pathology/tumor_detection/ignite/profiling_camelyon_pipeline.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Copyright (c) MONAI Consortium  \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",
    "&nbsp;&nbsp;&nbsp;&nbsp;http://www.apache.org/licenses/LICENSE-2.0  \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."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NVIDIA Nsight Systems\n",
    "[NVIDIA Nsight™ Systems](https://developer.nvidia.com/nsight-systems) is a system-wide performance analysis tool designed to visualize an application’s algorithms, help to identify the largest opportunities to optimize, and tune to scale efficiently across any quantity or size of CPUs and GPUs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NVIDIA Tools Extension (NVTX)\n",
    "\n",
    "The [NVIDIA® Tools Extension Library (NVTX)](https://github.com/NVIDIA/NVTX) is a powerful mechanism that allows users to manually instrument their application. With a C-based and a python-based Application Programming Interface (API) for annotating events, code ranges, and resources in your applications. Applications which integrate NVTX can use NVIDIA Nsight, Tegra System Profiler, and Visual Profiler to capture and visualize these events and ranges. In general, the NVTX can bring valuable insignt into the application while incurring almost no overhead."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "## MONAI Transforms and NVTX\n",
    "[MONAI](https://github.com/Project-MONAI/MONAI) is a high level framework for deep learning in healthcare imaging. One of its core concepts is [Transforms](https://github.com/Project-MONAI/MONAI/tree/dev/monai/transforms), similar to [TorchVision Transfoms](https://pytorch.org/vision/stable/transforms.html). Several of these transfoms are usualy chained together, using a [Compose](https://github.com/Project-MONAI/MONAI/blob/2f1c7a5d1b47c8dd21681dbe1b67213aa3278cd7/monai/transforms/compose.py#L35) class, to create a preprocessing or postprocessing pipeline that performs manipulation of the input data and make it suitable for training a deep learning model or inference. Using this chain of tranformaion does not allow for insertion of NVTX annotation directly in the code and may require modification of the source code. On the other hand, [MONAI NVTX Tranforms]() are a flexible way to insert the annotations in the chain of transforms by simplly adding additional tranforms that cause vitually no-overhead and does not alter the code.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup environment "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "!python -c \"import monai\" || pip install -q \"monai-weekly[gdown,ignite,torchvision,cucim]\"\n",
    "!python -c \"import ignite\" || pip install -q pytorch-ignite\n",
    "!python -c \"import torchvision\" || pip install -q torchvision\n",
    "!python -c \"import cucim\" || pip install -q cucim\n",
    "!python -c \"import pandas\" || pip install -q pandas\n",
    "!python -c \"import matplotlib\" || pip install -q matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup imports "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "\n",
    "import gdown\n",
    "import pandas as pd\n",
    "from monai.config import print_config\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "print_config()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Download data\n",
    "\n",
    "The pipeline that we are profiling `camelyon_train_evaluate_nvtx_profiling.py` required [Camelyon-16 Challenge](https://camelyon16.grand-challenge.org/) dataset.  You can download all the images for \"CAMELYON16\" data set from sources listed [here](https://camelyon17.grand-challenge.org/Data/). Also you can find the coordinations and labels for training (`training.csv`) [here](https://drive.google.com/file/d/1httIjgji6U6rMIb0P8pE0F-hXFAuvQEf/view?usp=sharing) and for validation (`validation.csv`) [here](https://drive.google.com/file/d/1tJulzl9m5LUm16IeFbOCoFnaSWoB6i5L/view?usp=sharing).\n",
    "\n",
    "However, for the demo of this notebook, we are downloading a very small subset of Camelyon dataset, which uses only one whole slide image `tumor_091.tif` .\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1uWS4CXKD-NP_6-SgiQbQfhFMzbs0UJIr\n",
      "To: /Users/bhashemian/workspace/tutorials/pathology/tumor_detection/ignite/training.csv\n",
      "100%|██████████| 153k/153k [00:00<00:00, 1.91MB/s]\n",
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1OxAeCMVqH9FGpIWpAXSEJe6cLinEGQtF\n",
      "To: /Users/bhashemian/workspace/tutorials/pathology/tumor_detection/ignite/training/images/tumor_091.tif\n",
      "100%|██████████| 546M/546M [00:22<00:00, 24.1MB/s] \n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'training/images/tumor_091.tif'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Download training.csv\n",
    "dataset_url = \"https://drive.google.com/uc?id=1uWS4CXKD-NP_6-SgiQbQfhFMzbs0UJIr\"\n",
    "dataset_path = \"training.csv\"\n",
    "gdown.download(dataset_url, dataset_path, quiet=False)\n",
    "\n",
    "# Download images\n",
    "# by default the images expect to be under training/images/\n",
    "image_dir = os.path.join(\"training\", \"images\", \"\")\n",
    "if not os.path.exists(image_dir):\n",
    "    os.makedirs(image_dir)\n",
    "image_url = \"https://drive.google.com/uc?id=1OxAeCMVqH9FGpIWpAXSEJe6cLinEGQtF\"\n",
    "gdown.download(image_url, image_dir, quiet=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Profiling Digital Pathology Tumor Detection Pipeline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!nsys profile \\\n",
    "    --trace nvtx,osrt,cudnn,cuda, \\\n",
    "    --delay 15 \\\n",
    "    --duration 60 \\\n",
    "    --show-output true \\\n",
    "    --force-overwrite true \\\n",
    "    --output profile_report.nsys-rep \\\n",
    "    python camelyon_train_evaluate_nvtx_profiling.py \\\n",
    "        --cpu 0 \\\n",
    "        --train-file ./training.csv \\\n",
    "        --valid-file ./training.csv \\\n",
    "        --root ./ \\\n",
    "        --bs 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Generating SQLite file profile_report.sqlite from profile_report.nsys-rep\n",
      "Exporting 265495 events: [=================================================100%]\n",
      "Using profile_report.sqlite for SQL queries.\n",
      "Running [/usr/local/cuda-11.6/NsightSystems-cli-2021.5.2/target-linux-x64/reports/nvtxppsum.py profile_report.sqlite]... \n",
      "\n",
      "+----------+-----------------+-----------+--------------+--------------+------------+-------------+--------------+--------------------------+\n",
      "| Time (%) | Total Time (ns) | Instances |   Avg (ns)   |   Med (ns)   |  Min (ns)  |  Max (ns)   | StdDev (ns)  |          Range           |\n",
      "+----------+-----------------+-----------+--------------+--------------+------------+-------------+--------------+--------------------------+\n",
      "|     28.7 |     33706579200 |         5 | 6741315840.0 | 6324451800.0 |  176995000 | 13682363400 | 4889241407.0 | Iteration                |\n",
      "|     21.3 |     25011936300 |         5 | 5002387260.0 | 4787481900.0 | 2873517700 |  8072929200 | 2035498721.7 | Batch                    |\n",
      "|     20.3 |     23839370600 |        50 |  476787412.0 |  376589400.0 |  220633100 |  1154097400 |  276441893.0 | Preprocessing            |\n",
      "|     19.3 |     22742525900 |       450 |   50538946.4 |   36570950.0 |   18874200 |   202062000 |   36166736.1 | TorchVisiond_ColorJitter |\n",
      "|      9.4 |     11044461700 |         5 | 2208892340.0 | 1996530400.0 |  148099300 |  4407900900 | 1534918799.1 | ResNet18                 |\n",
      "|      0.3 |       384269900 |       450 |     853933.1 |      65400.0 |      21000 |    22487800 |    2212634.3 | RandZoomd                |\n",
      "|      0.2 |       244892100 |       450 |     544204.7 |     441950.0 |     321800 |     8677700 |     541248.4 | ScaleIntensityRanged     |\n",
      "|      0.1 |       128083500 |       450 |     284630.0 |     243900.0 |     187400 |     4721600 |     230932.6 | Postprocessing           |\n",
      "|      0.1 |        91848800 |       450 |     204108.4 |     176450.0 |     128700 |      745700 |      87187.5 | ToNumpyd                 |\n",
      "|      0.1 |        65417500 |       450 |     145372.2 |     117000.0 |      90200 |     4613300 |     219185.3 | AsDiscreted              |\n",
      "|      0.1 |        59017500 |       450 |     131150.0 |      82250.0 |      17400 |     1050600 |     123950.6 | RandRotate90d            |\n",
      "|      0.0 |        55917100 |        50 |    1118342.0 |     882450.0 |     685900 |     5798000 |     801880.3 | GridSplitd               |\n",
      "|      0.0 |        54120900 |       450 |     120268.7 |     100700.0 |      68500 |      721200 |      59828.4 | ToTensord                |\n",
      "|      0.0 |        51677300 |       450 |     114838.4 |     101350.0 |      67500 |     1154000 |      60160.7 | CastToTyped              |\n",
      "|      0.0 |        50674700 |       450 |     112610.4 |      95650.0 |      71300 |      613700 |      51643.2 | ToTensord_2              |\n",
      "|      0.0 |        48966300 |       450 |     108814.0 |      95900.0 |      75200 |      428700 |      44791.2 | Activationsd             |\n",
      "|      0.0 |        39524100 |       450 |      87831.3 |      45950.0 |       7600 |     2748400 |     153536.8 | RandFlipd                |\n",
      "|      0.0 |         2460800 |        50 |      49216.0 |      39450.0 |      28700 |      146100 |      23219.6 | Lambdad                  |\n",
      "|      0.0 |         1074200 |         4 |     268550.0 |     194150.0 |     142200 |      543700 |     186523.3 | Loss                     |\n",
      "+----------+-----------------+-----------+--------------+--------------+------------+-------------+--------------+--------------------------+\n",
      "\n",
      "Running [/usr/local/cuda-11.6/NsightSystems-cli-2021.5.2/target-linux-x64/reports/nvtxppsum.py profile_report.sqlite] to [profile_report_nvtxppsum.csv]... PROCESSED\n",
      "\n",
      "Running [/usr/local/cuda-11.6/NsightSystems-cli-2021.5.2/target-linux-x64/reports/nvtxpptrace.py profile_report.sqlite] to [profile_report_nvtxpptrace.csv]... PROCESSED\n",
      "\n"
     ]
    }
   ],
   "source": [
    "!nsys stats \\\n",
    "    --report nvtxppsum,nvtxppsum,nvtxpptrace \\\n",
    "    --format table,csv \\\n",
    "    --output -,. \\\n",
    "    --force-overwrite true \\\n",
    "    profile_report.nsys-rep"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Performance Analysis\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "lines_to_next_cell": 2
   },
   "outputs": [],
   "source": [
    "# Ordered list of NVTX range for all training transforms\n",
    "transforms = [\n",
    "    \"ToTensord\",\n",
    "    \"Lambdad\",\n",
    "    \"GridSplitd\",\n",
    "    \"TorchVisiond_ColorJitter\",\n",
    "    \"ToNumpyd\",\n",
    "    \"RandFlipd\",\n",
    "    \"RandRotate90d\",\n",
    "    \"CastToTyped\",\n",
    "    \"RandZoomd\",\n",
    "    \"ScaleIntensityRanged\",\n",
    "    \"ToTensord_2\",\n",
    "    \"Activationsd\",\n",
    "    \"AsDiscreted\",\n",
    "]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using Nsight Summary Report"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Time (%)</th>\n",
       "      <th>Total Time (ns)</th>\n",
       "      <th>Instances</th>\n",
       "      <th>Avg (ns)</th>\n",
       "      <th>Med (ns)</th>\n",
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       "    <tr>\n",
       "      <th>Range</th>\n",
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       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>ToTensord</th>\n",
       "      <td>0.0</td>\n",
       "      <td>54120900</td>\n",
       "      <td>450</td>\n",
       "      <td>120268.7</td>\n",
       "      <td>100700.0</td>\n",
       "      <td>68500</td>\n",
       "      <td>721200</td>\n",
       "      <td>59828.4</td>\n",
       "      <td>0.222186</td>\n",
       "      <td>0.110528</td>\n",
       "      <td>0.126548</td>\n",
       "      <td>1.332357</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Lambdad</th>\n",
       "      <td>0.0</td>\n",
       "      <td>2460800</td>\n",
       "      <td>50</td>\n",
       "      <td>49216.0</td>\n",
       "      <td>39450.0</td>\n",
       "      <td>28700</td>\n",
       "      <td>146100</td>\n",
       "      <td>23219.6</td>\n",
       "      <td>0.090922</td>\n",
       "      <td>0.042896</td>\n",
       "      <td>0.053021</td>\n",
       "      <td>0.269908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GridSplitd</th>\n",
       "      <td>0.0</td>\n",
       "      <td>55917100</td>\n",
       "      <td>50</td>\n",
       "      <td>1118342.0</td>\n",
       "      <td>882450.0</td>\n",
       "      <td>685900</td>\n",
       "      <td>5798000</td>\n",
       "      <td>801880.3</td>\n",
       "      <td>2.066044</td>\n",
       "      <td>1.481407</td>\n",
       "      <td>1.267143</td>\n",
       "      <td>10.711323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>TorchVisiond_ColorJitter</th>\n",
       "      <td>19.3</td>\n",
       "      <td>22742525900</td>\n",
       "      <td>450</td>\n",
       "      <td>50538946.4</td>\n",
       "      <td>36570950.0</td>\n",
       "      <td>18874200</td>\n",
       "      <td>202062000</td>\n",
       "      <td>36166736.1</td>\n",
       "      <td>93.366500</td>\n",
       "      <td>66.815037</td>\n",
       "      <td>34.868515</td>\n",
       "      <td>373.292740</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ToNumpyd</th>\n",
       "      <td>0.1</td>\n",
       "      <td>91848800</td>\n",
       "      <td>450</td>\n",
       "      <td>204108.4</td>\n",
       "      <td>176450.0</td>\n",
       "      <td>128700</td>\n",
       "      <td>745700</td>\n",
       "      <td>87187.5</td>\n",
       "      <td>0.377073</td>\n",
       "      <td>0.161072</td>\n",
       "      <td>0.237763</td>\n",
       "      <td>1.377619</td>\n",
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       "    <tr>\n",
       "      <th>RandFlipd</th>\n",
       "      <td>0.0</td>\n",
       "      <td>39524100</td>\n",
       "      <td>450</td>\n",
       "      <td>87831.3</td>\n",
       "      <td>45950.0</td>\n",
       "      <td>7600</td>\n",
       "      <td>2748400</td>\n",
       "      <td>153536.8</td>\n",
       "      <td>0.162261</td>\n",
       "      <td>0.283646</td>\n",
       "      <td>0.014040</td>\n",
       "      <td>5.077440</td>\n",
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       "    <tr>\n",
       "      <th>RandRotate90d</th>\n",
       "      <td>0.1</td>\n",
       "      <td>59017500</td>\n",
       "      <td>450</td>\n",
       "      <td>131150.0</td>\n",
       "      <td>82250.0</td>\n",
       "      <td>17400</td>\n",
       "      <td>1050600</td>\n",
       "      <td>123950.6</td>\n",
       "      <td>0.242289</td>\n",
       "      <td>0.228988</td>\n",
       "      <td>0.032145</td>\n",
       "      <td>1.940896</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>CastToTyped</th>\n",
       "      <td>0.0</td>\n",
       "      <td>51677300</td>\n",
       "      <td>450</td>\n",
       "      <td>114838.4</td>\n",
       "      <td>101350.0</td>\n",
       "      <td>67500</td>\n",
       "      <td>1154000</td>\n",
       "      <td>60160.7</td>\n",
       "      <td>0.212154</td>\n",
       "      <td>0.111142</td>\n",
       "      <td>0.124701</td>\n",
       "      <td>2.131919</td>\n",
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       "    <tr>\n",
       "      <th>RandZoomd</th>\n",
       "      <td>0.3</td>\n",
       "      <td>384269900</td>\n",
       "      <td>450</td>\n",
       "      <td>853933.1</td>\n",
       "      <td>65400.0</td>\n",
       "      <td>21000</td>\n",
       "      <td>22487800</td>\n",
       "      <td>2212634.3</td>\n",
       "      <td>1.577570</td>\n",
       "      <td>4.087658</td>\n",
       "      <td>0.038796</td>\n",
       "      <td>41.544340</td>\n",
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       "    <tr>\n",
       "      <th>ScaleIntensityRanged</th>\n",
       "      <td>0.2</td>\n",
       "      <td>244892100</td>\n",
       "      <td>450</td>\n",
       "      <td>544204.7</td>\n",
       "      <td>441950.0</td>\n",
       "      <td>321800</td>\n",
       "      <td>8677700</td>\n",
       "      <td>541248.4</td>\n",
       "      <td>1.005373</td>\n",
       "      <td>0.999911</td>\n",
       "      <td>0.594499</td>\n",
       "      <td>16.031329</td>\n",
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       "    <tr>\n",
       "      <th>ToTensord_2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>50674700</td>\n",
       "      <td>450</td>\n",
       "      <td>112610.4</td>\n",
       "      <td>95650.0</td>\n",
       "      <td>71300</td>\n",
       "      <td>613700</td>\n",
       "      <td>51643.2</td>\n",
       "      <td>0.208038</td>\n",
       "      <td>0.095407</td>\n",
       "      <td>0.131721</td>\n",
       "      <td>1.133760</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Activationsd</th>\n",
       "      <td>0.0</td>\n",
       "      <td>48966300</td>\n",
       "      <td>450</td>\n",
       "      <td>108814.0</td>\n",
       "      <td>95900.0</td>\n",
       "      <td>75200</td>\n",
       "      <td>428700</td>\n",
       "      <td>44791.2</td>\n",
       "      <td>0.201025</td>\n",
       "      <td>0.082748</td>\n",
       "      <td>0.138926</td>\n",
       "      <td>0.791988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>AsDiscreted</th>\n",
       "      <td>0.1</td>\n",
       "      <td>65417500</td>\n",
       "      <td>450</td>\n",
       "      <td>145372.2</td>\n",
       "      <td>117000.0</td>\n",
       "      <td>90200</td>\n",
       "      <td>4613300</td>\n",
       "      <td>219185.3</td>\n",
       "      <td>0.268563</td>\n",
       "      <td>0.404927</td>\n",
       "      <td>0.166637</td>\n",
       "      <td>8.522688</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                          Time (%)  Total Time (ns)  Instances    Avg (ns)  \\\n",
       "Range                                                                        \n",
       "ToTensord                      0.0         54120900        450    120268.7   \n",
       "Lambdad                        0.0          2460800         50     49216.0   \n",
       "GridSplitd                     0.0         55917100         50   1118342.0   \n",
       "TorchVisiond_ColorJitter      19.3      22742525900        450  50538946.4   \n",
       "ToNumpyd                       0.1         91848800        450    204108.4   \n",
       "RandFlipd                      0.0         39524100        450     87831.3   \n",
       "RandRotate90d                  0.1         59017500        450    131150.0   \n",
       "CastToTyped                    0.0         51677300        450    114838.4   \n",
       "RandZoomd                      0.3        384269900        450    853933.1   \n",
       "ScaleIntensityRanged           0.2        244892100        450    544204.7   \n",
       "ToTensord_2                    0.0         50674700        450    112610.4   \n",
       "Activationsd                   0.0         48966300        450    108814.0   \n",
       "AsDiscreted                    0.1         65417500        450    145372.2   \n",
       "\n",
       "                            Med (ns)  Min (ns)   Max (ns)  StdDev (ns)  \\\n",
       "Range                                                                    \n",
       "ToTensord                   100700.0     68500     721200      59828.4   \n",
       "Lambdad                      39450.0     28700     146100      23219.6   \n",
       "GridSplitd                  882450.0    685900    5798000     801880.3   \n",
       "TorchVisiond_ColorJitter  36570950.0  18874200  202062000   36166736.1   \n",
       "ToNumpyd                    176450.0    128700     745700      87187.5   \n",
       "RandFlipd                    45950.0      7600    2748400     153536.8   \n",
       "RandRotate90d                82250.0     17400    1050600     123950.6   \n",
       "CastToTyped                 101350.0     67500    1154000      60160.7   \n",
       "RandZoomd                    65400.0     21000   22487800    2212634.3   \n",
       "ScaleIntensityRanged        441950.0    321800    8677700     541248.4   \n",
       "ToTensord_2                  95650.0     71300     613700      51643.2   \n",
       "Activationsd                 95900.0     75200     428700      44791.2   \n",
       "AsDiscreted                 117000.0     90200    4613300     219185.3   \n",
       "\n",
       "                               avg%       std%       min%        max%  \n",
       "Range                                                                  \n",
       "ToTensord                  0.222186   0.110528   0.126548    1.332357  \n",
       "Lambdad                    0.090922   0.042896   0.053021    0.269908  \n",
       "GridSplitd                 2.066044   1.481407   1.267143   10.711323  \n",
       "TorchVisiond_ColorJitter  93.366500  66.815037  34.868515  373.292740  \n",
       "ToNumpyd                   0.377073   0.161072   0.237763    1.377619  \n",
       "RandFlipd                  0.162261   0.283646   0.014040    5.077440  \n",
       "RandRotate90d              0.242289   0.228988   0.032145    1.940896  \n",
       "CastToTyped                0.212154   0.111142   0.124701    2.131919  \n",
       "RandZoomd                  1.577570   4.087658   0.038796   41.544340  \n",
       "ScaleIntensityRanged       1.005373   0.999911   0.594499   16.031329  \n",
       "ToTensord_2                0.208038   0.095407   0.131721    1.133760  \n",
       "Activationsd               0.201025   0.082748   0.138926    0.791988  \n",
       "AsDiscreted                0.268563   0.404927   0.166637    8.522688  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Load NVTX Push/Pop Range Summary\n",
    "summary = pd.read_csv(\"profile_report_nvtxppsum.csv\")\n",
    "# display(summary)\n",
    "\n",
    "# Set the Range (which is the name of each range) as the index\n",
    "summary.set_index(\"Range\", inplace=True)\n",
    "\n",
    "# Get the entries for training transforms only (to avoid nested ranges)\n",
    "summary = summary.loc[transforms]\n",
    "\n",
    "# Nsys output column names are different in different versions,\n",
    "# so we need to find the corresponding columns\n",
    "avg_col = [c for c in summary.columns if c.startswith(\"Av\")]\n",
    "std_col = [c for c in summary.columns if c.startswith(\"Std\")]\n",
    "min_col = [c for c in summary.columns if c.startswith(\"Min\")]\n",
    "max_col = [c for c in summary.columns if c.startswith(\"Max\")]\n",
    "\n",
    "# Normalize each transform range with total average time (percentage of transform time)\n",
    "summary[[\"avg%\", \"std%\", \"min%\", \"max%\"]] = (\n",
    "    summary[avg_col + std_col + min_col + max_col] / summary[avg_col].sum()[0] * 100\n",
    ")\n",
    "summary"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% of preprocessing time')"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 1080x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.style.use(\"fivethirtyeight\")\n",
    "plt.style.use(\"tableau-colorblind10\")\n",
    "axes = summary.plot.barh(\n",
    "    y=\"avg%\",\n",
    "    xerr=\"std%\",\n",
    "    title=\"Average Time\",\n",
    "    xlabel=\"\",\n",
    "    fontsize=16,\n",
    "    figsize=(15, 9),\n",
    "    alpha=0.5,\n",
    "    legend=\"\",\n",
    ")\n",
    "axes.set_xlim([-1, 101])\n",
    "axes.set_xticks(range(0, 101, 10))\n",
    "axes.set_xlabel(\"% of preprocessing time\", fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, '% of preprocessing time')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x1080 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "axes = summary.plot.barh(\n",
    "    y=[\"avg%\", \"min%\", \"max%\"],\n",
    "    xlabel=\"\",\n",
    "    fontsize=16,\n",
    "    figsize=(15, 15),\n",
    "    alpha=0.5,\n",
    "    subplots=True,\n",
    "    legend=\"\",\n",
    ")\n",
    "axes[-1].set_xlim([0, 101])\n",
    "axes[-1].set_xticks(range(0, 101, 10))\n",
    "axes[-1].set_xlabel(\"% of preprocessing time\", fontsize=20)"
   ]
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "98b0a9b7b4eaaa670588a142fd0a9b87eaafe866f1db4228be72b4211d12040f"
  },
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}