update logs

@@ -9,6 +9,8 @@ from torch.utils.tensorboard import SummaryWriter
 
 
 from utils import *
 from utils import *
 
 
+# command
+# python "eval.py" --model_path='logs/'
 
 
 def eval(model_path):
 def eval(model_path):
     print('\n[+] Parse arguments')
     print('\n[+] Parse arguments')

+{"use_cuda": true, "network": "resnet50", "dataset": "BraTS", "optimizer": "adam", "fast_auto_augment": true, "learning_rate": 0.0001, "seed": null, "num_workers": 4, "print_step": 100, "val_step": 100, "scheduler": "exp", "batch_size": 32, "start_step": 0, "max_step": 500, "augment_path": null}
\ No newline at end of file

+DEFALUT_CANDIDATES = [
+    ShearXY,
+    TranslateXY,
+    # Rotate,
+    # AutoContrast,
+    # Invert,
+    Equalize,
+    Solarize,
+    Posterize,
+    # Contrast,
+    # Color,
+    Brightness,
+    Sharpness,
+    Cutout,
+#     SamplePairing,
+]
+
+[+] Parse arguments
+Args(augment_path=None, batch_size=32, dataset='BraTS', fast_auto_augment=True, learning_rate=0.0001, 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)
+
+[+] Create log dir
+
+[+] Create network
+
+[+] Load dataset
+[+] Child 0 training started (GPU: 0)
+
+[+] Training step: 0/500        Elapsed time: 0.04min   Learning rate: 9.999283e-05     Device name: GeForce GTX 1080 Ti
+  Acc@1 : 0.000%
+  Acc@5 : 0.000%
+  Loss : 7.5784010887146
+
+[+] Training step: 100/500      Elapsed time: 0.48min   Learning rate: 9.927842001747633e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 78.125%
+  Acc@5 : 100.000%
+  Loss : 0.4084218442440033
+
+[+] Training step: 200/500      Elapsed time: 0.96min   Learning rate: 9.856911421715387e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 93.750%
+  Acc@5 : 100.000%
+  Loss : 0.2725507915019989
+
+[+] Training step: 300/500      Elapsed time: 1.42min   Learning rate: 9.786487613163069e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 90.625%
+  Acc@5 : 100.000%
+  Loss : 0.20991499722003937
+
+[+] Training step: 400/500      Elapsed time: 1.88min   Learning rate: 9.716566955405027e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 93.750%
+  Acc@5 : 100.000%
+  Loss : 0.2204296737909317
+100%|????????????????????????????????| 1/1 [00:01<00:00,  1.58s/trial, best loss: 0.8958249092102051]
+100%|?????????????????????????????????| 1/1 [00:01<00:00,  1.39s/trial, best loss: 1.509151816368103]
+[+] Child 1 training started (GPU: 0)
+
+[+] Training step: 0/500        Elapsed time: 0.03min   Learning rate: 9.999283e-05     Device name: GeForce GTX 1080 Ti
+  Acc@1 : 0.000%
+  Acc@5 : 0.000%
+  Loss : 7.634987831115723
+
+[+] Training step: 100/500      Elapsed time: 0.48min   Learning rate: 9.927842001747633e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 87.500%
+  Acc@5 : 100.000%
+  Loss : 0.29290342330932617
+
+[+] Training step: 200/500      Elapsed time: 0.96min   Learning rate: 9.856911421715387e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 90.625%
+  Acc@5 : 100.000%
+  Loss : 0.28638142347335815
+
+[+] Training step: 300/500      Elapsed time: 1.42min   Learning rate: 9.786487613163069e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 96.875%
+  Acc@5 : 100.000%
+  Loss : 0.06958930194377899
+
+[+] Training step: 400/500      Elapsed time: 1.88min   Learning rate: 9.716566955405027e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 100.000%
+  Acc@5 : 100.000%
+  Loss : 0.030036240816116333
+100%|??????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.54s/trial, best loss: 2.1128218173980713]
+100%|??????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.50s/trial, best loss: 1.9411643743515015]
+[+] Child 2 training started (GPU: 0)
+
+[+] Training step: 0/500        Elapsed time: 0.03min   Learning rate: 9.999283e-05     Device name: GeForce GTX 1080 Ti
+  Acc@1 : 0.000%
+  Acc@5 : 0.000%
+  Loss : 7.582807540893555
+
+[+] Training step: 100/500      Elapsed time: 0.49min   Learning rate: 9.927842001747633e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 75.000%
+  Acc@5 : 100.000%
+  Loss : 0.5312898755073547
+
+[+] Training step: 200/500      Elapsed time: 0.98min   Learning rate: 9.856911421715387e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 84.375%
+  Acc@5 : 100.000%
+  Loss : 0.4784519672393799
+
+[+] Training step: 300/500      Elapsed time: 1.45min   Learning rate: 9.786487613163069e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 100.000%
+  Acc@5 : 100.000%
+  Loss : 0.03968067467212677
+
+[+] Training step: 400/500      Elapsed time: 1.89min   Learning rate: 9.716566955405027e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 100.000%
+  Acc@5 : 100.000%
+  Loss : 0.025451302528381348
+100%|??????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.53s/trial, best loss: 2.5077414512634277]
+100%|???????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.59s/trial, best loss: 4.707443714141846]
+[+] Child 3 training started (GPU: 0)
+
+[+] Training step: 0/500        Elapsed time: 0.03min   Learning rate: 9.999283e-05     Device name: GeForce GTX 1080 Ti
+  Acc@1 : 0.000%
+  Acc@5 : 0.000%
+  Loss : 7.614710807800293
+
+[+] Training step: 100/500      Elapsed time: 0.49min   Learning rate: 9.927842001747633e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 75.000%
+  Acc@5 : 100.000%
+  Loss : 0.46335405111312866
+
+[+] Training step: 200/500      Elapsed time: 0.96min   Learning rate: 9.856911421715387e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 90.625%
+  Acc@5 : 100.000%
+  Loss : 0.16135810315608978
+
+[+] Training step: 300/500      Elapsed time: 1.44min   Learning rate: 9.786487613163069e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 84.375%
+  Acc@5 : 100.000%
+  Loss : 0.4632360339164734
+
+[+] Training step: 400/500      Elapsed time: 1.90min   Learning rate: 9.716566955405027e-05   Device name: GeForce GTX 1080 Ti
+  Acc@1 : 100.000%
+  Acc@5 : 100.000%
+  Loss : 0.04105471074581146
+100%|???????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.55s/trial, best loss: 2.492347240447998]
+100%|??????????????????????????????????????????????????????????| 1/1 [00:01<00:00,  1.56s/trial, best loss: 2.6143996715545654]
+RandomChoice(
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Brightness(prob=0.47, magnitude=0.06)
+    Sharpness(prob=0.52, magnitude=0.28)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Solarize(prob=0.70, magnitude=0.03)
+    Sharpness(prob=0.98, magnitude=0.62)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Posterize(prob=0.08, magnitude=0.88)
+    Solarize(prob=0.98, magnitude=0.76)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Posterize(prob=0.37, magnitude=0.60)
+    Cutout(prob=0.75, magnitude=0.83)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    ShearXY(prob=0.56, magnitude=0.86)
+    Cutout(prob=0.37, magnitude=0.00)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Sharpness(prob=0.09, magnitude=0.75)
+    Equalize(prob=0.70, magnitude=0.90)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    TranslateXY(prob=0.67, magnitude=0.95)
+    Posterize(prob=0.31, magnitude=0.92)
+    ToTensor()
+)
+    Compose(
+    Pad(padding=4, fill=0, padding_mode=constant)
+    RandomCrop(size=(32, 32), padding=None)
+    RandomHorizontalFlip(p=0.5)
+    Equalize(prob=0.32, magnitude=0.07)
+    Posterize(prob=0.83, magnitude=0.82)
+    ToTensor()
+)
+)
+
+[+] Start training
+
+[+] Use 1 GPUs
+
+[+] Using GPU: GeForce GTX 1080 Ti 
+/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2457: UserWarning: nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.
+  warnings.warn("nn.functional.upsample is deprecated. Use nn.functional.interpolate instead.")
+/opt/conda/lib/python3.7/site-packages/torch/nn/functional.py:2539: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
+  "See the documentation of nn.Upsample for details.".format(mode))
+
+[+] Training step: 0/500        Training epoch: 0/64    Elapsed time: 0.08min   Learning rate: 9.999283e-05
+  Acc@1 : 0.000%
+  Acc@5 : 0.000%
+  Loss : 7.677338123321533
+  FW Time : 200.173ms
+  BW Time : 22.200ms
+
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 17.083898544311523
+
+[+] Model saved
+
+[+] Training step: 100/500      Training epoch: 0/64    Elapsed time: 0.88min   Learning rate: 9.927842001747633e-05
+  Acc@1 : 34.375%
+  Acc@5 : 100.000%
+  Loss : 0.9044204950332642
+  FW Time : 20.693ms
+  BW Time : 19.411ms
+
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 10.917157173156738
+
+[+] Model saved
+
+[+] Training step: 200/500      Training epoch: 0/64    Elapsed time: 1.74min   Learning rate: 9.856911421715387e-05
+  Acc@1 : 34.375%
+  Acc@5 : 100.000%
+  Loss : 0.7641889452934265
+  FW Time : 20.088ms
+  BW Time : 9.569ms
+
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 26.895051956176758
+
+[+] Model saved
+
+[+] Training step: 300/500      Training epoch: 0/64    Elapsed time: 2.57min   Learning rate: 9.786487613163069e-05
+  Acc@1 : 56.250%
+  Acc@5 : 100.000%
+  Loss : 0.8696596622467041
+  FW Time : 19.580ms
+  BW Time : 9.993ms
+OMP: Warning #190: Forking a process while a parallel region is active is potentially unsafe.
+
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 11.694602966308594
+
+[+] Model saved
+
+[+] Training step: 400/500      Training epoch: 0/64    Elapsed time: 3.43min   Learning rate: 9.716566955405027e-05
+  Acc@1 : 71.875%
+  Acc@5 : 100.000%
+  Loss : 0.7189279198646545
+  FW Time : 19.634ms
+  BW Time : 16.867ms
+OMP: Warning #190: Forking a process while a parallel region is active is potentially unsafe.
+
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 12.062773704528809
+[+] Valid results
+  Acc@1 : 47.767%
+  Acc@5 : 100.000%
+  Loss : 12.063
+  Infer Time(per image) : 2.722ms
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