main.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "2210dd1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# importing pytorch and related package\n",
    "import torch\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from torch import optim\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "4e228b8e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from os import walk\n",
    "# return a tuple of arrays ([all directories inside input path], [all files inside input path])\n",
    "def get_content_of_path(pathname, get_dir=True, get_filenames=True):\n",
    "    f = []\n",
    "    for (dirpath, dirnames, filenames) in walk(pathname):\n",
    "        f.extend(filenames)\n",
    "        break\n",
    "    result = []\n",
    "    if get_dir is False:\n",
    "        return filenames\n",
    "    if get_filenames is False:\n",
    "        return dirnames\n",
    "    return dirnames, filenames\n",
    "\n",
    "def get_image_lists_from_dataset(datasets_path):\n",
    "    train_bmode_list = []\n",
    "    train_mask_list = []\n",
    "    test_bmode_list = []\n",
    "    test_mask_list = []\n",
    "\n",
    "    if datasets_path != None:\n",
    "        dataset_list = os.listdir(datasets_path)\n",
    "        for dataset in dataset_list:\n",
    "            bmode_list = [] # input\n",
    "            mask_list = [] # output\n",
    "            dataset_folder = datasets_path + dataset + \"/\"\n",
    "            dataset_dirnames = get_content_of_path(dataset_folder, get_filenames=False)\n",
    "\n",
    "            # loop through image files and put them in list corresponding to their filename\n",
    "            for filename in dataset_dirnames:\n",
    "                if filename != \"bmode\" and filename != \"mask\":\n",
    "                    continue\n",
    "                image_folder = dataset_folder + filename + \"/\"\n",
    "                image_paths = get_content_of_path(image_folder, get_dir=False)\n",
    "                image_paths = [dataset_folder + image_path for image_path in image_paths]\n",
    "                if filename == \"bmode\":\n",
    "                    bmode_list = image_paths\n",
    "                elif filename == \"mask\":\n",
    "                    mask_list = image_paths\n",
    "\n",
    "            # assign bmode and mask list depending on dataset type\n",
    "            if dataset == \"train\":\n",
    "                train_bmode_list = bmode_list\n",
    "                train_mask_list = mask_list\n",
    "            elif dataset == \"test\":\n",
    "                test_bmode_list = bmode_list\n",
    "                test_mask_list = mask_list\n",
    "    return train_bmode_list, train_mask_list, test_bmode_list, test_mask_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "2085bce2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_26.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_91.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_166.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_56.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_41.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_91.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_161.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_11.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_241.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_26.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_66.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_196.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_111.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_196.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_101.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_41.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_236.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_16.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_71.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_211.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_86.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_61.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_121.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_221.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_161.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_181.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_221.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_251.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_121.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_261.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_81.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_141.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_46.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_146.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_141.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_106.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_11.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_156.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_156.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_111.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_66.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_151.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_131.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_186.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_186.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_126.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_151.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_231.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_146.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_256.png'],\n",
       " ['/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_206.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_106.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_176.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_71.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_36.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_56.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_136.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_101.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_51.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_36.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_191.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_6.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_201.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_61.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_226.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_211.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_31.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_216.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_51.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_16.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_31.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_216.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_96.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_131.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_116.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_261.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_1.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_171.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_191.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_81.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_21.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_126.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_201.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_246.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_86.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_166.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_206.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_76.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_96.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_181.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_231.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_256.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_76.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_246.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_1.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_116.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_21.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_241.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_136.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_251.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_236.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_176.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_226.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_6.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_46.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_171.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_26.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_91.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_166.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_56.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_41.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_91.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_161.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_11.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_241.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_26.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_66.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_196.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_111.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_196.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_101.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_41.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_236.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_16.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_71.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_211.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_86.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_61.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_121.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_221.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_161.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_181.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_221.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_251.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_121.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_261.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_81.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_141.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_46.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_146.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_141.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_106.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_11.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_156.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_156.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_111.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_66.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_151.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_131.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_186.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_186.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_126.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_151.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATL_231.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_146.png',\n",
       "  '/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/test/VNS085_CCATR_256.png'])"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "datasets_path = \"/gpfs/fs2/scratch/mdoyley_lab/GPU_VA/DL_dataset/\"\n",
    "get_image_lists_from_dataset(datasets_path)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b30728ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "class deconvol(nn.Module):\n",
    "    def __init__(self, out_channels, kernel_size ,num_layer,name,channel_change, bias=True):\n",
    "        super().__init__()\n",
    "        self.sequence = nn.sequential()\n",
    "        sequence.add_module('Max Unpooling'+name,nn.MaxUnpool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False))\n",
    "        for i in range(num_layer-1):\n",
    "            sequence.add_module('Deconv'+name+str(i),nn.ConvTranspose2d(out_channels, out_channels, kernel_size=kernel_size, bias=bias))\n",
    "        if (channel_change):\n",
    "            sequence.add_module('Deconv'+name+str(i),nn.ConvTranspose2d(out_channels, out_channels/2, kernel_size=kernel_size, bias=bias))\n",
    "        else:\n",
    "            sequence.add_module('Deconv'+name+str(i),nn.ConvTranspose2d(out_channels, out_channels, kernel_size=kernel_size, bias=bias))\n",
    "    def forward(self,x):\n",
    "        return self.sequence(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "1a2fc112",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torchvision.models as models\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "model_ft = models.vgg16(pretrained=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "12a485e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cpu\n"
     ]
    }
   ],
   "source": [
    "print(device)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "169597aa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sequential(\n",
      "  (0): Sequential(\n",
      "    (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (1): ReLU(inplace)\n",
      "    (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (3): ReLU(inplace)\n",
      "    (4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (6): ReLU(inplace)\n",
      "    (7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (8): ReLU(inplace)\n",
      "    (9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (11): ReLU(inplace)\n",
      "    (12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (13): ReLU(inplace)\n",
      "    (14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (15): ReLU(inplace)\n",
      "    (16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (18): ReLU(inplace)\n",
      "    (19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (20): ReLU(inplace)\n",
      "    (21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (22): ReLU(inplace)\n",
      "    (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "    (24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (25): ReLU(inplace)\n",
      "    (26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (27): ReLU(inplace)\n",
      "    (28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
      "    (29): ReLU(inplace)\n",
      "    (30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
      "  )\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "model_encode = nn.Sequential(*[model_ft.features])\n",
    "print(model_encode)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f39c1c3d",
   "metadata": {},
   "outputs": [],
   "source": [
    "for param in model_encode.parameters():\n",
    "    param.requires_grad = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "09846833",
   "metadata": {},
   "outputs": [],
   "source": [
    "class main_model(nn.Module):\n",
    "    def __init__(self, in_channels, out_channels, kernel_size ,num_layer,name, bias=True):\n",
    "        super().__init__()\n",
    "        self.sequence = model_encode\n",
    "        sequence.add_module(nn.Flatten())\n",
    "        sequence.add_module(nn.Linear(in_features=25088, out_features=4096, bias=True))\n",
    "        sequence.add_module(nn.Linear(in_features=4096, out_features=4096, bias=True))\n",
    "        sequence.add_module(nn.Linear(in_features=4096, out_features=25088, bias=True))\n",
    "        self.sequence1 = nn.Sequential()\n",
    "        sequence1.add_module(deconvol(512, 3 ,3,'decov1',False, bias=True))\n",
    "        sequence1.add_module(deconvol(512, 3 ,3,'decov2',True, bias=True))\n",
    "        sequence1.add_module(deconvol(256, 3 ,3,'decov3',False, bias=True))\n",
    "        sequence1.add_module(deconvol(128, 3 ,2,'decov4',False, bias=True))\n",
    "        sequence1.add_module(deconvol(64, 3 ,2,'decov5',True, bias=True))\n",
    "        sequence1.add_module(nn.ConvTranspose2d(64, 64, kernel_size=1,stride=1, padding=1, bias=bias))\n",
    "    def forward(self,x):\n",
    "        x=self.sequence(x)\n",
    "        x=torch.reshape(x, (None, 7, 7, 512))\n",
    "        return self.sequence1(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "bdc12772",
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_model():\n",
    "    model = main_model()\n",
    "    return model, optim.SGD(model.parameters(), lr=lr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4750960c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def loss_batch(model, xb, yb, loss_func=F.cross_entropy, opt=None):\n",
    "    loss = loss_func(model(xb), yb)\n",
    "\n",
    "    if opt is not None:\n",
    "        loss.backward()\n",
    "        opt.step()\n",
    "        opt.zero_grad()\n",
    "\n",
    "    return loss.item(), len(xb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "23d71bda",
   "metadata": {},
   "outputs": [],
   "source": [
    "def fit(epochs, model, loss_func, opt, train_dl, valid_dl):\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        loss1 = []\n",
    "        num = []\n",
    "        for xb, yb in train_dl:\n",
    "            losses, nums = loss_batch(model, loss_func, xb, yb)\n",
    "        loss1.append(losses)\n",
    "        num.append(nums)\n",
    "        train_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)\n",
    "\n",
    "        model.eval()\n",
    "        with torch.no_grad():\n",
    "            losses, nums = zip(\n",
    "                *[loss_batch(model, loss_func, xb, yb) for xb, yb in valid_dl]\n",
    "            )\n",
    "        val_loss = np.sum(np.multiply(losses, nums)) / np.sum(nums)\n",
    "\n",
    "        print(epoch, val_loss, train_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "102fd353",
   "metadata": {},
   "outputs": [],
   "source": [
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import TensorDataset\n",
    "def get_data(x_train, y_train, x_valid, y_valid, bs):\n",
    "    train_ds = TensorDataset(x_train, y_train)\n",
    "    train_dl = DataLoader(train_ds, batch_size=bs, shuffle=True)\n",
    "    valid_ds = TensorDataset(x_valid, y_valid)\n",
    "    valid_dl = DataLoader(valid_ds, batch_size=bs * 2)\n",
    "    return (train_dl,valid_dl)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "113ee393",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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