cheXpert_final.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CheXpert : A Large Chest X-Ray Dataset and Competition\n",
    "\n",
    "This competition launched by the Stanford ML group aims at finding a prediction model which could perform as well as radiologist to find different pathologies thanks to chest X-Ray. The Dataset available to train our model is composed of 223,414 chest radiographs of 65,240 patients.\n",
    "\n",
    "<img src=\"view1_frontal.jpg\" title=\"X-Ray image of the dataset\" width = 320/>\n",
    "\n",
    "The website of the competition:\n",
    "https://stanfordmlgroup.github.io/competitions/chexpert/\n",
    "\n",
    "[Publication](https://arxiv.org/pdf/1901.07031.pdf) : Irvin, Jeremy, et al. \"CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparison.\" arXiv preprint arXiv:1901.07031 (2019).\n",
    "\n",
    "Our goal is first to reproduce main results obtained in the related paper, published in January 2019."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\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\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install -qU torchvision"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pillow in /opt/conda/lib/python3.7/site-packages (9.5.0)\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\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install pillow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: requests in /opt/conda/lib/python3.7/site-packages (2.31.0)\n",
      "Requirement already satisfied: charset-normalizer<4,>=2 in /opt/conda/lib/python3.7/site-packages (from requests) (2.0.4)\n",
      "Requirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.7/site-packages (from requests) (2.8)\n",
      "Requirement already satisfied: urllib3<3,>=1.21.1 in /opt/conda/lib/python3.7/site-packages (from requests) (2.0.3)\n",
      "Requirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.7/site-packages (from requests) (2023.5.7)\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\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install requests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Contents\n",
    "\n",
    "1. [Background](#Background)\n",
    "1. [Setup](#Setup)\n",
    "1. [Data](#Data)\n",
    "1. [Train](#Train)\n",
    "1. [Host](#Host)\n",
    "\n",
    "---\n",
    "\n",
    "## Background\n",
    "\n",
    "we used this open source https://www.kaggle.com/code/dnik007/pneumonia-detection-using-pytorch notebook.\n",
    "\n",
    "For more information about the PyTorch in SageMaker, please visit [sagemaker-pytorch-containers](https://github.com/aws/sagemaker-pytorch-containers) and [sagemaker-python-sdk](https://github.com/aws/sagemaker-python-sdk) github repositories.\n",
    "\n",
    "---\n",
    "\n",
    "## Setup\n",
    "\n",
    "_This notebook was created and tested on an ml.m4.xlarge notebook instance._\n",
    "\n",
    "Let's start by creating a SageMaker session and specifying:\n",
    "\n",
    "- The S3 bucket and prefix that you want to use for training and model data.  This should be within the same region as the Notebook Instance, training, and hosting.\n",
    "- The IAM role arn used to give training and hosting access to your data. See the documentation for how to create these.  Note, if more than one role is required for notebook instances, training, and/or hosting, please replace the `sagemaker.get_execution_role()` with a the appropriate full IAM role arn string(s).\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sagemaker\n",
    "\n",
    "sagemaker_session = sagemaker.Session()\n",
    "\n",
    "bucket = sagemaker_session.default_bucket()\n",
    "\n",
    "role = sagemaker.get_execution_role()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7f1569a2-45b7-47fa-9c63-1f81fe1ac06f'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import json\n",
    "metadata = json.load(open(\"/opt/ml/metadata/resource-metadata.json\",\"r\"))\n",
    "bucket = metadata[\"UserProfileName\"]\n",
    "bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'7f1569a2-45b7-47fa-9c63-1f81fe1ac06f'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
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      "data/train/e43454bd-d903-42d2-aea5-fdcd5cc1edd6.dcm.png\n",
      "data/train/e46345dc-097b-4b93-9923-897d294fc3ee.dcm.png\n",
      "data/train/e53ec60c-ab77-4aec-8f90-c51d3514d066.dcm.png\n",
      "data/train/e5440613-362f-4767-8ac8-420d849676cd.dcm.png\n",
      "data/train/e675c405-8913-4da6-b1fb-db228d661b3f.dcm.png\n",
      "data/train/e705b1de-4a53-4da9-a46d-9a4d977cc929.dcm.png\n",
      "data/train/e84f1a2e-0faf-4178-9f15-5a6fe43d13c3.dcm.png\n",
      "data/train/e8975ab7-9267-400a-9185-bd99faa6d693.dcm.png\n",
      "data/train/e8eb5cfd-93e2-4a4b-a783-2a62d690b7aa.dcm.png\n",
      "data/train/e9146e95-a29d-4ed3-af20-dd767041998b.dcm.png\n",
      "data/train/e9690c4d-1d24-412c-886a-beb80f4bdbc1.dcm.png\n",
      "data/train/e9fec511-d0b8-4baa-a870-0ac5ffb8ca0e.dcm.png\n",
      "data/train/eaf554e0-e9a5-48c0-9332-2b887e459ce7.dcm.png\n",
      "data/train/eb0ec612-496c-41bc-a2cb-3b32ce26a7cf.dcm.png\n",
      "data/train/ebd9a8a9-3544-4c09-b819-b62ce12163cc.dcm.png\n",
      "data/train/ec371492-a674-445f-a705-388dd0e6720e.dcm.png\n",
      "data/train/ec5a7a5a-9c9e-4a87-b1be-e0bdeb9f9f6b.dcm.png\n",
      "data/train/ec5a9670-9550-4893-9c83-eb81514ae4be.dcm.png\n",
      "data/train/ecb06228-5d00-4b34-8eb1-b9dc821ef620.dcm.png\n",
      "data/train/ed2ba79e-4be9-4c84-ad66-28bf1c481801.dcm.png\n",
      "data/train/f084bf25-9059-430f-a322-ae4acbfcd8a4.dcm.png\n",
      "data/train/f0924d6e-2a53-4953-95c6-e4450cd405bc.dcm.png\n",
      "data/train/f2635b2d-c607-4ffe-91dd-eb8fe7f5c185.dcm.png\n",
      "data/train/f3d8e001-f867-46c7-9bc5-451417cb6433.dcm.png\n",
      "data/train/f3dfab78-3666-4e54-bfe2-7b5e9acf1521.dcm.png\n",
      "data/train/f4649a51-465e-440a-b261-10620cb35e3d.dcm.png\n",
      "data/train/f5cd2898-ab9b-482b-8f95-b7c9f2f8c22e.dcm.png\n",
      "data/train/f6e07fba-553b-44c7-be64-b8d5ee40e732.dcm.png\n",
      "data/train/f7bc89d2-351e-4302-80b8-195aa9886d55.dcm.png\n",
      "data/train/f7dadb0d-3fe7-4bc8-b9f5-b958e43540f0.dcm.png\n",
      "data/train/f8d1a2dd-dcb5-4b02-b09c-1bc1cbe3d8f6.dcm.png\n",
      "data/train/fa39889a-8f13-4d88-8e36-17fe5cc2aeed.dcm.png\n",
      "data/train/fa66f935-ed22-47ba-9406-53d642368170.dcm.png\n",
      "data/train/fb7ebbed-ed72-4fe4-ae90-4219829b988f.dcm.png\n",
      "data/train/fbeae6ff-4426-4f45-a0b2-0cdb3b161789.dcm.png\n",
      "data/train/fcb9f613-e1ad-4f48-a021-88405fcb600d.dcm.png\n",
      "data/train/fd99544b-1324-4c25-bfab-c9a3764e62b1.dcm.png\n",
      "data/train/fd9f14c9-5fe4-43c8-a88a-f8e799682a0a.dcm.png\n",
      "data/train/fdbf0440-e56e-42ca-903e-de7bed58d2be.dcm.png\n",
      "data/train/ff85b50c-f7d1-49a8-a9af-5da7c0c486b1.dcm.png\n",
      "data/train/ffa42343-2731-40b2-80ba-2e9af6909a6f.dcm.png\n",
      "data/train/ffd117ec-6fe2-4e31-976a-1ac4f03e60cc.dcm.png\n",
      "labels/labels.csv\n",
      "labels/rois.json\n"
     ]
    }
   ],
   "source": [
    "from boto3 import client\n",
    "\n",
    "conn = client('s3')  # again assumes boto.cfg setup, assume AWS S3\n",
    "for key in conn.list_objects(Bucket=bucket)['Contents']:\n",
    "    print(key['Key'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "ename": "FileExistsError",
     "evalue": "[Errno 17] File exists: 'data'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mFileExistsError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-58fe9c9fb49e>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mboto3\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mclient\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"data\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0mos\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmkdir\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"labels\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mFileExistsError\u001b[0m: [Errno 17] File exists: 'data'"
     ]
    }
   ],
   "source": [
    "# Downloading Data from S3\n",
    "\n",
    "import boto3\n",
    "import botocore\n",
    "from boto3 import client\n",
    "import os\n",
    "os.mkdir(\"data\")\n",
    "os.mkdir(\"labels\")\n",
    "\n",
    "BUCKET_NAME = bucket\n",
    "c = 0\n",
    "conn = client('s3') \n",
    "for key in conn.list_objects(Bucket=bucket)['Contents']:\n",
    "    print(key['Key'])\n",
    "    KEY = key['Key']\n",
    "    s3 = boto3.resource('s3')\n",
    "    try:\n",
    "        s3.Bucket(BUCKET_NAME).download_file(KEY, f\"{key['Key']}\")\n",
    "    except botocore.exceptions.ClientError as e:\n",
    "        if e.response['Error']['Code'] == \"404\":\n",
    "            print(\"The object does not exist.\")\n",
    "        else:\n",
    "            raise\n",
    "    c = c+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting pydicom\n",
      "  Downloading pydicom-2.4.1-py3-none-any.whl (1.8 MB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.8/1.8 MB\u001b[0m \u001b[31m20.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m:00:01\u001b[0m\n",
      "\u001b[?25hInstalling collected packages: pydicom\n",
      "Successfully installed pydicom-2.4.1\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\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "\n",
    "# Dcm to Png\n",
    "!pip install pydicom\n",
    "import pydicom as dicom\n",
    "import pydicom\n",
    "import json\n",
    "import os\n",
    "from pathlib import Path\n",
    "from pydicom.pixel_data_handlers.util import apply_voi_lut\n",
    "from pydicom import dcmread\n",
    "import os\n",
    "import pickle\n",
    "from PIL import Image\n",
    "import numpy as np\n",
    "from PIL import Image\n",
    "import PIL\n",
    "\n",
    "def read_xray(path, voi_lut=True, fix_monochrome=True):\n",
    "    try:\n",
    "        print(\"Converting to PNG .........................\")\n",
    "        dicom = dcmread(path, force=True)\n",
    "        print(dicom.SOPInstanceUID, \">>>>>>\", dicom.StudyInstanceUID, \">>>>>\", dicom.SeriesInstanceUID)\n",
    "        #if voi_lut:\n",
    "        if voi_lut and len(dicom.get(\"VOILUTSequence\", [])):\n",
    "            data = apply_voi_lut(dicom.pixel_array, dicom)\n",
    "        else:\n",
    "            data = dicom.pixel_array\n",
    "        if fix_monochrome and dicom.PhotometricInterpretation == \"MONOCHROME1\":\n",
    "            data = np.amax(data) - data\n",
    "        data = data - np.min(data)\n",
    "        data = data / np.max(data)\n",
    "        data = (data * 255).astype(np.uint8)\n",
    "        return data,dicom.PatientName\n",
    "    except Exception as e:\n",
    "        print(e, \">>>>>>>>>>>>>>>>>>>>>>>>>>>>>\")\n",
    "        return \"corrupt\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['data/220395be-67bf-4352-9165-7882eb8a6f01.dcm', 'data/25fcfc3d-faf5-420c-bcaa-efe3c35a0027.dcm', 'data/cde17749-4a0f-4624-a555-289ee6820ade.dcm', 'data/dc042ef0-79da-4cfc-bfa9-e7ddfb74f761.dcm', 'data/9807e395-2211-40c3-9885-31c365504e65.dcm', 'data/79359da4-3e69-47d4-8221-0c237bfafdaf.dcm', 'data/23c3ed7a-5da6-4b62-ae23-e39c58348957.dcm', 'data/9abd652b-33cb-4131-a9bf-9b6442c0201d.dcm', 'data/f3dfab78-3666-4e54-bfe2-7b5e9acf1521.dcm', 'data/528ef9e9-9b34-4d8e-857b-8734f94850f0.dcm', 'data/7836d5bc-617d-4a4b-9592-7bd96af6d476.dcm', 'data/d37d05f9-8979-466a-beeb-1ca9fb38b4d9.dcm', 'data/2a2b3645-0860-4b0e-a6eb-51b89e2a12a5.dcm', 'data/c1870bd4-9e80-4db7-a9ad-9565b76613b3.dcm', 'data/39f9d924-dd5e-4b6f-93d4-fdd6f593080a.dcm', 'data/b465ca93-de94-4218-88e3-0a8ad12f793b.dcm', 'data/c765b546-68b4-4221-827d-ad51d72845c6.dcm', 'data/74049f1b-5826-4bae-af52-abc542fd3dd4.dcm', 'data/c926e886-d7ac-4eca-9ba7-90b4fa510835.dcm', 'data/bdc7e41f-3b1d-426b-91a2-e6d1547f6dad.dcm', 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'data/63e30cc5-37b4-4626-a5ad-fcacacabd417.dcm', 'data/8f5006c6-1bca-4a8b-b46a-cdab75107163.dcm', 'data/d67f9dd5-570c-4e4f-b30c-324053272bce.dcm', 'data/c30a4caf-5c33-466d-bf1e-19c1a0f2ac85.dcm', 'data/137dcc5e-c2f4-4456-94f4-4346bbf8a2f9.dcm', 'data/17298041-9f82-4725-a921-dcb2f74818bf.dcm', 'data/a5acdfb3-a555-43cb-8093-a07a6dc2a920.dcm', 'data/84cd6294-d50d-483a-90ed-bd45538a547f.dcm', 'data/0d242ebd-b777-434f-86c9-b2050a594d31.dcm', 'data/91dc9434-fbe6-4bbd-a820-d992a3104fcd.dcm', 'data/268af259-a802-4066-a748-6e2b969376dd.dcm', 'data/446d663e-1654-4b4e-ac8b-0369a3bb68f4.dcm', 'data/af658709-8a42-43c0-95b8-c82ec0b1f408.dcm', 'data/110e4b83-6a38-443c-9012-967e09743483.dcm', 'data/58514926-94ca-452c-985e-b69d57758257.dcm', 'data/60092729-bde8-4787-bbb3-78263bab2155.dcm', 'data/8dd9e03a-7655-4bf3-8f51-b173fa2604ed.dcm', 'data/cbc248f2-63b7-42f1-90cf-2ce76fe6fea2.dcm', 'data/55bf958f-9152-42ec-901c-37cdb0c99012.dcm', 'data/f6e07fba-553b-44c7-be64-b8d5ee40e732.dcm', 'data/41e48513-d1d7-4c81-bdbd-ef06f7487c3c.dcm', 'data/600dff41-7d42-4a45-943e-697f35439fd8.dcm', 'data/39cea3a6-d2d0-400b-9538-c5bf4bbd26e4.dcm', 'data/d9830b34-79aa-444b-be25-42583e196f14.dcm', 'data/55a243af-4c77-4f03-a28d-ce92bd5ca2b3.dcm', 'data/16712416-d486-4e68-bafc-65906dff9319.dcm', 'data/b9b25773-1087-42d4-8ed8-011923764a4c.dcm', 'data/133dd7dc-0c02-4b5a-89a3-15d9a48305df.dcm', 'data/a14c1f41-1664-4dec-8a20-2c46f61d7d90.dcm', 'data/4f1be4db-89fc-4b7e-ac5e-3e06254c3286.dcm', 'data/53e7fc74-94fb-41fb-a629-5cba44c283cc.dcm', 'data/bb5f898d-b3f5-4825-99a8-75bf2a007940.dcm', 'data/ac4bd7d6-d6c8-4da7-bffb-3b8cdfb54084.dcm', 'data/66aa54d4-78d6-4b3c-b8d3-70b44d514b69.dcm', 'data/16effb1e-6164-46c2-8cfa-2a5422808f90.dcm', 'data/a70b515b-e827-4014-a1ab-c3faba8bff97.dcm', 'data/ce48f7e2-b821-46c5-af73-cf49238e55af.dcm', 'data/a671612a-570c-4c79-8d48-bf0c73a8dbbd.dcm', 'data/a6925052-ec71-4810-a748-313eef4e4392.dcm', 'data/92dda07a-d234-4905-94f4-ffdf2eaea632.dcm', 'data/1468084b-d489-49c0-a593-e01780c40dd1.dcm', 'data/ec5a9670-9550-4893-9c83-eb81514ae4be.dcm', 'data/1fc2363a-d923-4776-965f-a08ea8302ccb.dcm', 'data/ab2fc3ec-a6e7-4080-9491-464cb75e19a3.dcm', 'data/69aa8cea-0acc-4d0c-b2c6-86db047eace3.dcm', 'data/df33f12c-88cf-49c9-86b8-36aaf8d6b9f7.dcm', 'data/e1ebc6e2-17c7-4d31-89f5-e2b80f5750aa.dcm', 'data/b7478589-93e2-408b-b086-8c16dd6c21ce.dcm', 'data/5ffbbb8c-f3e7-4c7a-a0c4-a0e1e04de622.dcm', 'data/7427009e-b9e1-4d6a-84f1-c6ebbcc50528.dcm', 'data/c9a4b79c-7870-497f-b35b-f1f4d024d5b9.dcm', 'data/656b52f3-e521-492d-b7c1-169b070320ab.dcm', 'data/da869fe7-1b73-4555-ba99-faca29fc64e0.dcm', 'data/28ca3acf-a5ad-498f-8ab8-41df5a05832f.dcm', 'data/12eabc77-768a-44c5-af33-19c899a0a2d3.dcm', 'data/57eed4ba-885a-4426-ade9-f4affb08a759.dcm', 'data/6087b7fc-30d5-4820-8751-0401b54fdc86.dcm', 'data/4b27584f-1e6c-4d91-8b11-0d338636d41d.dcm', 'data/98e1a809-72d5-4621-a75c-ad221e434951.dcm', 'data/55602395-42a5-42f9-8bba-a2d0a23d6cae.dcm', 'data/1fe15492-8b97-4725-acf3-086ce60847fb.dcm', 'data/7b22d939-c466-438f-b7d4-70301f28d9df.dcm', 'data/e9fec511-d0b8-4baa-a870-0ac5ffb8ca0e.dcm', 'data/3b89dc55-8f29-4983-b0e1-e5ef2624a6f1.dcm', 'data/8554824e-7b87-41f3-a7f6-e9b55c313271.dcm', 'data/cde055a2-7334-4986-8919-c2cbd9a66274.dcm', 'data/65ba6af7-b1cf-41c2-8448-1c2a0eecec05.dcm', 'data/a797f4d5-aec3-4d8e-b59a-798e9b03dc19.dcm', 'data/97ec4976-e851-4b34-8261-a7e89f79efe2.dcm', 'data/97192896-6d85-459f-96fb-c351082e8670.dcm', 'data/2c855322-05d7-4f42-b0a0-b07590dadaeb.dcm', 'data/fa66f935-ed22-47ba-9406-53d642368170.dcm', 'data/d411948e-f002-4c6c-8ed4-77ec273d34b9.dcm', 'data/cb395caa-3f35-42f6-b0eb-afc224964544.dcm', 'data/0026358a-7ad6-40a2-8705-f0f3610e9a8f.dcm', 'data/5ab37cf3-780c-4817-b966-d92ddfd1f1cb.dcm', 'data/c0e94329-9bfe-4757-8793-0537da6bc8a6.dcm', 'data/c05a534d-ee61-4944-9303-87130b8cee0a.dcm', 'data/bade1b5a-b163-4ebf-8345-a01cc3f637a1.dcm', 'data/ac5c3862-05cf-423f-95a7-68f99bb8ff18.dcm', 'data/7ca43bde-fcba-4882-b0c4-cbdf0f60961f.dcm', 'data/29219f0c-366a-46f0-b8ae-220ef90e5b41.dcm', 'data/bd07a0a1-2041-483b-ae9b-de90340003d0.dcm', 'data/3d66b6c0-97ae-448e-b788-6138d58e5c2e.dcm', 'data/5cde1b27-9c57-4d20-8138-d4a2f34fb231.dcm', 'data/03f1f601-95a6-4e1f-b9b3-8605e60b2425.dcm', 'data/3ac09a1d-bd1e-4d86-b1e5-08718f2da81f.dcm', 'data/fb7ebbed-ed72-4fe4-ae90-4219829b988f.dcm', 'data/182f9370-6aa5-4ee4-928c-64fbbb8cb91c.dcm', 'data/9aba4355-b4f4-441c-9dfa-a49845a8347e.dcm', 'data/1e9b8da2-cda9-457d-984d-24514beb823e.dcm', 'data/ffa42343-2731-40b2-80ba-2e9af6909a6f.dcm', 'data/2e2b4431-ae15-4550-8d34-28afb121a4c6.dcm', 'data/2c49bedb-78a8-4d6f-9a99-42d602c4b9f8.dcm']\n",
      "\n",
      ">>>  data/220395be-67bf-4352-9165-7882eb8a6f01.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.433.4318.40072.668594.4144571.95538922776006436555938221209 >>>>>> 9.22.602.1218.52185.737416.9005079.23887078277187142292868965379 >>>>> 6.36.404.1470.59934.218540.6902300.16514637602983726210307384354\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.7/site-packages/ipykernel_launcher.py:18: DeprecationWarning: `np.float` is a deprecated alias for the builtin `float`. To silence this warning, use `float` by itself. Doing this will not modify any behavior and is safe. If you specifically wanted the numpy scalar type, use `np.float64` here.\n",
      "Deprecated in NumPy 1.20; for more details and guidance: https://numpy.org/devdocs/release/1.20.0-notes.html#deprecations\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      ">> filename 220395be-67bf-4352-9165-7882eb8a6f01.dcm\n",
      "1 Done\n",
      "\n",
      ">>>  data/25fcfc3d-faf5-420c-bcaa-efe3c35a0027.dcm\n",
      "Converting to PNG .........................\n",
      "2.43.812.8930.67771.170953.6827875.86772537978690104226974692459 >>>>>> 9.21.880.7257.13475.143829.5741880.39160383226915128853849442907 >>>>> 1.12.547.1884.57148.533368.2261967.95395407883117570671724334316\n",
      "\n",
      ">> filename 25fcfc3d-faf5-420c-bcaa-efe3c35a0027.dcm\n",
      "2 Done\n",
      "\n",
      ">>>  data/cde17749-4a0f-4624-a555-289ee6820ade.dcm\n",
      "Converting to PNG .........................\n",
      "4.21.355.9222.34912.324311.8584344.81550036432378312720258838167 >>>>>> 8.24.380.7346.89430.526932.2935806.74519318306962707543314275776 >>>>> 1.17.478.2205.55414.591576.9212617.69808781944603168743602665431\n",
      "\n",
      ">> filename cde17749-4a0f-4624-a555-289ee6820ade.dcm\n",
      "3 Done\n",
      "\n",
      ">>>  data/dc042ef0-79da-4cfc-bfa9-e7ddfb74f761.dcm\n",
      "Converting to PNG .........................\n",
      "5.58.691.8966.65820.108092.4464498.26236783905020340898713896204 >>>>>> 1.17.426.1379.56997.944087.1843893.93416353014499306944420989686 >>>>> 9.59.127.6241.24825.932435.1596213.80927119509635395468044533265\n",
      "\n",
      ">> filename dc042ef0-79da-4cfc-bfa9-e7ddfb74f761.dcm\n",
      "4 Done\n",
      "\n",
      ">>>  data/9807e395-2211-40c3-9885-31c365504e65.dcm\n",
      "Converting to PNG .........................\n",
      "3.48.190.8164.23175.483910.8306682.38068357634124430905180733008 >>>>>> 1.35.591.6079.92123.614241.1277116.21917106500862770467242607439 >>>>> 5.65.783.7597.86331.959772.2741424.66704440264403611429220415575\n",
      "\n",
      ">> filename 9807e395-2211-40c3-9885-31c365504e65.dcm\n",
      "5 Done\n",
      "\n",
      ">>>  data/79359da4-3e69-47d4-8221-0c237bfafdaf.dcm\n",
      "Converting to PNG .........................\n",
      "8.20.944.2277.85748.434721.1478391.66464162570880977062931016097 >>>>>> 7.76.179.7095.88252.490500.2260285.65484445456428114818245115367 >>>>> 5.13.594.5494.29786.451772.9846501.11726439546967577430878036715\n",
      "\n",
      ">> filename 79359da4-3e69-47d4-8221-0c237bfafdaf.dcm\n",
      "6 Done\n",
      "\n",
      ">>>  data/23c3ed7a-5da6-4b62-ae23-e39c58348957.dcm\n",
      "Converting to PNG .........................\n",
      "7.25.718.4381.58367.354688.7731812.67989402138812150004100922678 >>>>>> 5.19.945.8556.69407.640783.5781278.32053088768591249125303866770 >>>>> 3.83.278.9686.86218.407317.2188228.37704696359291544644131419440\n",
      "\n",
      ">> filename 23c3ed7a-5da6-4b62-ae23-e39c58348957.dcm\n",
      "7 Done\n",
      "\n",
      ">>>  data/9abd652b-33cb-4131-a9bf-9b6442c0201d.dcm\n",
      "Converting to PNG .........................\n",
      "9.95.584.2360.68173.804526.1237670.69326646227104280657805884027 >>>>>> 5.97.904.8529.27921.377748.9238182.70038339830010278934132964861 >>>>> 7.19.122.2318.27103.562090.2975181.34022037571461951708860099261\n",
      "\n",
      ">> filename 9abd652b-33cb-4131-a9bf-9b6442c0201d.dcm\n",
      "8 Done\n",
      "\n",
      ">>>  data/f3dfab78-3666-4e54-bfe2-7b5e9acf1521.dcm\n",
      "Converting to PNG .........................\n",
      "1.17.559.4264.79736.512670.2839388.91450220491414040245238511812 >>>>>> 1.56.485.8525.23174.906737.1076392.73084768219088461946122290885 >>>>> 1.14.181.8449.56500.738128.6454493.47698805407401485975262797579\n",
      "\n",
      ">> filename f3dfab78-3666-4e54-bfe2-7b5e9acf1521.dcm\n",
      "9 Done\n",
      "\n",
      ">>>  data/528ef9e9-9b34-4d8e-857b-8734f94850f0.dcm\n",
      "Converting to PNG .........................\n",
      "1.88.982.6321.59239.238658.4348382.19822997007911499899632430051 >>>>>> 8.60.599.2167.55486.359028.7067883.84128010612707351169038116908 >>>>> 5.91.674.2425.12630.811843.6603782.13042019435650252210258566562\n",
      "\n",
      ">> filename 528ef9e9-9b34-4d8e-857b-8734f94850f0.dcm\n",
      "10 Done\n",
      "\n",
      ">>>  data/7836d5bc-617d-4a4b-9592-7bd96af6d476.dcm\n",
      "Converting to PNG .........................\n",
      "6.13.317.7878.68163.430288.5709972.12599360102972872166799683047 >>>>>> 2.63.467.4451.66933.124727.1077218.92041207822224005676551465348 >>>>> 6.43.899.1967.14301.230105.8551646.40954555194332783802601823408\n",
      "\n",
      ">> filename 7836d5bc-617d-4a4b-9592-7bd96af6d476.dcm\n",
      "11 Done\n",
      "\n",
      ">>>  data/d37d05f9-8979-466a-beeb-1ca9fb38b4d9.dcm\n",
      "Converting to PNG .........................\n",
      "4.64.892.3837.53573.344964.6127548.95627977496723025999426668465 >>>>>> 4.47.158.7232.69654.868750.1683255.18871855271569392433765954151 >>>>> 6.96.229.9371.48064.307258.2522117.11074211360119017385457329086\n",
      "\n",
      ">> filename d37d05f9-8979-466a-beeb-1ca9fb38b4d9.dcm\n",
      "12 Done\n",
      "\n",
      ">>>  data/2a2b3645-0860-4b0e-a6eb-51b89e2a12a5.dcm\n",
      "Converting to PNG .........................\n",
      "1.53.760.1853.15415.972333.1984397.24688146691691533766101905926 >>>>>> 4.82.826.2894.63260.251344.7358344.86212951276926517794570358159 >>>>> 3.20.142.1459.74391.750700.5068596.52201817677447034166545675300\n",
      "\n",
      ">> filename 2a2b3645-0860-4b0e-a6eb-51b89e2a12a5.dcm\n",
      "13 Done\n",
      "\n",
      ">>>  data/c1870bd4-9e80-4db7-a9ad-9565b76613b3.dcm\n",
      "Converting to PNG .........................\n",
      "7.41.967.1705.21911.487845.7349395.47713744452775663839567109543 >>>>>> 2.14.256.4340.80838.533823.9876423.99443904347405839811920371342 >>>>> 4.70.794.4769.77429.648537.2052984.21688005325667230661334023075\n",
      "\n",
      ">> filename c1870bd4-9e80-4db7-a9ad-9565b76613b3.dcm\n",
      "14 Done\n",
      "\n",
      ">>>  data/39f9d924-dd5e-4b6f-93d4-fdd6f593080a.dcm\n",
      "Converting to PNG .........................\n",
      "4.19.339.9661.49513.593192.6851287.45586584901773271997847184670 >>>>>> 2.82.976.2556.61146.451401.7633348.68901640271009302343590613198 >>>>> 3.59.239.4710.78702.275599.1543691.97475034818528089751261539134\n",
      "\n",
      ">> filename 39f9d924-dd5e-4b6f-93d4-fdd6f593080a.dcm\n",
      "15 Done\n",
      "\n",
      ">>>  data/b465ca93-de94-4218-88e3-0a8ad12f793b.dcm\n",
      "Converting to PNG .........................\n",
      "1.17.146.6372.65499.722789.2208732.48868726204648908529335926784 >>>>>> 2.39.836.6383.20061.380771.5372867.11645103863275204622239407641 >>>>> 8.43.675.4029.18637.598596.3126268.47498592539290257040041772460\n",
      "\n",
      ">> filename b465ca93-de94-4218-88e3-0a8ad12f793b.dcm\n",
      "16 Done\n",
      "\n",
      ">>>  data/c765b546-68b4-4221-827d-ad51d72845c6.dcm\n",
      "Converting to PNG .........................\n",
      "9.35.356.6603.84512.759960.1631610.82369286996071490245633545758 >>>>>> 3.37.651.3488.80304.663481.4545267.25670058234241941541457089675 >>>>> 7.88.808.4623.67515.106673.8343972.35020163643264120685892586925\n",
      "\n",
      ">> filename c765b546-68b4-4221-827d-ad51d72845c6.dcm\n",
      "17 Done\n",
      "\n",
      ">>>  data/74049f1b-5826-4bae-af52-abc542fd3dd4.dcm\n",
      "Converting to PNG .........................\n",
      "9.63.369.6566.98791.714581.4576503.97796693730013253105275963209 >>>>>> 6.67.871.5909.78860.164781.4707968.11472370016966900783798753669 >>>>> 8.22.100.7932.62563.431150.4084865.25068685914412791264129287647\n",
      "\n",
      ">> filename 74049f1b-5826-4bae-af52-abc542fd3dd4.dcm\n",
      "18 Done\n",
      "\n",
      ">>>  data/c926e886-d7ac-4eca-9ba7-90b4fa510835.dcm\n",
      "Converting to PNG .........................\n",
      "7.15.435.1320.19790.927733.6712340.64592975146827982207371425565 >>>>>> 7.74.555.1725.53378.159035.5966167.13467196861425294255192042452 >>>>> 1.49.298.2362.62584.191905.9912657.25087712912292330930841699332\n",
      "\n",
      ">> filename c926e886-d7ac-4eca-9ba7-90b4fa510835.dcm\n",
      "19 Done\n",
      "\n",
      ">>>  data/bdc7e41f-3b1d-426b-91a2-e6d1547f6dad.dcm\n",
      "Converting to PNG .........................\n",
      "8.18.172.5377.29360.341958.4842659.65715705787943526707583741651 >>>>>> 1.82.639.5998.96512.628322.8626016.84489111423785019067340458777 >>>>> 1.15.663.5559.71425.749086.7501810.23509514934703604142609948696\n",
      "\n",
      ">> filename bdc7e41f-3b1d-426b-91a2-e6d1547f6dad.dcm\n",
      "20 Done\n",
      "\n",
      ">>>  data/5613c36b-b003-41ae-8d1b-984fd512bff1.dcm\n",
      "Converting to PNG .........................\n",
      "1.14.634.8414.40371.751983.2327218.72640596205699425300030221868 >>>>>> 1.16.973.3312.67958.861683.8047496.20095963474049100872542584905 >>>>> 6.12.598.1134.11328.122710.7802038.41629846399147916783567202687\n",
      "\n",
      ">> filename 5613c36b-b003-41ae-8d1b-984fd512bff1.dcm\n",
      "21 Done\n",
      "\n",
      ">>>  data/7d6fc73f-d645-4f97-951c-69b9833a75c1.dcm\n",
      "Converting to PNG .........................\n",
      "7.13.690.7823.93808.461624.8537822.99898436242716472817419640566 >>>>>> 3.46.732.8391.74140.387728.1709837.80028698892163549203853872330 >>>>> 7.88.571.7838.45316.934467.8293381.50100446506709588814267260847\n",
      "\n",
      ">> filename 7d6fc73f-d645-4f97-951c-69b9833a75c1.dcm\n",
      "22 Done\n",
      "\n",
      ">>>  data/f2635b2d-c607-4ffe-91dd-eb8fe7f5c185.dcm\n",
      "Converting to PNG .........................\n",
      "7.24.365.1379.87141.516373.5248786.10819522065062781949080292855 >>>>>> 1.68.835.1053.64147.718408.8854013.21225319726007930865275613630 >>>>> 2.16.123.9684.13919.507976.2070417.19780297038553716121306132714\n",
      "\n",
      ">> filename f2635b2d-c607-4ffe-91dd-eb8fe7f5c185.dcm\n",
      "23 Done\n",
      "\n",
      ">>>  data/19b02908-8717-4275-a6fb-4ffd796c152f.dcm\n",
      "Converting to PNG .........................\n",
      "3.12.807.2621.15177.290029.8120807.62929995083434209904855360990 >>>>>> 1.17.228.5197.59170.547885.6512828.81858722340604451700796146527 >>>>> 8.62.313.2946.67253.159802.1857830.47806711490858981180283273319\n",
      "\n",
      ">> filename 19b02908-8717-4275-a6fb-4ffd796c152f.dcm\n",
      "24 Done\n",
      "\n",
      ">>>  data/e84f1a2e-0faf-4178-9f15-5a6fe43d13c3.dcm\n",
      "Converting to PNG .........................\n",
      "8.14.499.2224.57138.810965.4485169.75678883413635770959247812511 >>>>>> 1.10.501.9781.19672.828832.3827462.90512464327596221214585130346 >>>>> 2.57.630.5542.76275.964437.4116708.95993270613363137517559907212\n",
      "\n",
      ">> filename e84f1a2e-0faf-4178-9f15-5a6fe43d13c3.dcm\n",
      "25 Done\n",
      "\n",
      ">>>  data/46ccb9f9-aae7-4235-ad84-47a7921410e2.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.109.7436.95608.925709.9625239.86624521559343265596564405307 >>>>>> 3.77.935.3075.17268.434853.1052018.24995855801229819792762310635 >>>>> 4.26.163.5725.60990.110201.9885292.10825531011087712380054706006\n",
      "\n",
      ">> filename 46ccb9f9-aae7-4235-ad84-47a7921410e2.dcm\n",
      "26 Done\n",
      "\n",
      ">>>  data/e5440613-362f-4767-8ac8-420d849676cd.dcm\n",
      "Converting to PNG .........................\n",
      "3.67.192.2951.24955.234003.1290306.60255531971780560251660406492 >>>>>> 9.59.107.7905.19898.862787.5517081.12583781451310514025397387917 >>>>> 1.15.738.4219.67298.482791.8908235.12448315871728230520100738564\n",
      "\n",
      ">> filename e5440613-362f-4767-8ac8-420d849676cd.dcm\n",
      "27 Done\n",
      "\n",
      ">>>  data/e9146e95-a29d-4ed3-af20-dd767041998b.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.580.8266.61128.175443.7192084.42117095955616756351547592103 >>>>>> 4.75.263.4768.62264.743947.8479383.66983397617247354865438543706 >>>>> 3.37.345.5593.17145.641913.2615723.94048832958803078613985272720\n",
      "\n",
      ">> filename e9146e95-a29d-4ed3-af20-dd767041998b.dcm\n",
      "28 Done\n",
      "\n",
      ">>>  data/d59cc2da-4d9d-400f-a1d9-6cc3df74f614.dcm\n",
      "Converting to PNG .........................\n",
      "8.98.167.8096.24873.882284.4288144.62394580253238032242538329570 >>>>>> 4.76.644.1024.66557.111155.5715825.31241522442498389851643703416 >>>>> 8.56.752.4193.34265.830110.5901080.81690545785533635899835619509\n",
      "\n",
      ">> filename d59cc2da-4d9d-400f-a1d9-6cc3df74f614.dcm\n",
      "29 Done\n",
      "\n",
      ">>>  data/c31ebd5c-8a15-442a-8d4f-10030f29b7e5.dcm\n",
      "Converting to PNG .........................\n",
      "8.62.862.8161.76762.592830.1613987.38223846404346735498923693144 >>>>>> 2.77.676.4259.68495.857123.4169364.90217972679768079683150914452 >>>>> 7.92.188.3210.74081.847254.4265068.17421410061657816674809998705\n",
      "\n",
      ">> filename c31ebd5c-8a15-442a-8d4f-10030f29b7e5.dcm\n",
      "30 Done\n",
      "\n",
      ">>>  data/f0924d6e-2a53-4953-95c6-e4450cd405bc.dcm\n",
      "Converting to PNG .........................\n",
      "6.15.820.6636.13825.266122.8595856.64675843548381317563796981317 >>>>>> 2.85.652.8289.88055.582138.3740503.89323025851827681018841729291 >>>>> 3.89.562.3296.49623.763353.4899306.51436939471261969561794297545\n",
      "\n",
      ">> filename f0924d6e-2a53-4953-95c6-e4450cd405bc.dcm\n",
      "31 Done\n",
      "\n",
      ">>>  data/ecb06228-5d00-4b34-8eb1-b9dc821ef620.dcm\n",
      "Converting to PNG .........................\n",
      "6.84.900.7550.87029.417980.1525999.28297131895919678894273400092 >>>>>> 8.25.333.1460.21238.444362.7149852.81134968581265110354457973980 >>>>> 1.98.509.6449.17730.794729.3338163.36864718987449409034790989038\n",
      "\n",
      ">> filename ecb06228-5d00-4b34-8eb1-b9dc821ef620.dcm\n",
      "32 Done\n",
      "\n",
      ">>>  data/aa792217-5d1d-4d4c-ab7d-c60ccae3490d.dcm\n",
      "Converting to PNG .........................\n",
      "9.18.805.4683.31591.556988.8565438.55143931529910108634507592132 >>>>>> 6.78.632.9195.58781.520151.1450920.58049118300846398804097917746 >>>>> 4.60.334.3430.80593.750437.4422237.60008612043631177095758361615\n",
      "\n",
      ">> filename aa792217-5d1d-4d4c-ab7d-c60ccae3490d.dcm\n",
      "33 Done\n",
      "\n",
      ">>>  data/acedeb03-f38d-4806-8cb6-9150968c66c9.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.171.5887.97735.469149.3771903.39508706876836168630669298570 >>>>>> 6.11.256.7612.45822.828665.8509665.51217678500264117057150919466 >>>>> 1.14.114.3496.25402.323759.2308055.13709427687990851905083034824\n",
      "\n",
      ">> filename acedeb03-f38d-4806-8cb6-9150968c66c9.dcm\n",
      "34 Done\n",
      "\n",
      ">>>  data/e1159f57-dda2-4c91-8020-62dce379d1ed.dcm\n",
      "Converting to PNG .........................\n",
      "6.84.815.6227.24035.592022.3780755.95183613097196127868409447988 >>>>>> 1.22.260.9420.40694.272906.2053622.34314871227367897029378274207 >>>>> 9.33.216.9154.97033.270772.1862202.79748290837233039989793441760\n",
      "\n",
      ">> filename e1159f57-dda2-4c91-8020-62dce379d1ed.dcm\n",
      "35 Done\n",
      "\n",
      ">>>  data/8996d819-74f4-4898-b947-b52bc7c32a51.dcm\n",
      "Converting to PNG .........................\n",
      "1.51.646.9029.91312.882554.1570654.58625408961667942881736529036 >>>>>> 8.38.393.2947.80502.465565.8458576.82499569335318880571719847033 >>>>> 9.87.402.1254.99319.184055.5216185.94729045296989813584312697479\n",
      "\n",
      ">> filename 8996d819-74f4-4898-b947-b52bc7c32a51.dcm\n",
      "36 Done\n",
      "\n",
      ">>>  data/61b331d6-1f4a-44ad-8631-94adea0066cf.dcm\n",
      "Converting to PNG .........................\n",
      "9.34.387.3688.74045.271788.5404180.21951075927969886712796079640 >>>>>> 6.16.445.2594.37802.571807.8064040.75005776365760859152226339839 >>>>> 9.45.985.5310.47338.591095.9967822.86193226167554891688763244783\n",
      "\n",
      ">> filename 61b331d6-1f4a-44ad-8631-94adea0066cf.dcm\n",
      "37 Done\n",
      "\n",
      ">>>  data/64bc21bd-f485-4021-95b5-ed00fbc14284.dcm\n",
      "Converting to PNG .........................\n",
      "7.36.573.1123.31956.221242.4614209.74687534863531007866836622534 >>>>>> 1.17.366.4311.77513.532260.8352201.37693295484278343039384299075 >>>>> 1.10.959.4629.13318.490414.3185340.13826360887767264868962025597\n",
      "\n",
      ">> filename 64bc21bd-f485-4021-95b5-ed00fbc14284.dcm\n",
      "38 Done\n",
      "\n",
      ">>>  data/10979c8c-131d-478d-80f6-97985ce21c0b.dcm\n",
      "Converting to PNG .........................\n",
      "7.78.886.7724.13502.753910.8210147.30518521851166431791050710778 >>>>>> 5.48.774.9305.39435.170232.1553808.78488775772880366409936872180 >>>>> 9.90.120.1186.70823.758734.4241898.22313138659465241274118694824\n",
      "\n",
      ">> filename 10979c8c-131d-478d-80f6-97985ce21c0b.dcm\n",
      "39 Done\n",
      "\n",
      ">>>  data/3bab427f-ce2d-44c4-af20-7748110561b7.dcm\n",
      "Converting to PNG .........................\n",
      "2.68.344.7982.64565.362205.5539440.53930995895646065375292890077 >>>>>> 3.49.685.8825.43970.728384.8623663.42431129983970721489721100125 >>>>> 2.12.894.9280.23694.112753.2186453.23833839654690815954607439614\n",
      "\n",
      ">> filename 3bab427f-ce2d-44c4-af20-7748110561b7.dcm\n",
      "40 Done\n",
      "\n",
      ">>>  data/7bb2cf41-468e-45c8-a5f7-e8f8e6f21c61.dcm\n",
      "Converting to PNG .........................\n",
      "1.80.560.8781.23422.124848.6686336.53176473055147365387776687641 >>>>>> 2.44.754.3198.27426.692625.8426774.21728057770623461681159534853 >>>>> 6.62.741.7050.98065.170501.7375823.11864210646513166735502315590\n",
      "\n",
      ">> filename 7bb2cf41-468e-45c8-a5f7-e8f8e6f21c61.dcm\n",
      "41 Done\n",
      "\n",
      ">>>  data/cd84e639-965b-4f17-952b-45cf6a4426d1.dcm\n",
      "Converting to PNG .........................\n",
      "7.93.766.1142.42788.983823.3649629.40678038401475153052054459349 >>>>>> 1.11.166.4712.84914.799228.1357501.50313464987739897170072006791 >>>>> 1.65.723.9009.68704.140724.5447041.64951511068109871447763098532\n",
      "\n",
      ">> filename cd84e639-965b-4f17-952b-45cf6a4426d1.dcm\n",
      "42 Done\n",
      "\n",
      ">>>  data/8ff782b1-8aa0-4375-9a8d-a9bbce7e8f07.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.735.5113.55895.397038.4873115.10701630961011561669794364613 >>>>>> 1.14.313.3404.14047.605733.3256997.13237575229704284677342716564 >>>>> 1.10.659.6590.37955.226224.6936965.31228913465478237183697260799\n",
      "\n",
      ">> filename 8ff782b1-8aa0-4375-9a8d-a9bbce7e8f07.dcm\n",
      "43 Done\n",
      "\n",
      ">>>  data/6ca395f1-7f92-40b9-96c6-06127bb7980e.dcm\n",
      "Converting to PNG .........................\n",
      "9.89.357.8595.19606.166350.5367985.38207513403752438081721690191 >>>>>> 6.88.388.3987.48073.233935.9770938.12827066975909998748636838685 >>>>> 3.83.389.9521.11093.943683.7085354.95539849177650783475855081686\n",
      "\n",
      ">> filename 6ca395f1-7f92-40b9-96c6-06127bb7980e.dcm\n",
      "44 Done\n",
      "\n",
      ">>>  data/3a402ceb-c6b0-468a-8b41-744d36bfe008.dcm\n",
      "Converting to PNG .........................\n",
      "5.80.104.9910.97151.861143.1394187.15104306755970132873728296703 >>>>>> 1.17.710.8178.27478.391058.1423684.76349879375230786775923554147 >>>>> 2.30.514.1362.36369.357666.3429318.55577227136938023618157194331\n",
      "\n",
      ">> filename 3a402ceb-c6b0-468a-8b41-744d36bfe008.dcm\n",
      "45 Done\n",
      "\n",
      ">>>  data/d856ae63-8e1b-4e18-9fd5-a52e00e5b997.dcm\n",
      "Converting to PNG .........................\n",
      "4.38.457.5127.19610.596090.8908789.77547092599917388143058994670 >>>>>> 4.26.676.3636.84454.205407.1188846.51590107541413770026008290742 >>>>> 2.97.170.6101.45666.194331.8497826.49244499919643197881027797554\n",
      "\n",
      ">> filename d856ae63-8e1b-4e18-9fd5-a52e00e5b997.dcm\n",
      "46 Done\n",
      "\n",
      ">>>  data/823f9f3e-f886-469d-a840-d57b64bc91e4.dcm\n",
      "Converting to PNG .........................\n",
      "9.37.492.1855.37879.760924.5908881.41102660512258387946017116956 >>>>>> 9.11.183.7811.41164.953824.4223499.57471049911113416211747309258 >>>>> 5.35.906.4696.96653.125283.1948725.37375660987538126064033640724\n",
      "\n",
      ">> filename 823f9f3e-f886-469d-a840-d57b64bc91e4.dcm\n",
      "47 Done\n",
      "\n",
      ">>>  data/1b24fe41-9c54-4b45-8aef-458dd4cb7d79.dcm\n",
      "Converting to PNG .........................\n",
      "9.39.777.7847.25340.161049.6171844.30097970611505682982030186549 >>>>>> 5.52.698.4403.30595.681947.1458619.36801619256508535929587840478 >>>>> 5.97.300.4295.52548.253861.6525424.96445354263296189021306106189\n",
      "\n",
      ">> filename 1b24fe41-9c54-4b45-8aef-458dd4cb7d79.dcm\n",
      "48 Done\n",
      "\n",
      ">>>  data/07c5c1f0-edf8-4ff9-9866-4796db373b45.dcm\n",
      "Converting to PNG .........................\n",
      "8.46.544.7419.10381.261808.3655954.17768692735484474066371021381 >>>>>> 9.11.488.5857.78194.420604.1610004.27108068015394328052876730336 >>>>> 3.90.565.9999.89873.655984.1740975.98451997556279028327684766973\n",
      "\n",
      ">> filename 07c5c1f0-edf8-4ff9-9866-4796db373b45.dcm\n",
      "49 Done\n",
      "\n",
      ">>>  data/569addd2-bc4b-44ac-b6cb-9c527c2d5fc6.dcm\n",
      "Converting to PNG .........................\n",
      "5.25.185.3761.10939.406457.1648164.43124299404247862118836996976 >>>>>> 2.34.304.1294.39489.444093.2782332.99102876706922828791238306033 >>>>> 1.12.692.9319.81334.860484.5963722.73094001771413341111160951040\n",
      "\n",
      ">> filename 569addd2-bc4b-44ac-b6cb-9c527c2d5fc6.dcm\n",
      "50 Done\n",
      "\n",
      ">>>  data/2c36e595-7943-45ca-b5a1-75aa0abdf367.dcm\n",
      "Converting to PNG .........................\n",
      "9.38.548.6153.25380.359676.1663514.81729838682124466666617301893 >>>>>> 9.21.607.2852.93451.412279.2042579.88289652454030400793559641257 >>>>> 6.68.973.5367.82990.687274.3674945.92257059068223406494739700652\n",
      "\n",
      ">> filename 2c36e595-7943-45ca-b5a1-75aa0abdf367.dcm\n",
      "51 Done\n",
      "\n",
      ">>>  data/04ed79a6-ac5f-4dc1-8833-86a238d8ba8f.dcm\n",
      "Converting to PNG .........................\n",
      "8.66.957.9184.59933.814797.1601599.48989025567668565750447495609 >>>>>> 4.94.613.5374.13172.889471.8433947.14266756474465972032239346852 >>>>> 4.15.928.6592.88786.636302.8859084.92742395734922922940872840877\n",
      "\n",
      ">> filename 04ed79a6-ac5f-4dc1-8833-86a238d8ba8f.dcm\n",
      "52 Done\n",
      "\n",
      ">>>  data/11ef2bb6-aa69-495d-81d7-e42f6d325c99.dcm\n",
      "Converting to PNG .........................\n",
      "7.61.110.5217.38333.183603.5603023.81275292693645818827127763423 >>>>>> 9.46.337.6504.53927.554380.5436388.54740158076087732676089991069 >>>>> 9.20.137.6892.76980.219159.2500207.46435102355066162420604981634\n",
      "\n",
      ">> filename 11ef2bb6-aa69-495d-81d7-e42f6d325c99.dcm\n",
      "53 Done\n",
      "\n",
      ">>>  data/2e0236c5-89c7-4f2e-a675-57c4d4600d97.dcm\n",
      "Converting to PNG .........................\n",
      "2.52.950.4034.68825.654161.9443029.43790602557512709765171878751 >>>>>> 7.77.872.7402.60400.211666.1988244.24084019879778731881349850657 >>>>> 8.41.522.8520.12678.564577.6518982.46306215888201329008570800346\n",
      "\n",
      ">> filename 2e0236c5-89c7-4f2e-a675-57c4d4600d97.dcm\n",
      "54 Done\n",
      "\n",
      ">>>  data/08028bf1-d024-47d5-984f-fea252cf1128.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.289.4935.42050.641964.5498422.15863761474359872559079605373 >>>>>> 1.18.693.4919.18309.183582.1882364.69558230207561425026079962666 >>>>> 3.20.607.2545.50911.805764.8112914.77247799522046066103048899059\n",
      "\n",
      ">> filename 08028bf1-d024-47d5-984f-fea252cf1128.dcm\n",
      "55 Done\n",
      "\n",
      ">>>  data/45035d98-4953-4040-a89d-95a4df5b3626.dcm\n",
      "Converting to PNG .........................\n",
      "3.14.473.8973.64168.110425.4051393.62016822097400438153556633115 >>>>>> 7.12.157.5495.41611.360520.9799182.83627396233485276786203130371 >>>>> 7.82.724.3097.98772.262624.1566400.97375791335322707168361530628\n",
      "\n",
      ">> filename 45035d98-4953-4040-a89d-95a4df5b3626.dcm\n",
      "56 Done\n",
      "\n",
      ">>>  data/e46345dc-097b-4b93-9923-897d294fc3ee.dcm\n",
      "Converting to PNG .........................\n",
      "9.12.685.9643.33952.132888.4372210.91130746976146147409226666089 >>>>>> 6.59.108.7749.12514.132575.4815463.34661153122378484426342185940 >>>>> 5.26.491.4937.61045.558766.8150564.20191542063025268875396226354\n",
      "\n",
      ">> filename e46345dc-097b-4b93-9923-897d294fc3ee.dcm\n",
      "57 Done\n",
      "\n",
      ">>>  data/e705b1de-4a53-4da9-a46d-9a4d977cc929.dcm\n",
      "Converting to PNG .........................\n",
      "5.97.212.5395.82164.911156.4444775.54006695139051600759382118774 >>>>>> 9.13.254.7651.36551.743848.3161282.96268462027420384176141718660 >>>>> 7.11.922.2689.10325.642743.7018885.59975841892420048780205829666\n",
      "\n",
      ">> filename e705b1de-4a53-4da9-a46d-9a4d977cc929.dcm\n",
      "58 Done\n",
      "\n",
      ">>>  data/8b23fb56-2f51-4fcb-8f2d-fdc022d570f4.dcm\n",
      "Converting to PNG .........................\n",
      "1.27.135.7179.38114.237003.2413645.19817157176962491323589881476 >>>>>> 3.59.228.6835.10491.148524.1422812.59145877845635293883313411831 >>>>> 1.37.133.3068.19180.153935.7170089.89856723386144365728287347527\n",
      "\n",
      ">> filename 8b23fb56-2f51-4fcb-8f2d-fdc022d570f4.dcm\n",
      "59 Done\n",
      "\n",
      ">>>  data/6c637267-e705-4da7-96dd-2f9ec25044a5.dcm\n",
      "Converting to PNG .........................\n",
      "4.32.949.9370.58055.521025.6273872.83185215496849165740283034341 >>>>>> 7.61.817.1520.22982.277218.2396241.22446113620438897371760151844 >>>>> 6.40.130.5983.62039.433812.6899309.36446994634829871208713680290\n",
      "\n",
      ">> filename 6c637267-e705-4da7-96dd-2f9ec25044a5.dcm\n",
      "60 Done\n",
      "\n",
      ">>>  data/a962301a-ed5b-40f7-b807-13d9f4fba74f.dcm\n",
      "Converting to PNG .........................\n",
      "5.72.124.8459.56370.767427.7340480.12595772414934449625965206856 >>>>>> 7.17.473.7325.14666.982832.3682960.40886276647154088697166973244 >>>>> 1.11.155.1209.69084.835144.3735121.76581265039364961779613833116\n",
      "\n",
      ">> filename a962301a-ed5b-40f7-b807-13d9f4fba74f.dcm\n",
      "61 Done\n",
      "\n",
      ">>>  data/b1c2dbb4-4563-4a30-b238-bbb1f5cbf121.dcm\n",
      "Converting to PNG .........................\n",
      "6.26.129.2454.52036.682795.4978993.72848935428697858041923682225 >>>>>> 5.18.567.9003.81422.329054.4240238.12198901079192041552613525174 >>>>> 5.13.616.8074.57020.655662.2695812.13206833227116998346609738777\n",
      "\n",
      ">> filename b1c2dbb4-4563-4a30-b238-bbb1f5cbf121.dcm\n",
      "62 Done\n",
      "\n",
      ">>>  data/dff3d2ea-aebb-4857-83dc-5587337ea636.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.984.8512.43616.207284.1123343.41128888154392883603537485934 >>>>>> 1.14.333.5313.79068.127947.6089410.38542878747885166858871320078 >>>>> 4.83.976.5362.15190.187095.6764485.44886824399380633496954120139\n",
      "\n",
      ">> filename dff3d2ea-aebb-4857-83dc-5587337ea636.dcm\n",
      "63 Done\n",
      "\n",
      ">>>  data/2475ac8c-38fe-4477-9a4c-d4ed65eed150.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.352.2032.74222.123150.1868468.67461388194342843425514325126 >>>>>> 1.14.750.6764.15640.956748.9258103.70765991876799272551820976808 >>>>> 9.11.160.8413.50555.970674.4066105.68120531564660957538253101046\n",
      "\n",
      ">> filename 2475ac8c-38fe-4477-9a4c-d4ed65eed150.dcm\n",
      "64 Done\n",
      "\n",
      ">>>  data/69fedf16-25f2-4c4a-802c-59eaf3bd012e.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.500.8524.42756.325530.3736718.29121064577482510231104650419 >>>>>> 1.12.471.9072.51497.827807.4578139.92741002920179068651938200267 >>>>> 9.13.897.6408.10979.500003.6266206.11693891186346391735834970180\n",
      "\n",
      ">> filename 69fedf16-25f2-4c4a-802c-59eaf3bd012e.dcm\n",
      "65 Done\n",
      "\n",
      ">>>  data/f5cd2898-ab9b-482b-8f95-b7c9f2f8c22e.dcm\n",
      "Converting to PNG .........................\n",
      "7.63.436.7306.26189.772557.7357465.71396379041313156736867288077 >>>>>> 3.47.436.5534.34592.554653.1391049.67304427012011110570913433859 >>>>> 8.85.337.7690.77767.412763.8705445.84260084000315416516347047797\n",
      "\n",
      ">> filename f5cd2898-ab9b-482b-8f95-b7c9f2f8c22e.dcm\n",
      "66 Done\n",
      "\n",
      ">>>  data/06cbcff5-f280-4782-87bf-fc050b9ff4d6.dcm\n",
      "Converting to PNG .........................\n",
      "6.56.730.3291.91517.716857.9884318.95838744866683838852991536267 >>>>>> 5.20.224.6658.48001.379081.4190054.43985304468985944513952575730 >>>>> 1.13.168.7929.13723.296997.1388715.16412809968520345497631426869\n",
      "\n",
      ">> filename 06cbcff5-f280-4782-87bf-fc050b9ff4d6.dcm\n",
      "67 Done\n",
      "\n",
      ">>>  data/9e5529ea-6a3b-4545-9692-50f39009ebb4.dcm\n",
      "Converting to PNG .........................\n",
      "5.46.108.2834.73534.669223.3390311.97351677370699539652303452486 >>>>>> 5.12.484.8729.78767.188759.8614217.22871433002850542438483195202 >>>>> 9.68.195.5617.10235.349508.1253151.42877867676423701650393544679\n",
      "\n",
      ">> filename 9e5529ea-6a3b-4545-9692-50f39009ebb4.dcm\n",
      "68 Done\n",
      "\n",
      ">>>  data/59388f6f-42e7-453b-b342-bf93bab237ef.dcm\n",
      "Converting to PNG .........................\n",
      "3.60.137.1041.18333.408716.1870467.12015971391473505651481520988 >>>>>> 4.26.107.9401.62347.443144.3478582.18467344090145568139066163854 >>>>> 4.40.189.6965.69276.470979.4567824.92451333111017960717818713859\n",
      "\n",
      ">> filename 59388f6f-42e7-453b-b342-bf93bab237ef.dcm\n",
      "69 Done\n",
      "\n",
      ">>>  data/f4649a51-465e-440a-b261-10620cb35e3d.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.157.6281.53006.830201.1296366.77742113481927059738315231901 >>>>>> 9.25.589.4116.70072.494943.3500741.89465700206181650012969249570 >>>>> 7.37.206.5107.95725.630622.8852054.48049265294609618671155069181\n",
      "\n",
      ">> filename f4649a51-465e-440a-b261-10620cb35e3d.dcm\n",
      "70 Done\n",
      "\n",
      ">>>  data/f8d1a2dd-dcb5-4b02-b09c-1bc1cbe3d8f6.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.147.8532.88215.971899.4191105.13280510484531890680817798393 >>>>>> 4.54.203.7169.58363.380548.1987925.87552249687065700724263314741 >>>>> 1.13.684.7072.53581.153276.6285245.24190151771918494320433867027\n",
      "\n",
      ">> filename f8d1a2dd-dcb5-4b02-b09c-1bc1cbe3d8f6.dcm\n",
      "71 Done\n",
      "\n",
      ">>>  data/875856e3-752d-4306-8a59-1226f24d29a2.dcm\n",
      "Converting to PNG .........................\n",
      "5.21.141.2345.29412.175719.3426818.65787134395747250974767896909 >>>>>> 1.10.192.6790.67934.717054.5118942.75489686626792200486408414899 >>>>> 1.17.103.1189.14726.257493.7812244.99768264676749954150543585699\n",
      "\n",
      ">> filename 875856e3-752d-4306-8a59-1226f24d29a2.dcm\n",
      "72 Done\n",
      "\n",
      ">>>  data/e0d26d3b-ed90-4434-84b8-5b34e5c15453.dcm\n",
      "Converting to PNG .........................\n",
      "2.36.695.8328.39046.962513.1299767.17713710918759720283970550170 >>>>>> 5.54.984.4392.13624.630077.3174616.25093647273085797190892307684 >>>>> 6.15.193.8265.22046.384258.3552936.30311961861021354159210219458\n",
      "\n",
      ">> filename e0d26d3b-ed90-4434-84b8-5b34e5c15453.dcm\n",
      "73 Done\n",
      "\n",
      ">>>  data/a8b01534-ea8d-45f0-b456-cd9da18857a1.dcm\n",
      "Converting to PNG .........................\n",
      "8.15.445.2026.27399.876309.9851341.14070277666182881418949604321 >>>>>> 2.91.240.1746.18561.141274.8372640.90716513486783692345889315373 >>>>> 5.17.196.1573.60961.139445.1587151.43508260557180755307859351096\n",
      "\n",
      ">> filename a8b01534-ea8d-45f0-b456-cd9da18857a1.dcm\n",
      "74 Done\n",
      "\n",
      ">>>  data/b351b1f2-6751-4c69-833d-c323d86b9f96.dcm\n",
      "Converting to PNG .........................\n",
      "8.47.112.1697.64288.279914.6841839.22734844682125255034934460421 >>>>>> 7.93.132.6473.26988.362439.9166585.33200471875185515552555431031 >>>>> 3.22.104.5265.76589.135655.8037028.30771638889191511607162133960\n",
      "\n",
      ">> filename b351b1f2-6751-4c69-833d-c323d86b9f96.dcm\n",
      "75 Done\n",
      "\n",
      ">>>  data/d9d26c6f-63b9-478f-aab9-5f8e12c45cfd.dcm\n",
      "Converting to PNG .........................\n",
      "1.98.914.1399.34905.615786.7988199.48651957094997412159912821983 >>>>>> 6.13.164.3142.44564.179666.1144397.95511240202783603430228420155 >>>>> 6.24.496.9655.20444.147657.8475255.37316890846744515625068692932\n",
      "\n",
      ">> filename d9d26c6f-63b9-478f-aab9-5f8e12c45cfd.dcm\n",
      "76 Done\n",
      "\n",
      ">>>  data/869646ba-f1ae-4696-b17f-7b0990dbe583.dcm\n",
      "Converting to PNG .........................\n",
      "5.51.725.2360.49889.512784.6331987.19287377195314632268297580349 >>>>>> 8.59.335.3546.99608.576383.4879427.36235329284064423596859121624 >>>>> 2.57.490.3828.17547.118173.7739176.88738102353699631666832983934\n",
      "\n",
      ">> filename 869646ba-f1ae-4696-b17f-7b0990dbe583.dcm\n",
      "77 Done\n",
      "\n",
      ">>>  data/902778e0-9882-4700-90ae-92cc89e0e366.dcm\n",
      "Converting to PNG .........................\n",
      "3.55.143.5409.24520.147483.3537257.49019136932891720585444150808 >>>>>> 9.17.836.1506.97047.653754.3650391.19915090184986819708325465733 >>>>> 5.79.510.2169.17169.451811.7641724.19235279102408988492572776176\n",
      "\n",
      ">> filename 902778e0-9882-4700-90ae-92cc89e0e366.dcm\n",
      "78 Done\n",
      "\n",
      ">>>  data/2a8d85ab-e687-4e8c-a46c-1b517af0d916.dcm\n",
      "Converting to PNG .........................\n",
      "4.38.995.5030.81997.105384.1065577.63762206653974729411194971358 >>>>>> 6.14.262.9842.81186.383915.7472173.21607905522024918664702350454 >>>>> 5.56.426.6610.31310.980231.8976385.12397990650040783716936682735\n",
      "\n",
      ">> filename 2a8d85ab-e687-4e8c-a46c-1b517af0d916.dcm\n",
      "79 Done\n",
      "\n",
      ">>>  data/6e3906d3-93f9-40db-a13e-ae84adb68c09.dcm\n",
      "Converting to PNG .........................\n",
      "5.18.765.2794.59586.650061.6172113.29799606183810625580935512613 >>>>>> 9.69.266.3195.33156.711196.5133756.16891552125595721304997318341 >>>>> 9.10.434.3762.76188.140171.9109368.81028460207418030107247786499\n",
      "\n",
      ">> filename 6e3906d3-93f9-40db-a13e-ae84adb68c09.dcm\n",
      "80 Done\n",
      "\n",
      ">>>  data/d2c0e470-2f01-4fd3-8e1e-e21f5179b604.dcm\n",
      "Converting to PNG .........................\n",
      "2.87.129.9951.47064.156182.1904614.89355804538924908353123439992 >>>>>> 8.10.997.8257.53593.241846.8113439.55377609615140784547386561928 >>>>> 6.89.780.8445.56468.932271.6325746.67790269834849671057624301294\n",
      "\n",
      ">> filename d2c0e470-2f01-4fd3-8e1e-e21f5179b604.dcm\n",
      "81 Done\n",
      "\n",
      ">>>  data/8eba5099-e6ed-442e-b84c-bafe4e16a6e1.dcm\n",
      "Converting to PNG .........................\n",
      "5.58.962.5619.64636.726235.7554860.55361895714212351278781203991 >>>>>> 8.17.147.8960.15766.910826.7069011.69597277741824457145204428983 >>>>> 5.90.108.3940.48571.449094.9778440.17822638373181816142082393257\n",
      "\n",
      ">> filename 8eba5099-e6ed-442e-b84c-bafe4e16a6e1.dcm\n",
      "82 Done\n",
      "\n",
      ">>>  data/33dea50b-e8c3-47bb-906f-33e982eab657.dcm\n",
      "Converting to PNG .........................\n",
      "9.29.382.1625.81940.304343.1069013.24704870670284929372258796255 >>>>>> 8.32.142.3352.48257.294123.1210691.40936069428233195518484165003 >>>>> 1.10.655.1454.97322.777136.1504963.20605630804237276812608789093\n",
      "\n",
      ">> filename 33dea50b-e8c3-47bb-906f-33e982eab657.dcm\n",
      "83 Done\n",
      "\n",
      ">>>  data/6bff53d4-52c5-43bc-9065-68affbad2c48.dcm\n",
      "Converting to PNG .........................\n",
      "2.40.149.5909.33230.143137.4840832.25929147836549580927525928549 >>>>>> 6.64.881.2616.23841.825027.7632022.48153083479348261995902524855 >>>>> 9.97.717.9423.99398.830595.4826633.41626618917735468263451911651\n",
      "\n",
      ">> filename 6bff53d4-52c5-43bc-9065-68affbad2c48.dcm\n",
      "84 Done\n",
      "\n",
      ">>>  data/9864c2c3-4563-40be-9679-64ed68354d30.dcm\n",
      "Converting to PNG .........................\n",
      "3.14.394.9293.69554.553056.3182020.16174329905208485687533882006 >>>>>> 6.18.497.4011.60462.895732.1815300.31785708449993006089891944378 >>>>> 1.10.183.1751.13147.356633.1046239.30718949524181374249939573687\n",
      "\n",
      ">> filename 9864c2c3-4563-40be-9679-64ed68354d30.dcm\n",
      "85 Done\n",
      "\n",
      ">>>  data/225fc6dd-ff45-4091-aec5-b22a135be266.dcm\n",
      "Converting to PNG .........................\n",
      "2.73.682.1805.12105.175089.8170279.79772197355948497676926293815 >>>>>> 7.47.854.1669.82992.113200.7351837.35196982784466312531812562644 >>>>> 2.16.245.7239.18229.533718.3970231.67425788897336823919437371498\n",
      "\n",
      ">> filename 225fc6dd-ff45-4091-aec5-b22a135be266.dcm\n",
      "86 Done\n",
      "\n",
      ">>>  data/9eb2d624-f7b9-4b77-8b3b-1dd88851b970.dcm\n",
      "Converting to PNG .........................\n",
      "9.15.924.5506.58366.654996.5173044.17366523026969918458212043446 >>>>>> 7.30.391.7780.12956.630998.1376625.68213880692580248432414117360 >>>>> 1.10.552.3662.53250.561024.9429553.53095760433934742532335078250\n",
      "\n",
      ">> filename 9eb2d624-f7b9-4b77-8b3b-1dd88851b970.dcm\n",
      "87 Done\n",
      "\n",
      ">>>  data/5148ea4d-0a3e-42db-94a1-f8a0a0bba46c.dcm\n",
      "Converting to PNG .........................\n",
      "5.22.207.5188.20435.767239.1546774.24587931660318664091252574647 >>>>>> 9.19.193.2864.22507.863090.5409655.58242105088028245265124928765 >>>>> 4.35.425.4716.98565.430190.3379468.18442489985190378669813324053\n",
      "\n",
      ">> filename 5148ea4d-0a3e-42db-94a1-f8a0a0bba46c.dcm\n",
      "88 Done\n",
      "\n",
      ">>>  data/00f9eb96-0eac-4485-9273-edab2df09ec5.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.175.6073.11150.289095.5595822.97803865665390293993274896215 >>>>>> 2.91.389.7604.52840.650486.1425909.61066712926813302669870822903 >>>>> 2.39.133.1741.17568.952274.8901590.27409867305385382586196660737\n",
      "\n",
      ">> filename 00f9eb96-0eac-4485-9273-edab2df09ec5.dcm\n",
      "89 Done\n",
      "\n",
      ">>>  data/dfcb10ee-4be9-4fc9-82b3-f18f26301b97.dcm\n",
      "Converting to PNG .........................\n",
      "9.38.377.1184.26119.344315.4359317.73314476770284267116135356295 >>>>>> 8.17.181.4511.20757.964817.4370826.76156108523410088178028175575 >>>>> 7.20.383.9244.89782.765153.7037169.86479596620213700659627024255\n",
      "\n",
      ">> filename dfcb10ee-4be9-4fc9-82b3-f18f26301b97.dcm\n",
      "90 Done\n",
      "\n",
      ">>>  data/55e002bb-af13-42ec-8653-f2543aab304c.dcm\n",
      "Converting to PNG .........................\n",
      "8.75.161.1086.85464.212186.1484303.83997443282828845425355097857 >>>>>> 1.16.490.9957.30891.913244.2738373.81760641860950083275344200923 >>>>> 3.54.542.4803.97094.888977.1910373.34748973597709284926227177249\n",
      "\n",
      ">> filename 55e002bb-af13-42ec-8653-f2543aab304c.dcm\n",
      "91 Done\n",
      "\n",
      ">>>  data/82a1c6be-0e00-4ff8-a897-809234b8724c.dcm\n",
      "Converting to PNG .........................\n",
      "2.43.186.9286.65252.523710.9923726.64576476273730144729434686934 >>>>>> 6.87.542.9557.70416.216335.1605807.10062222728282046449194151503 >>>>> 1.16.942.7800.68233.137682.1556035.11237219922168290057524219056\n",
      "\n",
      ">> filename 82a1c6be-0e00-4ff8-a897-809234b8724c.dcm\n",
      "92 Done\n",
      "\n",
      ">>>  data/ae1b78c2-71f8-4bc8-ae1d-1c3b628cb4d1.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.779.1357.48305.699890.8671595.80833848531554041425206430330 >>>>>> 1.10.123.4152.64701.789860.7637951.87527961093481460947779111949 >>>>> 5.81.812.8931.32151.761316.5499552.68675103751382472074584512058\n",
      "\n",
      ">> filename ae1b78c2-71f8-4bc8-ae1d-1c3b628cb4d1.dcm\n",
      "93 Done\n",
      "\n",
      ">>>  data/92986ec3-fca2-4080-90b2-c4c40f90120d.dcm\n",
      "Converting to PNG .........................\n",
      "3.74.445.3907.97670.954129.3514169.37140671634977820107658444067 >>>>>> 7.81.717.1449.71740.128777.1359574.77684089277957073211787313706 >>>>> 7.27.697.3089.99002.370864.4776401.76686513267569321015364844447\n",
      "\n",
      ">> filename 92986ec3-fca2-4080-90b2-c4c40f90120d.dcm\n",
      "94 Done\n",
      "\n",
      ">>>  data/54e90916-8100-43ff-bc1a-d6bf8624072c.dcm\n",
      "Converting to PNG .........................\n",
      "5.11.930.4997.87408.887318.6573210.14967825200805191097674425217 >>>>>> 8.13.183.5191.42473.672232.6416348.11241076495731034565515516077 >>>>> 8.23.824.3802.38537.480754.9219088.54310871116356377016725438258\n",
      "\n",
      ">> filename 54e90916-8100-43ff-bc1a-d6bf8624072c.dcm\n",
      "95 Done\n",
      "\n",
      ">>>  data/ec5a7a5a-9c9e-4a87-b1be-e0bdeb9f9f6b.dcm\n",
      "Converting to PNG .........................\n",
      "2.66.643.1979.60644.629647.1587659.29215303821925240669534458674 >>>>>> 7.98.196.7205.16039.295852.5891159.72120504107642004968422960818 >>>>> 4.88.471.9803.60380.191815.5264435.15408129143279822362541412082\n",
      "\n",
      ">> filename ec5a7a5a-9c9e-4a87-b1be-e0bdeb9f9f6b.dcm\n",
      "96 Done\n",
      "\n",
      ">>>  data/1e623bfc-1528-4874-b800-7ef51aa1f239.dcm\n",
      "Converting to PNG .........................\n",
      "4.74.331.4758.96043.106013.5985546.14515305529252347231140480801 >>>>>> 4.49.670.8793.91906.229589.1478819.31310555634163065585802852464 >>>>> 9.44.136.5176.90834.756723.3959448.92197990118143243489612501116\n",
      "\n",
      ">> filename 1e623bfc-1528-4874-b800-7ef51aa1f239.dcm\n",
      "97 Done\n",
      "\n",
      ">>>  data/a9c81c4c-a394-42b2-b6d4-a0cb2a8d7d98.dcm\n",
      "Converting to PNG .........................\n",
      "8.16.607.3551.19878.198484.9689287.46074807125387282846837093746 >>>>>> 3.92.340.2457.67105.300696.9858414.96838754430522281554512081673 >>>>> 2.19.560.9400.31569.826437.1407379.56991416673442641014795125972\n",
      "\n",
      ">> filename a9c81c4c-a394-42b2-b6d4-a0cb2a8d7d98.dcm\n",
      "98 Done\n",
      "\n",
      ">>>  data/4b52f61c-412e-4e34-b39f-3e928d8e5a8e.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.909.2473.25215.598198.9789815.72884554242103349533385688245 >>>>>> 5.43.145.6195.79262.870195.7631025.86848846861386176052189469996 >>>>> 8.61.234.5152.34193.305257.9741620.74844829171173588356296605982\n",
      "\n",
      ">> filename 4b52f61c-412e-4e34-b39f-3e928d8e5a8e.dcm\n",
      "99 Done\n",
      "\n",
      ">>>  data/40c6dbc3-a7c5-49c1-97c4-04e4b29a4354.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.367.8084.93094.234888.5576047.88726706231090943547699324948 >>>>>> 1.43.466.1550.62934.370188.6450907.15178990686086310049724089263 >>>>> 9.45.894.7929.19956.279369.7160910.70328980098696125874300628058\n",
      "\n",
      ">> filename 40c6dbc3-a7c5-49c1-97c4-04e4b29a4354.dcm\n",
      "100 Done\n",
      "\n",
      ">>>  data/0ffa8cdf-751a-4257-a888-755945459246.dcm\n",
      "Converting to PNG .........................\n",
      "1.70.553.9351.75298.230480.3819591.54727411461359215715741468100 >>>>>> 1.54.204.1741.73860.339901.8928972.19822463797545700277879254483 >>>>> 3.35.240.2825.95220.517186.9600398.67279871112370135856441178369\n",
      "\n",
      ">> filename 0ffa8cdf-751a-4257-a888-755945459246.dcm\n",
      "101 Done\n",
      "\n",
      ">>>  data/794bf2b9-be99-4122-9f21-174d9744b885.dcm\n",
      "Converting to PNG .........................\n",
      "1.14.898.7559.52908.101298.3909937.92768026643115791936676123276 >>>>>> 2.17.368.7327.97396.821599.1583148.58364267795899748438871344475 >>>>> 9.14.128.5246.16027.313574.4929018.32231383769565961042627479409\n",
      "\n",
      ">> filename 794bf2b9-be99-4122-9f21-174d9744b885.dcm\n",
      "102 Done\n",
      "\n",
      ">>>  data/9b067a7a-5e74-4ed8-ab8b-cc24b7ac302f.dcm\n",
      "Converting to PNG .........................\n",
      "8.11.414.2372.52842.233268.2516691.36840320684714305673348236325 >>>>>> 4.53.163.1390.15449.938559.5247220.18955150536177671965029855363 >>>>> 2.69.508.6389.15195.701119.9911524.37261803320709580057394152203\n",
      "\n",
      ">> filename 9b067a7a-5e74-4ed8-ab8b-cc24b7ac302f.dcm\n",
      "103 Done\n",
      "\n",
      ">>>  data/3287ab13-97ce-41a8-94b7-f4971cf95ab0.dcm\n",
      "Converting to PNG .........................\n",
      "5.12.198.2485.68985.777804.1682353.30290899169207821774306296188 >>>>>> 6.56.604.4320.10958.256249.7877256.60483632386921331503127958296 >>>>> 1.18.403.4647.49718.105123.4299925.87894436659602730862834235809\n",
      "\n",
      ">> filename 3287ab13-97ce-41a8-94b7-f4971cf95ab0.dcm\n",
      "104 Done\n",
      "\n",
      ">>>  data/b345e94f-7d75-445a-a568-696192ef4f2e.dcm\n",
      "Converting to PNG .........................\n",
      "5.93.885.3790.12853.356226.4373055.51314729270289804209865829360 >>>>>> 2.23.447.5486.19857.189650.8568335.30045057241313958188549842469 >>>>> 2.19.101.1015.10865.195508.9730430.11600588486433577427265502717\n",
      "\n",
      ">> filename b345e94f-7d75-445a-a568-696192ef4f2e.dcm\n",
      "105 Done\n",
      "\n",
      ">>>  data/ad0db414-0512-4983-99e7-e2f56a37d2a0.dcm\n",
      "Converting to PNG .........................\n",
      "5.49.288.9047.73414.199412.4696860.94848222217181673179298422511 >>>>>> 1.13.684.3938.42715.115164.9847780.10594839607573076891902177195 >>>>> 6.94.166.7705.16884.736918.2703501.89510279689612642478500150777\n",
      "\n",
      ">> filename ad0db414-0512-4983-99e7-e2f56a37d2a0.dcm\n",
      "106 Done\n",
      "\n",
      ">>>  data/a6495a59-fb53-4edd-bfcc-510df0d13733.dcm\n",
      "Converting to PNG .........................\n",
      "9.85.920.3764.87260.514883.7321007.94791163540755521538828930367 >>>>>> 2.38.171.4696.66627.360936.7006581.22007877471194689166453390062 >>>>> 2.61.413.1368.91043.193135.5434192.90203946588761137193121635807\n",
      "\n",
      ">> filename a6495a59-fb53-4edd-bfcc-510df0d13733.dcm\n",
      "107 Done\n",
      "\n",
      ">>>  data/3eb7a66a-f36c-42c4-9af8-b5e1d6ecda1a.dcm\n",
      "Converting to PNG .........................\n",
      "8.73.348.7110.54861.541604.3363244.30925890079345502515754830520 >>>>>> 3.42.524.1961.72849.102409.1854688.82910524191476682873518180126 >>>>> 1.10.120.1830.79551.264227.4615529.55784129821968275491849650446\n",
      "\n",
      ">> filename 3eb7a66a-f36c-42c4-9af8-b5e1d6ecda1a.dcm\n",
      "108 Done\n",
      "\n",
      ">>>  data/6feb1b21-0bdd-433e-8709-6a42de5aef8c.dcm\n",
      "Converting to PNG .........................\n",
      "5.31.738.7614.47612.637031.1197502.27797234777018917850699532824 >>>>>> 7.98.197.7912.12758.340212.7926384.13856117205808393880755009329 >>>>> 5.18.732.7388.22234.632700.5330057.20306178307899562496929778855\n",
      "\n",
      ">> filename 6feb1b21-0bdd-433e-8709-6a42de5aef8c.dcm\n",
      "109 Done\n",
      "\n",
      ">>>  data/c97fc5f5-8a1b-4e4b-84c5-f1174166a781.dcm\n",
      "Converting to PNG .........................\n",
      "6.16.616.3596.93777.876268.2760584.24831004682755065489924856538 >>>>>> 7.26.342.1129.35124.205282.6453784.50888147149398008387701379155 >>>>> 3.37.580.6422.27988.876271.3776449.39510385508371616440273953875\n",
      "\n",
      ">> filename c97fc5f5-8a1b-4e4b-84c5-f1174166a781.dcm\n",
      "110 Done\n",
      "\n",
      ">>>  data/d0c3731a-af02-45dc-9d80-32d681b6a1a5.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.603.1214.42626.564969.6141770.29976015564097236060240428292 >>>>>> 8.12.669.1735.70365.866340.1224945.85270807272118798078916971377 >>>>> 6.10.993.1567.21771.164401.7743676.40572146292454054713543026989\n",
      "\n",
      ">> filename d0c3731a-af02-45dc-9d80-32d681b6a1a5.dcm\n",
      "111 Done\n",
      "\n",
      ">>>  data/69a37fc8-d89e-4578-8e03-87b71ab3ba85.dcm\n",
      "Converting to PNG .........................\n",
      "7.79.115.5924.16242.950973.4260689.39645715078913827242106753462 >>>>>> 9.87.626.4763.41073.845182.9528581.79166405510901105592541183268 >>>>> 1.13.843.9256.57665.565888.5062363.74079353731771677209090365877\n",
      "\n",
      ">> filename 69a37fc8-d89e-4578-8e03-87b71ab3ba85.dcm\n",
      "112 Done\n",
      "\n",
      ">>>  data/42de9b5e-5ba0-4d1c-acf7-ee583bd07f42.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.491.5441.84602.944407.5005995.74821507686318156158328833521 >>>>>> 2.42.211.1605.85009.405685.1961776.31216538115454902984698194427 >>>>> 4.14.935.8879.83215.803398.1751317.28355383566460648879850491479\n",
      "\n",
      ">> filename 42de9b5e-5ba0-4d1c-acf7-ee583bd07f42.dcm\n",
      "113 Done\n",
      "\n",
      ">>>  data/5efac429-e673-486b-977d-c0df9f9fa621.dcm\n",
      "Converting to PNG .........................\n",
      "8.72.611.9308.62993.513964.6223574.21454185754190049445079060217 >>>>>> 1.46.288.5492.71721.509888.5768918.77621021352023785003643795111 >>>>> 1.14.436.4212.37289.214074.1695316.25933471397402551798189890218\n",
      "\n",
      ">> filename 5efac429-e673-486b-977d-c0df9f9fa621.dcm\n",
      "114 Done\n",
      "\n",
      ">>>  data/411bda81-f265-41e9-b72c-1ec2ad8fff53.dcm\n",
      "Converting to PNG .........................\n",
      "8.81.579.1898.43153.719292.3947707.19293887203339453167130126375 >>>>>> 2.66.272.4089.14455.121688.8916184.26930505604246623005384463666 >>>>> 5.61.784.7177.50877.823162.4579285.53902109313631752651066915298\n",
      "\n",
      ">> filename 411bda81-f265-41e9-b72c-1ec2ad8fff53.dcm\n",
      "115 Done\n",
      "\n",
      ">>>  data/24467de3-7de0-4ad0-a9f8-476a3f195fd4.dcm\n",
      "Converting to PNG .........................\n",
      "9.86.681.8992.91597.646660.7453235.43835708734975959054391303193 >>>>>> 1.14.260.1204.64523.104668.7041922.56302423665550900806112859170 >>>>> 6.52.628.1976.73241.245022.2225355.32313840074316284519916944667\n",
      "\n",
      ">> filename 24467de3-7de0-4ad0-a9f8-476a3f195fd4.dcm\n",
      "116 Done\n",
      "\n",
      ">>>  data/e8975ab7-9267-400a-9185-bd99faa6d693.dcm\n",
      "Converting to PNG .........................\n",
      "2.98.816.5066.91326.647446.3229390.91022511394876944663463704509 >>>>>> 3.29.495.8680.90795.537522.2447087.14892680598268255823661937857 >>>>> 6.14.217.4115.30629.114817.9562427.14895905490665925593733496174\n",
      "\n",
      ">> filename e8975ab7-9267-400a-9185-bd99faa6d693.dcm\n",
      "117 Done\n",
      "\n",
      ">>>  data/fa39889a-8f13-4d88-8e36-17fe5cc2aeed.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.952.1139.94776.711332.8322329.13955323642361985335961233193 >>>>>> 5.51.248.8123.57079.784413.7430198.63790219849492336493153976957 >>>>> 4.92.478.2831.60666.134271.1821961.33832763541205153740075190421\n",
      "\n",
      ">> filename fa39889a-8f13-4d88-8e36-17fe5cc2aeed.dcm\n",
      "118 Done\n",
      "\n",
      ">>>  data/07857d71-5362-4c39-97c0-e8a27be4ac90.dcm\n",
      "Converting to PNG .........................\n",
      "8.68.573.6723.24238.289032.8990738.15290295468142010962377503301 >>>>>> 7.40.316.2929.25261.361396.9772296.90082633994113390028671157322 >>>>> 9.15.764.6469.83134.648298.4635416.87698834624570691603348091616\n",
      "\n",
      ">> filename 07857d71-5362-4c39-97c0-e8a27be4ac90.dcm\n",
      "119 Done\n",
      "\n",
      ">>>  data/29cf94e2-9b65-4e3d-b8c5-ad15f7a89e1d.dcm\n",
      "Converting to PNG .........................\n",
      "2.33.374.2761.70548.668625.9123855.58724734025942878841220375356 >>>>>> 2.30.976.1628.65261.419012.2710172.56126406619100695003203845203 >>>>> 1.91.504.2124.87790.967202.5738969.51452789508170456226240464835\n",
      "\n",
      ">> filename 29cf94e2-9b65-4e3d-b8c5-ad15f7a89e1d.dcm\n",
      "120 Done\n",
      "\n",
      ">>>  data/8e4d892c-4e89-432c-9b35-85ba451c1477.dcm\n",
      "Converting to PNG .........................\n",
      "1.18.149.9505.70941.446545.1659178.55178902813971835198956039227 >>>>>> 8.82.109.7316.14033.705340.3828673.98718445678424109856449274417 >>>>> 5.34.435.1079.28374.541413.1660775.18973933559047774086788410497\n",
      "\n",
      ">> filename 8e4d892c-4e89-432c-9b35-85ba451c1477.dcm\n",
      "121 Done\n",
      "\n",
      ">>>  data/2fccabfb-5e50-4594-aa59-4593aace7f91.dcm\n",
      "Converting to PNG .........................\n",
      "1.18.461.3258.18909.422737.1976459.67967821280417234372996043689 >>>>>> 1.12.174.5917.67586.921948.5232442.73996906433260391511078248699 >>>>> 1.81.189.9380.47779.127134.4377433.97156745972203349107420080914\n",
      "\n",
      ">> filename 2fccabfb-5e50-4594-aa59-4593aace7f91.dcm\n",
      "122 Done\n",
      "\n",
      ">>>  data/76bd8786-7d55-4a46-bab4-b5baf0f995d6.dcm\n",
      "Converting to PNG .........................\n",
      "9.12.580.3711.33742.838856.2374636.60469981184586426222134443519 >>>>>> 3.18.975.2904.47731.451472.7737536.13621342223017325680814427710 >>>>> 4.79.564.9494.33866.868142.2855588.81813773745880001429543620717\n",
      "\n",
      ">> filename 76bd8786-7d55-4a46-bab4-b5baf0f995d6.dcm\n",
      "123 Done\n",
      "\n",
      ">>>  data/d989e943-7d72-4acb-a085-e858bb9dc15a.dcm\n",
      "Converting to PNG .........................\n",
      "4.47.970.1694.91942.347523.3714069.20007525671095328001811359052 >>>>>> 2.83.481.3145.51246.397987.5320464.90905943963456480254759553787 >>>>> 8.70.901.3123.93312.267955.5404948.94965135619112456905715076988\n",
      "\n",
      ">> filename d989e943-7d72-4acb-a085-e858bb9dc15a.dcm\n",
      "124 Done\n",
      "\n",
      ">>>  data/0ef30c30-446f-42d4-a35a-17417c054f8f.dcm\n",
      "Converting to PNG .........................\n",
      "4.95.988.2277.22576.262137.3485254.19699996293593132480568228554 >>>>>> 8.98.553.3228.74044.314149.7873504.65798987555291314731657512043 >>>>> 5.33.981.8159.54224.950538.6922118.33056997594101765695416045078\n",
      "\n",
      ">> filename 0ef30c30-446f-42d4-a35a-17417c054f8f.dcm\n",
      "125 Done\n",
      "\n",
      ">>>  data/eaf554e0-e9a5-48c0-9332-2b887e459ce7.dcm\n",
      "Converting to PNG .........................\n",
      "7.93.637.7783.68693.586923.3623492.79169273246336791891336778176 >>>>>> 8.55.159.4059.99429.808212.2116518.39576854067974493658508149484 >>>>> 2.37.494.6607.49380.975117.8998784.11924092436731426491700444234\n",
      "\n",
      ">> filename eaf554e0-e9a5-48c0-9332-2b887e459ce7.dcm\n",
      "126 Done\n",
      "\n",
      ">>>  data/67b24fc6-b4dc-4254-9c6e-f559d3d2d76f.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.760.5175.13976.307338.8529780.19748478152123685587308086137 >>>>>> 4.17.783.9513.18703.456193.4667115.71753798712427687217871362448 >>>>> 3.53.747.5520.10081.896590.8450804.65738096125848650743789410586\n",
      "\n",
      ">> filename 67b24fc6-b4dc-4254-9c6e-f559d3d2d76f.dcm\n",
      "127 Done\n",
      "\n",
      ">>>  data/05e1f3b7-752b-4c7d-8114-7d7dbb50f89a.dcm\n",
      "Converting to PNG .........................\n",
      "8.60.345.4114.46817.168409.1728564.14540476005138455846809127367 >>>>>> 3.47.453.8106.44705.340895.6940088.98241049932915110580908440748 >>>>> 4.11.838.7280.45003.738813.8416660.44484523228498220530422335257\n",
      "\n",
      ">> filename 05e1f3b7-752b-4c7d-8114-7d7dbb50f89a.dcm\n",
      "128 Done\n",
      "\n",
      ">>>  data/637c3d9c-c202-491d-a6da-c64014806ea3.dcm\n",
      "Converting to PNG .........................\n",
      "9.61.291.2573.15505.938193.6796667.70171889341437951011813410697 >>>>>> 6.41.284.7243.67896.396191.6414457.54901142549109624533600142144 >>>>> 5.49.802.1027.11744.934887.9208358.36779445024948040103894187266\n",
      "\n",
      ">> filename 637c3d9c-c202-491d-a6da-c64014806ea3.dcm\n",
      "129 Done\n",
      "\n",
      ">>>  data/0f433a3e-093e-4818-bb04-6e8a16de0b43.dcm\n",
      "Converting to PNG .........................\n",
      "3.28.185.1946.55230.829332.4106522.74016142355322871968180375914 >>>>>> 7.21.189.1830.48626.775914.9341677.78735094667110606132121598461 >>>>> 3.98.852.3866.66961.305064.6737408.47799894385888091098892721328\n",
      "\n",
      ">> filename 0f433a3e-093e-4818-bb04-6e8a16de0b43.dcm\n",
      "130 Done\n",
      "\n",
      ">>>  data/4d927a5a-f51a-4045-9c4c-7fe1a3c6fe29.dcm\n",
      "Converting to PNG .........................\n",
      "3.44.125.6234.36874.939917.6720007.26303762427375598837193968568 >>>>>> 8.92.227.3640.55771.717533.4560082.45467182766485801575261128513 >>>>> 1.36.260.2968.94112.165596.4158600.38415697918597109062375980326\n",
      "\n",
      ">> filename 4d927a5a-f51a-4045-9c4c-7fe1a3c6fe29.dcm\n",
      "131 Done\n",
      "\n",
      ">>>  data/04c00891-1710-4ea3-acad-6001a287f73c.dcm\n",
      "Converting to PNG .........................\n",
      "9.71.603.6594.50873.959009.9981109.27540494164748473362605670061 >>>>>> 8.71.213.6735.26818.957514.9110924.95510193898258875098984418582 >>>>> 1.14.120.8311.43123.382004.6084368.28731213142266573444168797756\n",
      "\n",
      ">> filename 04c00891-1710-4ea3-acad-6001a287f73c.dcm\n",
      "132 Done\n",
      "\n",
      ">>>  data/2e50fac7-48b4-4002-ae6e-5bdd847c0081.dcm\n",
      "Converting to PNG .........................\n",
      "4.40.301.3081.12237.516030.9331372.57759542572588966040469144744 >>>>>> 1.14.259.4773.60984.223086.4006378.45008222921305973075191193737 >>>>> 3.16.687.3494.84770.860250.9058421.76445563864913008309783730931\n",
      "\n",
      ">> filename 2e50fac7-48b4-4002-ae6e-5bdd847c0081.dcm\n",
      "133 Done\n",
      "\n",
      ">>>  data/5aca9efc-70c8-4b1c-b6c9-f1741ae4a894.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.312.8811.10963.267585.2496868.62827968652166599103454216206 >>>>>> 3.92.340.2457.67105.300696.9858414.96838754430522281554512081673 >>>>> 2.19.560.9400.31569.826437.1407379.56991416673442641014795125972\n",
      "\n",
      ">> filename 5aca9efc-70c8-4b1c-b6c9-f1741ae4a894.dcm\n",
      "134 Done\n",
      "\n",
      ">>>  data/b59e7a9d-9ba4-466c-8dc6-7d221a3e1305.dcm\n",
      "Converting to PNG .........................\n",
      "1.17.710.3023.96235.514782.4221726.12378228763901105611499876126 >>>>>> 6.91.776.9406.83300.146315.7795809.92479928923756840966602551222 >>>>> 2.25.906.1072.96174.808534.1625944.80654815292669020411913099247\n",
      "\n",
      ">> filename b59e7a9d-9ba4-466c-8dc6-7d221a3e1305.dcm\n",
      "135 Done\n",
      "\n",
      ">>>  data/845f2027-8004-4056-bbfa-1ccb41d8d176.dcm\n",
      "Converting to PNG .........................\n",
      "4.99.557.2674.71833.873469.6663173.86619212040176718139519094928 >>>>>> 6.50.801.1937.38494.146635.4661236.91867971219880966974279341593 >>>>> 8.13.765.1311.90688.782283.6933674.64211862573510535040550691258\n",
      "\n",
      ">> filename 845f2027-8004-4056-bbfa-1ccb41d8d176.dcm\n",
      "136 Done\n",
      "\n",
      ">>>  data/987cf149-a42a-4397-9d54-b9213c011163.dcm\n",
      "Converting to PNG .........................\n",
      "3.97.766.8481.16903.215214.1911925.68389429255388005722854110118 >>>>>> 2.84.280.6770.36702.418956.3437903.31699439921399000752136499359 >>>>> 9.71.318.2150.30631.856793.4584406.16422113407406627805865774877\n",
      "\n",
      ">> filename 987cf149-a42a-4397-9d54-b9213c011163.dcm\n",
      "137 Done\n",
      "\n",
      ">>>  data/96c29482-6617-407e-ad9e-e425dac894bc.dcm\n",
      "Converting to PNG .........................\n",
      "8.93.893.1645.20450.629556.9882445.12951634337317023654509025441 >>>>>> 8.14.458.1191.35119.296032.6213162.32291674130653947099289995306 >>>>> 8.28.145.1999.64594.851970.5198394.87862855182317294792535617163\n",
      "\n",
      ">> filename 96c29482-6617-407e-ad9e-e425dac894bc.dcm\n",
      "138 Done\n",
      "\n",
      ">>>  data/999fca31-31dd-45df-a6d5-5ed32bcdd345.dcm\n",
      "Converting to PNG .........................\n",
      "3.82.520.2677.91687.114845.1375889.60124174204188156881254728447 >>>>>> 9.32.700.3463.70438.592951.7033604.35972638319222022053892634386 >>>>> 9.61.596.2158.24261.376555.7365982.55308450551222467199756579446\n",
      "\n",
      ">> filename 999fca31-31dd-45df-a6d5-5ed32bcdd345.dcm\n",
      "139 Done\n",
      "\n",
      ">>>  data/c57e905c-02cb-4647-b4c1-5097d4943c6c.dcm\n",
      "Converting to PNG .........................\n",
      "9.16.190.2166.10684.980726.3249304.12514826033063485803064297865 >>>>>> 9.61.168.5142.76034.564458.3067150.43193331502675435270952700081 >>>>> 1.16.817.7398.47876.792573.4879614.73593109979156709477784888072\n",
      "\n",
      ">> filename c57e905c-02cb-4647-b4c1-5097d4943c6c.dcm\n",
      "140 Done\n",
      "\n",
      ">>>  data/3ebfd9fe-f227-420a-8877-1338673286a6.dcm\n",
      "Converting to PNG .........................\n",
      "8.24.902.3796.34457.747998.9910669.91878463425867569796349309759 >>>>>> 4.94.767.1465.13712.635171.2769198.91750566240858591244140973679 >>>>> 3.73.806.4102.88070.172588.1890173.53683912834078846274504209553\n",
      "\n",
      ">> filename 3ebfd9fe-f227-420a-8877-1338673286a6.dcm\n",
      "141 Done\n",
      "\n",
      ">>>  data/be839b30-254b-4dcd-89eb-2a0862012dc3.dcm\n",
      "Converting to PNG .........................\n",
      "1.16.614.6195.11342.286693.5372305.75057511508420047623361728608 >>>>>> 1.85.548.9809.81692.493628.6350718.84914447866003941806736998420 >>>>> 2.75.913.5046.48417.112259.4665355.75227032318748918417686242946\n",
      "\n",
      ">> filename be839b30-254b-4dcd-89eb-2a0862012dc3.dcm\n",
      "142 Done\n",
      "\n",
      ">>>  data/23544618-185a-41d3-a0bb-1bdfeff27a34.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.102.5814.47775.774915.5879891.49392200064954022591343257916 >>>>>> 6.57.634.1022.25839.997973.1777697.45582645939873060690179432919 >>>>> 8.34.839.2631.14431.942642.5097257.73801873541061933085559792420\n",
      "\n",
      ">> filename 23544618-185a-41d3-a0bb-1bdfeff27a34.dcm\n",
      "143 Done\n",
      "\n",
      ">>>  data/734be2ca-7284-470f-bfa7-85c4fc0a149c.dcm\n",
      "Converting to PNG .........................\n",
      "4.55.331.6450.14218.746117.6072121.16121093488982482714369054851 >>>>>> 1.16.490.9957.30891.913244.2738373.81760641860950083275344200923 >>>>> 2.81.255.3680.45682.305270.3157489.30191920095241020865506481210\n",
      "\n",
      ">> filename 734be2ca-7284-470f-bfa7-85c4fc0a149c.dcm\n",
      "144 Done\n",
      "\n",
      ">>>  data/b006d993-b472-4238-8249-80daf9f6cfdc.dcm\n",
      "Converting to PNG .........................\n",
      "6.41.570.2616.37997.945535.8276718.56350695134508457057766013037 >>>>>> 2.73.121.1451.87466.607801.8600719.26496294665051306711532822441 >>>>> 1.68.504.6362.39848.294913.4746765.24714994020915635858567763169\n",
      "\n",
      ">> filename b006d993-b472-4238-8249-80daf9f6cfdc.dcm\n",
      "145 Done\n",
      "\n",
      ">>>  data/a813b762-3223-4f8e-917c-7d884c7d473b.dcm\n",
      "Converting to PNG .........................\n",
      "4.96.197.8775.17335.985412.3270221.91212002067432076601001585453 >>>>>> 3.49.233.8323.64730.972490.1022686.42546284299222112581944007096 >>>>> 4.45.495.9837.79718.251953.7903092.69286857559040054367812750777\n",
      "\n",
      ">> filename a813b762-3223-4f8e-917c-7d884c7d473b.dcm\n",
      "146 Done\n",
      "\n",
      ">>>  data/f7bc89d2-351e-4302-80b8-195aa9886d55.dcm\n",
      "Converting to PNG .........................\n",
      "5.47.104.3739.97498.201248.5447557.35886108810354779806570194922 >>>>>> 2.18.594.1026.41329.753632.8641178.21581320031449799163456999441 >>>>> 8.63.222.9899.14415.109323.9244842.33103419352482957378925986336\n",
      "\n",
      ">> filename f7bc89d2-351e-4302-80b8-195aa9886d55.dcm\n",
      "147 Done\n",
      "\n",
      ">>>  data/8cd2a1cf-0e76-4dd7-9f06-ce947880dbfb.dcm\n",
      "Converting to PNG .........................\n",
      "2.48.712.8898.19168.249907.6429523.63895772289588138669705800976 >>>>>> 1.31.960.5849.13538.468463.9663041.61896579186336898945117070233 >>>>> 5.13.935.2257.68495.358429.4151821.54440832127290706486067438009\n",
      "\n",
      ">> filename 8cd2a1cf-0e76-4dd7-9f06-ce947880dbfb.dcm\n",
      "148 Done\n",
      "\n",
      ">>>  data/b38524d9-2f59-45a5-bc86-d70c2683c5df.dcm\n",
      "Converting to PNG .........................\n",
      "8.27.634.1781.60462.900721.2322983.13742353403453692095423464932 >>>>>> 7.17.917.4491.12073.764027.3735233.61979531881572621305666660003 >>>>> 1.80.888.1875.97068.169341.9124380.90723085565467684184291815210\n",
      "\n",
      ">> filename b38524d9-2f59-45a5-bc86-d70c2683c5df.dcm\n",
      "149 Done\n",
      "\n",
      ">>>  data/73616186-27f4-40e7-b24f-c2d2b5da55c5.dcm\n",
      "Converting to PNG .........................\n",
      "8.38.802.5707.85166.976007.7311326.11521215625476040572246443099 >>>>>> 8.49.200.1727.79782.810238.5558218.21844122155480251295293074755 >>>>> 8.74.596.1371.11589.655938.6354862.28524420403101385918139954233\n",
      "\n",
      ">> filename 73616186-27f4-40e7-b24f-c2d2b5da55c5.dcm\n",
      "150 Done\n",
      "\n",
      ">>>  data/b36a5159-c036-4e22-8b2c-d505c73ce1ac.dcm\n",
      "Converting to PNG .........................\n",
      "9.88.479.1617.92389.307772.1280516.10886566051852014340643689754 >>>>>> 5.68.909.1773.44203.682238.2987919.94796029433617164189889287276 >>>>> 5.74.602.8856.28819.662682.6743807.11218986126559204998351123122\n",
      "\n",
      ">> filename b36a5159-c036-4e22-8b2c-d505c73ce1ac.dcm\n",
      "151 Done\n",
      "\n",
      ">>>  data/6ceff6d2-ecf7-4ba6-aaa5-48539329c864.dcm\n",
      "Converting to PNG .........................\n",
      "1.18.683.1028.38670.544154.9698354.56916385519565610795512929722 >>>>>> 3.58.975.1008.59483.497532.2221106.50041197391518845377250213264 >>>>> 3.87.329.1447.24621.593751.1413808.46384168923264176739940239915\n",
      "\n",
      ">> filename 6ceff6d2-ecf7-4ba6-aaa5-48539329c864.dcm\n",
      "152 Done\n",
      "\n",
      ">>>  data/35c634a4-ec27-458b-a181-a1d78b51b669.dcm\n",
      "Converting to PNG .........................\n",
      "9.65.169.5472.25818.936936.1579341.70784093769498309146211792268 >>>>>> 6.88.745.8784.99540.688140.7197454.47802531538788391482203988013 >>>>> 1.29.118.6387.49427.357432.2000830.71862940312641603836379703225\n",
      "\n",
      ">> filename 35c634a4-ec27-458b-a181-a1d78b51b669.dcm\n",
      "153 Done\n",
      "\n",
      ">>>  data/60c41e1f-733c-48f9-ad63-c57d645010ee.dcm\n",
      "Converting to PNG .........................\n",
      "8.92.628.4651.66537.810246.9921651.58925354521730170307578621847 >>>>>> 1.66.947.3582.33564.200184.7660321.95256626795996534686752864417 >>>>> 1.14.199.5575.21637.412612.6741287.11382731040725987694139677381\n",
      "\n",
      ">> filename 60c41e1f-733c-48f9-ad63-c57d645010ee.dcm\n",
      "154 Done\n",
      "\n",
      ">>>  data/fd9f14c9-5fe4-43c8-a88a-f8e799682a0a.dcm\n",
      "Converting to PNG .........................\n",
      "6.83.829.8534.42009.415692.7056561.78809334628026553575186624125 >>>>>> 6.95.176.5471.60806.272196.2663273.61440893253898667051617818496 >>>>> 9.59.925.9202.54676.839792.2991594.97037967768255377448170335950\n",
      "\n",
      ">> filename fd9f14c9-5fe4-43c8-a88a-f8e799682a0a.dcm\n",
      "155 Done\n",
      "\n",
      ">>>  data/9c2aae16-3108-44d6-91f5-8eabc959cb99.dcm\n",
      "Converting to PNG .........................\n",
      "5.67.995.9121.95827.124156.5267686.59710365505066063808146297811 >>>>>> 9.59.569.7708.15837.682030.3566343.86397754845211663708807657277 >>>>> 1.14.822.5757.36921.415694.1461177.19868744224427653378066763080\n",
      "\n",
      ">> filename 9c2aae16-3108-44d6-91f5-8eabc959cb99.dcm\n",
      "156 Done\n",
      "\n",
      ">>>  data/40da9678-83a4-4211-b9e4-2879f23db8df.dcm\n",
      "Converting to PNG .........................\n",
      "3.58.532.1055.74058.141020.2233493.81286429247120186515017860628 >>>>>> 7.77.763.9848.17837.314595.4036408.56507693509205424338140727305 >>>>> 5.67.148.2028.39251.652992.9024624.25544938181269272415620471696\n",
      "\n",
      ">> filename 40da9678-83a4-4211-b9e4-2879f23db8df.dcm\n",
      "157 Done\n",
      "\n",
      ">>>  data/5707a4f6-a585-4a0d-8a66-a774b3baeda6.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.733.6535.12665.682157.7236893.43020802145912761198948979511 >>>>>> 1.10.476.1067.34776.744584.3043073.38698766098678856565070314615 >>>>> 7.66.554.1068.33535.706413.4566077.75255020940095029264077777063\n",
      "\n",
      ">> filename 5707a4f6-a585-4a0d-8a66-a774b3baeda6.dcm\n",
      "158 Done\n",
      "\n",
      ">>>  data/15288f62-066a-4b23-865a-8fe33c196f75.dcm\n",
      "Converting to PNG .........................\n",
      "9.68.922.9429.67087.920201.6246164.97091276288921268567281206846 >>>>>> 7.88.771.6791.18800.184270.3667070.77240556342164875406722639996 >>>>> 3.39.753.1175.86598.158681.3966445.54409103866974284074381623476\n",
      "\n",
      ">> filename 15288f62-066a-4b23-865a-8fe33c196f75.dcm\n",
      "159 Done\n",
      "\n",
      ">>>  data/2fee0f4a-6886-4fda-b0d8-8e572878f002.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.573.6176.96941.989636.8649313.15108904033384368952549435012 >>>>>> 1.29.683.2511.58166.121772.9235852.90744807079835171450383370118 >>>>> 2.19.990.6482.14907.911331.1687162.66996505618586933371939523150\n",
      "\n",
      ">> filename 2fee0f4a-6886-4fda-b0d8-8e572878f002.dcm\n",
      "160 Done\n",
      "\n",
      ">>>  data/ba92ebf3-39dd-4905-9b6a-4d982cca3f9f.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.829.5047.95787.108281.5722081.35246110249442738525170834621 >>>>>> 4.33.965.4162.66969.554178.6952082.65915915238043627189792037223 >>>>> 9.68.419.4178.16456.633111.9222872.81822552208899656849037477340\n",
      "\n",
      ">> filename ba92ebf3-39dd-4905-9b6a-4d982cca3f9f.dcm\n",
      "161 Done\n",
      "\n",
      ">>>  data/46a5a71e-32cb-43fc-b735-9c1f6c9727a1.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.879.9043.95964.184226.9750975.66793474198227784552035483704 >>>>>> 1.16.258.7760.78043.976674.6677359.24381552852424990911503191184 >>>>> 8.48.113.6716.32321.845718.5240220.19000906789505817798375190163\n",
      "\n",
      ">> filename 46a5a71e-32cb-43fc-b735-9c1f6c9727a1.dcm\n",
      "162 Done\n",
      "\n",
      ">>>  data/cb55183e-31c2-4ca8-91e0-74512fc21eac.dcm\n",
      "Converting to PNG .........................\n",
      "7.10.169.6698.88135.565130.7585096.33057125044614402854683583442 >>>>>> 8.44.160.9591.87238.409477.1727368.76281732752820165368973967587 >>>>> 6.15.534.7196.35499.871291.1267855.14597724810995400254637546774\n",
      "\n",
      ">> filename cb55183e-31c2-4ca8-91e0-74512fc21eac.dcm\n",
      "163 Done\n",
      "\n",
      ">>>  data/667a6bb0-3d2a-4240-81af-3eb79780211a.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.817.1001.11065.170070.9849778.32646936343435827748072109515 >>>>>> 8.38.110.1926.15755.153022.2508826.61183900634400016202033710078 >>>>> 3.90.820.9671.52159.320114.5949959.68927679664491486540845110821\n",
      "\n",
      ">> filename 667a6bb0-3d2a-4240-81af-3eb79780211a.dcm\n",
      "164 Done\n",
      "\n",
      ">>>  data/ebd9a8a9-3544-4c09-b819-b62ce12163cc.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.246.1348.85400.862498.5421510.17866174673744764809359606433 >>>>>> 1.13.690.1978.17447.784739.1307756.32260372915387238020797510878 >>>>> 9.91.580.1767.92726.170865.3901715.12822482617811872223212426418\n",
      "\n",
      ">> filename ebd9a8a9-3544-4c09-b819-b62ce12163cc.dcm\n",
      "165 Done\n",
      "\n",
      ">>>  data/dad356d7-0f57-4c1a-a1a1-52923facc26e.dcm\n",
      "Converting to PNG .........................\n",
      "6.11.319.1985.59626.213845.8730668.49192973285768333096223033441 >>>>>> 6.96.429.3564.94728.908141.3400167.30867025181670139937593389296 >>>>> 2.69.668.6493.94168.781890.1614891.45572938803704675517722003317\n",
      "\n",
      ">> filename dad356d7-0f57-4c1a-a1a1-52923facc26e.dcm\n",
      "166 Done\n",
      "\n",
      ">>>  data/8ab7f593-77d4-465c-b670-ad0f93eefe13.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.288.1352.64339.662079.7912529.65963305947286728827100114954 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 7.86.575.1237.94595.624544.9005695.16224057615705871884971532793\n",
      "\n",
      ">> filename 8ab7f593-77d4-465c-b670-ad0f93eefe13.dcm\n",
      "167 Done\n",
      "\n",
      ">>>  data/13405c01-a304-4498-b083-5dcc5f7cbbb7.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.881.1118.34377.794311.4469703.70237693719486136581973495279 >>>>>> 6.15.110.9539.13445.174820.3407764.82323058835963696085676563741 >>>>> 8.95.324.1448.76177.926423.7740692.43470525501805061077358585701\n",
      "\n",
      ">> filename 13405c01-a304-4498-b083-5dcc5f7cbbb7.dcm\n",
      "168 Done\n",
      "\n",
      ">>>  data/e8eb5cfd-93e2-4a4b-a783-2a62d690b7aa.dcm\n",
      "Converting to PNG .........................\n",
      "8.68.992.1474.78637.398321.5630455.90942510125387836323599860324 >>>>>> 5.44.584.2605.72148.876757.1720738.42132439378705945538810254357 >>>>> 2.50.216.2201.13115.172769.1609462.11635345719109831964422825205\n",
      "\n",
      ">> filename e8eb5cfd-93e2-4a4b-a783-2a62d690b7aa.dcm\n",
      "169 Done\n",
      "\n",
      ">>>  data/79a36662-c51c-4f10-97f7-26bef518e5c4.dcm\n",
      "Converting to PNG .........................\n",
      "6.41.293.9211.90982.622732.4938383.24112315654366560177337677317 >>>>>> 2.84.437.9911.83203.977365.4360491.75767371368647209283711565905 >>>>> 3.89.191.2404.69855.799015.3855540.86115368458436788527155816800\n",
      "\n",
      ">> filename 79a36662-c51c-4f10-97f7-26bef518e5c4.dcm\n",
      "170 Done\n",
      "\n",
      ">>>  data/e675c405-8913-4da6-b1fb-db228d661b3f.dcm\n",
      "Converting to PNG .........................\n",
      "8.17.785.8517.37178.372647.6542840.99626580996730270618887282405 >>>>>> 1.71.233.6706.15454.116053.1751422.63682258106789997385993978851 >>>>> 8.38.447.3901.81684.287478.7398703.73399708231808716180497015156\n",
      "\n",
      ">> filename e675c405-8913-4da6-b1fb-db228d661b3f.dcm\n",
      "171 Done\n",
      "\n",
      ">>>  data/8a2cde9f-c544-46bd-bc8f-574484fb4cfb.dcm\n",
      "Converting to PNG .........................\n",
      "9.23.627.5931.12318.707939.6575427.17029118236335937209606146202 >>>>>> 6.58.803.5460.20789.101729.4616312.85225734798214572753612155853 >>>>> 3.19.876.4264.12456.662699.4094017.70141084737409049631977397369\n",
      "\n",
      ">> filename 8a2cde9f-c544-46bd-bc8f-574484fb4cfb.dcm\n",
      "172 Done\n",
      "\n",
      ">>>  data/9dbf7b72-52ea-423d-8d44-de7ac25652a2.dcm\n",
      "Converting to PNG .........................\n",
      "6.97.947.6122.89970.664820.9143170.97064377020914947556995567513 >>>>>> 9.14.155.7331.64508.107549.6568219.11400826248465298760405932825 >>>>> 8.18.859.1847.42459.315059.3132437.73261299254804835287734603754\n",
      "\n",
      ">> filename 9dbf7b72-52ea-423d-8d44-de7ac25652a2.dcm\n",
      "173 Done\n",
      "\n",
      ">>>  data/156b5048-db21-4efe-9b6c-3b746a58db24.dcm\n",
      "Converting to PNG .........................\n",
      "7.58.349.8721.42759.345596.1461546.41319150596979283063703282500 >>>>>> 5.45.966.9429.65199.734413.2528987.24073055537412135796465531449 >>>>> 1.13.893.9826.24041.416442.4859033.74512668530841758410717261216\n",
      "\n",
      ">> filename 156b5048-db21-4efe-9b6c-3b746a58db24.dcm\n",
      "174 Done\n",
      "\n",
      ">>>  data/6caa8ce4-d255-400f-96ad-6dde4d150a01.dcm\n",
      "Converting to PNG .........................\n",
      "7.16.168.2997.90514.288058.7555315.21579064673986739426698624832 >>>>>> 8.65.578.3442.58241.111418.2645325.76184103251870846823709659356 >>>>> 1.11.482.2382.74454.797506.9926153.91128696293315170061666806430\n",
      "\n",
      ">> filename 6caa8ce4-d255-400f-96ad-6dde4d150a01.dcm\n",
      "175 Done\n",
      "\n",
      ">>>  data/2f12a7dd-fe7d-4fe7-93a9-8fd4458dc2fe.dcm\n",
      "Converting to PNG .........................\n",
      "2.88.648.1295.70761.763150.3954278.77181527183951523566157631653 >>>>>> 5.61.431.2553.28932.992183.6352859.49582144702455582421645524441 >>>>> 3.49.243.9497.53682.205225.6837006.70818570746333203179431448660\n",
      "\n",
      ">> filename 2f12a7dd-fe7d-4fe7-93a9-8fd4458dc2fe.dcm\n",
      "176 Done\n",
      "\n",
      ">>>  data/9e8dd006-fd11-4e43-9919-e62b6a4d44b4.dcm\n",
      "Converting to PNG .........................\n",
      "9.74.747.2227.42772.128254.6275793.34816315267769413205707198758 >>>>>> 1.75.228.8229.19555.530338.2457794.54228364106683802039301770540 >>>>> 6.89.669.5952.13660.440762.2051506.19236906582168774011882368693\n",
      "\n",
      ">> filename 9e8dd006-fd11-4e43-9919-e62b6a4d44b4.dcm\n",
      "177 Done\n",
      "\n",
      ">>>  data/0c37ec91-ecdc-487a-a4e1-1ed6a4ac3f81.dcm\n",
      "Converting to PNG .........................\n",
      "5.80.407.4896.64276.147538.3907995.56558272034146943037313120655 >>>>>> 1.14.559.1395.65214.812832.8329285.15475130013415178850964565899 >>>>> 2.17.324.3164.10246.732746.8263623.12366503427319878649478536611\n",
      "\n",
      ">> filename 0c37ec91-ecdc-487a-a4e1-1ed6a4ac3f81.dcm\n",
      "178 Done\n",
      "\n",
      ">>>  data/256bd890-2c3b-483e-9d2d-60b1a5037532.dcm\n",
      "Converting to PNG .........................\n",
      "5.44.728.5778.11257.610510.9134524.34255210666150198915298997744 >>>>>> 1.16.490.9957.30891.913244.2738373.81760641860950083275344200923 >>>>> 3.54.542.4803.97094.888977.1910373.34748973597709284926227177249\n",
      "\n",
      ">> filename 256bd890-2c3b-483e-9d2d-60b1a5037532.dcm\n",
      "179 Done\n",
      "\n",
      ">>>  data/7fd858cd-9f27-4b15-8f1c-5fcf6fa2da94.dcm\n",
      "Converting to PNG .........................\n",
      "7.66.155.1614.14093.118583.8746616.34717731632405002336414930301 >>>>>> 9.13.944.2106.47357.940788.6258414.28199422351851457897598727248 >>>>> 7.58.287.9622.67011.199403.6942376.85764905680730654222549499317\n",
      "\n",
      ">> filename 7fd858cd-9f27-4b15-8f1c-5fcf6fa2da94.dcm\n",
      "180 Done\n",
      "\n",
      ">>>  data/6aa08f38-fa64-4ef8-869c-9e9d587c6f43.dcm\n",
      "Converting to PNG .........................\n",
      "6.20.303.1976.70333.629930.6292932.19105320509099622430685737077 >>>>>> 1.15.405.8121.19337.252039.3489264.35279045319794593635784253404 >>>>> 1.11.785.7096.55387.249788.8860129.13990651042558061723524457006\n",
      "\n",
      ">> filename 6aa08f38-fa64-4ef8-869c-9e9d587c6f43.dcm\n",
      "181 Done\n",
      "\n",
      ">>>  data/c2ebf6af-1af2-4904-a963-251a1888301e.dcm\n",
      "Converting to PNG .........................\n",
      "6.68.873.4460.58497.235019.6255070.13693518787201403065018737182 >>>>>> 2.41.418.6897.91972.784263.2080350.59452584708287891547571049037 >>>>> 7.65.506.9724.15351.301490.6666545.10560805078390249628429812939\n",
      "\n",
      ">> filename c2ebf6af-1af2-4904-a963-251a1888301e.dcm\n",
      "182 Done\n",
      "\n",
      ">>>  data/8f1a3cf1-5bfb-4a05-9568-e48aefea5a85.dcm\n",
      "Converting to PNG .........................\n",
      "5.11.928.5998.16670.743079.9004999.62192125888171211356800205698 >>>>>> 6.11.256.7612.45822.828665.8509665.51217678500264117057150919466 >>>>> 1.85.646.8632.69759.553375.4923824.82975555418556422798762740836\n",
      "\n",
      ">> filename 8f1a3cf1-5bfb-4a05-9568-e48aefea5a85.dcm\n",
      "183 Done\n",
      "\n",
      ">>>  data/8ea2e949-7b4e-40ba-82c2-8e4fcbf2b672.dcm\n",
      "Converting to PNG .........................\n",
      "5.83.461.2375.26738.679515.2567095.64731712439332599198677017795 >>>>>> 8.41.682.1103.11144.756160.3245571.70893321116138332464418021091 >>>>> 1.13.603.3127.84696.348027.8310589.54738974637594272122910756455\n",
      "\n",
      ">> filename 8ea2e949-7b4e-40ba-82c2-8e4fcbf2b672.dcm\n",
      "184 Done\n",
      "\n",
      ">>>  data/c503ebef-1f8f-49f7-92c0-35403c0099d6.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.145.5331.32216.166716.2020035.37199702249355779878720487291 >>>>>> 4.83.134.2169.83839.782262.8713601.36455560924908824683699733660 >>>>> 4.19.789.6567.45535.761179.4453640.19904124517062443905013893428\n",
      "\n",
      ">> filename c503ebef-1f8f-49f7-92c0-35403c0099d6.dcm\n",
      "185 Done\n",
      "\n",
      ">>>  data/d73a699d-1123-4d2f-a42c-35ef9d7ee7ac.dcm\n",
      "Converting to PNG .........................\n",
      "9.22.412.2507.11470.634475.5271774.18904763287700739962934540100 >>>>>> 9.74.839.6400.12154.363214.5002215.21907875848010090786822387385 >>>>> 8.27.103.5294.74922.281610.4665297.15558294145226860587051377991\n",
      "\n",
      ">> filename d73a699d-1123-4d2f-a42c-35ef9d7ee7ac.dcm\n",
      "186 Done\n",
      "\n",
      ">>>  data/ff85b50c-f7d1-49a8-a9af-5da7c0c486b1.dcm\n",
      "Converting to PNG .........................\n",
      "6.26.496.6128.90531.153254.9126968.20995460504983873204387636323 >>>>>> 1.45.757.8012.15873.711920.1031683.39813188326663627265354384249 >>>>> 2.93.409.7529.92763.138533.1502863.62985478828718403181906282788\n",
      "\n",
      ">> filename ff85b50c-f7d1-49a8-a9af-5da7c0c486b1.dcm\n",
      "187 Done\n",
      "\n",
      ">>>  data/cdc33c90-0707-48bd-9a97-29033b38f347.dcm\n",
      "Converting to PNG .........................\n",
      "3.19.415.9112.21879.248348.4611273.75345322601747225857543630724 >>>>>> 3.42.968.5107.83556.957402.6134631.92945707959455929584310415782 >>>>> 4.55.220.5982.49756.871107.8553773.37171442941249558326152166538\n",
      "\n",
      ">> filename cdc33c90-0707-48bd-9a97-29033b38f347.dcm\n",
      "188 Done\n",
      "\n",
      ">>>  data/6c1e2488-0b19-42dd-9e8c-8052cdc15f47.dcm\n",
      "Converting to PNG .........................\n",
      "4.99.135.9201.53675.966347.6682870.82983011839967887946726477309 >>>>>> 1.15.268.5612.10961.408072.9376765.92654823693540961239323951132 >>>>> 1.12.150.5325.90987.564158.8170413.28093814782291326927925754120\n",
      "\n",
      ">> filename 6c1e2488-0b19-42dd-9e8c-8052cdc15f47.dcm\n",
      "189 Done\n",
      "\n",
      ">>>  data/81ef4d0e-ac3e-44d6-9055-2fb0e4265132.dcm\n",
      "Converting to PNG .........................\n",
      "3.79.336.1865.40102.100712.1876784.11905254054695350219915608296 >>>>>> 1.49.139.4041.11271.211413.2157763.73348230164886506488327676864 >>>>> 4.52.383.3388.60668.693298.1021691.53925606254959833339991344318\n",
      "\n",
      ">> filename 81ef4d0e-ac3e-44d6-9055-2fb0e4265132.dcm\n",
      "190 Done\n",
      "\n",
      ">>>  data/59178e7d-cd46-4e44-acab-d78ba4af4c7b.dcm\n",
      "Converting to PNG .........................\n",
      "8.16.184.4278.47142.117360.3905222.74051858720435810971147363901 >>>>>> 6.52.215.1166.90723.374541.1996231.20422302220506681725457616824 >>>>> 3.51.916.9252.54705.113395.1003722.76144419991559656958184780011\n",
      "\n",
      ">> filename 59178e7d-cd46-4e44-acab-d78ba4af4c7b.dcm\n",
      "191 Done\n",
      "\n",
      ">>>  data/ec371492-a674-445f-a705-388dd0e6720e.dcm\n",
      "Converting to PNG .........................\n",
      "5.55.208.9684.98708.395583.6707264.59288758315195233043249071452 >>>>>> 5.92.730.5762.51965.105397.3901045.75719715311218570841970475887 >>>>> 9.53.513.5336.58269.180524.1592548.99967573256538786549299432570\n",
      "\n",
      ">> filename ec371492-a674-445f-a705-388dd0e6720e.dcm\n",
      "192 Done\n",
      "\n",
      ">>>  data/4169cf00-19c9-4745-adaa-ca2d83a0c872.dcm\n",
      "Converting to PNG .........................\n",
      "7.15.852.4534.64530.376262.3831228.92243943202035810313902027271 >>>>>> 7.11.933.7379.49862.164926.1783534.12725429571324385744083714932 >>>>> 5.59.700.3100.32099.433991.9243838.46815597678217837364000780493\n",
      "\n",
      ">> filename 4169cf00-19c9-4745-adaa-ca2d83a0c872.dcm\n",
      "193 Done\n",
      "\n",
      ">>>  data/3750478b-a292-4728-a654-fedce23550fe.dcm\n",
      "Converting to PNG .........................\n",
      "2.71.222.2905.37270.264390.2552747.40240902685919058103136744350 >>>>>> 3.94.557.5803.32000.222739.9547224.44132204786366993344050400961 >>>>> 5.16.247.9550.58183.161870.8148161.77613158493873699295757865925\n",
      "\n",
      ">> filename 3750478b-a292-4728-a654-fedce23550fe.dcm\n",
      "194 Done\n",
      "\n",
      ">>>  data/3e65bbc5-7149-42a9-83fa-713131dc99ed.dcm\n",
      "Converting to PNG .........................\n",
      "5.10.373.3095.56043.872243.3516066.38760033402478085457007799667 >>>>>> 5.86.158.9593.41305.165363.8733853.39997583369117529593852488124 >>>>> 5.12.989.6606.82727.540836.3659180.74857320778943215985636253501\n",
      "\n",
      ">> filename 3e65bbc5-7149-42a9-83fa-713131dc99ed.dcm\n",
      "195 Done\n",
      "\n",
      ">>>  data/f3d8e001-f867-46c7-9bc5-451417cb6433.dcm\n",
      "Converting to PNG .........................\n",
      "3.26.119.1874.95261.225466.9810748.19414263376708928487730904535 >>>>>> 8.88.275.6450.17337.114319.6520084.16336397374827794480674861136 >>>>> 1.10.628.1741.22215.212633.9168846.16622196013180289247802241275\n",
      "\n",
      ">> filename f3d8e001-f867-46c7-9bc5-451417cb6433.dcm\n",
      "196 Done\n",
      "\n",
      ">>>  data/27f4f2f9-3fac-4e38-97d8-6ea02641ab80.dcm\n",
      "Converting to PNG .........................\n",
      "7.69.966.8030.28102.437613.5634542.15171129358792541355285158971 >>>>>> 1.18.165.9407.57548.828967.8255190.81041298098910147945227645259 >>>>> 3.50.193.3431.63647.412885.5304131.80725522671475439482844671092\n",
      "\n",
      ">> filename 27f4f2f9-3fac-4e38-97d8-6ea02641ab80.dcm\n",
      "197 Done\n",
      "\n",
      ">>>  data/d6a503a2-acee-430d-9731-eb76a0a67c11.dcm\n",
      "Converting to PNG .........................\n",
      "1.46.319.1892.67066.305327.3495862.87531407130200266477490159638 >>>>>> 4.24.643.4372.41793.871975.5857433.21232004633209679230891544185 >>>>> 1.63.899.1737.14452.670420.8205649.57094518241925183619596693463\n",
      "\n",
      ">> filename d6a503a2-acee-430d-9731-eb76a0a67c11.dcm\n",
      "198 Done\n",
      "\n",
      ">>>  data/48f710d8-036c-4a8a-bad6-a9f37d637dff.dcm\n",
      "Converting to PNG .........................\n",
      "5.85.610.3457.49708.122144.9446545.38858506475201100240689078114 >>>>>> 1.13.979.9982.25598.338623.7350258.99579619346230176031224347019 >>>>> 8.94.385.5970.97062.187633.5304130.19088252869530112785754316746\n",
      "\n",
      ">> filename 48f710d8-036c-4a8a-bad6-a9f37d637dff.dcm\n",
      "199 Done\n",
      "\n",
      ">>>  data/506f8932-7f11-4565-ada0-da2efc1d56a5.dcm\n",
      "Converting to PNG .........................\n",
      "1.19.113.5227.52365.396719.2655104.10603854492141112847776679163 >>>>>> 8.92.942.3113.17950.910398.4840270.18907214147467621581481813744 >>>>> 2.41.259.6301.16412.761249.2333294.56634835248888382398881803002\n",
      "\n",
      ">> filename 506f8932-7f11-4565-ada0-da2efc1d56a5.dcm\n",
      "200 Done\n",
      "\n",
      ">>>  data/ffd117ec-6fe2-4e31-976a-1ac4f03e60cc.dcm\n",
      "Converting to PNG .........................\n",
      "4.17.544.5221.96714.217907.9741571.95801204284701452549459734329 >>>>>> 1.46.379.4270.36677.352134.8830551.10404127374884738135493956313 >>>>> 3.52.695.1289.13892.435884.3973129.67132445598198646622464466397\n",
      "\n",
      ">> filename ffd117ec-6fe2-4e31-976a-1ac4f03e60cc.dcm\n",
      "201 Done\n",
      "\n",
      ">>>  data/1e023b43-a7a2-4b30-b99f-9bf68ec7522b.dcm\n",
      "Converting to PNG .........................\n",
      "8.85.189.8358.67894.581174.9633214.47998645696493094989532894181 >>>>>> 3.39.650.8043.27860.379568.6207449.76517569932700567556779310967 >>>>> 1.11.434.5586.14084.575906.4320002.22873167204578468274353194984\n",
      "\n",
      ">> filename 1e023b43-a7a2-4b30-b99f-9bf68ec7522b.dcm\n",
      "202 Done\n",
      "\n",
      ">>>  data/c01673f8-c798-4206-aa41-fa182f880c79.dcm\n",
      "Converting to PNG .........................\n",
      "1.69.802.5104.47699.322380.7370664.88821025130777469212798774143 >>>>>> 3.19.915.7620.25311.191764.6326429.49409619517454763247067940295 >>>>> 1.14.852.5968.60817.109406.6584194.47322738179893209859264375852\n",
      "\n",
      ">> filename c01673f8-c798-4206-aa41-fa182f880c79.dcm\n",
      "203 Done\n",
      "\n",
      ">>>  data/8a25fbe5-7b43-4c23-8c3a-d90fbaae5d6c.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.587.2189.69034.176977.8723780.87266449655042299285561475682 >>>>>> 5.20.224.6658.48001.379081.4190054.43985304468985944513952575730 >>>>> 1.13.168.7929.13723.296997.1388715.16412809968520345497631426869\n",
      "\n",
      ">> filename 8a25fbe5-7b43-4c23-8c3a-d90fbaae5d6c.dcm\n",
      "204 Done\n",
      "\n",
      ">>>  data/7bc60d56-038e-48b1-b24c-3f70ff788cf4.dcm\n",
      "Converting to PNG .........................\n",
      "1.67.990.6169.13077.145073.1446529.99690471239805733750274402951 >>>>>> 9.13.852.1925.16586.578385.1187184.95179615778646374774043954907 >>>>> 2.25.373.1968.51293.192782.4968739.19911859141531972285600705671\n",
      "\n",
      ">> filename 7bc60d56-038e-48b1-b24c-3f70ff788cf4.dcm\n",
      "205 Done\n",
      "\n",
      ">>>  data/d23ee80d-46e1-485e-92ef-633ccb3e671f.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.803.3761.82555.946572.1629325.47804340127633049463728053232 >>>>>> 2.17.890.6874.95910.367108.1067269.17594504263613105133127716558 >>>>> 1.57.103.4815.12692.736670.4640921.89354237784484305020551430385\n",
      "\n",
      ">> filename d23ee80d-46e1-485e-92ef-633ccb3e671f.dcm\n",
      "206 Done\n",
      "\n",
      ">>>  data/3db1852c-e903-4955-90ac-8ab4ad9396ef.dcm\n",
      "Converting to PNG .........................\n",
      "1.49.463.5698.57486.560931.4774455.13929601809470863289842467175 >>>>>> 6.40.453.1499.36549.160698.1531970.98562356591614847003385733752 >>>>> 1.55.709.1957.30714.401653.5837483.90167779403409340520600277195\n",
      "\n",
      ">> filename 3db1852c-e903-4955-90ac-8ab4ad9396ef.dcm\n",
      "207 Done\n",
      "\n",
      ">>>  data/e07b60a7-1999-46f6-a3ae-4485135ea960.dcm\n",
      "Converting to PNG .........................\n",
      "8.56.836.5680.26635.457943.9325094.14661141096693725421578395386 >>>>>> 1.11.971.8309.61676.699043.3640634.43921156397910204297317658981 >>>>> 1.12.802.4620.15781.151889.2666506.89523863883473997132019978937\n",
      "\n",
      ">> filename e07b60a7-1999-46f6-a3ae-4485135ea960.dcm\n",
      "208 Done\n",
      "\n",
      ">>>  data/2d0f0923-6c46-4074-9de5-54c35537df83.dcm\n",
      "Converting to PNG .........................\n",
      "6.64.129.6024.59886.260732.2081405.14992169189221104247428781869 >>>>>> 8.99.310.2883.81333.906389.8099036.74789641792384745496947993976 >>>>> 8.10.151.1279.19660.883143.4533072.77796150816565898184203180215\n",
      "\n",
      ">> filename 2d0f0923-6c46-4074-9de5-54c35537df83.dcm\n",
      "209 Done\n",
      "\n",
      ">>>  data/322a8bc6-ea5d-4275-b037-df6001ae126c.dcm\n",
      "Converting to PNG .........................\n",
      "1.37.595.5220.21353.110791.1247981.26490784941715973666496950283 >>>>>> 3.92.340.2457.67105.300696.9858414.96838754430522281554512081673 >>>>> 6.64.732.2522.33846.655579.1772552.85109782609307846543320991622\n",
      "\n",
      ">> filename 322a8bc6-ea5d-4275-b037-df6001ae126c.dcm\n",
      "210 Done\n",
      "\n",
      ">>>  data/759ade86-dd45-4dbc-a2ad-e52009ed2512.dcm\n",
      "Converting to PNG .........................\n",
      "2.66.129.4139.34949.783927.2787570.78716776809714413825948563622 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 9.18.474.1031.62996.639896.4166871.10665793450993916098123037597\n",
      "\n",
      ">> filename 759ade86-dd45-4dbc-a2ad-e52009ed2512.dcm\n",
      "211 Done\n",
      "\n",
      ">>>  data/830e3a29-427d-4a70-bd80-ecb85e4c4ecb.dcm\n",
      "Converting to PNG .........................\n",
      "9.89.171.7868.17087.642146.6501328.84578433174530770609076975289 >>>>>> 1.16.138.2848.17577.348136.4909773.11635122081227377689249923650 >>>>> 2.65.396.5174.48735.904837.5682614.26254816402325258724058716674\n",
      "\n",
      ">> filename 830e3a29-427d-4a70-bd80-ecb85e4c4ecb.dcm\n",
      "212 Done\n",
      "\n",
      ">>>  data/41640bc7-5a1b-42e9-a46c-a0946e0d8e7d.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.981.7638.50043.959650.9268234.60732729942355021693939055134 >>>>>> 5.69.732.2681.61239.339557.7016303.26120258664616112926610161728 >>>>> 3.69.196.6666.18889.400542.4933190.48270145618719161479913823702\n",
      "\n",
      ">> filename 41640bc7-5a1b-42e9-a46c-a0946e0d8e7d.dcm\n",
      "213 Done\n",
      "\n",
      ">>>  data/fbeae6ff-4426-4f45-a0b2-0cdb3b161789.dcm\n",
      "Converting to PNG .........................\n",
      "1.77.907.7096.35735.102137.1370122.91534290365584512463951960194 >>>>>> 7.34.855.1616.66719.906265.9358354.16521947621930930320548780583 >>>>> 7.15.571.3128.11155.424360.1600121.87199508439259481280855927545\n",
      "\n",
      ">> filename fbeae6ff-4426-4f45-a0b2-0cdb3b161789.dcm\n",
      "214 Done\n",
      "\n",
      ">>>  data/f7dadb0d-3fe7-4bc8-b9f5-b958e43540f0.dcm\n",
      "Converting to PNG .........................\n",
      "2.23.616.6590.57774.945257.8836643.18868028519290507113185750057 >>>>>> 6.95.580.6958.10275.575676.9454576.16615658386491139529373665404 >>>>> 1.81.249.4053.82291.679202.5188913.18743351770509411498678767147\n",
      "\n",
      ">> filename f7dadb0d-3fe7-4bc8-b9f5-b958e43540f0.dcm\n",
      "215 Done\n",
      "\n",
      ">>>  data/eb0ec612-496c-41bc-a2cb-3b32ce26a7cf.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.721.1211.91426.264502.2459050.99502534693317849736586846666 >>>>>> 7.65.108.1873.97099.973260.4044127.11399424856783796755190155834 >>>>> 6.61.197.7734.12937.357690.7457930.55496454866303679525206209308\n",
      "\n",
      ">> filename eb0ec612-496c-41bc-a2cb-3b32ce26a7cf.dcm\n",
      "216 Done\n",
      "\n",
      ">>>  data/59bea832-8530-4a73-be5b-5289d5a84d45.dcm\n",
      "Converting to PNG .........................\n",
      "7.21.357.1307.80031.774727.3367371.36941630750655893705403752040 >>>>>> 3.46.999.4845.25221.649157.7653346.96056117663788015465611048624 >>>>> 1.71.508.1549.22228.619605.6981246.88434799006852835397835794398\n",
      "\n",
      ">> filename 59bea832-8530-4a73-be5b-5289d5a84d45.dcm\n",
      "217 Done\n",
      "\n",
      ">>>  data/cd3552c0-4fd2-4025-81c0-50e4f742c9e9.dcm\n",
      "Converting to PNG .........................\n",
      "1.18.123.3032.96079.142522.1071851.85942621018204400219694649482 >>>>>> 6.59.108.7749.12514.132575.4815463.34661153122378484426342185940 >>>>> 1.15.742.7787.41603.473316.4173377.21448323456723017714345835116\n",
      "\n",
      ">> filename cd3552c0-4fd2-4025-81c0-50e4f742c9e9.dcm\n",
      "218 Done\n",
      "\n",
      ">>>  data/af43418d-690b-437d-b391-1e16b455bb4e.dcm\n",
      "Converting to PNG .........................\n",
      "6.53.743.9211.65088.410746.7240994.62319267625040719132277247229 >>>>>> 8.95.808.8362.37715.714942.8888607.53180463015018704221824604282 >>>>> 1.27.742.7793.43572.974476.5964169.10972875988587913314442284374\n",
      "\n",
      ">> filename af43418d-690b-437d-b391-1e16b455bb4e.dcm\n",
      "219 Done\n",
      "\n",
      ">>>  data/e9690c4d-1d24-412c-886a-beb80f4bdbc1.dcm\n",
      "Converting to PNG .........................\n",
      "3.68.982.4845.89650.530322.4169510.50329888859793028853215083663 >>>>>> 1.18.727.1422.63575.120940.9789243.62480738202695866525310894716 >>>>> 6.52.483.4585.31178.164534.3794434.75637172428795285075831124522\n",
      "\n",
      ">> filename e9690c4d-1d24-412c-886a-beb80f4bdbc1.dcm\n",
      "220 Done\n",
      "\n",
      ">>>  data/51ea8d1c-664f-4885-b7f6-adbe2a7a2972.dcm\n",
      "Converting to PNG .........................\n",
      "1.54.829.1195.96440.920293.1547897.15137357481506714841883758815 >>>>>> 7.65.552.1734.11743.926012.6083946.44662914974625477865621539096 >>>>> 1.69.447.8088.36218.614742.3510446.34255131408733354670061092949\n",
      "\n",
      ">> filename 51ea8d1c-664f-4885-b7f6-adbe2a7a2972.dcm\n",
      "221 Done\n",
      "\n",
      ">>>  data/72e9947d-e670-4cc3-a55b-8911e4ea33e3.dcm\n",
      "Converting to PNG .........................\n",
      "1.33.770.9662.78056.851768.8499895.28665890834368360978546791258 >>>>>> 3.39.882.3468.47844.883870.7734517.11062332355345975972947504789 >>>>> 9.47.268.8701.34202.972419.9816158.15288582082602045462812390121\n",
      "\n",
      ">> filename 72e9947d-e670-4cc3-a55b-8911e4ea33e3.dcm\n",
      "222 Done\n",
      "\n",
      ">>>  data/24510056-fdfa-42c1-9c5f-b17b045e97b6.dcm\n",
      "Converting to PNG .........................\n",
      "8.89.627.2999.15366.613668.2723012.21970032990084093692553841720 >>>>>> 5.57.520.4733.87717.324839.5329102.27643162087590080346677497794 >>>>> 1.84.545.3480.17801.269003.1667241.92607525371620837087428384332\n",
      "\n",
      ">> filename 24510056-fdfa-42c1-9c5f-b17b045e97b6.dcm\n",
      "223 Done\n",
      "\n",
      ">>>  data/32e94f18-2ba2-45db-8586-6446b8342409.dcm\n",
      "Converting to PNG .........................\n",
      "6.89.943.4086.24593.130922.1017991.82884405602899631858039050541 >>>>>> 1.13.504.1646.90912.195780.2252701.76848530334394626720110300443 >>>>> 7.47.805.1550.15507.772851.2651493.75188486384437059937522186130\n",
      "\n",
      ">> filename 32e94f18-2ba2-45db-8586-6446b8342409.dcm\n",
      "224 Done\n",
      "\n",
      ">>>  data/752471fb-a105-4f40-97e2-98426ad14697.dcm\n",
      "Converting to PNG .........................\n",
      "2.65.577.2718.50703.944307.1449383.54062259700699459252920297149 >>>>>> 4.16.293.6510.19903.958122.2029634.73088396495417593021756769688 >>>>> 7.15.450.8259.20955.106203.7849287.49649075032032378941021290675\n",
      "\n",
      ">> filename 752471fb-a105-4f40-97e2-98426ad14697.dcm\n",
      "225 Done\n",
      "\n",
      ">>>  data/707d9b97-2f8f-496d-9e97-39d612628811.dcm\n",
      "Converting to PNG .........................\n",
      "9.96.140.9595.15327.911059.1809788.81647341459169388697918644807 >>>>>> 5.31.801.1550.14241.833129.9612532.94128873681348949033185453496 >>>>> 9.47.456.1240.23900.162682.1824421.76893143658027931519668053324\n",
      "\n",
      ">> filename 707d9b97-2f8f-496d-9e97-39d612628811.dcm\n",
      "226 Done\n",
      "\n",
      ">>>  data/2ae7b5f1-93a0-457d-8699-64ccede0abd0.dcm\n",
      "Converting to PNG .........................\n",
      "5.43.203.4109.16434.658516.5667303.78007636340986821401937792111 >>>>>> 3.34.133.2995.80663.268846.9513047.23566864868829355735529440512 >>>>> 8.52.935.1962.78141.898863.5979372.37771406498810435418652966003\n",
      "\n",
      ">> filename 2ae7b5f1-93a0-457d-8699-64ccede0abd0.dcm\n",
      "227 Done\n",
      "\n",
      ">>>  data/1cb132e7-c390-4d29-b224-b1ae6f16e5d5.dcm\n",
      "Converting to PNG .........................\n",
      "5.34.631.3917.36646.136597.8502953.13644880362717359223647719481 >>>>>> 8.14.212.3514.25579.885129.2907029.78054186466435595083928127749 >>>>> 1.48.129.8099.80027.143537.3832178.61648721064863545085482003404\n",
      "\n",
      ">> filename 1cb132e7-c390-4d29-b224-b1ae6f16e5d5.dcm\n",
      "228 Done\n",
      "\n",
      ">>>  data/64e7fd96-bc45-4696-aad3-f373e43e4d6b.dcm\n",
      "Converting to PNG .........................\n",
      "2.37.950.5706.35633.561163.5556224.36465727617430680392168049965 >>>>>> 9.33.373.5595.35269.885813.8849151.74524263966502734103389056958 >>>>> 1.12.859.8849.27167.747269.1339739.22873655845835885534293179400\n",
      "\n",
      ">> filename 64e7fd96-bc45-4696-aad3-f373e43e4d6b.dcm\n",
      "229 Done\n",
      "\n",
      ">>>  data/25e32d3a-fc1c-4a08-809c-c65d3c34ce05.dcm\n",
      "Converting to PNG .........................\n",
      "1.95.833.6495.15845.898070.3984337.73538066855084481763859440629 >>>>>> 3.23.762.3307.88743.857978.7216010.11656748453628647224000247752 >>>>> 7.24.696.9571.26523.505521.9599966.34946860519049884159612929454\n",
      "\n",
      ">> filename 25e32d3a-fc1c-4a08-809c-c65d3c34ce05.dcm\n",
      "230 Done\n",
      "\n",
      ">>>  data/4d2c19fb-8e69-4dec-b730-1277cb35dafc.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.200.2745.28690.819425.4619557.49934946359772761699832162339 >>>>>> 1.16.258.7760.78043.976674.6677359.24381552852424990911503191184 >>>>> 7.13.180.5686.15277.837918.6941529.62058603929481705017913371980\n",
      "\n",
      ">> filename 4d2c19fb-8e69-4dec-b730-1277cb35dafc.dcm\n",
      "231 Done\n",
      "\n",
      ">>>  data/54c3313b-c737-4ca9-9f77-e4fb232aad47.dcm\n",
      "Converting to PNG .........................\n",
      "6.21.969.9430.15324.130686.5340807.33935387998045725923940847933 >>>>>> 1.14.559.1395.65214.812832.8329285.15475130013415178850964565899 >>>>> 1.15.137.8629.51378.628906.8715893.23737122100542312814904316758\n",
      "\n",
      ">> filename 54c3313b-c737-4ca9-9f77-e4fb232aad47.dcm\n",
      "232 Done\n",
      "\n",
      ">>>  data/1b752fd2-ce33-44f8-9f3e-01eec1f7c552.dcm\n",
      "Converting to PNG .........................\n",
      "2.48.312.6894.95541.473706.7462464.51637727612807504676890785532 >>>>>> 6.54.538.3490.93514.804741.5197201.33224177718794637220624976033 >>>>> 2.21.841.9351.12445.379513.9583090.49567042653915561934460481682\n",
      "\n",
      ">> filename 1b752fd2-ce33-44f8-9f3e-01eec1f7c552.dcm\n",
      "233 Done\n",
      "\n",
      ">>>  data/1cc24e5e-b2a7-4390-aa9a-b220e2b6488f.dcm\n",
      "Converting to PNG .........................\n",
      "1.27.566.2934.28611.179463.2884091.27985045726497400100595370453 >>>>>> 7.45.621.2238.59152.373509.8087947.14914491399796501782226795715 >>>>> 1.37.107.9622.79124.248004.7857904.83970428518374729170712653882\n",
      "\n",
      ">> filename 1cc24e5e-b2a7-4390-aa9a-b220e2b6488f.dcm\n",
      "234 Done\n",
      "\n",
      ">>>  data/20f30072-fbbd-4a19-bfbd-b6aca4533620.dcm\n",
      "Converting to PNG .........................\n",
      "6.70.326.4404.40232.333973.9363627.61718998226799464489384499303 >>>>>> 4.68.299.8176.11581.496757.3779228.98407938641897234512588054427 >>>>> 5.68.437.9379.31799.883598.7822973.96332366748861849452298725182\n",
      "\n",
      ">> filename 20f30072-fbbd-4a19-bfbd-b6aca4533620.dcm\n",
      "235 Done\n",
      "\n",
      ">>>  data/20633fb4-8e0b-41e7-a44a-063bdaa9f36f.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.518.5384.20435.723992.5632213.63309144407269953014690486048 >>>>>> 9.38.730.4926.61978.123659.9242320.80094824751399256559993036446 >>>>> 8.61.183.3353.28020.194618.5754078.20561075707826101613254446913\n",
      "\n",
      ">> filename 20633fb4-8e0b-41e7-a44a-063bdaa9f36f.dcm\n",
      "236 Done\n",
      "\n",
      ">>>  data/aaf8bcf1-a236-476f-9c56-0e5a462a5556.dcm\n",
      "Converting to PNG .........................\n",
      "9.82.807.4765.15815.573797.4129095.32019974200061623326725629767 >>>>>> 1.10.137.1519.64629.311194.9804074.64722337181471826289301546600 >>>>> 8.22.266.6795.15048.275821.4974745.96645874783342869455175447757\n",
      "\n",
      ">> filename aaf8bcf1-a236-476f-9c56-0e5a462a5556.dcm\n",
      "237 Done\n",
      "\n",
      ">>>  data/afc3b0c1-b71d-42ca-9196-2fa378bf36a2.dcm\n",
      "Converting to PNG .........................\n",
      "8.37.838.3581.73773.892319.3121235.81720388027008349608426725765 >>>>>> 2.73.121.1451.87466.607801.8600719.26496294665051306711532822441 >>>>> 1.68.504.6362.39848.294913.4746765.24714994020915635858567763169\n",
      "\n",
      ">> filename afc3b0c1-b71d-42ca-9196-2fa378bf36a2.dcm\n",
      "238 Done\n",
      "\n",
      ">>>  data/3895243d-83ab-4f6e-813a-2c4acb8d04f8.dcm\n",
      "Converting to PNG .........................\n",
      "3.96.259.5640.11710.754918.5562113.62365415463606718718309648677 >>>>>> 6.87.816.3316.60434.770187.3183556.74557548133568146859941168450 >>>>> 4.79.197.5206.17745.539710.6976695.21435470866997453663117008469\n",
      "\n",
      ">> filename 3895243d-83ab-4f6e-813a-2c4acb8d04f8.dcm\n",
      "239 Done\n",
      "\n",
      ">>>  data/9f830dcc-d17a-44f8-a8a9-df9f92110210.dcm\n",
      "Converting to PNG .........................\n",
      "9.33.246.4530.34613.273813.5267842.87336042287306193267489541938 >>>>>> 8.77.142.1661.78728.476458.7541058.57904548273763553825967096070 >>>>> 2.11.157.7901.96115.169021.1682320.31282682296326691453222976073\n",
      "\n",
      ">> filename 9f830dcc-d17a-44f8-a8a9-df9f92110210.dcm\n",
      "240 Done\n",
      "\n",
      ">>>  data/24d61d54-dbef-40c5-b85b-48355c39a90e.dcm\n",
      "Converting to PNG .........................\n",
      "6.51.396.4907.40133.196657.6589933.69896489565974481490257352944 >>>>>> 2.56.693.4631.42957.963156.9819355.45869064839653243902174972188 >>>>> 7.57.982.1880.81831.533366.2128109.81892017128094169864835291260\n",
      "\n",
      ">> filename 24d61d54-dbef-40c5-b85b-48355c39a90e.dcm\n",
      "241 Done\n",
      "\n",
      ">>>  data/85dbe477-4716-4df4-9529-78290ea8dc94.dcm\n",
      "Converting to PNG .........................\n",
      "9.51.236.5543.30724.569856.9534201.88621046669447685136079021852 >>>>>> 8.47.308.9819.76673.105576.9779000.72057188961925103707644525443 >>>>> 3.19.305.9341.77723.419726.2628831.59359991362380621777268200827\n",
      "\n",
      ">> filename 85dbe477-4716-4df4-9529-78290ea8dc94.dcm\n",
      "242 Done\n",
      "\n",
      ">>>  data/3cbdeb0c-162f-4e60-928f-3551a7af74e9.dcm\n",
      "Converting to PNG .........................\n",
      "3.21.140.5592.52726.549296.9437249.62797931856523788409980746516 >>>>>> 7.71.414.4905.23523.384814.7553130.42692013049128823536756796192 >>>>> 3.81.268.1772.35604.302240.6929996.60584634278099859045789941706\n",
      "\n",
      ">> filename 3cbdeb0c-162f-4e60-928f-3551a7af74e9.dcm\n",
      "243 Done\n",
      "\n",
      ">>>  data/43588495-c927-423f-a0ee-bc1347a53a17.dcm\n",
      "Converting to PNG .........................\n",
      "9.10.555.4298.79059.205087.5154952.78817640654432331148070899975 >>>>>> 5.22.136.4175.90562.281349.5270273.50870790535167308255892211706 >>>>> 6.68.214.4793.84219.934558.9374424.25756280152584189415945873980\n",
      "\n",
      ">> filename 43588495-c927-423f-a0ee-bc1347a53a17.dcm\n",
      "244 Done\n",
      "\n",
      ">>>  data/13dc5900-8f3e-444c-a9be-fd9bc49ab5d3.dcm\n",
      "Converting to PNG .........................\n",
      "6.58.139.3799.38529.740059.2828541.44759188494781111064185667284 >>>>>> 9.61.623.8967.48485.204597.2772839.33863090671485360453900314395 >>>>> 3.80.118.4557.45089.883717.1984056.13258641936402088343094591430\n",
      "\n",
      ">> filename 13dc5900-8f3e-444c-a9be-fd9bc49ab5d3.dcm\n",
      "245 Done\n",
      "\n",
      ">>>  data/8036830d-c196-4c37-b790-b831170e51c9.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.276.8870.73046.297546.2430097.35282293929618037582069249840 >>>>>> 8.58.413.4733.74720.620365.2736619.17370070005874370742407265697 >>>>> 3.18.694.6811.37976.163319.6268108.19346329186187635279933048249\n",
      "\n",
      ">> filename 8036830d-c196-4c37-b790-b831170e51c9.dcm\n",
      "246 Done\n",
      "\n",
      ">>>  data/9efa7c3c-b4be-4c7a-a0dd-3b6d5ef22a50.dcm\n",
      "Converting to PNG .........................\n",
      "9.62.709.4249.32614.115407.8699024.20392748185716568720386303843 >>>>>> 2.73.121.1451.87466.607801.8600719.26496294665051306711532822441 >>>>> 1.15.123.3369.16349.961495.5563560.25824932269380410201874177528\n",
      "\n",
      ">> filename 9efa7c3c-b4be-4c7a-a0dd-3b6d5ef22a50.dcm\n",
      "247 Done\n",
      "\n",
      ">>>  data/92e7ef6e-2eb6-4325-badd-4b43a895589d.dcm\n",
      "Converting to PNG .........................\n",
      "8.26.496.1324.18355.118877.7874841.32664286776976745205037538750 >>>>>> 6.99.187.6124.98617.146460.3289762.41735311186339711363946689314 >>>>> 9.13.763.9173.51569.205562.3359809.38529590530967307444607726193\n",
      "\n",
      ">> filename 92e7ef6e-2eb6-4325-badd-4b43a895589d.dcm\n",
      "248 Done\n",
      "\n",
      ">>>  data/89aa7b6b-3fa8-4e09-8fa0-b0b529ee6aa6.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.388.5931.10709.764068.4729023.12152979608723181637547431404 >>>>>> 1.29.383.8061.42614.286286.4648890.96417463007641724704816004438 >>>>> 8.74.692.3243.23153.148211.9442227.71143293425299932648242668678\n",
      "\n",
      ">> filename 89aa7b6b-3fa8-4e09-8fa0-b0b529ee6aa6.dcm\n",
      "249 Done\n",
      "\n",
      ">>>  data/a37f87b5-acdc-4871-91db-0f904b6d4fac.dcm\n",
      "Converting to PNG .........................\n",
      "3.97.524.1091.43334.422170.1592556.96772700388951442063837970877 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 7.86.575.1237.94595.624544.9005695.16224057615705871884971532793\n",
      "\n",
      ">> filename a37f87b5-acdc-4871-91db-0f904b6d4fac.dcm\n",
      "250 Done\n",
      "\n",
      ">>>  data/3fa39dbc-b996-4598-b86b-246a137e2047.dcm\n",
      "Converting to PNG .........................\n",
      "2.19.827.1030.91084.727839.9738080.25946650998084390498380646692 >>>>>> 4.98.424.7981.59087.536488.1198918.64533264052157464986028629247 >>>>> 8.37.418.2088.32883.799629.3908421.90753788259554133414967633816\n",
      "\n",
      ">> filename 3fa39dbc-b996-4598-b86b-246a137e2047.dcm\n",
      "251 Done\n",
      "\n",
      ">>>  data/db84bba4-602a-456e-bb17-d44ed02d266b.dcm\n",
      "Converting to PNG .........................\n",
      "3.31.155.2820.12810.602811.9065211.95022306126842436774338268007 >>>>>> 1.85.495.1451.95192.792715.7847960.22137396558127408729513482103 >>>>> 3.91.123.3314.85684.890696.5793893.45925374381651564939089868693\n",
      "\n",
      ">> filename db84bba4-602a-456e-bb17-d44ed02d266b.dcm\n",
      "252 Done\n",
      "\n",
      ">>>  data/cc3082b9-919b-4b10-a8f1-26b27664a7a9.dcm\n",
      "Converting to PNG .........................\n",
      "9.26.955.5589.56294.311991.1066260.23066781296570935232936557258 >>>>>> 6.12.592.3302.58323.491563.4979807.58246561492857219117016147031 >>>>> 9.97.850.1548.37753.418719.1744962.13336780886880758506367390306\n",
      "\n",
      ">> filename cc3082b9-919b-4b10-a8f1-26b27664a7a9.dcm\n",
      "253 Done\n",
      "\n",
      ">>>  data/1698be61-b4a7-4d09-8daa-9b46729685b0.dcm\n",
      "Converting to PNG .........................\n",
      "4.15.147.1084.67267.351101.6968396.53035488337814306833370463911 >>>>>> 5.69.779.3744.36021.458017.3768030.18849852431318780223752727447 >>>>> 7.88.630.4527.65057.263181.1313435.43998451719683141217056947321\n",
      "\n",
      ">> filename 1698be61-b4a7-4d09-8daa-9b46729685b0.dcm\n",
      "254 Done\n",
      "\n",
      ">>>  data/88aa3658-5ff5-4390-9f15-3091cb9ead43.dcm\n",
      "Converting to PNG .........................\n",
      "6.73.639.6132.44830.147241.9130139.79973447469182263430464583748 >>>>>> 8.29.171.2723.10512.975829.4036751.98651487990088353999444085918 >>>>> 1.35.824.1206.16397.108568.7935949.64134189027504834007246807188\n",
      "\n",
      ">> filename 88aa3658-5ff5-4390-9f15-3091cb9ead43.dcm\n",
      "255 Done\n",
      "\n",
      ">>>  data/742e4de4-87fc-4a26-9f0e-228a364f8c5c.dcm\n",
      "Converting to PNG .........................\n",
      "8.96.624.6243.27585.954773.3845003.14462488210142165473483471358 >>>>>> 2.66.272.4089.14455.121688.8916184.26930505604246623005384463666 >>>>> 2.83.635.6167.95318.499740.8952336.51386916021399854572010558274\n",
      "\n",
      ">> filename 742e4de4-87fc-4a26-9f0e-228a364f8c5c.dcm\n",
      "256 Done\n",
      "\n",
      ">>>  data/7889e59f-7fd3-47b8-9df9-36fca903708c.dcm\n",
      "Converting to PNG .........................\n",
      "3.19.833.4113.16730.175463.2515861.33913992282260353326383239496 >>>>>> 7.79.125.7990.12086.630231.3331694.12776142753458047714933009874 >>>>> 5.27.786.8021.51965.127744.8419946.28060176761042525021233293015\n",
      "\n",
      ">> filename 7889e59f-7fd3-47b8-9df9-36fca903708c.dcm\n",
      "257 Done\n",
      "\n",
      ">>>  data/5dd90ad1-e4cc-4345-80a9-f71eeb74da7a.dcm\n",
      "Converting to PNG .........................\n",
      "5.98.567.7838.96138.740232.6387330.59046681930561660108085200740 >>>>>> 2.61.309.1751.95959.125927.5941540.10899408096718014928687316301 >>>>> 1.13.656.8592.35405.383944.7941729.73342052532139651394809832645\n",
      "\n",
      ">> filename 5dd90ad1-e4cc-4345-80a9-f71eeb74da7a.dcm\n",
      "258 Done\n",
      "\n",
      ">>>  data/8fe48972-6021-43e0-8aa8-47d559a309ce.dcm\n",
      "Converting to PNG .........................\n",
      "4.27.233.5238.68889.654369.6072728.75703511844781511888930977674 >>>>>> 5.43.145.6195.79262.870195.7631025.86848846861386176052189469996 >>>>> 8.61.234.5152.34193.305257.9741620.74844829171173588356296605982\n",
      "\n",
      ">> filename 8fe48972-6021-43e0-8aa8-47d559a309ce.dcm\n",
      "259 Done\n",
      "\n",
      ">>>  data/fdbf0440-e56e-42ca-903e-de7bed58d2be.dcm\n",
      "Converting to PNG .........................\n",
      "4.82.946.2267.19925.712378.2940818.94695083916632953911084421270 >>>>>> 4.79.758.9620.91151.252341.7629117.90234043918196221366497500068 >>>>> 1.16.489.8030.17991.967948.1592506.16953939486505051358144712193\n",
      "\n",
      ">> filename fdbf0440-e56e-42ca-903e-de7bed58d2be.dcm\n",
      "260 Done\n",
      "\n",
      ">>>  data/424633a2-7757-4405-9316-856744af5590.dcm\n",
      "Converting to PNG .........................\n",
      "6.69.876.1746.53500.273311.2782139.13697434064466837007111325120 >>>>>> 6.26.955.6459.68024.657972.1252238.86908194959353353928011963013 >>>>> 5.10.200.2167.16172.205778.6669909.47245687169197591999200777288\n",
      "\n",
      ">> filename 424633a2-7757-4405-9316-856744af5590.dcm\n",
      "261 Done\n",
      "\n",
      ">>>  data/fd99544b-1324-4c25-bfab-c9a3764e62b1.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.936.9267.97783.652661.7048313.58494594909421423336471295363 >>>>>> 3.53.934.9294.15123.256005.3180593.42763177037528060397002446429 >>>>> 1.12.554.4886.94403.331288.6495147.40158753769158564222845197436\n",
      "\n",
      ">> filename fd99544b-1324-4c25-bfab-c9a3764e62b1.dcm\n",
      "262 Done\n",
      "\n",
      ">>>  data/d0c7d5f9-d5b4-4d6c-ae4c-fef58b21b340.dcm\n",
      "Converting to PNG .........................\n",
      "5.82.774.7309.83030.750413.5811451.16772173804657126378892339907 >>>>>> 1.18.727.1422.63575.120940.9789243.62480738202695866525310894716 >>>>> 2.39.291.4264.56943.669095.3372278.62133616828947523005474143396\n",
      "\n",
      ">> filename d0c7d5f9-d5b4-4d6c-ae4c-fef58b21b340.dcm\n",
      "263 Done\n",
      "\n",
      ">>>  data/888d900f-2644-42cd-a6a9-92559d5a35ea.dcm\n",
      "Converting to PNG .........................\n",
      "7.20.399.4425.10773.398439.9833413.54071490162717533709650752613 >>>>>> 9.18.233.2531.19706.736107.6145874.60365496122025635709380495159 >>>>> 2.96.127.6657.45589.518357.4403519.14057033972937533486057099048\n",
      "\n",
      ">> filename 888d900f-2644-42cd-a6a9-92559d5a35ea.dcm\n",
      "264 Done\n",
      "\n",
      ">>>  data/c4813c5f-a9ce-4fca-ae88-2b77036a4d22.dcm\n",
      "Converting to PNG .........................\n",
      "3.52.191.1914.88207.949081.1224661.77135989016644963243357636799 >>>>>> 2.47.110.6024.95026.332051.1065915.60676543657417218397079040390 >>>>> 7.67.960.4306.19567.966037.8724144.39801894017235507801123348636\n",
      "\n",
      ">> filename c4813c5f-a9ce-4fca-ae88-2b77036a4d22.dcm\n",
      "265 Done\n",
      "\n",
      ">>>  data/8956c65f-249b-49a0-8437-215e3c924512.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.964.2828.87853.306372.4310275.80480985411600623984049385644 >>>>>> 1.16.490.9957.30891.913244.2738373.81760641860950083275344200923 >>>>> 3.54.542.4803.97094.888977.1910373.34748973597709284926227177249\n",
      "\n",
      ">> filename 8956c65f-249b-49a0-8437-215e3c924512.dcm\n",
      "266 Done\n",
      "\n",
      ">>>  data/c1880b20-1486-4aa8-9256-bde003091987.dcm\n",
      "Converting to PNG .........................\n",
      "1.61.934.7752.84928.826600.5230359.20603685911759964029699852381 >>>>>> 8.44.423.2907.52252.524679.1634243.10792048293108453739400568294 >>>>> 8.47.764.2956.50728.145539.1855112.28591050477083027029465054342\n",
      "\n",
      ">> filename c1880b20-1486-4aa8-9256-bde003091987.dcm\n",
      "267 Done\n",
      "\n",
      ">>>  data/dc442240-1200-4c7d-9630-caa1def8945b.dcm\n",
      "Converting to PNG .........................\n",
      "7.65.441.8351.92586.475317.3962830.75424719243471248441982934113 >>>>>> 1.37.842.1602.96662.780578.7807971.48792907772125169180816011523 >>>>> 6.74.132.4689.10817.369628.4275175.20863427571784787613068644288\n",
      "\n",
      ">> filename dc442240-1200-4c7d-9630-caa1def8945b.dcm\n",
      "268 Done\n",
      "\n",
      ">>>  data/4c1a7cb6-ae42-4357-bebf-76b5e6dc8dac.dcm\n",
      "Converting to PNG .........................\n",
      "1.34.106.3067.96917.139362.1238473.76160747599118150748303567329 >>>>>> 1.63.673.1271.79133.159830.8201078.54817074946630512777872097401 >>>>> 7.51.280.1836.56994.818180.1391777.46199939781763807595365197276\n",
      "\n",
      ">> filename 4c1a7cb6-ae42-4357-bebf-76b5e6dc8dac.dcm\n",
      "269 Done\n",
      "\n",
      ">>>  data/18eb21ca-cc57-4697-8180-5fea87efb347.dcm\n",
      "Converting to PNG .........................\n",
      "6.11.857.8200.64641.664584.9578302.33687912757279561814606150718 >>>>>> 1.48.768.7917.91975.722489.5272558.70681508194043133104002619603 >>>>> 6.62.363.4522.10210.106440.8599330.39355606690213953628197714219\n",
      "\n",
      ">> filename 18eb21ca-cc57-4697-8180-5fea87efb347.dcm\n",
      "270 Done\n",
      "\n",
      ">>>  data/4a65cbbc-7181-485e-a481-e45caf6b14e6.dcm\n",
      "Converting to PNG .........................\n",
      "1.27.168.1191.12170.958953.4739840.86786551719969330390205194110 >>>>>> 3.17.484.9679.19203.492232.3418596.24983054922498592453088610436 >>>>> 1.30.474.8556.66285.656603.8947125.52302580296099755296307356670\n",
      "\n",
      ">> filename 4a65cbbc-7181-485e-a481-e45caf6b14e6.dcm\n",
      "271 Done\n",
      "\n",
      ">>>  data/9be77445-77a4-40d3-a2ed-62f4bb10f2b9.dcm\n",
      "Converting to PNG .........................\n",
      "6.83.997.5133.37211.297862.1631806.67174679042283947895209943634 >>>>>> 6.12.758.1365.72772.958062.7810391.76658508686004333812041073630 >>>>> 3.11.112.5130.51347.479084.6007334.92919745990144098948548893580\n",
      "\n",
      ">> filename 9be77445-77a4-40d3-a2ed-62f4bb10f2b9.dcm\n",
      "272 Done\n",
      "\n",
      ">>>  data/9079797d-5649-42eb-b08f-893c3fcaf4e3.dcm\n",
      "Converting to PNG .........................\n",
      "5.17.868.8688.33131.199558.3862013.61476953925034555288645418245 >>>>>> 9.62.390.2979.43314.545916.8843121.72197919518873754760689953913 >>>>> 7.89.387.3614.22496.514734.6319814.23420988263011846073480213010\n",
      "\n",
      ">> filename 9079797d-5649-42eb-b08f-893c3fcaf4e3.dcm\n",
      "273 Done\n",
      "\n",
      ">>>  data/73410229-bee1-4aa5-a0d4-052a5a410be8.dcm\n",
      "Converting to PNG .........................\n",
      "1.33.749.2519.67376.371635.1503670.32658638637359911911986778593 >>>>>> 6.23.376.5349.28185.868943.5982891.90022145407708310412078994378 >>>>> 2.18.907.3951.17495.941387.1883129.24644475026988785396757553218\n",
      "\n",
      ">> filename 73410229-bee1-4aa5-a0d4-052a5a410be8.dcm\n",
      "274 Done\n",
      "\n",
      ">>>  data/63b7122d-85bc-4667-8d2e-62bfcbeab43d.dcm\n",
      "Converting to PNG .........................\n",
      "2.35.323.5548.73840.580415.6442421.40382253795959949927557620049 >>>>>> 1.18.727.1422.63575.120940.9789243.62480738202695866525310894716 >>>>> 2.39.291.4264.56943.669095.3372278.62133616828947523005474143396\n",
      "\n",
      ">> filename 63b7122d-85bc-4667-8d2e-62bfcbeab43d.dcm\n",
      "275 Done\n",
      "\n",
      ">>>  data/d8c19503-9aaa-41d3-9030-d7d87192d29a.dcm\n",
      "Converting to PNG .........................\n",
      "4.14.782.6259.24659.337238.9781136.19945738885508850149715154876 >>>>>> 7.53.452.2809.80738.812549.7277537.30279578739865970088885629411 >>>>> 2.14.742.5027.50847.128797.1951166.81701254614198902236185412298\n",
      "\n",
      ">> filename d8c19503-9aaa-41d3-9030-d7d87192d29a.dcm\n",
      "276 Done\n",
      "\n",
      ">>>  data/19364a5e-4bb9-4f59-a431-ccfe7854dcf5.dcm\n",
      "Converting to PNG .........................\n",
      "8.20.442.9330.54873.585196.8238695.35539682728771873075748939418 >>>>>> 9.21.880.7257.13475.143829.5741880.39160383226915128853849442907 >>>>> 1.12.547.1884.57148.533368.2261967.95395407883117570671724334316\n",
      "\n",
      ">> filename 19364a5e-4bb9-4f59-a431-ccfe7854dcf5.dcm\n",
      "277 Done\n",
      "\n",
      ">>>  data/9c758d74-6bc3-41f0-99ab-81ef820c0603.dcm\n",
      "Converting to PNG .........................\n",
      "2.52.837.3321.79633.922960.2358042.24418708003551688095939031435 >>>>>> 1.63.557.6426.98631.226884.4754622.31668276550309671148334218445 >>>>> 4.84.622.4138.39028.537687.2970334.98439002532511352558497024082\n",
      "\n",
      ">> filename 9c758d74-6bc3-41f0-99ab-81ef820c0603.dcm\n",
      "278 Done\n",
      "\n",
      ">>>  data/0ce63f24-4224-4571-8126-7c5873693f30.dcm\n",
      "Converting to PNG .........................\n",
      "5.54.908.4753.52230.113600.1423521.78607918997274004403350202072 >>>>>> 3.20.281.3254.13748.465968.8835087.89533532037185658948944153937 >>>>> 3.57.606.6735.27273.603907.3284427.68236427684535826373350603821\n",
      "\n",
      ">> filename 0ce63f24-4224-4571-8126-7c5873693f30.dcm\n",
      "279 Done\n",
      "\n",
      ">>>  data/f084bf25-9059-430f-a322-ae4acbfcd8a4.dcm\n",
      "Converting to PNG .........................\n",
      "3.99.295.1167.51995.199698.8617655.32408928193514605748319117761 >>>>>> 8.66.826.7151.81462.189665.8423482.72746823694405139734343116771 >>>>> 7.22.262.6241.11599.224187.5859418.47504062442378405452181321711\n",
      "\n",
      ">> filename f084bf25-9059-430f-a322-ae4acbfcd8a4.dcm\n",
      "280 Done\n",
      "\n",
      ">>>  data/6e42e940-9061-4b15-acff-f7b5b265295e.dcm\n",
      "Converting to PNG .........................\n",
      "7.78.422.2926.86179.400172.6273942.40197121016025490373090046625 >>>>>> 1.10.753.9150.91356.159837.1261521.96034693397580442233346662334 >>>>> 1.32.655.3220.70407.527462.7537091.75725616383298629975355625542\n",
      "\n",
      ">> filename 6e42e940-9061-4b15-acff-f7b5b265295e.dcm\n",
      "281 Done\n",
      "\n",
      ">>>  data/b9458998-6545-4e05-bf1b-d68df96047f8.dcm\n",
      "Converting to PNG .........................\n",
      "8.13.385.2063.33367.221210.1477034.95800196733597220814267751015 >>>>>> 3.81.152.1528.94730.394655.6030115.65145943570632816219031526135 >>>>> 1.15.350.2860.52614.714931.2301038.15781321366621632091956221388\n",
      "\n",
      ">> filename b9458998-6545-4e05-bf1b-d68df96047f8.dcm\n",
      "282 Done\n",
      "\n",
      ">>>  data/2d18213a-101a-4772-9de8-8de20906c4b5.dcm\n",
      "Converting to PNG .........................\n",
      "4.49.136.1531.23414.520644.5731054.56807107800392426583518161858 >>>>>> 6.48.150.4708.72863.331154.4821381.78477940608602943352706430540 >>>>> 5.10.174.4567.10701.569596.8367013.73964772913100007868992588200\n",
      "\n",
      ">> filename 2d18213a-101a-4772-9de8-8de20906c4b5.dcm\n",
      "283 Done\n",
      "\n",
      ">>>  data/da215343-474e-4f37-9ddf-4edf157f7a12.dcm\n",
      "Converting to PNG .........................\n",
      "1.51.260.1880.82089.174975.1685224.17783292348728079081884152939 >>>>>> 9.13.591.1283.48259.661123.4242262.17622418217732530048484616507 >>>>> 5.34.617.1792.98868.327264.6678365.17440441393735453645134607480\n",
      "\n",
      ">> filename da215343-474e-4f37-9ddf-4edf157f7a12.dcm\n",
      "284 Done\n",
      "\n",
      ">>>  data/90c747f3-fc03-432c-bec1-e3277e748157.dcm\n",
      "Converting to PNG .........................\n",
      "5.34.149.1469.23607.193980.1751919.80103366294908261532323428419 >>>>>> 7.18.593.3455.15293.172759.4858257.77094797183573275180752458536 >>>>> 5.45.132.1989.66608.235959.3826493.17078835083500172743721181199\n",
      "\n",
      ">> filename 90c747f3-fc03-432c-bec1-e3277e748157.dcm\n",
      "285 Done\n",
      "\n",
      ">>>  data/71e0a2cd-eb59-47c4-b4df-4ff1f85514ef.dcm\n",
      "Converting to PNG .........................\n",
      "7.11.205.3847.81996.391988.2997769.94587993964492519745998016457 >>>>>> 4.26.645.7635.82987.135760.2032734.51934801300817586228979876252 >>>>> 2.81.710.3778.12039.146745.7621243.89420139232693068713164567999\n",
      "\n",
      ">> filename 71e0a2cd-eb59-47c4-b4df-4ff1f85514ef.dcm\n",
      "286 Done\n",
      "\n",
      ">>>  data/b63a25cd-e601-4d6a-b7dd-eb3311c5a171.dcm\n",
      "Converting to PNG .........................\n",
      "8.40.573.1285.73327.992144.6278758.95606972713660822477064949364 >>>>>> 8.29.175.5003.84006.183177.1294070.39951916059462720123003417161 >>>>> 3.79.693.6982.48832.978849.1872359.15678068690224950810257249040\n",
      "\n",
      ">> filename b63a25cd-e601-4d6a-b7dd-eb3311c5a171.dcm\n",
      "287 Done\n",
      "\n",
      ">>>  data/bd392729-fab7-414d-a50b-f691542b3859.dcm\n",
      "Converting to PNG .........................\n",
      "1.43.481.2770.36402.988144.4100115.69435085233492097786613680184 >>>>>> 9.84.475.9546.91445.141291.1966287.19246697787771526553394285492 >>>>> 3.16.992.1993.74359.387297.4998702.18956101600547070756921376898\n",
      "\n",
      ">> filename bd392729-fab7-414d-a50b-f691542b3859.dcm\n",
      "288 Done\n",
      "\n",
      ">>>  data/c4d445f0-1085-4fa0-b38a-b9583878a5f4.dcm\n",
      "Converting to PNG .........................\n",
      "2.37.759.9866.54511.197323.5101855.37314212027395195805930470334 >>>>>> 7.38.633.9168.60386.964975.3750306.17419507083157905659684472644 >>>>> 1.11.654.8776.36666.957635.2819923.28324046830014348947293742683\n",
      "\n",
      ">> filename c4d445f0-1085-4fa0-b38a-b9583878a5f4.dcm\n",
      "289 Done\n",
      "\n",
      ">>>  data/12179788-aa34-454b-bbb1-fa512015c13a.dcm\n",
      "Converting to PNG .........................\n",
      "9.61.306.3786.18590.670865.9369299.64414019131165719134758673194 >>>>>> 7.72.471.7563.31949.436186.6725949.18027789259430181242227526196 >>>>> 8.75.145.8298.29894.699727.9656518.70145406392180943553338451209\n",
      "\n",
      ">> filename 12179788-aa34-454b-bbb1-fa512015c13a.dcm\n",
      "290 Done\n",
      "\n",
      ">>>  data/c0d2e26e-3ae8-416b-b142-f70e47d44633.dcm\n",
      "Converting to PNG .........................\n",
      "6.36.682.5216.70457.281211.8636065.55451241715372887492879074761 >>>>>> 1.73.131.1909.26266.134108.6056755.42268264619150356363198896060 >>>>> 4.56.516.6350.34654.687390.8346321.51199713564545488722702901466\n",
      "\n",
      ">> filename c0d2e26e-3ae8-416b-b142-f70e47d44633.dcm\n",
      "291 Done\n",
      "\n",
      ">>>  data/ed2ba79e-4be9-4c84-ad66-28bf1c481801.dcm\n",
      "Converting to PNG .........................\n",
      "3.18.418.6361.96634.860351.6710556.11079156566537271729826873837 >>>>>> 6.11.256.7612.45822.828665.8509665.51217678500264117057150919466 >>>>> 1.85.646.8632.69759.553375.4923824.82975555418556422798762740836\n",
      "\n",
      ">> filename ed2ba79e-4be9-4c84-ad66-28bf1c481801.dcm\n",
      "292 Done\n",
      "\n",
      ">>>  data/2a7781a3-3b13-4a65-894a-ed051e455b2d.dcm\n",
      "Converting to PNG .........................\n",
      "2.22.660.5660.13242.183271.9775753.12711890779026369538732115693 >>>>>> 8.81.767.7433.57079.598781.8447205.94852762033478855242280706437 >>>>> 7.39.614.7286.66923.183557.9734631.77479301904348057232256331113\n",
      "\n",
      ">> filename 2a7781a3-3b13-4a65-894a-ed051e455b2d.dcm\n",
      "293 Done\n",
      "\n",
      ">>>  data/e43454bd-d903-42d2-aea5-fdcd5cc1edd6.dcm\n",
      "Converting to PNG .........................\n",
      "7.61.893.1076.87968.849622.4304756.83544002615125149172533558190 >>>>>> 6.12.758.1365.72772.958062.7810391.76658508686004333812041073630 >>>>> 1.92.351.3338.71391.798896.1411874.54362474337199460845688993002\n",
      "\n",
      ">> filename e43454bd-d903-42d2-aea5-fdcd5cc1edd6.dcm\n",
      "294 Done\n",
      "\n",
      ">>>  data/7b608ad3-8f13-4d26-8561-8aa1e99ee868.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.498.2956.17923.794321.6565639.24355354966531304458712402691 >>>>>> 3.40.495.2878.23604.442372.3139186.68042810806825778895365027687 >>>>> 5.11.699.8257.56256.863373.1258686.20364510602369181764514330129\n",
      "\n",
      ">> filename 7b608ad3-8f13-4d26-8561-8aa1e99ee868.dcm\n",
      "295 Done\n",
      "\n",
      ">>>  data/33e07487-dd82-4465-a8cb-1c8ea373813d.dcm\n",
      "Converting to PNG .........................\n",
      "2.29.888.1711.83980.139257.1244792.67457628535217229919760027935 >>>>>> 4.19.658.5507.76469.702419.4939893.60427206906722598899952258723 >>>>> 8.52.412.9345.63833.197809.9890560.12024501169082901892183901205\n",
      "\n",
      ">> filename 33e07487-dd82-4465-a8cb-1c8ea373813d.dcm\n",
      "296 Done\n",
      "\n",
      ">>>  data/8a9e3d6b-41fd-41f8-9d7a-da057667a99c.dcm\n",
      "Converting to PNG .........................\n",
      "2.99.133.1277.30684.621483.1079565.26178138358904399630779355956 >>>>>> 1.63.279.3424.95763.888667.6650591.69498609327501215420291178014 >>>>> 6.72.649.6247.65796.773059.1181541.74603568190491514882191601727\n",
      "\n",
      ">> filename 8a9e3d6b-41fd-41f8-9d7a-da057667a99c.dcm\n",
      "297 Done\n",
      "\n",
      ">>>  data/fcb9f613-e1ad-4f48-a021-88405fcb600d.dcm\n",
      "Converting to PNG .........................\n",
      "5.46.228.8971.80961.895983.9737515.34653737026995677023528570991 >>>>>> 1.83.232.3350.48637.543824.3857217.24887023930240326958393163362 >>>>> 4.88.336.5770.32099.492144.6404341.30331058624999862353943209686\n",
      "\n",
      ">> filename fcb9f613-e1ad-4f48-a021-88405fcb600d.dcm\n",
      "298 Done\n",
      "\n",
      ">>>  data/631a8b01-7f79-46a0-a76a-c462aa10e694.dcm\n",
      "Converting to PNG .........................\n",
      "9.24.358.9046.33366.445319.1435910.13440734863217109998025391840 >>>>>> 8.66.730.2394.92835.699331.4209103.70607715294048730774645608825 >>>>> 1.50.450.8429.93233.691062.9888859.60836334964332308543480324467\n",
      "\n",
      ">> filename 631a8b01-7f79-46a0-a76a-c462aa10e694.dcm\n",
      "299 Done\n",
      "\n",
      ">>>  data/a5bad11b-4d04-486d-b4f3-d0fac7f44b97.dcm\n",
      "Converting to PNG .........................\n",
      "4.85.782.7573.51094.991308.4041629.50468196936213788983508426868 >>>>>> 1.80.621.4954.19713.206148.3662563.37519400192262929444603305339 >>>>> 1.95.185.9517.89830.163762.2897400.74210410705248782799214322879\n",
      "\n",
      ">> filename a5bad11b-4d04-486d-b4f3-d0fac7f44b97.dcm\n",
      "300 Done\n",
      "\n",
      ">>>  data/8c964d39-33a8-421b-bfa0-d1b69e1279c7.dcm\n",
      "Converting to PNG .........................\n",
      "8.93.138.1998.85518.114908.2704607.15113517255115104255159992337 >>>>>> 8.50.387.2947.60816.140073.2519311.16631323510558921224217990900 >>>>> 5.57.830.1981.52330.317116.1390740.44640192955625059874327532693\n",
      "\n",
      ">> filename 8c964d39-33a8-421b-bfa0-d1b69e1279c7.dcm\n",
      "301 Done\n",
      "\n",
      ">>>  data/a36a5a08-a381-4725-8d9f-1c8227210244.dcm\n",
      "Converting to PNG .........................\n",
      "8.40.941.4124.64915.603418.4898462.83837292980634510236981821438 >>>>>> 9.57.126.9723.24834.119686.5848109.49673078128285419773097086573 >>>>> 4.76.788.2010.30002.463852.8643599.91458969221064524713285846132\n",
      "\n",
      ">> filename a36a5a08-a381-4725-8d9f-1c8227210244.dcm\n",
      "302 Done\n",
      "\n",
      ">>>  data/042818b7-04f4-41e1-bec9-9f0e5396adef.dcm\n",
      "Converting to PNG .........................\n",
      "4.25.267.7930.40389.724312.9027842.18871708446517172261159021333 >>>>>> 3.92.340.2457.67105.300696.9858414.96838754430522281554512081673 >>>>> 2.19.560.9400.31569.826437.1407379.56991416673442641014795125972\n",
      "\n",
      ">> filename 042818b7-04f4-41e1-bec9-9f0e5396adef.dcm\n",
      "303 Done\n",
      "\n",
      ">>>  data/a832d4b4-5413-49a9-bdc2-ec9639e8e0f7.dcm\n",
      "Converting to PNG .........................\n",
      "1.70.887.1877.57270.467547.8975538.41799463585167046381121766098 >>>>>> 2.19.677.4669.32436.173880.2121543.18321177566935251542569082917 >>>>> 1.11.633.8493.36895.451053.1947425.79655372513244439838921223725\n",
      "\n",
      ">> filename a832d4b4-5413-49a9-bdc2-ec9639e8e0f7.dcm\n",
      "304 Done\n",
      "\n",
      ">>>  data/e53ec60c-ab77-4aec-8f90-c51d3514d066.dcm\n",
      "Converting to PNG .........................\n",
      "2.94.169.2833.15066.243598.5754735.28330827856258799861040870552 >>>>>> 6.47.924.8374.12238.737561.7408499.20103085156976735308232509598 >>>>> 7.18.904.1360.26599.227900.1626151.45773054296802375096982520015\n",
      "\n",
      ">> filename e53ec60c-ab77-4aec-8f90-c51d3514d066.dcm\n",
      "305 Done\n",
      "\n",
      ">>>  data/91ba569a-2499-4c81-ab32-7ca90a2b1d67.dcm\n",
      "Converting to PNG .........................\n",
      "6.96.526.2581.65641.979078.4742315.20210684039798807509422157307 >>>>>> 3.86.489.7007.80520.433101.3638818.55054996812928413188433007952 >>>>> 5.61.874.1819.64053.459515.3315906.64701349866090189295885478995\n",
      "\n",
      ">> filename 91ba569a-2499-4c81-ab32-7ca90a2b1d67.dcm\n",
      "306 Done\n",
      "\n",
      ">>>  data/7c37c801-cc79-4efb-afb5-62a187eab340.dcm\n",
      "Converting to PNG .........................\n",
      "3.58.140.8021.32512.782498.9547394.59482275687234663792611803315 >>>>>> 1.14.702.1624.37435.708897.8569259.56189911418190258093330824396 >>>>> 3.10.800.5617.96220.510189.2512986.96256726101976391604828562313\n",
      "\n",
      ">> filename 7c37c801-cc79-4efb-afb5-62a187eab340.dcm\n",
      "307 Done\n",
      "\n",
      ">>>  data/2f3dbc21-71d8-46e7-8e7c-e3a1fbea9f77.dcm\n",
      "Converting to PNG .........................\n",
      "1.18.744.8045.14014.560943.1150500.92893045150793404071112837138 >>>>>> 9.56.447.5785.40241.380132.8714084.26686001800999792739630734533 >>>>> 3.19.604.1003.49346.528071.1539003.37957603253016872644907775757\n",
      "\n",
      ">> filename 2f3dbc21-71d8-46e7-8e7c-e3a1fbea9f77.dcm\n",
      "308 Done\n",
      "\n",
      ">>>  data/1645b6b5-5f55-4661-aed6-43829078ab93.dcm\n",
      "Converting to PNG .........................\n",
      "5.34.398.4784.66022.445433.1827605.20420854995923573078424323091 >>>>>> 7.15.154.8196.18143.443519.8692903.14131936792965290472425690021 >>>>> 1.17.118.3685.57375.365152.8792812.44079872852233908541199711053\n",
      "\n",
      ">> filename 1645b6b5-5f55-4661-aed6-43829078ab93.dcm\n",
      "309 Done\n",
      "\n",
      ">>>  data/cc3edf24-2df1-4702-adb8-c51ddf6473c8.dcm\n",
      "Converting to PNG .........................\n",
      "9.92.695.6786.86606.437447.3465726.48930367254003105966336813403 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 7.10.475.9203.98020.853500.4169022.61368515953698240913782332380\n",
      "\n",
      ">> filename cc3edf24-2df1-4702-adb8-c51ddf6473c8.dcm\n",
      "310 Done\n",
      "\n",
      ">>>  data/1971a6ac-12d3-4381-ba41-2d1897f9614e.dcm\n",
      "Converting to PNG .........................\n",
      "7.24.101.1551.33723.131878.3216502.61284768911722722382356310760 >>>>>> 7.45.183.9376.63094.667559.5000258.23547453263788110912049130463 >>>>> 6.83.114.9093.30688.210609.9622676.25346736269487963823452328125\n",
      "\n",
      ">> filename 1971a6ac-12d3-4381-ba41-2d1897f9614e.dcm\n",
      "311 Done\n",
      "\n",
      ">>>  data/da01eb4b-b249-4642-b0be-5315be01c421.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.354.5142.64983.633931.2936548.96357926339781219234004666433 >>>>>> 1.93.676.6374.56799.751139.8495597.64458229234772094096695148502 >>>>> 2.51.191.5781.86530.638815.1375839.18047152288742300517149157931\n",
      "\n",
      ">> filename da01eb4b-b249-4642-b0be-5315be01c421.dcm\n",
      "312 Done\n",
      "\n",
      ">>>  data/c7a46800-7199-4be0-8a29-1e2babf541d6.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.423.3139.66207.382790.9380627.47285704228900122485956645469 >>>>>> 1.18.727.1422.63575.120940.9789243.62480738202695866525310894716 >>>>> 2.39.291.4264.56943.669095.3372278.62133616828947523005474143396\n",
      "\n",
      ">> filename c7a46800-7199-4be0-8a29-1e2babf541d6.dcm\n",
      "313 Done\n",
      "\n",
      ">>>  data/e3906e98-f1f4-4bcd-bcac-b9bd1963c9b7.dcm\n",
      "Converting to PNG .........................\n",
      "8.98.150.1663.97842.211394.6341461.78301136505364878188366564773 >>>>>> 7.86.601.1934.12677.700950.8880064.65598193467270470605519624269 >>>>> 1.13.936.3208.13599.355387.7340977.63190968483169724617406799318\n",
      "\n",
      ">> filename e3906e98-f1f4-4bcd-bcac-b9bd1963c9b7.dcm\n",
      "314 Done\n",
      "\n",
      ">>>  data/26937c14-46a6-4bcf-b3e9-17fa51b31c3f.dcm\n",
      "Converting to PNG .........................\n",
      "9.31.944.8805.44279.514134.3299448.14773960189393102123040919158 >>>>>> 1.15.707.3818.90205.410875.6125522.95763852488885953682953885288 >>>>> 6.11.950.5392.17399.110394.1975741.26138734303288479720830445979\n",
      "\n",
      ">> filename 26937c14-46a6-4bcf-b3e9-17fa51b31c3f.dcm\n",
      "315 Done\n",
      "\n",
      ">>>  data/ca23324e-ab94-4eec-b90b-ddb28f45d9ce.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.629.3124.39491.874750.6633893.24205496359856118129830225989 >>>>>> 5.82.554.3950.19073.637821.1595127.16518649060458580032764362224 >>>>> 1.36.561.3708.96343.438113.1665701.35198962018502536735663780339\n",
      "\n",
      ">> filename ca23324e-ab94-4eec-b90b-ddb28f45d9ce.dcm\n",
      "316 Done\n",
      "\n",
      ">>>  data/2f46f5de-b3b4-4bd8-a991-e80a36d2e569.dcm\n",
      "Converting to PNG .........................\n",
      "9.15.446.7952.39496.800274.2081699.51946471217920157743146880632 >>>>>> 5.21.977.9927.58594.350273.6302904.34511921621715956961184387832 >>>>> 8.90.124.1742.48972.545572.9233839.37874156227610503857484568641\n",
      "\n",
      ">> filename 2f46f5de-b3b4-4bd8-a991-e80a36d2e569.dcm\n",
      "317 Done\n",
      "\n",
      ">>>  data/be568299-1171-419d-9e5b-14d7240f29f1.dcm\n",
      "Converting to PNG .........................\n",
      "7.57.612.5962.70492.183997.3953630.95312212700654709235668446421 >>>>>> 5.55.907.1035.11593.319393.1251928.97589118117071911373076699729 >>>>> 6.36.188.8041.41203.341969.5941852.10214979620290448728758833333\n",
      "\n",
      ">> filename be568299-1171-419d-9e5b-14d7240f29f1.dcm\n",
      "318 Done\n",
      "\n",
      ">>>  data/24a4da7f-ae3f-44f9-9573-52bc391f74fb.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.704.7188.84193.324062.3699537.14884631895958493682223993273 >>>>>> 2.66.753.6530.64410.721009.7534958.20988328793158588305469413732 >>>>> 4.39.455.2391.84919.953534.2853405.91689591214130476534970624393\n",
      "\n",
      ">> filename 24a4da7f-ae3f-44f9-9573-52bc391f74fb.dcm\n",
      "319 Done\n",
      "\n",
      ">>>  data/b9231ca8-1d40-444d-bef2-327694275740.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.106.7797.91617.522977.6410587.17628871739619024200747271641 >>>>>> 1.88.387.7279.37751.829985.2222354.85922053429313345416590477599 >>>>> 3.29.181.4752.65144.154252.7286535.83778695288041458210923348185\n",
      "\n",
      ">> filename b9231ca8-1d40-444d-bef2-327694275740.dcm\n",
      "320 Done\n",
      "\n",
      ">>>  data/63e30cc5-37b4-4626-a5ad-fcacacabd417.dcm\n",
      "Converting to PNG .........................\n",
      "6.84.680.1850.26344.957738.7230053.84101358583184637293292535061 >>>>>> 1.14.110.2578.24061.172881.3989602.39354062059478936998475278206 >>>>> 3.35.961.1960.62599.271958.1487884.17049870705480627548327441101\n",
      "\n",
      ">> filename 63e30cc5-37b4-4626-a5ad-fcacacabd417.dcm\n",
      "321 Done\n",
      "\n",
      ">>>  data/8f5006c6-1bca-4a8b-b46a-cdab75107163.dcm\n",
      "Converting to PNG .........................\n",
      "9.98.190.8282.70157.967008.3125566.97198235743595357408089264743 >>>>>> 1.10.294.1470.86460.725485.5493258.90675366016408775021956228560 >>>>> 3.56.538.6190.90016.670741.1476595.98221129886335179962465235764\n",
      "\n",
      ">> filename 8f5006c6-1bca-4a8b-b46a-cdab75107163.dcm\n",
      "322 Done\n",
      "\n",
      ">>>  data/d67f9dd5-570c-4e4f-b30c-324053272bce.dcm\n",
      "Converting to PNG .........................\n",
      "6.71.666.7136.88773.661085.6232020.98143254673799276620322393816 >>>>>> 2.71.731.4065.60639.231269.1796125.95804546327962942447692639807 >>>>> 8.10.634.5384.74955.470233.3777164.75525727926501683651604814086\n",
      "\n",
      ">> filename d67f9dd5-570c-4e4f-b30c-324053272bce.dcm\n",
      "323 Done\n",
      "\n",
      ">>>  data/c30a4caf-5c33-466d-bf1e-19c1a0f2ac85.dcm\n",
      "Converting to PNG .........................\n",
      "2.87.643.8186.27746.288390.1412660.82609199615013150363707391677 >>>>>> 5.22.136.4175.90562.281349.5270273.50870790535167308255892211706 >>>>> 9.67.522.1283.84288.326159.8950538.90846995942316559198312799951\n",
      "\n",
      ">> filename c30a4caf-5c33-466d-bf1e-19c1a0f2ac85.dcm\n",
      "324 Done\n",
      "\n",
      ">>>  data/137dcc5e-c2f4-4456-94f4-4346bbf8a2f9.dcm\n",
      "Converting to PNG .........................\n",
      "1.80.559.9355.45258.690013.6167663.70413665928354793329774823013 >>>>>> 9.51.534.2815.62272.164829.7866458.18499296831272757071909679564 >>>>> 1.19.940.8802.21897.700252.3428010.15713720854920127339693821776\n",
      "\n",
      ">> filename 137dcc5e-c2f4-4456-94f4-4346bbf8a2f9.dcm\n",
      "325 Done\n",
      "\n",
      ">>>  data/17298041-9f82-4725-a921-dcb2f74818bf.dcm\n",
      "Converting to PNG .........................\n",
      "4.30.774.5783.68345.833713.4090397.68356790563903813711433892217 >>>>>> 5.49.469.8623.73483.108490.5797762.25686526429133168762220898593 >>>>> 5.78.364.8240.92100.980694.4399174.40873624612058478488902506029\n",
      "\n",
      ">> filename 17298041-9f82-4725-a921-dcb2f74818bf.dcm\n",
      "326 Done\n",
      "\n",
      ">>>  data/a5acdfb3-a555-43cb-8093-a07a6dc2a920.dcm\n",
      "Converting to PNG .........................\n",
      "5.41.429.8870.64425.107517.1436606.64175017854570422336013993491 >>>>>> 9.53.975.1841.58550.438657.8689501.42112569608420145638641209634 >>>>> 1.15.621.1803.70737.987502.7375053.93006289222124636373395375639\n",
      "\n",
      ">> filename a5acdfb3-a555-43cb-8093-a07a6dc2a920.dcm\n",
      "327 Done\n",
      "\n",
      ">>>  data/84cd6294-d50d-483a-90ed-bd45538a547f.dcm\n",
      "Converting to PNG .........................\n",
      "5.16.559.7440.41847.821906.9657981.29933920517868142288053516668 >>>>>> 6.11.256.7612.45822.828665.8509665.51217678500264117057150919466 >>>>> 1.85.646.8632.69759.553375.4923824.82975555418556422798762740836\n",
      "\n",
      ">> filename 84cd6294-d50d-483a-90ed-bd45538a547f.dcm\n",
      "328 Done\n",
      "\n",
      ">>>  data/0d242ebd-b777-434f-86c9-b2050a594d31.dcm\n",
      "Converting to PNG .........................\n",
      "3.28.700.1789.70138.957329.9817163.12438181102695509709656078278 >>>>>> 1.14.467.8963.91470.804174.2845080.35193333355131784998084281842 >>>>> 6.59.117.1639.93057.958071.9605674.97713666088598270189528263497\n",
      "\n",
      ">> filename 0d242ebd-b777-434f-86c9-b2050a594d31.dcm\n",
      "329 Done\n",
      "\n",
      ">>>  data/91dc9434-fbe6-4bbd-a820-d992a3104fcd.dcm\n",
      "Converting to PNG .........................\n",
      "3.67.587.8495.39917.974271.8004975.35646828705616372813796014199 >>>>>> 6.91.403.4003.13377.624971.4469589.12170510489750072957675038019 >>>>> 6.39.172.5269.64346.699003.7145423.17237339411349237136400219146\n",
      "\n",
      ">> filename 91dc9434-fbe6-4bbd-a820-d992a3104fcd.dcm\n",
      "330 Done\n",
      "\n",
      ">>>  data/268af259-a802-4066-a748-6e2b969376dd.dcm\n",
      "Converting to PNG .........................\n",
      "2.49.780.2015.98411.151420.9355003.12656364356381817999336143621 >>>>>> 1.19.924.1250.56784.934508.8180195.21800735618855968637891626947 >>>>> 8.97.428.9030.63698.157119.6731659.44450028330135392628535918237\n",
      "\n",
      ">> filename 268af259-a802-4066-a748-6e2b969376dd.dcm\n",
      "331 Done\n",
      "\n",
      ">>>  data/446d663e-1654-4b4e-ac8b-0369a3bb68f4.dcm\n",
      "Converting to PNG .........................\n",
      "4.34.187.3746.37957.619083.2552627.29752143368040492787681606722 >>>>>> 7.83.682.7830.98212.168682.5193972.84357900065167284985564978873 >>>>> 5.64.258.9176.90285.535887.2469985.22293405376314434853320247435\n",
      "\n",
      ">> filename 446d663e-1654-4b4e-ac8b-0369a3bb68f4.dcm\n",
      "332 Done\n",
      "\n",
      ">>>  data/af658709-8a42-43c0-95b8-c82ec0b1f408.dcm\n",
      "Converting to PNG .........................\n",
      "8.72.913.2480.71289.743756.4883302.41525067854449174548981976703 >>>>>> 1.14.559.1395.65214.812832.8329285.15475130013415178850964565899 >>>>> 2.17.324.3164.10246.732746.8263623.12366503427319878649478536611\n",
      "\n",
      ">> filename af658709-8a42-43c0-95b8-c82ec0b1f408.dcm\n",
      "333 Done\n",
      "\n",
      ">>>  data/110e4b83-6a38-443c-9012-967e09743483.dcm\n",
      "Converting to PNG .........................\n",
      "5.77.742.1628.45829.492573.1478607.81262074533050671847679492984 >>>>>> 1.10.576.6695.85846.669661.5487308.97623632451027249006349006356 >>>>> 5.19.923.5412.83778.187290.1716720.70839948081407894740771753106\n",
      "\n",
      ">> filename 110e4b83-6a38-443c-9012-967e09743483.dcm\n",
      "334 Done\n",
      "\n",
      ">>>  data/58514926-94ca-452c-985e-b69d57758257.dcm\n",
      "Converting to PNG .........................\n",
      "3.32.453.6464.18608.975377.6628333.37926865081003409643236230994 >>>>>> 1.17.156.1730.99784.590998.8205038.97556367916572341913046465990 >>>>> 4.13.697.1577.95101.106294.9887600.17378797731904792639140699292\n",
      "\n",
      ">> filename 58514926-94ca-452c-985e-b69d57758257.dcm\n",
      "335 Done\n",
      "\n",
      ">>>  data/60092729-bde8-4787-bbb3-78263bab2155.dcm\n",
      "Converting to PNG .........................\n",
      "8.57.233.5684.79411.828312.5688902.11833390811800816284154887890 >>>>>> 8.59.109.3518.69626.615823.2483000.22132334745647876291412088174 >>>>> 8.70.414.5292.43368.719118.1307581.99463112530153026701515893911\n",
      "\n",
      ">> filename 60092729-bde8-4787-bbb3-78263bab2155.dcm\n",
      "336 Done\n",
      "\n",
      ">>>  data/8dd9e03a-7655-4bf3-8f51-b173fa2604ed.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.376.4379.63783.721984.4732024.86206143698995226353425670363 >>>>>> 3.62.126.6898.12200.770613.1846649.56343923655546105867751876841 >>>>> 6.59.184.7407.11835.159753.1023915.89396798610440009562713234699\n",
      "\n",
      ">> filename 8dd9e03a-7655-4bf3-8f51-b173fa2604ed.dcm\n",
      "337 Done\n",
      "\n",
      ">>>  data/cbc248f2-63b7-42f1-90cf-2ce76fe6fea2.dcm\n",
      "Converting to PNG .........................\n",
      "8.91.523.7305.91619.632233.4066861.45541417028537128013258874060 >>>>>> 5.65.390.2271.64737.718327.1374643.92338661731239771035407775570 >>>>> 4.18.253.4171.55258.623152.1900587.17879176910112378319650348721\n",
      "\n",
      ">> filename cbc248f2-63b7-42f1-90cf-2ce76fe6fea2.dcm\n",
      "338 Done\n",
      "\n",
      ">>>  data/55bf958f-9152-42ec-901c-37cdb0c99012.dcm\n",
      "Converting to PNG .........................\n",
      "1.54.299.1126.13476.807613.8653912.45858548409255054321023118159 >>>>>> 6.68.832.8736.19154.855116.2329735.66060363501661942737772238080 >>>>> 9.22.831.7339.67134.383975.1830286.41114677436139054565293349517\n",
      "\n",
      ">> filename 55bf958f-9152-42ec-901c-37cdb0c99012.dcm\n",
      "339 Done\n",
      "\n",
      ">>>  data/f6e07fba-553b-44c7-be64-b8d5ee40e732.dcm\n",
      "Converting to PNG .........................\n",
      "4.25.464.3565.26922.192131.3722281.31958341665417238939744675616 >>>>>> 1.10.123.1736.11299.630124.9505170.32109886440064412428588017558 >>>>> 7.76.479.5346.59971.724998.2730109.10469862434465272552110170434\n",
      "\n",
      ">> filename f6e07fba-553b-44c7-be64-b8d5ee40e732.dcm\n",
      "340 Done\n",
      "\n",
      ">>>  data/41e48513-d1d7-4c81-bdbd-ef06f7487c3c.dcm\n",
      "Converting to PNG .........................\n",
      "5.67.606.9342.72211.513401.5095540.47595596137076960150310759677 >>>>>> 1.11.615.5007.69994.713943.7994173.86384785261747687962905208547 >>>>> 3.85.116.9306.31985.968973.8098005.98130231209369876268770222975\n",
      "\n",
      ">> filename 41e48513-d1d7-4c81-bdbd-ef06f7487c3c.dcm\n",
      "341 Done\n",
      "\n",
      ">>>  data/600dff41-7d42-4a45-943e-697f35439fd8.dcm\n",
      "Converting to PNG .........................\n",
      "9.53.850.6483.11950.175042.7670093.14260741391059066253693032686 >>>>>> 8.59.143.5441.58387.107500.1391126.58974462285332494441156162347 >>>>> 7.71.691.1945.16207.560412.1736127.13490570827020623925241068504\n",
      "\n",
      ">> filename 600dff41-7d42-4a45-943e-697f35439fd8.dcm\n",
      "342 Done\n",
      "\n",
      ">>>  data/39cea3a6-d2d0-400b-9538-c5bf4bbd26e4.dcm\n",
      "Converting to PNG .........................\n",
      "1.15.874.1213.59602.148446.7289773.16364798249721365333035375546 >>>>>> 1.15.161.2249.63643.761758.3515121.31506488276916566951753792238 >>>>> 1.53.441.8841.14579.156122.5509336.84109588828771567136392627959\n",
      "\n",
      ">> filename 39cea3a6-d2d0-400b-9538-c5bf4bbd26e4.dcm\n",
      "343 Done\n",
      "\n",
      ">>>  data/d9830b34-79aa-444b-be25-42583e196f14.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.502.1752.19591.911767.6029461.60351185929004712871584005429 >>>>>> 5.61.189.2219.15139.284066.2039740.86681186383254537986929502532 >>>>> 5.34.190.7387.41881.114794.2758111.32270383188496796117821733937\n",
      "\n",
      ">> filename d9830b34-79aa-444b-be25-42583e196f14.dcm\n",
      "344 Done\n",
      "\n",
      ">>>  data/55a243af-4c77-4f03-a28d-ce92bd5ca2b3.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.296.7051.90621.591755.5422708.84492530820525279341882387895 >>>>>> 1.70.567.2479.11791.349880.2531745.44347893866333354096732011412 >>>>> 9.78.137.7831.18815.526847.5795732.16500478958016861927250471217\n",
      "\n",
      ">> filename 55a243af-4c77-4f03-a28d-ce92bd5ca2b3.dcm\n",
      "345 Done\n",
      "\n",
      ">>>  data/16712416-d486-4e68-bafc-65906dff9319.dcm\n",
      "Converting to PNG .........................\n",
      "6.74.672.8612.24494.520822.6310428.12831859060075323693585536600 >>>>>> 1.16.258.7760.78043.976674.6677359.24381552852424990911503191184 >>>>> 7.13.180.5686.15277.837918.6941529.62058603929481705017913371980\n",
      "\n",
      ">> filename 16712416-d486-4e68-bafc-65906dff9319.dcm\n",
      "346 Done\n",
      "\n",
      ">>>  data/b9b25773-1087-42d4-8ed8-011923764a4c.dcm\n",
      "Converting to PNG .........................\n",
      "1.70.314.3593.93672.533666.2758981.38379286749611540392614336165 >>>>>> 1.12.500.7953.25268.246303.8139298.19093887209061868056289346770 >>>>> 7.46.320.8109.77845.779753.1363249.42528033493537629565291460065\n",
      "\n",
      ">> filename b9b25773-1087-42d4-8ed8-011923764a4c.dcm\n",
      "347 Done\n",
      "\n",
      ">>>  data/133dd7dc-0c02-4b5a-89a3-15d9a48305df.dcm\n",
      "Converting to PNG .........................\n",
      "2.12.767.3057.10001.550319.4160362.69748602653209848183132531467 >>>>>> 9.26.505.5576.54137.989055.1597005.95656529860429558993861778671 >>>>> 4.71.199.1791.42551.126802.2248512.87863300945407838599320217248\n",
      "\n",
      ">> filename 133dd7dc-0c02-4b5a-89a3-15d9a48305df.dcm\n",
      "348 Done\n",
      "\n",
      ">>>  data/a14c1f41-1664-4dec-8a20-2c46f61d7d90.dcm\n",
      "Converting to PNG .........................\n",
      "3.99.467.8539.77854.431690.3662789.72312837503545907701009306578 >>>>>> 2.38.968.6586.77174.341203.3512430.90670290958005482105811597651 >>>>> 7.35.343.8671.68953.374224.9064367.68973937389498004095068547385\n",
      "\n",
      ">> filename a14c1f41-1664-4dec-8a20-2c46f61d7d90.dcm\n",
      "349 Done\n",
      "\n",
      ">>>  data/4f1be4db-89fc-4b7e-ac5e-3e06254c3286.dcm\n",
      "Converting to PNG .........................\n",
      "2.45.842.3591.93385.678921.9455162.19343357070370525833733547203 >>>>>> 8.15.152.4609.40166.647327.8723679.23339096416927778642640733691 >>>>> 9.18.158.5710.46310.842735.4272544.96804035405296613075087820811\n",
      "\n",
      ">> filename 4f1be4db-89fc-4b7e-ac5e-3e06254c3286.dcm\n",
      "350 Done\n",
      "\n",
      ">>>  data/53e7fc74-94fb-41fb-a629-5cba44c283cc.dcm\n",
      "Converting to PNG .........................\n",
      "8.70.797.6123.11875.314677.8197833.83449413469182548806414527992 >>>>>> 8.37.656.4914.70283.943008.3689028.13583709425911319796293272548 >>>>> 9.20.576.1017.54134.275488.1776302.41068094407368355937600731632\n",
      "\n",
      ">> filename 53e7fc74-94fb-41fb-a629-5cba44c283cc.dcm\n",
      "351 Done\n",
      "\n",
      ">>>  data/bb5f898d-b3f5-4825-99a8-75bf2a007940.dcm\n",
      "Converting to PNG .........................\n",
      "2.76.831.1251.34219.736881.1498699.64804029891008857581641417899 >>>>>> 8.96.410.1437.99253.133503.1452954.90951495145390369774383320971 >>>>> 3.35.133.1753.98547.847882.8235989.85566500336825306613286341266\n",
      "\n",
      ">> filename bb5f898d-b3f5-4825-99a8-75bf2a007940.dcm\n",
      "352 Done\n",
      "\n",
      ">>>  data/ac4bd7d6-d6c8-4da7-bffb-3b8cdfb54084.dcm\n",
      "Converting to PNG .........................\n",
      "6.16.224.4551.59919.164037.3449014.20626935837627433646793876441 >>>>>> 7.68.186.7960.47822.501255.2299777.66671149668088952515432132427 >>>>> 5.63.273.7934.64419.476821.2810558.15289403037415126490721840695\n",
      "\n",
      ">> filename ac4bd7d6-d6c8-4da7-bffb-3b8cdfb54084.dcm\n",
      "353 Done\n",
      "\n",
      ">>>  data/66aa54d4-78d6-4b3c-b8d3-70b44d514b69.dcm\n",
      "Converting to PNG .........................\n",
      "8.71.151.1394.47985.929037.1970169.56466585172476105945467609570 >>>>>> 8.18.116.5355.71491.711512.4890793.75271098987369009414733229264 >>>>> 6.17.326.4874.60187.417434.3294102.89131322182407205870732753750\n",
      "\n",
      ">> filename 66aa54d4-78d6-4b3c-b8d3-70b44d514b69.dcm\n",
      "354 Done\n",
      "\n",
      ">>>  data/16effb1e-6164-46c2-8cfa-2a5422808f90.dcm\n",
      "Converting to PNG .........................\n",
      "7.55.990.8362.93629.195562.8781945.87217703389613577360324032938 >>>>>> 4.14.731.3617.50764.175794.7778239.24046685744555802365234343797 >>>>> 7.64.740.1420.26326.390257.3243419.19197697724044013738393866153\n",
      "\n",
      ">> filename 16effb1e-6164-46c2-8cfa-2a5422808f90.dcm\n",
      "355 Done\n",
      "\n",
      ">>>  data/a70b515b-e827-4014-a1ab-c3faba8bff97.dcm\n",
      "Converting to PNG .........................\n",
      "7.80.174.8804.77855.774869.4314692.62016850854510412519812449247 >>>>>> 3.23.762.3307.88743.857978.7216010.11656748453628647224000247752 >>>>> 1.18.987.8936.86095.579349.3936526.39043049036527419904747707690\n",
      "\n",
      ">> filename a70b515b-e827-4014-a1ab-c3faba8bff97.dcm\n",
      "356 Done\n",
      "\n",
      ">>>  data/ce48f7e2-b821-46c5-af73-cf49238e55af.dcm\n",
      "Converting to PNG .........................\n",
      "5.87.196.1395.10149.455056.9774760.19053637280830706579872265031 >>>>>> 1.43.613.1559.78578.831345.7588800.88937795852799803916642975891 >>>>> 7.67.109.9991.39860.580663.7172597.30534546705804185396120353321\n",
      "\n",
      ">> filename ce48f7e2-b821-46c5-af73-cf49238e55af.dcm\n",
      "357 Done\n",
      "\n",
      ">>>  data/a671612a-570c-4c79-8d48-bf0c73a8dbbd.dcm\n",
      "Converting to PNG .........................\n",
      "1.17.700.2272.95780.875526.1052732.60488044118934251700098924460 >>>>>> 9.40.953.1843.47949.698787.1582472.66434382180632967952874702834 >>>>> 1.75.133.2693.29037.266097.8247440.49101476615275501387098041124\n",
      "\n",
      ">> filename a671612a-570c-4c79-8d48-bf0c73a8dbbd.dcm\n",
      "358 Done\n",
      "\n",
      ">>>  data/a6925052-ec71-4810-a748-313eef4e4392.dcm\n",
      "Converting to PNG .........................\n",
      "7.15.329.4335.35459.219987.6593827.14060728653952174773243014805 >>>>>> 5.17.963.4465.72632.336257.2403190.46780328746215562363554508995 >>>>> 7.21.953.7170.36753.780505.9387157.12252489030491221272174171169\n",
      "\n",
      ">> filename a6925052-ec71-4810-a748-313eef4e4392.dcm\n",
      "359 Done\n",
      "\n",
      ">>>  data/92dda07a-d234-4905-94f4-ffdf2eaea632.dcm\n",
      "Converting to PNG .........................\n",
      "1.13.297.1819.79396.330042.4704536.47685662387441678507320276369 >>>>>> 1.32.989.6466.84499.138900.5215701.77699478557334759033499956739 >>>>> 5.59.237.1040.85229.524708.3632261.98728678618873989771474246791\n",
      "\n",
      ">> filename 92dda07a-d234-4905-94f4-ffdf2eaea632.dcm\n",
      "360 Done\n",
      "\n",
      ">>>  data/1468084b-d489-49c0-a593-e01780c40dd1.dcm\n",
      "Converting to PNG .........................\n",
      "9.51.372.1047.94806.195862.8800643.26098834027587471200836127698 >>>>>> 5.18.821.1937.13737.320747.8422577.81387353452494923037423473605 >>>>> 8.51.383.7566.95125.723652.2205745.99796628786991374964438375443\n",
      "\n",
      ">> filename 1468084b-d489-49c0-a593-e01780c40dd1.dcm\n",
      "361 Done\n",
      "\n",
      ">>>  data/ec5a9670-9550-4893-9c83-eb81514ae4be.dcm\n",
      "Converting to PNG .........................\n",
      "5.69.866.1734.29218.342026.6156475.75307769365293154697784383837 >>>>>> 8.69.452.4369.77548.720555.5673315.90880059539012700699611001282 >>>>> 1.19.158.9600.88337.123696.1955670.34899677696598304977420207783\n",
      "\n",
      ">> filename ec5a9670-9550-4893-9c83-eb81514ae4be.dcm\n",
      "362 Done\n",
      "\n",
      ">>>  data/1fc2363a-d923-4776-965f-a08ea8302ccb.dcm\n",
      "Converting to PNG .........................\n",
      "4.30.383.2326.89283.608424.1398890.68566027128785646264010349318 >>>>>> 1.11.531.8255.23472.993194.3899191.33454583020231479119957380087 >>>>> 3.14.184.9085.97609.278653.9178203.54464550450946441455730395823\n",
      "\n",
      ">> filename 1fc2363a-d923-4776-965f-a08ea8302ccb.dcm\n",
      "363 Done\n",
      "\n",
      ">>>  data/ab2fc3ec-a6e7-4080-9491-464cb75e19a3.dcm\n",
      "Converting to PNG .........................\n",
      "9.40.794.8986.32729.407711.7181237.68630635657850337052157504213 >>>>>> 5.24.276.8664.48110.593219.7908520.43259329549293120836932466461 >>>>> 1.72.670.4648.53896.688967.3693793.14062503816623354146078360658\n",
      "\n",
      ">> filename ab2fc3ec-a6e7-4080-9491-464cb75e19a3.dcm\n",
      "364 Done\n",
      "\n",
      ">>>  data/69aa8cea-0acc-4d0c-b2c6-86db047eace3.dcm\n",
      "Converting to PNG .........................\n",
      "7.64.963.7920.29572.162049.9682675.55069452871516050277089073515 >>>>>> 1.14.585.6018.37383.151201.9834410.90131380657479446155732930511 >>>>> 6.65.383.9140.19498.499037.6952264.10550975076213230980831342130\n",
      "\n",
      ">> filename 69aa8cea-0acc-4d0c-b2c6-86db047eace3.dcm\n",
      "365 Done\n",
      "\n",
      ">>>  data/df33f12c-88cf-49c9-86b8-36aaf8d6b9f7.dcm\n",
      "Converting to PNG .........................\n",
      "4.21.236.4163.10879.591025.1764772.65100101479535756904013413552 >>>>>> 6.24.858.8865.32523.583020.5464149.94123544721238461023900057423 >>>>> 7.11.390.6260.48392.951413.4305783.34412336166274947771578955685\n",
      "\n",
      ">> filename df33f12c-88cf-49c9-86b8-36aaf8d6b9f7.dcm\n",
      "366 Done\n",
      "\n",
      ">>>  data/e1ebc6e2-17c7-4d31-89f5-e2b80f5750aa.dcm\n",
      "Converting to PNG .........................\n",
      "9.79.871.6021.34896.891192.7710839.53760265319586030301642889601 >>>>>> 8.20.441.4732.94088.147308.4974784.47709834088569618212995831625 >>>>> 8.42.567.7971.14186.404225.6706581.29843189494727297515115036231\n",
      "\n",
      ">> filename e1ebc6e2-17c7-4d31-89f5-e2b80f5750aa.dcm\n",
      "367 Done\n",
      "\n",
      ">>>  data/b7478589-93e2-408b-b086-8c16dd6c21ce.dcm\n",
      "Converting to PNG .........................\n",
      "2.78.215.8627.48663.209032.1673433.86095526290822910713701538059 >>>>>> 4.74.486.1592.78001.509440.3265222.43986743799796481528731289104 >>>>> 1.15.408.4721.15676.461633.5899448.35708024368577581121284551434\n",
      "\n",
      ">> filename b7478589-93e2-408b-b086-8c16dd6c21ce.dcm\n",
      "368 Done\n",
      "\n",
      ">>>  data/5ffbbb8c-f3e7-4c7a-a0c4-a0e1e04de622.dcm\n",
      "Converting to PNG .........................\n",
      "5.51.589.7300.12870.639980.4828362.24628684755958865831845713650 >>>>>> 8.20.441.4732.94088.147308.4974784.47709834088569618212995831625 >>>>> 8.42.567.7971.14186.404225.6706581.29843189494727297515115036231\n",
      "\n",
      ">> filename 5ffbbb8c-f3e7-4c7a-a0c4-a0e1e04de622.dcm\n",
      "369 Done\n",
      "\n",
      ">>>  data/7427009e-b9e1-4d6a-84f1-c6ebbcc50528.dcm\n",
      "Converting to PNG .........................\n",
      "3.48.546.1276.16255.158599.2823595.36758027381125819832222478672 >>>>>> 1.14.694.8486.99833.689403.2585197.51005749455254023987107939398 >>>>> 8.19.439.7567.78786.164337.5952205.70307494126793814776073385989\n",
      "\n",
      ">> filename 7427009e-b9e1-4d6a-84f1-c6ebbcc50528.dcm\n",
      "370 Done\n",
      "\n",
      ">>>  data/c9a4b79c-7870-497f-b35b-f1f4d024d5b9.dcm\n",
      "Converting to PNG .........................\n",
      "5.44.340.4216.46731.919287.3785105.11944393601366660117126773430 >>>>>> 8.16.503.2817.74671.935847.5888191.78658892474419556707116965553 >>>>> 4.33.812.4745.36841.138580.3955183.34201620023171288013818900151\n",
      "\n",
      ">> filename c9a4b79c-7870-497f-b35b-f1f4d024d5b9.dcm\n",
      "371 Done\n",
      "\n",
      ">>>  data/656b52f3-e521-492d-b7c1-169b070320ab.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.955.2042.79494.813704.8531038.21957035039768157243115541657 >>>>>> 7.16.745.5037.10041.955418.1344765.78700349688886167264817840128 >>>>> 8.79.294.7313.97226.665724.3451670.48695028957974503003402176548\n",
      "\n",
      ">> filename 656b52f3-e521-492d-b7c1-169b070320ab.dcm\n",
      "372 Done\n",
      "\n",
      ">>>  data/da869fe7-1b73-4555-ba99-faca29fc64e0.dcm\n",
      "Converting to PNG .........................\n",
      "7.41.503.6014.88123.222691.4302781.41437404567448231897151638802 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 1.90.146.6210.21554.430938.8756620.21455290826111106876775037802\n",
      "\n",
      ">> filename da869fe7-1b73-4555-ba99-faca29fc64e0.dcm\n",
      "373 Done\n",
      "\n",
      ">>>  data/28ca3acf-a5ad-498f-8ab8-41df5a05832f.dcm\n",
      "Converting to PNG .........................\n",
      "8.18.430.6930.98129.736553.6345751.89534041011166771369851674693 >>>>>> 3.31.655.2382.14737.568322.4890399.96184489017556678999160539489 >>>>> 5.36.570.2408.11301.190157.1859731.73991386108202024539660056829\n",
      "\n",
      ">> filename 28ca3acf-a5ad-498f-8ab8-41df5a05832f.dcm\n",
      "374 Done\n",
      "\n",
      ">>>  data/12eabc77-768a-44c5-af33-19c899a0a2d3.dcm\n",
      "Converting to PNG .........................\n",
      "1.54.835.9907.82016.658122.4326122.55978489278085469048606429036 >>>>>> 8.20.441.4732.94088.147308.4974784.47709834088569618212995831625 >>>>> 8.83.496.6183.11177.631531.9218219.64633443863922210228274245351\n",
      "\n",
      ">> filename 12eabc77-768a-44c5-af33-19c899a0a2d3.dcm\n",
      "375 Done\n",
      "\n",
      ">>>  data/57eed4ba-885a-4426-ade9-f4affb08a759.dcm\n",
      "Converting to PNG .........................\n",
      "8.17.663.5654.88453.905498.7577454.22696666457688826678114495254 >>>>>> 1.16.623.1443.53453.125128.3187855.16304909936633440650110723537 >>>>> 2.80.588.4760.79205.657596.6033205.13595405830701406101474581984\n",
      "\n",
      ">> filename 57eed4ba-885a-4426-ade9-f4affb08a759.dcm\n",
      "376 Done\n",
      "\n",
      ">>>  data/6087b7fc-30d5-4820-8751-0401b54fdc86.dcm\n",
      "Converting to PNG .........................\n",
      "4.94.332.3671.66655.784057.8370798.30397463293178171445444083819 >>>>>> 1.14.559.1395.65214.812832.8329285.15475130013415178850964565899 >>>>> 2.17.324.3164.10246.732746.8263623.12366503427319878649478536611\n",
      "\n",
      ">> filename 6087b7fc-30d5-4820-8751-0401b54fdc86.dcm\n",
      "377 Done\n",
      "\n",
      ">>>  data/4b27584f-1e6c-4d91-8b11-0d338636d41d.dcm\n",
      "Converting to PNG .........................\n",
      "1.92.175.8105.70481.433876.5575528.41453177641324507583155614937 >>>>>> 9.45.916.1447.72726.396103.7390718.78233520479498638827778003047 >>>>> 6.73.774.2924.99277.558934.4500918.10456336119498657703638314762\n",
      "\n",
      ">> filename 4b27584f-1e6c-4d91-8b11-0d338636d41d.dcm\n",
      "378 Done\n",
      "\n",
      ">>>  data/98e1a809-72d5-4621-a75c-ad221e434951.dcm\n",
      "Converting to PNG .........................\n",
      "3.68.362.5920.12518.877200.8627962.38097959803193963208142030163 >>>>>> 1.10.504.2908.42061.445406.2506588.95031427778119119298139049105 >>>>> 9.18.802.2314.75217.807096.3938889.64735610649231732112981197410\n",
      "\n",
      ">> filename 98e1a809-72d5-4621-a75c-ad221e434951.dcm\n",
      "379 Done\n",
      "\n",
      ">>>  data/55602395-42a5-42f9-8bba-a2d0a23d6cae.dcm\n",
      "Converting to PNG .........................\n",
      "3.52.415.4661.35123.838093.1451398.13028162429128949061313372858 >>>>>> 1.11.116.2223.81434.812844.1442093.70740726946841083712663641594 >>>>> 3.59.248.3924.53362.770161.6645709.94854932738736107741963826509\n",
      "\n",
      ">> filename 55602395-42a5-42f9-8bba-a2d0a23d6cae.dcm\n",
      "380 Done\n",
      "\n",
      ">>>  data/1fe15492-8b97-4725-acf3-086ce60847fb.dcm\n",
      "Converting to PNG .........................\n",
      "5.45.535.1512.31223.140329.7757077.36874762587774257501161007527 >>>>>> 5.72.237.9227.84306.910984.1490475.65994945141663672758163312156 >>>>> 5.83.715.1642.92868.229597.3586713.78743737772020778754018539926\n",
      "\n",
      ">> filename 1fe15492-8b97-4725-acf3-086ce60847fb.dcm\n",
      "381 Done\n",
      "\n",
      ">>>  data/7b22d939-c466-438f-b7d4-70301f28d9df.dcm\n",
      "Converting to PNG .........................\n",
      "2.22.230.3353.35690.927351.1543617.90075705410503600427290897356 >>>>>> 1.11.419.6075.69005.683453.4626684.85109567965064728954131712860 >>>>> 2.77.100.1023.10416.190216.7252707.88797232142016375228006022861\n",
      "\n",
      ">> filename 7b22d939-c466-438f-b7d4-70301f28d9df.dcm\n",
      "382 Done\n",
      "\n",
      ">>>  data/e9fec511-d0b8-4baa-a870-0ac5ffb8ca0e.dcm\n",
      "Converting to PNG .........................\n",
      "1.14.673.3926.85092.182285.6651883.84689494877177317402876011832 >>>>>> 7.45.585.4147.73186.201463.8063808.29500915485840324604511381741 >>>>> 1.12.962.7506.41560.346505.6764094.35153841565106850131736031621\n",
      "\n",
      ">> filename e9fec511-d0b8-4baa-a870-0ac5ffb8ca0e.dcm\n",
      "383 Done\n",
      "\n",
      ">>>  data/3b89dc55-8f29-4983-b0e1-e5ef2624a6f1.dcm\n",
      "Converting to PNG .........................\n",
      "2.28.409.5421.75002.633682.6912914.32709485994344617748824296829 >>>>>> 3.53.951.9150.30281.729483.6059244.73238151046150614242886152708 >>>>> 2.97.549.7119.49196.837359.4404033.42606960476142754649196759819\n",
      "\n",
      ">> filename 3b89dc55-8f29-4983-b0e1-e5ef2624a6f1.dcm\n",
      "384 Done\n",
      "\n",
      ">>>  data/8554824e-7b87-41f3-a7f6-e9b55c313271.dcm\n",
      "Converting to PNG .........................\n",
      "4.42.226.1553.29677.449651.5049162.22137639638936820798016726103 >>>>>> 9.73.145.6634.34545.804527.4302771.93482250005693172580279092374 >>>>> 8.55.333.1056.10797.628958.3299830.44967227046427972190855918721\n",
      "\n",
      ">> filename 8554824e-7b87-41f3-a7f6-e9b55c313271.dcm\n",
      "385 Done\n",
      "\n",
      ">>>  data/cde055a2-7334-4986-8919-c2cbd9a66274.dcm\n",
      "Converting to PNG .........................\n",
      "4.55.317.8113.16958.942183.9131900.98955190126470576447231252733 >>>>>> 3.54.734.6296.71037.454669.6354249.50534629686427117928114089581 >>>>> 7.48.846.5590.70992.374020.2063942.12040119872235622257798640159\n",
      "\n",
      ">> filename cde055a2-7334-4986-8919-c2cbd9a66274.dcm\n",
      "386 Done\n",
      "\n",
      ">>>  data/65ba6af7-b1cf-41c2-8448-1c2a0eecec05.dcm\n",
      "Converting to PNG .........................\n",
      "8.10.842.8232.19807.108858.8614682.64324493491727035298979420191 >>>>>> 9.99.940.6185.89814.430804.9623814.88859854058954647858509864360 >>>>> 3.94.315.5821.21070.621401.5651211.31212286086008741699030178824\n",
      "\n",
      ">> filename 65ba6af7-b1cf-41c2-8448-1c2a0eecec05.dcm\n",
      "387 Done\n",
      "\n",
      ">>>  data/a797f4d5-aec3-4d8e-b59a-798e9b03dc19.dcm\n",
      "Converting to PNG .........................\n",
      "2.32.936.6646.52004.865803.9359311.16091919138549723770309346988 >>>>>> 5.49.157.1781.29630.167444.8486414.53615475301977282315391519512 >>>>> 1.55.460.8113.91637.366731.5642284.58270133873576384136098063503\n",
      "\n",
      ">> filename a797f4d5-aec3-4d8e-b59a-798e9b03dc19.dcm\n",
      "388 Done\n",
      "\n",
      ">>>  data/97ec4976-e851-4b34-8261-a7e89f79efe2.dcm\n",
      "Converting to PNG .........................\n",
      "8.53.953.5531.86225.979746.7974505.12833923627488299639042264679 >>>>>> 1.78.623.2646.75093.490619.1028059.71337637139917354971564934645 >>>>> 7.84.524.9862.18309.159108.9360521.22317100642929132195185141451\n",
      "\n",
      ">> filename 97ec4976-e851-4b34-8261-a7e89f79efe2.dcm\n",
      "389 Done\n",
      "\n",
      ">>>  data/97192896-6d85-459f-96fb-c351082e8670.dcm\n",
      "Converting to PNG .........................\n",
      "1.64.260.2772.77314.609643.8756740.60536688825839366491944382477 >>>>>> 5.53.665.1635.76771.835508.2484750.59132847561124753003973475928 >>>>> 1.12.102.3520.58427.287853.2086370.29124176692892121595098013030\n",
      "\n",
      ">> filename 97192896-6d85-459f-96fb-c351082e8670.dcm\n",
      "390 Done\n",
      "\n",
      ">>>  data/2c855322-05d7-4f42-b0a0-b07590dadaeb.dcm\n",
      "Converting to PNG .........................\n",
      "7.74.705.1800.99257.713884.4307539.46654250637317249505577340589 >>>>>> 4.24.114.5207.91768.144563.3987397.32857959431948638718653841573 >>>>> 3.36.334.1970.46588.445414.4263107.78457489247105829603006799775\n",
      "\n",
      ">> filename 2c855322-05d7-4f42-b0a0-b07590dadaeb.dcm\n",
      "391 Done\n",
      "\n",
      ">>>  data/fa66f935-ed22-47ba-9406-53d642368170.dcm\n",
      "Converting to PNG .........................\n",
      "1.11.548.9759.89128.306598.1650425.19268896257526740541511203275 >>>>>> 1.13.140.2447.39397.113953.5308567.75822403996408684876080017316 >>>>> 1.16.600.1613.72492.611487.4256416.19386511359253424056076465589\n",
      "\n",
      ">> filename fa66f935-ed22-47ba-9406-53d642368170.dcm\n",
      "392 Done\n",
      "\n",
      ">>>  data/d411948e-f002-4c6c-8ed4-77ec273d34b9.dcm\n",
      "Converting to PNG .........................\n",
      "7.27.432.5709.14339.194843.4490917.69403419221669125171074357152 >>>>>> 7.24.455.8851.94930.850264.1771985.95616490146439237334948275259 >>>>> 1.12.789.7105.90849.736263.4337773.86173134080886200144704745562\n",
      "\n",
      ">> filename d411948e-f002-4c6c-8ed4-77ec273d34b9.dcm\n",
      "393 Done\n",
      "\n",
      ">>>  data/cb395caa-3f35-42f6-b0eb-afc224964544.dcm\n",
      "Converting to PNG .........................\n",
      "1.10.793.2288.13156.245366.5011541.64762254652345882130920803638 >>>>>> 6.11.606.9370.88962.113976.4754424.48995203284809829496901599108 >>>>> 2.17.981.1642.32100.140323.6169591.72333896204261082071093585318\n",
      "\n",
      ">> filename cb395caa-3f35-42f6-b0eb-afc224964544.dcm\n",
      "394 Done\n",
      "\n",
      ">>>  data/0026358a-7ad6-40a2-8705-f0f3610e9a8f.dcm\n",
      "Converting to PNG .........................\n",
      "2.10.868.1331.26537.470523.9390014.92694268046363599732589032123 >>>>>> 4.21.691.9715.86402.743578.1237073.20782877631977487542465611317 >>>>> 7.29.766.8956.16161.178711.6451525.97010622813868949335609416568\n",
      "\n",
      ">> filename 0026358a-7ad6-40a2-8705-f0f3610e9a8f.dcm\n",
      "395 Done\n",
      "\n",
      ">>>  data/5ab37cf3-780c-4817-b966-d92ddfd1f1cb.dcm\n",
      "Converting to PNG .........................\n",
      "2.10.514.1613.11842.432349.1625085.72785053910108013879293112688 >>>>>> 5.53.782.4402.76372.420418.5354357.32478839537162666340891121955 >>>>> 7.41.658.6281.55638.152853.8928769.21823371762295235937295429186\n",
      "\n",
      ">> filename 5ab37cf3-780c-4817-b966-d92ddfd1f1cb.dcm\n",
      "396 Done\n",
      "\n",
      ">>>  data/c0e94329-9bfe-4757-8793-0537da6bc8a6.dcm\n",
      "Converting to PNG .........................\n",
      "7.51.163.1555.66022.818515.8391730.31329511983471961204762283747 >>>>>> 9.90.149.8334.99165.636292.9289651.59333451565427271806445688282 >>>>> 7.86.575.1237.94595.624544.9005695.16224057615705871884971532793\n",
      "\n",
      ">> filename c0e94329-9bfe-4757-8793-0537da6bc8a6.dcm\n",
      "397 Done\n",
      "\n",
      ">>>  data/c05a534d-ee61-4944-9303-87130b8cee0a.dcm\n",
      "Converting to PNG .........................\n",
      "8.20.434.3071.22802.299328.1665760.97814151352561025768146866741 >>>>>> 5.16.790.4613.72359.103738.4734948.37124802574214445870316297425 >>>>> 6.65.114.1389.83170.143837.1920623.70252907514941389770134240136\n",
      "\n",
      ">> filename c05a534d-ee61-4944-9303-87130b8cee0a.dcm\n",
      "398 Done\n",
      "\n",
      ">>>  data/bade1b5a-b163-4ebf-8345-a01cc3f637a1.dcm\n",
      "Converting to PNG .........................\n",
      "1.91.314.5788.86539.154029.1019992.84179264632075781411809180441 >>>>>> 5.43.590.4118.27533.106536.4474084.70580763297963077417213371051 >>>>> 1.14.829.2895.94677.189993.1664708.19964949957733930247487250591\n",
      "\n",
      ">> filename bade1b5a-b163-4ebf-8345-a01cc3f637a1.dcm\n",
      "399 Done\n",
      "\n",
      ">>>  data/ac5c3862-05cf-423f-95a7-68f99bb8ff18.dcm\n",
      "Converting to PNG .........................\n",
      "5.94.674.1149.83094.482118.2486767.53334162825239477799003757991 >>>>>> 2.12.937.7769.64948.998824.7790544.29131150936487442458974179728 >>>>> 8.65.874.2386.92734.635834.2014642.84165727495303135166856196391\n",
      "\n",
      ">> filename ac5c3862-05cf-423f-95a7-68f99bb8ff18.dcm\n",
      "400 Done\n",
      "\n",
      ">>>  data/7ca43bde-fcba-4882-b0c4-cbdf0f60961f.dcm\n",
      "Converting to PNG .........................\n",
      "1.27.857.5133.98063.731562.4657449.88572773002910890058378676966 >>>>>> 8.80.979.7728.87491.733835.5268016.98717920121715444426569481152 >>>>> 1.13.737.8116.75237.369045.1160360.59752495107609034327199880406\n",
      "\n",
      ">> filename 7ca43bde-fcba-4882-b0c4-cbdf0f60961f.dcm\n",
      "401 Done\n",
      "\n",
      ">>>  data/29219f0c-366a-46f0-b8ae-220ef90e5b41.dcm\n",
      "Converting to PNG .........................\n",
      "5.10.899.1150.27535.436795.7514943.62414493147598723430622543236 >>>>>> 9.82.530.1277.35381.122524.1406548.83119894608652102461833251386 >>>>> 7.84.123.5109.74795.721963.5326708.96371020319759785178860976981\n",
      "\n",
      ">> filename 29219f0c-366a-46f0-b8ae-220ef90e5b41.dcm\n",
      "402 Done\n",
      "\n",
      ">>>  data/bd07a0a1-2041-483b-ae9b-de90340003d0.dcm\n",
      "Converting to PNG .........................\n",
      "2.82.621.4782.41543.350446.1946110.84672329294627688737317793511 >>>>>> 2.20.999.7904.62871.667406.6024176.81351619140759517338095303422 >>>>> 8.66.976.8760.16964.396732.4009146.67514872253164835331991578692\n",
      "\n",
      ">> filename bd07a0a1-2041-483b-ae9b-de90340003d0.dcm\n",
      "403 Done\n",
      "\n",
      ">>>  data/3d66b6c0-97ae-448e-b788-6138d58e5c2e.dcm\n",
      "Converting to PNG .........................\n",
      "9.95.215.1911.31666.516903.5256333.62776795272272671438345018441 >>>>>> 5.55.985.7154.56804.330270.1943517.94492468677334159361120423087 >>>>> 5.16.524.8866.90115.158989.7475137.99170789265912559406336150763\n",
      "\n",
      ">> filename 3d66b6c0-97ae-448e-b788-6138d58e5c2e.dcm\n",
      "404 Done\n",
      "\n",
      ">>>  data/5cde1b27-9c57-4d20-8138-d4a2f34fb231.dcm\n",
      "Converting to PNG .........................\n",
      "9.42.525.1581.88865.268825.1134948.92349667078870391481517440950 >>>>>> 4.24.643.4372.41793.871975.5857433.21232004633209679230891544185 >>>>> 8.97.813.6217.19855.271255.3923822.14727860351534215596514739227\n",
      "\n",
      ">> filename 5cde1b27-9c57-4d20-8138-d4a2f34fb231.dcm\n",
      "405 Done\n",
      "\n",
      ">>>  data/03f1f601-95a6-4e1f-b9b3-8605e60b2425.dcm\n",
      "Converting to PNG .........................\n",
      "8.97.863.6603.98266.446009.7371682.63906180739906331263058227273 >>>>>> 1.10.437.9038.61052.142881.5851585.23113826836899828044060796691 >>>>> 7.34.195.9551.13176.963232.1587421.11279876012320958947304215893\n",
      "\n",
      ">> filename 03f1f601-95a6-4e1f-b9b3-8605e60b2425.dcm\n",
      "406 Done\n",
      "\n",
      ">>>  data/3ac09a1d-bd1e-4d86-b1e5-08718f2da81f.dcm\n",
      "Converting to PNG .........................\n",
      "4.68.855.6328.29840.863811.2131728.12993061398053365547108175713 >>>>>> 5.66.264.2639.84922.449155.6115544.60073035594741267580270037564 >>>>> 4.47.468.6231.62808.332364.8223691.68589038358321584283733806933\n",
      "\n",
      ">> filename 3ac09a1d-bd1e-4d86-b1e5-08718f2da81f.dcm\n",
      "407 Done\n",
      "\n",
      ">>>  data/fb7ebbed-ed72-4fe4-ae90-4219829b988f.dcm\n",
      "Converting to PNG .........................\n",
      "6.30.151.6818.11256.831644.4404612.75932397248735915979369922299 >>>>>> 6.58.107.4976.70920.444865.9113945.76439830756114727791453717526 >>>>> 3.70.550.9125.22349.281763.1451460.97514431569270947642018894921\n",
      "\n",
      ">> filename fb7ebbed-ed72-4fe4-ae90-4219829b988f.dcm\n",
      "408 Done\n",
      "\n",
      ">>>  data/182f9370-6aa5-4ee4-928c-64fbbb8cb91c.dcm\n",
      "Converting to PNG .........................\n",
      "3.51.397.7155.12242.248630.5968085.23002809209370795602720743317 >>>>>> 2.66.272.4089.14455.121688.8916184.26930505604246623005384463666 >>>>> 5.61.784.7177.50877.823162.4579285.53902109313631752651066915298\n",
      "\n",
      ">> filename 182f9370-6aa5-4ee4-928c-64fbbb8cb91c.dcm\n",
      "409 Done\n",
      "\n",
      ">>>  data/9aba4355-b4f4-441c-9dfa-a49845a8347e.dcm\n",
      "Converting to PNG .........................\n",
      "5.13.876.9185.11655.327493.1929573.89338664327994304636365207794 >>>>>> 8.88.435.4411.30094.451998.5560944.76475767112363352558287773913 >>>>> 6.22.253.4095.67430.874611.2945825.66263705853096254364417758021\n",
      "\n",
      ">> filename 9aba4355-b4f4-441c-9dfa-a49845a8347e.dcm\n",
      "410 Done\n",
      "\n",
      ">>>  data/1e9b8da2-cda9-457d-984d-24514beb823e.dcm\n",
      "Converting to PNG .........................\n",
      "1.12.400.4488.32966.109977.1649124.43720589707510096619850902386 >>>>>> 1.13.805.5581.56285.187379.8563632.57369358350969302674194197876 >>>>> 8.89.455.4513.19037.178001.7888526.34925462293768685809405016395\n",
      "\n",
      ">> filename 1e9b8da2-cda9-457d-984d-24514beb823e.dcm\n",
      "411 Done\n",
      "\n",
      ">>>  data/ffa42343-2731-40b2-80ba-2e9af6909a6f.dcm\n",
      "Converting to PNG .........................\n",
      "5.24.172.6781.78890.298166.8412411.18311384122039058684990503749 >>>>>> 8.60.923.5175.32577.905738.5037119.15414683871417300715232855399 >>>>> 8.62.889.5843.56065.442796.5585698.90873065729665431366623959446\n",
      "\n",
      ">> filename ffa42343-2731-40b2-80ba-2e9af6909a6f.dcm\n",
      "412 Done\n",
      "\n",
      ">>>  data/2e2b4431-ae15-4550-8d34-28afb121a4c6.dcm\n",
      "Converting to PNG .........................\n",
      "3.34.161.5664.74180.111077.3931287.29855887250739294029561600881 >>>>>> 6.30.376.6906.83266.947370.4715582.14481014024091053496860287845 >>>>> 4.10.450.3440.63875.657737.5332600.81801920219534620819771901932\n",
      "\n",
      ">> filename 2e2b4431-ae15-4550-8d34-28afb121a4c6.dcm\n",
      "413 Done\n",
      "\n",
      ">>>  data/2c49bedb-78a8-4d6f-9a99-42d602c4b9f8.dcm\n",
      "Converting to PNG .........................\n",
      "7.68.751.9861.19643.836112.1735706.84654120940961160322751365079 >>>>>> 7.98.572.1902.70581.560364.6606868.84546547604554735905298507377 >>>>> 2.11.569.6177.42634.331450.3817475.81233455424918816235279320967\n",
      "\n",
      ">> filename 2c49bedb-78a8-4d6f-9a99-42d602c4b9f8.dcm\n",
      "414 Done\n"
     ]
    }
   ],
   "source": [
    "# convert dicoms to png and store in training_data_png\n",
    "\n",
    "from glob import glob\n",
    "files_ = glob('data/*.dcm',recursive=True)\n",
    "try:\n",
    "    os.mkdir(\"training_data_png\")\n",
    "except:\n",
    "    pass\n",
    "print(files_)\n",
    "c = 0\n",
    "for i in files_:\n",
    "    try:\n",
    "        print(\"\\n>>> \",i)\n",
    "        img,patientname = read_xray(i)\n",
    "        with open(\"filename.pkl\", 'wb') as f:\n",
    "            pickle.dump(img, f)\n",
    "        ims = pickle.load(open(\"filename.pkl\", \"rb\"))\n",
    "        norm = (ims.astype(np.float) - ims.min()) * 255.0 / (ims.max() - ims.min())\n",
    "        filename = str(i).split(\"/\")[1].split(\"_\")[0]\n",
    "        print(\"\\n>> filename\",filename)\n",
    "        Image.fromarray(norm.astype(np.uint8)).save(f\"training_data_png/{filename}.png\")\n",
    "        c = c + 1\n",
    "        print(c, \"Done\")\n",
    "    except Exception as e:\n",
    "        print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Uploading the data to S3\n",
    "We are going to use the `sagemaker.Session.upload_data` function to upload our datasets to an S3 location. The return value inputs identifies the location -- we will use later when we start the training job.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   0 Case Number Patient ID Patient Name  \\\n",
      "0  1  Case 10375  ssss-0006    ssss-0006   \n",
      "1  2  Case 10377  ssss-0008    ssss-0008   \n",
      "2  3  Case 10378  ssss-0009    ssss-0009   \n",
      "3  4  Case 10374  ssss-0005    ssss-0005   \n",
      "4  5  Case 10373  ssss-0004    ssss-0004   \n",
      "5  6  Case 10370       Anon        AA001   \n",
      "6  7  Case 10371  ssss-0002    ssss-0002   \n",
      "7  8  Case 10376  ssss-0007    ssss-0007   \n",
      "8  9  Case 10372  ssss-0003    ssss-0003   \n",
      "\n",
      "                                    StudyInstanceUID           User  Comments  \\\n",
      "0  3.73.860.9192.16202.831749.3290149.62853582098...  sudo@carpl.ai       NaN   \n",
      "1  1.86.711.5694.23884.235814.2324468.17132268458...  sudo@carpl.ai       NaN   \n",
      "2  6.42.185.8295.15199.978388.6046825.48050093409...  sudo@carpl.ai       NaN   \n",
      "3  3.25.639.9624.60891.908848.1133013.29224464766...  sudo@carpl.ai       NaN   \n",
      "4  7.39.190.7770.15802.706866.7064726.62775422404...  sudo@carpl.ai       NaN   \n",
      "5  1.2.840.113619.2.203.4.2147483647.1474011852.1...  sudo@carpl.ai       NaN   \n",
      "6  5.30.143.2195.51539.564465.4182368.38009059141...  sudo@carpl.ai       NaN   \n",
      "7  5.41.642.2793.11955.920385.3813713.65744642995...  sudo@carpl.ai       NaN   \n",
      "8  6.47.433.5951.69303.740620.5024974.86325227258...  sudo@carpl.ai       NaN   \n",
      "\n",
      "  test  \n",
      "0  yes  \n",
      "1   no  \n",
      "2  yes  \n",
      "3  yes  \n",
      "4   no  \n",
      "5  yes  \n",
      "6   no  \n",
      "7   no  \n",
      "8  yes  \n"
     ]
    }
   ],
   "source": [
    "from glob import glob\n",
    "import pandas as pd\n",
    "labels = pd.read_csv(\"./labels/labels.csv\",header=1)\n",
    "print(labels)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "files_ = glob('training_data_png/*.png',recursive=True)\n",
    "\n",
    "for file in files_:\n",
    "    sagemaker_session.upload_data(path=file  , bucket=bucket, key_prefix=\"data/train\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Train\n",
    "### Training script\n",
    "The `classifier.py` script provides all the code we need for training and hosting a SageMaker model (`model_fn` function to load a model).\n",
    "The training script is very similar to a training script you might run outside of SageMaker, but you can access useful properties about the training environment through various environment variables, such as:\n",
    "\n",
    "* `SM_MODEL_DIR`: A string representing the path to the directory to write model artifacts to.\n",
    "  These artifacts are uploaded to S3 for model hosting.\n",
    "* `SM_NUM_GPUS`: The number of gpus available in the current container.\n",
    "* `SM_CURRENT_HOST`: The name of the current container on the container network.\n",
    "* `SM_HOSTS`: JSON encoded list containing all the hosts .\n",
    "\n",
    "Supposing one input channel, 'training', was used in the call to the PyTorch estimator's `fit()` method, the following will be set, following the format `SM_CHANNEL_[channel_name]`:\n",
    "\n",
    "* `SM_CHANNEL_TRAINING`: A string representing the path to the directory containing data in the 'training' channel.\n",
    "\n",
    "For more information about training environment variables, please visit [SageMaker Containers](https://github.com/aws/sagemaker-containers).\n",
    "\n",
    "A typical training script loads data from the input channels, configures training with hyperparameters, trains a model, and saves a model to `model_dir` so that it can be hosted later. Hyperparameters are passed to your script as arguments and can be retrieved with an `argparse.ArgumentParser` instance.\n",
    "\n",
    "Because the SageMaker imports the training script, you should put your training code in a main guard (``if __name__=='__main__':``) if you are using the same script to host your model as we do in this example, so that SageMaker does not inadvertently run your training code at the wrong point in execution.\n",
    "\n",
    "For example, the script run by this notebook:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mjson\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtime\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msys\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcsv\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mcv2\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mmatplotlib\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mpyplot\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mplt\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mbackends\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mcudnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mcudnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mtransforms\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mtransforms\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36moptim\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36moptim\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mfunctional\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mtfunc\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Dataset\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdataset\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m random_split\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m DataLoader\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36moptim\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mlr_scheduler\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m ReduceLROnPlateau\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Image\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mfunctional\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mfunc\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36msklearn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmetrics\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m roc_auc_score\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msklearn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmetrics\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mmetrics\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrandom\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mpandas\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mpd\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mcopy\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m deepcopy\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36margparse\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mjson\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mlogging\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mos\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36msys\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#import sagemaker_containers\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdistributed\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mdist\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mnn\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mfunctional\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mF\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36moptim\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36moptim\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m cuda\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtimeit\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m default_timer \u001b[34mas\u001b[39;49;00m timer\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdistributed\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m datasets, transforms, models\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorch\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mutils\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mdata\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m DataLoader, sampler\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mpandas\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mpd\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mshlex\u001b[39;49;00m, \u001b[04m\u001b[36msubprocess\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchsummary\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m summary\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mPIL\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m Image\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrequests\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mpydicom\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mglob\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m densenet121\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "logger = logging.getLogger(\u001b[31m__name__\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "logger.setLevel(logging.DEBUG)\u001b[37m\u001b[39;49;00m\n",
      "logger.addHandler(logging.StreamHandler(sys.stdout))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "class_names = [\u001b[33m'\u001b[39;49;00m\u001b[33mNo Finding\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEnlarged Cardiomediastinum\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mCardiomegaly\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mLung Opacity\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mLung Lesion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEdema\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mConsolidation\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumonia\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mAtelectasis\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumothorax\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Effusion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Other\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mFracture\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mSupport Devices\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "nnClassCount = \u001b[34m14\u001b[39;49;00m \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mfind_file_path\u001b[39;49;00m(filename):\u001b[37m\u001b[39;49;00m\n",
      "    file_path = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m path \u001b[35min\u001b[39;49;00m glob.glob(\u001b[33m'\u001b[39;49;00m\u001b[33m**/\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + filename, recursive=\u001b[34mTrue\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m file_path \u001b[35mis\u001b[39;49;00m \u001b[34mNone\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            file_path = os.path.abspath(path)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mbreak\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m file_path\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[33m''' INFERENCING'''\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_json\u001b[39;49;00m(coord,og_img_size):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# final_coord = [[(coord[x]+16)*(og_img_size[0]/256),(coord[y]+16)*(og_img_size[1]/256)] for x,y in zip(list(range(0,len(coord),2)),list(range(1,len(coord),2)))]\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    final_coord = [[coord[x],coord[y]] \u001b[34mfor\u001b[39;49;00m x,y \u001b[35min\u001b[39;49;00m \u001b[36mzip\u001b[39;49;00m(\u001b[36mlist\u001b[39;49;00m(\u001b[36mrange\u001b[39;49;00m(\u001b[34m0\u001b[39;49;00m,\u001b[36mlen\u001b[39;49;00m(coord),\u001b[34m2\u001b[39;49;00m)),\u001b[36mlist\u001b[39;49;00m(\u001b[36mrange\u001b[39;49;00m(\u001b[34m1\u001b[39;49;00m,\u001b[36mlen\u001b[39;49;00m(coord),\u001b[34m2\u001b[39;49;00m)))]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#final_coord = np.array(final_coord)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m(final_coord)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_coord_dict\u001b[39;49;00m(cont_new):\u001b[37m\u001b[39;49;00m\n",
      "    final_pairs = get_pairs(cont_new)\u001b[37m\u001b[39;49;00m\n",
      "    line_list = []\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[34m0\u001b[39;49;00m,\u001b[36mlen\u001b[39;49;00m(final_pairs)):\u001b[37m\u001b[39;49;00m\n",
      "        coord_cur,coord_after = final_pairs[i][\u001b[34m0\u001b[39;49;00m],final_pairs[i][\u001b[34m1\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "        z = {\u001b[33m'\u001b[39;49;00m\u001b[33mactive\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[34mTrue\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mhighlight\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: \u001b[34mTrue\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mlines\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: [{\u001b[33m'\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: coord_after[\u001b[34m0\u001b[39;49;00m], \u001b[33m'\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: coord_after[\u001b[34m1\u001b[39;49;00m]}],\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mx\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: coord_cur[\u001b[34m0\u001b[39;49;00m],\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33my\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: coord_cur[\u001b[34m1\u001b[39;49;00m]}\u001b[37m\u001b[39;49;00m\n",
      "        line_list.append(z)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m line_list\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_1D_coord\u001b[39;49;00m(contours):\u001b[37m\u001b[39;49;00m\n",
      "    global_list=[]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m contour_id \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[36mlen\u001b[39;49;00m(contours)):\u001b[37m\u001b[39;49;00m\n",
      "        local_list=[]\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m point_idx \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(contours[contour_id].shape[\u001b[34m0\u001b[39;49;00m]):\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m(point_idx==\u001b[34m0\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "                X_0= contours[contour_id][point_idx][\u001b[34m0\u001b[39;49;00m][\u001b[34m0\u001b[39;49;00m].astype(\u001b[33m'\u001b[39;49;00m\u001b[33mfloat\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                Y_0 = contours[contour_id][point_idx][\u001b[34m0\u001b[39;49;00m][\u001b[34m1\u001b[39;49;00m].astype(\u001b[33m'\u001b[39;49;00m\u001b[33mfloat\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            X = contours[contour_id][point_idx][\u001b[34m0\u001b[39;49;00m][\u001b[34m0\u001b[39;49;00m].astype(\u001b[33m'\u001b[39;49;00m\u001b[33mfloat\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            Y = contours[contour_id][point_idx][\u001b[34m0\u001b[39;49;00m][\u001b[34m1\u001b[39;49;00m].astype(\u001b[33m'\u001b[39;49;00m\u001b[33mfloat\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            local_list.append(X)\u001b[37m\u001b[39;49;00m\n",
      "            local_list.append(Y)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m# If the last point is reached, then append the first point\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m(point_idx == contours[contour_id].shape[\u001b[34m0\u001b[39;49;00m]-\u001b[34m1\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "                local_list.append(X_0)\u001b[37m\u001b[39;49;00m\n",
      "                local_list.append(Y_0)\u001b[37m\u001b[39;49;00m\n",
      "        global_list.append(deepcopy(local_list))\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m(global_list)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mthreshold\u001b[39;49;00m(minimum,maximum,image,binary=\u001b[34mTrue\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mif\u001b[39;49;00m(binary): \u001b[37m# If binary is True, then the image is converted to binary\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image[image<minimum]=\u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image[image>maximum]=\u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image[(image>\u001b[34m0\u001b[39;49;00m)]=\u001b[34m1\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34melse\u001b[39;49;00m: \u001b[37m# If binary is False, then the image is converted to grayscale\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image[image<minimum]=\u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image[image>maximum]=\u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m image\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mget_pairs\u001b[39;49;00m(cont_new):\u001b[37m\u001b[39;49;00m\n",
      "    pairs=[]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[34m0\u001b[39;49;00m,cont_new.shape[\u001b[34m0\u001b[39;49;00m]):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m (i < (cont_new.shape[\u001b[34m0\u001b[39;49;00m]-\u001b[34m1\u001b[39;49;00m)):\u001b[37m\u001b[39;49;00m\n",
      "            pairs.append((cont_new[i],cont_new[i+\u001b[34m1\u001b[39;49;00m]))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            pairs.append((cont_new[i],cont_new[\u001b[34m0\u001b[39;49;00m]))\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m(pairs)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mmodel_fn\u001b[39;49;00m(model_dir):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m densenet121\u001b[37m\u001b[39;49;00m\n",
      "    IMAGENET_MEAN = np.array([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    IMAGENET_STD = np.array([\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    model =  ChexNet()\u001b[37m\u001b[39;49;00m\n",
      "    device = torch.device(\u001b[33m\"\u001b[39;49;00m\u001b[33mcuda\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[34mif\u001b[39;49;00m torch.cuda.is_available() \u001b[34melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mcpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    f = \u001b[33m\"\u001b[39;49;00m\u001b[33mmodele.pth\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33m>>>>>>>>>>>>>>> model dir >>>>>>>>\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(model_dir)\u001b[37m\u001b[39;49;00m\n",
      "    model_def_path = os.path.join(model_dir, \u001b[33m\"\u001b[39;49;00m\u001b[33mmodel.pth\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mif\u001b[39;49;00m \u001b[35mnot\u001b[39;49;00m os.path.isfile(model_def_path):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33mfile not found in \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mmodel_def_path\u001b[33m}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mraise\u001b[39;49;00m \u001b[36mRuntimeError\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mMissing the model definition file\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# model = torch.nn.DataParallel(model)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    modelCheckpoint = torch.load(model_def_path)\u001b[37m\u001b[39;49;00m\n",
      "    model.load_state_dict(modelCheckpoint[\u001b[33m'\u001b[39;49;00m\u001b[33mstate_dict\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    model.eval()\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m model.to(device)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32msave_model\u001b[39;49;00m(model, model_dir):\u001b[37m\u001b[39;49;00m\n",
      "    logger.info(\u001b[33m\"\u001b[39;49;00m\u001b[33mSaving the model.\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    path = os.path.join(model_dir, \u001b[33m\"\u001b[39;49;00m\u001b[33mmodel.pth\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    torch.save(model, path)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32minput_fn\u001b[39;49;00m(request_body, content_type=\u001b[33m'\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "    logger.info(\u001b[33m'\u001b[39;49;00m\u001b[33mDeserializing the input data.\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mrequests\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    device = torch.device(\u001b[33m\"\u001b[39;49;00m\u001b[33mcuda\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[34mif\u001b[39;49;00m torch.cuda.is_available() \u001b[34melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mcpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mif\u001b[39;49;00m content_type == \u001b[33m'\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:    \u001b[37m\u001b[39;49;00m\n",
      "        input_data = json.loads(request_body)\u001b[37m\u001b[39;49;00m\n",
      "        url = input_data[\u001b[33m'\u001b[39;49;00m\u001b[33murl\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "        logger.info(\u001b[33mf\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mImage url: \u001b[39;49;00m\u001b[33m{\u001b[39;49;00murl\u001b[33m}\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        device = torch.device(\u001b[33m\"\u001b[39;49;00m\u001b[33mcuda\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[34mif\u001b[39;49;00m torch.cuda.is_available() \u001b[34melse\u001b[39;49;00m \u001b[33m\"\u001b[39;49;00m\u001b[33mcpu\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        image_data = Image.open(requests.get(url, stream=\u001b[34mTrue\u001b[39;49;00m).raw).convert(\u001b[33m'\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        image_transform = transforms.Compose([\u001b[37m\u001b[39;49;00m\n",
      "            transforms.Resize(size=\u001b[34m224\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "            transforms.ToTensor(),\u001b[37m\u001b[39;49;00m\n",
      "            transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m], [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "        ])\u001b[37m\u001b[39;49;00m\n",
      "        data = image_transform(image_data)\u001b[37m\u001b[39;49;00m\n",
      "        logger.info(\u001b[36mstr\u001b[39;49;00m(data.shape))\u001b[37m\u001b[39;49;00m\n",
      "        data = torch.tensor(data, dtype=torch.float32, device=device).unsqueeze(\u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        logger.info(\u001b[36mstr\u001b[39;49;00m(data.shape))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m [data, image_data.size]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mraise\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[33mRequested unsupported ContentType in content_type \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mcontent_type\u001b[33m}\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mpredict_fn\u001b[39;49;00m(input_object, model):\u001b[37m\u001b[39;49;00m\n",
      "    input_object, og_img_size = input_object\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mwith\u001b[39;49;00m torch.no_grad():\u001b[37m\u001b[39;49;00m\n",
      "        logger.info(\u001b[36mstr\u001b[39;49;00m(input_object.shape))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# logger.info(model.classifier)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        output = model.backbone(input_object)\u001b[37m\u001b[39;49;00m\n",
      "        l = model.forward(input_object)\u001b[37m\u001b[39;49;00m\n",
      "        l = torch.sigmoid(l)\u001b[37m\u001b[39;49;00m\n",
      "        heatmap = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        weights = \u001b[36mlist\u001b[39;49;00m(model.backbone.parameters())[-\u001b[34m2\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m (\u001b[34m0\u001b[39;49;00m, \u001b[36mlen\u001b[39;49;00m(weights)):\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[36mmap\u001b[39;49;00m = output[\u001b[34m0\u001b[39;49;00m,i,:,:]\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m i == \u001b[34m0\u001b[39;49;00m: \u001b[37m\u001b[39;49;00m\n",
      "                heatmap = weights[i] * \u001b[36mmap\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34melse\u001b[39;49;00m: \u001b[37m\u001b[39;49;00m\n",
      "                heatmap += weights[i] * \u001b[36mmap\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            npHeatmap = heatmap.cpu().data.numpy()\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m [output,l,npHeatmap, og_img_size]\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32moutput_fn\u001b[39;49;00m(predictions, content_type):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34massert\u001b[39;49;00m content_type == \u001b[33m'\u001b[39;49;00m\u001b[33mapplication/json\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    output,l,npHeatmap,og_img_size = predictions\u001b[37m\u001b[39;49;00m\n",
      "    heatmap = npHeatmap\u001b[37m\u001b[39;49;00m\n",
      "    heatmap = ((heatmap - heatmap.min()) * (\u001b[34m1\u001b[39;49;00m / (heatmap.max() - heatmap.min())) * \u001b[34m255\u001b[39;49;00m).astype(np.uint8)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# cam = npHeatmap / np.max(npHeatmap)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    cam = cv2.resize(heatmap, og_img_size)\u001b[37m\u001b[39;49;00m\n",
      "    heatmap = cam\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# heatmap = cv2.applyColorMap(cam, cv2.COLORMAP_JET)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#---- Blend original and heatmap \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    temp = heatmap.copy()\u001b[37m\u001b[39;49;00m\n",
      "    img = temp\u001b[37m#[:,:,0]\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# img = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(img.min())\u001b[37m\u001b[39;49;00m\n",
      "    img = (img/\u001b[34m1\u001b[39;49;00m).astype(\u001b[33m'\u001b[39;49;00m\u001b[33muint8\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    binary = threshold(\u001b[34m220\u001b[39;49;00m,img.max(),img)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# binary = binary.astype(np.int32)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    contours, _ = cv2.findContours(binary,cv2.RETR_CCOMP, cv2.CHAIN_APPROX_SIMPLE) \u001b[37m\u001b[39;49;00m\n",
      "    sorted_contours = \u001b[36msorted\u001b[39;49;00m(contours, key=cv2.contourArea, reverse=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    contours = []\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m countour \u001b[35min\u001b[39;49;00m sorted_contours[:\u001b[34m4\u001b[39;49;00m]:\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m cv2.contourArea(countour) > (\u001b[34m0.005\u001b[39;49;00m*og_img_size[\u001b[34m0\u001b[39;49;00m]*og_img_size[\u001b[34m1\u001b[39;49;00m]):\u001b[37m\u001b[39;49;00m\n",
      "            contours.append(countour)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    coords = get_1D_coord(contours)\u001b[37m\u001b[39;49;00m\n",
      "    data_final = []\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m coord \u001b[35min\u001b[39;49;00m coords:\u001b[37m\u001b[39;49;00m\n",
      "        data_final.append(get_json(coord,og_img_size))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mFINAL JSON\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    output = response_converter(l.tolist(),data_final)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m json.dumps(output)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mresponse_converter\u001b[39;49;00m(labels,coords):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(labels,coords)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m    \u001b[39;49;00m\u001b[33m'''\u001b[39;49;00m\n",
      "\u001b[33m    THIS IS A CUSTOM RESPONSE CCONVERTER\u001b[39;49;00m\n",
      "\u001b[33m    CARPL EXPECTS OUTPUT IN THIS FORMAT:\u001b[39;49;00m\n",
      "\u001b[33m    {\u001b[39;49;00m\n",
      "\u001b[33m        \"response\": {\u001b[39;49;00m\n",
      "\u001b[33m            \"findings\":[\u001b[39;49;00m\n",
      "\u001b[33m                {\u001b[39;49;00m\n",
      "\u001b[33m                    \"name\":\"class_A\",\u001b[39;49;00m\n",
      "\u001b[33m                    \"probability\":score_A\u001b[39;49;00m\n",
      "\u001b[33m                },\u001b[39;49;00m\n",
      "\u001b[33m                {\u001b[39;49;00m\n",
      "\u001b[33m                    \"name\":\"class_B\",\u001b[39;49;00m\n",
      "\u001b[33m                    \"probability\":score_B\u001b[39;49;00m\n",
      "\u001b[33m                }\u001b[39;49;00m\n",
      "\u001b[33m            ],\u001b[39;49;00m\n",
      "\u001b[33m            \"rois\":[\u001b[39;49;00m\n",
      "\u001b[33m                    {\u001b[39;49;00m\n",
      "\u001b[33m                        \"finding_name\":\"class1\",\u001b[39;49;00m\n",
      "\u001b[33m                        \"type\":\"Rectangle\",\u001b[39;49;00m\n",
      "\u001b[33m                        \"points\":[\u001b[39;49;00m\n",
      "\u001b[33m                            [50,300],\u001b[39;49;00m\n",
      "\u001b[33m                            [100,200]\u001b[39;49;00m\n",
      "\u001b[33m                        ]\u001b[39;49;00m\n",
      "\u001b[33m                    },\u001b[39;49;00m\n",
      "\u001b[33m                    {\u001b[39;49;00m\n",
      "\u001b[33m                        \"finding_name\":\"class2\",\u001b[39;49;00m\n",
      "\u001b[33m                        \"type\":\"Freehand\",\u001b[39;49;00m\n",
      "\u001b[33m                        \"points\":[\u001b[39;49;00m\n",
      "\u001b[33m                            [500,300],\u001b[39;49;00m\n",
      "\u001b[33m                            [500,320],\u001b[39;49;00m\n",
      "\u001b[33m                            [500,420],\u001b[39;49;00m\n",
      "\u001b[33m                            [800,420],\u001b[39;49;00m\n",
      "\u001b[33m                            [800,370],\u001b[39;49;00m\n",
      "\u001b[33m                            [800,320]\u001b[39;49;00m\n",
      "\u001b[33m                        ]\u001b[39;49;00m\n",
      "\u001b[33m                    }\u001b[39;49;00m\n",
      "\u001b[33m                ]\u001b[39;49;00m\n",
      "\u001b[33m        }\u001b[39;49;00m\n",
      "\u001b[33m    }\u001b[39;49;00m\n",
      "\u001b[33m    '''\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mnumpy\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m \u001b[04m\u001b[36mnp\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    class_names = [\u001b[33m'\u001b[39;49;00m\u001b[33mNo Finding\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEnlarged Cardiomediastinum\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mCardiomegaly\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mLung Opacity\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mLung Lesion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEdema\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mConsolidation\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumonia\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mAtelectasis\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumothorax\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Effusion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Other\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mFracture\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mSupport Devices\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mtry\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m {\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            \u001b[33m\"\u001b[39;49;00m\u001b[33mresponse\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m : {\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[33m\"\u001b[39;49;00m\u001b[33mfindings\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:[{\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[33m\"\u001b[39;49;00m\u001b[33mname\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:class_names[e],\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[33m\"\u001b[39;49;00m\u001b[33mprobability\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\u001b[36mround\u001b[39;49;00m(l * \u001b[34m100\u001b[39;49;00m, \u001b[34m2\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                } \u001b[34mfor\u001b[39;49;00m e,l \u001b[35min\u001b[39;49;00m \u001b[36menumerate\u001b[39;49;00m(labels[\u001b[34m0\u001b[39;49;00m])]+[{\u001b[33m\"\u001b[39;49;00m\u001b[33mname\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\u001b[33m\"\u001b[39;49;00m\u001b[33mROIS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33mprobability\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m : \u001b[36mstr\u001b[39;49;00m(\u001b[36mlen\u001b[39;49;00m(coords))}],\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[33m\"\u001b[39;49;00m\u001b[33mrois\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:[\u001b[37m\u001b[39;49;00m\n",
      "                    {\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[33m\"\u001b[39;49;00m\u001b[33mfinding_name\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\u001b[33m\"\u001b[39;49;00m\u001b[33mAbnormality\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[33m\"\u001b[39;49;00m\u001b[33mtype\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\u001b[33m\"\u001b[39;49;00m\u001b[33mFreehand\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[33m\"\u001b[39;49;00m\u001b[33mpoints\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:coords[e],\u001b[37m\u001b[39;49;00m\n",
      "                    } \u001b[34mfor\u001b[39;49;00m e,l \u001b[35min\u001b[39;49;00m \u001b[36menumerate\u001b[39;49;00m(coords)\u001b[37m\u001b[39;49;00m\n",
      "                ]\u001b[37m\u001b[39;49;00m\n",
      "            }\u001b[37m\u001b[39;49;00m\n",
      "        }\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mexcept\u001b[39;49;00m \u001b[36mException\u001b[39;49;00m \u001b[34mas\u001b[39;49;00m e:\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m(e)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mCheXpertDataSet\u001b[39;49;00m(Dataset):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, image_list_file, transform=\u001b[34mNone\u001b[39;49;00m, policy=\u001b[33m\"\u001b[39;49;00m\u001b[33mones\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m        \u001b[39;49;00m\u001b[33m\"\"\"\u001b[39;49;00m\n",
      "\u001b[33m        image_list_file: path to the file containing images with corresponding labels.\u001b[39;49;00m\n",
      "\u001b[33m        transform: optional transform to be applied on a sample.\u001b[39;49;00m\n",
      "\u001b[33m        Upolicy: name the policy with regard to the uncertain labels\u001b[39;49;00m\n",
      "\u001b[33m        \"\"\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mimport\u001b[39;49;00m \u001b[04m\u001b[36mglob\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image_names = glob.glob(image_list_file+\u001b[33m\"\u001b[39;49;00m\u001b[33m/*.png\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,recursive = \u001b[34mTrue\u001b[39;49;00m )\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[37m\u001b[39;49;00m\n",
      "        labels = np.ones((\u001b[36mlen\u001b[39;49;00m(image_names),nnClassCount))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.image_names = image_names\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.labels = labels\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.transform = transform\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32m__getitem__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, index):\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m        \u001b[39;49;00m\u001b[33m\"\"\"Take the index of item and returns the image and its labels\"\"\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        image_name = \u001b[36mself\u001b[39;49;00m.image_names[index]\u001b[37m\u001b[39;49;00m\n",
      "        image = Image.open(image_name).convert(\u001b[33m'\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        label = \u001b[36mself\u001b[39;49;00m.labels[index]\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.transform \u001b[35mis\u001b[39;49;00m \u001b[35mnot\u001b[39;49;00m \u001b[34mNone\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            image = \u001b[36mself\u001b[39;49;00m.transform(image)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m image, torch.FloatTensor(label)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32m__len__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m \u001b[36mlen\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m.image_names)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mCheXpertTrainer\u001b[39;49;00m():\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mtrain\u001b[39;49;00m (model, dataLoaderTrain, dataLoaderVal, nnClassCount, trMaxEpoch, launchTimestamp, checkpoint, save_path):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#SETTINGS: OPTIMIZER & SCHEDULER\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        optimizer = optim.Adam (model.parameters(), lr=\u001b[34m0.0001\u001b[39;49;00m, betas=(\u001b[34m0.9\u001b[39;49;00m, \u001b[34m0.999\u001b[39;49;00m), eps=\u001b[34m1e-08\u001b[39;49;00m, weight_decay=\u001b[34m1e-5\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#SETTINGS: LOSS\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        loss = torch.nn.CrossEntropyLoss()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#LOAD CHECKPOINT \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m checkpoint != \u001b[34mNone\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            state_dict = torch.load(checkpoint)\u001b[37m\u001b[39;49;00m\n",
      "            model.load_state_dict(state_dict)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33m>>>>>>>>>>>>>>>>>>> MODEL LOADED SUCCESSFULLY >>>>>>>>>>>>>>\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#TRAIN THE NETWORK\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        lossMIN = \u001b[34m100000\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        epochID = \u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        lossMIN = \u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        torch.save({\u001b[33m'\u001b[39;49;00m\u001b[33mepoch\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: epochID + \u001b[34m1\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mstate_dict\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: model.state_dict(), \u001b[33m'\u001b[39;49;00m\u001b[33mbest_loss\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: lossMIN, \u001b[33m'\u001b[39;49;00m\u001b[33moptimizer\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m : optimizer.state_dict()}, save_path +  \u001b[33m\"\u001b[39;49;00m\u001b[33m/model.pth\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m epochID \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[34m0\u001b[39;49;00m, trMaxEpoch):\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            timestampTime = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mH\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mM\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            timestampDate = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%d\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mm\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mY\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            timestampSTART = timestampDate + \u001b[33m'\u001b[39;49;00m\u001b[33m-\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + timestampTime\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            batchs, losst, losse = CheXpertTrainer.epochTrain(model, dataLoaderTrain, dataLoaderVal, optimizer, trMaxEpoch, nnClassCount, loss)\u001b[37m\u001b[39;49;00m\n",
      "            lossVal = CheXpertTrainer.epochVal(model, dataLoaderVal, optimizer, trMaxEpoch, nnClassCount, loss)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "            timestampTime = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mH\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mM\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            timestampDate = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%d\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mm\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mY\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            timestampEND = timestampDate + \u001b[33m'\u001b[39;49;00m\u001b[33m-\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + timestampTime\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m lossVal < lossMIN:\u001b[37m\u001b[39;49;00m\n",
      "                lossMIN = lossVal    \u001b[37m\u001b[39;49;00m\n",
      "                torch.save({\u001b[33m'\u001b[39;49;00m\u001b[33mepoch\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: epochID + \u001b[34m1\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mstate_dict\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: model.state_dict(), \u001b[33m'\u001b[39;49;00m\u001b[33mbest_loss\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: lossMIN, \u001b[33m'\u001b[39;49;00m\u001b[33moptimizer\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m : optimizer.state_dict()}, save_path +  \u001b[33m\"\u001b[39;49;00m\u001b[33m/model_base.pth\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m (\u001b[33m'\u001b[39;49;00m\u001b[33mEpoch [\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + \u001b[36mstr\u001b[39;49;00m(epochID + \u001b[34m1\u001b[39;49;00m) + \u001b[33m'\u001b[39;49;00m\u001b[33m] [save] [\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + timestampEND + \u001b[33m'\u001b[39;49;00m\u001b[33m] loss= \u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + \u001b[36mstr\u001b[39;49;00m(lossVal))\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m# save_model(model, save_path)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m (\u001b[33m'\u001b[39;49;00m\u001b[33mEpoch [\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + \u001b[36mstr\u001b[39;49;00m(epochID + \u001b[34m1\u001b[39;49;00m) + \u001b[33m'\u001b[39;49;00m\u001b[33m] [----] [\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + timestampEND + \u001b[33m'\u001b[39;49;00m\u001b[33m] loss= \u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + \u001b[36mstr\u001b[39;49;00m(lossVal))\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m batchs, losst, losse        \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#-------------------------------------------------------------------------------- \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "       \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mepochTrain\u001b[39;49;00m(model, dataLoaderTrain, dataLoaderVal, optimizer, trMaxEpoch, classCount, loss):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        batch = []\u001b[37m\u001b[39;49;00m\n",
      "        losstrain = []\u001b[37m\u001b[39;49;00m\n",
      "        losseval = []\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        model.train()\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m batchID, (varInput, target) \u001b[35min\u001b[39;49;00m \u001b[36menumerate\u001b[39;49;00m(dataLoaderTrain):\u001b[37m\u001b[39;49;00m\n",
      "            use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "                varTarget = target.cuda(non_blocking = \u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                varTarget = target\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m#varTarget = target.cuda()         \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "            varOutput = model(varInput)\u001b[37m\u001b[39;49;00m\n",
      "            lossvalue = loss(varOutput, varTarget)\u001b[37m\u001b[39;49;00m\n",
      "                       \u001b[37m\u001b[39;49;00m\n",
      "            optimizer.zero_grad()\u001b[37m\u001b[39;49;00m\n",
      "            lossvalue.backward()\u001b[37m\u001b[39;49;00m\n",
      "            optimizer.step()\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            l = lossvalue.item()\u001b[37m\u001b[39;49;00m\n",
      "            losstrain.append(l)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m batchID%\u001b[34m35\u001b[39;49;00m==\u001b[34m0\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m(batchID//\u001b[34m35\u001b[39;49;00m, \u001b[33m\"\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33m batches computed\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m#Fill three arrays to see the evolution of the loss\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "                batch.append(batchID)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "                le = CheXpertTrainer.epochVal(model, dataLoaderVal, optimizer, trMaxEpoch, classCount, loss).item()\u001b[37m\u001b[39;49;00m\n",
      "                losseval.append(le)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m(batchID)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m(l)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mprint\u001b[39;49;00m(le)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m batch, losstrain, losseval\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#-------------------------------------------------------------------------------- \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mepochVal\u001b[39;49;00m(model, dataLoaderVal, optimizer, epochMax, classCount, loss):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        model.eval()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        lossVal = \u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        lossValNorm = \u001b[34m0\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mwith\u001b[39;49;00m torch.no_grad():\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mfor\u001b[39;49;00m i, (varInput, target) \u001b[35min\u001b[39;49;00m \u001b[36menumerate\u001b[39;49;00m(dataLoaderVal):\u001b[37m\u001b[39;49;00m\n",
      "                use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "                    target = target.cuda(non_blocking = \u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                    target = target\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[37m\u001b[39;49;00m\n",
      "                varOutput = model(varInput)\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "                losstensor = loss(varOutput, target)\u001b[37m\u001b[39;49;00m\n",
      "                lossVal += losstensor\u001b[37m\u001b[39;49;00m\n",
      "                lossValNorm += \u001b[34m1\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[37m\u001b[39;49;00m\n",
      "        outLoss = lossVal / lossValNorm\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m outLoss\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#--------------------------------------------------------------------------------     \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "     \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#---- Computes area under ROC curve \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#---- dataGT - ground truth data\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#---- dataPRED - predicted data\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#---- classCount - number of classes\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mcomputeAUROC\u001b[39;49;00m (dataGT, dataPRED, classCount):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        outAUROC = []\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        datanpGT = dataGT.cpu().numpy()\u001b[37m\u001b[39;49;00m\n",
      "        datanpPRED = dataPRED.cpu().numpy()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(classCount):\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mtry\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                outAUROC.append(roc_auc_score(datanpGT[:, i], datanpPRED[:, i]))\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mexcept\u001b[39;49;00m \u001b[36mValueError\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34mpass\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m outAUROC\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#-------------------------------------------------------------------------------- \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mtest\u001b[39;49;00m(model, dataLoaderTest, nnClassCount, checkpoint, class_names):   \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        cudnn.benchmark = \u001b[34mTrue\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m checkpoint != \u001b[34mNone\u001b[39;49;00m \u001b[35mand\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "            modelCheckpoint = torch.load(checkpoint)\u001b[37m\u001b[39;49;00m\n",
      "            model.load_state_dict(modelCheckpoint[\u001b[33m'\u001b[39;49;00m\u001b[33mstate_dict\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "            outGT = torch.FloatTensor().cuda()\u001b[37m\u001b[39;49;00m\n",
      "            outPRED = torch.FloatTensor().cuda()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            outGT = torch.FloatTensor()\u001b[37m\u001b[39;49;00m\n",
      "            outPRED = torch.FloatTensor()\u001b[37m\u001b[39;49;00m\n",
      "       \u001b[37m\u001b[39;49;00m\n",
      "        model.eval()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mwith\u001b[39;49;00m torch.no_grad():\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mfor\u001b[39;49;00m i, (\u001b[36minput\u001b[39;49;00m, target) \u001b[35min\u001b[39;49;00m \u001b[36menumerate\u001b[39;49;00m(dataLoaderTest):\u001b[37m\u001b[39;49;00m\n",
      "                use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "                    target = target.cuda()\u001b[37m\u001b[39;49;00m\n",
      "                    outGT = torch.cat((outGT, target), \u001b[34m0\u001b[39;49;00m).cuda()\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "                    target = target\u001b[37m\u001b[39;49;00m\n",
      "                    outGT = torch.cat((outGT, target), \u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "                bs, c, h, w = \u001b[36minput\u001b[39;49;00m.size()\u001b[37m\u001b[39;49;00m\n",
      "                varInput = \u001b[36minput\u001b[39;49;00m.view(-\u001b[34m1\u001b[39;49;00m, c, h, w)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m\u001b[39;49;00m\n",
      "                out = model(varInput)\u001b[37m\u001b[39;49;00m\n",
      "                outPRED = torch.cat((outPRED, out), \u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        aurocIndividual = CheXpertTrainer.computeAUROC(outGT, outPRED, nnClassCount)\u001b[37m\u001b[39;49;00m\n",
      "        aurocMean = np.array(aurocIndividual).mean()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m (\u001b[33m'\u001b[39;49;00m\u001b[33mAUROC mean \u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, aurocMean)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m (\u001b[34m0\u001b[39;49;00m, \u001b[36mlen\u001b[39;49;00m(aurocIndividual)):\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[36mprint\u001b[39;49;00m (class_names[i], \u001b[33m'\u001b[39;49;00m\u001b[33m \u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, aurocIndividual[i])\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m outGT, outPRED\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mFlatten\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, x):\u001b[37m\u001b[39;49;00m\n",
      "        x = x.view(x.size()[\u001b[34m0\u001b[39;49;00m], -\u001b[34m1\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m x\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "IMAGENET_MEAN = np.array([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "IMAGENET_STD = np.array([\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m densenet121\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mChexNet\u001b[39;49;00m(nn.Module):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfrom\u001b[39;49;00m \u001b[04m\u001b[36mtorchvision\u001b[39;49;00m\u001b[04m\u001b[36m.\u001b[39;49;00m\u001b[04m\u001b[36mmodels\u001b[39;49;00m \u001b[34mimport\u001b[39;49;00m densenet121\u001b[37m\u001b[39;49;00m\n",
      "    tfm = transforms.Compose([\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Resize((\u001b[34m224\u001b[39;49;00m, \u001b[34m224\u001b[39;49;00m)),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.ToTensor(),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Normalize(IMAGENET_MEAN, IMAGENET_STD)])\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36msuper\u001b[39;49;00m().\u001b[32m__init__\u001b[39;49;00m()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.backbone = densenet121(\u001b[34mFalse\u001b[39;49;00m).features\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.head = nn.Sequential(nn.AdaptiveAvgPool2d(\u001b[34m1\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "                                 Flatten(),\u001b[37m\u001b[39;49;00m\n",
      "                                 nn.Linear(\u001b[34m1024\u001b[39;49;00m, \u001b[34m14\u001b[39;49;00m))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mforward\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, x):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m \u001b[36mself\u001b[39;49;00m.head(\u001b[36mself\u001b[39;49;00m.backbone(x))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mpredict\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m, image):\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m        \u001b[39;49;00m\u001b[33m\"\"\"\u001b[39;49;00m\n",
      "\u001b[33m        input: PIL image (w, h, c)\u001b[39;49;00m\n",
      "\u001b[33m        output: prob np.array\u001b[39;49;00m\n",
      "\u001b[33m        \"\"\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        image = V(\u001b[36mself\u001b[39;49;00m.tfm(image)[\u001b[34mNone\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "        image=image.cpu()\u001b[37m\u001b[39;49;00m\n",
      "        py = torch.sigmoid(\u001b[36mself\u001b[39;49;00m(image))\u001b[37m\u001b[39;49;00m\n",
      "        prob = py.detach().cpu().numpy()[\u001b[34m0\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mreturn\u001b[39;49;00m prob\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mcreate_dataloader\u001b[39;49;00m(traindir,testdir,validdir,batch_size=\u001b[34m128\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    image_transforms = {\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Train uses data augmentation\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mtrain\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "    transforms.Compose([\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Resize(size=\u001b[34m256\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.CenterCrop(size=\u001b[34m224\u001b[39;49;00m), \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# transforms.RandomHorizontalFlip(),\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        transforms.ToTensor(),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m],\u001b[37m\u001b[39;49;00m\n",
      "                             [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])  \u001b[37m# Imagenet standards\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    ])\u001b[37m\u001b[39;49;00m\n",
      "        ,\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Validation does not use augmentation\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mval\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "    transforms.Compose([\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Resize(size=\u001b[34m256\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.CenterCrop(size=\u001b[34m224\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.ToTensor(),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m], [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    ]),\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Test does not use augmentation\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mtest\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "    transforms.Compose([\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Resize(size=\u001b[34m256\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.CenterCrop(size=\u001b[34m224\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.ToTensor(),\u001b[37m\u001b[39;49;00m\n",
      "        transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m], [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    ]),\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    }\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    data = {\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mtrain\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "    datasets.ImageFolder(root=traindir, transform=image_transforms[\u001b[33m'\u001b[39;49;00m\u001b[33mtrain\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]),\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mval\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "    datasets.ImageFolder(root=validdir, transform=image_transforms[\u001b[33m'\u001b[39;49;00m\u001b[33mval\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]),\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[33m'\u001b[39;49;00m\u001b[33mtest\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "    datasets.ImageFolder(root=testdir, transform=image_transforms[\u001b[33m'\u001b[39;49;00m\u001b[33mtest\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    }\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Dataloader iterators\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    dataloaders = {\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mtrain\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: DataLoader(data[\u001b[33m'\u001b[39;49;00m\u001b[33mtrain\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m], batch_size=batch_size, shuffle=\u001b[34mTrue\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mval\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: DataLoader(data[\u001b[33m'\u001b[39;49;00m\u001b[33mval\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m], batch_size=batch_size, shuffle=\u001b[34mTrue\u001b[39;49;00m),\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mtest\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: DataLoader(data[\u001b[33m'\u001b[39;49;00m\u001b[33mtest\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m], batch_size=batch_size, shuffle=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    }\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m data,dataloaders\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mpreops\u001b[39;49;00m(traindir,testdir,validdir):\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33m********* inside preops *************\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33mf\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[33m{\u001b[39;49;00mtraindir\u001b[33m}\u001b[39;49;00m\u001b[33m , \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mtestdir\u001b[33m}\u001b[39;49;00m\u001b[33m , \u001b[39;49;00m\u001b[33m{\u001b[39;49;00mvaliddir\u001b[33m}\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Empty lists\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    categories = []\u001b[37m\u001b[39;49;00m\n",
      "    img_categories = []\u001b[37m\u001b[39;49;00m\n",
      "    n_train = []\u001b[37m\u001b[39;49;00m\n",
      "    n_valid = []\u001b[37m\u001b[39;49;00m\n",
      "    n_test = []\u001b[37m\u001b[39;49;00m\n",
      "    hs = []\u001b[37m\u001b[39;49;00m\n",
      "    ws = []\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Iterate through each category\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m d \u001b[35min\u001b[39;49;00m os.listdir(traindir):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m \u001b[35mnot\u001b[39;49;00m d.startswith(\u001b[33m'\u001b[39;49;00m\u001b[33m.\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "            categories.append(d)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m# Number of each image\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            train_imgs = os.listdir(traindir + d)\u001b[37m\u001b[39;49;00m\n",
      "            valid_imgs = os.listdir(validdir + d)\u001b[37m\u001b[39;49;00m\n",
      "            test_imgs = os.listdir(testdir + d)\u001b[37m\u001b[39;49;00m\n",
      "            n_train.append(\u001b[36mlen\u001b[39;49;00m(train_imgs))\u001b[37m\u001b[39;49;00m\n",
      "            n_valid.append(\u001b[36mlen\u001b[39;49;00m(valid_imgs))\u001b[37m\u001b[39;49;00m\n",
      "            n_test.append(\u001b[36mlen\u001b[39;49;00m(test_imgs))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m# Find stats for train images\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m train_imgs:\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34mif\u001b[39;49;00m \u001b[35mnot\u001b[39;49;00m i.startswith(\u001b[33m'\u001b[39;49;00m\u001b[33m.\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "                    img_categories.append(d)\u001b[37m\u001b[39;49;00m\n",
      "                    img = Image.open(traindir + d + \u001b[33m'\u001b[39;49;00m\u001b[33m/\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + i).convert(\u001b[33m\"\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "                    img_array = np.array(img)\u001b[37m\u001b[39;49;00m\n",
      "                    \u001b[37m# Shape\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "                    hs.append(img_array.shape[\u001b[34m0\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "                    ws.append(img_array.shape[\u001b[34m1\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Dataframe of categories\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    cat_df = pd.DataFrame({\u001b[33m'\u001b[39;49;00m\u001b[33mcategory\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: categories,\u001b[37m\u001b[39;49;00m\n",
      "                           \u001b[33m'\u001b[39;49;00m\u001b[33mn_train\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: n_train,\u001b[37m\u001b[39;49;00m\n",
      "                           \u001b[33m'\u001b[39;49;00m\u001b[33mn_valid\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: n_valid, \u001b[33m'\u001b[39;49;00m\u001b[33mn_test\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: n_test}).\\\n",
      "        sort_values(\u001b[33m'\u001b[39;49;00m\u001b[33mcategory\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Dataframe of training images\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    image_df = pd.DataFrame({\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mcategory\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: img_categories,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mheight\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: hs,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m'\u001b[39;49;00m\u001b[33mwidth\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m: ws\u001b[37m\u001b[39;49;00m\n",
      "    })\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m cat_df\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mclass\u001b[39;49;00m \u001b[04m\u001b[32mHeatmapGenerator\u001b[39;49;00m():\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32m__init__\u001b[39;49;00m (\u001b[36mself\u001b[39;49;00m, pathModel=\u001b[34mNone\u001b[39;49;00m, nnClassCount=\u001b[34m14\u001b[39;49;00m, transCrop=\u001b[34m224\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# model = DenseNet121(nnClassCount)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        use_gpu = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "            model = torch.nn.DataParallel(model)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            model = torch.nn.DataParallel(model)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        model = torch.load(pathModel)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# model.load_state_dict(modelCheckpoint['state_dict'])\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.model = model\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.model.eval()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#---- Initialize the weights\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.weights = \u001b[36mlist\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m.model.module.densenet121.features.parameters())[-\u001b[34m2\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#---- Initialize the image transform\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        normalize = transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m], [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "        transformList = []\u001b[37m\u001b[39;49;00m\n",
      "        transformList.append(transforms.Resize((transCrop, transCrop)))\u001b[37m\u001b[39;49;00m\n",
      "        transformList.append(transforms.ToTensor())\u001b[37m\u001b[39;49;00m\n",
      "        transformList.append(normalize)  \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mself\u001b[39;49;00m.transformSequence = transforms.Compose(transformList)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#--------------------------------------------------------------------------------\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "     \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mdef\u001b[39;49;00m \u001b[32mgenerate\u001b[39;49;00m (\u001b[36mself\u001b[39;49;00m, pathImageFile, pathOutputFile, transCrop):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#---- Load image, transform, convert \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mwith\u001b[39;49;00m torch.no_grad():\u001b[37m\u001b[39;49;00m\n",
      "            use_gpu = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      " \u001b[37m\u001b[39;49;00m\n",
      "            imageData = Image.open(pathImageFile).convert(\u001b[33m'\u001b[39;49;00m\u001b[33mRGB\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            imageData = \u001b[36mself\u001b[39;49;00m.transformSequence(imageData)\u001b[37m\u001b[39;49;00m\n",
      "            imageData = imageData.unsqueeze_(\u001b[34m0\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "                imageData = imageData\u001b[37m\u001b[39;49;00m\n",
      "            l = \u001b[36mself\u001b[39;49;00m.model(imageData)\u001b[37m\u001b[39;49;00m\n",
      "            output = \u001b[36mself\u001b[39;49;00m.model.module.densenet121.features(imageData)\u001b[37m\u001b[39;49;00m\n",
      "            label = class_names[torch.max(l,\u001b[34m1\u001b[39;49;00m)[\u001b[34m1\u001b[39;49;00m]]\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[37m#---- Generate heatmap\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            heatmap = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m (\u001b[34m0\u001b[39;49;00m, \u001b[36mlen\u001b[39;49;00m(\u001b[36mself\u001b[39;49;00m.weights)):\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[36mmap\u001b[39;49;00m = output[\u001b[34m0\u001b[39;49;00m,i,:,:]\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34mif\u001b[39;49;00m i == \u001b[34m0\u001b[39;49;00m: heatmap = \u001b[36mself\u001b[39;49;00m.weights[i] * \u001b[36mmap\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "                \u001b[34melse\u001b[39;49;00m: heatmap += \u001b[36mself\u001b[39;49;00m.weights[i] * \u001b[36mmap\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "                npHeatmap = heatmap.cpu().data.numpy()\u001b[37m\u001b[39;49;00m\n",
      "        cam = npHeatmap / np.max(npHeatmap)\u001b[37m\u001b[39;49;00m\n",
      "        cam = cv2.resize(cam, (transCrop, transCrop))\u001b[37m\u001b[39;49;00m\n",
      "        heatmap = cv2.applyColorMap(np.uint8(\u001b[34m255\u001b[39;49;00m*cam), cv2.COLORMAP_JET)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m#---- Blend original and heatmap \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        temp = heatmap.copy()\u001b[37m\u001b[39;49;00m\n",
      "        img = cv2.cvtColor(temp, cv2.COLOR_BGR2GRAY)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m(img.min())\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# print(img.shape)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        img = (img/\u001b[34m1\u001b[39;49;00m).astype(\u001b[33m'\u001b[39;49;00m\u001b[33muint8\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        binary = threshold(\u001b[34m200\u001b[39;49;00m,\u001b[34m255\u001b[39;49;00m,img)\u001b[37m\u001b[39;49;00m\n",
      "        binary = binary.astype(np.int32)\u001b[37m\u001b[39;49;00m\n",
      "        contours, _ = cv2.findContours(binary,cv2.RETR_FLOODFILL, cv2.CHAIN_APPROX_SIMPLE) \u001b[37m\u001b[39;49;00m\n",
      "        multi_data = []\u001b[37m\u001b[39;49;00m\n",
      "        multidata_dict={}\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mwith\u001b[39;49;00m \u001b[36mopen\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mROIFormat.txt\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m) \u001b[34mas\u001b[39;49;00m json_file:\u001b[37m\u001b[39;49;00m\n",
      "            data_orig = json.load(json_file)\u001b[37m\u001b[39;49;00m\n",
      "        coords = get_1D_coord(contours)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mfor\u001b[39;49;00m coord \u001b[35min\u001b[39;49;00m coords:\u001b[37m\u001b[39;49;00m\n",
      "            data_final = get_json(data = data_orig, coord=coord)\u001b[37m\u001b[39;49;00m\n",
      "            multi_data.append(deepcopy(data_final[\u001b[33m'\u001b[39;49;00m\u001b[33mallTools\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m][\u001b[34m0\u001b[39;49;00m]))\u001b[37m\u001b[39;49;00m\n",
      "        multidata_dict[\u001b[33m'\u001b[39;49;00m\u001b[33mallTools\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m] = multi_data\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mFINAL JSON\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mprint\u001b[39;49;00m(multidata_dict)\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         imgOriginal = cv2.imread(pathImageFile, 1)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         imgOriginal = cv2.resize(imgOriginal, (transCrop, transCrop))\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         img = cv2.addWeighted(imgOriginal,1,heatmap,0.35,0)            \u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.title(label)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.imshow(img)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.plot()\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.axis('off')\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.savefig(pathOutputFile)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m#         plt.show()\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mfind_file_path\u001b[39;49;00m(filename):\u001b[37m\u001b[39;49;00m\n",
      "    file_path = \u001b[34mNone\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m path \u001b[35min\u001b[39;49;00m glob.glob(\u001b[33m'\u001b[39;49;00m\u001b[33m**/\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + filename, recursive=\u001b[34mTrue\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[34mif\u001b[39;49;00m file_path \u001b[35mis\u001b[39;49;00m \u001b[34mNone\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "            file_path = os.path.abspath(path)\u001b[37m\u001b[39;49;00m\n",
      "            \u001b[34mbreak\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mreturn\u001b[39;49;00m file_path\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mdef\u001b[39;49;00m \u001b[32mrun\u001b[39;49;00m(args):\u001b[37m\u001b[39;49;00m\n",
      "    batch_size = \u001b[34m128\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    traindir = args.data_dir_train + \u001b[33m\"\u001b[39;49;00m\u001b[33m/\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m#datadir + '/train/'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    validdir = args.data_dir_val + \u001b[33m\"\u001b[39;49;00m\u001b[33m/\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m#datadir + '/val/'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    testdir = args.data_dir_test + \u001b[33m\"\u001b[39;49;00m\u001b[33m/\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m \u001b[37m#datadir + '/test/'\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "    pathFileTrain = args.data_dir_train\u001b[37m\u001b[39;49;00m\n",
      "    pathFileValid = args.data_dir_val\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Neural network parameters:\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    nnIsTrained = \u001b[34mFalse\u001b[39;49;00m                 \u001b[37m#pre-trained using ImageNet\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    nnClassCount = \u001b[34m14\u001b[39;49;00m                   \u001b[37m#dimension of the output\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Training settings: batch size, maximum number of epochs\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    trBatchSize = \u001b[34m64\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    trMaxEpoch = \u001b[34m1\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Parameters related to image transforms: size of the down-scaled image, cropped image\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    imgtransResize = (\u001b[34m320\u001b[39;49;00m, \u001b[34m320\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    imgtransCrop = \u001b[34m224\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# Class names\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    class_names = [\u001b[33m'\u001b[39;49;00m\u001b[33mNo Finding\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEnlarged Cardiomediastinum\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mCardiomegaly\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mLung Opacity\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[37m\u001b[39;49;00m\n",
      "                \u001b[33m'\u001b[39;49;00m\u001b[33mLung Lesion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mEdema\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mConsolidation\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumonia\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mAtelectasis\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPneumothorax\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[37m\u001b[39;49;00m\n",
      "                \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Effusion\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mPleural Other\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mFracture\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m, \u001b[33m'\u001b[39;49;00m\u001b[33mSupport Devices\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m]\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#TRANSFORM DATA\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    normalize = transforms.Normalize([\u001b[34m0.485\u001b[39;49;00m, \u001b[34m0.456\u001b[39;49;00m, \u001b[34m0.406\u001b[39;49;00m], [\u001b[34m0.229\u001b[39;49;00m, \u001b[34m0.224\u001b[39;49;00m, \u001b[34m0.225\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    transformList = []\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#transformList.append(transforms.Resize(imgtransCrop))\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    transformList.append(transforms.RandomResizedCrop(imgtransCrop))\u001b[37m\u001b[39;49;00m\n",
      "    transformList.append(transforms.RandomHorizontalFlip())\u001b[37m\u001b[39;49;00m\n",
      "    transformList.append(transforms.ToTensor())\u001b[37m\u001b[39;49;00m\n",
      "    transformList.append(normalize)      \u001b[37m\u001b[39;49;00m\n",
      "    transformSequence=transforms.Compose(transformList)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m#LOAD DATASET\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# cat_df = preops(traindir,testdir,validdir)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    nnClassCount = \u001b[36mlen\u001b[39;49;00m(class_names)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# data,dataloaders = create_dataloader(traindir,testdir,validdir,batch_size)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    dataset = CheXpertDataSet(pathFileTrain ,transformSequence, policy=\u001b[33m\"\u001b[39;49;00m\u001b[33mones\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# datasetTest, datasetTrain = random_split(dataset, [len(dataset), len(dataset)])\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    datasetTest = dataset\u001b[37m\u001b[39;49;00m\n",
      "    datasetTrain = dataset\u001b[37m\u001b[39;49;00m\n",
      "    datasetValid = CheXpertDataSet(pathFileValid, transformSequence)            \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# dataLoaderTrain = dataloaders[\"train\"]#DataLoader(dataset=datasetTrain, batch_size=trBatchSize, shuffle=True,  num_workers=24, pin_memory=True)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# dataLoaderVal = dataloaders[\"val\"]#DataLoader(dataset=datasetValid, batch_size=trBatchSize, shuffle=False, num_workers=24, pin_memory=True)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# dataLoaderTest = dataloaders[\"test\"]#DataLoader(dataset=datasetTest, num_workers=24, pin_memory=True)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    dataLoaderTrain = DataLoader(dataset=datasetTrain, batch_size=trBatchSize, shuffle=\u001b[34mTrue\u001b[39;49;00m,  num_workers=\u001b[34m24\u001b[39;49;00m, pin_memory=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    dataLoaderVal = DataLoader(dataset=datasetValid, batch_size=trBatchSize, shuffle=\u001b[34mFalse\u001b[39;49;00m, num_workers=\u001b[34m24\u001b[39;49;00m, pin_memory=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    dataLoaderTest = DataLoader(dataset=datasetTest, num_workers=\u001b[34m24\u001b[39;49;00m, pin_memory=\u001b[34mTrue\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m# initialize and load the model\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    use_gpu = torch.cuda.is_available()\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mif\u001b[39;49;00m use_gpu:\u001b[37m\u001b[39;49;00m\n",
      "        model = ChexNet().cuda()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# model = torch.nn.DataParallel(model).cuda()\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34melse\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "        model = ChexNet()\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m# model = torch.nn.DataParallel(model)\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    timestampTime = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mH\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mM\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    timestampDate = time.strftime(\u001b[33m\"\u001b[39;49;00m\u001b[33m%d\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mm\u001b[39;49;00m\u001b[33m%\u001b[39;49;00m\u001b[33mY\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    timestampLaunch = timestampDate + \u001b[33m'\u001b[39;49;00m\u001b[33m-\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m + timestampTime\u001b[37m\u001b[39;49;00m\n",
      "    save_path = args.model_dir\u001b[37m\u001b[39;49;00m\n",
      "    path = find_file_path(\u001b[33m'\u001b[39;49;00m\u001b[33mchexnet.h5\u001b[39;49;00m\u001b[33m'\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    batch, losst, losse = CheXpertTrainer.train(model, dataLoaderTrain, dataLoaderVal, nnClassCount, trMaxEpoch, timestampLaunch, path, save_path)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(\u001b[33m\"\u001b[39;49;00m\u001b[33mModel trained\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    losstn = []\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[34mfor\u001b[39;49;00m i \u001b[35min\u001b[39;49;00m \u001b[36mrange\u001b[39;49;00m(\u001b[34m0\u001b[39;49;00m, \u001b[36mlen\u001b[39;49;00m(losst), \u001b[34m35\u001b[39;49;00m):\u001b[37m\u001b[39;49;00m\n",
      "        losstn.append(np.mean(losst[i:i+\u001b[34m35\u001b[39;49;00m]))\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(losstn)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[36mprint\u001b[39;49;00m(losse)\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "\u001b[34mif\u001b[39;49;00m \u001b[31m__name__\u001b[39;49;00m == \u001b[33m\"\u001b[39;49;00m\u001b[33m__main__\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m:\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    parser = argparse.ArgumentParser()\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--batch-size\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        default=\u001b[34m64\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        help=\u001b[33m\"\u001b[39;49;00m\u001b[33minput batch size for training (default: 64)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--test-batch-size\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        default=\u001b[34m1000\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        help=\u001b[33m\"\u001b[39;49;00m\u001b[33minput batch size for testing (default: 1000)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--epochs\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        default=\u001b[34m10\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        help=\u001b[33m\"\u001b[39;49;00m\u001b[33mnumber of epochs to train (default: 10)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--lr\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mfloat\u001b[39;49;00m, default=\u001b[34m0.01\u001b[39;49;00m, metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mLR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mlearning rate (default: 0.01)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--momentum\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mfloat\u001b[39;49;00m, default=\u001b[34m0.5\u001b[39;49;00m, metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mM\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mSGD momentum (default: 0.5)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "   \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--seed\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=\u001b[34m1\u001b[39;49;00m, metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, help=\u001b[33m\"\u001b[39;49;00m\u001b[33mrandom seed (default: 1)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--log-interval\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        default=\u001b[34m100\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        metavar=\u001b[33m\"\u001b[39;49;00m\u001b[33mN\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        help=\u001b[33m\"\u001b[39;49;00m\u001b[33mhow many batches to wait before logging training status\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[33m\"\u001b[39;49;00m\u001b[33m--backend\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        default=\u001b[34mNone\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "        help=\u001b[33m\"\u001b[39;49;00m\u001b[33mbackend for distributed training (tcp, gloo on cpu and gloo, nccl on gpu)\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m,\u001b[37m\u001b[39;49;00m\n",
      "    )\u001b[37m\u001b[39;49;00m\n",
      "\u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--hosts\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mlist\u001b[39;49;00m, default=json.loads(os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_HOSTS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m]))\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--current-host\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CURRENT_HOST\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--model-dir\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_MODEL_DIR\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--data-dir-train\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CHANNEL_TRAINING\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--data-dir-test\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CHANNEL_TESTING\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--data-dir-val\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mstr\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_CHANNEL_VALIDATING\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    parser.add_argument(\u001b[33m\"\u001b[39;49;00m\u001b[33m--num-gpus\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m, \u001b[36mtype\u001b[39;49;00m=\u001b[36mint\u001b[39;49;00m, default=os.environ[\u001b[33m\"\u001b[39;49;00m\u001b[33mSM_NUM_GPUS\u001b[39;49;00m\u001b[33m\"\u001b[39;49;00m])\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    args = parser.parse_args()\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n",
      "    run(args)\u001b[37m\u001b[39;49;00m\n",
      "    \u001b[37m\u001b[39;49;00m\n"
     ]
    }
   ],
   "source": [
    "!pygmentize code/classifier.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Run training in SageMaker\n",
    "\n",
    "The `PyTorch` class allows us to run our training function as a training job on SageMaker infrastructure. We need to configure it with our training script, an IAM role, the number of training instances, the training instance type, and hyperparameters. In this case we are going to run our training job on 2 ```ml.c4.xlarge``` instances. But this example can be ran on one or multiple, cpu or gpu instances ([full list of available instances](https://aws.amazon.com/sagemaker/pricing/instance-types/)). The hyperparameters parameter is a dict of values that will be passed to your training script -- you can see how to access these values in the `classifier.py` script above.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Collecting sagemaker==2.152.0\n",
      "  Downloading sagemaker-2.152.0.tar.gz (751 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m751.1/751.1 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25h  Preparing metadata (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25hCollecting attrs<23,>=20.3.0 (from sagemaker==2.152.0)\n",
      "  Downloading attrs-22.2.0-py3-none-any.whl (60 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m60.0/60.0 kB\u001b[0m \u001b[31m1.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: boto3<2.0,>=1.26.28 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (1.26.154)\n",
      "Requirement already satisfied: cloudpickle==2.2.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (2.2.1)\n",
      "Requirement already satisfied: google-pasta in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (0.2.0)\n",
      "Requirement already satisfied: numpy<2.0,>=1.9.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (1.21.6)\n",
      "Requirement already satisfied: protobuf<4.0,>=3.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (3.20.3)\n",
      "Requirement already satisfied: protobuf3-to-dict<1.0,>=0.1.5 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (0.1.5)\n",
      "Requirement already satisfied: smdebug_rulesconfig==1.0.1 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (1.0.1)\n",
      "Collecting importlib-metadata<5.0,>=1.4.0 (from sagemaker==2.152.0)\n",
      "  Downloading importlib_metadata-4.13.0-py3-none-any.whl (23 kB)\n",
      "Requirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (20.1)\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (1.3.5)\n",
      "Requirement already satisfied: pathos in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (0.3.0)\n",
      "Requirement already satisfied: schema in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (0.7.5)\n",
      "Collecting PyYAML==5.4.1 (from sagemaker==2.152.0)\n",
      "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m636.6/636.6 kB\u001b[0m \u001b[31m10.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m\n",
      "\u001b[?25hRequirement already satisfied: jsonschema in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (3.2.0)\n",
      "Requirement already satisfied: platformdirs in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (3.5.3)\n",
      "Requirement already satisfied: tblib==1.7.0 in /opt/conda/lib/python3.7/site-packages (from sagemaker==2.152.0) (1.7.0)\n",
      "Requirement already satisfied: botocore<1.30.0,>=1.29.154 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker==2.152.0) (1.29.154)\n",
      "Requirement already satisfied: jmespath<2.0.0,>=0.7.1 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker==2.152.0) (1.0.1)\n",
      "Requirement already satisfied: s3transfer<0.7.0,>=0.6.0 in /opt/conda/lib/python3.7/site-packages (from boto3<2.0,>=1.26.28->sagemaker==2.152.0) (0.6.1)\n",
      "Requirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker==2.152.0) (2.2.0)\n",
      "Requirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.7/site-packages (from importlib-metadata<5.0,>=1.4.0->sagemaker==2.152.0) (4.6.3)\n",
      "Requirement already satisfied: pyparsing>=2.0.2 in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker==2.152.0) (2.4.6)\n",
      "Requirement already satisfied: six in /opt/conda/lib/python3.7/site-packages (from packaging>=20.0->sagemaker==2.152.0) (1.14.0)\n",
      "Requirement already satisfied: pyrsistent>=0.14.0 in /opt/conda/lib/python3.7/site-packages (from jsonschema->sagemaker==2.152.0) (0.15.7)\n",
      "Requirement already satisfied: setuptools in /opt/conda/lib/python3.7/site-packages (from jsonschema->sagemaker==2.152.0) (65.5.1)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker==2.152.0) (2.8.2)\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas->sagemaker==2.152.0) (2019.3)\n",
      "Requirement already satisfied: ppft>=1.7.6.6 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker==2.152.0) (1.7.6.6)\n",
      "Requirement already satisfied: dill>=0.3.6 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker==2.152.0) (0.3.6)\n",
      "Requirement already satisfied: pox>=0.3.2 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker==2.152.0) (0.3.2)\n",
      "Requirement already satisfied: multiprocess>=0.70.14 in /opt/conda/lib/python3.7/site-packages (from pathos->sagemaker==2.152.0) (0.70.14)\n",
      "Requirement already satisfied: contextlib2>=0.5.5 in /opt/conda/lib/python3.7/site-packages (from schema->sagemaker==2.152.0) (0.6.0.post1)\n",
      "Collecting urllib3<1.27,>=1.25.4 (from botocore<1.30.0,>=1.29.154->boto3<2.0,>=1.26.28->sagemaker==2.152.0)\n",
      "  Downloading urllib3-1.26.16-py2.py3-none-any.whl (143 kB)\n",
      "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m143.1/143.1 kB\u001b[0m \u001b[31m2.6 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0ma \u001b[36m0:00:01\u001b[0m\n",
      "\u001b[?25hBuilding wheels for collected packages: sagemaker\n",
      "  Building wheel for sagemaker (setup.py) ... \u001b[?25ldone\n",
      "\u001b[?25h  Created wheel for sagemaker: filename=sagemaker-2.152.0-py2.py3-none-any.whl size=1007493 sha256=802694518fa24102545b8bd7e076c878da846e423cfff5fcec65dcb3ad00f524\n",
      "  Stored in directory: /root/.cache/pip/wheels/e5/5b/a4/ad61c362425ff3ffeb666f9dc85274cb0d6271f7d897c976df\n",
      "Successfully built sagemaker\n",
      "Installing collected packages: urllib3, PyYAML, importlib-metadata, attrs, sagemaker\n",
      "  Attempting uninstall: urllib3\n",
      "    Found existing installation: urllib3 2.0.3\n",
      "    Uninstalling urllib3-2.0.3:\n",
      "      Successfully uninstalled urllib3-2.0.3\n",
      "  Attempting uninstall: PyYAML\n",
      "    Found existing installation: PyYAML 6.0\n",
      "    Uninstalling PyYAML-6.0:\n",
      "      Successfully uninstalled PyYAML-6.0\n",
      "  Attempting uninstall: importlib-metadata\n",
      "    Found existing installation: importlib-metadata 6.6.0\n",
      "    Uninstalling importlib-metadata-6.6.0:\n",
      "      Successfully uninstalled importlib-metadata-6.6.0\n",
      "  Attempting uninstall: attrs\n",
      "    Found existing installation: attrs 23.1.0\n",
      "    Uninstalling attrs-23.1.0:\n",
      "      Successfully uninstalled attrs-23.1.0\n",
      "  Attempting uninstall: sagemaker\n",
      "    Found existing installation: sagemaker 2.165.0\n",
      "    Uninstalling sagemaker-2.165.0:\n",
      "      Successfully uninstalled sagemaker-2.165.0\n",
      "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
      "pytest-astropy 0.8.0 requires pytest-cov>=2.0, which is not installed.\n",
      "pytest-astropy 0.8.0 requires pytest-filter-subpackage>=0.1, which is not installed.\n",
      "spyder 4.0.1 requires pyqt5<5.13; python_version >= \"3\", which is not installed.\n",
      "spyder 4.0.1 requires pyqtwebengine<5.13; python_version >= \"3\", which is not installed.\n",
      "awscli 1.27.154 requires rsa<4.8,>=3.1.2, but you have rsa 4.9 which is incompatible.\n",
      "python-language-server 0.31.7 requires jedi<0.16,>=0.14.1, but you have jedi 0.18.2 which is incompatible.\n",
      "python-language-server 0.31.7 requires ujson<=1.35; platform_system != \"Windows\", but you have ujson 5.7.0 which is incompatible.\n",
      "sparkmagic 0.20.4 requires nest-asyncio==1.5.5, but you have nest-asyncio 1.5.6 which is incompatible.\n",
      "spyder 4.0.1 requires jedi==0.14.1, but you have jedi 0.18.2 which is incompatible.\u001b[0m\u001b[31m\n",
      "\u001b[0mSuccessfully installed PyYAML-5.4.1 attrs-22.2.0 importlib-metadata-4.13.0 sagemaker-2.152.0 urllib3-1.26.16\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\u001b[33m\n",
      "\u001b[0m"
     ]
    }
   ],
   "source": [
    "!pip install --upgrade sagemaker==2.152.0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.pytorch import PyTorch"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Version: 2.152.0\n"
     ]
    }
   ],
   "source": [
    "!pip show sagemaker | grep Version"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PLEASE CHECK PRICING HERE : https://aws.amazon.com/sagemaker/pricing/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sagemaker.pytorch import PyTorch\n",
    "\n",
    "estimator = PyTorch(\n",
    "    entry_point=\"classifier.py\",\n",
    "    role=role,\n",
    "    py_version=\"py38\",\n",
    "    framework_version=\"1.12.0\",\n",
    "    instance_count=1,\n",
    "    instance_type=\"ml.m5.large\",\n",
    "    # instance_type=\"ml.g4dn.xlarge\",\n",
    "    hyperparameters={\"epochs\": 1, \"backend\": \"gloo\"},\n",
    "    dependencies=['code/requirements.txt'],\n",
    "    source_dir = \"code\",\n",
    "    max_run=86400,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker.image_uris:image_uri is not presented, retrieving image_uri based on instance_type, framework etc.\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using provided s3_resource\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker.image_uris:image_uri is not presented, retrieving image_uri based on instance_type, framework etc.\n",
      "INFO:sagemaker:Creating training-job with name: pytorch-training-2023-06-27-07-20-10-603\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2023-06-27 07:20:12 Starting - Starting the training job...\n",
      "2023-06-27 07:20:27 Starting - Preparing the instances for training......\n",
      "2023-06-27 07:21:17 Downloading - Downloading input data...\n",
      "2023-06-27 07:22:02 Training - Downloading the training image...\n",
      "2023-06-27 07:22:27 Training - Training image download completed. Training in progress..\u001b[34mbash: cannot set terminal process group (-1): Inappropriate ioctl for device\u001b[0m\n",
      "\u001b[34mbash: no job control in this shell\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:35,364 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:35,366 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:35,374 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:35,376 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:36,032 sagemaker-training-toolkit INFO     Installing dependencies from requirements.txt:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.8 -m pip install -r requirements.txt\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: requests in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 1)) (2.28.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: pillow in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 2)) (9.2.0)\u001b[0m\n",
      "\u001b[34mCollecting pydicom\u001b[0m\n",
      "\u001b[34mDownloading pydicom-2.4.1-py3-none-any.whl (1.8 MB)\u001b[0m\n",
      "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.8/1.8 MB 57.1 MB/s eta 0:00:00\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: numpy in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 4)) (1.22.2)\u001b[0m\n",
      "\u001b[34mCollecting torchsummary\u001b[0m\n",
      "\u001b[34mDownloading torchsummary-1.5.1-py3-none-any.whl (2.8 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: scikit-learn in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 6)) (1.1.2)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: pandas in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 7)) (1.4.3)\u001b[0m\n",
      "\u001b[34mCollecting opencv-python-headless\u001b[0m\n",
      "\u001b[34mDownloading opencv_python_headless-4.7.0.72-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (49.2 MB)\u001b[0m\n",
      "\u001b[34m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 49.2/49.2 MB 31.5 MB/s eta 0:00:00\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: matplotlib in /opt/conda/lib/python3.8/site-packages (from -r requirements.txt (line 9)) (3.5.3)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.8/site-packages (from requests->-r requirements.txt (line 1)) (2022.6.15)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: urllib3<1.27,>=1.21.1 in /opt/conda/lib/python3.8/site-packages (from requests->-r requirements.txt (line 1)) (1.26.11)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: idna<4,>=2.5 in /opt/conda/lib/python3.8/site-packages (from requests->-r requirements.txt (line 1)) (3.3)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: charset-normalizer<3,>=2 in /opt/conda/lib/python3.8/site-packages (from requests->-r requirements.txt (line 1)) (2.1.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: joblib>=1.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->-r requirements.txt (line 6)) (1.1.0)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: scipy>=1.3.2 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->-r requirements.txt (line 6)) (1.9.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: threadpoolctl>=2.0.0 in /opt/conda/lib/python3.8/site-packages (from scikit-learn->-r requirements.txt (line 6)) (3.1.0)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: python-dateutil>=2.8.1 in /opt/conda/lib/python3.8/site-packages (from pandas->-r requirements.txt (line 7)) (2.8.2)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: pytz>=2020.1 in /opt/conda/lib/python3.8/site-packages (from pandas->-r requirements.txt (line 7)) (2022.2.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: fonttools>=4.22.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 9)) (4.37.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: kiwisolver>=1.0.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 9)) (1.4.4)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: pyparsing>=2.2.1 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 9)) (3.0.9)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: packaging>=20.0 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 9)) (21.3)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: cycler>=0.10 in /opt/conda/lib/python3.8/site-packages (from matplotlib->-r requirements.txt (line 9)) (0.11.0)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: six>=1.5 in /opt/conda/lib/python3.8/site-packages (from python-dateutil>=2.8.1->pandas->-r requirements.txt (line 7)) (1.16.0)\u001b[0m\n",
      "\u001b[34mInstalling collected packages: torchsummary, pydicom, opencv-python-headless\u001b[0m\n",
      "\u001b[34mSuccessfully installed opencv-python-headless-4.7.0.72 pydicom-2.4.1 torchsummary-1.5.1\u001b[0m\n",
      "\u001b[34mWARNING: 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",
      "\u001b[34m[notice] A new release of pip available: 22.2.2 -> 23.1.2\u001b[0m\n",
      "\u001b[34m[notice] To update, run: pip install --upgrade pip\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,514 sagemaker-training-toolkit INFO     Waiting for the process to finish and give a return code.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,514 sagemaker-training-toolkit INFO     Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,518 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,528 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,538 sagemaker-training-toolkit INFO     No GPUs detected (normal if no gpus installed)\u001b[0m\n",
      "\u001b[34m2023-06-27 07:22:40,547 sagemaker-training-toolkit INFO     Invoking user script\u001b[0m\n",
      "\u001b[34mTraining Env:\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"testing\": \"/opt/ml/input/data/testing\",\n",
      "        \"training\": \"/opt/ml/input/data/training\",\n",
      "        \"validating\": \"/opt/ml/input/data/validating\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"current_instance_group\": \"homogeneousCluster\",\n",
      "    \"current_instance_group_hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"current_instance_type\": \"ml.m5.xlarge\",\n",
      "    \"distribution_hosts\": [],\n",
      "    \"distribution_instance_groups\": [],\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"backend\": \"gloo\",\n",
      "        \"epochs\": 1\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"testing\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"training\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        },\n",
      "        \"validating\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"instance_groups\": [\n",
      "        \"homogeneousCluster\"\n",
      "    ],\n",
      "    \"instance_groups_dict\": {\n",
      "        \"homogeneousCluster\": {\n",
      "            \"instance_group_name\": \"homogeneousCluster\",\n",
      "            \"instance_type\": \"ml.m5.xlarge\",\n",
      "            \"hosts\": [\n",
      "                \"algo-1\"\n",
      "            ]\n",
      "        }\n",
      "    },\n",
      "    \"is_hetero\": false,\n",
      "    \"is_master\": true,\n",
      "    \"is_modelparallel_enabled\": null,\n",
      "    \"job_name\": \"pytorch-training-2023-06-27-07-20-10-603\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-ap-south-1-023180687239/pytorch-training-2023-06-27-07-20-10-603/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"classifier\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 0,\n",
      "    \"output_data_dir\": \"/opt/ml/output/data\",\n",
      "    \"output_dir\": \"/opt/ml/output\",\n",
      "    \"output_intermediate_dir\": \"/opt/ml/output/intermediate\",\n",
      "    \"resource_config\": {\n",
      "        \"current_host\": \"algo-1\",\n",
      "        \"current_instance_type\": \"ml.m5.xlarge\",\n",
      "        \"current_group_name\": \"homogeneousCluster\",\n",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"instance_groups\": [\n",
      "            {\n",
      "                \"instance_group_name\": \"homogeneousCluster\",\n",
      "                \"instance_type\": \"ml.m5.xlarge\",\n",
      "                \"hosts\": [\n",
      "                    \"algo-1\"\n",
      "                ]\n",
      "            }\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"classifier.py\"\u001b[0m\n",
      "\u001b[34m}\u001b[0m\n",
      "\u001b[34mEnvironment variables:\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"backend\":\"gloo\",\"epochs\":1}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=classifier.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.xlarge\"}],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validating\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"testing\",\"training\",\"validating\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_TYPE=ml.m5.xlarge\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP=homogeneousCluster\u001b[0m\n",
      "\u001b[34mSM_CURRENT_INSTANCE_GROUP_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS=[\"homogeneousCluster\"]\u001b[0m\n",
      "\u001b[34mSM_INSTANCE_GROUPS_DICT={\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.xlarge\"}}\u001b[0m\n",
      "\u001b[34mSM_DISTRIBUTION_INSTANCE_GROUPS=[]\u001b[0m\n",
      "\u001b[34mSM_IS_HETERO=false\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=classifier\u001b[0m\n",
      "\u001b[34mSM_LOG_LEVEL=20\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_MODULE=sagemaker_pytorch_container.training:main\u001b[0m\n",
      "\u001b[34mSM_INPUT_DIR=/opt/ml/input\u001b[0m\n",
      "\u001b[34mSM_INPUT_CONFIG_DIR=/opt/ml/input/config\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DIR=/opt/ml/output\u001b[0m\n",
      "\u001b[34mSM_NUM_CPUS=4\u001b[0m\n",
      "\u001b[34mSM_NUM_GPUS=0\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-ap-south-1-023180687239/pytorch-training-2023-06-27-07-20-10-603/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"testing\":\"/opt/ml/input/data/testing\",\"training\":\"/opt/ml/input/data/training\",\"validating\":\"/opt/ml/input/data/validating\"},\"current_host\":\"algo-1\",\"current_instance_group\":\"homogeneousCluster\",\"current_instance_group_hosts\":[\"algo-1\"],\"current_instance_type\":\"ml.m5.xlarge\",\"distribution_hosts\":[],\"distribution_instance_groups\":[],\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"backend\":\"gloo\",\"epochs\":1},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"testing\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"},\"validating\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"instance_groups\":[\"homogeneousCluster\"],\"instance_groups_dict\":{\"homogeneousCluster\":{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.xlarge\"}},\"is_hetero\":false,\"is_master\":true,\"is_modelparallel_enabled\":null,\"job_name\":\"pytorch-training-2023-06-27-07-20-10-603\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-ap-south-1-023180687239/pytorch-training-2023-06-27-07-20-10-603/source/sourcedir.tar.gz\",\"module_name\":\"classifier\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":0,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_group_name\":\"homogeneousCluster\",\"current_host\":\"algo-1\",\"current_instance_type\":\"ml.m5.xlarge\",\"hosts\":[\"algo-1\"],\"instance_groups\":[{\"hosts\":[\"algo-1\"],\"instance_group_name\":\"homogeneousCluster\",\"instance_type\":\"ml.m5.xlarge\"}],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"classifier.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--backend\",\"gloo\",\"--epochs\",\"1\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TESTING=/opt/ml/input/data/testing\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_VALIDATING=/opt/ml/input/data/validating\u001b[0m\n",
      "\u001b[34mSM_HP_BACKEND=gloo\u001b[0m\n",
      "\u001b[34mSM_HP_EPOCHS=1\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python38.zip:/opt/conda/lib/python3.8:/opt/conda/lib/python3.8/lib-dynload:/opt/conda/lib/python3.8/site-packages:/opt/conda/lib/python3.8/site-packages/smdebug-1.0.19b20220829-py3.8.egg:/opt/conda/lib/python3.8/site-packages/pyinstrument-3.4.2-py3.8.egg:/opt/conda/lib/python3.8/site-packages/pyinstrument_cext-0.2.4-py3.8-linux-x86_64.egg\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.8 classifier.py --backend gloo --epochs 1\u001b[0m\n",
      "\u001b[34m/opt/conda/lib/python3.8/site-packages/torchvision/models/_utils.py:135: UserWarning: Using 'weights' as positional parameter(s) is deprecated since 0.13 and will be removed in 0.15. Please use keyword parameter(s) instead.\n",
      "  warnings.warn(\u001b[0m\n",
      "\u001b[34m/opt/conda/lib/python3.8/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=None`.\n",
      "  warnings.warn(msg)\u001b[0m\n",
      "\u001b[34m>>>>>>>>>>>>>>>>>>> MODEL LOADED SUCCESSFULLY >>>>>>>>>>>>>>\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.200 algo-1:40 INFO utils.py:27] RULE_JOB_STOP_SIGNAL_FILENAME: None\u001b[0m\n",
      "\u001b[34m/opt/conda/lib/python3.8/site-packages/smdebug-1.0.19b20220829-py3.8.egg/smdebug/profiler/system_metrics_reader.py:63: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\u001b[0m\n",
      "\u001b[34m/opt/conda/lib/python3.8/site-packages/smdebug-1.0.19b20220829-py3.8.egg/smdebug/profiler/system_metrics_reader.py:63: SyntaxWarning: \"is not\" with a literal. Did you mean \"!=\"?\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.367 algo-1:40 INFO profiler_config_parser.py:111] User has disabled profiler.\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.368 algo-1:40 INFO json_config.py:91] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.369 algo-1:40 INFO hook.py:201] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.369 algo-1:40 INFO hook.py:254] Saving to /opt/ml/output/tensors\u001b[0m\n",
      "\u001b[34m[2023-06-27 07:22:43.369 algo-1:40 INFO state_store.py:77] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n",
      "\u001b[34m0 % batches computed\u001b[0m\n",
      "\u001b[34m0\u001b[0m\n",
      "\u001b[34m51.18600845336914\u001b[0m\n",
      "\u001b[34m57.70515823364258\u001b[0m\n",
      "\u001b[34mEpoch [1] [----] [27062023-072526] loss= tensor(37.1877)\u001b[0m\n",
      "\u001b[34mModel trained\u001b[0m\n",
      "\u001b[34m[41.05522742638221]\u001b[0m\n",
      "\u001b[34m[57.70515823364258]\u001b[0m\n",
      "\u001b[34m2023-06-27 07:25:27,261 sagemaker-training-toolkit INFO     Waiting for the process to finish and give a return code.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:25:27,261 sagemaker-training-toolkit INFO     Done waiting for a return code. Received 0 from exiting process.\u001b[0m\n",
      "\u001b[34m2023-06-27 07:25:27,261 sagemaker-training-toolkit INFO     Reporting training SUCCESS\u001b[0m\n",
      "\n",
      "2023-06-27 07:25:44 Uploading - Uploading generated training model\n",
      "2023-06-27 07:25:44 Completed - Training job completed\n",
      "Training seconds: 268\n",
      "Billable seconds: 268\n"
     ]
    }
   ],
   "source": [
    "estimator.fit(\n",
    "    {\n",
    "        \"training\":\"s3://\"+bucket+\"/data/train\" ,\n",
    "        \"testing\":\"s3://\"+bucket+\"/data/train\",\n",
    "        \"validating\":\"s3://\"+bucket+\"/data/train\"\n",
    "        }\n",
    "    )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Host your model in Sagemaker\n",
    "### Create endpoint\n",
    "After training, we use the `PyTorch` estimator object to build and deploy a `PyTorchPredictor`. This creates a Sagemaker Endpoint -- a hosted prediction service that we can use to perform inference.\n",
    "\n",
    "As mentioned above we have implementation of `model_fn` in the `classifier.py` script that is required. We are going to use default implementations of `input_fn`, `predict_fn`, `output_fn` and `transform_fm` defined in [sagemaker-pytorch-containers](https://github.com/aws/sagemaker-pytorch-containers).\n",
    "\n",
    "The arguments to the deploy function allow us to set the number and type of instances that will be used for the Endpoint. These do not need to be the same as the values we used for the training job. For example, you can train a model on a set of GPU-based instances, and then deploy the Endpoint to a fleet of CPU-based instances, but you need to make sure that you return or save your model as a cpu model similar to what we did in `classifier.py`. Here we will deploy the model to a single ```ml.m4.xlarge``` instance."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'framework_version': '1.12.0',\n",
       " 'py_version': 'py38',\n",
       " 'instance_count': 1,\n",
       " 'instance_type': 'ml.m5.large',\n",
       " 'keep_alive_period_in_seconds': None,\n",
       " 'instance_groups': None,\n",
       " 'volume_size': 30,\n",
       " 'max_run': 86400,\n",
       " 'input_mode': 'File',\n",
       " 'metric_definitions': None,\n",
       " 'model_uri': None,\n",
       " 'model_channel_name': 'model',\n",
       " 'code_uri': None,\n",
       " 'code_channel_name': 'code',\n",
       " 'source_dir': 'code',\n",
       " 'git_config': None,\n",
       " 'container_log_level': 20,\n",
       " '_hyperparameters': {'epochs': 1,\n",
       "  'backend': 'gloo',\n",
       "  'sagemaker_submit_directory': 's3://sagemaker-ap-south-1-023180687239/pytorch-training-2023-06-27-07-00-46-005/source/sourcedir.tar.gz',\n",
       "  'sagemaker_program': 'classifier.py',\n",
       "  'sagemaker_container_log_level': 20,\n",
       "  'sagemaker_job_name': 'pytorch-training-2023-06-27-07-00-46-005',\n",
       "  'sagemaker_region': 'ap-south-1'},\n",
       " 'code_location': None,\n",
       " 'entry_point': 'classifier.py',\n",
       " 'dependencies': ['code/requirements.txt'],\n",
       " 'uploaded_code': UserCode(s3_prefix='s3://sagemaker-ap-south-1-023180687239/pytorch-training-2023-06-27-07-00-46-005/source/sourcedir.tar.gz', script_name='classifier.py'),\n",
       " 'tags': None,\n",
       " 'sagemaker_session': <sagemaker.session.Session at 0x7f7c888c7fd0>,\n",
       " 'base_job_name': 'pytorch-training',\n",
       " '_current_job_name': 'pytorch-training-2023-06-27-07-00-46-005',\n",
       " 'output_path': 's3://sagemaker-ap-south-1-023180687239/',\n",
       " 'latest_training_job': <sagemaker.estimator._TrainingJob at 0x7f7c888c7e10>,\n",
       " 'jobs': [<sagemaker.estimator._TrainingJob at 0x7f7c888c7e10>],\n",
       " 'deploy_instance_type': None,\n",
       " '_compiled_models': {},\n",
       " 'role': 'arn:aws:iam::023180687239:role/service-role/AmazonSageMaker-ExecutionRole-20220906T142944',\n",
       " 'output_kms_key': None,\n",
       " 'volume_kms_key': None,\n",
       " 'subnets': None,\n",
       " 'security_group_ids': None,\n",
       " 'training_repository_access_mode': None,\n",
       " 'training_repository_credentials_provider_arn': None,\n",
       " 'encrypt_inter_container_traffic': False,\n",
       " 'use_spot_instances': False,\n",
       " 'max_wait': None,\n",
       " 'checkpoint_s3_uri': None,\n",
       " 'checkpoint_local_path': None,\n",
       " 'rules': None,\n",
       " 'debugger_hook_config': <sagemaker.debugger.debugger.DebuggerHookConfig at 0x7f7c88126a90>,\n",
       " 'tensorboard_output_config': None,\n",
       " 'debugger_rule_configs': [],\n",
       " 'collection_configs': set(),\n",
       " 'enable_sagemaker_metrics': True,\n",
       " '_enable_network_isolation': False,\n",
       " 'profiler_config': <sagemaker.debugger.profiler_config.ProfilerConfig at 0x7f7c881268d0>,\n",
       " 'disable_profiler': False,\n",
       " 'environment': None,\n",
       " 'max_retry_attempts': None,\n",
       " 'profiler_rule_configs': [],\n",
       " 'profiler_rules': [],\n",
       " 'debugger_rules': [],\n",
       " 'image_uri': None,\n",
       " 'distribution': {},\n",
       " 'compiler_config': None}"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "estimator.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "sagemaker_job_name = estimator._hyperparameters[\"sagemaker_job_name\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "INFO:sagemaker:Creating model with name: pytorch-training-2023-06-27-07-11-06-277\n",
      "INFO:sagemaker:Creating endpoint-config with name pytorch-training-2023-06-27-07-11-06-277\n",
      "INFO:sagemaker:Creating endpoint with name pytorch-training-2023-06-27-07-11-06-277\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "----!"
     ]
    }
   ],
   "source": [
    "predictor = estimator.deploy(initial_instance_count=1, instance_type=\"ml.c5.2xlarge\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluate\n",
    "\n",
    "You can use the test images to evalute the endpoint. The accuracy of the model depends on how many it is trained. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'endpoint_name': 'pytorch-training-2023-06-27-07-11-06-277',\n",
       " 'sagemaker_session': <sagemaker.session.Session at 0x7f7c888c7fd0>,\n",
       " 'serializer': <sagemaker.serializers.NumpySerializer at 0x7f7c8a1db190>,\n",
       " 'deserializer': <sagemaker.deserializers.NumpyDeserializer at 0x7f7c8a1db1d0>,\n",
       " '_endpoint_config_name': None,\n",
       " '_model_names': None,\n",
       " '_context': None}"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'pytorch-training-2023-06-27-07-11-06-277'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "predictor.endpoint_name "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Invoke endpoint and test your model in Sagemaker"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'ResponseMetadata': {'RequestId': 'a22946e8-216a-4d45-9441-850fac5bd421', 'HTTPStatusCode': 200, 'HTTPHeaders': {'x-amzn-requestid': 'a22946e8-216a-4d45-9441-850fac5bd421', 'x-amzn-invoked-production-variant': 'AllTraffic', 'date': 'Mon, 26 Jun 2023 09:06:28 GMT', 'content-type': 'application/json', 'content-length': '4210', 'connection': 'keep-alive'}, 'RetryAttempts': 0}, 'ContentType': 'application/json', 'InvokedProductionVariant': 'AllTraffic', 'Body': <botocore.response.StreamingBody object at 0x7f215b62bad0>}\n"
     ]
    }
   ],
   "source": [
    "import boto3\n",
    "\n",
    "client = boto3.client('sagemaker-runtime')\n",
    "\n",
    "custom_attributes = \"c000b4f9-df62-4c85-a0bf-7c525f9104a4\"  # An example of a trace ID.\n",
    "endpoint_name = predictor.endpoint_name                                        # Your endpoint name.\n",
    "content_type = \"application/json\"                                        # The MIME type of the input data in the request body.\n",
    "accept = \"application/json\"                                              # The desired MIME type of the inference in the response.\n",
    "payload = json.dumps({\"url\":\"https://storage.googleapis.com/kaggle-datasets-images/17810/23340/c8372ebbe20b0f671c2f3c501ba51412/dataset-cover.jpeg?t=2018-03-24-19-05-18\"})                                           # Payload for inference.\n",
    "response = client.invoke_endpoint(\n",
    "    EndpointName=endpoint_name, \n",
    "    CustomAttributes=custom_attributes, \n",
    "    ContentType=content_type,\n",
    "    Accept=accept,\n",
    "    Body=payload\n",
    "    )\n",
    "\n",
    "print(response)   "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from pprint import pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'Body': <botocore.response.StreamingBody object at 0x7f215b62bad0>,\n",
      " 'ContentType': 'application/json',\n",
      " 'InvokedProductionVariant': 'AllTraffic',\n",
      " 'ResponseMetadata': {'HTTPHeaders': {'connection': 'keep-alive',\n",
      "                                      'content-length': '4210',\n",
      "                                      'content-type': 'application/json',\n",
      "                                      'date': 'Mon, 26 Jun 2023 09:06:28 GMT',\n",
      "                                      'x-amzn-invoked-production-variant': 'AllTraffic',\n",
      "                                      'x-amzn-requestid': 'a22946e8-216a-4d45-9441-850fac5bd421'},\n",
      "                      'HTTPStatusCode': 200,\n",
      "                      'RequestId': 'a22946e8-216a-4d45-9441-850fac5bd421',\n",
      "                      'RetryAttempts': 0}}\n"
     ]
    }
   ],
   "source": [
    "pprint(response) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "r = json.load(response[\"Body\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'response': {'findings': [{'name': 'No Finding', 'probability': 15.9},\n",
       "   {'name': 'Enlarged Cardiomediastinum', 'probability': 6.0},\n",
       "   {'name': 'Cardiomegaly', 'probability': 29.16},\n",
       "   {'name': 'Lung Opacity', 'probability': 48.87},\n",
       "   {'name': 'Lung Lesion', 'probability': 8.38},\n",
       "   {'name': 'Edema', 'probability': 21.4},\n",
       "   {'name': 'Consolidation', 'probability': 13.96},\n",
       "   {'name': 'Pneumonia', 'probability': 47.53},\n",
       "   {'name': 'Atelectasis', 'probability': 21.94},\n",
       "   {'name': 'Pneumothorax', 'probability': 35.23},\n",
       "   {'name': 'Pleural Effusion', 'probability': 28.07},\n",
       "   {'name': 'Pleural Other', 'probability': 3.51},\n",
       "   {'name': 'Fracture', 'probability': 8.78},\n",
       "   {'name': 'Support Devices', 'probability': 3.43},\n",
       "   {'name': 'ROIS', 'probability': '1'}],\n",
       "  'rois': [{'finding_name': 'Abnormality',\n",
       "    'type': 'Freehand',\n",
       "    'points': [[1269.0, 199.0],\n",
       "     [1268.0, 200.0],\n",
       "     [1247.0, 200.0],\n",
       "     [1246.0, 201.0],\n",
       "     [1244.0, 201.0],\n",
       "     [1243.0, 200.0],\n",
       "     [1242.0, 201.0],\n",
       "     [1227.0, 201.0],\n",
       "     [1226.0, 202.0],\n",
       "     [1225.0, 202.0],\n",
       "     [1224.0, 203.0],\n",
       "     [1223.0, 203.0],\n",
       "     [1220.0, 206.0],\n",
       "     [1219.0, 206.0],\n",
       "     [1217.0, 208.0],\n",
       "     [1216.0, 208.0],\n",
       "     [1213.0, 211.0],\n",
       "     [1212.0, 211.0],\n",
       "     [1209.0, 214.0],\n",
       "     [1208.0, 214.0],\n",
       "     [1206.0, 216.0],\n",
       "     [1205.0, 216.0],\n",
       "     [1201.0, 220.0],\n",
       "     [1200.0, 220.0],\n",
       "     [1195.0, 225.0],\n",
       "     [1194.0, 225.0],\n",
       "     [1187.0, 232.0],\n",
       "     [1186.0, 232.0],\n",
       "     [1186.0, 233.0],\n",
       "     [1185.0, 234.0],\n",
       "     [1184.0, 234.0],\n",
       "     [1175.0, 243.0],\n",
       "     [1175.0, 250.0],\n",
       "     [1176.0, 251.0],\n",
       "     [1176.0, 252.0],\n",
       "     [1175.0, 253.0],\n",
       "     [1176.0, 254.0],\n",
       "     [1176.0, 272.0],\n",
       "     [1177.0, 273.0],\n",
       "     [1176.0, 274.0],\n",
       "     [1176.0, 275.0],\n",
       "     [1177.0, 276.0],\n",
       "     [1177.0, 277.0],\n",
       "     [1176.0, 278.0],\n",
       "     [1176.0, 279.0],\n",
       "     [1177.0, 280.0],\n",
       "     [1177.0, 292.0],\n",
       "     [1178.0, 293.0],\n",
       "     [1178.0, 294.0],\n",
       "     [1177.0, 295.0],\n",
       "     [1177.0, 296.0],\n",
       "     [1179.0, 298.0],\n",
       "     [1180.0, 298.0],\n",
       "     [1182.0, 300.0],\n",
       "     [1184.0, 300.0],\n",
       "     [1186.0, 302.0],\n",
       "     [1187.0, 302.0],\n",
       "     [1188.0, 303.0],\n",
       "     [1189.0, 303.0],\n",
       "     [1190.0, 304.0],\n",
       "     [1191.0, 304.0],\n",
       "     [1193.0, 306.0],\n",
       "     [1194.0, 306.0],\n",
       "     [1195.0, 307.0],\n",
       "     [1196.0, 307.0],\n",
       "     [1197.0, 308.0],\n",
       "     [1198.0, 308.0],\n",
       "     [1199.0, 309.0],\n",
       "     [1200.0, 309.0],\n",
       "     [1201.0, 310.0],\n",
       "     [1202.0, 310.0],\n",
       "     [1204.0, 312.0],\n",
       "     [1205.0, 312.0],\n",
       "     [1206.0, 313.0],\n",
       "     [1207.0, 313.0],\n",
       "     [1208.0, 314.0],\n",
       "     [1209.0, 314.0],\n",
       "     [1210.0, 315.0],\n",
       "     [1211.0, 315.0],\n",
       "     [1212.0, 316.0],\n",
       "     [1213.0, 316.0],\n",
       "     [1215.0, 318.0],\n",
       "     [1216.0, 318.0],\n",
       "     [1217.0, 319.0],\n",
       "     [1218.0, 319.0],\n",
       "     [1219.0, 320.0],\n",
       "     [1220.0, 320.0],\n",
       "     [1222.0, 322.0],\n",
       "     [1223.0, 322.0],\n",
       "     [1224.0, 323.0],\n",
       "     [1225.0, 323.0],\n",
       "     [1226.0, 324.0],\n",
       "     [1228.0, 324.0],\n",
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       "     [1234.0, 321.0],\n",
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       "     [1242.0, 318.0],\n",
       "     [1243.0, 317.0],\n",
       "     [1244.0, 317.0],\n",
       "     [1245.0, 316.0],\n",
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       "     [1248.0, 315.0],\n",
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       "     [1250.0, 314.0],\n",
       "     [1251.0, 314.0],\n",
       "     [1252.0, 313.0],\n",
       "     [1253.0, 313.0],\n",
       "     [1254.0, 312.0],\n",
       "     [1255.0, 312.0],\n",
       "     [1256.0, 311.0],\n",
       "     [1258.0, 311.0],\n",
       "     [1259.0, 310.0],\n",
       "     [1260.0, 310.0],\n",
       "     [1261.0, 309.0],\n",
       "     [1262.0, 309.0],\n",
       "     [1263.0, 308.0],\n",
       "     [1264.0, 308.0],\n",
       "     [1265.0, 307.0],\n",
       "     [1266.0, 307.0],\n",
       "     [1267.0, 306.0],\n",
       "     [1269.0, 306.0],\n",
       "     [1271.0, 304.0],\n",
       "     [1272.0, 304.0],\n",
       "     [1273.0, 303.0],\n",
       "     [1275.0, 303.0],\n",
       "     [1276.0, 302.0],\n",
       "     [1277.0, 302.0],\n",
       "     [1278.0, 301.0],\n",
       "     [1279.0, 301.0],\n",
       "     [1280.0, 300.0],\n",
       "     [1281.0, 300.0],\n",
       "     [1281.0, 299.0],\n",
       "     [1282.0, 298.0],\n",
       "     [1282.0, 297.0],\n",
       "     [1283.0, 296.0],\n",
       "     [1283.0, 293.0],\n",
       "     [1284.0, 292.0],\n",
       "     [1284.0, 289.0],\n",
       "     [1285.0, 288.0],\n",
       "     [1285.0, 285.0],\n",
       "     [1286.0, 284.0],\n",
       "     [1286.0, 281.0],\n",
       "     [1287.0, 280.0],\n",
       "     [1287.0, 277.0],\n",
       "     [1288.0, 276.0],\n",
       "     [1288.0, 273.0],\n",
       "     [1289.0, 272.0],\n",
       "     [1289.0, 269.0],\n",
       "     [1290.0, 268.0],\n",
       "     [1290.0, 265.0],\n",
       "     [1291.0, 264.0],\n",
       "     [1291.0, 260.0],\n",
       "     [1292.0, 259.0],\n",
       "     [1292.0, 256.0],\n",
       "     [1293.0, 255.0],\n",
       "     [1293.0, 251.0],\n",
       "     [1294.0, 250.0],\n",
       "     [1294.0, 247.0],\n",
       "     [1295.0, 246.0],\n",
       "     [1295.0, 241.0],\n",
       "     [1294.0, 240.0],\n",
       "     [1294.0, 237.0],\n",
       "     [1293.0, 236.0],\n",
       "     [1293.0, 234.0],\n",
       "     [1292.0, 233.0],\n",
       "     [1292.0, 230.0],\n",
       "     [1291.0, 229.0],\n",
       "     [1291.0, 227.0],\n",
       "     [1290.0, 226.0],\n",
       "     [1290.0, 224.0],\n",
       "     [1289.0, 223.0],\n",
       "     [1289.0, 220.0],\n",
       "     [1288.0, 219.0],\n",
       "     [1288.0, 217.0],\n",
       "     [1287.0, 216.0],\n",
       "     [1287.0, 214.0],\n",
       "     [1286.0, 213.0],\n",
       "     [1286.0, 212.0],\n",
       "     [1285.0, 211.0],\n",
       "     [1285.0, 209.0],\n",
       "     [1284.0, 208.0],\n",
       "     [1284.0, 206.0],\n",
       "     [1283.0, 205.0],\n",
       "     [1283.0, 203.0],\n",
       "     [1282.0, 202.0],\n",
       "     [1282.0, 201.0],\n",
       "     [1281.0, 200.0],\n",
       "     [1281.0, 199.0],\n",
       "     [1273.0, 199.0],\n",
       "     [1272.0, 200.0],\n",
       "     [1270.0, 200.0],\n",
       "     [1269.0, 199.0]]}]}}"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Push code to your S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import boto3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "bucket_name = bucket\n",
    "key = 'artifact/code'\n",
    "from boto3 import client\n",
    "s3 = client('s3')\n",
    "BUCKET_NAME = bucket\n",
    "DIR_NAME = 'code'\n",
    "# Iterate through the files in the directory\n",
    "for root, dirs, files in os.walk(DIR_NAME):\n",
    "    for file in files:\n",
    "        # Construct the full local path of the file\n",
    "        local_path = os.path.join(root, file)\n",
    "        # Construct the full S3 path of the file\n",
    "        s3_path = os.path.join(root.replace(DIR_NAME, key), file)\n",
    "        # Upload the file to S3\n",
    "        s3.upload_file(local_path, BUCKET_NAME, s3_path)\n",
    "        print(f'Uploaded {local_path} to s3://{BUCKET_NAME}/{s3_path}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Push model to your S3 bucket"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "modelbucket = estimator.output_path.split(\"/\")[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "try:\n",
    "    os.mkdir(\"model\")\n",
    "except:\n",
    "    pass\n",
    "BUCKET_NAME = modelbucket\n",
    "s3 = boto3.resource('s3')\n",
    "s3.Bucket(BUCKET_NAME).download_file(sagemaker_job_name + \"/output/model.tar.gz\", \"model/model.tar.gz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "s3 = client('s3')\n",
    "BUCKET_NAME = bucket\n",
    "s3.upload_file(\"model/model.tar.gz\", BUCKET_NAME, \"model/model.tar.gz\")"
   ]
  }
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