4bbbe411d44b91bec7d07f500e8093", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 078/080 ] loss = 0.54202, acc = 0.62273\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "447bbc2df52e44f2b0994ea2c9c477b8", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 079/080 ] loss = 0.41360, acc = 0.72453\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c139b9fd1da54c6a8c4ed42742f2c41f", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 079/080 ] loss = 0.70552, acc = 0.51667\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d3d1b6e3084d42a9b1d2022442b1fc7c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 080/080 ] loss = 0.41217, acc = 0.72575\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32f94eded649446382bb1011edab53cf", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 080/080 ] loss = 0.57082, acc = 0.61667\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nAIDw2P-l2lN" + }, + "source": [ + "## **Testing** *(same as HW3)*\n", + "\n", + "For inference, we need to make sure the model is in eval mode, and the order of the dataset should not be shuffled (\"shuffle=False\" in test_loader).\n", + "\n", + "Last but not least, don't forget to save the predictions into a single CSV file.\n", + "The format of CSV file should follow the rules mentioned in the slides.\n", + "\n", + "### **WARNING -- Keep in Mind**\n", + "\n", + "Cheating includes but not limited to:\n", + "1. using testing labels,\n", + "2. submitting results to previous Kaggle competitions,\n", + "3. sharing predictions with others,\n", + "4. copying codes from any creatures on Earth,\n", + "5. asking other people to do it for you.\n", + "\n", + "Any violations bring you punishments from getting a discount on the final grade to failing the course.\n", + "\n", + "It is your responsibility to check whether your code violates the rules.\n", + "When citing codes from the Internet, you should know what these codes exactly do.\n", + "You will **NOT** be tolerated if you break the rule and claim you don't know what these codes do.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YJfC0qOD3z05", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "fab41f155b754c089e4a330b4abef427", + "26e45d44ae6243ad803d7efc6275a7ce", + "33c3c294d4664f7cbbf9d429625ada1a", + "2349e7a21f3249648025ee569efffe2c", + "efb5bb9368a14dd4a45fe3281627cb63", + "d61e4732193749918dae219a7011f7af", + "558d1a2b2ff44e1b89eb31d7219f2b3b", + "998ec630dc8a4cad8ccdf476fcab8840" + ] + }, + "outputId": "ca6bef34-b846-456a-ec3a-9c6cf0797ce3" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Make sure the model is in eval mode.\n", + "# Some modules like Dropout or BatchNorm affect if the model is in training mode.\n", + "student_net.eval()\n", + "\n", + "# Initialize a list to store the predictions.\n", + "predictions = []\n", + "\n", + "# Iterate the testing set by batches.\n", + "for batch in tqdm(test_loader):\n", + " # A batch consists of image data and corresponding labels.\n", + " # But here the variable \"labels\" is useless since we do not have the ground-truth.\n", + " # If printing out the labels, you will find that it is always 0.\n", + " # This is because the wrapper (DatasetFolder) returns images and labels for each batch,\n", + " # so we have to create fake labels to make it work normally.\n", + " imgs, labels = batch\n", + "\n", + " # We don't need gradient in testing, and we don't even have labels to compute loss.\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = student_net(imgs.to(device))\n", + "\n", + " # Take the class with greatest logit as prediction and record it.\n", + " predictions.extend(logits.argmax(dim=-1).cpu().numpy().tolist())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fab41f155b754c089e4a330b4abef427", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=53.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wSHk4ockETPT" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Save predictions into the file.\n", + "with open(\"predict.csv\", \"w\") as f:\n", + "\n", + " # The first row must be \"Id, Category\"\n", + " f.write(\"Id,Category\\n\")\n", + "\n", + " # For the rest of the rows, each image id corresponds to a predicted class.\n", + " for i, pred in enumerate(predictions):\n", + " f.write(f\"{i},{pred}\\n\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HmCWl8uU8V14" + }, + "source": [ + "## **Statistics**\n", + "\n", + "|Baseline|Accuracy|Training Time|\n", + "|-|-|-|\n", + "|Simple Baseline |0.59856|2 Hours|\n", + "|Medium Baseline |0.65412|2 Hours|\n", + "|Strong Baseline |0.72819|4 Hours|\n", + "|Boss Baseline |0.81003|Unmeasueable|\n", + "\n", + "## **Learning Curve**\n", + "\n", + "![img](https://lh5.googleusercontent.com/amMLGa7dkqvXGmsJlrVN49VfSjClk5d-n7nCi_Y3ROK4himsBSHhB7SpdWe7Zm06ctRO77VdDkD9u_aKfAh1tMW-KcyYX7vF7LPlKqOo2fVtt3SyfsLv0KTYDB0YbAk6ZhyOIKT8Zfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zPTYk9w-B_yt" + }, + "source": [ + "\n", + "\n", + "## **Q&A**\n", + "\n", + "If you have any question about this colab, please send a email to ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3w5w7nMNd3R" + }, + "source": [ + "## **Backup Links**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J337ak5VNZUT" + }, + "source": [ + "# resnet_model \n", + "# !gdown --id '1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m' --output resnet_model.ckpt\n", + "# !gdown --id '1VBIeQKH4xRHfToUxuDxtEPsqz0MHvrgd' --output resnet_model.ckpt\n", + "# !gdown --id '1Er2azErvXWS5m1jboKN7BLxNXnuAatYw' --output resnet_model.ckpt\n", + "# !gdown --id '1Qya0vmf3nRl11IyxxF7nudDpZI_Q4Amh' --output resnet_model.ckpt\n", + "# !gdown --id '1fGOOb5ndljraBIkRkLp3bW9orR4YN97U' --output resnet_model.ckpt\n", + "# !gdown --id '1apHLvZBZ3GYEMxXxToGKF7qDLn1XbOfJ' --output resnet_model.ckpt\n", + "# !gdown --id '1vsDylNsLaAqxonop7Mw3dBAig0EO7tlF' --output resnet_model.ckpt\n", + "# !gdown --id '1V_hXJM_V9-10i6wldRyl0SOiivPp4SNt' --output resnet_model.ckpt\n", + "# !gdown --id '11HzaJM2M2yg6KYhLaWpWy8WmPIIvJgnk' --output resnet_model.ckpt\n", + "\n", + "# food-11\n", + "# !gdown --id '1qdyNN0Ek4S5yi-pAqHes1yjj5cNkENCc' --output food-11.zip\n", + "# !gdown --id '1c0Q1EP6yIx0O2rqVMIVInIt8wFjLxmRh' --output food-11.zip\n", + "# !gdown --id '1hKO054nT1R8egcXY2-tgQbwX4EjowRLz' --output food-11.zip\n", + "# !gdown --id '1_7_uC1WUvX6H51gQaYmI4q3AezdQJhud' --output food-11.zip\n", + "# !gdown --id '12bz82Zpx0_7BDGXq4nRt7E_fMFmILoc9' --output food-11.zip\n", + "# !gdown --id '1oiqRKrDQXVBM5y63MeEaHxFmCIzNXx1Q' --output food-11.zip\n", + "# !gdown --id '1qaL43sl4qUMeCT1OVpk4aOFycnLL5ZJX' --output food-11.zip" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/范例/HW13/HW13.ipynb b/范例/HW13/HW13.ipynb new file mode 100644 index 0000000..a26fecf --- /dev/null +++ b/范例/HW13/HW13.ipynb @@ -0,0 +1,45828 @@ 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"padding": null, + "grid_auto_rows": null, + "grid_gap": null, + "max_width": null, + "order": null, + "_view_module_version": "1.2.0", + "grid_template_areas": null, + "object_position": null, + "object_fit": null, + "grid_auto_columns": null, + "margin": null, + "display": null, + "left": null + } + } + } + } + }, + "cells": [ + { + "cell_type": "markdown", + "metadata": { + "id": "AeGB_b7IbKs1" + }, + "source": [ + "Homework 13 - Network Compression\n", + "===\n", + "\n", + "> Author: Arvin Liu (r09922071@ntu.edu.tw), this colab is modified from ML2021-HW3\n", + "\n", + "If you have any questions, feel free to ask: ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "JY4wUHY3bNx1" + }, + "source": [ + "## **Intro**\n", + "\n", + "HW13 is about network compression\n", + "\n", + "There are many types of Network/Model Compression, here we introduce two:\n", + "* Knowledge Distillation\n", + "* Design Architecture\n", + "\n", + "\n", + "The process of this notebook is as follows:
\n", + "1. Introduce depthwise, pointwise and group convolution in MobileNet.\n", + "2. Design the model of this colab\n", + "3. Introduce Knowledge-Distillation\n", + "4. Set up TeacherNet and it would be helpful in training\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "w3PoggZ91dRA" + }, + "source": [ + "## **About the Dataset** *(same as HW3)*\n", + "\n", + "The dataset used here is food-11, a collection of food images in 11 classes.\n", + "\n", + "For the requirement in the homework, TAs slightly modified the data.\n", + "Please DO NOT access the original fully-labeled training data or testing labels.\n", + "\n", + "Also, the modified dataset is for this course only, and any further distribution or commercial use is forbidden." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BiNaf9gu1pu2", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "39da2215-344a-4955-8461-435c40bebf88" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Download the dataset\n", + "# You may choose where to download the data.\n", + "\n", + "# Google Drive\n", + "!gdown --id '1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy' --output food-11.zip\n", + "# If you cannot successfully gdown, you can change a link. (Backup link is provided at the bottom of this colab tutorial).\n", + "\n", + "# Dropbox\n", + "# !wget https://www.dropbox.com/s/m9q6273jl3djall/food-11.zip -O food-11.zip\n", + "\n", + "# MEGA\n", + "# !sudo apt install megatools\n", + "# !megadl \"https://mega.nz/#!zt1TTIhK!ZuMbg5ZjGWzWX1I6nEUbfjMZgCmAgeqJlwDkqdIryfg\"\n", + "\n", + "# Unzip the dataset.\n", + "# This may take some time.\n", + "!unzip -q food-11.zip" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1awF7pZ9Dz7X1jn1_QAiKN-_v56veCEKy\n", + "To: /content/food-11.zip\n", + "963MB [00:14, 67.3MB/s]\n", + "replace food-11/training/unlabeled/00/5176.jpg? [y]es, [n]o, [A]ll, [N]one, [r]ename: N\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vg8nOEmK1saF" + }, + "source": [ + "## **Import Packages** *(same as HW3)*\n", + "\n", + "First, we need to import packages that will be used later.\n", + "\n", + "In this homework, we highly rely on **torchvision**, a library of PyTorch." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BAk_whhx1xuf" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Import necessary packages.\n", + "import numpy as np\n", + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "import torch\n", + "import torchvision.transforms as transforms\n", + "import torchvision.models as models\n", + "\n", + "from PIL import Image\n", + "# \"ConcatDataset\" and \"Subset\" are possibly useful when doing semi-supervised learning.\n", + "from torch.utils.data import ConcatDataset, DataLoader, Subset\n", + "from torchvision.datasets import DatasetFolder\n", + "\n", + "# This is for the progress bar.\n", + "from tqdm.auto import tqdm" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "vPjS3Q9WRakn" + }, + "source": [ + "## **Dataset, Data Loader, and Transforms** *(similar to HW3)*\n", + "\n", + "Torchvision provides lots of useful utilities for image preprocessing, data wrapping as well as data augmentation.\n", + "\n", + "Here, since our data are stored in folders by class labels, we can directly apply **torchvision.datasets.DatasetFolder** for wrapping data without much effort.\n", + "\n", + "Please refer to [PyTorch official website](https://pytorch.org/vision/stable/transforms.html) for details about different transforms.\n", + "\n", + "---\n", + "**The only diffference with HW3 is that the transform functions are different.**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "IImuu-Po11L-" + }, + "source": [ + "### This block is similar to HW3 ###\n", + "# It is important to do data augmentation in training.\n", + "# However, not every augmentation is useful.\n", + "# Please think about what kind of augmentation is helpful for food recognition.\n", + "\n", + "train_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 142)\n", + "\ttransforms.Resize((142, 142)),\n", + " transforms.RandomHorizontalFlip(),\n", + " transforms.RandomRotation(15),\n", + "\ttransforms.RandomCrop(128),\n", + "\ttransforms.ToTensor(),\n", + "])\n", + "\n", + "# We don't need augmentations in testing and validation.\n", + "# All we need here is to resize the PIL image and transform it into Tensor.\n", + "test_tfm = transforms.Compose([\n", + " # Resize the image into a fixed shape (height = width = 142)\n", + " transforms.Resize((142, 142)),\n", + " transforms.CenterCrop(128),\n", + " transforms.ToTensor(),\n", + "])\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "code", + "metadata": { + "id": "rcU_xd_C12ce" + }, + "source": [ + "### This block is similar to HW3 ###\n", + "# Batch size for training, validation, and testing.\n", + "# A greater batch size usually gives a more stable gradient.\n", + "# But the GPU memory is limited, so please adjust it carefully.\n", + "batch_size = 64\n", + "\n", + "# Construct datasets.\n", + "# The argument \"loader\" tells how torchvision reads the data.\n", + "train_set = DatasetFolder(\"food-11/training/labeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "valid_set = DatasetFolder(\"food-11/validation\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "unlabeled_set = DatasetFolder(\"food-11/training/unlabeled\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=train_tfm)\n", + "test_set = DatasetFolder(\"food-11/testing\", loader=lambda x: Image.open(x), extensions=\"jpg\", transform=test_tfm)\n", + "\n", + "# Construct data loaders.\n", + "train_loader = DataLoader(train_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "valid_loader = DataLoader(valid_set, batch_size=batch_size, shuffle=True, num_workers=2, pin_memory=True)\n", + "test_loader = DataLoader(test_set, batch_size=batch_size, shuffle=False)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zggTBkI6bPkn" + }, + "source": [ + "# **Architecture / Model Design**\n", + "The following are types of convolution layer design that has fewer parameters.\n", + "\n", + "## **Depthwise & Pointwise Convolution**\n", + "![](https://i.imgur.com/FBgcA0s.png)\n", + "> Blue: the connection between layers \\\n", + "> Green: the expansion of **receptive field** \\\n", + "> (reference: arxiv:1810.04231)\n", + "\n", + "(a) normal convolution layer: It is fully connected. The difference between fully connected layer and fully connected convolution layer is the operation. (multiply --> convolution)\n", + "\n", + "(b) Depthwise convolution layer(DW): You can consider each feature map pass through their own filter and then pass through pointwise convolution layer(PW) to combine the information of all pixels in feature maps.\n", + "\n", + "\n", + "(c) Group convolution layer(GC): Group the feature maps. Each group passes their filter then concate together. If group_size = input_feature_size, then GC becomes DC (channels are independent). If group_size = 1, then GC becomes fully connected.\n", + "\n", + "\n", + "\n", + "\n", + "## **Implementation details**\n", + "```python\n", + "# Regular Convolution, # of params = in_chs * out_chs * kernel_size^2\n", + "nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding)\n", + "\n", + "# Group Convolution, \"groups\" controls the connections between inputs and\n", + "# outputs. in_chs and out_chs must both be divisible by groups.\n", + "nn.Conv2d(in_chs, out_chs, kernel_size, stride, padding, groups=groups)\n", + "\n", + "# Depthwise Convolution, out_chs=in_chs=groups, # of params = in_chs * kernel_size^2\n", + "nn.Conv2d(in_chs, out_chs=in_chs, kernel_size, stride, padding, groups=in_chs)\n", + "\n", + "# Pointwise Convolution, a.k.a 1 by 1 convolution, # of params = in_chs * out_chs\n", + "nn.Conv2d(in_chs, out_chs, 1)\n", + "\n", + "# Merge Depthwise and Pointwise Convolution (without )\n", + "def dwpw_conv(in_chs, out_chs, kernel_size, stride, padding):\n", + " return nn.Sequential(\n", + " nn.Conv2d(in_chs, in_chs, kernels, stride, padding, groups=in_chs),\n", + " nn.Conv2d(in_chs, out_chs, 1),\n", + " )\n", + "```\n", + "\n", + "## **Model**\n", + "\n", + "The basic model here is simply a stack of convolutional layers followed by some fully-connected layers. You can take advatage of depthwise & pointwise convolution to make your model deeper, but still follow the size constraint." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "TRwpE9eebVXK" + }, + "source": [ + "class StudentNet(nn.Module):\n", + " def __init__(self):\n", + " super(StudentNet, self).__init__()\n", + "\n", + " # ---------- TODO ----------\n", + " # Modify your model architecture\n", + "\n", + " self.cnn = nn.Sequential(\n", + " nn.Conv2d(3, 32, 3), \n", + " nn.BatchNorm2d(32),\n", + " nn.ReLU(),\n", + " nn.Conv2d(32, 32, 3), \n", + " nn.BatchNorm2d(32),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0), \n", + "\n", + " nn.Conv2d(32, 64, 3), \n", + " nn.BatchNorm2d(64),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0), \n", + "\n", + " nn.Conv2d(64, 100, 3), \n", + " nn.BatchNorm2d(100),\n", + " nn.ReLU(),\n", + " nn.MaxPool2d(2, 2, 0),\n", + " \n", + " # Here we adopt Global Average Pooling for various input size.\n", + " nn.AdaptiveAvgPool2d((1, 1)),\n", + " )\n", + " self.fc = nn.Sequential(\n", + " nn.Linear(100, 11),\n", + " )\n", + " \n", + " def forward(self, x):\n", + " out = self.cnn(x)\n", + " out = out.view(out.size()[0], -1)\n", + " return self.fc(out)\n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "MvHiN7q1BCRs" + }, + "source": [ + "## **Model Analysis**\n", + "\n", + "Use `torchsummary` to get your model architecture (screenshot or pasting text are allowed.) and numbers of \n", + "parameters, these two information should be submit to your NTU Cool questions.\n", + "\n", + "Note that the number of parameters **should not greater than 100,000**, or you'll get penalty in this homework.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/" + }, + "id": "0gB2f5dO-ktA", + "outputId": "3def2925-be08-48e0-8b66-89cd032284d7" + }, + "source": [ + "from torchsummary import summary\n", + "\n", + "student_net = StudentNet()\n", + "summary(student_net, (3, 128, 128), device=\"cpu\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "----------------------------------------------------------------\n", + " Layer (type) Output Shape Param #\n", + "================================================================\n", + " Conv2d-1 [-1, 32, 126, 126] 896\n", + " BatchNorm2d-2 [-1, 32, 126, 126] 64\n", + " ReLU-3 [-1, 32, 126, 126] 0\n", + " Conv2d-4 [-1, 32, 124, 124] 9,248\n", + " BatchNorm2d-5 [-1, 32, 124, 124] 64\n", + " ReLU-6 [-1, 32, 124, 124] 0\n", + " MaxPool2d-7 [-1, 32, 62, 62] 0\n", + " Conv2d-8 [-1, 64, 60, 60] 18,496\n", + " BatchNorm2d-9 [-1, 64, 60, 60] 128\n", + " ReLU-10 [-1, 64, 60, 60] 0\n", + " MaxPool2d-11 [-1, 64, 30, 30] 0\n", + " Conv2d-12 [-1, 100, 28, 28] 57,700\n", + " BatchNorm2d-13 [-1, 100, 28, 28] 200\n", + " ReLU-14 [-1, 100, 28, 28] 0\n", + " MaxPool2d-15 [-1, 100, 14, 14] 0\n", + "AdaptiveAvgPool2d-16 [-1, 100, 1, 1] 0\n", + " Linear-17 [-1, 11] 1,111\n", + "================================================================\n", + "Total params: 87,907\n", + "Trainable params: 87,907\n", + "Non-trainable params: 0\n", + "----------------------------------------------------------------\n", + "Input size (MB): 0.19\n", + "Forward/backward pass size (MB): 31.49\n", + "Params size (MB): 0.34\n", + "Estimated Total Size (MB): 32.01\n", + "----------------------------------------------------------------\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "NQXY4vLmR8ae" + }, + "source": [ + "## **Knowledge Distillation**\n", + "\n", + "\n", + "\n", + "Since we have a learned big model, let it teach the other small model. In implementation, let the training target be the prediction of big model instead of the ground truth.\n", + "\n", + "## **Why it works?**\n", + "* If the data is not clean, then the prediction of big model could ignore the noise of the data with wrong labeled.\n", + "* The labels might have some relations. Number 8 is more similar to 6, 9, 0 than 1, 7, for example.\n", + "\n", + "\n", + "## **How to implement?**\n", + "* $Loss = \\alpha T^2 \\times KL(\\frac{\\text{Teacher's Logits}}{T} || \\frac{\\text{Student's Logits}}{T}) + (1-\\alpha)(\\text{Original Loss})$\n", + "* Note that the logits here should have passed softmax." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "6TMCyqrWTSsz" + }, + "source": [ + "def loss_fn_kd(outputs, labels, teacher_outputs, alpha=0.5):\n", + " hard_loss = F.cross_entropy(outputs, labels) * (1. - alpha) \n", + " # ---------- TODO ----------\n", + " # Complete soft loss in knowledge distillation\n", + " soft_loss = 0 \n", + " return hard_loss + soft_loss" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "97zXxEd3Thlb" + }, + "source": [ + "## **Teacher Model Setting**\n", + "We provide a well-trained teacher model to help you knowledge distillation to student model.\n", + "Note that if you want to change the transform function, you should consider if suitable for this well-trained teacher model.\n", + "* If you cannot successfully gdown, you can change a link. (Backup link is provided at the bottom of this colab tutorial).\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nR9J169XTjki", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "3086910d-9a5e-4747-ce1d-8ff0a3609648" + }, + "source": [ + "# Download teacherNet\n", + "!gdown --id '1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m' --output teacher_net.ckpt\n", + "# Load teacherNet\n", + "teacher_net = torch.load('./teacher_net.ckpt')\n", + "teacher_net.eval()" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading...\n", + "From: https://drive.google.com/uc?id=1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m\n", + "To: /content/teacher_net.ckpt\n", + "44.8MB [00:00, 58.6MB/s]\n" + ], + "name": "stdout" + }, + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "ResNet(\n", + " (conv1): Conv2d(3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (maxpool): MaxPool2d(kernel_size=3, stride=2, padding=1, dilation=1, ceil_mode=False)\n", + " (layer1): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer2): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer3): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(128, 256, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (layer4): Sequential(\n", + " (0): BasicBlock(\n", + " (conv1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (downsample): Sequential(\n", + " (0): Conv2d(256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False)\n", + " (1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (1): BasicBlock(\n", + " (conv1): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " (relu): ReLU(inplace=True)\n", + " (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False)\n", + " (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n", + " )\n", + " )\n", + " (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n", + " (fc): Linear(in_features=512, out_features=11, bias=True)\n", + ")" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "0DMKD-bnuIRF" + }, + "source": [ + "## **Generate Pseudo Labels in Unlabeled Data**\n", + "\n", + "Since we have a well-trained model, we can use this model to predict pseudo-labels and help the student network train well. Note that you \n", + "**CANNOT** use well-trained model to pseudo-label the test data. \n", + "\n", + "\n", + "---\n", + "\n", + "**AGAIN, DO NOT USE TEST DATA FOR PURPOSE OTHER THAN INFERENCING**\n", + "\n", + "* Because If you use teacher network to predict pseudo-labels of the test data, you can only use student network to overfit these pseudo-labels without train/unlabeled data. In this way, your kaggle accuracy will be as high as the teacher network, but the fact is that you just overfit the test data and your true testing accuracy is very low. \n", + "* These contradict the purpose of these assignment (network compression); therefore, you should not misuse the test data.\n", + "* If you have any concerns, you can email us.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "58608c3ebf3b4662b6208b0d12ed5082", + "ac9d4431b6034315b696e71756e718f6", + "a711cf06b5e84b18b05141116cc19639", + "322582864f8843ec8b3860ead19c7f92", + "d0aaa49ccc4f4b6c8244193118ad4217", + "c12fa6ff16644a638bf05d0c1c5110bb", + "bb0205c810d94c8f942515528b91fbc9", + "e14750af9cc447cf9f37edf4f7e312e6" + ] + }, + "id": "tyRc7MktuJQN", + "outputId": "b7641e78-7e27-4198-d5db-da5b72c5967f" + }, + "source": [ + "# \"cuda\" only when GPUs are available.\n", + "device = \"cuda\" if torch.cuda.is_available() else \"cpu\"\n", + "\n", + "# Initialize a model, and put it on the device specified.\n", + "student_net = student_net.to(device)\n", + "teacher_net = teacher_net.to(device)\n", + "\n", + "# Whether to do pseudo label.\n", + "do_semi = True\n", + "\n", + "def get_pseudo_labels(dataset, model):\n", + " loader = DataLoader(dataset, batch_size=batch_size*3, shuffle=False, pin_memory=True)\n", + " pseudo_labels = []\n", + " for batch in tqdm(loader):\n", + " # A batch consists of image data and corresponding labels.\n", + " img, _ = batch\n", + "\n", + " # Forward the data\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = model(img.to(device))\n", + " pseudo_labels.append(logits.argmax(dim=-1).detach().cpu())\n", + " # Obtain the probability distributions by applying softmax on logits.\n", + " pseudo_labels = torch.cat(pseudo_labels)\n", + " # Update the labels by replacing with pseudo labels.\n", + " for idx, ((img, _), pseudo_label) in enumerate(zip(dataset.samples, pseudo_labels)):\n", + " dataset.samples[idx] = (img, pseudo_label.item())\n", + " return dataset\n", + "\n", + "if do_semi:\n", + " # Generate new trainloader with unlabeled set.\n", + " unlabeled_set = get_pseudo_labels(unlabeled_set, teacher_net)\n", + " concat_dataset = ConcatDataset([train_set, unlabeled_set])\n", + " train_loader = DataLoader(concat_dataset, batch_size=batch_size, shuffle=True, pin_memory=True, drop_last=True)\n", + "\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "58608c3ebf3b4662b6208b0d12ed5082", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=36.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XeprOVBhT7cJ" + }, + "source": [ + "## **Training** *(similar to HW3)*\n", + "\n", + "You can finish supervised learning by simply running the provided code without any modification.\n", + "\n", + "The function \"get_pseudo_labels\" is used for semi-supervised learning.\n", + "It is expected to get better performance if you use unlabeled data for semi-supervised learning.\n", + "However, you have to implement the function on your own and need to adjust several hyperparameters manually.\n", + "\n", + "For more details about semi-supervised learning, please refer to [Prof. Lee's slides](https://speech.ee.ntu.edu.tw/~tlkagk/courses/ML_2016/Lecture/semi%20(v3).pdf).\n", + "\n", + "Again, please notice that utilizing external data (or pre-trained model) for training is **prohibited**.\n", + "\n", + "---\n", + "**The only diffference with HW3 is that you should use loss in knowledge distillation.**\n", + "\n", + "\n" + ] + }, + { + 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before training.\n", + " student_net.train()\n", + "\n", + " # These are used to record information in training.\n", + " train_loss = []\n", + " train_accs = []\n", + "\n", + " # Iterate the training set by batches.\n", + " for batch in tqdm(train_loader):\n", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # Forward the data. (Make sure data and model are on the same device.)\n", + " logits = student_net(imgs.to(device))\n", + " # Teacher net will not be updated. And we use torch.no_grad\n", + " # to tell torch do not retain the intermediate values\n", + " # (which are for backpropgation) and save the memory.\n", + " with torch.no_grad():\n", + " soft_labels = teacher_net(imgs.to(device))\n", + " \n", + " # Calculate the loss in knowledge distillation method.\n", + " loss = loss_fn_kd(logits, labels.to(device), soft_labels)\n", + "\n", + " # Gradients stored in the parameters in the previous step should be cleared out first.\n", + " optimizer.zero_grad()\n", + "\n", + " # Compute the gradients for parameters.\n", + " loss.backward()\n", + "\n", + " # Clip the gradient norms for stable training.\n", + " grad_norm = nn.utils.clip_grad_norm_(student_net.parameters(), max_norm=10)\n", + "\n", + " # Update the parameters with computed gradients.\n", + " optimizer.step()\n", + "\n", + " # Compute the accuracy for current batch.\n", + " acc = (logits.argmax(dim=-1) == labels.to(device)).float().mean()\n", + "\n", + " # Record the loss and accuracy.\n", + " train_loss.append(loss.item())\n", + " train_accs.append(acc)\n", + "\n", + " # The average loss and accuracy of the training set is the average of the recorded values.\n", + " train_loss = sum(train_loss) / len(train_loss)\n", + " train_acc = sum(train_accs) / len(train_accs)\n", + "\n", + " # Print the information.\n", + " print(f\"[ Train | {epoch + 1:03d}/{n_epochs:03d} ] loss = {train_loss:.5f}, acc = {train_acc:.5f}\")\n", + "\n", + "\n", + " # ---------- Validation ----------\n", + " # Make sure the model is in eval mode so that some modules like dropout are disabled and work normally.\n", + " student_net.eval()\n", + "\n", + " # These are used to record information in validation.\n", + " valid_loss = []\n", + " valid_accs = []\n", + "\n", + " # Iterate the validation set by batches.\n", + " for batch in tqdm(valid_loader):\n", + "\n", + " # A batch consists of image data and corresponding labels.\n", + " imgs, labels = batch\n", + "\n", + " # We don't need gradient in validation.\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = student_net(imgs.to(device))\n", + " soft_labels = teacher_net(imgs.to(device))\n", + " # We can still compute the loss (but not the gradient).\n", + " loss = loss_fn_kd(logits, labels.to(device), soft_labels)\n", + "\n", + " # Compute the accuracy for current batch.\n", + " acc = (logits.argmax(dim=-1) == labels.to(device)).float().detach().cpu().view(-1).numpy()\n", + "\n", + " # Record the loss and accuracy.\n", + " valid_loss.append(loss.item())\n", + " valid_accs += list(acc)\n", + "\n", + " # The average loss and accuracy for entire validation set is the average of the recorded values.\n", + " valid_loss = sum(valid_loss) / len(valid_loss)\n", + " valid_acc = sum(valid_accs) / len(valid_accs)\n", + "\n", + " # Print the information.\n", + " print(f\"[ Valid | {epoch + 1:03d}/{n_epochs:03d} ] loss = {valid_loss:.5f}, acc = {valid_acc:.5f}\")" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2f757b82d83242ddac2e390dc9e48c65", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 001/080 ] loss = 0.98192, acc = 0.32518\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "28c0f9ba585b4f499e50d37cdcd97325", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 001/080 ] loss = 0.97429, acc = 0.29848\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8146f082f24b428886130d1325b4ce89", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 002/080 ] loss = 0.89496, acc = 0.39265\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "2881e1484106480f9a82ae1515e30e39", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 002/080 ] loss = 0.97479, acc = 0.31818\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "23cc357e30374caca096994653215b13", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 003/080 ] loss = 0.85041, acc = 0.42390\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ab3e94fd039f496ea5bb97cfc0db28e0", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 003/080 ] loss = 0.90649, acc = 0.35758\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": 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"name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "0e4bbbe411d44b91bec7d07f500e8093", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 078/080 ] loss = 0.54202, acc = 0.62273\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "447bbc2df52e44f2b0994ea2c9c477b8", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 079/080 ] loss = 0.41360, acc = 0.72453\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c139b9fd1da54c6a8c4ed42742f2c41f", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 079/080 ] loss = 0.70552, acc = 0.51667\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "d3d1b6e3084d42a9b1d2022442b1fc7c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=154.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Train | 080/080 ] loss = 0.41217, acc = 0.72575\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "32f94eded649446382bb1011edab53cf", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=11.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "[ Valid | 080/080 ] loss = 0.57082, acc = 0.61667\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "nAIDw2P-l2lN" + }, + "source": [ + "## **Testing** *(same as HW3)*\n", + "\n", + "For inference, we need to make sure the model is in eval mode, and the order of the dataset should not be shuffled (\"shuffle=False\" in test_loader).\n", + "\n", + "Last but not least, don't forget to save the predictions into a single CSV file.\n", + "The format of CSV file should follow the rules mentioned in the slides.\n", + "\n", + "### **WARNING -- Keep in Mind**\n", + "\n", + "Cheating includes but not limited to:\n", + "1. using testing labels,\n", + "2. submitting results to previous Kaggle competitions,\n", + "3. sharing predictions with others,\n", + "4. copying codes from any creatures on Earth,\n", + "5. asking other people to do it for you.\n", + "\n", + "Any violations bring you punishments from getting a discount on the final grade to failing the course.\n", + "\n", + "It is your responsibility to check whether your code violates the rules.\n", + "When citing codes from the Internet, you should know what these codes exactly do.\n", + "You will **NOT** be tolerated if you break the rule and claim you don't know what these codes do.\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "YJfC0qOD3z05", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 67, + "referenced_widgets": [ + "fab41f155b754c089e4a330b4abef427", + "26e45d44ae6243ad803d7efc6275a7ce", + "33c3c294d4664f7cbbf9d429625ada1a", + "2349e7a21f3249648025ee569efffe2c", + "efb5bb9368a14dd4a45fe3281627cb63", + "d61e4732193749918dae219a7011f7af", + "558d1a2b2ff44e1b89eb31d7219f2b3b", + "998ec630dc8a4cad8ccdf476fcab8840" + ] + }, + "outputId": "ca6bef34-b846-456a-ec3a-9c6cf0797ce3" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Make sure the model is in eval mode.\n", + "# Some modules like Dropout or BatchNorm affect if the model is in training mode.\n", + "student_net.eval()\n", + "\n", + "# Initialize a list to store the predictions.\n", + "predictions = []\n", + "\n", + "# Iterate the testing set by batches.\n", + "for batch in tqdm(test_loader):\n", + " # A batch consists of image data and corresponding labels.\n", + " # But here the variable \"labels\" is useless since we do not have the ground-truth.\n", + " # If printing out the labels, you will find that it is always 0.\n", + " # This is because the wrapper (DatasetFolder) returns images and labels for each batch,\n", + " # so we have to create fake labels to make it work normally.\n", + " imgs, labels = batch\n", + "\n", + " # We don't need gradient in testing, and we don't even have labels to compute loss.\n", + " # Using torch.no_grad() accelerates the forward process.\n", + " with torch.no_grad():\n", + " logits = student_net(imgs.to(device))\n", + "\n", + " # Take the class with greatest logit as prediction and record it.\n", + " predictions.extend(logits.argmax(dim=-1).cpu().numpy().tolist())" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "fab41f155b754c089e4a330b4abef427", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=53.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wSHk4ockETPT" + }, + "source": [ + "### This block is same as HW3 ###\n", + "# Save predictions into the file.\n", + "with open(\"predict.csv\", \"w\") as f:\n", + "\n", + " # The first row must be \"Id, Category\"\n", + " f.write(\"Id,Category\\n\")\n", + "\n", + " # For the rest of the rows, each image id corresponds to a predicted class.\n", + " for i, pred in enumerate(predictions):\n", + " f.write(f\"{i},{pred}\\n\")" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "HmCWl8uU8V14" + }, + "source": [ + "## **Statistics**\n", + "\n", + "|Baseline|Accuracy|Training Time|\n", + "|-|-|-|\n", + "|Simple Baseline |0.59856|2 Hours|\n", + "|Medium Baseline |0.65412|2 Hours|\n", + "|Strong Baseline |0.72819|4 Hours|\n", + "|Boss Baseline |0.81003|Unmeasueable|\n", + "\n", + "## **Learning Curve**\n", + "\n", + "![img](https://lh5.googleusercontent.com/amMLGa7dkqvXGmsJlrVN49VfSjClk5d-n7nCi_Y3ROK4himsBSHhB7SpdWe7Zm06ctRO77VdDkD9u_aKfAh1tMW-KcyYX7vF7LPlKqOo2fVtt3SyfsLv0KTYDB0YbAk6ZhyOIKT8Zfg)" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zPTYk9w-B_yt" + }, + "source": [ + "\n", + "\n", + "## **Q&A**\n", + "\n", + "If you have any question about this colab, please send a email to ntu-ml-2021spring-ta@googlegroups.com" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Q3w5w7nMNd3R" + }, + "source": [ + "## **Backup Links**" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "J337ak5VNZUT" + }, + "source": [ + "# resnet_model \n", + "# !gdown --id '1zH1x39Y8a0XyOORG7TWzAnFf_YPY8e-m' --output resnet_model.ckpt\n", + "# !gdown --id '1VBIeQKH4xRHfToUxuDxtEPsqz0MHvrgd' --output resnet_model.ckpt\n", + "# !gdown --id '1Er2azErvXWS5m1jboKN7BLxNXnuAatYw' --output resnet_model.ckpt\n", + "# !gdown --id '1Qya0vmf3nRl11IyxxF7nudDpZI_Q4Amh' --output resnet_model.ckpt\n", + "# !gdown --id '1fGOOb5ndljraBIkRkLp3bW9orR4YN97U' --output resnet_model.ckpt\n", + "# !gdown --id '1apHLvZBZ3GYEMxXxToGKF7qDLn1XbOfJ' --output resnet_model.ckpt\n", + "# !gdown --id '1vsDylNsLaAqxonop7Mw3dBAig0EO7tlF' --output resnet_model.ckpt\n", + "# !gdown --id '1V_hXJM_V9-10i6wldRyl0SOiivPp4SNt' --output resnet_model.ckpt\n", + "# !gdown --id '11HzaJM2M2yg6KYhLaWpWy8WmPIIvJgnk' --output resnet_model.ckpt\n", + "\n", + "# food-11\n", + "# !gdown --id '1qdyNN0Ek4S5yi-pAqHes1yjj5cNkENCc' --output food-11.zip\n", + "# !gdown --id '1c0Q1EP6yIx0O2rqVMIVInIt8wFjLxmRh' --output food-11.zip\n", + "# !gdown --id '1hKO054nT1R8egcXY2-tgQbwX4EjowRLz' --output food-11.zip\n", + "# !gdown --id '1_7_uC1WUvX6H51gQaYmI4q3AezdQJhud' --output food-11.zip\n", + "# !gdown --id '12bz82Zpx0_7BDGXq4nRt7E_fMFmILoc9' --output food-11.zip\n", + "# !gdown --id '1oiqRKrDQXVBM5y63MeEaHxFmCIzNXx1Q' --output food-11.zip\n", + "# !gdown --id '1qaL43sl4qUMeCT1OVpk4aOFycnLL5ZJX' --output food-11.zip" + ], + "execution_count": null, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/范例/HW13/HW13.pdf b/范例/HW13/HW13.pdf new file mode 100644 index 0000000..d71539a Binary files /dev/null and b/范例/HW13/HW13.pdf differ diff 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please refer to [LifeLong learning](https://youtu.be/7qT5P9KJnWo) \n", + "\n", + "\n", + "### Methods\n", + "Someone proposed a survey paper for LifeLong Learning at the end of 2019 to distinguish 2016-2019 LigeLong Learning methods into three families.\n", + "\n", + "We can distinguish LifeLong Learning methods into three families, based on how task\n", + "specific information is stored and used throughout the sequential learning process:\n", + "* Replay-based methods\n", + "* Regularization-based methods\n", + "* Parameter isolation methods\n", + "\n", + "\n", + "\n", + "In this assignment, we have to go through EWC, MAS, SI, Remanian Walk, SCP Methods in the prior-focused methods of the regularization-based methods. \n", + "\n", + "Source: [Continual Learning in Neural\n", + "Networks](https://arxiv.org/pdf/1910.02718.pdf)\n", + "\n", + "Please feel free to mail us if you have any questions.\n", + "\n", + "ntu-ml-2020spring-ta@googlegroups.com\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "tuBc9bznXoo3" + }, + "source": [ + "### Table of Content\n", + "- Utils\n", + "- Visualization\n", + "- Methods" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "jwKUnZeByvhv" + }, + "source": [ + "### Utility\n", + "We utilize permuted MNIST as our training dataset.\n", + "\n", + "So, first we utilize 5 different permutations to generate 10 different permuted MNIST as different task." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "atDSdG4TUAAn" + }, + "source": [ + " #### - Pemutation" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "lsWYJnC7ymjz" + }, + "source": [ + "import torch.utils.data as data\n", + "import torch.utils.data.sampler as sampler\n", + "import torchvision\n", + "import os\n", + "import torch.nn.functional as F\n", + "from torchvision import datasets, transforms\n", + "\n", + "# Permute MNIST to generate 10 tasks\n", + "\n", + "def _permutate_image_pixels(image, permutation):\n", + " if permutation is None:\n", + " return image\n", + "\n", + " c, h, w = image.size()\n", + " image = image.view(-1, c)\n", + " image = image[permutation, :]\n", + " image.view(c, h, w)\n", + " return image\n", + "\n", + "def get_transform(permutation=None, normalize=True):\n", + " if normalize == True:\n", + " transform = transforms.Compose([transforms.ToTensor(),\n", + " Pad(28),\n", + " transforms.Normalize((0.1307,), (0.3081,)),\n", + " transforms.Lambda(lambda x: _permutate_image_pixels(x, permutation)),\n", + " ])\n", + " else:\n", + " transform = transforms.Compose([transforms.ToTensor(),\n", + " Pad(28),\n", + " transforms.Lambda(lambda x: _permutate_image_pixels(x, permutation)),\n", + " ])\n", + " return transform\n", + "\n", + "class Pad(object):\n", + " def __init__(self, size, fill=0, padding_mode='constant'):\n", + " self.size = size\n", + " self.fill = fill\n", + " self.padding_mode = padding_mode\n", + " \n", + " def __call__(self, img):\n", + " # If the H and W of img is not equal to desired size,\n", + " # then pad the channel of img to desired size.\n", + " img_size = img.size()[1]\n", + " assert ((self.size - img_size) % 2 == 0)\n", + " padding = (self.size - img_size) // 2\n", + " padding = (padding, padding, padding, padding)\n", + " return F.pad(img, padding, self.padding_mode, self.fill)\n", + "\n", + "class Data():\n", + " def __init__(self, path, train=True, permutation=None, normalize=True):\n", + "\n", + " transform = get_transform(permutation, normalize)\n", + " self.dataset = datasets.MNIST(root = os.path.join(path, \"MNIST\"),\n", + " transform=transform,\n", + " train = train,\n", + " download = True)" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "X6JZjHmi6CGU" + }, + "source": [ + "#### - Dataloader and Argument\n", + "- Training Arguments\n", + "- Setup 5 different Permutation\n", + "- 5 Train DataLoader\n", + "- 5 Test DataLoader \n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "QQNEXhES4Zr9", + "colab": { + "base_uri": "https://localhost:8080/", + "referenced_widgets": [ + "c15dd9522b8249148461a81ef0e26d73", + "5e83b7cdf85947ff9e400b43cc446cef", + "917efdc2c9bc4c1eabb645367898711f", + "626117a83cec44db8abbe58b5a76da45", + "712e7f7ae0e94c0ebfb4096c9001e634", + "b7ad10b739bd445787585cd6c1ae8df7", + "9891764f51c84a1e9d5a3d84ca65a31d", + "f387feb29f8442c3b0ed1be2ffcbe20c", + "7717afd84e434d25b3068ff2ff733e1c", + "6dcc743d8e394905bde8004815269778", + "ef0a89c0c777497f8c50f15581ee7c71", + "a8d78a9649a1491b8939a03f07cc5acd", + "420a094c784f4b00bda227741fc0c737", + "a7dbef8db36c41b5861e00e1a4309a61", + "2566de9a6037414381f8ce9549dc9599", + "2b6d48829eb64962ba5f98b7a92aadaf", + "37ed32a2e0544b9886e0b63c96ea869c", + "84c42524a4d64ec992ae870762413dcb", + "0c38f88361f94cf68220c87fa6c46753", + "d1fda42d453a497d9c35b09c711cfe2b", + "f80a9d9f08c5401eac29e9be7c141ce2", + "6bf0a703aca1467b985bc1c23fdf9175", + "9cfae7e0cd5645f8a481f8f465b596bf", + "8f159e35751247b781020c5b3d5f2e79", + "79739e2492de4d0a9c4cb6ff7e32f14d", + "66678803c5674e6d9e9ba97bb0949651", + "c2f89e420b4b4161b4ae6e51b0dca61e", + "96854f96a7ff4c429aa886e40bde1e90", + "c84f35f2fc17434facff99bf495f3528", + "0b2d4905381e4bdb81e66bc90458f3e0", + "38b7710713484a4e9842e6f854c4f167", + "d01a9cb0685741d1a7fef71e5e7857ca" + ] + }, + "outputId": "90ee5fb1-6dc8-4e2a-da17-d6163d272cc2" + }, + "source": [ + "### Main Process\n", + "\n", + "import argparse\n", + "import numpy as np\n", + "import torch\n", + "import matplotlib.pyplot as plt\n", + "import tqdm\n", + "from torch.utils.data import DataLoader\n", + "from tqdm import trange\n", + "\n", + "class Args:\n", + " task_number = 5\n", + " epochs_per_task = 10\n", + " lr = 1.0e-4\n", + " batch_size = 128\n", + " test_size=8192\n", + " random_seed=0\n", + "\n", + "args=Args()\n", + "\n", + "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n", + "# generate permutations for the tasks.\n", + "np.random.seed(args.random_seed)\n", + "\n", + "#generate permuted MNIST data from 10 different permutation.\n", + "permutations = [\n", + " np.random.permutation(784) if index !=0 else np.arange(784) for index in range(args.task_number) ]\n", + "\n", + "# prepare permuted mnist datasets.\n", + "train_datasets = [\n", + " Data('data', permutation=permutations[index]) for index in range(len(permutations))\n", + "]\n", + "train_dataloaders = [\n", + " DataLoader(data.dataset, batch_size=args.batch_size, shuffle=True) for data in train_datasets\n", + "]\n", + "\n", + "\n", + "test_datasets = [\n", + " Data('data',train=False, permutation=permutations[index]) for index in range(len(permutations))\n", + "]\n", + "test_dataloaders = [\n", + " DataLoader(data.dataset, batch_size=args.test_size, shuffle=True) for data in test_datasets\n", + "]\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c15dd9522b8249148461a81ef0e26d73", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=9912422.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/train-images-idx3-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7717afd84e434d25b3068ff2ff733e1c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=28881.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/train-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n", + "Failed to download (trying next):\n", + "HTTP Error 503: Service Unavailable\n", + "\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n", + "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to data/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "37ed32a2e0544b9886e0b63c96ea869c", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=1648877.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/t10k-images-idx3-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n", + "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "79739e2492de4d0a9c4cb6ff7e32f14d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, max=4542.0), HTML(value='')))" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "stream", + "text": [ + "\n", + "Extracting data/MNIST/MNIST/raw/t10k-labels-idx1-ubyte.gz to data/MNIST/MNIST/raw\n", + "\n", + "Processing...\n", + "Done!\n" + ], + "name": "stdout" + }, + { + "output_type": "stream", + "text": [ + "/usr/local/lib/python3.7/dist-packages/torchvision/datasets/mnist.py:502: UserWarning: The given NumPy array is not writeable, and PyTorch does not support non-writeable tensors. This means you can write to the underlying (supposedly non-writeable) NumPy array using the tensor. You may want to copy the array to protect its data or make it writeable before converting it to a tensor. This type of warning will be suppressed for the rest of this program. (Triggered internally at /pytorch/torch/csrc/utils/tensor_numpy.cpp:143.)\n", + " return torch.from_numpy(parsed.astype(m[2], copy=False)).view(*s)\n" + ], + "name": "stderr" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "yemZCVfzzLHK" + }, + "source": [ + "#### - Model\n", + "To fair comparison, \n", + "\n", + "We fix our model architecture to do this homework. \n", + "\n", + "The model architecture consist 4 layers fully-connected network." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "wDfCEemDyTvN", + "colab": { + "base_uri": "https://localhost:8080/" + }, + "outputId": "cb0cb76a-1587-4e9e-dbb0-0b5ecade6f2c" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "import torch.nn.functional as F\n", + "\n", + "class Model(nn.Module):\n", + " \"\"\"\n", + " Model architecture \n", + " 784 (input) → 1024 → 512 → 256 → 10\n", + " \"\"\"\n", + " def __init__(self):\n", + " super(Model, self).__init__()\n", + " self.fc1 = nn.Linear(784, 1024)\n", + " self.fc2 = nn.Linear(1024, 512)\n", + " self.fc3 = nn.Linear(512, 256)\n", + " self.fc4 = nn.Linear(256, 10)\n", + " self.relu = nn.ReLU()\n", + "\n", + " def forward(self, x):\n", + " x = x.view(-1, 1*28*28)\n", + " x = self.fc1(x)\n", + " x = self.relu(x)\n", + " x = self.fc2(x)\n", + " x = self.relu(x)\n", + " x = self.fc3(x)\n", + " x = self.relu(x)\n", + " x = self.fc4(x)\n", + " return x\n", + "\n", + "example = Model()\n", + "print(example)\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "Model(\n", + " (fc1): Linear(in_features=784, out_features=1024, bias=True)\n", + " (fc2): Linear(in_features=1024, out_features=512, bias=True)\n", + " (fc3): Linear(in_features=512, out_features=256, bias=True)\n", + " (fc4): Linear(in_features=256, out_features=10, bias=True)\n", + " (relu): ReLU()\n", + ")\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "Vpi2lffuzuhv" + }, + "source": [ + "#### - Train\n", + "This is our function of training process.\n", + "\n", + "It can generally applied in different regularization-based lifelong learning algorithm in this homework." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "zFmbMAc6rJ2Z" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import tqdm\n", + "import numpy as np\n", + "from tqdm import trange\n", + "\n", + "def train(model, optimizer, dataloader, epochs_per_task, lll_object, lll_lambda, test_dataloaders, evaluate, device, log_step=1):\n", + " model.train()\n", + " model.zero_grad()\n", + " objective = nn.CrossEntropyLoss()\n", + " acc_per_epoch = []\n", + " loss = 1.0\n", + " bar = tqdm.auto.trange(epochs_per_task, leave=False, desc=f\"Epoch 1, Loss: {loss:.7f}\")\n", + " for epoch in bar:\n", + " for imgs, labels in tqdm.auto.tqdm(dataloader, leave=False): \n", + " imgs, labels = imgs.to(device), labels.to(device)\n", + " outputs = model(imgs)\n", + " loss = objective(outputs, labels)\n", + " total_loss = loss\n", + " lll_loss = lll_object.penalty(model)\n", + " total_loss += lll_lambda * lll_loss \n", + " lll_object.update(model)\n", + " optimizer.zero_grad()\n", + " total_loss.backward()\n", + " optimizer.step()\n", + "\n", + " loss = total_loss.item()\n", + " bar.set_description_str(desc=f\"Epoch {epoch+1:2}, Loss: {loss:.7f}\", refresh=True)\n", + " acc_average = []\n", + " for test_dataloader in test_dataloaders: \n", + " acc_test = evaluate(model, test_dataloader, device)\n", + " acc_average.append(acc_test)\n", + " average=np.mean(np.array(acc_average))\n", + " acc_per_epoch.append(average*100.0)\n", + " bar.set_description_str(desc=f\"Epoch {epoch+2:2}, Loss: {loss:.7f}\", refresh=True)\n", + " \n", + " return model, optimizer, acc_per_epoch" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "rjOiSSVD0GXB" + }, + "source": [ + "#### - Evaluate\n", + "This is our function of evaluation process.\n", + "\n", + "It can generally applied in different regularization-based lifelong learning algorithm in this homework." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "BUh6J6LZrQVW" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "\n", + "\n", + "def evaluate(model, test_dataloader, device):\n", + " model.eval()\n", + " correct_cnt = 0\n", + " total = 0\n", + " for imgs, labels in test_dataloader:\n", + " imgs, labels = imgs.to(device), labels.to(device)\n", + " outputs = model(imgs)\n", + " _, pred_label = torch.max(outputs.data, 1)\n", + "\n", + " correct_cnt += (pred_label == labels.data).sum().item()\n", + " total += torch.ones_like(labels.data).sum().item()\n", + " return correct_cnt / total" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "gqAoSbiYzRiZ" + }, + "source": [ + "#### - Evaluation Metric\n", + "We utilize **Average Accuracy** as our evaluation metric, \n", + "\n", + "which average the accuracy from the all previous and current test set to measure the performance of lifelong learning . " + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6JTAisD5uogo" + }, + "source": [ + "### Visualization\n" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ktQWQcVKTvnK" + }, + "source": [ + "#### - Permuted MNIST dataset" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HXj1AIU3u7Hu", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 512 + }, + "outputId": "a34fdda7-7370-426d-d504-9df4f0eb0d61" + }, + "source": [ + "### Visualize label 0-9 1 sample MNIST picture in first 3 task.\n", + "\n", + "sample = [\n", + " Data('data', permutation=permutations[index], normalize=False) for index in range(len(permutations))\n", + "]\n", + "import matplotlib.pyplot as plt\n", + "plt.figure(figsize=(30, 10))\n", + "for task in range(3):\n", + " labels = [list(sample[task].dataset.targets).index(l) for l in range(10)]\n", + " for idx, label in enumerate(labels):\n", + " plt.subplot(3, 10, (task)*10 + idx + 1)\n", + " curr_img = np.reshape(sample[task].dataset[label][0], (28, 28))\n", + " plt.matshow(curr_img, cmap=plt.get_cmap('gray'), fignum=False)\n", + " ax = plt.gca()\n", + " ax.axes.xaxis.set_ticks([])\n", + " ax.axes.yaxis.set_ticks([])\n", + " plt.title(\"task: \" + str(task+1) + \" \" + \"label: \" + str(idx), y=1)\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "display_data", + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "tags": [] + } + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "f5iwmr4TWLFy" + }, + "source": [ + "### Methods\n", + "- Baseline\n", + "- EWC\n", + "- SI\n", + "- MAS\n", + "- RWalk\n", + "-SCP" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "wXN4EDl71sBu" + }, + "source": [ + "#### - Baseline\n", + "The baseline class will do nothing in regularization term." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "ZcyKKyrb0i2_" + }, + "source": [ + "# Baseline \n", + "import torch\n", + "import torch.nn as nn\n", + "\n", + "\n", + "class baseline(object):\n", + " \"\"\"\n", + " baseline technique: do nothing in regularization term [initialize and all weight is zero]\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + "\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #extract all parameters in models\n", + " self.p_old = {} # store current parameters\n", + " self._precision_matrices = self._calculate_importance() # generate weight matrix \n", + "\n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def _calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): # initialize weight matrix（fill zero）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "ib50m1HZ5QAo" + }, + "source": [ + "Main process for baseline" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "SgKgYHJz5HrW", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141, + "referenced_widgets": [ + "ec2588ef879e419fa19b5b27e004c342", + "92bfa33413c54f4bb1df7a2aedb6c256", + "daac70865f1e4f5f8c96bfd846572352", + "00de71facd75413985fa5c3d7eb892ed", + "0f4dd12881e24eb888e21b9e5a3eb60f", + "c6ed98d9308e45679a7d117a5a100b5a", + "6c3447f5589f4167b39ef090dea13e08", + "29c741ce25534c8eaaf80b3fc01130e8", + "3d538393b0304acab0c9a67690c002b5", + "7d4e79d051ad49b6a94573b5e1d86fe9", + "48526f88886d4eb4ad59d7fc9600a74d", + "e5ba9e62395344a0879128f09ed423c9", + "a502c6e96efd46f081fa70ae7ed5f21e", + "98fdce97a2bd4c339d88c44beeafdf22", + "5442b88d556346a786fa6beada9ddc45", + "38ae0f4faf3e4846964055cc3cf04a31", + "17fd4fab27944c4a85d65bc96c02b857", + "2b75388b5af94af3a7d08d1103b8e225", + "b15bdd9c7f044b76bfad2d2de7e97549", + 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tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "\n", + "# iterate training on each task continually.\n", + "for train_indexes in task_bar:\n", + " # Train each task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + "\n", + " # get model weight to baseline class and do nothing!\n", + " lll_object=baseline(model=model, dataloaders=test_dataloaders[:train_indexes],device=device)\n", + "\n", + " # new a optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " baseline_acc.extend(acc_list)\n", + " \n", + " # display the information of the next task.\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(baseline_acc)\n", + 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"name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "QIv2EUeu2lXv" + }, + "source": [ + "#### - EWC\n", + "\n", + "Elastic Weight Consolidation\n", + "\n", + "The ewc class applied EWC algorithm to calculate regularization term.\n", + "The central concept is included in Prof.Hung-yi's lectures. Here we will focus on the algorithm of EWC. \n", + "\n", + "In this assignment, we want to let our model learn 10 tasks successively.\n", + "Here we show a simple example that we let the model learn 2 tasks(task A, task B) successively.\n", + "\n", + "In EWC algorithm, the definition of loss function is shown below:\n", + " $$\\mathcal{L}_B = \\mathcal{L}(\\theta) + \\sum_{i} \\frac{\\lambda}{2} F_i (\\theta_{i} - \\theta_{A,i}^{*})^2 $$\n", + " \n", + "Assume we have a neural network with more than two parameters.\n", + "\n", + "$F_i$ correspond to the $i^{th}$ guard in Prof. Hung-yi's lecture. Please do not modify this parameters, because it's important to task A.\n", + "\n", + "The definition of $F$ is shown below.\n", + "$$ F = [ \\nabla \\log(p(y_n | x_n, \\theta_{A}^{*}) \\nabla \\log(p(y_n | x_n, \\theta_{A}^{*})^T ] $$ \n", + "\n", + "We only take the diagonal value of matrix to approximate each parameters' $F_i$.\n", + "\n", + "The detail infromation and derivation are shown in 2.4.1 and 2.4 of [Continual Learning in Neural\n", + "Networks](https://arxiv.org/pdf/1910.02718.pdf)\n", + "\n", + "For You Information: [Elastic Weight Consolidation](https://arxiv.org/pdf/1612.00796.pdf)\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "4zf7sGSD2FID" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class ewc(object):\n", + " \"\"\"\n", + " @article{kirkpatrick2017overcoming,\n", + " title={Overcoming catastrophic forgetting in neural networks},\n", + " author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},\n", + " journal={Proceedings of the national academy of sciences},\n", + " year={2017},\n", + " url={https://arxiv.org/abs/1612.00796}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + "\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} # extract all parameters in models\n", + " self.p_old = {} # initialize parameters\n", + " self._precision_matrices = self._calculate_importance() # generate Fisher (F) matrix for EWC \n", + "\n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def _calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): \n", + " # initialize Fisher (F) matrix（all fill zero）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num=len(self.dataloaders)\n", + " number_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " # get image data\n", + " input = data[0].to(self.device)\n", + " # image data forward model\n", + " output = self.model(input)\n", + " # Simply use groud truth label of dataset. \n", + " label = data[1].to(self.device)\n", + " # print(output.shape, label.shape)\n", + " \n", + " ############################################################################\n", + " ##### generate Fisher(F) matrix for EWC #####\n", + " ############################################################################ \n", + " loss = F.nll_loss(F.log_softmax(output, dim=1), label) \n", + " loss.backward() \n", + " ############################################################################\n", + "\n", + " for n, p in self.model.named_parameters():\n", + " # get the gradient of each parameter and square it, then average it in all validation set. \n", + " precision_matrices[n].data += p.grad.data ** 2 / number_data \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + "\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " # generate the final regularization term by the ewc weight (self._precision_matrices[n]) and the square of weight difference ((p - self.p_old[n]) ** 2). \n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return \n" + ], + 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coefficient value of regularization term.\n", + "lll_lambda=100\n", + "ewc_acc= []\n", + "task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "\n", + "# iterate training on each task continually.\n", + "for train_indexes in task_bar:\n", + " # Train Each Task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=ewc(model=model, dataloaders=test_dataloaders[:train_indexes+1],device=device)\n", + "\n", + " # new a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # collect average accuracy in each epoch\n", + " ewc_acc.extend(acc_list)\n", + "\n", + " # Update tqdm displayer\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(ewc_acc)\n", + "print(\"==================================================================================================\")\n", + "\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN EWC\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "7fdb56e98d0d43c4b7c3f4ff2dd3dc3e", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Task 1', max=5.0, style=ProgressStyle(description_width…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1ec628eb300c43ab84d9d08a816e1d8d", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Epoch 1, Loss: 1.0000000', max=10.0, 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"==================================================================================================\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "aPz7wBXo7rkr" + }, + "source": [ + "#### - MAS\n", + "Memory Aware Synapses\n", + "\n", + "The mas class applied MAS algorithm to calculate regularization term.\n", + "\n", + "The concept of MAS is similar to EWC, the only difference is the calculation of the important weight. \n", + "The details are mentioned in following blocks.\n", + "\n", + "MAS:\n", + "\n", + "In MAS, the Loss function is shown below, the model learn task A before it learned task B.\n", + "\n", + "$$\\mathcal{L}_B = \\mathcal{L}(\\theta) + \\sum_{i} \\frac{\\lambda}{2} \\Omega_i (\\theta_{i} - \\theta_{A,i}^{*})^2$$\n", + "\n", + "Compare with EWC, the $F_i$ in the loss function is replaced with $\\Omega_i$ in the following function.\n", + "\n", + "$$\\Omega_i = || \\frac{\\partial \\ell_2^2(M(x_k; \\theta))}{\\partial \\theta_i} || $$ \n", + "\n", + "$x_k$ is the sample data of the previous task. So the $\\Omega$ is obtained gradients of the squared L2-norm of the learned network output.\n", + "\n", + "The methods that proposed from paper is the local version by taking squared L2-norm outputs from the each layers of the model.\n", + "\n", + "Here we only implmented the global version by taking outputs from the last layer of the model. \n", + "\n", + "\n", + "For Your Information: \n", + "[Memory Aware Synapses](https://arxiv.org/pdf/1711.09601.pdf)\n", + " \n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "nznnDrFP3UYf" + }, + "source": [ + "class mas(object):\n", + " \"\"\"\n", + " @article{aljundi2017memory,\n", + " title={Memory Aware Synapses: Learning what (not) to forget},\n", + " author={Aljundi, Rahaf and Babiloni, Francesca and Elhoseiny, Mohamed and Rohrbach, Marcus and Tuytelaars, Tinne},\n", + " booktitle={ECCV},\n", + " year={2018},\n", + " url={https://eccv2018.org/openaccess/content_ECCV_2018/papers/Rahaf_Aljundi_Memory_Aware_Synapses_ECCV_2018_paper.pdf}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model: nn.Module, dataloaders: list, device):\n", + " self.model = model \n", + " self.dataloaders = dataloaders\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #extract all parameters in models\n", + " self.p_old = {} # initialize parameters\n", + " self.device = device\n", + " self._precision_matrices = self.calculate_importance() # generate Omega(Ω) matrix for MAS\n", + " \n", + " for n, p in self.params.items():\n", + " self.p_old[n] = p.clone().detach() # keep the old parameter in self.p_old\n", + " \n", + " def calculate_importance(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items():\n", + " precision_matrices[n] = p.clone().detach().fill_(0) # initialize Omega(Ω) matrix（all filled zero）\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num = len(self.dataloaders)\n", + " num_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " output = self.model(data[0].to(self.device))\n", + "\n", + " ###########################################################################################################################################\n", + " ##### TODO BLOCK: generate Omega(Ω) matrix for MAS. (Hint: square of l2 norm of output vector, then backward and take its gradients #####\n", + " ###########################################################################################################################################\n", + " output.pow_(2) \n", + " loss = torch.sum(output,dim=1) \n", + " loss = loss.mean() \n", + " loss.backward() \n", + " ########################################################################################################################################### \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " precision_matrices[n].data += p.grad.abs() / num_data ## difference with EWC \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self.p_old[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "zfbD-yPG-LkN" + }, + "source": [ + "Main process for MAS" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "HLoLPPmD-BHD", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 141, + "referenced_widgets": [ + "4ca31f1971c9488d872c47bad7f96889", + "9e915c9b2c664887b1d0d4937c97fe41", + "ccb22c8cee03438c8326c13a081c5fc7", + "09e1f35617a741bf800c6ac0b1488e4a", + "a5e656639fdb421090070d8a3a9f8afa", + "07ce24c5ee95478cbdbdb5c73ef74668", + "6ca43c3d9f044f96b00d72514dbb2c86", + "bf597639a51f462f87e985a2836d5a89", + "71f38e1d1c1545cd9eda838dfcf4bac2", + "cc1af4d131464c01964043964d730a6d", + "2282b2571aa440a4add872d652f9717a", + "060a9076eceb4cfbb6637f1e70562f06", + "f9982380a4974221943b2eab766956ed", + "b0fabb1a5b884970b8bbe2ee4d58061f", + "658d0ad8216642be82d59bbe2c7cbee1", + 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mas_acc= []\n", + " task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + " for train_indexes in task_bar:\n", + " # Train Each Task\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=mas(model=model, dataloaders=test_dataloaders[:train_indexes+1],device=device)\n", + "\n", + " # New a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " mas_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + " \n", + " # average accuracy in each task per epoch! \n", + " print(mas_acc)\n", + " print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN MAS\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "4ca31f1971c9488d872c47bad7f96889", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Task 1', max=5.0, style=ProgressStyle(description_width…" + ] + }, + "metadata": { + "tags": [] + } + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "71f38e1d1c1545cd9eda838dfcf4bac2", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + "HBox(children=(FloatProgress(value=0.0, description='Epoch 1, Loss: 1.0000000', max=10.0, style=ProgressStyle(…" + ] + }, + "metadata": { + "tags": [] + } + }, + { 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"metadata": { + "id": "OJ4LYprnRO73" + }, + "source": [ + "#### - SI\n", + "The si class applied SI (Synaptic Intelligence) algorithm to calculate regularization term." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "vN4PS1nSRaEx" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "\n", + "class si(object):\n", + " \"\"\"\n", + " @article{kirkpatrick2017overcoming,\n", + " title={Overcoming catastrophic forgetting in neural networks},\n", + " author={Kirkpatrick, James and Pascanu, Razvan and Rabinowitz, Neil and Veness, Joel and Desjardins, Guillaume and Rusu, Andrei A and Milan, Kieran and Quan, John and Ramalho, Tiago and Grabska-Barwinska, Agnieszka and others},\n", + " journal={Proceedings of the national academy of sciences},\n", + " year={2017},\n", + " url={https://arxiv.org/abs/1612.00796}\n", + " }\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, epsilon, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + " self.epsilon = epsilon\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} #抓出模型的所有參數\n", + " self._n_p_prev, self._n_omega = self._calculate_importance() \n", + " self.W, self.p_old = self._init_()\n", + "\n", + " def _init_(self):\n", + " W = {}\n", + " p_old = {}\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " W[n] = p.data.clone().zero_()\n", + " p_old[n] = p.data.clone()\n", + " return W, p_old\n", + "\n", + " def _calculate_importance(self):\n", + " n_p_prev = {}\n", + " n_omega = {}\n", + "\n", + " if self.dataloaders[0] != None:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + "\n", + " # Find/calculate new values for quadratic penalty on parameters\n", + " p_prev = getattr(self.model, '{}_SI_prev_task'.format(n))\n", + " W = getattr(self.model, '{}_W'.format(n))\n", + " p_current = p.detach().clone()\n", + " p_change = p_current - p_prev\n", + " omega_add = W/(p_change**2 + self.epsilon)\n", + " try:\n", + " omega = getattr(self.model, '{}_SI_omega'.format(n))\n", + " except AttributeError:\n", + " omega = p.detach().clone().zero_()\n", + " omega_new = omega + omega_add\n", + " n_omega[n] = omega_new\n", + " n_p_prev[n] = p_current\n", + "\n", + "\n", + " # Store these new values in the model\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p_current)\n", + " self.model.register_buffer('{}_SI_omega'.format(n), omega_new)\n", + " else:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " n_p_prev[n] = p.detach().clone()\n", + " n_omega[n] = p.detach().clone().zero_()\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p.detach().clone())\n", + "\n", + "\n", + " return n_p_prev, n_omega\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0.0\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " prev_values = self._n_p_prev[n]\n", + " omega = self._n_omega[n]\n", + " _loss = omega * (p - prev_values) ** 2\n", + " loss += _loss.sum()\n", + " \n", + " return loss\n", + " \n", + " def update(self, model):\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " if p.grad is not None:\n", + " self.W[n].add_(-p.grad * (p.detach() - self.p_old[n]))\n", + " self.model.register_buffer('{}_W'.format(n), self.W[n])\n", + " self.p_old[n] = p.detach().clone()\n", + " return \n", + " \n" + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "z1yDmFFeRxi9" + }, + "source": [ + "Main Process for SI\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "RDwVo5W9R5cx", + 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evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " \n", + " # get model weight and calculate guidance for each weight\n", + " lll_object=si(model=model, dataloaders=test_dataloaders[:train_indexes+1], epsilon=0.1, device=device)\n", + "\n", + " # New a Optimizer\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + " # Collect average accuracy in each epoch\n", + " si_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + " # average accuracy in each task per epoch! \n", + " print(si_acc)" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN SI\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "531cbe4dc8b14d4b9184c7c87fb511a3", + "version_minor": 0, + "version_major": 2 + }, + "text/plain": [ + 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95.90333333333334, 96.06333333333333, 95.71666666666665, 95.11666666666666, 95.73, 95.33333333333333, 95.39666666666666, 95.13666666666666, 95.28666666666666, 94.90333333333334, 93.1575, 92.99499999999999, 92.0825, 92.14750000000001, 91.7425, 91.6175, 92.4725, 92.015, 91.41999999999999, 92.58, 91.05000000000001, 90.41799999999999, 90.25399999999999, 90.43599999999999, 90.28399999999999, 89.524, 90.168, 90.66000000000001, 89.87399999999998, 90.428]\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "64UECuLG66GM" + }, + "source": [ + "#### - RWalk\n", + "\n", + "#### Remanian Walk for Incremental Learning\n", + "\n", + "The rwalk class applied Remanian Walk algorithm to calculate regularization term.\n", + "\n", + "The details are mentioned in following blocks." + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "9hzivWHj8KFF" + }, + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "\n", + "class rwalk(object):\n", + " \"\"\"\n", + "\n", + " \"\"\"\n", + " def __init__(self, model, dataloaders, epsilon, device):\n", + " \n", + " self.model = model\n", + " self.dataloaders = dataloaders\n", + " self.device = device\n", + " self.epsilon = epsilon\n", + " self.update_ewc_parameter = 0.4\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad} # extract model parameters and store in dictionary\n", + " self._means = {} # initialize the guidance matrix\n", + " self._precision_matrices = self._calculate_importance_ewc() # Generate Fisher (F) Information Matrix \n", + " self._n_p_prev, self._n_omega = self._calculate_importance() \n", + " self.W, self.p_old = self._init_()\n", + "\n", + "\n", + " def _init_(self):\n", + " W = {}\n", + " p_old = {}\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " W[n] = p.data.clone().zero_()\n", + " p_old[n] = p.data.clone()\n", + " return W, p_old\n", + "\n", + " def _calculate_importance(self):\n", + " n_p_prev = {}\n", + " n_omega = {}\n", + "\n", + " if self.dataloaders[0] != None:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + "\n", + " # Find/calculate new values for quadratic penalty on parameters\n", + " p_prev = getattr(self.model, '{}_SI_prev_task'.format(n))\n", + " W = getattr(self.model, '{}_W'.format(n))\n", + " p_current = p.detach().clone()\n", + " p_change = p_current - p_prev\n", + " omega_add = W / (1.0 / 2.0*self._precision_matrices[n] *p_change**2 + self.epsilon)\n", + " try:\n", + " omega = getattr(self.model, '{}_SI_omega'.format(n))\n", + " except AttributeError:\n", + " omega = p.detach().clone().zero_()\n", + " omega_new = 0.5 * omega + 0.5 *omega_add\n", + " n_omega[n] = omega_new\n", + " n_p_prev[n] = p_current\n", + "\n", + "\n", + " # Store these new values in the model\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p_current)\n", + " self.model.register_buffer('{}_SI_omega'.format(n), omega_new)\n", + " else:\n", + " for n, p in self.model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " n_p_prev[n] = p.detach().clone()\n", + " n_omega[n] = p.detach().clone().zero_()\n", + " self.model.register_buffer('{}_SI_prev_task'.format(n), p.detach().clone())\n", + "\n", + "\n", + " return n_p_prev, n_omega\n", + " \n", + "\n", + " def _calculate_importance_ewc(self):\n", + " precision_matrices = {}\n", + " for n, p in self.params.items(): \n", + " n = n.replace('.', '__') # 初始化 Fisher (F) 的矩陣（都補零）\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num=len(self.dataloaders)\n", + " number_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for n, p in self.model.named_parameters(): \n", + " n = n.replace('.', '__')\n", + " precision_matrices[n].data *= (1 -self.update_ewc_parameter) \n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " input = data[0].to(self.device)\n", + " output = self.model(input)\n", + " label = data[1].to(self.device)\n", + "\n", + " \n", + " ############################################################################\n", + " ##### Generate Fisher Matrix #####\n", + " ############################################################################ \n", + " loss = F.nll_loss(F.log_softmax(output, dim=1), label) \n", + " loss.backward() \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " n = n.replace('.', '__')\n", + " precision_matrices[n].data += self.update_ewc_parameter*p.grad.data ** 2 / number_data \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + "\n", + " return precision_matrices\n", + "\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0.0\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " prev_values = self._n_p_prev[n]\n", + " omega = self._n_omega[n]\n", + "\n", + " #################################################################################\n", + " #### Generate regularization term _loss by omega and Fisher Matrix ####\n", + " #################################################################################\n", + " _loss = (omega + self._precision_matrices[n]) * (p - prev_values) ** 2\n", + " loss += _loss.sum()\n", + " \n", + " return loss\n", + " \n", + " def update(self, model):\n", + " for n, p in model.named_parameters():\n", + " n = n.replace('.', '__')\n", + " if p.requires_grad:\n", + " if p.grad is not None:\n", + " self.W[n].add_(-p.grad * (p.detach() - self.p_old[n]))\n", + " self.model.register_buffer('{}_W'.format(n), self.W[n])\n", + " self.p_old[n] = p.detach().clone()\n", + " return \n", + " \n" + ], + 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tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "for train_indexes in task_bar:\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " lll_object=rwalk(model=model, dataloaders=test_dataloaders[:train_indexes+1], epsilon=0.1, device=device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + " rwalk_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(rwalk_acc)\n", + "print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN Rwalk\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + 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97.57000000000001, 97.75, 97.7, 98.00999999999999, 97.76, 97.89999999999999, 96.06, 97.03500000000001, 96.9, 97.275, 97.16, 97.21, 97.38, 97.255, 97.39999999999999, 97.32, 95.37666666666667, 96.08666666666666, 96.07333333333334, 96.30666666666666, 96.31333333333333, 96.22333333333333, 96.36333333333334, 96.08666666666666, 95.90666666666667, 96.07666666666667, 94.65, 95.075, 94.9325, 95.06500000000001, 94.80499999999999, 95.0975, 95.2225, 95.1975, 94.9925, 94.78, 93.96000000000001, 94.214, 93.836, 94.036, 94.152, 94.00800000000001, 94.17, 94.178, 93.89800000000001, 93.986]\n", + "==================================================================================================\n" + ], + "name": "stdout" + } + ] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "XicStkkOA_gg" + }, + "source": [ + "#### - SCP\n", + "Sliced Cramer Preservation\n", + "\n", + "\n", + "\n", + "Pseudo Code:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "metadata": { + "id": "72o_U5uxA8kX" + }, + "source": [ + "import torch\n", + "from torch import nn\n", + "import numpy as np\n", + "\n", + "\n", + "\n", + "def sample_spherical(npoints, ndim=3):\n", + " vec = np.random.randn(ndim, npoints)\n", + " vec /= np.linalg.norm(vec, axis=0)\n", + " return torch.from_numpy(vec)\n", + "\n", + "class scp(object):\n", + " \"\"\"\n", + " OPEN REVIEW VERSION:\n", + " https://openreview.net/forum?id=BJge3TNKwH\n", + " \"\"\"\n", + " def __init__(self, model: nn.Module, dataloaders: list, L: int, device):\n", + " self.model = model \n", + " self.dataloaders = dataloaders\n", + " self.params = {n: p for n, p in self.model.named_parameters() if p.requires_grad}\n", + " self._state_parameters = {}\n", + " self.L= L\n", + " self.device = device\n", + " self._precision_matrices = self.calculate_importance()\n", + " \n", + " for n, p in self.params.items():\n", + " self._state_parameters[n] = p.clone().detach()\n", + " \n", + " def calculate_importance(self):\n", + "\n", + " precision_matrices = {}\n", + " for n, p in self.params.items():\n", + " precision_matrices[n] = p.clone().detach().fill_(0)\n", + "\n", + " self.model.eval()\n", + " if self.dataloaders[0] is not None:\n", + " dataloader_num = len(self.dataloaders)\n", + " num_data = sum([len(loader) for loader in self.dataloaders])\n", + " for dataloader in self.dataloaders:\n", + " for data in dataloader:\n", + " self.model.zero_grad()\n", + " output = self.model(data[0].to(self.device))\n", + " \n", + " ####################################################################################\n", + " ##### generate SCP's Gamma(Γ) matrix (like MAS's Omega(Ω) and EWC's Fisher(F)) #####\n", + " ####################################################################################\n", + " ##### 1.take average on a batch of Output vector to get vector φ(:,θ_A* )####\n", + " ####################################################################################\n", + " mean_vec = output.mean(dim=0)\n", + "\n", + " ####################################################################################\n", + " ##### 2. random sample L vectors ξ #（ Hint: sample_spherical() ） #####\n", + " ####################################################################################\n", + " L_vectors = sample_spherical(self.L, output.shape[-1])\n", + " L_vectors = L_vectors.transpose(1,0).to(self.device).float()\n", + "\n", + " ####################################################################################\n", + " ##### 3. 每一個 vector ξ 和 vector φ( :,θ_A* )內積得到 scalar ρ ####\n", + " ##### 對 scalar ρ 取 backward ， 每個參數得到各自的 gradient ∇ρ ####\n", + " ##### 每個參數的 gradient ∇ρ 取平方 取 L 平均 得到 各個參數的 Γ scalar #### \n", + " ##### 所有參數的 Γ scalar 組合而成其實就是 Γ 矩陣 ####\n", + " ####(hint: 記得 每次 backward 之後 要 zero_grad 去 清 gradient, 不然 gradient會累加 )#### \n", + " ####################################################################################\n", + " total_scalar = 0\n", + " for vec in L_vectors:\n", + " scalar=torch.matmul(vec, mean_vec)\n", + " total_scalar += scalar\n", + " total_scalar /= L_vectors.shape[0] \n", + " total_scalar.backward()\n", + " ################################################################################## \n", + " \n", + " \n", + " for n, p in self.model.named_parameters(): \n", + " precision_matrices[n].data += p.grad.abs() / num_data ## difference with EWC \n", + " \n", + " precision_matrices = {n: p for n, p in precision_matrices.items()}\n", + " return precision_matrices\n", + "\n", + " def penalty(self, model: nn.Module):\n", + " loss = 0\n", + " for n, p in model.named_parameters():\n", + " _loss = self._precision_matrices[n] * (p - self._state_parameters[n]) ** 2\n", + " loss += _loss.sum()\n", + " return loss\n", + " \n", + " def update(self, model):\n", + " # do nothing\n", + " return " + ], + "execution_count": null, + "outputs": [] + }, + { + "cell_type": "markdown", + "metadata": { + "id": "6uBo7pzH-eQx" + }, + "source": [ + "Main process for 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"bcf36e5d2da943dd9ba1957aa79131e5", + "83007f139b2d485899e8afcb647bea89", + "5b9ae06c4547401193c5c892271e6c74", + "e275e141b65c45ac8a4972c53c77e364", + "cf88d6cc80e24e28be56c699398d39b6", + "ffa2a96da3374802b5ec2009c2ea8e29", + "2266a36f65134ab8806435850d7e2527", + "34928e149ac5468383ae967167778845", + "f0866b229a3944fc9151a1903658cba0", + "bcfbed0138214264b649fb289a6d8612", + "21e7b76309004f9d916d62880610b9e8" + ] + }, + "outputId": "1c409ff8-903a-4bf6-d752-0aba3fdc274c" + }, + "source": [ + "# SCP\n", + "print(\"RUN SLICE CRAMER PRESERVATION\")\n", + "model = Model()\n", + "model = model.to(device)\n", + "optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + "\n", + "lll_object=scp(model=model, dataloaders=[None], L=100, device=device)\n", + "lll_lambda=100\n", + "scp_acc= []\n", + "task_bar = tqdm.auto.trange(len(train_dataloaders),desc=\"Task 1\")\n", + "for train_indexes in task_bar:\n", + " model, _, acc_list = train(model, optimizer, train_dataloaders[train_indexes], args.epochs_per_task, lll_object, lll_lambda, evaluate=evaluate,device=device, test_dataloaders=test_dataloaders[:train_indexes+1])\n", + " lll_object=scp(model=model, dataloaders=test_dataloaders[:train_indexes+1], L=100, device=device)\n", + " optimizer = torch.optim.Adam(model.parameters(), lr=args.lr)\n", + " scp_acc.extend(acc_list)\n", + " task_bar.set_description_str(f\"Task {train_indexes+2:2}\")\n", + "\n", + "# average accuracy in each task per epoch! \n", + "print(scp_acc)\n", + "print(\"==================================================================================================\")\n" + ], + "execution_count": null, + "outputs": [ + { + "output_type": "stream", + "text": [ + "RUN SLICE CRAMER PRESERVATION\n" + ], + "name": "stdout" + }, + { + "output_type": "display_data", + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "c4b9a5c6e5434bcbaa46720747035d9b", + "version_minor": 0, + "version_major": 2 + }, + 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