get 1 random policy

+import os
+import fire
+import json
+from pprint import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+from torch.utils.tensorboard import SummaryWriter
+
+from utils import *
+
+# command
+# python getAugmented.py --model_path='logs/April_24_21:05:15__resnet50__None/'
+
+def eval(model_path):
+    print('\n[+] Parse arguments')
+    kwargs_path = os.path.join(model_path, 'kwargs.json')
+    kwargs = json.loads(open(kwargs_path).read())
+    args, kwargs = parse_args(kwargs)
+    pprint(args)
+    device = torch.device('cuda' if args.use_cuda else 'cpu')
+
+    cp_path = os.path.join(model_path, 'augmentation.cp')
+    
+    writer = SummaryWriter(log_dir=model_path)
+
+
+    print('\n[+] Load transform')
+    # list
+    with open(cp_path, 'rb') as f:
+        aug_transform_list = pickle.load(f)
+
+    augmented_image_list = [torch.Tensor(240,0)] * len(get_dataset(args, None, 'test'))
+
+
+    print('\n[+] Load dataset')
+    for aug_idx, aug_transform in enumerate(aug_transform_list):
+        dataset = get_dataset(args, aug_transform, 'test') 
+
+        loader = iter(get_aug_dataloader(args, dataset))
+
+        for i, (images, target) in enumerate(loader):
+            images = images.view(240, 240)
+
+            # concat image
+            augmented_image_list[i] = torch.cat([augmented_image_list[i], images], dim = 1)
+            
+            if i % 1000 == 0: 
+                print("\n images size: ", augmented_image_list[i].size()) # [240, 240]
+
+            break
+        # break
+
+
+   # print(augmented_image_list)
+
+
+    print('\n[+] Write on tensorboard')   
+    if writer:
+        for i, data in enumerate(augmented_image_list):
+            tag = 'img/' + str(i) 
+            writer.add_image(tag, data.view(1, 240, -1), global_step=0)
+            break
+
+    writer.close()      
+
+
+    # if writer:
+    #     for j in range():
+    #         tag = 'img/' + str(img_count) + '_' + str(j) 
+    #         # writer.add_image(tag,
+    #         #         concat_image_features(images[j], first[j]), global_step=step) 
+    #         # if j > 0:
+    #         #     fore = concat_image_features(fore, images[j])
+
+    #     writer.add_image(tag, fore, global_step=0)
+    #     img_count = img_count + 1
+
+    # writer.close()
+
+if __name__ == '__main__':
+    fire.Fire(eval)

+import os
+import fire
+import json
+from pprint import pprint
+import pickle
+
+import torch
+import torch.nn as nn
+from torch.utils.tensorboard import SummaryWriter
+
+from utils import *
+
+# command
+# python getAugmented.py --model_path='logs/April_24_21:05:15__resnet50__None/'
+
+def eval(model_path):
+    print('\n[+] Parse arguments')
+    kwargs_path = os.path.join(model_path, 'kwargs.json')
+    kwargs = json.loads(open(kwargs_path).read())
+    args, kwargs = parse_args(kwargs)
+    pprint(args)
+    device = torch.device('cuda' if args.use_cuda else 'cpu')
+
+    cp_path = os.path.join(model_path, 'augmentation.cp')
+    
+    writer = SummaryWriter(log_dir=model_path)
+
+
+    print('\n[+] Load transform')
+    # list
+    with open(cp_path, 'rb') as f:
+        aug_transform_list = pickle.load(f)
+
+    augmented_image_list = [torch.Tensor(240,0)] * len(get_dataset(args, None, 'train'))
+
+
+    print('\n[+] Load dataset')
+    for aug_idx, aug_transform in enumerate(aug_transform_list):
+        dataset = get_dataset(args, aug_transform, 'train') 
+
+        loader = iter(get_aug_dataloader(args, dataset))
+
+        for i, (images, target) in enumerate(loader):
+            images = images.view(240, 240)
+
+            # concat image
+            augmented_image_list[i] = torch.cat([augmented_image_list[i], images], dim = 1)
+
+
+
+
+    print('\n[+] Write on tensorboard')   
+    if writer:
+        for i, data in enumerate(augmented_image_list):
+            tag = 'img/' + str(i) 
+            writer.add_image(tag, data.view(1, 240, -1), global_step=0)
+
+    writer.close()
+   
+
+if __name__ == '__main__':
+    fire.Fire(eval)

+import os
+import fire
+import json
+from pprint import pprint
+import pickle
+import random
+
+import torch
+import torch.nn as nn
+from torchvision.utils import save_image
+from torch.utils.tensorboard import SummaryWriter
+
+from utils import *
+
+# command
+# python getAugmented_saveimg.py --model_path='logs/April_26_00:55:16__resnet50__None/'
+
+def eval(model_path):
+    print('\n[+] Parse arguments')
+    kwargs_path = os.path.join(model_path, 'kwargs.json')
+    kwargs = json.loads(open(kwargs_path).read())
+    args, kwargs = parse_args(kwargs)
+    pprint(args)
+    device = torch.device('cuda' if args.use_cuda else 'cpu')
+
+    cp_path = os.path.join(model_path, 'augmentation.cp')
+    
+    writer = SummaryWriter(log_dir=model_path)
+
+
+    print('\n[+] Load transform')
+    # list to tensor
+    with open(cp_path, 'rb') as f:
+        aug_transform_list = pickle.load(f)
+
+    transform = transforms.RandomChoice(aug_transform_list)
+
+
+    print('\n[+] Load dataset')
+
+    dataset = get_dataset(args, transform, 'train')
+    loader = iter(get_aug_dataloader(args, dataset))
+
+
+    print('\n[+] Save 1 random policy')   
+    os.makedirs(os.path.join(model_path, 'augmented_imgs'))
+    save_dir = os.path.join(model_path, 'augmented_imgs')
+
+    for i, (image, target) in enumerate(loader):
+        image = image.view(240, 240)
+        # save img
+        save_image(image, os.path.join(save_dir, 'aug_'+ str(i) + '.png'))
+
+        if(i % 100 == 0):
+            print("\n saved images: ", i)
+                
+    print('\n[+] Finished to save')
+
+if __name__ == '__main__':
+    fire.Fire(eval)
+
+    
+    

+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "name": "classify normal/lesion.ipynb",
+      "provenance": [],
+      "collapsed_sections": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "accelerator": "GPU"
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "AjoTMXMCrFYX",
+        "colab_type": "code",
+        "outputId": "2548434e-72b7-4946-9748-070103017379",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 129
+        }
+      },
+      "source": [
+        "from google.colab import drive\n",
+        "drive.mount('/content/drive')"
+      ],
+      "execution_count": 1,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
+            "\n",
+            "Enter your authorization code:\n",
+            "··········\n",
+            "Mounted at /content/drive\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "lXK8NZfIyzeB",
+        "colab_type": "code",
+        "outputId": "17508683-94fb-45fa-b9df-f7ddb9401b68",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 146
+        }
+      },
+      "source": [
+        "!git clone http://khuhub.khu.ac.kr/2020-1-capstone-design2/2016104167.git"
+      ],
+      "execution_count": 2,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Cloning into '2016104167'...\n",
+            "remote: Counting objects: 11451, done.\u001b[K\n",
+            "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
+            "remote: Total 11451 (delta 15), reused 0 (delta 0)\u001b[K\n",
+            "Receiving objects: 100% (11451/11451), 292.82 MiB | 384.00 KiB/s, done.\n",
+            "Resolving deltas: 100% (1109/1109), done.\n",
+            "Checking out files: 100% (15684/15684), done.\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "TmGc36H2y5sI",
+        "colab_type": "code",
+        "outputId": "49ce70f0-10bb-48d8-bd89-de98a28c7893",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 35
+        }
+      },
+      "source": [
+        "%cd '2016104167/code/classifier/'"
+      ],
+      "execution_count": 3,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "/content/2016104167/code/classifier\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "oJ08JUJCzEEE",
+        "colab_type": "code",
+        "outputId": "f9454032-73ad-444e-b708-e1228f6b3a0b",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "source": [
+        "!python -m pip install -r \"requirements.txt\""
+      ],
+      "execution_count": 4,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.16.0)\n",
+            "Collecting tb-nightly\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/4e/46/4b95936aed44f2154d936160de6c58e3bd4cf8501152db21945617c84694/tb_nightly-2.3.0a20200425-py3-none-any.whl (2.9MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.9MB 39.9MB/s \n",
+            "\u001b[?25hRequirement already satisfied: hyperopt in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (0.1.2)\n",
+            "Collecting pillow==6.2.1\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/10/5c/0e94e689de2476c4c5e644a3bd223a1c1b9e2bdb7c510191750be74fa786/Pillow-6.2.1-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
+            "\u001b[K     |████████████████████████████████| 2.1MB 48.1MB/s \n",
+            "\u001b[?25hRequirement already satisfied: natsort in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (5.5.0)\n",
+            "Collecting fire\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/34/a7/0e22e70778aca01a52b9c899d9c145c6396d7b613719cd63db97ffa13f2f/fire-0.3.1.tar.gz (81kB)\n",
+            "\u001b[K     |████████████████████████████████| 81kB 12.1MB/s \n",
+            "\u001b[?25hCollecting torchvision==0.2.2\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/ce/a1/66d72a2fe580a9f0fcbaaa5b976911fbbde9dce9b330ba12791997b856e9/torchvision-0.2.2-py2.py3-none-any.whl (64kB)\n",
+            "\u001b[K     |████████████████████████████████| 71kB 11.8MB/s \n",
+            "\u001b[?25hCollecting torch==1.1.0\n",
+            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/69/60/f685fb2cfb3088736bafbc9bdbb455327bdc8906b606da9c9a81bae1c81e/torch-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (676.9MB)\n",
+            "\u001b[K     |████████████████████████████████| 676.9MB 18kB/s \n",
+            "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 9)) (1.0.3)\n",
+            "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 10)) (0.0)\n",
+            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (3.2.1)\n",
+            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (3.10.0)\n",
+            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.7.2)\n",
+            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.18.3)\n",
+            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.6.0.post3)\n",
+            "Requirement already satisfied: wheel>=0.26; python_version >= \"3\" in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.34.2)\n",
+            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.9.0)\n",
+            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (46.1.3)\n",
+            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (2.21.0)\n",
+            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.12.0)\n",
+            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.0.1)\n",
+            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (1.28.1)\n",
+            "Requirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.6/dist-packages (from tb-nightly->-r requirements.txt (line 2)) (0.4.1)\n",
+            "Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (2.4)\n",
+            "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (4.38.0)\n",
+            "Requirement already satisfied: pymongo in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (3.10.1)\n",
+            "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (1.4.1)\n",
+            "Requirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from fire->-r requirements.txt (line 6)) (1.1.0)\n",
+            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 9)) (2.8.1)\n",
+            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 9)) (2018.9)\n",
+            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.6/dist-packages (from sklearn->-r requirements.txt (line 10)) (0.22.2.post1)\n",
+            "Requirement already satisfied: rsa<4.1,>=3.1.4 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (4.0)\n",
+            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (0.2.8)\n",
+            "Requirement already satisfied: cachetools<3.2,>=2.0.0 in /usr/local/lib/python3.6/dist-packages (from google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (3.1.1)\n",
+            "Requirement already satisfied: chardet<3.1.0,>=3.0.2 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (3.0.4)\n",
+            "Requirement already satisfied: idna<2.9,>=2.5 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (2.8)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (2020.4.5.1)\n",
+            "Requirement already satisfied: urllib3<1.25,>=1.21.1 in /usr/local/lib/python3.6/dist-packages (from requests<3,>=2.21.0->tb-nightly->-r requirements.txt (line 2)) (1.24.3)\n",
+            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.6/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->-r requirements.txt (line 2)) (1.3.0)\n",
+            "Requirement already satisfied: decorator>=4.3.0 in /usr/local/lib/python3.6/dist-packages (from networkx->hyperopt->-r requirements.txt (line 3)) (4.4.2)\n",
+            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.6/dist-packages (from scikit-learn->sklearn->-r requirements.txt (line 10)) (0.14.1)\n",
+            "Requirement already satisfied: pyasn1>=0.1.3 in /usr/local/lib/python3.6/dist-packages (from rsa<4.1,>=3.1.4->google-auth<2,>=1.6.3->tb-nightly->-r requirements.txt (line 2)) (0.4.8)\n",
+            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.6/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tb-nightly->-r requirements.txt (line 2)) (3.1.0)\n",
+            "Building wheels for collected packages: fire\n",
+            "  Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
+            "  Created wheel for fire: filename=fire-0.3.1-py2.py3-none-any.whl size=111005 sha256=090cc0b99c44969c5594966fd1af925b4ff73b02719d44b05fb4aacd07b9bfb3\n",
+            "  Stored in directory: /root/.cache/pip/wheels/c1/61/df/768b03527bf006b546dce284eb4249b185669e65afc5fbb2ac\n",
+            "Successfully built fire\n",
+            "\u001b[31mERROR: torchvision 0.2.2 has requirement tqdm==4.19.9, but you'll have tqdm 4.38.0 which is incompatible.\u001b[0m\n",
+            "\u001b[31mERROR: albumentations 0.1.12 has requirement imgaug<0.2.7,>=0.2.5, but you'll have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
+            "Installing collected packages: tb-nightly, pillow, fire, torch, torchvision\n",
+            "  Found existing installation: Pillow 7.0.0\n",
+            "    Uninstalling Pillow-7.0.0:\n",
+            "      Successfully uninstalled Pillow-7.0.0\n",
+            "  Found existing installation: torch 1.4.0\n",
+            "    Uninstalling torch-1.4.0:\n",
+            "      Successfully uninstalled torch-1.4.0\n",
+            "  Found existing installation: torchvision 0.5.0\n",
+            "    Uninstalling torchvision-0.5.0:\n",
+            "      Successfully uninstalled torchvision-0.5.0\n",
+            "Successfully installed fire-0.3.1 pillow-6.2.1 tb-nightly-2.3.0a20200425 torch-1.1.0 torchvision-0.2.2\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "jdayOoSYHJDf",
+        "colab_type": "code",
+        "outputId": "f663313d-1166-4f6c-f617-d3d331ce7793",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "source": [
+        "!python train.py --use_cuda=True --network=resnet50 --optimizer=adam "
+      ],
+      "execution_count": 9,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "[+] Parse arguments\n",
+            "Args(augment_path=None, batch_size=8, dataset='BraTS', learning_rate=1e-06, max_step=2000, network='resnet50', num_workers=4, optimizer='adam', print_step=50, scheduler='exp', seed=None, start_step=0, use_cuda=True, val_step=50)\n",
+            "\n",
+            "[+] Create log dir\n",
+            "2020-04-26 04:35:05.510266: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
+            "\n",
+            "[+] Create network\n",
+            "\n",
+            "[+] Load dataset\n",
+            "\n",
+            "[+] Start training\n",
+            "\n",
+            "[+] Use 1 GPUs\n",
+            "\n",
+            "[+] Using GPU: Tesla P4 \n",
+            "\n",
+            "[+] Training step: 0/2000\tTraining epoch: 0/580\tElapsed time: 0.01min\tLearning rate: 9.999283e-07\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 7.1226911544799805\n",
+            "  FW Time : 125.206ms\n",
+            "  BW Time : 14.758ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 49/2000\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 7.333343982696533\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 50/2000\tTraining epoch: 0/580\tElapsed time: 0.17min\tLearning rate: 9.963498469652168e-07\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 6.839444160461426\n",
+            "  FW Time : 15.798ms\n",
+            "  BW Time : 10.145ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 99/2000\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 7.0915045738220215\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 100/2000\tTraining epoch: 0/580\tElapsed time: 0.33min\tLearning rate: 9.92784200174764e-07\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 6.130648136138916\n",
+            "  FW Time : 15.954ms\n",
+            "  BW Time : 13.310ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 149/2000\n",
+            "  Acc@1 : 12.000%\n",
+            "  Loss : 6.664271354675293\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 150/2000\tTraining epoch: 0/580\tElapsed time: 0.49min\tLearning rate: 9.89231313798811e-07\n",
+            "  Acc@1 : 25.000%\n",
+            "  Loss : 5.820688247680664\n",
+            "  FW Time : 15.717ms\n",
+            "  BW Time : 12.132ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 199/2000\n",
+            "  Acc@1 : 38.400%\n",
+            "  Loss : 6.647609233856201\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 200/2000\tTraining epoch: 0/580\tElapsed time: 0.65min\tLearning rate: 9.85691142171539e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 5.464842319488525\n",
+            "  FW Time : 18.873ms\n",
+            "  BW Time : 14.734ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 249/2000\n",
+            "  Acc@1 : 51.600%\n",
+            "  Loss : 6.3311767578125\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 250/2000\tTraining epoch: 0/580\tElapsed time: 0.80min\tLearning rate: 9.821636397905564e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 5.102325439453125\n",
+            "  FW Time : 20.299ms\n",
+            "  BW Time : 13.266ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 299/2000\n",
+            "  Acc@1 : 65.000%\n",
+            "  Loss : 5.964324951171875\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 300/2000\tTraining epoch: 0/580\tElapsed time: 0.97min\tLearning rate: 9.786487613163075e-07\n",
+            "  Acc@1 : 75.000%\n",
+            "  Loss : 4.567136287689209\n",
+            "  FW Time : 22.482ms\n",
+            "  BW Time : 11.849ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 349/2000\n",
+            "  Acc@1 : 67.000%\n",
+            "  Loss : 5.935492038726807\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 350/2000\tTraining epoch: 0/580\tElapsed time: 1.13min\tLearning rate: 9.751464615714972e-07\n",
+            "  Acc@1 : 75.000%\n",
+            "  Loss : 4.528119087219238\n",
+            "  FW Time : 15.663ms\n",
+            "  BW Time : 12.312ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 399/2000\n",
+            "  Acc@1 : 66.200%\n",
+            "  Loss : 6.177009105682373\n",
+            "\n",
+            "[+] Training step: 400/2000\tTraining epoch: 0/580\tElapsed time: 1.28min\tLearning rate: 9.716566955405052e-07\n",
+            "  Acc@1 : 50.000%\n",
+            "  Loss : 5.060157775878906\n",
+            "  FW Time : 18.538ms\n",
+            "  BW Time : 15.068ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 449/2000\n",
+            "  Acc@1 : 70.400%\n",
+            "  Loss : 5.513169288635254\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 450/2000\tTraining epoch: 0/580\tElapsed time: 1.44min\tLearning rate: 9.681794183688074e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 4.341537952423096\n",
+            "  FW Time : 20.281ms\n",
+            "  BW Time : 11.464ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 499/2000\n",
+            "  Acc@1 : 71.200%\n",
+            "  Loss : 5.211921691894531\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 500/2000\tTraining epoch: 0/580\tElapsed time: 1.60min\tLearning rate: 9.647145853624042e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 3.643502712249756\n",
+            "  FW Time : 35.242ms\n",
+            "  BW Time : 13.355ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 549/2000\n",
+            "  Acc@1 : 73.000%\n",
+            "  Loss : 5.065425872802734\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 550/2000\tTraining epoch: 0/580\tElapsed time: 1.76min\tLearning rate: 9.61262151987242e-07\n",
+            "  Acc@1 : 37.500%\n",
+            "  Loss : 4.352468013763428\n",
+            "  FW Time : 15.172ms\n",
+            "  BW Time : 14.894ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 599/2000\n",
+            "  Acc@1 : 72.800%\n",
+            "  Loss : 5.094676971435547\n",
+            "\n",
+            "[+] Training step: 600/2000\tTraining epoch: 0/580\tElapsed time: 1.92min\tLearning rate: 9.578220738686398e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 2.67336106300354\n",
+            "  FW Time : 16.094ms\n",
+            "  BW Time : 11.950ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 649/2000\n",
+            "  Acc@1 : 74.200%\n",
+            "  Loss : 4.8995866775512695\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 650/2000\tTraining epoch: 0/580\tElapsed time: 2.08min\tLearning rate: 9.543943067907226e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 2.3277320861816406\n",
+            "  FW Time : 22.967ms\n",
+            "  BW Time : 16.513ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 699/2000\n",
+            "  Acc@1 : 78.000%\n",
+            "  Loss : 4.331630706787109\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 700/2000\tTraining epoch: 0/580\tElapsed time: 2.24min\tLearning rate: 9.509788066958503e-07\n",
+            "  Acc@1 : 50.000%\n",
+            "  Loss : 4.2444071769714355\n",
+            "  FW Time : 26.178ms\n",
+            "  BW Time : 20.318ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 749/2000\n",
+            "  Acc@1 : 79.800%\n",
+            "  Loss : 4.202180862426758\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 750/2000\tTraining epoch: 0/580\tElapsed time: 2.40min\tLearning rate: 9.475755296840536e-07\n",
+            "  Acc@1 : 75.000%\n",
+            "  Loss : 2.0681729316711426\n",
+            "  FW Time : 14.539ms\n",
+            "  BW Time : 9.137ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 799/2000\n",
+            "  Acc@1 : 81.800%\n",
+            "  Loss : 3.595407009124756\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 800/2000\tTraining epoch: 0/580\tElapsed time: 2.56min\tLearning rate: 9.441844320124666e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 1.7526265382766724\n",
+            "  FW Time : 16.346ms\n",
+            "  BW Time : 13.979ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 849/2000\n",
+            "  Acc@1 : 82.800%\n",
+            "  Loss : 3.435865879058838\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 850/2000\tTraining epoch: 0/580\tElapsed time: 2.72min\tLearning rate: 9.408054700947673e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 1.327768325805664\n",
+            "  FW Time : 20.280ms\n",
+            "  BW Time : 11.583ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 899/2000\n",
+            "  Acc@1 : 86.800%\n",
+            "  Loss : 3.0217819213867188\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 900/2000\tTraining epoch: 0/580\tElapsed time: 2.88min\tLearning rate: 9.37438600500616e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 1.7023265361785889\n",
+            "  FW Time : 16.003ms\n",
+            "  BW Time : 11.639ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 949/2000\n",
+            "  Acc@1 : 89.600%\n",
+            "  Loss : 2.5056638717651367\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 950/2000\tTraining epoch: 0/580\tElapsed time: 3.04min\tLearning rate: 9.340837799550989e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 2.0191428661346436\n",
+            "  FW Time : 26.238ms\n",
+            "  BW Time : 11.809ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 999/2000\n",
+            "  Acc@1 : 92.600%\n",
+            "  Loss : 1.8181736469268799\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1000/2000\tTraining epoch: 0/580\tElapsed time: 3.20min\tLearning rate: 9.307409653381686e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 0.9778107404708862\n",
+            "  FW Time : 18.434ms\n",
+            "  BW Time : 17.896ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1049/2000\n",
+            "  Acc@1 : 94.400%\n",
+            "  Loss : 1.7727973461151123\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1050/2000\tTraining epoch: 0/580\tElapsed time: 3.36min\tLearning rate: 9.274101136840937e-07\n",
+            "  Acc@1 : 75.000%\n",
+            "  Loss : 1.6472196578979492\n",
+            "  FW Time : 22.264ms\n",
+            "  BW Time : 11.575ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1099/2000\n",
+            "  Acc@1 : 96.400%\n",
+            "  Loss : 1.3225497007369995\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1100/2000\tTraining epoch: 0/580\tElapsed time: 3.52min\tLearning rate: 9.240911821809037e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 1.520838737487793\n",
+            "  FW Time : 17.655ms\n",
+            "  BW Time : 11.358ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1149/2000\n",
+            "  Acc@1 : 97.200%\n",
+            "  Loss : 1.0447773933410645\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1150/2000\tTraining epoch: 0/580\tElapsed time: 3.69min\tLearning rate: 9.207841281698394e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.45810914039611816\n",
+            "  FW Time : 21.189ms\n",
+            "  BW Time : 21.055ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1199/2000\n",
+            "  Acc@1 : 97.200%\n",
+            "  Loss : 0.6157697439193726\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1200/2000\tTraining epoch: 0/580\tElapsed time: 3.85min\tLearning rate: 9.174889091448058e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.2990947961807251\n",
+            "  FW Time : 16.315ms\n",
+            "  BW Time : 13.309ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1249/2000\n",
+            "  Acc@1 : 98.200%\n",
+            "  Loss : 0.9130725860595703\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1250/2000\tTraining epoch: 0/580\tElapsed time: 4.01min\tLearning rate: 9.142054827518248e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.2261216640472412\n",
+            "  FW Time : 30.053ms\n",
+            "  BW Time : 18.239ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1299/2000\n",
+            "  Acc@1 : 98.000%\n",
+            "  Loss : 0.4017503261566162\n",
+            "\n",
+            "[+] Training step: 1300/2000\tTraining epoch: 0/580\tElapsed time: 4.17min\tLearning rate: 9.109338067884897e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.3248733878135681\n",
+            "  FW Time : 21.094ms\n",
+            "  BW Time : 13.526ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1349/2000\n",
+            "  Acc@1 : 98.000%\n",
+            "  Loss : 0.3138556480407715\n",
+            "\n",
+            "[+] Training step: 1350/2000\tTraining epoch: 0/580\tElapsed time: 4.32min\tLearning rate: 9.076738392034251e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.21796566247940063\n",
+            "  FW Time : 25.850ms\n",
+            "  BW Time : 19.265ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1399/2000\n",
+            "  Acc@1 : 98.000%\n",
+            "  Loss : 0.16485238075256348\n",
+            "\n",
+            "[+] Training step: 1400/2000\tTraining epoch: 0/580\tElapsed time: 4.48min\tLearning rate: 9.044255380957452e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.28114575147628784\n",
+            "  FW Time : 22.245ms\n",
+            "  BW Time : 15.607ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1449/2000\n",
+            "  Acc@1 : 98.200%\n",
+            "  Loss : 0.20785784721374512\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1450/2000\tTraining epoch: 0/580\tElapsed time: 4.64min\tLearning rate: 9.011888617145144e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 1.18643319606781\n",
+            "  FW Time : 19.665ms\n",
+            "  BW Time : 12.618ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1499/2000\n",
+            "  Acc@1 : 98.400%\n",
+            "  Loss : 0.17331039905548096\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1500/2000\tTraining epoch: 0/580\tElapsed time: 4.80min\tLearning rate: 8.979637684582136e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.27240896224975586\n",
+            "  FW Time : 24.560ms\n",
+            "  BW Time : 14.096ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1549/2000\n",
+            "  Acc@1 : 99.600%\n",
+            "  Loss : 0.17186295986175537\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1550/2000\tTraining epoch: 0/580\tElapsed time: 4.96min\tLearning rate: 8.947502168742003e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 0.6044453382492065\n",
+            "  FW Time : 20.771ms\n",
+            "  BW Time : 16.470ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1599/2000\n",
+            "  Acc@1 : 99.200%\n",
+            "  Loss : 0.06833314895629883\n",
+            "\n",
+            "[+] Training step: 1600/2000\tTraining epoch: 0/580\tElapsed time: 5.12min\tLearning rate: 8.915481656581816e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.1431320309638977\n",
+            "  FW Time : 20.891ms\n",
+            "  BW Time : 12.692ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1649/2000\n",
+            "  Acc@1 : 99.600%\n",
+            "  Loss : 0.09469139575958252\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1650/2000\tTraining epoch: 0/580\tElapsed time: 5.28min\tLearning rate: 8.8835757365368e-07\n",
+            "  Acc@1 : 87.500%\n",
+            "  Loss : 0.8603015542030334\n",
+            "  FW Time : 15.960ms\n",
+            "  BW Time : 16.599ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1699/2000\n",
+            "  Acc@1 : 99.600%\n",
+            "  Loss : 0.061615824699401855\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1700/2000\tTraining epoch: 0/580\tElapsed time: 5.44min\tLearning rate: 8.851783998515047e-07\n",
+            "  Acc@1 : 62.500%\n",
+            "  Loss : 1.4177227020263672\n",
+            "  FW Time : 14.728ms\n",
+            "  BW Time : 14.786ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1749/2000\n",
+            "  Acc@1 : 99.600%\n",
+            "  Loss : 0.03962230682373047\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1750/2000\tTraining epoch: 0/580\tElapsed time: 5.61min\tLearning rate: 8.820106033892254e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.13233846426010132\n",
+            "  FW Time : 15.973ms\n",
+            "  BW Time : 13.815ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1799/2000\n",
+            "  Acc@1 : 99.800%\n",
+            "  Loss : 0.044447898864746094\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1800/2000\tTraining epoch: 0/580\tElapsed time: 5.77min\tLearning rate: 8.788541435506462e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.08350610733032227\n",
+            "  FW Time : 18.745ms\n",
+            "  BW Time : 13.062ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1849/2000\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.06921112537384033\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1850/2000\tTraining epoch: 0/580\tElapsed time: 5.93min\tLearning rate: 8.757089797652821e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.14804929494857788\n",
+            "  FW Time : 41.547ms\n",
+            "  BW Time : 15.775ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1899/2000\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.06944191455841064\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1900/2000\tTraining epoch: 0/580\tElapsed time: 6.09min\tLearning rate: 8.725750716078392e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.027304232120513916\n",
+            "  FW Time : 21.632ms\n",
+            "  BW Time : 17.365ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1949/2000\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.05875754356384277\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 1950/2000\tTraining epoch: 0/580\tElapsed time: 6.25min\tLearning rate: 8.694523787976934e-07\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.13626277446746826\n",
+            "  FW Time : 40.104ms\n",
+            "  BW Time : 14.362ms\n",
+            "\n",
+            "[+] (Valid results) Valid step: 1999/2000\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.0167849063873291\n",
+            "\n",
+            "[+] Model saved\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "3opAMwCLZYJC",
+        "colab_type": "code",
+        "outputId": "208d8102-ce39-4897-9f89-ed7f8f626c33",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 350
+        }
+      },
+      "source": [
+        "!python \"eval.py\" --model_path='/content/drive/My Drive/CD2 Project/runs/classify/April_26_13:35:05__resnet50__None/' "
+      ],
+      "execution_count": 10,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "[+] Parse arguments\n",
+            "Args(augment_path=None, batch_size=8, dataset='BraTS', learning_rate=1e-06, max_step=2000, network='resnet50', num_workers=4, optimizer='adam', print_step=50, scheduler='exp', seed=None, start_step=0, use_cuda=True, val_step=50)\n",
+            "\n",
+            "[+] Create network\n",
+            "\n",
+            "[+] Load model\n",
+            "\n",
+            "[+] Load dataset\n",
+            "\n",
+            "[+] Start testing\n",
+            "2020-04-26 05:02:26.970817: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcudart.so.10.1\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 100.000%\n",
+            "  Loss : 0.020\n",
+            "  Infer Time(per image) : 4.062ms\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "F70Y3J9DHJwy",
+        "colab_type": "code",
+        "outputId": "502a62a8-de44-42c8-ec77-afc838a068c8",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        }
+      },
+      "source": [
+        "# train cifar10\n",
+        "# resnet50 from pytorch\n",
+        "# !python train.py --dataset=cifar10 --use_cuda=True --optimizer=adam --network=resnet50"
+      ],
+      "execution_count": 0,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "text": [
+            "\n",
+            "[+] Parse arguments\n",
+            "Args(augment_path=None, batch_size=32, dataset='cifar10', learning_rate=0.001, max_step=500, network='resnet50', num_workers=4, optimizer='adam', print_step=100, scheduler='exp', seed=None, start_step=0, use_cuda=True, val_step=100)\n",
+            "\n",
+            "[+] Create log dir\n",
+            "\n",
+            "[+] Create network\n",
+            "\n",
+            "[+] Load dataset\n",
+            "Downloading https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz to /content/drive/My Drive/CD2 Project/cifar-10-python.tar.gz\n",
+            "171MB [00:02, 70.7MB/s]               \n",
+            "Files already downloaded and verified\n",
+            "\n",
+            "[+] Start training\n",
+            "\n",
+            "[+] Use 1 GPUs\n",
+            "\n",
+            "[+] Using GPU: Tesla P100-PCIE-16GB \n",
+            "\n",
+            "[+] Training step: 0/500\tTraining epoch: 0/1406\tElapsed time: 0.01min\tLearning rate: 0.0009999283\n",
+            "  Acc@1 : 0.000%\n",
+            "  Loss : 6.797711372375488\n",
+            "  FW Time : 63.167ms\n",
+            "  BW Time : 14.364ms\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 22.620%\n",
+            "  Loss : 2.110281229019165\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 100/500\tTraining epoch: 0/1406\tElapsed time: 0.20min\tLearning rate: 0.000992784200174764\n",
+            "  Acc@1 : 12.500%\n",
+            "  Loss : 2.331458568572998\n",
+            "  FW Time : 28.192ms\n",
+            "  BW Time : 17.216ms\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 27.320%\n",
+            "  Loss : 1.5727815628051758\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 200/500\tTraining epoch: 0/1406\tElapsed time: 0.36min\tLearning rate: 0.0009856911421715388\n",
+            "  Acc@1 : 25.000%\n",
+            "  Loss : 1.8888376951217651\n",
+            "  FW Time : 26.493ms\n",
+            "  BW Time : 17.836ms\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 30.800%\n",
+            "  Loss : 1.4916565418243408\n",
+            "\n",
+            "[+] Model saved\n",
+            "\n",
+            "[+] Training step: 300/500\tTraining epoch: 0/1406\tElapsed time: 0.53min\tLearning rate: 0.0009786487613163062\n",
+            "  Acc@1 : 25.000%\n",
+            "  Loss : 2.3930575847625732\n",
+            "  FW Time : 24.473ms\n",
+            "  BW Time : 22.187ms\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 24.680%\n",
+            "  Loss : 1.992270827293396\n",
+            "\n",
+            "[+] Training step: 400/500\tTraining epoch: 0/1406\tElapsed time: 0.70min\tLearning rate: 0.000971656695540503\n",
+            "  Acc@1 : 25.000%\n",
+            "  Loss : 2.1769983768463135\n",
+            "  FW Time : 28.696ms\n",
+            "  BW Time : 22.384ms\n",
+            "\n",
+            "[+] Valid results\n",
+            "  Acc@1 : 32.320%\n",
+            "  Loss : 1.5474934577941895\n",
+            "\n",
+            "[+] Model saved\n"
+          ],
+          "name": "stdout"
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "metadata": {
+        "id": "Mhw6fBwCpHRd",
+        "colab_type": "code",
+        "colab": {}
+      },
+      "source": [
+        ""
+      ],
+      "execution_count": 0,
+      "outputs": []
+    }
+  ]
+}
\ No newline at end of file

-import os
-import time
-import importlib
-import collections
-import pickle as cp
-import glob
-import numpy as np
-import pandas as pd
-
-from natsort import natsorted
-from PIL import Image
-import torch
-import torchvision
-import torch.nn.functional as F
-import torchvision.models as models
-import torchvision.transforms as transforms
-from torch.utils.data import Subset
-from torch.utils.data import Dataset, DataLoader
-
-from sklearn.model_selection import StratifiedShuffleSplit
-from sklearn.model_selection import train_test_split
-from sklearn.model_selection import KFold
-
-from networks import *
-
-
-TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/nonaug+Normal_train/'
-TRAIN_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/train_nonaug_classify_target.csv'
-# VAL_DATASET_PATH = '../../data/MICCAI_BraTS_2019_Data_Training/ce_valid/'
-# VAL_TARGET_PATH = '../../data/MICCAI_BraTS_2019_Data_Training/ce_valid_targets.csv'
-
-current_epoch = 0
-
-
-def split_dataset(args, dataset, k):
-    # load dataset
-    X = list(range(len(dataset)))
-    Y = dataset.targets
-
-    # split to k-fold
-    assert len(X) == len(Y)
-
-    def _it_to_list(_it):
-        return list(zip(*list(_it)))
-
-    sss = StratifiedShuffleSplit(n_splits=k, random_state=args.seed, test_size=0.1)
-    Dm_indexes, Da_indexes = _it_to_list(sss.split(X, Y))
-
-    return Dm_indexes, Da_indexes
-
-
-
-def get_model_name(args):
-    from datetime import datetime, timedelta, timezone
-    now = datetime.now(timezone.utc)
-    tz = timezone(timedelta(hours=9))
-    now = now.astimezone(tz)
-    date_time = now.strftime("%B_%d_%H:%M:%S")
-    model_name = '__'.join([date_time, args.network, str(args.seed)])
-    return model_name
-
-
-def dict_to_namedtuple(d):
-    Args = collections.namedtuple('Args', sorted(d.keys()))
-
-    for k,v in d.items():
-        if type(v) is dict:
-            d[k] = dict_to_namedtuple(v)
-
-        elif type(v) is str:
-            try:
-                d[k] = eval(v)
-            except:
-                d[k] = v
-
-    args = Args(**d)
-    return args
-
-
-def parse_args(kwargs):
-    # combine with default args
-    kwargs['dataset'] =  kwargs['dataset'] if 'dataset' in kwargs else 'BraTS'
-    kwargs['network'] =  kwargs['network'] if 'network' in kwargs else 'resnet50'
-    kwargs['optimizer'] = kwargs['optimizer'] if 'optimizer' in kwargs else 'adam'
-    kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.0001
-    kwargs['seed'] =  kwargs['seed'] if 'seed' in kwargs else None
-    kwargs['use_cuda'] =  kwargs['use_cuda'] if 'use_cuda' in kwargs else True
-    kwargs['use_cuda'] =  kwargs['use_cuda'] and torch.cuda.is_available()
-    kwargs['num_workers'] = kwargs['num_workers'] if 'num_workers' in kwargs else 4
-    kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 500
-    kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 500
-    kwargs['scheduler'] = kwargs['scheduler'] if 'scheduler' in kwargs else 'exp'
-    kwargs['batch_size'] = kwargs['batch_size'] if 'batch_size' in kwargs else 128
-    kwargs['start_step'] = kwargs['start_step'] if 'start_step' in kwargs else 0
-    kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 5000
-    kwargs['augment_path'] = kwargs['augment_path'] if 'augment_path' in kwargs else None
-
-    # to named tuple
-    args = dict_to_namedtuple(kwargs)
-    return args, kwargs
-
-
-def select_model(args):
-    # grayResNet2
-    resnet_dict = {'resnet18':grayResNet2.resnet18(), 'resnet34':grayResNet2.resnet34(), 
-    'resnet50':grayResNet2.resnet50(), 'resnet101':grayResNet2.resnet101(), 'resnet152':grayResNet2.resnet152()}
-
-    if args.network in resnet_dict:
-        backbone = resnet_dict[args.network]
-        model = basenet.BaseNet(backbone, args)
-    else:
-        Net = getattr(importlib.import_module('networks.{}'.format(args.network)), 'Net')
-        model = Net(args)
-
-    #print(model) # print model architecture
-    return model
-
-
-def select_optimizer(args, model):
-    if args.optimizer == 'sgd':
-        optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=0.0001)
-    elif args.optimizer == 'rms':
-        optimizer = torch.optim.RMSprop(model.parameters(), lr=args.learning_rate)
-    elif args.optimizer == 'adam':
-        optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
-    else:
-        raise Exception('Unknown Optimizer')
-    return optimizer
-
-
-def select_scheduler(args, optimizer):
-    if not args.scheduler or args.scheduler == 'None':
-        return None
-    elif args.scheduler =='clr':
-        return torch.optim.lr_scheduler.CyclicLR(optimizer, 0.01, 0.015, mode='triangular2', step_size_up=250000, cycle_momentum=False)
-    elif args.scheduler =='exp':
-        return torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9999283, last_epoch=-1)
-    else:
-        raise Exception('Unknown Scheduler')
-
-
-class CustomDataset(Dataset): 
-  def __init__(self, data_path, csv_path):
-    self.len = len(self.imgs)
-    self.path = data_path
-    self.imgs = natsorted(os.listdir(data_path))
-  
-    df = pd.read_csv(csv_path)
-    targets_list = []
-
-    for fname in self.imgs:
-        row = df.loc[df['filename'] == fname]
-        targets_list.append(row.iloc[0, 1])
-
-    self.targets = targets_list
-
-  def __len__(self):
-      return self.len
-
-  def __getitem__(self, idx):
-    img_loc = os.path.join(self.path, self.imgs[idx])
-    targets = self.targets[idx]
-    image = Image.open(img_loc)
-    return image, targets
-
-
-
-def get_dataset(args, transform, split='train'):
-    assert split in ['train', 'val', 'test', 'trainval']
-
-    if split in ['train']:
-        dataset = CustomDataset(TRAIN_DATASET_PATH, TRAIN_TARGET_PATH, transform=transform)
-    else: #test
-        dataset = CustomDataset(VAL_DATASET_PATH, VAL_TARGET_PATH, transform=transform)
-
-    return dataset
-
-
-def get_dataloader(args, dataset, shuffle=False, pin_memory=True):
-    data_loader = torch.utils.data.DataLoader(dataset,
-                                              batch_size=args.batch_size,
-                                              shuffle=shuffle,
-                                              num_workers=args.num_workers,
-                                              pin_memory=pin_memory)
-    return data_loader
-
-
-def get_aug_dataloader(args, dataset, shuffle=False, pin_memory=True):
-    data_loader = torch.utils.data.DataLoader(dataset,
-                                              batch_size=args.batch_size,
-                                              shuffle=shuffle,
-                                              num_workers=args.num_workers,
-                                              pin_memory=pin_memory)
-    return data_loader
-
-
-def get_inf_dataloader(args, dataset):
-    global current_epoch
-    data_loader = iter(get_dataloader(args, dataset, shuffle=True))
-
-    while True:
-        try:
-            batch = next(data_loader)
-
-        except StopIteration:
-            current_epoch += 1
-            data_loader = iter(get_dataloader(args, dataset, shuffle=True))
-            batch = next(data_loader)
-
-        yield batch
-
-
-
-
-def train_step(args, model, optimizer, scheduler, criterion, batch, step, writer, device=None):
-    model.train()
-    images, target = batch
-
-    if device:
-        images = images.to(device)
-        target = target.to(device)
-
-    elif args.use_cuda:
-        images = images.cuda(non_blocking=True)
-        target = target.cuda(non_blocking=True)
-
-    # compute output
-    start_t = time.time()
-    output, first = model(images)
-    forward_t = time.time() - start_t
-    loss = criterion(output, target)
-
-    # measure accuracy and record loss
-    acc1, acc5 = accuracy(output, target, topk=(1, 5))
-    acc1 /= images.size(0)
-    acc5 /= images.size(0)
-
-    # compute gradient and do SGD step
-    optimizer.zero_grad()
-    start_t = time.time()
-    loss.backward()
-    backward_t = time.time() - start_t
-    optimizer.step()
-    if scheduler: scheduler.step()
-
-    if writer and step % args.print_step == 0:
-        n_imgs = min(images.size(0), 10)
-        tag = 'train/' + str(step)
-        for j in range(n_imgs):
-            writer.add_image(tag,
-                    concat_image_features(images[j], first[j]), global_step=step)
-
-    return acc1, acc5, loss, forward_t, backward_t
-
-
-#_acc1, _acc5 = accuracy(output, target, topk=(1, 5))
-def accuracy(output, target, topk=(1,)):
-    """Computes the accuracy over the k top predictions for the specified values of k"""
-    with torch.no_grad():
-        maxk = max(topk)
-        batch_size = target.size(0)
-
-        _, pred = output.topk(maxk, 1, True, True)
-        pred = pred.t()
-        correct = pred.eq(target.view(1, -1).expand_as(pred))
-
-        res = []
-        for k in topk:
-            correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
-            res.append(correct_k)
-        return res
-