==========================================
gpu_id:0
s:0
t:1
max_epoch:10
batch_size:64
worker:4
dset:VISDA-C
lr:0.001
net:resnet101
seed:2020
bottleneck:512
epsilon:1e-05
layer:wn
classifier:bn
smooth:0.1
output:ckps/source/
da:uda
trte:val
class_num:12
s_dset_path:./data/VISDA-C/train_list.txt
test_dset_path:./data/VISDA-C/validation_list.txt
output_dir_src:ckps/source/uda/VISDA-C/T
name_src:T
out_file:<_io.TextIOWrapper name='ckps/source/uda/VISDA-C/T/log.txt' mode='w' encoding='UTF-8'>

Task: T, Iter:2144/21440; Accuracy = 97.59%
99.73 99.02 92.97 94.37 99.44 99.04 98.85 99.33 99.9 99.58 96.24 92.58
Task: T, Iter:4288/21440; Accuracy = 98.27%
99.8 99.16 97.54 95.84 99.55 98.97 99.14 99.83 99.81 99.83 95.93 93.83
Task: T, Iter:6432/21440; Accuracy = 98.66%
99.93 99.3 97.36 97.92 99.78 99.66 99.2 99.83 100.0 99.92 97.23 93.83
Task: T, Iter:8576/21440; Accuracy = 98.92%
99.93 99.44 98.2 97.46 99.89 99.66 99.31 99.92 100.0 99.92 98.71 94.67
Task: T, Iter:10720/21440; Accuracy = 98.99%
99.93 99.72 98.26 97.46 99.89 99.66 99.26 99.92 100.0 99.92 98.83 95.09
Task: T, Iter:12864/21440; Accuracy = 99.03%
99.93 99.58 98.26 98.54 100.0 99.73 99.31 99.92 100.0 100.0 98.77 94.36
Task: T, Iter:15008/21440; Accuracy = 99.16%
100.0 99.86 98.56 97.76 99.89 99.66 99.54 100.0 100.0 99.92 98.64 96.13
Task: T, Iter:17152/21440; Accuracy = 99.25%
99.93 99.58 98.02 98.38 99.89 99.73 99.71 99.92 100.0 100.0 99.2 96.66
Task: T, Iter:19296/21440; Accuracy = 99.18%
100.0 99.58 98.8 98.69 99.89 99.66 99.66 100.0 100.0 100.0 98.95 94.88
Task: T, Iter:21440/21440; Accuracy = 99.39%
100.0 99.16 98.92 98.84 100.0 99.73 99.89 99.92 100.0 100.0 99.2 97.07
