pytorch_vision_resnet.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "lightweight-electricity",
      "metadata": {
        "id": "lightweight-electricity"
      },
      "source": [
        "### This notebook is optionally accelerated with a GPU runtime.\n",
        "### If you would like to use this acceleration, please select the menu option \"Runtime\" -> \"Change runtime type\", select \"Hardware Accelerator\" -> \"GPU\" and click \"SAVE\"\n",
        "\n",
        "----------------------------------------------------------------------\n",
        "\n",
        "# ResNet\n",
        "\n",
        "*Author: Pytorch Team*\n",
        "\n",
        "**Deep residual networks pre-trained on ImageNet**\n",
        "\n",
        "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "brilliant-supplier",
      "metadata": {
        "id": "brilliant-supplier"
      },
      "outputs": [],
      "source": [
        "import torch\n",
        "model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet18', pretrained=True)\n",
        "# or any of these variants\n",
        "# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet34', pretrained=True)\n",
        "# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
        "# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet101', pretrained=True)\n",
        "# model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet152', pretrained=True)\n",
        "model.eval()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "faced-chuck",
      "metadata": {
        "id": "faced-chuck"
      },
      "source": [
        "All pre-trained models expect input images normalized in the same way,\n",
        "i.e. mini-batches of 3-channel RGB images of shape `(3 x H x W)`, where `H` and `W` are expected to be at least `224`.\n",
        "The images have to be loaded in to a range of `[0, 1]` and then normalized using `mean = [0.485, 0.456, 0.406]`\n",
        "and `std = [0.229, 0.224, 0.225]`.\n",
        "\n",
        "Here's a sample execution."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "medium-greensboro",
      "metadata": {
        "id": "medium-greensboro"
      },
      "outputs": [],
      "source": [
        "# Download an example image from the pytorch website\n",
        "import urllib\n",
        "url, filename = (\"https://github.com/pytorch/hub/raw/master/images/dog.jpg\", \"dog.jpg\")\n",
        "try: urllib.URLopener().retrieve(url, filename)\n",
        "except: urllib.request.urlretrieve(url, filename)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "other-johnston",
      "metadata": {
        "id": "other-johnston"
      },
      "outputs": [],
      "source": [
        "# sample execution (requires torchvision)\n",
        "from PIL import Image\n",
        "from torchvision import transforms\n",
        "input_image = Image.open(filename)\n",
        "preprocess = transforms.Compose([\n",
        "    transforms.Resize(256),\n",
        "    transforms.CenterCrop(224),\n",
        "    transforms.ToTensor(),\n",
        "    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),\n",
        "])\n",
        "input_tensor = preprocess(input_image)\n",
        "input_batch = input_tensor.unsqueeze(0) # create a mini-batch as expected by the model\n",
        "\n",
        "# move the input and model to GPU for speed if available\n",
        "if torch.cuda.is_available():\n",
        "    input_batch = input_batch.to('cuda')\n",
        "    model.to('cuda')\n",
        "\n",
        "with torch.no_grad():\n",
        "    output = model(input_batch)\n",
        "# Tensor of shape 1000, with confidence scores over Imagenet's 1000 classes\n",
        "print(output[0])\n",
        "# The output has unnormalized scores. To get probabilities, you can run a softmax on it.\n",
        "probabilities = torch.nn.functional.softmax(output[0], dim=0)\n",
        "print(probabilities)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "front-bearing",
      "metadata": {
        "id": "front-bearing"
      },
      "outputs": [],
      "source": [
        "# Download ImageNet labels\n",
        "!wget https://raw.githubusercontent.com/pytorch/hub/master/imagenet_classes.txt"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "occupational-seller",
      "metadata": {
        "id": "occupational-seller"
      },
      "outputs": [],
      "source": [
        "# Read the categories\n",
        "with open(\"imagenet_classes.txt\", \"r\") as f:\n",
        "    categories = [s.strip() for s in f.readlines()]\n",
        "# Show top categories per image\n",
        "top5_prob, top5_catid = torch.topk(probabilities, 5)\n",
        "for i in range(top5_prob.size(0)):\n",
        "    print(categories[top5_catid[i]], top5_prob[i].item())"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "studied-honey",
      "metadata": {
        "id": "studied-honey"
      },
      "source": [
        "### Model Description\n",
        "\n",
        "Resnet models were proposed in \"Deep Residual Learning for Image Recognition\".\n",
        "Here we have the 5 versions of resnet models, which contains 18, 34, 50, 101, 152 layers respectively.\n",
        "Detailed model architectures can be found in Table 1.\n",
        "Their 1-crop error rates on imagenet dataset with pretrained models are listed below.\n",
        "\n",
        "| Model structure | Top-1 error | Top-5 error |\n",
        "| --------------- | ----------- | ----------- |\n",
        "|  resnet18       | 30.24       | 10.92       |\n",
        "|  resnet34       | 26.70       | 8.58        |\n",
        "|  resnet50       | 23.85       | 7.13        |\n",
        "|  resnet101      | 22.63       | 6.44        |\n",
        "|  resnet152      | 21.69       | 5.94        |\n",
        "\n",
        "### References\n",
        "\n",
        " - [Deep Residual Learning for Image Recognition](https://arxiv.org/abs/1512.03385)"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "name": "pytorch_vision_resnet.ipynb",
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}