2020-1-capstone-design2 / 2016104167

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get 1 random policy

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Showing 5 changed files with 1071 additions and 272 deletions
1 +import os
2 +import fire
3 +import json
4 +from pprint import pprint
5 +import pickle
6 +
7 +import torch
8 +import torch.nn as nn
9 +from torch.utils.tensorboard import SummaryWriter
10 +
11 +from utils import *
12 +
13 +# command
14 +# python getAugmented.py --model_path='logs/April_24_21:05:15__resnet50__None/'
15 +
16 +def eval(model_path):
17 + print('\n[+] Parse arguments')
18 + kwargs_path = os.path.join(model_path, 'kwargs.json')
19 + kwargs = json.loads(open(kwargs_path).read())
20 + args, kwargs = parse_args(kwargs)
21 + pprint(args)
22 + device = torch.device('cuda' if args.use_cuda else 'cpu')
23 +
24 + cp_path = os.path.join(model_path, 'augmentation.cp')
25 +
26 + writer = SummaryWriter(log_dir=model_path)
27 +
28 +
29 + print('\n[+] Load transform')
30 + # list
31 + with open(cp_path, 'rb') as f:
32 + aug_transform_list = pickle.load(f)
33 +
34 + augmented_image_list = [torch.Tensor(240,0)] * len(get_dataset(args, None, 'test'))
35 +
36 +
37 + print('\n[+] Load dataset')
38 + for aug_idx, aug_transform in enumerate(aug_transform_list):
39 + dataset = get_dataset(args, aug_transform, 'test')
40 +
41 + loader = iter(get_aug_dataloader(args, dataset))
42 +
43 + for i, (images, target) in enumerate(loader):
44 + images = images.view(240, 240)
45 +
46 + # concat image
47 + augmented_image_list[i] = torch.cat([augmented_image_list[i], images], dim = 1)
48 +
49 + if i % 1000 == 0:
50 + print("\n images size: ", augmented_image_list[i].size()) # [240, 240]
51 +
52 + break
53 + # break
54 +
55 +
56 + # print(augmented_image_list)
57 +
58 +
59 + print('\n[+] Write on tensorboard')
60 + if writer:
61 + for i, data in enumerate(augmented_image_list):
62 + tag = 'img/' + str(i)
63 + writer.add_image(tag, data.view(1, 240, -1), global_step=0)
64 + break
65 +
66 + writer.close()
67 +
68 +
69 + # if writer:
70 + # for j in range():
71 + # tag = 'img/' + str(img_count) + '_' + str(j)
72 + # # writer.add_image(tag,
73 + # # concat_image_features(images[j], first[j]), global_step=step)
74 + # # if j > 0:
75 + # # fore = concat_image_features(fore, images[j])
76 +
77 + # writer.add_image(tag, fore, global_step=0)
78 + # img_count = img_count + 1
79 +
80 + # writer.close()
81 +
82 +if __name__ == '__main__':
83 + fire.Fire(eval)
1 +import os
2 +import fire
3 +import json
4 +from pprint import pprint
5 +import pickle
6 +
7 +import torch
8 +import torch.nn as nn
9 +from torch.utils.tensorboard import SummaryWriter
10 +
11 +from utils import *
12 +
13 +# command
14 +# python getAugmented.py --model_path='logs/April_24_21:05:15__resnet50__None/'
15 +
16 +def eval(model_path):
17 + print('\n[+] Parse arguments')
18 + kwargs_path = os.path.join(model_path, 'kwargs.json')
19 + kwargs = json.loads(open(kwargs_path).read())
20 + args, kwargs = parse_args(kwargs)
21 + pprint(args)
22 + device = torch.device('cuda' if args.use_cuda else 'cpu')
23 +
24 + cp_path = os.path.join(model_path, 'augmentation.cp')
25 +
26 + writer = SummaryWriter(log_dir=model_path)
27 +
28 +
29 + print('\n[+] Load transform')
30 + # list
31 + with open(cp_path, 'rb') as f:
32 + aug_transform_list = pickle.load(f)
33 +
34 + augmented_image_list = [torch.Tensor(240,0)] * len(get_dataset(args, None, 'train'))
35 +
36 +
37 + print('\n[+] Load dataset')
38 + for aug_idx, aug_transform in enumerate(aug_transform_list):
39 + dataset = get_dataset(args, aug_transform, 'train')
40 +
41 + loader = iter(get_aug_dataloader(args, dataset))
42 +
43 + for i, (images, target) in enumerate(loader):
44 + images = images.view(240, 240)
45 +
46 + # concat image
47 + augmented_image_list[i] = torch.cat([augmented_image_list[i], images], dim = 1)
48 +
49 +
50 +
51 +
52 + print('\n[+] Write on tensorboard')
53 + if writer:
54 + for i, data in enumerate(augmented_image_list):
55 + tag = 'img/' + str(i)
56 + writer.add_image(tag, data.view(1, 240, -1), global_step=0)
57 +
58 + writer.close()
59 +
60 +
61 +if __name__ == '__main__':
62 + fire.Fire(eval)
1 +import os
2 +import fire
3 +import json
4 +from pprint import pprint
5 +import pickle
6 +import random
7 +
8 +import torch
9 +import torch.nn as nn
10 +from torchvision.utils import save_image
11 +from torch.utils.tensorboard import SummaryWriter
12 +
13 +from utils import *
14 +
15 +# command
16 +# python getAugmented_saveimg.py --model_path='logs/April_26_00:55:16__resnet50__None/'
17 +
18 +def eval(model_path):
19 + print('\n[+] Parse arguments')
20 + kwargs_path = os.path.join(model_path, 'kwargs.json')
21 + kwargs = json.loads(open(kwargs_path).read())
22 + args, kwargs = parse_args(kwargs)
23 + pprint(args)
24 + device = torch.device('cuda' if args.use_cuda else 'cpu')
25 +
26 + cp_path = os.path.join(model_path, 'augmentation.cp')
27 +
28 + writer = SummaryWriter(log_dir=model_path)
29 +
30 +
31 + print('\n[+] Load transform')
32 + # list to tensor
33 + with open(cp_path, 'rb') as f:
34 + aug_transform_list = pickle.load(f)
35 +
36 + transform = transforms.RandomChoice(aug_transform_list)
37 +
38 +
39 + print('\n[+] Load dataset')
40 +
41 + dataset = get_dataset(args, transform, 'train')
42 + loader = iter(get_aug_dataloader(args, dataset))
43 +
44 +
45 + print('\n[+] Save 1 random policy')
46 + os.makedirs(os.path.join(model_path, 'augmented_imgs'))
47 + save_dir = os.path.join(model_path, 'augmented_imgs')
48 +
49 + for i, (image, target) in enumerate(loader):
50 + image = image.view(240, 240)
51 + # save img
52 + save_image(image, os.path.join(save_dir, 'aug_'+ str(i) + '.png'))
53 +
54 + if(i % 100 == 0):
55 + print("\n saved images: ", i)
56 +
57 + print('\n[+] Finished to save')
58 +
59 +if __name__ == '__main__':
60 + fire.Fire(eval)
61 +
62 +
63 +
1 +{
2 + "nbformat": 4,
3 + "nbformat_minor": 0,
4 + "metadata": {
5 + "colab": {
6 + "name": "classify normal/lesion.ipynb",
7 + "provenance": [],
8 + "collapsed_sections": []
9 + },
10 + "kernelspec": {
11 + "name": "python3",
12 + "display_name": "Python 3"
13 + },
14 + "accelerator": "GPU"
15 + },
16 + "cells": [
17 + {
18 + "cell_type": "code",
19 + "metadata": {
20 + "id": "AjoTMXMCrFYX",
21 + "colab_type": "code",
22 + "outputId": "2548434e-72b7-4946-9748-070103017379",
23 + "colab": {
24 + "base_uri": "https://localhost:8080/",
25 + "height": 129
26 + }
27 + },
28 + "source": [
29 + "from google.colab import drive\n",
30 + "drive.mount('/content/drive')"
31 + ],
32 + "execution_count": 1,
33 + "outputs": [
34 + {
35 + "output_type": "stream",
36 + "text": [
37 + "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",
38 + "\n",
39 + "Enter your authorization code:\n",
40 + "··········\n",
41 + "Mounted at /content/drive\n"
42 + ],
43 + "name": "stdout"
44 + }
45 + ]
46 + },
47 + {
48 + "cell_type": "code",
49 + "metadata": {
50 + "id": "lXK8NZfIyzeB",
51 + "colab_type": "code",
52 + "outputId": "17508683-94fb-45fa-b9df-f7ddb9401b68",
53 + "colab": {
54 + "base_uri": "https://localhost:8080/",
55 + "height": 146
56 + }
57 + },
58 + "source": [
59 + "!git clone http://khuhub.khu.ac.kr/2020-1-capstone-design2/2016104167.git"
60 + ],
61 + "execution_count": 2,
62 + "outputs": [
63 + {
64 + "output_type": "stream",
65 + "text": [
66 + "Cloning into '2016104167'...\n",
67 + "remote: Counting objects: 11451, done.\u001b[K\n",
68 + "remote: Compressing objects: 100% (39/39), done.\u001b[K\n",
69 + "remote: Total 11451 (delta 15), reused 0 (delta 0)\u001b[K\n",
70 + "Receiving objects: 100% (11451/11451), 292.82 MiB | 384.00 KiB/s, done.\n",
71 + "Resolving deltas: 100% (1109/1109), done.\n",
72 + "Checking out files: 100% (15684/15684), done.\n"
73 + ],
74 + "name": "stdout"
75 + }
76 + ]
77 + },
78 + {
79 + "cell_type": "code",
80 + "metadata": {
81 + "id": "TmGc36H2y5sI",
82 + "colab_type": "code",
83 + "outputId": "49ce70f0-10bb-48d8-bd89-de98a28c7893",
84 + "colab": {
85 + "base_uri": "https://localhost:8080/",
86 + "height": 35
87 + }
88 + },
89 + "source": [
90 + "%cd '2016104167/code/classifier/'"
91 + ],
92 + "execution_count": 3,
93 + "outputs": [
94 + {
95 + "output_type": "stream",
96 + "text": [
97 + "/content/2016104167/code/classifier\n"
98 + ],
99 + "name": "stdout"
100 + }
101 + ]
102 + },
103 + {
104 + "cell_type": "code",
105 + "metadata": {
106 + "id": "oJ08JUJCzEEE",
107 + "colab_type": "code",
108 + "outputId": "f9454032-73ad-444e-b708-e1228f6b3a0b",
109 + "colab": {
110 + "base_uri": "https://localhost:8080/",
111 + "height": 1000
112 + }
113 + },
114 + "source": [
115 + "!python -m pip install -r \"requirements.txt\""
116 + ],
117 + "execution_count": 4,
118 + "outputs": [
119 + {
120 + "output_type": "stream",
121 + "text": [
122 + "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 1)) (0.16.0)\n",
123 + "Collecting tb-nightly\n",
124 + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/4e/46/4b95936aed44f2154d936160de6c58e3bd4cf8501152db21945617c84694/tb_nightly-2.3.0a20200425-py3-none-any.whl (2.9MB)\n",
125 + "\u001b[K |████████████████████████████████| 2.9MB 39.9MB/s \n",
126 + "\u001b[?25hRequirement already satisfied: hyperopt in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 3)) (0.1.2)\n",
127 + "Collecting pillow==6.2.1\n",
128 + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/10/5c/0e94e689de2476c4c5e644a3bd223a1c1b9e2bdb7c510191750be74fa786/Pillow-6.2.1-cp36-cp36m-manylinux1_x86_64.whl (2.1MB)\n",
129 + "\u001b[K |████████████████████████████████| 2.1MB 48.1MB/s \n",
130 + "\u001b[?25hRequirement already satisfied: natsort in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 5)) (5.5.0)\n",
131 + "Collecting fire\n",
132 + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/34/a7/0e22e70778aca01a52b9c899d9c145c6396d7b613719cd63db97ffa13f2f/fire-0.3.1.tar.gz (81kB)\n",
133 + "\u001b[K |████████████████████████████████| 81kB 12.1MB/s \n",
134 + "\u001b[?25hCollecting torchvision==0.2.2\n",
135 + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/ce/a1/66d72a2fe580a9f0fcbaaa5b976911fbbde9dce9b330ba12791997b856e9/torchvision-0.2.2-py2.py3-none-any.whl (64kB)\n",
136 + "\u001b[K |████████████████████████████████| 71kB 11.8MB/s \n",
137 + "\u001b[?25hCollecting torch==1.1.0\n",
138 + "\u001b[?25l Downloading https://files.pythonhosted.org/packages/69/60/f685fb2cfb3088736bafbc9bdbb455327bdc8906b606da9c9a81bae1c81e/torch-1.1.0-cp36-cp36m-manylinux1_x86_64.whl (676.9MB)\n",
139 + "\u001b[K |████████████████████████████████| 676.9MB 18kB/s \n",
140 + "\u001b[?25hRequirement already satisfied: pandas in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 9)) (1.0.3)\n",
141 + "Requirement already satisfied: sklearn in /usr/local/lib/python3.6/dist-packages (from -r requirements.txt (line 10)) (0.0)\n",
142 + "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",
143 + "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",
144 + "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",
145 + "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",
146 + "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",
147 + "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",
148 + "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",
149 + "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",
150 + "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",
151 + "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",
152 + "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",
153 + "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",
154 + "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",
155 + "Requirement already satisfied: networkx in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (2.4)\n",
156 + "Requirement already satisfied: tqdm in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (4.38.0)\n",
157 + "Requirement already satisfied: pymongo in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (3.10.1)\n",
158 + "Requirement already satisfied: scipy in /usr/local/lib/python3.6/dist-packages (from hyperopt->-r requirements.txt (line 3)) (1.4.1)\n",
159 + "Requirement already satisfied: termcolor in /usr/local/lib/python3.6/dist-packages (from fire->-r requirements.txt (line 6)) (1.1.0)\n",
160 + "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",
161 + "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas->-r requirements.txt (line 9)) (2018.9)\n",
162 + "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",
163 + "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",
164 + "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",
165 + "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",
166 + "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",
167 + "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",
168 + "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",
169 + "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",
170 + "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",
171 + "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",
172 + "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",
173 + "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",
174 + "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",
175 + "Building wheels for collected packages: fire\n",
176 + " Building wheel for fire (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
177 + " Created wheel for fire: filename=fire-0.3.1-py2.py3-none-any.whl size=111005 sha256=090cc0b99c44969c5594966fd1af925b4ff73b02719d44b05fb4aacd07b9bfb3\n",
178 + " Stored in directory: /root/.cache/pip/wheels/c1/61/df/768b03527bf006b546dce284eb4249b185669e65afc5fbb2ac\n",
179 + "Successfully built fire\n",
180 + "\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",
181 + "\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",
182 + "Installing collected packages: tb-nightly, pillow, fire, torch, torchvision\n",
183 + " Found existing installation: Pillow 7.0.0\n",
184 + " Uninstalling Pillow-7.0.0:\n",
185 + " Successfully uninstalled Pillow-7.0.0\n",
186 + " Found existing installation: torch 1.4.0\n",
187 + " Uninstalling torch-1.4.0:\n",
188 + " Successfully uninstalled torch-1.4.0\n",
189 + " Found existing installation: torchvision 0.5.0\n",
190 + " Uninstalling torchvision-0.5.0:\n",
191 + " Successfully uninstalled torchvision-0.5.0\n",
192 + "Successfully installed fire-0.3.1 pillow-6.2.1 tb-nightly-2.3.0a20200425 torch-1.1.0 torchvision-0.2.2\n"
193 + ],
194 + "name": "stdout"
195 + }
196 + ]
197 + },
198 + {
199 + "cell_type": "code",
200 + "metadata": {
201 + "id": "jdayOoSYHJDf",
202 + "colab_type": "code",
203 + "outputId": "f663313d-1166-4f6c-f617-d3d331ce7793",
204 + "colab": {
205 + "base_uri": "https://localhost:8080/",
206 + "height": 1000
207 + }
208 + },
209 + "source": [
210 + "!python train.py --use_cuda=True --network=resnet50 --optimizer=adam "
211 + ],
212 + "execution_count": 9,
213 + "outputs": [
214 + {
215 + "output_type": "stream",
216 + "text": [
217 + "\n",
218 + "[+] Parse arguments\n",
219 + "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",
220 + "\n",
221 + "[+] Create log dir\n",
222 + "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",
223 + "\n",
224 + "[+] Create network\n",
225 + "\n",
226 + "[+] Load dataset\n",
227 + "\n",
228 + "[+] Start training\n",
229 + "\n",
230 + "[+] Use 1 GPUs\n",
231 + "\n",
232 + "[+] Using GPU: Tesla P4 \n",
233 + "\n",
234 + "[+] Training step: 0/2000\tTraining epoch: 0/580\tElapsed time: 0.01min\tLearning rate: 9.999283e-07\n",
235 + " Acc@1 : 0.000%\n",
236 + " Loss : 7.1226911544799805\n",
237 + " FW Time : 125.206ms\n",
238 + " BW Time : 14.758ms\n",
239 + "\n",
240 + "[+] (Valid results) Valid step: 49/2000\n",
241 + " Acc@1 : 0.000%\n",
242 + " Loss : 7.333343982696533\n",
243 + "\n",
244 + "[+] Model saved\n",
245 + "\n",
246 + "[+] Training step: 50/2000\tTraining epoch: 0/580\tElapsed time: 0.17min\tLearning rate: 9.963498469652168e-07\n",
247 + " Acc@1 : 0.000%\n",
248 + " Loss : 6.839444160461426\n",
249 + " FW Time : 15.798ms\n",
250 + " BW Time : 10.145ms\n",
251 + "\n",
252 + "[+] (Valid results) Valid step: 99/2000\n",
253 + " Acc@1 : 0.000%\n",
254 + " Loss : 7.0915045738220215\n",
255 + "\n",
256 + "[+] Model saved\n",
257 + "\n",
258 + "[+] Training step: 100/2000\tTraining epoch: 0/580\tElapsed time: 0.33min\tLearning rate: 9.92784200174764e-07\n",
259 + " Acc@1 : 0.000%\n",
260 + " Loss : 6.130648136138916\n",
261 + " FW Time : 15.954ms\n",
262 + " BW Time : 13.310ms\n",
263 + "\n",
264 + "[+] (Valid results) Valid step: 149/2000\n",
265 + " Acc@1 : 12.000%\n",
266 + " Loss : 6.664271354675293\n",
267 + "\n",
268 + "[+] Model saved\n",
269 + "\n",
270 + "[+] Training step: 150/2000\tTraining epoch: 0/580\tElapsed time: 0.49min\tLearning rate: 9.89231313798811e-07\n",
271 + " Acc@1 : 25.000%\n",
272 + " Loss : 5.820688247680664\n",
273 + " FW Time : 15.717ms\n",
274 + " BW Time : 12.132ms\n",
275 + "\n",
276 + "[+] (Valid results) Valid step: 199/2000\n",
277 + " Acc@1 : 38.400%\n",
278 + " Loss : 6.647609233856201\n",
279 + "\n",
280 + "[+] Model saved\n",
281 + "\n",
282 + "[+] Training step: 200/2000\tTraining epoch: 0/580\tElapsed time: 0.65min\tLearning rate: 9.85691142171539e-07\n",
283 + " Acc@1 : 62.500%\n",
284 + " Loss : 5.464842319488525\n",
285 + " FW Time : 18.873ms\n",
286 + " BW Time : 14.734ms\n",
287 + "\n",
288 + "[+] (Valid results) Valid step: 249/2000\n",
289 + " Acc@1 : 51.600%\n",
290 + " Loss : 6.3311767578125\n",
291 + "\n",
292 + "[+] Model saved\n",
293 + "\n",
294 + "[+] Training step: 250/2000\tTraining epoch: 0/580\tElapsed time: 0.80min\tLearning rate: 9.821636397905564e-07\n",
295 + " Acc@1 : 62.500%\n",
296 + " Loss : 5.102325439453125\n",
297 + " FW Time : 20.299ms\n",
298 + " BW Time : 13.266ms\n",
299 + "\n",
300 + "[+] (Valid results) Valid step: 299/2000\n",
301 + " Acc@1 : 65.000%\n",
302 + " Loss : 5.964324951171875\n",
303 + "\n",
304 + "[+] Model saved\n",
305 + "\n",
306 + "[+] Training step: 300/2000\tTraining epoch: 0/580\tElapsed time: 0.97min\tLearning rate: 9.786487613163075e-07\n",
307 + " Acc@1 : 75.000%\n",
308 + " Loss : 4.567136287689209\n",
309 + " FW Time : 22.482ms\n",
310 + " BW Time : 11.849ms\n",
311 + "\n",
312 + "[+] (Valid results) Valid step: 349/2000\n",
313 + " Acc@1 : 67.000%\n",
314 + " Loss : 5.935492038726807\n",
315 + "\n",
316 + "[+] Model saved\n",
317 + "\n",
318 + "[+] Training step: 350/2000\tTraining epoch: 0/580\tElapsed time: 1.13min\tLearning rate: 9.751464615714972e-07\n",
319 + " Acc@1 : 75.000%\n",
320 + " Loss : 4.528119087219238\n",
321 + " FW Time : 15.663ms\n",
322 + " BW Time : 12.312ms\n",
323 + "\n",
324 + "[+] (Valid results) Valid step: 399/2000\n",
325 + " Acc@1 : 66.200%\n",
326 + " Loss : 6.177009105682373\n",
327 + "\n",
328 + "[+] Training step: 400/2000\tTraining epoch: 0/580\tElapsed time: 1.28min\tLearning rate: 9.716566955405052e-07\n",
329 + " Acc@1 : 50.000%\n",
330 + " Loss : 5.060157775878906\n",
331 + " FW Time : 18.538ms\n",
332 + " BW Time : 15.068ms\n",
333 + "\n",
334 + "[+] (Valid results) Valid step: 449/2000\n",
335 + " Acc@1 : 70.400%\n",
336 + " Loss : 5.513169288635254\n",
337 + "\n",
338 + "[+] Model saved\n",
339 + "\n",
340 + "[+] Training step: 450/2000\tTraining epoch: 0/580\tElapsed time: 1.44min\tLearning rate: 9.681794183688074e-07\n",
341 + " Acc@1 : 62.500%\n",
342 + " Loss : 4.341537952423096\n",
343 + " FW Time : 20.281ms\n",
344 + " BW Time : 11.464ms\n",
345 + "\n",
346 + "[+] (Valid results) Valid step: 499/2000\n",
347 + " Acc@1 : 71.200%\n",
348 + " Loss : 5.211921691894531\n",
349 + "\n",
350 + "[+] Model saved\n",
351 + "\n",
352 + "[+] Training step: 500/2000\tTraining epoch: 0/580\tElapsed time: 1.60min\tLearning rate: 9.647145853624042e-07\n",
353 + " Acc@1 : 62.500%\n",
354 + " Loss : 3.643502712249756\n",
355 + " FW Time : 35.242ms\n",
356 + " BW Time : 13.355ms\n",
357 + "\n",
358 + "[+] (Valid results) Valid step: 549/2000\n",
359 + " Acc@1 : 73.000%\n",
360 + " Loss : 5.065425872802734\n",
361 + "\n",
362 + "[+] Model saved\n",
363 + "\n",
364 + "[+] Training step: 550/2000\tTraining epoch: 0/580\tElapsed time: 1.76min\tLearning rate: 9.61262151987242e-07\n",
365 + " Acc@1 : 37.500%\n",
366 + " Loss : 4.352468013763428\n",
367 + " FW Time : 15.172ms\n",
368 + " BW Time : 14.894ms\n",
369 + "\n",
370 + "[+] (Valid results) Valid step: 599/2000\n",
371 + " Acc@1 : 72.800%\n",
372 + " Loss : 5.094676971435547\n",
373 + "\n",
374 + "[+] Training step: 600/2000\tTraining epoch: 0/580\tElapsed time: 1.92min\tLearning rate: 9.578220738686398e-07\n",
375 + " Acc@1 : 62.500%\n",
376 + " Loss : 2.67336106300354\n",
377 + " FW Time : 16.094ms\n",
378 + " BW Time : 11.950ms\n",
379 + "\n",
380 + "[+] (Valid results) Valid step: 649/2000\n",
381 + " Acc@1 : 74.200%\n",
382 + " Loss : 4.8995866775512695\n",
383 + "\n",
384 + "[+] Model saved\n",
385 + "\n",
386 + "[+] Training step: 650/2000\tTraining epoch: 0/580\tElapsed time: 2.08min\tLearning rate: 9.543943067907226e-07\n",
387 + " Acc@1 : 87.500%\n",
388 + " Loss : 2.3277320861816406\n",
389 + " FW Time : 22.967ms\n",
390 + " BW Time : 16.513ms\n",
391 + "\n",
392 + "[+] (Valid results) Valid step: 699/2000\n",
393 + " Acc@1 : 78.000%\n",
394 + " Loss : 4.331630706787109\n",
395 + "\n",
396 + "[+] Model saved\n",
397 + "\n",
398 + "[+] Training step: 700/2000\tTraining epoch: 0/580\tElapsed time: 2.24min\tLearning rate: 9.509788066958503e-07\n",
399 + " Acc@1 : 50.000%\n",
400 + " Loss : 4.2444071769714355\n",
401 + " FW Time : 26.178ms\n",
402 + " BW Time : 20.318ms\n",
403 + "\n",
404 + "[+] (Valid results) Valid step: 749/2000\n",
405 + " Acc@1 : 79.800%\n",
406 + " Loss : 4.202180862426758\n",
407 + "\n",
408 + "[+] Model saved\n",
409 + "\n",
410 + "[+] Training step: 750/2000\tTraining epoch: 0/580\tElapsed time: 2.40min\tLearning rate: 9.475755296840536e-07\n",
411 + " Acc@1 : 75.000%\n",
412 + " Loss : 2.0681729316711426\n",
413 + " FW Time : 14.539ms\n",
414 + " BW Time : 9.137ms\n",
415 + "\n",
416 + "[+] (Valid results) Valid step: 799/2000\n",
417 + " Acc@1 : 81.800%\n",
418 + " Loss : 3.595407009124756\n",
419 + "\n",
420 + "[+] Model saved\n",
421 + "\n",
422 + "[+] Training step: 800/2000\tTraining epoch: 0/580\tElapsed time: 2.56min\tLearning rate: 9.441844320124666e-07\n",
423 + " Acc@1 : 87.500%\n",
424 + " Loss : 1.7526265382766724\n",
425 + " FW Time : 16.346ms\n",
426 + " BW Time : 13.979ms\n",
427 + "\n",
428 + "[+] (Valid results) Valid step: 849/2000\n",
429 + " Acc@1 : 82.800%\n",
430 + " Loss : 3.435865879058838\n",
431 + "\n",
432 + "[+] Model saved\n",
433 + "\n",
434 + "[+] Training step: 850/2000\tTraining epoch: 0/580\tElapsed time: 2.72min\tLearning rate: 9.408054700947673e-07\n",
435 + " Acc@1 : 100.000%\n",
436 + " Loss : 1.327768325805664\n",
437 + " FW Time : 20.280ms\n",
438 + " BW Time : 11.583ms\n",
439 + "\n",
440 + "[+] (Valid results) Valid step: 899/2000\n",
441 + " Acc@1 : 86.800%\n",
442 + " Loss : 3.0217819213867188\n",
443 + "\n",
444 + "[+] Model saved\n",
445 + "\n",
446 + "[+] Training step: 900/2000\tTraining epoch: 0/580\tElapsed time: 2.88min\tLearning rate: 9.37438600500616e-07\n",
447 + " Acc@1 : 87.500%\n",
448 + " Loss : 1.7023265361785889\n",
449 + " FW Time : 16.003ms\n",
450 + " BW Time : 11.639ms\n",
451 + "\n",
452 + "[+] (Valid results) Valid step: 949/2000\n",
453 + " Acc@1 : 89.600%\n",
454 + " Loss : 2.5056638717651367\n",
455 + "\n",
456 + "[+] Model saved\n",
457 + "\n",
458 + "[+] Training step: 950/2000\tTraining epoch: 0/580\tElapsed time: 3.04min\tLearning rate: 9.340837799550989e-07\n",
459 + " Acc@1 : 87.500%\n",
460 + " Loss : 2.0191428661346436\n",
461 + " FW Time : 26.238ms\n",
462 + " BW Time : 11.809ms\n",
463 + "\n",
464 + "[+] (Valid results) Valid step: 999/2000\n",
465 + " Acc@1 : 92.600%\n",
466 + " Loss : 1.8181736469268799\n",
467 + "\n",
468 + "[+] Model saved\n",
469 + "\n",
470 + "[+] Training step: 1000/2000\tTraining epoch: 0/580\tElapsed time: 3.20min\tLearning rate: 9.307409653381686e-07\n",
471 + " Acc@1 : 87.500%\n",
472 + " Loss : 0.9778107404708862\n",
473 + " FW Time : 18.434ms\n",
474 + " BW Time : 17.896ms\n",
475 + "\n",
476 + "[+] (Valid results) Valid step: 1049/2000\n",
477 + " Acc@1 : 94.400%\n",
478 + " Loss : 1.7727973461151123\n",
479 + "\n",
480 + "[+] Model saved\n",
481 + "\n",
482 + "[+] Training step: 1050/2000\tTraining epoch: 0/580\tElapsed time: 3.36min\tLearning rate: 9.274101136840937e-07\n",
483 + " Acc@1 : 75.000%\n",
484 + " Loss : 1.6472196578979492\n",
485 + " FW Time : 22.264ms\n",
486 + " BW Time : 11.575ms\n",
487 + "\n",
488 + "[+] (Valid results) Valid step: 1099/2000\n",
489 + " Acc@1 : 96.400%\n",
490 + " Loss : 1.3225497007369995\n",
491 + "\n",
492 + "[+] Model saved\n",
493 + "\n",
494 + "[+] Training step: 1100/2000\tTraining epoch: 0/580\tElapsed time: 3.52min\tLearning rate: 9.240911821809037e-07\n",
495 + " Acc@1 : 87.500%\n",
496 + " Loss : 1.520838737487793\n",
497 + " FW Time : 17.655ms\n",
498 + " BW Time : 11.358ms\n",
499 + "\n",
500 + "[+] (Valid results) Valid step: 1149/2000\n",
501 + " Acc@1 : 97.200%\n",
502 + " Loss : 1.0447773933410645\n",
503 + "\n",
504 + "[+] Model saved\n",
505 + "\n",
506 + "[+] Training step: 1150/2000\tTraining epoch: 0/580\tElapsed time: 3.69min\tLearning rate: 9.207841281698394e-07\n",
507 + " Acc@1 : 100.000%\n",
508 + " Loss : 0.45810914039611816\n",
509 + " FW Time : 21.189ms\n",
510 + " BW Time : 21.055ms\n",
511 + "\n",
512 + "[+] (Valid results) Valid step: 1199/2000\n",
513 + " Acc@1 : 97.200%\n",
514 + " Loss : 0.6157697439193726\n",
515 + "\n",
516 + "[+] Model saved\n",
517 + "\n",
518 + "[+] Training step: 1200/2000\tTraining epoch: 0/580\tElapsed time: 3.85min\tLearning rate: 9.174889091448058e-07\n",
519 + " Acc@1 : 100.000%\n",
520 + " Loss : 0.2990947961807251\n",
521 + " FW Time : 16.315ms\n",
522 + " BW Time : 13.309ms\n",
523 + "\n",
524 + "[+] (Valid results) Valid step: 1249/2000\n",
525 + " Acc@1 : 98.200%\n",
526 + " Loss : 0.9130725860595703\n",
527 + "\n",
528 + "[+] Model saved\n",
529 + "\n",
530 + "[+] Training step: 1250/2000\tTraining epoch: 0/580\tElapsed time: 4.01min\tLearning rate: 9.142054827518248e-07\n",
531 + " Acc@1 : 100.000%\n",
532 + " Loss : 0.2261216640472412\n",
533 + " FW Time : 30.053ms\n",
534 + " BW Time : 18.239ms\n",
535 + "\n",
536 + "[+] (Valid results) Valid step: 1299/2000\n",
537 + " Acc@1 : 98.000%\n",
538 + " Loss : 0.4017503261566162\n",
539 + "\n",
540 + "[+] Training step: 1300/2000\tTraining epoch: 0/580\tElapsed time: 4.17min\tLearning rate: 9.109338067884897e-07\n",
541 + " Acc@1 : 100.000%\n",
542 + " Loss : 0.3248733878135681\n",
543 + " FW Time : 21.094ms\n",
544 + " BW Time : 13.526ms\n",
545 + "\n",
546 + "[+] (Valid results) Valid step: 1349/2000\n",
547 + " Acc@1 : 98.000%\n",
548 + " Loss : 0.3138556480407715\n",
549 + "\n",
550 + "[+] Training step: 1350/2000\tTraining epoch: 0/580\tElapsed time: 4.32min\tLearning rate: 9.076738392034251e-07\n",
551 + " Acc@1 : 100.000%\n",
552 + " Loss : 0.21796566247940063\n",
553 + " FW Time : 25.850ms\n",
554 + " BW Time : 19.265ms\n",
555 + "\n",
556 + "[+] (Valid results) Valid step: 1399/2000\n",
557 + " Acc@1 : 98.000%\n",
558 + " Loss : 0.16485238075256348\n",
559 + "\n",
560 + "[+] Training step: 1400/2000\tTraining epoch: 0/580\tElapsed time: 4.48min\tLearning rate: 9.044255380957452e-07\n",
561 + " Acc@1 : 100.000%\n",
562 + " Loss : 0.28114575147628784\n",
563 + " FW Time : 22.245ms\n",
564 + " BW Time : 15.607ms\n",
565 + "\n",
566 + "[+] (Valid results) Valid step: 1449/2000\n",
567 + " Acc@1 : 98.200%\n",
568 + " Loss : 0.20785784721374512\n",
569 + "\n",
570 + "[+] Model saved\n",
571 + "\n",
572 + "[+] Training step: 1450/2000\tTraining epoch: 0/580\tElapsed time: 4.64min\tLearning rate: 9.011888617145144e-07\n",
573 + " Acc@1 : 87.500%\n",
574 + " Loss : 1.18643319606781\n",
575 + " FW Time : 19.665ms\n",
576 + " BW Time : 12.618ms\n",
577 + "\n",
578 + "[+] (Valid results) Valid step: 1499/2000\n",
579 + " Acc@1 : 98.400%\n",
580 + " Loss : 0.17331039905548096\n",
581 + "\n",
582 + "[+] Model saved\n",
583 + "\n",
584 + "[+] Training step: 1500/2000\tTraining epoch: 0/580\tElapsed time: 4.80min\tLearning rate: 8.979637684582136e-07\n",
585 + " Acc@1 : 100.000%\n",
586 + " Loss : 0.27240896224975586\n",
587 + " FW Time : 24.560ms\n",
588 + " BW Time : 14.096ms\n",
589 + "\n",
590 + "[+] (Valid results) Valid step: 1549/2000\n",
591 + " Acc@1 : 99.600%\n",
592 + " Loss : 0.17186295986175537\n",
593 + "\n",
594 + "[+] Model saved\n",
595 + "\n",
596 + "[+] Training step: 1550/2000\tTraining epoch: 0/580\tElapsed time: 4.96min\tLearning rate: 8.947502168742003e-07\n",
597 + " Acc@1 : 87.500%\n",
598 + " Loss : 0.6044453382492065\n",
599 + " FW Time : 20.771ms\n",
600 + " BW Time : 16.470ms\n",
601 + "\n",
602 + "[+] (Valid results) Valid step: 1599/2000\n",
603 + " Acc@1 : 99.200%\n",
604 + " Loss : 0.06833314895629883\n",
605 + "\n",
606 + "[+] Training step: 1600/2000\tTraining epoch: 0/580\tElapsed time: 5.12min\tLearning rate: 8.915481656581816e-07\n",
607 + " Acc@1 : 100.000%\n",
608 + " Loss : 0.1431320309638977\n",
609 + " FW Time : 20.891ms\n",
610 + " BW Time : 12.692ms\n",
611 + "\n",
612 + "[+] (Valid results) Valid step: 1649/2000\n",
613 + " Acc@1 : 99.600%\n",
614 + " Loss : 0.09469139575958252\n",
615 + "\n",
616 + "[+] Model saved\n",
617 + "\n",
618 + "[+] Training step: 1650/2000\tTraining epoch: 0/580\tElapsed time: 5.28min\tLearning rate: 8.8835757365368e-07\n",
619 + " Acc@1 : 87.500%\n",
620 + " Loss : 0.8603015542030334\n",
621 + " FW Time : 15.960ms\n",
622 + " BW Time : 16.599ms\n",
623 + "\n",
624 + "[+] (Valid results) Valid step: 1699/2000\n",
625 + " Acc@1 : 99.600%\n",
626 + " Loss : 0.061615824699401855\n",
627 + "\n",
628 + "[+] Model saved\n",
629 + "\n",
630 + "[+] Training step: 1700/2000\tTraining epoch: 0/580\tElapsed time: 5.44min\tLearning rate: 8.851783998515047e-07\n",
631 + " Acc@1 : 62.500%\n",
632 + " Loss : 1.4177227020263672\n",
633 + " FW Time : 14.728ms\n",
634 + " BW Time : 14.786ms\n",
635 + "\n",
636 + "[+] (Valid results) Valid step: 1749/2000\n",
637 + " Acc@1 : 99.600%\n",
638 + " Loss : 0.03962230682373047\n",
639 + "\n",
640 + "[+] Model saved\n",
641 + "\n",
642 + "[+] Training step: 1750/2000\tTraining epoch: 0/580\tElapsed time: 5.61min\tLearning rate: 8.820106033892254e-07\n",
643 + " Acc@1 : 100.000%\n",
644 + " Loss : 0.13233846426010132\n",
645 + " FW Time : 15.973ms\n",
646 + " BW Time : 13.815ms\n",
647 + "\n",
648 + "[+] (Valid results) Valid step: 1799/2000\n",
649 + " Acc@1 : 99.800%\n",
650 + " Loss : 0.044447898864746094\n",
651 + "\n",
652 + "[+] Model saved\n",
653 + "\n",
654 + "[+] Training step: 1800/2000\tTraining epoch: 0/580\tElapsed time: 5.77min\tLearning rate: 8.788541435506462e-07\n",
655 + " Acc@1 : 100.000%\n",
656 + " Loss : 0.08350610733032227\n",
657 + " FW Time : 18.745ms\n",
658 + " BW Time : 13.062ms\n",
659 + "\n",
660 + "[+] (Valid results) Valid step: 1849/2000\n",
661 + " Acc@1 : 100.000%\n",
662 + " Loss : 0.06921112537384033\n",
663 + "\n",
664 + "[+] Model saved\n",
665 + "\n",
666 + "[+] Training step: 1850/2000\tTraining epoch: 0/580\tElapsed time: 5.93min\tLearning rate: 8.757089797652821e-07\n",
667 + " Acc@1 : 100.000%\n",
668 + " Loss : 0.14804929494857788\n",
669 + " FW Time : 41.547ms\n",
670 + " BW Time : 15.775ms\n",
671 + "\n",
672 + "[+] (Valid results) Valid step: 1899/2000\n",
673 + " Acc@1 : 100.000%\n",
674 + " Loss : 0.06944191455841064\n",
675 + "\n",
676 + "[+] Model saved\n",
677 + "\n",
678 + "[+] Training step: 1900/2000\tTraining epoch: 0/580\tElapsed time: 6.09min\tLearning rate: 8.725750716078392e-07\n",
679 + " Acc@1 : 100.000%\n",
680 + " Loss : 0.027304232120513916\n",
681 + " FW Time : 21.632ms\n",
682 + " BW Time : 17.365ms\n",
683 + "\n",
684 + "[+] (Valid results) Valid step: 1949/2000\n",
685 + " Acc@1 : 100.000%\n",
686 + " Loss : 0.05875754356384277\n",
687 + "\n",
688 + "[+] Model saved\n",
689 + "\n",
690 + "[+] Training step: 1950/2000\tTraining epoch: 0/580\tElapsed time: 6.25min\tLearning rate: 8.694523787976934e-07\n",
691 + " Acc@1 : 100.000%\n",
692 + " Loss : 0.13626277446746826\n",
693 + " FW Time : 40.104ms\n",
694 + " BW Time : 14.362ms\n",
695 + "\n",
696 + "[+] (Valid results) Valid step: 1999/2000\n",
697 + " Acc@1 : 100.000%\n",
698 + " Loss : 0.0167849063873291\n",
699 + "\n",
700 + "[+] Model saved\n"
701 + ],
702 + "name": "stdout"
703 + }
704 + ]
705 + },
706 + {
707 + "cell_type": "code",
708 + "metadata": {
709 + "id": "3opAMwCLZYJC",
710 + "colab_type": "code",
711 + "outputId": "208d8102-ce39-4897-9f89-ed7f8f626c33",
712 + "colab": {
713 + "base_uri": "https://localhost:8080/",
714 + "height": 350
715 + }
716 + },
717 + "source": [
718 + "!python \"eval.py\" --model_path='/content/drive/My Drive/CD2 Project/runs/classify/April_26_13:35:05__resnet50__None/' "
719 + ],
720 + "execution_count": 10,
721 + "outputs": [
722 + {
723 + "output_type": "stream",
724 + "text": [
725 + "\n",
726 + "[+] Parse arguments\n",
727 + "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",
728 + "\n",
729 + "[+] Create network\n",
730 + "\n",
731 + "[+] Load model\n",
732 + "\n",
733 + "[+] Load dataset\n",
734 + "\n",
735 + "[+] Start testing\n",
736 + "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",
737 + "\n",
738 + "[+] Valid results\n",
739 + " Acc@1 : 100.000%\n",
740 + " Loss : 0.020\n",
741 + " Infer Time(per image) : 4.062ms\n"
742 + ],
743 + "name": "stdout"
744 + }
745 + ]
746 + },
747 + {
748 + "cell_type": "code",
749 + "metadata": {
750 + "id": "F70Y3J9DHJwy",
751 + "colab_type": "code",
752 + "outputId": "502a62a8-de44-42c8-ec77-afc838a068c8",
753 + "colab": {
754 + "base_uri": "https://localhost:8080/",
755 + "height": 1000
756 + }
757 + },
758 + "source": [
759 + "# train cifar10\n",
760 + "# resnet50 from pytorch\n",
761 + "# !python train.py --dataset=cifar10 --use_cuda=True --optimizer=adam --network=resnet50"
762 + ],
763 + "execution_count": 0,
764 + "outputs": [
765 + {
766 + "output_type": "stream",
767 + "text": [
768 + "\n",
769 + "[+] Parse arguments\n",
770 + "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",
771 + "\n",
772 + "[+] Create log dir\n",
773 + "\n",
774 + "[+] Create network\n",
775 + "\n",
776 + "[+] Load dataset\n",
777 + "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",
778 + "171MB [00:02, 70.7MB/s] \n",
779 + "Files already downloaded and verified\n",
780 + "\n",
781 + "[+] Start training\n",
782 + "\n",
783 + "[+] Use 1 GPUs\n",
784 + "\n",
785 + "[+] Using GPU: Tesla P100-PCIE-16GB \n",
786 + "\n",
787 + "[+] Training step: 0/500\tTraining epoch: 0/1406\tElapsed time: 0.01min\tLearning rate: 0.0009999283\n",
788 + " Acc@1 : 0.000%\n",
789 + " Loss : 6.797711372375488\n",
790 + " FW Time : 63.167ms\n",
791 + " BW Time : 14.364ms\n",
792 + "\n",
793 + "[+] Valid results\n",
794 + " Acc@1 : 22.620%\n",
795 + " Loss : 2.110281229019165\n",
796 + "\n",
797 + "[+] Model saved\n",
798 + "\n",
799 + "[+] Training step: 100/500\tTraining epoch: 0/1406\tElapsed time: 0.20min\tLearning rate: 0.000992784200174764\n",
800 + " Acc@1 : 12.500%\n",
801 + " Loss : 2.331458568572998\n",
802 + " FW Time : 28.192ms\n",
803 + " BW Time : 17.216ms\n",
804 + "\n",
805 + "[+] Valid results\n",
806 + " Acc@1 : 27.320%\n",
807 + " Loss : 1.5727815628051758\n",
808 + "\n",
809 + "[+] Model saved\n",
810 + "\n",
811 + "[+] Training step: 200/500\tTraining epoch: 0/1406\tElapsed time: 0.36min\tLearning rate: 0.0009856911421715388\n",
812 + " Acc@1 : 25.000%\n",
813 + " Loss : 1.8888376951217651\n",
814 + " FW Time : 26.493ms\n",
815 + " BW Time : 17.836ms\n",
816 + "\n",
817 + "[+] Valid results\n",
818 + " Acc@1 : 30.800%\n",
819 + " Loss : 1.4916565418243408\n",
820 + "\n",
821 + "[+] Model saved\n",
822 + "\n",
823 + "[+] Training step: 300/500\tTraining epoch: 0/1406\tElapsed time: 0.53min\tLearning rate: 0.0009786487613163062\n",
824 + " Acc@1 : 25.000%\n",
825 + " Loss : 2.3930575847625732\n",
826 + " FW Time : 24.473ms\n",
827 + " BW Time : 22.187ms\n",
828 + "\n",
829 + "[+] Valid results\n",
830 + " Acc@1 : 24.680%\n",
831 + " Loss : 1.992270827293396\n",
832 + "\n",
833 + "[+] Training step: 400/500\tTraining epoch: 0/1406\tElapsed time: 0.70min\tLearning rate: 0.000971656695540503\n",
834 + " Acc@1 : 25.000%\n",
835 + " Loss : 2.1769983768463135\n",
836 + " FW Time : 28.696ms\n",
837 + " BW Time : 22.384ms\n",
838 + "\n",
839 + "[+] Valid results\n",
840 + " Acc@1 : 32.320%\n",
841 + " Loss : 1.5474934577941895\n",
842 + "\n",
843 + "[+] Model saved\n"
844 + ],
845 + "name": "stdout"
846 + }
847 + ]
848 + },
849 + {
850 + "cell_type": "code",
851 + "metadata": {
852 + "id": "Mhw6fBwCpHRd",
853 + "colab_type": "code",
854 + "colab": {}
855 + },
856 + "source": [
857 + ""
858 + ],
859 + "execution_count": 0,
860 + "outputs": []
861 + }
862 + ]
863 +}
...\ No newline at end of file ...\ No newline at end of file
1 -import os
2 -import time
3 -import importlib
4 -import collections
5 -import pickle as cp
6 -import glob
7 -import numpy as np
8 -import pandas as pd
9 -
10 -from natsort import natsorted
11 -from PIL import Image
12 -import torch
13 -import torchvision
14 -import torch.nn.functional as F
15 -import torchvision.models as models
16 -import torchvision.transforms as transforms
17 -from torch.utils.data import Subset
18 -from torch.utils.data import Dataset, DataLoader
19 -
20 -from sklearn.model_selection import StratifiedShuffleSplit
21 -from sklearn.model_selection import train_test_split
22 -from sklearn.model_selection import KFold
23 -
24 -from networks import *
25 -
26 -
27 -TRAIN_DATASET_PATH = '/content/drive/My Drive/CD2 Project/data/nonaug+Normal_train/'
28 -TRAIN_TARGET_PATH = '/content/drive/My Drive/CD2 Project/data/train_nonaug_classify_target.csv'
29 -# VAL_DATASET_PATH = '../../data/MICCAI_BraTS_2019_Data_Training/ce_valid/'
30 -# VAL_TARGET_PATH = '../../data/MICCAI_BraTS_2019_Data_Training/ce_valid_targets.csv'
31 -
32 -current_epoch = 0
33 -
34 -
35 -def split_dataset(args, dataset, k):
36 - # load dataset
37 - X = list(range(len(dataset)))
38 - Y = dataset.targets
39 -
40 - # split to k-fold
41 - assert len(X) == len(Y)
42 -
43 - def _it_to_list(_it):
44 - return list(zip(*list(_it)))
45 -
46 - sss = StratifiedShuffleSplit(n_splits=k, random_state=args.seed, test_size=0.1)
47 - Dm_indexes, Da_indexes = _it_to_list(sss.split(X, Y))
48 -
49 - return Dm_indexes, Da_indexes
50 -
51 -
52 -
53 -def get_model_name(args):
54 - from datetime import datetime, timedelta, timezone
55 - now = datetime.now(timezone.utc)
56 - tz = timezone(timedelta(hours=9))
57 - now = now.astimezone(tz)
58 - date_time = now.strftime("%B_%d_%H:%M:%S")
59 - model_name = '__'.join([date_time, args.network, str(args.seed)])
60 - return model_name
61 -
62 -
63 -def dict_to_namedtuple(d):
64 - Args = collections.namedtuple('Args', sorted(d.keys()))
65 -
66 - for k,v in d.items():
67 - if type(v) is dict:
68 - d[k] = dict_to_namedtuple(v)
69 -
70 - elif type(v) is str:
71 - try:
72 - d[k] = eval(v)
73 - except:
74 - d[k] = v
75 -
76 - args = Args(**d)
77 - return args
78 -
79 -
80 -def parse_args(kwargs):
81 - # combine with default args
82 - kwargs['dataset'] = kwargs['dataset'] if 'dataset' in kwargs else 'BraTS'
83 - kwargs['network'] = kwargs['network'] if 'network' in kwargs else 'resnet50'
84 - kwargs['optimizer'] = kwargs['optimizer'] if 'optimizer' in kwargs else 'adam'
85 - kwargs['learning_rate'] = kwargs['learning_rate'] if 'learning_rate' in kwargs else 0.0001
86 - kwargs['seed'] = kwargs['seed'] if 'seed' in kwargs else None
87 - kwargs['use_cuda'] = kwargs['use_cuda'] if 'use_cuda' in kwargs else True
88 - kwargs['use_cuda'] = kwargs['use_cuda'] and torch.cuda.is_available()
89 - kwargs['num_workers'] = kwargs['num_workers'] if 'num_workers' in kwargs else 4
90 - kwargs['print_step'] = kwargs['print_step'] if 'print_step' in kwargs else 500
91 - kwargs['val_step'] = kwargs['val_step'] if 'val_step' in kwargs else 500
92 - kwargs['scheduler'] = kwargs['scheduler'] if 'scheduler' in kwargs else 'exp'
93 - kwargs['batch_size'] = kwargs['batch_size'] if 'batch_size' in kwargs else 128
94 - kwargs['start_step'] = kwargs['start_step'] if 'start_step' in kwargs else 0
95 - kwargs['max_step'] = kwargs['max_step'] if 'max_step' in kwargs else 5000
96 - kwargs['augment_path'] = kwargs['augment_path'] if 'augment_path' in kwargs else None
97 -
98 - # to named tuple
99 - args = dict_to_namedtuple(kwargs)
100 - return args, kwargs
101 -
102 -
103 -def select_model(args):
104 - # grayResNet2
105 - resnet_dict = {'resnet18':grayResNet2.resnet18(), 'resnet34':grayResNet2.resnet34(),
106 - 'resnet50':grayResNet2.resnet50(), 'resnet101':grayResNet2.resnet101(), 'resnet152':grayResNet2.resnet152()}
107 -
108 - if args.network in resnet_dict:
109 - backbone = resnet_dict[args.network]
110 - model = basenet.BaseNet(backbone, args)
111 - else:
112 - Net = getattr(importlib.import_module('networks.{}'.format(args.network)), 'Net')
113 - model = Net(args)
114 -
115 - #print(model) # print model architecture
116 - return model
117 -
118 -
119 -def select_optimizer(args, model):
120 - if args.optimizer == 'sgd':
121 - optimizer = torch.optim.SGD(model.parameters(), lr=args.learning_rate, momentum=0.9, weight_decay=0.0001)
122 - elif args.optimizer == 'rms':
123 - optimizer = torch.optim.RMSprop(model.parameters(), lr=args.learning_rate)
124 - elif args.optimizer == 'adam':
125 - optimizer = torch.optim.Adam(model.parameters(), lr=args.learning_rate)
126 - else:
127 - raise Exception('Unknown Optimizer')
128 - return optimizer
129 -
130 -
131 -def select_scheduler(args, optimizer):
132 - if not args.scheduler or args.scheduler == 'None':
133 - return None
134 - elif args.scheduler =='clr':
135 - return torch.optim.lr_scheduler.CyclicLR(optimizer, 0.01, 0.015, mode='triangular2', step_size_up=250000, cycle_momentum=False)
136 - elif args.scheduler =='exp':
137 - return torch.optim.lr_scheduler.ExponentialLR(optimizer, gamma=0.9999283, last_epoch=-1)
138 - else:
139 - raise Exception('Unknown Scheduler')
140 -
141 -
142 -class CustomDataset(Dataset):
143 - def __init__(self, data_path, csv_path):
144 - self.len = len(self.imgs)
145 - self.path = data_path
146 - self.imgs = natsorted(os.listdir(data_path))
147 -
148 - df = pd.read_csv(csv_path)
149 - targets_list = []
150 -
151 - for fname in self.imgs:
152 - row = df.loc[df['filename'] == fname]
153 - targets_list.append(row.iloc[0, 1])
154 -
155 - self.targets = targets_list
156 -
157 - def __len__(self):
158 - return self.len
159 -
160 - def __getitem__(self, idx):
161 - img_loc = os.path.join(self.path, self.imgs[idx])
162 - targets = self.targets[idx]
163 - image = Image.open(img_loc)
164 - return image, targets
165 -
166 -
167 -
168 -def get_dataset(args, transform, split='train'):
169 - assert split in ['train', 'val', 'test', 'trainval']
170 -
171 - if split in ['train']:
172 - dataset = CustomDataset(TRAIN_DATASET_PATH, TRAIN_TARGET_PATH, transform=transform)
173 - else: #test
174 - dataset = CustomDataset(VAL_DATASET_PATH, VAL_TARGET_PATH, transform=transform)
175 -
176 - return dataset
177 -
178 -
179 -def get_dataloader(args, dataset, shuffle=False, pin_memory=True):
180 - data_loader = torch.utils.data.DataLoader(dataset,
181 - batch_size=args.batch_size,
182 - shuffle=shuffle,
183 - num_workers=args.num_workers,
184 - pin_memory=pin_memory)
185 - return data_loader
186 -
187 -
188 -def get_aug_dataloader(args, dataset, shuffle=False, pin_memory=True):
189 - data_loader = torch.utils.data.DataLoader(dataset,
190 - batch_size=args.batch_size,
191 - shuffle=shuffle,
192 - num_workers=args.num_workers,
193 - pin_memory=pin_memory)
194 - return data_loader
195 -
196 -
197 -def get_inf_dataloader(args, dataset):
198 - global current_epoch
199 - data_loader = iter(get_dataloader(args, dataset, shuffle=True))
200 -
201 - while True:
202 - try:
203 - batch = next(data_loader)
204 -
205 - except StopIteration:
206 - current_epoch += 1
207 - data_loader = iter(get_dataloader(args, dataset, shuffle=True))
208 - batch = next(data_loader)
209 -
210 - yield batch
211 -
212 -
213 -
214 -
215 -def train_step(args, model, optimizer, scheduler, criterion, batch, step, writer, device=None):
216 - model.train()
217 - images, target = batch
218 -
219 - if device:
220 - images = images.to(device)
221 - target = target.to(device)
222 -
223 - elif args.use_cuda:
224 - images = images.cuda(non_blocking=True)
225 - target = target.cuda(non_blocking=True)
226 -
227 - # compute output
228 - start_t = time.time()
229 - output, first = model(images)
230 - forward_t = time.time() - start_t
231 - loss = criterion(output, target)
232 -
233 - # measure accuracy and record loss
234 - acc1, acc5 = accuracy(output, target, topk=(1, 5))
235 - acc1 /= images.size(0)
236 - acc5 /= images.size(0)
237 -
238 - # compute gradient and do SGD step
239 - optimizer.zero_grad()
240 - start_t = time.time()
241 - loss.backward()
242 - backward_t = time.time() - start_t
243 - optimizer.step()
244 - if scheduler: scheduler.step()
245 -
246 - if writer and step % args.print_step == 0:
247 - n_imgs = min(images.size(0), 10)
248 - tag = 'train/' + str(step)
249 - for j in range(n_imgs):
250 - writer.add_image(tag,
251 - concat_image_features(images[j], first[j]), global_step=step)
252 -
253 - return acc1, acc5, loss, forward_t, backward_t
254 -
255 -
256 -#_acc1, _acc5 = accuracy(output, target, topk=(1, 5))
257 -def accuracy(output, target, topk=(1,)):
258 - """Computes the accuracy over the k top predictions for the specified values of k"""
259 - with torch.no_grad():
260 - maxk = max(topk)
261 - batch_size = target.size(0)
262 -
263 - _, pred = output.topk(maxk, 1, True, True)
264 - pred = pred.t()
265 - correct = pred.eq(target.view(1, -1).expand_as(pred))
266 -
267 - res = []
268 - for k in topk:
269 - correct_k = correct[:k].view(-1).float().sum(0, keepdim=True)
270 - res.append(correct_k)
271 - return res
272 -