{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "![MLU Logo](../../data/MLU_Logo.png)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Machine Learning Accelerator - Computer Vision - Lecture 3\n", "\n", "\n", "## Inference with Pre-trained ResNet Model\n", "\n", "In this notebook, we use pre-trained [ResNet](https://d2l.ai/chapter_convolutional-modern/resnet.html) with only a few lines of code.\n", "\n", "1. Downloading a Pretrained Model \n", "2. Preprocessing an Image\n", "3. Using ResNet50 for Inference\n", " " ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\u001b[33mWARNING: You are using pip version 21.2.4; however, version 21.3.1 is available.\r\n", "You should consider upgrading via the '/home/ec2-user/anaconda3/envs/pytorch_p36/bin/python -m pip install --upgrade pip' command.\u001b[0m\r\n" ] } ], "source": [ "! pip install -q -r ../../requirements.txt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's import the necessary libraries:" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "from PIL import Image\n", "from matplotlib.image import imread\n", "import torch\n", "import torchvision\n", "from torchvision import transforms" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 1. Downloading a Pretrained Model\n", "(Go to top)\n", "\n", "With torchvision, we will start with a ResNet 50 neural net trained on ImageNet dataset as our base model. By specifying\n", "`pretrained=True`, it will automatically download the model from the model\n", "zoo if necessary. For more pretrained models, please refer to [torchvision Model Zoo](https://pytorch.org/docs/stable/torchvision/models.html)." ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /home/ec2-user/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n" ] }, { "data": { "application/vnd.jupyter.widget-view+json": { "model_id": "493ca843e1da427fb8fd82b9968e0e73", "version_major": 2, "version_minor": 0 }, "text/plain": [ " 0%| | 0.00/97.8M [00:00Pre-processing an Image\n", "(Go to top)\n", "\n", "Next we read a sample image, and pre-process it with preset data transforms `transform_eval`." ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).\n" ] }, { "data": { "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "# im1 = imread(train_path + \"/sw/0.jpg\")\n", "# im2 = imread(train_path + \"/vg/0.jpg\")\n", "\n", "img_raw = Image.open('../../data/catdog.png').convert('RGB')\n", "transform_eval = transforms.Compose([\n", " transforms.Resize(256),\n", " transforms.CenterCrop(224),\n", " transforms.ToTensor(),\n", " transforms.Normalize(mean=[0.485, 0.456, 0.406],\n", " std=[0.229, 0.224, 0.225])\n", "])\n", "\n", "img = transform_eval(img_raw)\n", "plt.imshow(img.permute(1,2,0).numpy())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 3. Inference Using ResNet50\n", "(Go to top)\n", "\n", "Now let's generate the predictions from the pretrained ResNet50.\n", "`pred` will be a list of ndarray, where each ndarray is of length 1000.\n", "Each number of this 1000-length ndarray can be applied `softmax` to \n", "represent the prediction confidence towards each of the [subject classes in ImageNet](http://image-net.org/explore)." ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/functional.py:718: UserWarning: Named tensors and all their associated APIs are an experimental feature and subject to change. Please do not use them for anything important until they are released as stable. (Triggered internally at /pytorch/c10/core/TensorImpl.h:1156.)\n", " return torch.max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode)\n" ] }, { "data": { "text/plain": [ "1000" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "net.eval()\n", "pred = net(img.unsqueeze(0))\n", "len(pred[0])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To access all the existing classes of ImageNet, pytorch doesn't offer an in-built function. So we use json to load an external list which has a mapping for the ImageNet classes to the indices. Let's take a look of the first 5 classes!" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "['tench', 'goldfish', 'great white shark', 'tiger shark', 'hammerhead']" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import json\n", "\n", "with open(\"../../data/imagenet_idx_to_class.json\", \"r\") as f:\n", " classes = json.load(f)\n", " \n", "classes[:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's see how does the model think of our input test image?!" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "The input picture is classified to be\n", "\t[Chihuahua], with probability 0.399.\n", "\t[tabby], with probability 0.085.\n", "\t[Egyptian cat], with probability 0.085.\n", "\t[Brabancon griffon], with probability 0.081.\n", "\t[pug], with probability 0.023.\n" ] } ], "source": [ "topK = 5\n", "conf, ind = torch.topk(pred.squeeze(0), k=topK)\n", "ind = ind.squeeze(0).numpy()\n", "print('The input picture is classified to be')\n", "for i in range(topK):\n", " print('\\t[%s], with probability %.3f.'%\n", " (classes[ind[i]], torch.softmax(pred.squeeze(0), dim=0)[ind[i]].item()))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Feel free to download and try other ResNet versions (ResNet18, ResNet101, ResNet152, etc.) in your own experiment. What is more, try to fineture on other datasets to see if you can improve the model performance." ] } ], "metadata": { "kernelspec": { "display_name": "conda_pytorch_p36", "language": "python", "name": "conda_pytorch_p36" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.13" } }, "nbformat": 4, "nbformat_minor": 1 }