Train on UCF101

[Morning-YU]

I use the following parameters and take mobilenet and resnet-50 which trained by TSN as pre-trained. But the training results are strange！From the beginning, the training accuracy has reached 100%, while the test accuracy is basically unchanged. CUDA_VISIBLE_DEVICES=4 python stage1.py
dataset=ucf101
data_dir=/data/ymy/data/
train_stage=1
batch_size=32
num_segments_glancer=8
num_segments_focuser=12
glance_size=224
patch_size=144
random_patch=True
epochs=50
backbone_lr=0.001
fc_lr=0.01
lr_type=step
dropout=0.5
load_pretrained_focuser_fc=False
dist_url=tcp://127.0.0.1:8816
eval_freq=1
start_eval=0
print_freq=25
workers=16
pretrained_glancer='/AdaFocus-main/new_mobile.tar'
pretrained_focuser='/AdaFocus-main/new_resnet.tar'

Epoch: [5][ 0/298] Time 43.183 (43.183) Data 42.607 (42.607) Loss 1.1841e-03 (1.1841e-03) Acc@1 100.00 (100.00) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][ 25/298] Time 0.674 ( 2.839) Data 0.107 ( 2.276) Loss 1.7993e-03 (8.2321e-03) Acc@1 100.00 ( 99.76) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][ 50/298] Time 1.080 ( 2.122) Data 0.526 ( 1.560) Loss 1.7797e-02 (1.1389e-02) Acc@1 100.00 ( 99.63) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][ 75/298] Time 0.615 ( 1.833) Data 0.048 ( 1.272) Loss 2.5565e-04 (1.1153e-02) Acc@1 100.00 ( 99.63) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][100/298] Time 0.624 ( 1.724) Data 0.056 ( 1.163) Loss 1.6186e-03 (9.6181e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][125/298] Time 0.640 ( 1.601) Data 0.082 ( 1.041) Loss 6.2654e-02 (9.9088e-03) Acc@1 96.88 ( 99.68) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][150/298] Time 0.618 ( 1.596) Data 0.061 ( 1.036) Loss 1.9718e-04 (9.0484e-03) Acc@1 100.00 ( 99.71) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][175/298] Time 0.673 ( 1.526) Data 0.107 ( 0.965) Loss 1.8096e-03 (9.6376e-03) Acc@1 100.00 ( 99.70) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][200/298] Time 0.630 ( 1.523) Data 0.061 ( 0.962) Loss 2.6468e-03 (9.3167e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][225/298] Time 11.313 ( 1.514) Data 10.754 ( 0.952) Loss 9.3352e-03 (9.5301e-03) Acc@1 100.00 ( 99.72) Acc@5 100.00 (100.00) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][250/298] Time 0.643 ( 1.475) Data 0.086 ( 0.913) Loss 1.7089e-03 (1.0416e-02) Acc@1 100.00 ( 99.70) Acc@5 100.00 ( 99.99) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][275/298] Time 0.604 ( 1.472) Data 0.046 ( 0.910) Loss 1.3999e-03 (9.9850e-03) Acc@1 100.00 ( 99.73) Acc@5 100.00 ( 99.99) Focuser BackBone LR: 0.001 FC LR: 0 Epoch: [5][297/298] Time 0.647 ( 1.410) Data 0.094 ( 0.848) Loss 1.0134e-03 (1.0606e-02) Acc@1 100.00 ( 99.72) Acc@5 100.00 ( 99.98) Focuser BackBone LR: 0.001 FC LR: 0 Test: [ 0/119] Time 21.262 (21.262) Loss 6.7998e-01 (6.7998e-01) Acc@1 81.25 ( 81.25) Acc@5 100.00 (100.00) Test: [ 25/119] Time 0.381 ( 1.223) Loss 2.3228e-01 (6.1051e-01) Acc@1 93.75 ( 85.10) Acc@5 100.00 ( 97.60) Test: [ 50/119] Time 0.366 ( 0.818) Loss 4.2509e-01 (8.5970e-01) Acc@1 93.75 ( 81.07) Acc@5 96.88 ( 95.22) Test: [ 75/119] Time 0.406 ( 0.680) Loss 2.0299e-01 (1.0306e+00) Acc@1 93.75 ( 78.12) Acc@5 100.00 ( 93.09) Test: [100/119] Time 0.362 ( 0.609) Loss 3.9213e-01 (9.9937e-01) Acc@1 96.88 ( 78.53) Acc@5 96.88 ( 93.56) Test: [118/119] Time 0.122 ( 0.571) Loss 1.6555e+00 (9.4728e-01) Acc@1 28.57 ( 79.33) Acc@5 100.00 ( 94.00) Testing Results: Prec@1 79.329 Prec@5 93.999 Loss 0.94728