01_data_exploration.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "329c2c6b",
   "metadata": {},
   "source": [
    "# **Brain Tumor Radiogenomic Classification** --> 3D CNN with PyTorch"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0b3e6d2",
   "metadata": {},
   "source": [
    "### Outline  \n",
    "1. [Problem Description](#description)    \n",
    "2. [Modeling Approach](#approach)\n",
    "3. [Exploring the data](#exploring)\n",
    "4. [Data Processing & Loading](#process)\n",
    "5. [Training a Pytorch Estimator](#training)\n",
    "6. [Make Predictions](#predict)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e866404b",
   "metadata": {},
   "source": [
    "<a id='description'></a>\n",
    "### Problem Description<sup>[[1]](#1)</sup> \n",
    "A malignant tumor in the brain is a life-threatening condition. Known as glioblastoma, it's both the most common form of brain cancer in adults and the one with the worst prognosis, with median survival being less than a year. The presence of a specific genetic sequence in the tumor known as MGMT promoter methylation has been shown to be a favorable prognostic factor and a strong predictor of responsiveness to chemotherapy.\n",
    "\n",
    "Currently, genetic analysis of cancer requires surgery to extract a tissue sample. Then it can take several weeks to determine the genetic characterization of the tumor. Depending upon the results and type of initial therapy chosen, a subsequent surgery may be necessary. If an accurate method to predict the genetics of the cancer through imaging (i.e., radiogenomics) alone could be developed, this would potentially minimize the number of surgeries and refine the type of therapy required.\n",
    "\n",
    "This project will attempt to predict the genetic subtype of glioblastoma using MRI (magnetic resonance imaging) scans by training a model to detect for the presence of MGMT promoter methylation. This essentially is a binary classification problem. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c4004f",
   "metadata": {},
   "source": [
    "<a id='approach'></a>\n",
    "### Modeling Approach\n",
    "Due to the sequential nature of MRI images, the modeling appoach lends itself to using a 3D convolutional neural network. This approach will capture the 3D spatial data in the sequence. Another approach could be to look at this problem through the lense of a LSTM network however it is not necessary to capture, in memory, previous image sequences. We must look at the sequence of MRI images as parts of a full three dimensional reconstruction of the brain. This notebook will walk through the steps, using AWS Sagemaker, to build a custom PyTorch 3D CNN model for training and inference purposes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "00905ce4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# !pip install pydicom\n",
    "# !pip install git+https://github.com/shijianjian/EfficientNet-PyTorch-3D\n",
    "# !pip install tensorboard"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "849a63b1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# necessary imports\n",
    "import os\n",
    "import io\n",
    "import sys\n",
    "import glob\n",
    "from tqdm import tqdm\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "from time import time\n",
    "import pydicom\n",
    "from pydicom.pixel_data_handlers.util import apply_voi_lut\n",
    "import cv2\n",
    "import pprint as pp\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.metrics import roc_auc_score\n",
    "from sklearn.metrics import accuracy_score\n",
    "\n",
    "import torch \n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import Dataset, DataLoader\n",
    "from torchvision import models, transforms, utils\n",
    "from torch.utils.tensorboard import SummaryWriter\n",
    "\n",
    "from efficientnet_pytorch_3d import EfficientNet3D\n",
    "\n",
    "%load_ext tensorboard\n",
    "%matplotlib inline\n",
    "%config InlineBackend.figure_format = 'retina'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "597019c2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "sagemaker-us-west-2-904606187431\n"
     ]
    }
   ],
   "source": [
    "# sagemaker specific imports\n",
    "import boto3\n",
    "import sagemaker\n",
    "from sagemaker.pytorch import PyTorch\n",
    "from sagemaker.pytorch import PyTorchModel\n",
    "from sagemaker.debugger import TensorBoardOutputConfig\n",
    "\n",
    "# session and role\n",
    "sagemaker_session = sagemaker.Session()\n",
    "role = sagemaker.get_execution_role()\n",
    "\n",
    "sagemaker_bucket = sagemaker_session.default_bucket()\n",
    "print(sagemaker_bucket)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f174fe5e",
   "metadata": {},
   "source": [
    "<a id='exploring'></a>\n",
    "### Exploring the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f7185dd8",
   "metadata": {},
   "source": [
    "The data<sup>[[2]](#2)</sup>  is defined by three cohorts: _Training_, _Test (Validation)_, and _Testing_. The _Testing_ dataset is kept private and is used to test the competition submission entries. The _Test_ dataset, available for use, represents about 20% the size of the private _Testing_ dataset. Each top level folder represents a subject idicated by a five digit number. Within each of the folders are the four different MRI tests, FLAIR, T1w, T1wCE, and T2w. Each of these test folders has the respective imgages in DICOM format. The Tests and data structure are as follows:\n",
    "\n",
    "1.  **FLAIR** - Fluid Attenuated Inversion Recovery\n",
    "2.  **T1w** - T1-weighted pre-contrast\n",
    "    * fluid (black) - low signal intensity\n",
    "    * fat (white) - high signal intensity\n",
    "    * grey matter (grey) - intermediate signal intensity\n",
    "    * white matter (white-ish)\n",
    "3.  **T1wCE** - T1-weighted post-contrast \n",
    "4.  **T2w** - T2-weighted \n",
    "    * fluid (white) - high signal intensity\n",
    "    * fat (white) - high signal intensity\n",
    "    * grey matter (grey) - intermediate signal intensity\n",
    "    * white matter (black-ish)\n",
    "<p align=\"center\">\n",
    "<img width=\"900\" src = \"images/MRI Data Structure.png\">\n",
    "</p>\n",
    "\n",
    "**NOTE:** There are some unexpected issues with the following three cases in the training dataset, participants can exclude the cases during training:   \n",
    "[00109, 00123, 00709]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "39c09804",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model artifacts and data will be stored in  s3://sagemaker-us-west-2-904606187431/Radiogenomic/\n",
      "Num train labels:  585\n"
     ]
    },
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BraTS21ID</th>\n",
       "      <th>MGMT_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   BraTS21ID  MGMT_value\n",
       "0          0           1\n",
       "1          2           1\n",
       "2          3           0\n",
       "3          5           1\n",
       "4          6           1"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# project specific data paths\n",
    "train_labels_key = 'train_labels.csv'\n",
    "\n",
    "s3_prefix = 'Radiogenomic'\n",
    "output_path = 's3://{}/{}/'.format(sagemaker_bucket, s3_prefix)\n",
    "print('Model artifacts and data will be stored in ', output_path)\n",
    "\n",
    "train_labels_df = pd.read_csv(os.path.join(output_path, train_labels_key))\n",
    "print(\"Num train labels: \", len(train_labels_df))\n",
    "train_labels_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "264da4f8",
   "metadata": {},
   "source": [
    "#### Split the Train Labels into train/val DataFrames\n",
    "This could be done in the Pytorch Dataloader or Dataset Class implementation but those methods would introduce potential bias in the split. Because the labels are split fairly evenly between positive and negative MGMT values, the data can be split from the begining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f6a1b9b",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Train labels dataframe length:  494\n",
      "Validation labels dataframe length:  88\n",
      "582\n",
      "582\n"
     ]
    }
   ],
   "source": [
    "# read in train_labels.csv file\n",
    "train_labels_copy_df = pd.read_csv(os.path.join(output_path, train_labels_key))\n",
    "val_split = 0.15 # 10 % of the training data will be for validation during training\n",
    "\n",
    "# drop [00109, 00123, 00709]\n",
    "train_labels_copy_df.drop(train_labels_copy_df.loc[train_labels_copy_df['BraTS21ID']==109].index, inplace=True)\n",
    "train_labels_copy_df.drop(train_labels_copy_df.loc[train_labels_copy_df['BraTS21ID']==123].index, inplace=True)\n",
    "train_labels_copy_df.drop(train_labels_copy_df.loc[train_labels_copy_df['BraTS21ID']==709].index, inplace=True)\n",
    "\n",
    "# separate into two dataframes. \n",
    "mask = train_labels_copy_df['MGMT_value'] == 1\n",
    "df_pos = train_labels_copy_df[mask] # MGMT_value == 1\n",
    "df_neg = train_labels_copy_df[~mask] # MGMT_value == 0\n",
    "\n",
    "# use scikit-learn to split each pos/neg DataFrame into train/val\n",
    "train_pos, val_pos = train_test_split(df_pos, test_size=val_split)\n",
    "train_neg, val_neg = train_test_split(df_neg, test_size=val_split)\n",
    "\n",
    "# concatenate the pos with the negative\n",
    "train_df = pd.DataFrame(pd.concat([train_pos, train_neg])).sort_values(by='BraTS21ID')\n",
    "train_df['Split'] = 'train'\n",
    "print('Train labels dataframe length: ', len(train_df))\n",
    "val_df = pd.DataFrame(pd.concat([val_pos, val_neg])).sort_values(by='BraTS21ID')\n",
    "val_df['Split'] = 'valid'\n",
    "print('Validation labels dataframe length: ', len(val_df))\n",
    "\n",
    "# the merged_df should be exactly the same as our original train_labels_copy_df\n",
    "merged_df = pd.concat([train_df, val_df]).sort_values(by='BraTS21ID')\n",
    "assert(len(train_df) + len(val_df) == len(train_labels_copy_df))\n",
    "\n",
    "# [00109, 00123, 00709]\n",
    "\n",
    "print(len(train_labels_copy_df))\n",
    "print(len(merged_df))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3b4dbb23",
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 426.1x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 380,
       "width": 430
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = merged_df[['MGMT_value', 'Split']]\n",
    "g = sns.catplot(x='MGMT_value',\n",
    "                kind='count',\n",
    "                hue='Split',\n",
    "                data=df)\n",
    "ax = g.facet_axis(0,0)\n",
    "for p in ax.patches:\n",
    "    ax.text(x=p.get_x() + 0.1,\n",
    "            y=p.get_height() + 3,\n",
    "            s=p.get_height(),\n",
    "            color='black',\n",
    "            rotation='horizontal',\n",
    "            size='large')\n",
    "# df.head()\n",
    "ax = sns.set_theme(style='whitegrid')\n",
    "plt.title('Ground Truth Label Distribution')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9bd01ef2",
   "metadata": {},
   "source": [
    "#### Read DICOM Metadata\n",
    "Read in DICOM image metadata and extract image pixel data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "e95eb270",
   "metadata": {},
   "outputs": [],
   "source": [
    "s3_train_prefix = \"Radiogenomic/train\"\n",
    "local_train_prefix = 'data/train'\n",
    "patient_num = '00000'\n",
    "image_type = ['FLAIR', 'T1w', 'T1wCE', 'T2w']\n",
    "image_num = 'Image-222.dcm'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "28726386",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3 image path:  Radiogenomic/train/00000/FLAIR/Image-222.dcm\n",
      "Dataset.file_meta -------------------------------\n",
      "(0002, 0010) Transfer Syntax UID                 UI: Implicit VR Little Endian\n",
      "-------------------------------------------------\n",
      "(0008, 0005) Specific Character Set              CS: 'ISO_IR 100'\n",
      "(0008, 0008) Image Type                          CS: ['DERIVED', 'SECONDARY']\n",
      "(0008, 0016) SOP Class UID                       UI: MR Image Storage\n",
      "(0008, 0018) SOP Instance UID                    UI: 1.2.826.0.1.3680043.8.498.95236764925427671601379047905341503092\n",
      "(0008, 0050) Accession Number                    SH: '00000'\n",
      "(0008, 0060) Modality                            CS: 'MR'\n",
      "(0008, 103e) Series Description                  LO: 'FLAIR'\n",
      "(0010, 0010) Patient's Name                      PN: '00000'\n",
      "(0010, 0020) Patient ID                          LO: '00000'\n",
      "(0018, 0023) MR Acquisition Type                 CS: '3D'\n",
      "(0018, 0050) Slice Thickness                     DS: '1.2'\n",
      "(0018, 0081) Echo Time                           DS: None\n",
      "(0018, 0082) Inversion Time                      DS: None\n",
      "(0018, 0083) Number of Averages                  DS: '1.0'\n",
      "(0018, 0084) Imaging Frequency                   DS: '127.742358'\n",
      "(0018, 0085) Imaged Nucleus                      SH: '1H'\n",
      "(0018, 0086) Echo Number(s)                      IS: '1'\n",
      "(0018, 0087) Magnetic Field Strength             DS: '3.0'\n",
      "(0018, 0088) Spacing Between Slices              DS: '1.0'\n",
      "(0018, 0091) Echo Train Length                   IS: '200'\n",
      "(0018, 0093) Percent Sampling                    DS: '100.0'\n",
      "(0018, 0094) Percent Phase Field of View         DS: '80.0'\n",
      "(0018, 0095) Pixel Bandwidth                     DS: '139.492'\n",
      "(0018, 1094) Trigger Window                      IS: '0'\n",
      "(0018, 1100) Reconstruction Diameter             DS: '256.0'\n",
      "(0018, 1310) Acquisition Matrix                  US: [0, 256, 256, 0]\n",
      "(0018, 1312) In-plane Phase Encoding Direction   CS: 'ROW'\n",
      "(0018, 1314) Flip Angle                          DS: '90.0'\n",
      "(0018, 1316) SAR                                 DS: '0.677622'\n",
      "(0018, 5100) Patient Position                    CS: 'HFS'\n",
      "(0020, 000d) Study Instance UID                  UI: 1.2.826.0.1.3680043.8.498.21557373119637153130481842401167746353\n",
      "(0020, 000e) Series Instance UID                 UI: 1.2.826.0.1.3680043.8.498.75098256064787219771161442192873622098\n",
      "(0020, 0011) Series Number                       IS: '4'\n",
      "(0020, 0013) Instance Number                     IS: '222'\n",
      "(0020, 0032) Image Position (Patient)            DS: [-125.094, -42.2135, 127.74]\n",
      "(0020, 0037) Image Orientation (Patient)         DS: [1, -0, 0, -0, -0, -1]\n",
      "(0020, 0060) Laterality                          CS: ''\n",
      "(0020, 1040) Position Reference Indicator        LO: ''\n",
      "(0020, 1041) Slice Location                      DS: '42.21351624'\n",
      "(0020, 9057) In-Stack Position Number            UL: 113\n",
      "(0028, 0002) Samples per Pixel                   US: 1\n",
      "(0028, 0004) Photometric Interpretation          CS: 'MONOCHROME2'\n",
      "(0028, 0010) Rows                                US: 512\n",
      "(0028, 0011) Columns                             US: 512\n",
      "(0028, 0030) Pixel Spacing                       DS: [0.5, 0.5]\n",
      "(0028, 0100) Bits Allocated                      US: 16\n",
      "(0028, 0101) Bits Stored                         US: 16\n",
      "(0028, 0102) High Bit                            US: 15\n",
      "(0028, 0103) Pixel Representation                US: 1\n",
      "(0028, 1050) Window Center                       DS: '1207.0'\n",
      "(0028, 1051) Window Width                        DS: '2415.0'\n",
      "(0028, 1052) Rescale Intercept                   DS: '0.0'\n",
      "(0028, 1053) Rescale Slope                       DS: '1.0'\n",
      "(0028, 1054) Rescale Type                        LO: 'US'\n",
      "(7fe0, 0010) Pixel Data                          OW: Array of 524288 elements\n",
      "\n",
      "Size of DICOM image: (512, 512)\n"
     ]
    }
   ],
   "source": [
    "# reading from s3 bucket\n",
    "s3_image_path = os.path.join(s3_train_prefix, patient_num, image_type[0], image_num)\n",
    "print('s3 image path: ', s3_image_path)\n",
    "s3_client = boto3.client('s3')\n",
    "obj = s3_client.get_object(Bucket=sagemaker_bucket, Key=s3_image_path)\n",
    "s3_test_dicom = pydicom.dcmread(io.BytesIO(obj['Body'].read()))\n",
    "\n",
    "# # reading from local sagemaker instance storage\n",
    "# image_path = os.path.join(local_train_prefix, patient_num, image_type[0], image_num)\n",
    "# print('Local image path: ', image_path)\n",
    "# print()\n",
    "# test_dicom = pydicom.dcmread(s3_image_path)\n",
    "\n",
    "print(s3_test_dicom)\n",
    "print(\"\\nSize of DICOM image:\", s3_test_dicom.pixel_array.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "6a453dd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'PixelData'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# accessing a particular element in the dicom image metadata\n",
    "elem = s3_test_dicom[0x7fe0, 0x0010]\n",
    "elem.keyword"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "492caadd",
   "metadata": {},
   "source": [
    "#### Helper Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "8c7d9892",
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper functions\n",
    "def process_dicom_image(path: str, resize=True) -> np.ndarray:\n",
    "    \"\"\" Given a path to a DICOM image, process and return the image. \n",
    "        Reduces the size in memory.\n",
    "    \"\"\"\n",
    "    # TODO: add functionality to read from s3 NOT local\n",
    "#     s3_client = boto3.client('s3')\n",
    "#     obj = s3_client.get_object(Bucket=sagemaker_bucket, Key=s3_image_path)\n",
    "#     s3_test_dicom = pydicom.dcmread(io.BytesIO(obj['Body'].read()))\n",
    "\n",
    "    dicom = pydicom.dcmread(path)\n",
    "    image = dicom.pixel_array\n",
    "    image = image - np.min(image)\n",
    "    \n",
    "    if np.max(image) != 0:\n",
    "        image = image / np.max(image)\n",
    "    \n",
    "    image = (image * 255).astype(np.uint8)\n",
    "    # resize the image 256px\n",
    "    if resize:\n",
    "        image = cv2.resize(image, (256,256))\n",
    "#     print(image.shape)\n",
    "#     print(type(image))\n",
    "    return image\n",
    "\n",
    "def get_sequence_images(path: str) -> list():\n",
    "    \"\"\" Returns a sorted list of images from a MRI sequence subfolder. \n",
    "        Excludes images that have no image. i.e. - only black.\n",
    "    \n",
    "        Arguments:\n",
    "            path (str): path to a MRI sequence folder. ex. ./train/00000/FLAIR\n",
    "        Returns:\n",
    "            images (list): List of np.ndarray images \n",
    "    \"\"\"\n",
    "    images = []\n",
    "    image_path_list = glob.glob(path + '/*') # at the MRI sequence level\n",
    "    # sort the path list in place by image number \n",
    "    image_path_list.sort(key=lambda x: int(x.split('/')[-1].split('-')[-1].split('.')[0]))\n",
    "    \n",
    "    for p in image_path_list:\n",
    "        img = process_dicom_image(p)\n",
    "        # only add if there is an visual image. i.e. - if it is not black\n",
    "        if np.max(img) == 0:\n",
    "            continue\n",
    "        images.append(img)\n",
    "        \n",
    "    return images\n",
    "\n",
    "def get_middle_image(path: str) -> np.ndarray:\n",
    "    \"\"\" Returns the middle image in a sequence of MRI images. Removes\n",
    "        images that are only black.\n",
    "        \n",
    "        Arguments:\n",
    "            path (str): path to a MRI sequence folder. ex. ./train/00000/FLAIR\n",
    "        Returns:\n",
    "            images (np.ndarray)\n",
    "    \"\"\"\n",
    "    image_path_list = glob.glob(path + '/*') # at the MRI sequence level\n",
    "    image_path_list.sort(key=lambda x: int(x.split('/')[-1].split('-')[-1].split('.')[0]))\n",
    "    idx = len(image_path_list)//2\n",
    "    image = process_dicom_image(image_path_list[idx])\n",
    "    \n",
    "    return image\n",
    "\n",
    "def train_val_split(path: str, val_ratio):\n",
    "    \"\"\" Splits the train_labels.csv file into training and validation\n",
    "        dataframes to be used in the DataSet Class.\n",
    "        \n",
    "        Arguments:\n",
    "            path (str): path to the data folder\n",
    "            val_ratio (float): ratio to split for validation\n",
    "        \n",
    "        Returns:\n",
    "            train_df, val_df (pd.DataFrame): labels for training and validation\n",
    "    \"\"\"\n",
    "    # read in train_labels.csv file\n",
    "    train_labels_df = pd.read_csv(os.path.join(path, 'train_labels.csv'))\n",
    "    \n",
    "    # drop [00109, 00123, 00709]\n",
    "    train_labels_df.drop(train_labels_df.loc[train_labels_df['BraTS21ID']==109].index, inplace=True)\n",
    "    train_labels_df.drop(train_labels_df.loc[train_labels_df['BraTS21ID']==123].index, inplace=True)\n",
    "    train_labels_df.drop(train_labels_df.loc[train_labels_df['BraTS21ID']==709].index, inplace=True)\n",
    "\n",
    "    # separate into two dataframes. \n",
    "    mask = train_labels_df['MGMT_value'] == 1\n",
    "    df_pos = train_labels_df[mask] # MGMT_value == 1\n",
    "    df_neg = train_labels_df[~mask] # MGMT_value == 0\n",
    "\n",
    "    # use scikit-learn to split each pos/neg DataFrame into train/val\n",
    "    train_pos, val_pos = train_test_split(df_pos, test_size=val_ratio)\n",
    "    train_neg, val_neg = train_test_split(df_neg, test_size=val_ratio)\n",
    "\n",
    "    # concatenate the pos with the negative\n",
    "    train_df = pd.DataFrame(pd.concat([train_pos, train_neg])).sort_values(by='BraTS21ID')\n",
    "    train_df['Split'] = 'train'\n",
    "#     print('Train labels dataframe length: ', len(train_df))\n",
    "    val_df = pd.DataFrame(pd.concat([val_pos, val_neg])).sort_values(by='BraTS21ID')\n",
    "    val_df['Split'] = 'valid'\n",
    "#     print('Validation labels dataframe length: ', len(val_df))\n",
    "\n",
    "    assert(len(train_df) + len(val_df) == len(train_labels_df))\n",
    "    \n",
    "    return train_df, val_df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "94310e1e",
   "metadata": {},
   "source": [
    "### Visualize MRI Sequence Types for Patient 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cd27da5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1152x576 with 4 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 223,
       "width": 907
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot one image in each MRI sequence type for 3 patients\n",
    "patients = ['00000', '00033', '00698']\n",
    "image_type = ['FLAIR', 'T1w', 'T1wCE', 'T2w']\n",
    "\n",
    "fig = plt.figure(figsize=[16,8])\n",
    "\n",
    "for i in range(4):\n",
    "    fig.add_subplot(1, 4, i+1)\n",
    "    path = os.path.join(local_train_prefix, patients[1], image_type[i])\n",
    "    img = get_middle_image(path)\n",
    "    plt.imshow(img, cmap='gray')\n",
    "    plt.axis('off')\n",
    "    plt.title('Patient {}'.format(patients[1]+': '+image_type[i]))\n",
    "    \n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "03c681e5",
   "metadata": {},
   "source": [
    "#### Visualize Full Set of T1w Images for Patient 33"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "06e80a79",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Patient Number:  00033\n",
      "Image Type:  FLAIR\n",
      "Num Images:  153\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1440x1440 with 153 Axes>"
      ]
     },
     "metadata": {
      "image/png": {
       "height": 608,
       "width": 1113
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "images_33 = get_sequence_images(os.path.join(local_train_prefix, patients[1], image_type[0]))\n",
    "fig = plt.figure(figsize=[20,20])\n",
    "num_imgs = len(images_33)\n",
    "print('Patient Number: ', patients[1])\n",
    "print('Image Type: ', image_type[0])\n",
    "print('Num Images: ', num_imgs)\n",
    "rows = 20\n",
    "cols = 15\n",
    "\n",
    "for idx, image in enumerate(images_33):\n",
    "    fig.add_subplot(rows, cols, idx+1)\n",
    "    plt.imshow(image, cmap='gray')\n",
    "    plt.axis('off')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e745bdb",
   "metadata": {},
   "source": [
    "<a id='process'></a>\n",
    "### Data Processing & Loading"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "fa0764c3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/train/00033/FLAIR\n",
      "Sorted image path list length:  216\n",
      "Function sorted images:  153\n",
      "Image array shape:  (256, 256, 153)\n"
     ]
    }
   ],
   "source": [
    "# s3_train_prefix = \"Radiogenomic/train\"\n",
    "# local_train_prefix = 'data/train'\n",
    "# patient_num = '00794'\n",
    "patient_num = '00033'\n",
    "# image_type = ['FLAIR', 'T1w', 'T1wCE', 'T2w']\n",
    "# image_num = 'Image-222.dcm'\n",
    "test_path = os.path.join(local_train_prefix, patient_num, image_type[0])\n",
    "print(test_path)\n",
    "img_list = glob.glob(test_path +'/*')\n",
    "img_list.sort(key=lambda x: int(x.split('/')[-1].split('-')[-1].split('.')[0]))\n",
    "print('Sorted image path list length: ',len(img_list))\n",
    "\n",
    "images = get_sequence_images(test_path)\n",
    "print('Function sorted images: ', len(images))\n",
    "print('Image array shape: ', np.array(images).T.shape)\n",
    "patient_list = glob.glob(local_train_prefix+'/*')\n",
    "patient_list.sort(key=lambda x: int(x.split('/')[-1]))\n",
    "# print(patient_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a23689fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# helper function to process DICOM images. \n",
    "def get_patient_images(path):\n",
    "    \"\"\" Process all images in a patient subfolders FLAIR, T1w, T1wCE, T2w. Sorts images and returns\n",
    "        an array of images of size (W,H,N,C) where:\n",
    "            W=image width reduced from 512 to 256 px\n",
    "            H=image height reduced from 512 to 256 px\n",
    "            N=number if images in each MRI sequence. \n",
    "            C=number if MRI seqence types\n",
    "        If the number of images in each subset is less than 128 the remaining images are created \n",
    "        as np.array with pixel values == 0.\n",
    "        \n",
    "        Parameters:\n",
    "            path (str): patient id path ./train/00000\n",
    "        Returns:\n",
    "            np.array of size (256, 256, 128, 4)\n",
    "    \"\"\"\n",
    "    seq_len = 64\n",
    "\n",
    "    # get the path of MRI seq subfolder\n",
    "    flair_path = os.path.join(path, 'FLAIR')\n",
    "    t1w_path = os.path.join(path, 'T1w')\n",
    "    t1wce_path = os.path.join(path, 'T1wCE')\n",
    "    t2w_path = os.path.join(path, 'T2w')\n",
    "    \n",
    "    # get the images in each sequence\n",
    "    # FLAIR\n",
    "    flair_imgs = get_sequence_images(flair_path)\n",
    "    if len(flair_imgs) >= seq_len:\n",
    "        start = (len(flair_imgs)//2)-int(seq_len/2)\n",
    "        end = (len(flair_imgs)//2)+int(seq_len/2)\n",
    "        flair_imgs = np.array(flair_imgs[start:end]).T\n",
    "    else:\n",
    "        diff = seq_len - len(flair_imgs)\n",
    "        flair_imgs = np.concatenate((np.array(flair_imgs).T, np.zeros((256,256,diff))),axis=-1)\n",
    "    \n",
    "    # T1w\n",
    "    t1w_imgs = get_sequence_images(t1w_path)\n",
    "    if len(t1w_imgs) >= seq_len:\n",
    "        start = (len(t1w_imgs)//2)-int(seq_len/2)\n",
    "        end = (len(t1w_imgs)//2)+int(seq_len/2)\n",
    "        t1w_imgs = np.array(t1w_imgs[start:end]).T\n",
    "    else:\n",
    "        diff = seq_len - len(t1w_imgs)\n",
    "        t1w_imgs = np.concatenate((np.array(t1w_imgs).T, np.zeros((256,256,diff))),axis=-1)\n",
    "        \n",
    "    # T1wCE\n",
    "    t1wce_imgs = get_sequence_images(t1wce_path)\n",
    "    if len(t1wce_imgs) >= seq_len:\n",
    "        start = (len(t1wce_imgs)//2)-int(seq_len/2)\n",
    "        end = (len(t1wce_imgs)//2)+int(seq_len/2)\n",
    "        t1wce_imgs = np.array(t1wce_imgs[start:end]).T\n",
    "    else:\n",
    "        diff = seq_len - len(t1wce_imgs)\n",
    "        t1wce_imgs = np.concatenate((np.array(t1wce_imgs).T, np.zeros((256,256,diff))),axis=-1)\n",
    "        \n",
    "    # T2w\n",
    "    t2w_imgs = get_sequence_images(t2w_path)\n",
    "    if len(t2w_imgs) >= seq_len:\n",
    "        start = (len(t2w_imgs)//2)-int(seq_len/2)\n",
    "        end = (len(t2w_imgs)//2)+int(seq_len/2)\n",
    "        t2w_imgs = np.array(t2w_imgs[start:end]).T\n",
    "    else:\n",
    "        diff = seq_len - len(t2w_imgs)\n",
    "        t2w_imgs = np.concatenate((np.array(t2w_imgs).T, np.zeros((256,256,diff))),axis=-1)\n",
    "    \n",
    "    return np.moveaxis(np.array((flair_imgs, t1w_imgs, t1wce_imgs, t2w_imgs)), 0, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "0cd9d1b3",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Array shape:  (256, 256, 64, 4)\n"
     ]
    }
   ],
   "source": [
    "seq_arr = get_patient_images(os.path.join(local_train_prefix, patient_num))\n",
    "print('Array shape: ', seq_arr.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f44d5039",
   "metadata": {},
   "source": [
    "#### Pytorch Dataloader"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "18df5fc2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MGMT_value\n",
      "3\n",
      "['data/train/00000', 'data/train/00002', 'data/train/00005']\n",
      "87\n"
     ]
    }
   ],
   "source": [
    "# just experimenting with ways to access elements in DataFrames\n",
    "tl_id = train_labels_df.columns[0]\n",
    "tl_y = train_labels_df.columns[1]\n",
    "print(tl_y)\n",
    "label_id = train_labels_df[tl_id]\n",
    "label_y = train_labels_df[tl_y]\n",
    "zip_label = zip(label_id, label_y)\n",
    "\n",
    "train_labels_df.BraTS21ID.apply(lambda x: str(x).zfill(5))\n",
    "# label_test = {str(l_id).zfill(5): y for l_id, y in zip(label_id, label_y)}\n",
    "label_test = {'00000': 1, '00002': 0, '00005': 1}\n",
    "ids = [l for l in sorted(glob.glob(local_train_prefix + '/*')) if l.split('/')[-1] in label_test]\n",
    "print(len(ids))\n",
    "print(ids)\n",
    "# print(label_test)\n",
    "# print(ids[:5])\n",
    "# # print(label_test)\n",
    "\n",
    "test_ids = [p.split('/')[-1] for p in sorted(glob.glob('data/test/*'))]\n",
    "print(len(test_ids))\n",
    "# print(test_ids)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "454f3d6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a PyTorch Custom Dataset to be used in DataLoader\n",
    "class BrainScanDataset(Dataset):\n",
    "    \"\"\" MRI brain scan dataset. \"\"\"\n",
    "    def __init__(self, data_dir, split, val_ratio, transform=None):\n",
    "        \"\"\"\n",
    "            Args:\n",
    "             data_dir (str): Path to the data folder\n",
    "             split (str): 'train' or 'valid' \n",
    "             transform (bool): Apply transforms\n",
    "        \"\"\"\n",
    "        self.data_dir = data_dir\n",
    "        \n",
    "        # get training labels\n",
    "        t_df, v_df = train_val_split(data_dir, val_ratio)\n",
    "        \n",
    "        if split == 'train':\n",
    "            labels_df = t_df\n",
    "        elif split == 'valid':\n",
    "            labels_df = v_df\n",
    "            \n",
    "        label_id = labels_df[labels_df.columns[0]] # BraTS21ID\n",
    "        label_y = labels_df[labels_df.columns[1]] # MGMT_value\n",
    "        self.labels_dict = {str(l_id).zfill(5): y for l_id, y in zip(label_id, label_y)}\n",
    "        \n",
    "        # TODO: Correct for Testing and Training\n",
    "        self.data_path = os.path.join(data_dir, 'train')\n",
    "        \n",
    "        # get patient ids\n",
    "        self.id_path_list = [path for path in sorted(glob.glob(self.data_path + '/*')) \n",
    "                             if path.split('/')[-1] in self.labels_dict]\n",
    "        self.id_list = [path.split('/')[-1] for path in sorted(glob.glob(self.data_path + '/*'))\n",
    "                        if path.split('/')[-1] in self.labels_dict]\n",
    "        \n",
    "        # TODO: image transforms\n",
    "        self.transform = transform\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.id_path_list)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "            \n",
    "        images = get_patient_images(self.id_path_list[idx])\n",
    "        labels = self.labels_dict[self.id_list[idx]]\n",
    "        \n",
    "        imgs_tensor = torch.tensor(images, dtype=torch.float32).permute(-1, 0, 1, 2) # need to reshape\n",
    "#         print(imgs_tensor.shape)\n",
    "        labels_tensor = torch.tensor(labels, dtype=torch.long)\n",
    "        \n",
    "        return imgs_tensor, labels_tensor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b68392e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "val_ratio = 0.15\n",
    "train_dataset = BrainScanDataset(data_dir='./data', split='train', val_ratio=val_ratio)\n",
    "valid_dataset = BrainScanDataset(data_dir='./data', split='valid', val_ratio=val_ratio)\n",
    "batch_size = 4 \n",
    "train_loader = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)\n",
    "batch_size = 2\n",
    "valid_loader = DataLoader(valid_dataset, batch_size=batch_size, shuffle=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "6e49abdc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "124\n"
     ]
    }
   ],
   "source": [
    "print(len(train_loader))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "1aa1919f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Train Data ---\n",
      "Image shape:  torch.Size([4, 4, 256, 256, 64])\n",
      "Class label shape: torch.Size([4])\n",
      "Class label:  tensor([0, 1, 1, 1])\n",
      "--- Valid Data ---\n",
      "Image shape:  torch.Size([2, 4, 256, 256, 64])\n",
      "Class label shape: torch.Size([2])\n",
      "Class label:  tensor([0, 1])\n"
     ]
    }
   ],
   "source": [
    "for img, label in train_loader:\n",
    "    print('--- Train Data ---')\n",
    "    print('Image shape: ', img.shape)\n",
    "    print('Class label shape:', label.shape)\n",
    "    print('Class label: ', label)\n",
    "    break\n",
    "    \n",
    "for img, label in valid_loader:\n",
    "    print('--- Valid Data ---')\n",
    "    print('Image shape: ', img.shape)\n",
    "    print('Class label shape:', label.shape)\n",
    "    print('Class label: ', label)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "cc0d982c",
   "metadata": {},
   "outputs": [],
   "source": [
    "model_info = {'model_name': 'efficientnet-b5',\n",
    "              'input_dim': 4,\n",
    "              'output_dim': {'num_classes': 2}}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f4593c91",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "EfficientNet3D(\n",
      "  (_conv_stem): Conv3dStaticSamePadding(\n",
      "    4, 48, kernel_size=(3, 3, 3), stride=(2, 2, 2), bias=False\n",
      "    (static_padding): ZeroPad2d(padding=(0, 1, 0, 1, 0, 1), value=0.0)\n",
      "  )\n",
      "  (_bn0): BatchNorm3d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "  (_blocks): ModuleList(\n",
      "    (0): MBConvBlock3D(\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        48, 48, kernel_size=(3, 3, 3), stride=[2, 2, 2], groups=48, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1, 0, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(48, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        48, 12, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        12, 48, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        48, 24, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (1): MBConvBlock3D(\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        24, 24, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=24, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        24, 6, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        6, 24, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        24, 24, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (2): MBConvBlock3D(\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        24, 24, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=24, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        24, 6, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        6, 24, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        24, 24, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(24, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (3): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        24, 144, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        144, 144, kernel_size=(3, 3, 3), stride=[2, 2, 2], groups=144, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1, 0, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(144, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        144, 6, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        6, 144, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        144, 40, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (4): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        40, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        240, 240, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=240, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        240, 10, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        10, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        240, 40, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (5): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        40, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        240, 240, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=240, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        240, 10, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        10, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        240, 40, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (6): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        40, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        240, 240, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=240, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        240, 10, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        10, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        240, 40, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (7): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        40, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        240, 240, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=240, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        240, 10, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        10, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        240, 40, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(40, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (8): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        40, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        240, 240, kernel_size=(5, 5, 5), stride=[2, 2, 2], groups=240, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 2, 1, 2, 1, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(240, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        240, 10, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        10, 240, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        240, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(64, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (9): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        64, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        384, 384, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=384, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        384, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        16, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        384, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(64, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (10): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        64, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        384, 384, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=384, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        384, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        16, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        384, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(64, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (11): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        64, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        384, 384, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=384, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        384, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        16, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        384, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(64, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (12): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        64, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        384, 384, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=384, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        384, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        16, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        384, 64, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(64, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (13): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        64, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        384, 384, kernel_size=(3, 3, 3), stride=[2, 2, 2], groups=384, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(0, 1, 0, 1, 0, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(384, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        384, 16, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        16, 384, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        384, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (14): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (15): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (16): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (17): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (18): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (19): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(128, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (20): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        128, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        768, 768, kernel_size=(5, 5, 5), stride=[1, 1, 1], groups=768, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(768, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        768, 32, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        32, 768, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        768, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (21): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (22): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (23): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (24): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (25): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (26): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 176, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(176, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (27): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        176, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1056, 1056, kernel_size=(5, 5, 5), stride=[2, 2, 2], groups=1056, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 2, 1, 2, 1, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1056, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1056, 44, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        44, 1056, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1056, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (28): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (29): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (30): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (31): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (32): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (33): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (34): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (35): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(5, 5, 5), stride=(1, 1, 1), groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(2, 2, 2, 2, 2, 2), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 304, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(304, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (36): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        304, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        1824, 1824, kernel_size=(3, 3, 3), stride=[1, 1, 1], groups=1824, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(1824, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        1824, 76, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        76, 1824, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        1824, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(512, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (37): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        512, 3072, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(3072, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        3072, 3072, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=3072, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(3072, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        3072, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        128, 3072, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        3072, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(512, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "    (38): MBConvBlock3D(\n",
      "      (_expand_conv): Conv3dStaticSamePadding(\n",
      "        512, 3072, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn0): BatchNorm3d(3072, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_depthwise_conv): Conv3dStaticSamePadding(\n",
      "        3072, 3072, kernel_size=(3, 3, 3), stride=(1, 1, 1), groups=3072, bias=False\n",
      "        (static_padding): ZeroPad2d(padding=(1, 1, 1, 1, 1, 1), value=0.0)\n",
      "      )\n",
      "      (_bn1): BatchNorm3d(3072, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_se_reduce): Conv3dStaticSamePadding(\n",
      "        3072, 128, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_se_expand): Conv3dStaticSamePadding(\n",
      "        128, 3072, kernel_size=(1, 1, 1), stride=(1, 1, 1)\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_project_conv): Conv3dStaticSamePadding(\n",
      "        3072, 512, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "        (static_padding): Identity()\n",
      "      )\n",
      "      (_bn2): BatchNorm3d(512, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "      (_swish): MemoryEfficientSwish()\n",
      "    )\n",
      "  )\n",
      "  (_conv_head): Conv3dStaticSamePadding(\n",
      "    512, 2048, kernel_size=(1, 1, 1), stride=(1, 1, 1), bias=False\n",
      "    (static_padding): Identity()\n",
      "  )\n",
      "  (_bn1): BatchNorm3d(2048, eps=0.001, momentum=0.010000000000000009, affine=True, track_running_stats=True)\n",
      "  (_avg_pooling): AdaptiveAvgPool3d(output_size=1)\n",
      "  (_dropout): Dropout(p=0.4, inplace=False)\n",
      "  (_fc): Linear(in_features=2048, out_features=2, bias=True)\n",
      "  (_swish): MemoryEfficientSwish()\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model = EfficientNet3D.from_name(model_name=model_info['model_name'], \n",
    "                                 override_params=model_info['output_dim'], \n",
    "                                 in_channels=model_info['input_dim']) \n",
    "print(model)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9807d9d0",
   "metadata": {},
   "source": [
    "<a id='training'></a>\n",
    "### Training an Estimator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "07690a26",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "s3://sagemaker-us-west-2-904606187431/Radiogenomic/\n"
     ]
    }
   ],
   "source": [
    "print(output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "ff4612d4",
   "metadata": {},
   "outputs": [],
   "source": [
    "# tensorboard output configuration \n",
    "# tensorboard_output_config = TensorBoardOutputConfig(s3_output_path=output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "59824a29",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate a PyTorch Estimator\n",
    "pytorch_estimator = PyTorch(entry_point='train.py',\n",
    "                            source_dir='source',\n",
    "                            role=role,\n",
    "                            framework_version='1.5.0',\n",
    "                            py_version='py3',\n",
    "                            output_path=output_path,\n",
    "                            instance_count=1,\n",
    "                            instance_type='ml.p2.xlarge',\n",
    "                            volume_size=150, # size in GB of the EBS storage volume attached to instance type\n",
    "                            sagemaker_session=sagemaker_session,\n",
    "                            hyperparameters={\n",
    "                                'epochs': 16\n",
    "                            })"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e321eadc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2021-09-01 16:17:10 Starting - Starting the training job...\n",
      "2021-09-01 16:17:34 Starting - Launching requested ML instancesProfilerReport-1630513030: InProgress\n",
      "......\n",
      "2021-09-01 16:18:34 Starting - Preparing the instances for training...............\n",
      "2021-09-01 16:21:11 Downloading - Downloading input data......................................................................................................................................................................................................................................................................................................................................................................................................................................................................................................\n",
      "2021-09-01 17:42:35 Training - Downloading the training image...\n",
      "2021-09-01 17:42:59 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[34m2021-09-01 17:43:00,578 sagemaker-training-toolkit INFO     Imported framework sagemaker_pytorch_container.training\u001b[0m\n",
      "\u001b[34m2021-09-01 17:43:00,616 sagemaker_pytorch_container.training INFO     Block until all host DNS lookups succeed.\u001b[0m\n",
      "\u001b[34m2021-09-01 17:43:00,625 sagemaker_pytorch_container.training INFO     Invoking user training script.\u001b[0m\n",
      "\u001b[34m2021-09-01 17:43:01,499 sagemaker-training-toolkit INFO     Installing dependencies from requirements.txt:\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.6 -m pip install -r requirements.txt\u001b[0m\n",
      "\u001b[34mCollecting git+https://github.com/shijianjian/EfficientNet-PyTorch-3D (from -r requirements.txt (line 8))\n",
      "  Cloning https://github.com/shijianjian/EfficientNet-PyTorch-3D to /tmp/pip-req-build-t7ypsavg\u001b[0m\n",
      "\u001b[34mCollecting pydicom\n",
      "  Downloading pydicom-2.2.1-py3-none-any.whl (2.0 MB)\u001b[0m\n",
      "\u001b[34mCollecting tensorboard\n",
      "  Downloading tensorboard-2.6.0-py3-none-any.whl (5.6 MB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: torch in /opt/conda/lib/python3.6/site-packages (from efficientnet-pytorch-3d==0.6.3->-r requirements.txt (line 8)) (1.5.0)\u001b[0m\n",
      "\u001b[34mCollecting absl-py>=0.4\n",
      "  Downloading absl_py-0.13.0-py3-none-any.whl (132 kB)\u001b[0m\n",
      "\u001b[34mCollecting tensorboard-data-server<0.7.0,>=0.6.0\n",
      "  Downloading tensorboard_data_server-0.6.1-py3-none-manylinux2010_x86_64.whl (4.9 MB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: werkzeug>=0.11.15 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (1.0.1)\u001b[0m\n",
      "\u001b[34mCollecting google-auth<2,>=1.6.3\n",
      "  Downloading google_auth-1.35.0-py2.py3-none-any.whl (152 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: setuptools>=41.0.0 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (49.6.0.post20210108)\u001b[0m\n",
      "\u001b[34mCollecting grpcio>=1.24.3\n",
      "  Downloading grpcio-1.39.0-cp36-cp36m-manylinux2014_x86_64.whl (4.3 MB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: protobuf>=3.6.0 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (3.15.6)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: wheel>=0.26 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (0.36.2)\u001b[0m\n",
      "\u001b[34mCollecting markdown>=2.6.8\n",
      "  Downloading Markdown-3.3.4-py3-none-any.whl (97 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: numpy>=1.12.0 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (1.16.4)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: requests<3,>=2.21.0 in /opt/conda/lib/python3.6/site-packages (from tensorboard->-r requirements.txt (line 5)) (2.22.0)\u001b[0m\n",
      "\u001b[34mCollecting tensorboard-plugin-wit>=1.6.0\n",
      "  Downloading tensorboard_plugin_wit-1.8.0-py3-none-any.whl (781 kB)\u001b[0m\n",
      "\u001b[34mCollecting google-auth-oauthlib<0.5,>=0.4.1\n",
      "  Downloading google_auth_oauthlib-0.4.6-py2.py3-none-any.whl (18 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: six in /opt/conda/lib/python3.6/site-packages (from absl-py>=0.4->tensorboard->-r requirements.txt (line 5)) (1.15.0)\u001b[0m\n",
      "\u001b[34mCollecting cachetools<5.0,>=2.0.0\n",
      "  Downloading cachetools-4.2.2-py3-none-any.whl (11 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: rsa<5,>=3.1.4 in /opt/conda/lib/python3.6/site-packages (from google-auth<2,>=1.6.3->tensorboard->-r requirements.txt (line 5)) (4.7.2)\u001b[0m\n",
      "\u001b[34mCollecting pyasn1-modules>=0.2.1\n",
      "  Downloading pyasn1_modules-0.2.8-py2.py3-none-any.whl (155 kB)\u001b[0m\n",
      "\u001b[34mCollecting requests-oauthlib>=0.7.0\n",
      "  Downloading requests_oauthlib-1.3.0-py2.py3-none-any.whl (23 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: importlib-metadata in /opt/conda/lib/python3.6/site-packages (from markdown>=2.6.8->tensorboard->-r requirements.txt (line 5)) (3.7.2)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /opt/conda/lib/python3.6/site-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard->-r requirements.txt (line 5)) (0.4.8)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: chardet<3.1.0,>=3.0.2 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->-r requirements.txt (line 5)) (3.0.4)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: idna<2.9,>=2.5 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->-r requirements.txt (line 5)) (2.8)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: certifi>=2017.4.17 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->-r requirements.txt (line 5)) (2020.12.5)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /opt/conda/lib/python3.6/site-packages (from requests<3,>=2.21.0->tensorboard->-r requirements.txt (line 5)) (1.25.11)\u001b[0m\n",
      "\u001b[34mCollecting oauthlib>=3.0.0\n",
      "  Downloading oauthlib-3.1.1-py2.py3-none-any.whl (146 kB)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: zipp>=0.5 in /opt/conda/lib/python3.6/site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard->-r requirements.txt (line 5)) (3.4.1)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: typing-extensions>=3.6.4 in /opt/conda/lib/python3.6/site-packages (from importlib-metadata->markdown>=2.6.8->tensorboard->-r requirements.txt (line 5)) (3.7.4.3)\u001b[0m\n",
      "\u001b[34mRequirement already satisfied: future in /opt/conda/lib/python3.6/site-packages (from torch->efficientnet-pytorch-3d==0.6.3->-r requirements.txt (line 8)) (0.17.1)\u001b[0m\n",
      "\u001b[34mBuilding wheels for collected packages: efficientnet-pytorch-3d\n",
      "  Building wheel for efficientnet-pytorch-3d (setup.py): started\n",
      "  Building wheel for efficientnet-pytorch-3d (setup.py): finished with status 'done'\n",
      "  Created wheel for efficientnet-pytorch-3d: filename=efficientnet_pytorch_3d-0.6.3-py3-none-any.whl size=16760 sha256=47d70cb7377a3d2d10bdddc063fafb2f576975a805d6a6c7cdd8a59b6c627234\n",
      "  Stored in directory: /tmp/pip-ephem-wheel-cache-igq4cyce/wheels/39/9e/ca/4948ca6da0345cd2faf76f061fc76f1c06b9a7bae8463c8b7f\u001b[0m\n",
      "\u001b[34mSuccessfully built efficientnet-pytorch-3d\u001b[0m\n",
      "\u001b[34mInstalling collected packages: pyasn1-modules, oauthlib, cachetools, requests-oauthlib, google-auth, tensorboard-plugin-wit, tensorboard-data-server, markdown, grpcio, google-auth-oauthlib, absl-py, tensorboard, pydicom, efficientnet-pytorch-3d\u001b[0m\n",
      "\u001b[34mSuccessfully installed absl-py-0.13.0 cachetools-4.2.2 efficientnet-pytorch-3d-0.6.3 google-auth-1.35.0 google-auth-oauthlib-0.4.6 grpcio-1.39.0 markdown-3.3.4 oauthlib-3.1.1 pyasn1-modules-0.2.8 pydicom-2.2.1 requests-oauthlib-1.3.0 tensorboard-2.6.0 tensorboard-data-server-0.6.1 tensorboard-plugin-wit-1.8.0\n",
      "  Running command git clone -q https://github.com/shijianjian/EfficientNet-PyTorch-3D /tmp/pip-req-build-t7ypsavg\n",
      "\u001b[0m\n",
      "\u001b[34m2021-09-01 17:43:11,703 sagemaker-training-toolkit INFO     Invoking user script\n",
      "\u001b[0m\n",
      "\u001b[34mTraining Env:\n",
      "\u001b[0m\n",
      "\u001b[34m{\n",
      "    \"additional_framework_parameters\": {},\n",
      "    \"channel_input_dirs\": {\n",
      "        \"training\": \"/opt/ml/input/data/training\"\n",
      "    },\n",
      "    \"current_host\": \"algo-1\",\n",
      "    \"framework_module\": \"sagemaker_pytorch_container.training:main\",\n",
      "    \"hosts\": [\n",
      "        \"algo-1\"\n",
      "    ],\n",
      "    \"hyperparameters\": {\n",
      "        \"epochs\": 16\n",
      "    },\n",
      "    \"input_config_dir\": \"/opt/ml/input/config\",\n",
      "    \"input_data_config\": {\n",
      "        \"training\": {\n",
      "            \"TrainingInputMode\": \"File\",\n",
      "            \"S3DistributionType\": \"FullyReplicated\",\n",
      "            \"RecordWrapperType\": \"None\"\n",
      "        }\n",
      "    },\n",
      "    \"input_dir\": \"/opt/ml/input\",\n",
      "    \"is_master\": true,\n",
      "    \"job_name\": \"pytorch-training-2021-09-01-16-17-10-574\",\n",
      "    \"log_level\": 20,\n",
      "    \"master_hostname\": \"algo-1\",\n",
      "    \"model_dir\": \"/opt/ml/model\",\n",
      "    \"module_dir\": \"s3://sagemaker-us-west-2-904606187431/pytorch-training-2021-09-01-16-17-10-574/source/sourcedir.tar.gz\",\n",
      "    \"module_name\": \"train\",\n",
      "    \"network_interface_name\": \"eth0\",\n",
      "    \"num_cpus\": 4,\n",
      "    \"num_gpus\": 1,\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",
      "        \"hosts\": [\n",
      "            \"algo-1\"\n",
      "        ],\n",
      "        \"network_interface_name\": \"eth0\"\n",
      "    },\n",
      "    \"user_entry_point\": \"train.py\"\u001b[0m\n",
      "\u001b[34m}\n",
      "\u001b[0m\n",
      "\u001b[34mEnvironment variables:\n",
      "\u001b[0m\n",
      "\u001b[34mSM_HOSTS=[\"algo-1\"]\u001b[0m\n",
      "\u001b[34mSM_NETWORK_INTERFACE_NAME=eth0\u001b[0m\n",
      "\u001b[34mSM_HPS={\"epochs\":16}\u001b[0m\n",
      "\u001b[34mSM_USER_ENTRY_POINT=train.py\u001b[0m\n",
      "\u001b[34mSM_FRAMEWORK_PARAMS={}\u001b[0m\n",
      "\u001b[34mSM_RESOURCE_CONFIG={\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"}\u001b[0m\n",
      "\u001b[34mSM_INPUT_DATA_CONFIG={\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}}\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_DATA_DIR=/opt/ml/output/data\u001b[0m\n",
      "\u001b[34mSM_CHANNELS=[\"training\"]\u001b[0m\n",
      "\u001b[34mSM_CURRENT_HOST=algo-1\u001b[0m\n",
      "\u001b[34mSM_MODULE_NAME=train\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=1\u001b[0m\n",
      "\u001b[34mSM_MODEL_DIR=/opt/ml/model\u001b[0m\n",
      "\u001b[34mSM_MODULE_DIR=s3://sagemaker-us-west-2-904606187431/pytorch-training-2021-09-01-16-17-10-574/source/sourcedir.tar.gz\u001b[0m\n",
      "\u001b[34mSM_TRAINING_ENV={\"additional_framework_parameters\":{},\"channel_input_dirs\":{\"training\":\"/opt/ml/input/data/training\"},\"current_host\":\"algo-1\",\"framework_module\":\"sagemaker_pytorch_container.training:main\",\"hosts\":[\"algo-1\"],\"hyperparameters\":{\"epochs\":16},\"input_config_dir\":\"/opt/ml/input/config\",\"input_data_config\":{\"training\":{\"RecordWrapperType\":\"None\",\"S3DistributionType\":\"FullyReplicated\",\"TrainingInputMode\":\"File\"}},\"input_dir\":\"/opt/ml/input\",\"is_master\":true,\"job_name\":\"pytorch-training-2021-09-01-16-17-10-574\",\"log_level\":20,\"master_hostname\":\"algo-1\",\"model_dir\":\"/opt/ml/model\",\"module_dir\":\"s3://sagemaker-us-west-2-904606187431/pytorch-training-2021-09-01-16-17-10-574/source/sourcedir.tar.gz\",\"module_name\":\"train\",\"network_interface_name\":\"eth0\",\"num_cpus\":4,\"num_gpus\":1,\"output_data_dir\":\"/opt/ml/output/data\",\"output_dir\":\"/opt/ml/output\",\"output_intermediate_dir\":\"/opt/ml/output/intermediate\",\"resource_config\":{\"current_host\":\"algo-1\",\"hosts\":[\"algo-1\"],\"network_interface_name\":\"eth0\"},\"user_entry_point\":\"train.py\"}\u001b[0m\n",
      "\u001b[34mSM_USER_ARGS=[\"--epochs\",\"16\"]\u001b[0m\n",
      "\u001b[34mSM_OUTPUT_INTERMEDIATE_DIR=/opt/ml/output/intermediate\u001b[0m\n",
      "\u001b[34mSM_CHANNEL_TRAINING=/opt/ml/input/data/training\u001b[0m\n",
      "\u001b[34mSM_HP_EPOCHS=16\u001b[0m\n",
      "\u001b[34mPYTHONPATH=/opt/ml/code:/opt/conda/bin:/opt/conda/lib/python36.zip:/opt/conda/lib/python3.6:/opt/conda/lib/python3.6/lib-dynload:/opt/conda/lib/python3.6/site-packages\n",
      "\u001b[0m\n",
      "\u001b[34mInvoking script with the following command:\n",
      "\u001b[0m\n",
      "\u001b[34m/opt/conda/bin/python3.6 train.py --epochs 16\n",
      "\n",
      "\u001b[0m\n",
      "\u001b[34mUsing device cuda.\u001b[0m\n",
      "\u001b[34mGetting training and validation data loaders...\u001b[0m\n",
      "\u001b[34mDone getting dataloaders!\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.540 algo-1:48 INFO json_config.py:90] Creating hook from json_config at /opt/ml/input/config/debughookconfig.json.\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.541 algo-1:48 INFO hook.py:192] tensorboard_dir has not been set for the hook. SMDebug will not be exporting tensorboard summaries.\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.541 algo-1:48 INFO hook.py:237] Saving to /opt/ml/output/tensors\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.541 algo-1:48 INFO state_store.py:67] The checkpoint config file /opt/ml/input/config/checkpointconfig.json does not exist.\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.594 algo-1:48 INFO hook.py:382] Monitoring the collections: losses\u001b[0m\n",
      "\u001b[34m[2021-09-01 17:43:36.594 algo-1:48 INFO hook.py:443] Hook is writing from the hook with pid: 48\n",
      "\u001b[0m\n",
      "\u001b[34mEpoch: 1 -- Training Loss: 0.79752 -- Validation Loss: 0.72193 -- Accuracy: 47.4576271\u001b[0m\n"
     ]
    }
   ],
   "source": [
    "pytorch_estimator.fit(output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "809f13a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pytorch-training-2021-08-30-21-16-06-096\n",
      "\n",
      "2021-08-31 13:44:53 Starting - Preparing the instances for training\n",
      "2021-08-31 13:44:53 Downloading - Downloading input data\n",
      "2021-08-31 13:44:53 Training - Training image download completed. Training in progress.\n",
      "2021-08-31 13:44:53 Uploading - Uploading generated training model\n",
      "2021-08-31 13:44:53 Completed - Training job completed\n"
     ]
    }
   ],
   "source": [
    "# use for previous training jobs\n",
    "# training_job_name = 'pytorch-training-2021-09-01-16-17-10-574'\n",
    "# training_job_name = 'pytorch-training-2021-08-25-23-08-19-905'\n",
    "training_job_name = 'pytorch-training-2021-08-30-21-16-06-096'\n",
    "print(training_job_name)\n",
    "attached_estimator = sagemaker.estimator.Estimator.attach(training_job_name)\n",
    "\n",
    "# # get the path to tensorboard outputs\n",
    "# tensorboard_s3_output_path = pytorch_estimator.latest_job_tensorboard_artifacts_path()\n",
    "# # tensorboard_path = os.path.join(output_path, 'tensorboard-output/')\n",
    "# print('Tensorboard output path: ', tensorboard_s3_output_path)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "a85e4379",
   "metadata": {},
   "outputs": [],
   "source": [
    "### Tensorboard URL for this notebook\n",
    "### https://ml-capstone-5kqg.notebook.us-west-2.sagemaker.aws/proxy/6006/\n",
    "\n",
    "### start tensorboard\n",
    "# %tensorboard --logdir tensorboard_s3_output_path"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "88f06dcb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# check if the event files have data\n",
    "# %tensorboard --inspect --logdir tensorboard_s3_output_path"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f2f2bfb",
   "metadata": {},
   "source": [
    "<a id='predict'></a>\n",
    "## Make Predictions\n",
    "Due to the large size of the test dataset deploying a model endpoint is not viable. The largest size of of data packets is 5 MB. Processed data for each patient is approximatly 64 MB. This is simply to large to send to an endpoint. Instead we will utilize Batch Transform to process the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9a425ee1",
   "metadata": {},
   "outputs": [],
   "source": [
    "class BrainScanTestDataset(Dataset):\n",
    "    \"\"\" MRI brain scan test dataset. \"\"\"\n",
    "    def __init__(self, data_dir, transform=None):\n",
    "        \"\"\"\n",
    "            Args:\n",
    "             data_dir (str): Path to the data folder\n",
    "        \"\"\"\n",
    "        self.data_dir = data_dir\n",
    "        \n",
    "        # get test ids\n",
    "        labels_df = pd.read_csv(os.path.join(data_dir, 'sample_submission.csv'))\n",
    "            \n",
    "        label_id = labels_df[labels_df.columns[0]] # BraTS21ID\n",
    "        label_y = labels_df[labels_df.columns[1]] # MGMT_value\n",
    "        self.labels_dict = {str(l_id).zfill(5): y for l_id, y in zip(label_id, label_y)}\n",
    "        \n",
    "        # test data directory\n",
    "        self.data_path = os.path.join(data_dir, 'test')\n",
    "        \n",
    "        # get patient ids\n",
    "        self.id_path_list = [path for path in sorted(glob.glob(self.data_path + '/*')) \n",
    "                             if path.split('/')[-1] in self.labels_dict]\n",
    "        self.id_list = [path.split('/')[-1] for path in sorted(glob.glob(self.data_path + '/*'))\n",
    "                        if path.split('/')[-1] in self.labels_dict]\n",
    "        \n",
    "    def __len__(self):\n",
    "        return len(self.id_path_list)\n",
    "    \n",
    "    def __getitem__(self, idx):\n",
    "        if torch.is_tensor(idx):\n",
    "            idx = idx.tolist()\n",
    "            \n",
    "        images = get_patient_images(self.id_path_list[idx])\n",
    "        labels = self.labels_dict[self.id_list[idx]]\n",
    "        id_num = self.id_list[idx]\n",
    "        \n",
    "        imgs_tensor = torch.tensor(images, dtype=torch.float32).permute(-1, 0, 1, 2) # need to reshape\n",
    "#         print(imgs_tensor.shape)\n",
    "        labels_tensor = torch.tensor(labels, dtype=torch.long)\n",
    "        \n",
    "        return imgs_tensor, labels_tensor, id_num"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "876d4af1",
   "metadata": {},
   "outputs": [],
   "source": [
    "# instantiate the test dataset to use for processing\n",
    "test_dataset = BrainScanTestDataset(data_dir='./data')\n",
    "\n",
    "# batch_size = 4 \n",
    "# test_loader = DataLoader(test_dataset, batch_size=batch_size, shuffle=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "08c1f731",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "--- Test Data ---\n",
      "Image shape:  torch.Size([4, 4, 256, 256, 64])\n",
      "Class label shape: torch.Size([4])\n",
      "Class label:  tensor([0, 0, 0, 0])\n"
     ]
    }
   ],
   "source": [
    "# testing tensor size\n",
    "for img, label in test_loader:\n",
    "    print('--- Test Data ---')\n",
    "    print('Image shape: ', img.shape)\n",
    "    print('Class label shape:', label.shape)\n",
    "    print('Class label: ', label)\n",
    "    break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "cc3c3d74",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 256, 256, 64)\n",
      "67.108864\n",
      "67.108864\n"
     ]
    }
   ],
   "source": [
    "# looking at the size of the np.array\n",
    "# data is too large to send to an endpoint\n",
    "\n",
    "data = test_dataset[0]\n",
    "image = data[0]\n",
    "image = image.unsqueeze(0).numpy()\n",
    "print(image.shape)\n",
    "print(image.size * image.itemsize / 1000000)\n",
    "print(image.nbytes / 1000000)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9ca7946",
   "metadata": {},
   "source": [
    "#### Process Test Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "73a56fe9",
   "metadata": {},
   "outputs": [],
   "source": [
    "def format_test_images(test_data_folder, test_dataset):\n",
    "    \"\"\" Load, process and save the test data numpy arrays to \n",
    "        a binary file in NumPy .npy format. Numpy array will \n",
    "        have shape (1, 4, 256, 256, 64)\n",
    "        \n",
    "        Arguments:\n",
    "            test_data_path (str): path to store test data\n",
    "            test_dataset (DataSet): PyTorch DatsSet Class\n",
    "    \"\"\"\n",
    "    \n",
    "    for i in range(len(test_dataset)):\n",
    "        data = test_dataset[i] # first record in dataset\n",
    "        image = data[0] # tensor(image, label, id_num)\n",
    "        id_num = data[2]\n",
    "        path = os.path.join('./data/test_np', id_num)\n",
    "        np.save(path, image.unsqueeze(0).numpy())\n",
    "    print('Completed formating test dataset with length: ', len(test_dataset))\n",
    "          \n",
    "test_data_folder = './data/test_np'\n",
    "format_test_images(test_data_folder, test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "c0ace4e8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device cpu.\n",
      "<class 'numpy.ndarray'>\n",
      "Image shape:  torch.Size([1, 4, 256, 256, 64])\n",
      "Numpy image shape:  (1, 4, 256, 256, 64)\n",
      "ID number:  00001\n",
      "tensor([1])\n",
      "<class 'numpy.ndarray'>\n",
      "Image shape:  torch.Size([1, 4, 256, 256, 64])\n",
      "Numpy image shape:  (1, 4, 256, 256, 64)\n",
      "ID number:  00013\n",
      "tensor([1])\n"
     ]
    }
   ],
   "source": [
    "# test predict function to look at output dimemsions\n",
    "def predict(model, test_dataset):\n",
    "    \n",
    "    device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "    print(\"Using device {}.\".format(device))\n",
    "    model.to(device).eval()\n",
    "    \n",
    "    for i in range(2):\n",
    "        data = test_dataset[i]\n",
    "        image = data[0]\n",
    "        print(type(image.numpy()))\n",
    "        id_num = data[2]\n",
    "        print('Image shape: ', image.unsqueeze(0).shape)\n",
    "        print('Numpy image shape: ', image.unsqueeze(0).numpy().shape)\n",
    "        print('ID number: ', id_num)\n",
    "        path = os.path.join('./data/test_np', id_num)\n",
    "        np.save(path, image.unsqueeze(0).numpy())\n",
    "        \n",
    "        out = model(image.unsqueeze(0))\n",
    "        _, preds = torch.max(out, 1) \n",
    "        print(preds)\n",
    "        \n",
    "        \n",
    "predict(model, test_dataset)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "696c9aae",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "87\n"
     ]
    }
   ],
   "source": [
    "print(len(test_dataset))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "64b1d08d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Inference data is located: s3://sagemaker-us-west-2-904606187431/Radiogenomic/batch_transform\n"
     ]
    }
   ],
   "source": [
    "# upload data to s3 when using batch transform\n",
    "inference_prefix = 'Radiogenomic/batch_transform'\n",
    "inference_data = sagemaker_session.upload_data(path='./data/test_np', bucket=sagemaker_bucket, key_prefix=inference_prefix)\n",
    "print(\"Inference data is located: {}\".format(inference_data))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c47d05d3",
   "metadata": {},
   "source": [
    "### Predict Class \n",
    "Performing inference on the test dataset in the notebook. This will utilize`model_fn` and `predict_fn` to predict the labels. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d9e06c95",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Loading model.\n",
      "model_info: {'model_name': 'efficientnet-b5', 'input_dim': 4, 'output_dim': {'num_classes': 2}, 'epochs': 20, 'val_ratio': 0.15, 'lr': 0.001}\n",
      "Done loading model.\n"
     ]
    }
   ],
   "source": [
    "from source.predict import model_fn\n",
    "from source.predict import predict_fn\n",
    "\n",
    "# load model with recent checkpoint\n",
    "model = model_fn('./data')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "95e538ee",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 4, 256, 256, 64)\n"
     ]
    }
   ],
   "source": [
    "# Predict class labels for test data\n",
    "test_dir = './data/test_np'\n",
    "# patient_id_list = [path.split('/')[-1].split('.')[0] for path in sorted(glob.glob(test_dir + '/*'))]\n",
    "\n",
    "file = np.load('./data/test_np/00833.npy')\n",
    "print(file.shape)\n",
    "prediction = predict_fn(file, model)['predictions'][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "4daccf46",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n"
     ]
    }
   ],
   "source": [
    "print(prediction)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "20038a10",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'00001': 0, '00013': 0, '00015': 0, '00027': 0, '00037': 0, '00047': 0, '00079': 0, '00080': 0, '00082': 0, '00091': 0, '00114': 0, '00119': 0, '00125': 0, '00129': 0, '00135': 0, '00145': 0, '00153': 1, '00161': 0, '00163': 0, '00174': 0, '00181': 0, '00182': 1, '00190': 0, '00200': 0, '00208': 0, '00213': 0, '00229': 1, '00252': 0, '00256': 0, '00264': 0, '00287': 0, '00307': 0, '00323': 0, '00333': 1, '00335': 1, '00337': 0, '00355': 1, '00372': 0, '00381': 0, '00384': 0, '00393': 0, '00422': 0, '00428': 0, '00434': 0, '00438': 0, '00447': 0, '00450': 0, '00458': 0, '00460': 0, '00462': 0, '00463': 0, '00467': 0, '00474': 0, '00489': 0, '00492': 0, '00503': 0, '00521': 0, '00535': 0, '00553': 0, '00560': 0, '00573': 0, '00585': 0, '00592': 0, '00595': 0, '00603': 0, '00644': 0, '00647': 0, '00662': 0, '00671': 0, '00681': 0, '00699': 0, '00702': 0, '00712': 0, '00719': 0, '00721': 0, '00749': 0, '00762': 1, '00769': 0, '00779': 0, '00821': 0, '00822': 0, '00825': 0, '00826': 0, '00829': 0, '00833': 1, '00997': 0, '01006': 0}\n"
     ]
    }
   ],
   "source": [
    "def predict_MGMT(test_dir, model):\n",
    "    \"\"\" Predict the class labels for patients in the test dataset. \n",
    "        \n",
    "        Arguments:\n",
    "            test_dir (str): path to processed test data \n",
    "            model: loaded model with currect checkpoint \n",
    "        \n",
    "        Returns:\n",
    "            submission (.csv): csv file containing predictions\n",
    "    \"\"\"\n",
    "    image_path_list = [path for path in sorted(glob.glob(test_dir + '/*'))]\n",
    "    preds_dict = {}\n",
    "    \n",
    "    for image_path in image_path_list:\n",
    "        file = np.load(image_path)\n",
    "        pred_np = predict_fn(file, model)['predictions'][0] # get the value directly\n",
    "        preds_dict[image_path.split('/')[-1].split('.')[0]] = pred_np\n",
    "        \n",
    "    return preds_dict\n",
    "\n",
    "preds_dict = predict_MGMT(test_dir, model)\n",
    "print(preds_dict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "07284a80",
   "metadata": {},
   "outputs": [],
   "source": [
    "# convert to the format required by Kaggle\n",
    "submission = pd.DataFrame(columns=['BraTS21ID', 'MGMT_value'])\n",
    "submission['BraTS21ID'] = [key for key in preds_dict]\n",
    "submission['MGMT_value'] = [val for (key, val) in preds_dict.items()]\n",
    "submission.to_csv('./data/submission.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "030e9d76",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>BraTS21ID</th>\n",
       "      <th>MGMT_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>00001</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>00013</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>00015</td>\n",
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       "      <th>3</th>\n",
       "      <td>00027</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>00037</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>00829</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>00833</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>00997</td>\n",
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       "      <th>86</th>\n",
       "      <td>01006</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   BraTS21ID  MGMT_value\n",
       "0      00001           0\n",
       "1      00013           0\n",
       "2      00015           0\n",
       "3      00027           0\n",
       "4      00037           0\n",
       "..       ...         ...\n",
       "82     00826           0\n",
       "83     00829           0\n",
       "84     00833           1\n",
       "85     00997           0\n",
       "86     01006           0\n",
       "\n",
       "[87 rows x 2 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "submission"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a410101d",
   "metadata": {},
   "source": [
    "### Batch Transform Job\n",
    "Error 413: Get this error when trying to lauch a batch transform. Need to investigate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "93b77776",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a model from the trained estimator data \n",
    "# and point to the predict script\n",
    "pytorch_model = PyTorchModel(model_data=attached_estimator.model_data,\n",
    "                             role=role,\n",
    "                             framework_version='1.5.0',\n",
    "                             py_version='py3',\n",
    "                             entry_point='predict.py',\n",
    "                             source_dir='source')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "e187efbd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a transformer \n",
    "pytorch_transformer = pytorch_model.transformer(instance_count=2,\n",
    "                                                instance_type='ml.c5.2xlarge',\n",
    "                                                strategy='SingleRecord',\n",
    "                                                max_payload=100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "445024dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# pytorch_transformer.transform(data=inference_data,\n",
    "#                               data_type=\"S3Prefix\",\n",
    "#                               content_type=\"application/x-npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a730a4fd",
   "metadata": {},
   "source": [
    "## <a id='references'></a>\n",
    "### References\n",
    "[1]<a id='1'></a> https://www.kaggle.com/c/rsna-miccai-brain-tumor-radiogenomic-classification/overview  \n",
    "[2]<a id='2'></a> U.Baid, et al., “The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification”, arXiv:2107.02314, 2021.  \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e167aab8",
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true,
     "source_hidden": true
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "----- x -----\n",
      "1\n",
      "Correct 0\n",
      "\n",
      "----- y -----\n",
      "1\n",
      "Correct:  1\n",
      "Item in tensor is:  <class 'bool'>\n",
      "False\n",
      "1\n",
      "<class 'int'>\n"
     ]
    }
   ],
   "source": [
    "correct = 0\n",
    "x = torch.tensor([1], device='cpu').detach().item()\n",
    "y = torch.tensor([0], device='cpu').detach().item()\n",
    "print('\\n----- x -----')\n",
    "print(x)\n",
    "# print(x.shape)\n",
    "# print(y.size(0))\n",
    "# print(x[0].item())\n",
    "# print(y[0].item())\n",
    "# print(x[1].item())\n",
    "correct += (x == y)\n",
    "print('Correct', correct)\n",
    "\n",
    "y = torch.tensor([1]).item()\n",
    "print('\\n----- y -----')\n",
    "print(y)\n",
    "# print(y.shape)\n",
    "# print(y.size())\n",
    "# print(y[0].item())\n",
    "# print(y[1].item())\n",
    "correct += (x == y)\n",
    "\n",
    "print('Correct: ', correct)\n",
    "\n",
    "\n",
    "preds = torch.tensor([1], device='cpu').detach().item()\n",
    "batch_y = torch.tensor([0], device='cpu').detach().item()\n",
    "print('Item in tensor is: ',type((preds == batch_y)))\n",
    "print((preds == batch_y))\n",
    "print(preds)\n",
    "print(type(batch_y))"
   ]
  }
 ],
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