2020-1-capstone-design2 / 2016104167

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cad58740 1 parent a5ce1660

classifier tensorboard

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Showing 2 changed files with 19 additions and 13 deletions
...@@ -6,7 +6,7 @@ from pprint import pprint ...@@ -6,7 +6,7 @@ from pprint import pprint
6 import torch 6 import torch
7 import torch.nn as nn 7 import torch.nn as nn
8 import torchvision.transforms as transforms 8 import torchvision.transforms as transforms
9 -#from torch.utils.tensorboard import SummaryWriter 9 +from torch.utils.tensorboard import SummaryWriter
10 10
11 from utils import * 11 from utils import *
12 12
...@@ -44,8 +44,9 @@ def eval(model_path): ...@@ -44,8 +44,9 @@ def eval(model_path):
44 44
45 test_loader = iter(get_dataloader(args, test_dataset)) ### 45 test_loader = iter(get_dataloader(args, test_dataset)) ###
46 46
47 - # print('\n[+] Start testing') 47 + print('\n[+] Start testing')
48 - # writer = SummaryWriter(log_dir=model_path) 48 + os.makedirs(os.path.join(model_path, 'test'))
49 + writer = SummaryWriter(log_dir=os.path.join(model_path, 'test'))
49 _test_res = validate(args, model, criterion, test_loader, step=0) 50 _test_res = validate(args, model, criterion, test_loader, step=0)
50 51
51 print('\n[+] Valid results') 52 print('\n[+] Valid results')
...@@ -53,7 +54,10 @@ def eval(model_path): ...@@ -53,7 +54,10 @@ def eval(model_path):
53 print(' Loss : {:.3f}'.format(_test_res[1].data)) 54 print(' Loss : {:.3f}'.format(_test_res[1].data))
54 print(' Infer Time(per image) : {:.3f}ms'.format(_test_res[2]*1000 / len(test_dataset))) 55 print(' Infer Time(per image) : {:.3f}ms'.format(_test_res[2]*1000 / len(test_dataset)))
55 56
56 - #writer.close() 57 + writer.add_scalar('test/acc1', _test_res[0])
58 + writer.add_scalar('test/loss', _test_res[1])
59 +
60 + writer.close()
57 61
58 if __name__ == '__main__': 62 if __name__ == '__main__':
59 fire.Fire(eval) 63 fire.Fire(eval)
......
...@@ -7,7 +7,7 @@ from pprint import pprint ...@@ -7,7 +7,7 @@ from pprint import pprint
7 7
8 import torch.nn as nn 8 import torch.nn as nn
9 import torch.backends.cudnn as cudnn 9 import torch.backends.cudnn as cudnn
10 -#from torch.utils.tensorboard import SummaryWriter 10 +from torch.utils.tensorboard import SummaryWriter
11 11
12 from networks import * 12 from networks import *
13 from utils import * 13 from utils import *
...@@ -27,7 +27,9 @@ def train(**kwargs): ...@@ -27,7 +27,9 @@ def train(**kwargs):
27 log_dir = os.path.join('/content/drive/My Drive/CD2 Project/runs/classify/', model_name) 27 log_dir = os.path.join('/content/drive/My Drive/CD2 Project/runs/classify/', model_name)
28 os.makedirs(os.path.join(log_dir, 'model')) 28 os.makedirs(os.path.join(log_dir, 'model'))
29 json.dump(kwargs, open(os.path.join(log_dir, 'kwargs.json'), 'w')) 29 json.dump(kwargs, open(os.path.join(log_dir, 'kwargs.json'), 'w'))
30 - #writer = SummaryWriter(log_dir=log_dir) 30 +
31 + os.makedirs(os.path.join(log_dir, 'train'))
32 + writer = SummaryWriter(log_dir=os.path.join(log_dir, 'train'))
31 33
32 if args.seed is not None: 34 if args.seed is not None:
33 random.seed(args.seed) 35 random.seed(args.seed)
...@@ -69,11 +71,11 @@ def train(**kwargs): ...@@ -69,11 +71,11 @@ def train(**kwargs):
69 if step % args.print_step == 0: 71 if step % args.print_step == 0:
70 print('\n[+] Training step: {}/{}\tTraining epoch: {}/{}\tElapsed time: {:.2f}min\tLearning rate: {}'.format( 72 print('\n[+] Training step: {}/{}\tTraining epoch: {}/{}\tElapsed time: {:.2f}min\tLearning rate: {}'.format(
71 step, args.max_step, current_epoch, max_epoch, (time.time()-start_t)/60, optimizer.param_groups[0]['lr'])) 73 step, args.max_step, current_epoch, max_epoch, (time.time()-start_t)/60, optimizer.param_groups[0]['lr']))
72 - # writer.add_scalar('train/learning_rate', optimizer.param_groups[0]['lr'], global_step=step) 74 + writer.add_scalar('train/learning_rate', optimizer.param_groups[0]['lr'], global_step=step)
73 - # writer.add_scalar('train/acc1', _train_res[0], global_step=step) 75 + writer.add_scalar('train/acc1', _train_res[0], global_step=step)
74 - # writer.add_scalar('train/loss', _train_res[1], global_step=step) 76 + writer.add_scalar('train/loss', _train_res[1], global_step=step)
75 - # writer.add_scalar('train/forward_time', _train_res[2], global_step=step) 77 + writer.add_scalar('train/forward_time', _train_res[2], global_step=step)
76 - # writer.add_scalar('train/backward_time', _train_res[3], global_step=step) 78 + writer.add_scalar('train/backward_time', _train_res[3], global_step=step)
77 print(' Acc@1 : {:.3f}%'.format(_train_res[0].data.cpu().numpy()[0]*100)) 79 print(' Acc@1 : {:.3f}%'.format(_train_res[0].data.cpu().numpy()[0]*100))
78 print(' Loss : {}'.format(_train_res[1].data)) 80 print(' Loss : {}'.format(_train_res[1].data))
79 print(' FW Time : {:.3f}ms'.format(_train_res[2]*1000)) 81 print(' FW Time : {:.3f}ms'.format(_train_res[2]*1000))
...@@ -84,8 +86,8 @@ def train(**kwargs): ...@@ -84,8 +86,8 @@ def train(**kwargs):
84 valid_loader = iter(get_dataloader(args, valid_dataset)) 86 valid_loader = iter(get_dataloader(args, valid_dataset))
85 _valid_res = validate(args, model, criterion, valid_loader, step) 87 _valid_res = validate(args, model, criterion, valid_loader, step)
86 print('\n[+] (Valid results) Valid step: {}/{}'.format(step, args.max_step)) 88 print('\n[+] (Valid results) Valid step: {}/{}'.format(step, args.max_step))
87 - # writer.add_scalar('valid/acc1', _valid_res[0], global_step=step) 89 + writer.add_scalar('valid/acc1', _valid_res[0], global_step=step)
88 - # writer.add_scalar('valid/loss', _valid_res[1], global_step=step) 90 + writer.add_scalar('valid/loss', _valid_res[1], global_step=step)
89 print(' Acc@1 : {:.3f}%'.format(_valid_res[0].data.cpu().numpy()[0]*100)) 91 print(' Acc@1 : {:.3f}%'.format(_valid_res[0].data.cpu().numpy()[0]*100))
90 print(' Loss : {}'.format(_valid_res[1].data)) 92 print(' Loss : {}'.format(_valid_res[1].data))
91 93
......