{ "cells": [ { "cell_type": "markdown", "source": [ "# Train resnet18 operator on mnist dataset." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import torch\n", "import matplotlib.pyplot as plt\n", "import random\n", "from resnet_image_embedding import ResnetImageEmbedding\n", "from towhee.trainer.training_config import TrainingConfig\n", "from torchvision import transforms\n", "from towhee import dataset\n", "from torchvision.transforms import Lambda\n", "\n", "# build a resnet op with 10 classes output, because the mnist has 10 classes:\n", "op = ResnetImageEmbedding('resnet18', num_classes=10)" ] }, { "cell_type": "code", "execution_count": 2, "outputs": [], "source": [ "# build a training config:\n", "training_config = TrainingConfig()\n", "training_config.batch_size = 64\n", "training_config.epoch_num = 2\n", "training_config.tensorboard = None\n", "training_config.output_dir = 'mnist_output'" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 3, "outputs": [], "source": [ "# prepare the mnist data\n", "mean = 0.1307\n", "std = 0.3081\n", "mnist_transform = transforms.Compose([transforms.ToTensor(),\n", " Lambda(lambda x: x.repeat(3, 1, 1)),\n", " transforms.Normalize(mean=[mean] * 3, std=[std] * 3)])\n", "train_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=True)\n", "eval_data = dataset('mnist', transform=mnist_transform, download=True, root='data', train=False)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "code", "execution_count": 4, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "2022-03-02 18:43:32,380 - 8612169216 - trainer.py-trainer:390 - WARNING: TrainingConfig(output_dir='mnist_output', overwrite_output_dir=True, eval_strategy='epoch', eval_steps=None, batch_size=64, val_batch_size=-1, seed=42, epoch_num=2, dataloader_pin_memory=True, dataloader_drop_last=True, dataloader_num_workers=0, lr=5e-05, metric='Accuracy', print_steps=None, load_best_model_at_end=False, early_stopping={'monitor': 'eval_epoch_metric', 'patience': 4, 'mode': 'max'}, model_checkpoint={'every_n_epoch': 1}, tensorboard=None, loss='CrossEntropyLoss', optimizer='Adam', lr_scheduler_type='linear', warmup_ratio=0.0, warmup_steps=0, device_str=None, n_gpu=-1, sync_bn=False, freeze_bn=False)\n", "[epoch 1/2] loss=0.203, metric=0.945, eval_loss=0.182, eval_metric=0.988: 100%|██████████| 937/937 [19:04<00:00, 1.22s/step]\n", "[epoch 2/2] loss=0.044, metric=0.994, eval_loss=0.045, eval_metric=0.989: 100%|██████████| 937/937 [19:08<00:00, 1.20s/step]" ] } ], "source": [ "# start to train mnist, it will take about 30-100 minutes on a cpu machine.\n", "# if you train on a gpu machine, it will be much faster.\n", "op.train(training_config, train_dataset=train_data, eval_dataset=eval_data)" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### If you observe loss is decreasing and metric is increasing, it means you are training the model correctly.\n", "### After finishing training, we can use this model to do predict." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": 12, "outputs": [ { "data": { "text/plain": "
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}, "metadata": { "needs_background": "light" }, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "this picture is number 9\n" ] } ], "source": [ "# get random picture and predict it.\n", "img_index = random.randint(0, len(eval_data))\n", "img = eval_data.dataset[img_index][0]\n", "img = img.numpy().transpose(1, 2, 0) # (C, H, W) -> (H, W, C)\n", "pil_img = img * std + mean\n", "plt.imshow(pil_img)\n", "plt.show()\n", "test_img = eval_data.dataset[img_index][0].unsqueeze(0)\n", "out = op.trainer.predict(test_img)\n", "predict_num = torch.argmax(torch.softmax(out, dim=-1)).item()\n", "print('this picture is number {}'.format(predict_num))" ], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } }, { "cell_type": "markdown", "source": [ "### You can repeat running this prediction code cell multiple times and check if the prediction result is right." ], "metadata": { "collapsed": false } }, { "cell_type": "code", "execution_count": null, "outputs": [], "source": [], "metadata": { "collapsed": false, "pycharm": { "name": "#%%\n" } } } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }