From 713f5f12e24a1ee12083e79b802bf1a69643d5eb Mon Sep 17 00:00:00 2001 From: shivamsaboo17 Date: Fri, 29 Mar 2019 22:27:47 +0530 Subject: [PATCH] bug fixed, added demo and tests --- demo.ipynb | 349 +++++ elastic_weight_consolidation.py | 11 +- ewc_test.ipynb | 2254 ------------------------------- 3 files changed, 355 insertions(+), 2259 deletions(-) create mode 100644 demo.ipynb delete mode 100644 ewc_test.ipynb diff --git a/demo.ipynb b/demo.ipynb new file mode 100644 index 0000000..4e40f73 --- /dev/null +++ b/demo.ipynb @@ -0,0 +1,349 @@ +{ + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "Untitled1.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + } + }, + "cells": [ + { + "metadata": { + "id": "C7Ppv-uc9DWM", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import torch\n", + "import torch.nn as nn\n", + "import torch.nn.functional as F\n", + "from elastic_weight_consolidation import ElasticWeightConsolidation" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "qe22sCzx9DWQ", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from torch.utils.data import Dataset, DataLoader\n", + "from torchvision import datasets, transforms" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "Y_LMkmXG9DWV", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "mnist_train = datasets.MNIST(\"../data\", train=True, download=True, transform=transforms.ToTensor())\n", + "mnist_test = datasets.MNIST(\"../data\", train=False, download=True, transform=transforms.ToTensor())\n", + "train_loader = DataLoader(mnist_train, batch_size = 100, shuffle=True)\n", + "test_loader = DataLoader(mnist_test, batch_size = 100, shuffle=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "YrKlgL6t9zJe", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "class LinearLayer(nn.Module):\n", + " def __init__(self, input_dim, output_dim, act='relu', use_bn=False):\n", + " super(LinearLayer, self).__init__()\n", + " self.use_bn = use_bn\n", + " self.lin = nn.Linear(input_dim, output_dim)\n", + " self.act = nn.ReLU() if act == 'relu' else act\n", + " if use_bn:\n", + " self.bn = nn.BatchNorm1d(output_dim)\n", + " def forward(self, x):\n", + " if self.use_bn:\n", + " return self.bn(self.act(self.lin(x)))\n", + " return self.act(self.lin(x))\n", + "\n", + "class Flatten(nn.Module):\n", + "\n", + " def forward(self, x):\n", + " return x.view(x.shape[0], -1)\n" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "44d9meQa9DWc", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "class BaseModel(nn.Module):\n", + " \n", + " def __init__(self, num_inputs, num_hidden, num_outputs):\n", + " super(BaseModel, self).__init__()\n", + " self.f1 = Flatten()\n", + " self.lin1 = LinearLayer(num_inputs, num_hidden, use_bn=True)\n", + " self.lin2 = LinearLayer(num_hidden, num_hidden, use_bn=True)\n", + " self.lin3 = nn.Linear(num_hidden, num_outputs)\n", + " \n", + " def forward(self, x):\n", + " return self.lin3(self.lin2(self.lin1(self.f1(x))))" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "_17XW9359DWf", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "crit = nn.CrossEntropyLoss()" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SpBrwjk89DWi", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ewc = ElasticWeightConsolidation(BaseModel(28 * 28, 100, 10), crit=crit, lr=1e-4)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "CMQGk-E19DWl", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "from tqdm import tqdm" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "gmbrFvJm9DWn", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "outputId": "db2ca466-76dc-4d1c-fa32-8dc672a12a8f" + }, + "cell_type": "code", + "source": [ + "for _ in range(4):\n", + " for input, target in tqdm(train_loader):\n", + " ewc.forward_backward_update(input, target)" + ], + "execution_count": 41, + "outputs": [ + { + "output_type": "stream", + "text": [ + "100%|██████████| 600/600 [00:07<00:00, 75.32it/s]\n", + "100%|██████████| 600/600 [00:07<00:00, 76.07it/s]\n", + "100%|██████████| 600/600 [00:08<00:00, 74.12it/s]\n", + "100%|██████████| 600/600 [00:08<00:00, 73.90it/s]\n" + ], + "name": "stderr" + } + ] + }, + { + "metadata": { + "id": "8HwlRJkI9DWt", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ewc.register_ewc_params(mnist_train, 100, 300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "NvJW68IB9DWw", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "f_mnist_train = datasets.FashionMNIST(\"../data\", train=True, download=True, transform=transforms.ToTensor())\n", + "f_mnist_test = datasets.FashionMNIST(\"../data\", train=False, download=True, transform=transforms.ToTensor())\n", + "f_train_loader = DataLoader(f_mnist_train, batch_size = 100, shuffle=True)\n", + "f_test_loader = DataLoader(f_mnist_test, batch_size = 100, shuffle=False)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "SzQbVudz9DWy", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 87 + }, + "outputId": "bdcb55c5-d40a-4a7a-dca5-4652076e8033" + }, + "cell_type": "code", + "source": [ + "for _ in range(4):\n", + " for input, target in tqdm(f_train_loader):\n", + " ewc.forward_backward_update(input, target)" + ], + "execution_count": 44, + "outputs": [ + { + "output_type": "stream", + "text": [ + "100%|██████████| 600/600 [00:09<00:00, 62.14it/s]\n", + "100%|██████████| 600/600 [00:09<00:00, 66.03it/s]\n", + "100%|██████████| 600/600 [00:09<00:00, 66.56it/s]\n", + "100%|██████████| 600/600 [00:09<00:00, 65.95it/s]\n" + ], + "name": "stderr" + } + ] + }, + { + "metadata": { + "id": "L8n6PX5w9DW2", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "ewc.register_ewc_params(f_mnist_train, 100, 300)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "fUqvbeO79DW4", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "def accu(model, dataloader):\n", + " model = model.eval()\n", + " acc = 0\n", + " for input, target in dataloader:\n", + " o = model(input)\n", + " acc += (o.argmax(dim=1).long() == target).float().mean()\n", + " return acc / len(dataloader)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "aOIOBZhp9DW6", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "b260dee2-3c7d-4a9f-be83-8ac412a32f5c" + }, + "cell_type": "code", + "source": [ + "accu(ewc.model, f_test_loader)" + ], + "execution_count": 47, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor(0.8188)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 47 + } + ] + }, + { + "metadata": { + "id": "hFdW_33Y9DW-", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 35 + }, + "outputId": "38b2dad0-bfb4-48e5-ec01-d848cc8c1593" + }, + "cell_type": "code", + "source": [ + "accu(ewc.model, test_loader)" + ], + "execution_count": 48, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "tensor(0.7027)" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 48 + } + ] + }, + { + "metadata": { + "id": "Fkni7xkY-tRI", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "" + ], + "execution_count": 0, + "outputs": [] + } + ] +} \ No newline at end of file diff --git a/elastic_weight_consolidation.py b/elastic_weight_consolidation.py index 46ca1c4..82293ae 100644 --- a/elastic_weight_consolidation.py +++ b/elastic_weight_consolidation.py @@ -4,11 +4,12 @@ import torch.optim as optim from torch import autograd import numpy as np +from torch.utils.data import DataLoader class ElasticWeightConsolidation: - def __init__(self, model, crit, lr=0.001, weight=0.1): + def __init__(self, model, crit, lr=0.001, weight=1000000): self.model = model self.weight = weight self.crit = crit @@ -20,7 +21,7 @@ def _update_mean_params(self): self.model.register_buffer(_buff_param_name+'_estimated_mean', param.data.clone()) def _update_fisher_params(self, current_ds, batch_size, num_batch): - dl = DataLoader(ds, batch_size, shuffle=True) + dl = DataLoader(current_ds, batch_size, shuffle=True) log_liklihoods = [] for i, (input, target) in enumerate(dl): if i > num_batch: @@ -56,8 +57,8 @@ def forward_backward_update(self, input, target): loss.backward() self.optimizer.step() - def save(self): - torch.save(self.model) - + def save(self, filename): + torch.save(self.model, filename) + def load(self, filename): self.model = torch.load(filename) diff --git a/ewc_test.ipynb b/ewc_test.ipynb deleted file mode 100644 index 146d4ca..0000000 --- a/ewc_test.ipynb +++ /dev/null @@ -1,2254 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [], - "source": [ - "import torch\n", - "import torch.nn as nn\n", - "import torch.nn.functional as F\n", - "import torch.optim as optim\n", - "from torch import autograd" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "class BaseModel(nn.Module):\n", - " def __init__(self, num_ip, num_op):\n", - " super(BaseModel, self).__init__()\n", - " self.l1 = nn.Linear(num_ip, 20)\n", - " self.l2 = nn.Linear(20, num_op)\n", - " def forward(self, x):\n", - " o = self.l2(self.l1(x))\n", - " return o" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "model = BaseModel(32, 4)" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "from torch.utils.data import Dataset, DataLoader" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "class ElasticWeightConsolidation:\n", - " def __init__(self, model, crit, lr=0.001, weight=0.1):\n", - " self.model = model\n", - " self.weight = weight\n", - " self.crit = crit\n", - " self.optimizer = optim.Adam(self.model.parameters(), lr)\n", - " def _update_mean_params(self):\n", - " for param_name, param in self.model.named_parameters():\n", - " _buff_param_name = param_name.replace('.', '__')\n", - " self.model.register_buffer(_buff_param_name+'_estimated_mean', param.data.clone())\n", - " def _update_fisher_params(self, current_ds, batch_size, num_batch):\n", - " dl = DataLoader(ds, batch_size, shuffle=True)\n", - " log_liklihoods = []\n", - " for i, (input, target) in enumerate(dl):\n", - " if i > num_batch:\n", - " break\n", - " output = F.log_softmax(self.model(input), dim=1)\n", - " log_liklihoods.append(output[:, target])\n", - " log_likelihood = torch.cat(log_liklihoods).mean()\n", - " grad_log_liklihood = autograd.grad(log_likelihood, self.model.parameters())\n", - " _buff_param_names = [param[0].replace('.', '__') for param in self.model.named_parameters()]\n", - " for _buff_param_name, param in zip(_buff_param_names, grad_log_liklihood):\n", - " self.model.register_buffer(_buff_param_name+'_estimated_fisher', param.data.clone() ** 2)\n", - " def register_ewc_params(self, dataset, batch_size, num_batches):\n", - " self._update_fisher_params(dataset, batch_size, num_batches)\n", - " self._update_mean_params()\n", - " def _compute_consolidation_loss(self, weight):\n", - " try:\n", - " losses = []\n", - " for param_name, param in self.model.named_parameters():\n", - " _buff_param_name = param_name.replace('.', '__')\n", - " estimated_mean = getattr(self.model, '{}_estimated_mean'.format(_buff_param_name))\n", - " estimated_fisher = getattr(self.model, '{}_estimated_fisher'.format(_buff_param_name))\n", - " losses.append((estimated_fisher * (param - estimated_mean) ** 2).sum())\n", - " return (weight / 2) * sum(losses)\n", - " except AttributeError:\n", - " return 0 \n", - " def forward_backward_update(self, input, target):\n", - " output = self.model(input)\n", - " loss = self._compute_consolidation_loss(self.weight) + self.crit(output, target)\n", - " self.optimizer.zero_grad()\n", - " loss.backward()\n", - " self.optimizer.step()\n", - " def save(self):\n", - " torch.save(self.model)\n", - " def load(self, filename):\n", - " self.model = torch.load(filename)" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "class RandomDataset(Dataset):\n", - " def __getitem__(self, index):\n", - " input = np.random.rand(32)\n", - " output = np.random.randint(0, 3, 1)\n", - " return torch.Tensor(input).float(), torch.Tensor(output).long()\n", - " def __len__(self):\n", - " return 10000" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "ds = RandomDataset()" - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [], - "source": [ - "crit = nn.CrossEntropyLoss()" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "metadata": {}, - "outputs": [], - "source": [ - "ewc = ElasticWeightConsolidation(model, crit)" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [], - "source": [ - "dl = DataLoader(ds, batch_size=5, shuffle=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "tensor(0., grad_fn=)\n", - "tensor(3.6783e-09, grad_fn=)\n", - "tensor(1.2652e-08, grad_fn=)\n", - "tensor(2.7241e-08, grad_fn=)\n", - "tensor(3.6557e-08, grad_fn=)\n", - "tensor(4.1845e-08, grad_fn=)\n", - "tensor(5.2557e-08, grad_fn=)\n", - "tensor(6.1444e-08, grad_fn=)\n", - "tensor(5.9770e-08, grad_fn=)\n", - "tensor(5.0217e-08, grad_fn=)\n", - "tensor(4.1888e-08, grad_fn=)\n", - "tensor(4.0935e-08, grad_fn=)\n", - "tensor(3.8803e-08, grad_fn=)\n", - "tensor(3.5675e-08, grad_fn=)\n", - "tensor(3.4343e-08, grad_fn=)\n", - "tensor(3.4015e-08, grad_fn=)\n", - "tensor(3.2781e-08, grad_fn=)\n", - "tensor(3.0843e-08, grad_fn=)\n", - "tensor(2.7784e-08, grad_fn=)\n", - "tensor(3.0785e-08, grad_fn=)\n", - "tensor(4.2405e-08, 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