{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Copyright 2019 NVIDIA Corporation. All Rights Reserved.\n",
    "#\n",
    "# Licensed under the Apache License, Version 2.0 (the \"License\");\n",
    "# you may not use this file except in compliance with the License.\n",
    "# You may obtain a copy of the License at\n",
    "#\n",
    "#     http://www.apache.org/licenses/LICENSE-2.0\n",
    "#\n",
    "# Unless required by applicable law or agreed to in writing, software\n",
    "# distributed under the License is distributed on an \"AS IS\" BASIS,\n",
    "# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
    "# See the License for the specific language governing permissions and\n",
    "# limitations under the License.\n",
    "# =============================================================================="
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<img src=\"https://developer.download.nvidia.com/tesla/notebook_assets/nv_logo_torch_trt_resnet_notebook.png\" style=\"width: 90px; float: right;\">\n",
    "\n",
    "# Torch-TensorRT Getting Started - ResNet 50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Overview\n",
    "\n",
    "In the practice of developing machine learning models, there are few tools as approachable as PyTorch for developing and experimenting in designing machine learning models. The power of PyTorch comes from its deep integration into Python, its flexibility and its approach to automatic differentiation and execution (eager execution). However, when moving from research into production, the requirements change and we may no longer want that deep Python integration and we want optimization to get the best performance we can on our deployment platform. In PyTorch 1.0, TorchScript was introduced as a method to separate your PyTorch model from Python, make it portable and optimizable. TorchScript uses PyTorch's JIT compiler to transform your normal PyTorch code which gets interpreted by the Python interpreter to an intermediate representation (IR) which can have optimizations run on it and at runtime can get interpreted by the PyTorch JIT interpreter. For PyTorch this has opened up a whole new world of possibilities, including deployment in other languages like C++. It also introduces a structured graph based format that we can use to do down to the kernel level optimization of models for inference.\n",
    "\n",
    "When deploying on NVIDIA GPUs TensorRT, NVIDIA's Deep Learning Optimization SDK and Runtime is able to take models from any major framework and specifically tune them to perform better on specific target hardware in the NVIDIA family be it an A100, TITAN V, Jetson Xavier or NVIDIA's Deep Learning Accelerator. TensorRT performs a couple sets of optimizations to achieve this. TensorRT fuses layers and tensors in the model graph, it then uses a large kernel library to select implementations that perform best on the target GPU. TensorRT also has strong support for reduced operating precision execution which allows users to leverage the Tensor Cores on Volta and newer GPUs as well as reducing memory and computation footprints on device.\n",
    "\n",
    "Torch-TensorRT is a compiler that uses TensorRT to optimize TorchScript code, compiling standard TorchScript modules into ones that internally run with TensorRT optimizations. This enables you to continue to remain in the PyTorch ecosystem, using all the great features PyTorch has such as module composability, its flexible tensor implementation, data loaders and more. Torch-TensorRT is available to use with both PyTorch and LibTorch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Learning objectives\n",
    "\n",
    "This notebook demonstrates the steps for compiling a TorchScript module with Torch-TensorRT on a pretrained ResNet-50 network, and running it to test the speedup obtained.\n",
    "\n",
    "## Content\n",
    "1. [Requirements](#1)\n",
    "1. [ResNet-50 Overview](#2)\n",
    "1. [Running the model without optimizations](#3)\n",
    "1. [Accelerating with Torch-TensorRT](#4)\n",
    "1. [Conclusion](#5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Feb  8 19:16:53 2022       \n",
      "+-----------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 510.39.01    Driver Version: 510.39.01    CUDA Version: 11.6     |\n",
      "|-------------------------------+----------------------+----------------------+\n",
      "| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |\n",
      "|                               |                      |               MIG M. |\n",
      "|===============================+======================+======================|\n",
      "|   0  NVIDIA GeForce ...  On   | 00000000:65:00.0 Off |                  N/A |\n",
      "| 30%   29C    P8    15W / 350W |      0MiB / 24576MiB |      0%      Default |\n",
      "|                               |                      |                  N/A |\n",
      "+-------------------------------+----------------------+----------------------+\n",
      "                                                                               \n",
      "+-----------------------------------------------------------------------------+\n",
      "| Processes:                                                                  |\n",
      "|  GPU   GI   CI        PID   Type   Process name                  GPU Memory |\n",
      "|        ID   ID                                                   Usage      |\n",
      "|=============================================================================|\n",
      "|  No running processes found                                                 |\n",
      "+-----------------------------------------------------------------------------+\n",
      "Looking in indexes: https://pypi.org/simple, https://pypi.ngc.nvidia.com\n",
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     ]
    },
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      "Installing collected packages: widgetsnbextension, jupyterlab-widgets, ipywidgets\n",
      "Successfully installed ipywidgets-7.6.5 jupyterlab-widgets-1.0.2 widgetsnbextension-3.5.2\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi\n",
    "!pip install ipywidgets --trusted-host pypi.org --trusted-host pypi.python.org --trusted-host=files.pythonhosted.org"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"1\"></a>\n",
    "## 1. Requirements\n",
    "\n",
    "NVIDIA's NGC provides PyTorch Docker Container which contains PyTorch and Torch-TensorRT. We can make use of [latest pytorch](https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch) container to run this notebook.\n",
    "\n",
    "Otherwise, you can follow the steps in `notebooks/README` to prepare a Docker container yourself, within which you can run this demo notebook."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"2\"></a>\n",
    "## 2. ResNet-50 Overview\n",
    "\n",
    "\n",
    "PyTorch has a model repository called the PyTorch Hub, which is a source for high quality implementations of common models. We can get our ResNet-50 model from there pretrained on ImageNet.\n",
    "\n",
    "### Model Description\n",
    "\n",
    "This ResNet-50 model is based on the [Deep Residual Learning for Image Recognition](https://arxiv.org/pdf/1512.03385.pdf) paper, which describes ResNet as “a method for detecting objects in images using a single deep neural network\". The input size is fixed to 32x32.\n",
    "\n",
    "<img src=\"https://pytorch.org/assets/images/resnet.png\" alt=\"alt\" width=\"50%\"/>\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"3\"></a>\n",
    "## 3. Running the model without optimizations\n",
    "\n",
    "\n",
    "PyTorch has a model repository called `timm`, which is a source for high quality implementations of computer vision models. We can get our EfficientNet model from there pretrained on ImageNet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading: \"https://github.com/pytorch/vision/archive/v0.10.0.zip\" to /root/.cache/torch/hub/v0.10.0.zip\n",
      "Downloading: \"https://download.pytorch.org/models/resnet50-0676ba61.pth\" to /root/.cache/torch/hub/checkpoints/resnet50-0676ba61.pth\n"
     ]
    },
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     "data": {
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       "model_id": "a7036a6122874340b14aaef7b8319260",
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     "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): Bottleneck(\n",
       "      (conv1): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "        (1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer2): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=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): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer3): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (3): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (4): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (5): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(256, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(1024, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (layer4): Sequential(\n",
       "    (0): Bottleneck(\n",
       "      (conv1): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv2): Conv2d(512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False)\n",
       "      (bn2): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "      (downsample): Sequential(\n",
       "        (0): Conv2d(1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False)\n",
       "        (1): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      )\n",
       "    )\n",
       "    (1): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "    (2): Bottleneck(\n",
       "      (conv1): Conv2d(2048, 512, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn1): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=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",
       "      (conv3): Conv2d(512, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False)\n",
       "      (bn3): BatchNorm2d(2048, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)\n",
       "      (relu): ReLU(inplace=True)\n",
       "    )\n",
       "  )\n",
       "  (avgpool): AdaptiveAvgPool2d(output_size=(1, 1))\n",
       "  (fc): Linear(in_features=2048, out_features=1000, bias=True)\n",
       ")"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import torch\n",
    "import torchvision\n",
    "\n",
    "torch.hub._validate_not_a_forked_repo=lambda a,b,c: True\n",
    "\n",
    "resnet50_model = torch.hub.load('pytorch/vision:v0.10.0', 'resnet50', pretrained=True)\n",
    "resnet50_model.eval()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With our model loaded, let's proceed to downloading some images!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--2022-02-08 19:17:19--  https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\n",
      "Resolving d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)... 18.65.227.99, 18.65.227.37, 18.65.227.223, ...\n",
      "Connecting to d17fnq9dkz9hgj.cloudfront.net (d17fnq9dkz9hgj.cloudfront.net)|18.65.227.99|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 24112 (24K) [image/jpeg]\n",
      "Saving to: ‘./data/img0.JPG’\n",
      "\n",
      "./data/img0.JPG     100%[===================>]  23.55K  --.-KB/s    in 0.002s  \n",
      "\n",
      "2022-02-08 19:17:19 (14.6 MB/s) - ‘./data/img0.JPG’ saved [24112/24112]\n",
      "\n",
      "--2022-02-08 19:17:19--  https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\n",
      "Resolving www.hakaimagazine.com (www.hakaimagazine.com)... 164.92.73.117\n",
      "Connecting to www.hakaimagazine.com (www.hakaimagazine.com)|164.92.73.117|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 452718 (442K) [image/jpeg]\n",
      "Saving to: ‘./data/img1.JPG’\n",
      "\n",
      "./data/img1.JPG     100%[===================>] 442.11K  2.40MB/s    in 0.2s    \n",
      "\n",
      "2022-02-08 19:17:19 (2.40 MB/s) - ‘./data/img1.JPG’ saved [452718/452718]\n",
      "\n",
      "--2022-02-08 19:17:20--  https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\n",
      "Resolving www.artis.nl (www.artis.nl)... 94.75.225.20\n",
      "Connecting to www.artis.nl (www.artis.nl)|94.75.225.20|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 361413 (353K) [image/jpeg]\n",
      "Saving to: ‘./data/img2.JPG’\n",
      "\n",
      "./data/img2.JPG     100%[===================>] 352.94K   366KB/s    in 1.0s    \n",
      "\n",
      "2022-02-08 19:17:24 (366 KB/s) - ‘./data/img2.JPG’ saved [361413/361413]\n",
      "\n",
      "--2022-02-08 19:17:24--  https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\n",
      "Resolving www.familyhandyman.com (www.familyhandyman.com)... 104.18.201.107, 104.18.202.107, 2606:4700::6812:ca6b, ...\n",
      "Connecting to www.familyhandyman.com (www.familyhandyman.com)|104.18.201.107|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 94328 (92K) [image/jpeg]\n",
      "Saving to: ‘./data/img3.JPG’\n",
      "\n",
      "./data/img3.JPG     100%[===================>]  92.12K  --.-KB/s    in 0.005s  \n",
      "\n",
      "2022-02-08 19:17:25 (16.8 MB/s) - ‘./data/img3.JPG’ saved [94328/94328]\n",
      "\n",
      "--2022-02-08 19:17:25--  https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\n",
      "Resolving s3.amazonaws.com (s3.amazonaws.com)... 52.217.41.222\n",
      "Connecting to s3.amazonaws.com (s3.amazonaws.com)|52.217.41.222|:443... connected.\n",
      "HTTP request sent, awaiting response... 200 OK\n",
      "Length: 35363 (35K) [application/octet-stream]\n",
      "Saving to: ‘./data/imagenet_class_index.json’\n",
      "\n",
      "./data/imagenet_cla 100%[===================>]  34.53K  --.-KB/s    in 0.07s   \n",
      "\n",
      "2022-02-08 19:17:26 (481 KB/s) - ‘./data/imagenet_class_index.json’ saved [35363/35363]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!mkdir -p ./data\n",
    "!wget  -O ./data/img0.JPG \"https://d17fnq9dkz9hgj.cloudfront.net/breed-uploads/2018/08/siberian-husky-detail.jpg?bust=1535566590&width=630\"\n",
    "!wget  -O ./data/img1.JPG \"https://www.hakaimagazine.com/wp-content/uploads/header-gulf-birds.jpg\"\n",
    "!wget  -O ./data/img2.JPG \"https://www.artis.nl/media/filer_public_thumbnails/filer_public/00/f1/00f1b6db-fbed-4fef-9ab0-84e944ff11f8/chimpansee_amber_r_1920x1080.jpg__1920x1080_q85_subject_location-923%2C365_subsampling-2.jpg\"\n",
    "!wget  -O ./data/img3.JPG \"https://www.familyhandyman.com/wp-content/uploads/2018/09/How-to-Avoid-Snakes-Slithering-Up-Your-Toilet-shutterstock_780480850.jpg\"\n",
    "\n",
    "!wget  -O ./data/imagenet_class_index.json \"https://s3.amazonaws.com/deep-learning-models/image-models/imagenet_class_index.json\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "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": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from PIL import Image\n",
    "from torchvision import transforms\n",
    "import matplotlib.pyplot as plt\n",
    "import json \n",
    "\n",
    "fig, axes = plt.subplots(nrows=2, ncols=2)\n",
    "\n",
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\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(img)      \n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img)\n",
    "    plt.axis('off')\n",
    "\n",
    "# loading labels    \n",
    "with open(\"./data/imagenet_class_index.json\") as json_file: \n",
    "    d = json.load(json_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Throughout this tutorial, we will be making use of some utility functions; `rn50_preprocess` for preprocessing input images, `predict` to use the model for prediction and `benchmark` to benchmark the inference. You do not need to understand/go through these utilities to make use of Torch TensorRT, but are welecomed to do so if you choose."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import time\n",
    "import torch.backends.cudnn as cudnn\n",
    "cudnn.benchmark = True\n",
    "\n",
    "def rn50_preprocess():\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",
    "    return preprocess\n",
    "\n",
    "# decode the results into ([predicted class, description], probability)\n",
    "def predict(img_path, model):\n",
    "    img = Image.open(img_path)\n",
    "    preprocess = rn50_preprocess()\n",
    "    input_tensor = preprocess(img)\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",
    "        sm_output = torch.nn.functional.softmax(output[0], dim=0)\n",
    "        \n",
    "    ind = torch.argmax(sm_output)\n",
    "    return d[str(ind.item())], sm_output[ind] #([predicted class, description], probability)\n",
    "\n",
    "def benchmark(model, input_shape=(1024, 1, 224, 224), dtype='fp32', nwarmup=50, nruns=10000):\n",
    "    input_data = torch.randn(input_shape)\n",
    "    input_data = input_data.to(\"cuda\")\n",
    "    if dtype=='fp16':\n",
    "        input_data = input_data.half()\n",
    "        \n",
    "    print(\"Warm up ...\")\n",
    "    with torch.no_grad():\n",
    "        for _ in range(nwarmup):\n",
    "            features = model(input_data)\n",
    "    torch.cuda.synchronize()\n",
    "    print(\"Start timing ...\")\n",
    "    timings = []\n",
    "    with torch.no_grad():\n",
    "        for i in range(1, nruns+1):\n",
    "            start_time = time.time()\n",
    "            features = model(input_data)\n",
    "            torch.cuda.synchronize()\n",
    "            end_time = time.time()\n",
    "            timings.append(end_time - start_time)\n",
    "            if i%10==0:\n",
    "                print('Iteration %d/%d, ave batch time %.2f ms'%(i, nruns, np.mean(timings)*1000))\n",
    "\n",
    "    print(\"Input shape:\", input_data.size())\n",
    "    print(\"Output features size:\", features.size())\n",
    "    print('Average batch time: %.2f ms'%(np.mean(timings)*1000))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the model downloaded and the util functions written, let's just quickly see some predictions, and benchmark the model in its current un-optimized state."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "./data/img0.JPG - Predicted: ['n02110185', 'Siberian_husky'], Probablility: 0.49785590171813965\n",
      "./data/img1.JPG - Predicted: ['n01820546', 'lorikeet'], Probablility: 0.6445754766464233\n",
      "./data/img2.JPG - Predicted: ['n02481823', 'chimpanzee'], Probablility: 0.9899807572364807\n",
      "./data/img3.JPG - Predicted: ['n01749939', 'green_mamba'], Probablility: 0.39485082030296326\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in range(4):\n",
    "    img_path = './data/img%d.JPG'%i\n",
    "    img = Image.open(img_path)\n",
    "    \n",
    "    pred, prob = predict(img_path, resnet50_model)\n",
    "    print('{} - Predicted: {}, Probablility: {}'.format(img_path, pred, prob))\n",
    "\n",
    "    plt.subplot(2,2,i+1)\n",
    "    plt.imshow(img);\n",
    "    plt.axis('off');\n",
    "    plt.title(pred[1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 75.34 ms\n",
      "Iteration 20/100, ave batch time 75.33 ms\n",
      "Iteration 30/100, ave batch time 75.35 ms\n",
      "Iteration 40/100, ave batch time 75.37 ms\n",
      "Iteration 50/100, ave batch time 75.38 ms\n",
      "Iteration 60/100, ave batch time 75.38 ms\n",
      "Iteration 70/100, ave batch time 75.39 ms\n",
      "Iteration 80/100, ave batch time 75.39 ms\n",
      "Iteration 90/100, ave batch time 75.40 ms\n",
      "Iteration 100/100, ave batch time 75.41 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 75.41 ms\n"
     ]
    }
   ],
   "source": [
    "# Model benchmark without Torch-TensorRT\n",
    "model = resnet50_model.eval().to(\"cuda\")\n",
    "benchmark(model, input_shape=(128, 3, 224, 224), nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"4\"></a>\n",
    "## 4. Accelerating with Torch-TensorRT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Onwards to the next step, accelerating with Torch TensorRT. In these examples we showcase the results for FP32 (single precision) and FP16 (half precision). We do not demonstrat specific tuning, just showcase the simplicity of usage. If you want to learn more about the possible customizations, visit our [documentation](https://nvidia.github.io/Torch-TensorRT/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP32 (single precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
     ]
    }
   ],
   "source": [
    "import torch_tensorrt\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP32 precision.\n",
    "trt_model_fp32 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.float32)],\n",
    "    enabled_precisions = torch.float32, # Run with FP32\n",
    "    workspace_size = 1 << 22\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 41.02 ms\n",
      "Iteration 20/100, ave batch time 41.12 ms\n",
      "Iteration 30/100, ave batch time 41.22 ms\n",
      "Iteration 40/100, ave batch time 41.14 ms\n",
      "Iteration 50/100, ave batch time 41.20 ms\n",
      "Iteration 60/100, ave batch time 41.20 ms\n",
      "Iteration 70/100, ave batch time 41.19 ms\n",
      "Iteration 80/100, ave batch time 41.23 ms\n",
      "Iteration 90/100, ave batch time 41.20 ms\n",
      "Iteration 100/100, ave batch time 41.21 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 41.21 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model_fp32, input_shape=(128, 3, 224, 224), nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### FP16 (half precision)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: [Torch-TensorRT] - For input x.1, found user specified input dtype as Float16, however when inspecting the graph, the input type expected was inferred to be Float\n",
      "The compiler is going to use the user setting Float16\n",
      "This conflict may cause an error at runtime due to partial compilation being enabled and therefore\n",
      "compatibility with PyTorch's data type convention is required.\n",
      "If you do indeed see errors at runtime either:\n",
      "- Remove the dtype spec for x.1\n",
      "- Disable partial compilation by setting require_full_compilation to True\n",
      "WARNING: [Torch-TensorRT] - Dilation not used in Max pooling converter\n"
     ]
    }
   ],
   "source": [
    "import torch_tensorrt\n",
    "\n",
    "# The compiled module will have precision as specified by \"op_precision\".\n",
    "# Here, it will have FP16 precision.\n",
    "trt_model_fp16 = torch_tensorrt.compile(model, inputs = [torch_tensorrt.Input((128, 3, 224, 224), dtype=torch.half)],\n",
    "    enabled_precisions = {torch.half}, # Run with FP32\n",
    "    workspace_size = 1 << 22\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Warm up ...\n",
      "Start timing ...\n",
      "Iteration 10/100, ave batch time 14.48 ms\n",
      "Iteration 20/100, ave batch time 14.58 ms\n",
      "Iteration 30/100, ave batch time 14.72 ms\n",
      "Iteration 40/100, ave batch time 14.73 ms\n",
      "Iteration 50/100, ave batch time 14.70 ms\n",
      "Iteration 60/100, ave batch time 14.79 ms\n",
      "Iteration 70/100, ave batch time 14.73 ms\n",
      "Iteration 80/100, ave batch time 14.69 ms\n",
      "Iteration 90/100, ave batch time 14.68 ms\n",
      "Iteration 100/100, ave batch time 14.69 ms\n",
      "Input shape: torch.Size([128, 3, 224, 224])\n",
      "Output features size: torch.Size([128, 1000])\n",
      "Average batch time: 14.69 ms\n"
     ]
    }
   ],
   "source": [
    "# Obtain the average time taken by a batch of input\n",
    "benchmark(trt_model_fp16, input_shape=(128, 3, 224, 224), dtype='fp16', nruns=100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<a id=\"5\"></a>\n",
    "## 5. Conclusion\n",
    "\n",
    "In this notebook, we have walked through the complete process of compiling TorchScript models with Torch-TensorRT for EfficientNet-B0 model and test the performance impact of the optimization. With Torch-TensorRT, we observe a speedup of **1.84x** with FP32, and **5.2x** with FP16 on an NVIDIA 3090 GPU. These acceleration numbers will vary from GPU to GPU(as well as implementation to implementation based on the ops used) and we encorage you to try out latest generation of Data center compute cards for maximum acceleration.\n",
    "\n",
    "### What's next\n",
    "Now it's time to try Torch-TensorRT on your own model. If you run into any issues, you can fill them at https://github.com/NVIDIA/Torch-TensorRT. Your involvement will help future development of Torch-TensorRT.\n"
   ]
  }
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