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TrainOffice.txt
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=======Arguments=======
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/bin/python
dataset: Office
MnistTrain: 1
batch_size: 512
train_epoch: 300
NetVersion: Shuffle
GroupWiseEncoder: False
GroupWiseDecoder: 1
WithFeatureB: False
FeatureType: GlT_GaussainlizeAndTanh
BatchNormType: 1001
RepresentationType: Normalize
ActivationType: Tanh
representation_dim: 0
RunInManuel: False
VisualFreq: 20
DrawTSNE: False
DrawUmap: False
WarmAll: 20
WarmBalance: 0
WarmConsistency: 0
WarmUpProto: 0
WarmOneHot: 0
SoftAssignmentTemperatureBalance: 0.1
SoftAssignmentTemperatureHot: 0.1
SoftAssignmentTemperatureSelfCons: 0.2
Reconstruction: 1.0
ReconstructionEpoch: 999
GlobalBalanceLoss: 0.0
InfoGlobalLoss: 0.0
InfoBalanceLoss: 0.04
InfoFairLoss: 0.2
GroupWiseBalanceLoss: 0.0
ClusterWiseBalanceLoss: 0.0
BalanceLossType: KL
BalanceLossNoDetach: False
OneHot: 0.04
loss_cons: 0.0
loss_self_cons: 0.0
SelfConsLossType: Assignment
Discriminative: 0.0
DiscriminativeTest: 0
reconstruct_all: 0
Decenc: 0.0
LearnRate: 0.001
LearnRateDecayType: None
WeightDecay: 0
LearnRateWarm: 0
betas_a: 0.9
betas_v: 0.999
seed: 0
resume: /xlearning/pengxin/FC_Cps/OFFICE/Epoch299.checkpoint
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/lib/python3.10/site-packages/torchvision/models/_utils.py:208: UserWarning: The parameter 'pretrained' is deprecated since 0.13 and will be removed in 0.15, please use 'weights' instead.
warnings.warn(
/xlearning/pengxin/Softwares/anaconda3/envs/torch1120/lib/python3.10/site-packages/torchvision/models/_utils.py:223: UserWarning: Arguments other than a weight enum or `None` for 'weights' are deprecated since 0.13 and will be removed in 0.15. The current behavior is equivalent to passing `weights=ResNet50_Weights.IMAGENET1K_V1`. You can also use `weights=ResNet50_Weights.DEFAULT` to get the most up-to-date weights.
warnings.warn(msg)
torch.mean(x)==-0.10849952697753906, torch.std(x)==0.5449516773223877
torch.min(x)==-0.7269272804260254, torch.max(x)==1.0
torch.sqrt(torch.sum(x ** 2, dim=1, keepdim=True))==tensor([[24.3025],
[24.2172],
[25.0194],
...,
[25.5545],
[25.0868],
[25.8095]])
=> loading checkpoint '/xlearning/pengxin/FC_Cps/OFFICE/Epoch299.checkpoint'
=> loaded checkpoint '/xlearning/pengxin/FC_Cps/OFFICE/Epoch299.checkpoint' (epoch 299)
Save check point into /xlearning/pengxin/Codes/230520/RunSet0520_RePo5/ --dataset Office --resume Epoch299 --seed 0/Checkpoints/Epoch299.checkpoint
ACC= 70.0|69.989, NMI= 71.2|71.160, Bal= 22.6|22.581, MNCE= 90.6|90.643, Fmeasure= 79.7|79.728
Clu\g 0, 1,
123: 94, 29,
103: 82, 21,
125: 97, 28,
100: 80, 20,
117: 93, 24,
221: 176, 45,
201: 152, 49,
92: 70, 22,
84: 64, 20,
4: 1, 3,
148: 117, 31,
118: 93, 25,
145: 114, 31,
130: 102, 28,
132: 103, 29,
140: 109, 31,
172: 134, 38,
130: 101, 29,
167: 132, 35,
78: 62, 16,
48: 38, 10,
138: 108, 30,
119: 94, 25,
66: 51, 15,
122: 94, 28,
114: 85, 29,
86: 64, 22,
109: 83, 26,
96: 77, 19,
108: 85, 23,
76: 62, 14,
Typ\g 0, 1,
121: 92, 29,
103: 82, 21,
100: 72, 28,
94: 82, 12,
52: 36, 16,
125: 94, 31,
131: 91, 40,
115: 97, 18,
118: 97, 21,
100: 81, 19,
126: 99, 27,
127: 100, 27,
130: 100, 30,
117: 98, 19,
130: 100, 30,
142: 99, 43,
130: 100, 30,
121: 94, 27,
124: 96, 28,
127: 95, 32,
109: 93, 16,
120: 100, 20,
128: 98, 30,
125: 98, 27,
130: 90, 40,
86: 75, 11,
125: 100, 25,
129: 99, 30,
123: 99, 24,
119: 96, 23,
85: 64, 21,
t\c 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30
0: 87 29| | | | | | | | | | | | 1 | | | | 4 | | | | | | | | | | | | | | |
1: |81 21| | | | | 1 | | | | | | | | | | | | | | | | | | | | | | | | |
2: | |70 28| | | | 1 | | | | | | | | | | 1 | | | | | | | | | | | | | | |
3: 1 | | 1 |71 12| | | | 4 | | | | | | | | | | | 1 | | | | | | 1 | 1 | | 1 | 1 | | |
4: 1 | | | |24 16| | | | | | 3 | | | | | | | 1 | 3 | | | | | | | | 2 | | | 2 | |
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6: | | | 1 | 1 | 1 |80 39| 3 1| | | 4 | | | | | | | 1 | | | | | | | | | | | | | |
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8: | | | | | 1 | | |47 18| | | 2 |15 | | |15 | | | 2 | 1 | | 1| 1| 2 | 1| | | 4 | | 4 | 4 |
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19: | | | 1 | 6 | 1 | | | | | 1 | | | | | | | | 2 |55 10| | | | | | 1 22| 2 | | | 3 |23 |
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23: | | 3 | 1 | 4 | | | | | | 1 3| | | 2 7| | | 1 | |13 | |16 | 1 1| 4 |19 5| | 3 | 6 | | 7 5|14 6| 3 |
24: 2 | | | 1 | | 1 | | | | | | | | 3 2| | | | |11 5| | | 1 | | |64 21| 1 | | | 4 12| | 2 |
25: | | | | 2 | 2 | | 1 | | | 2 | | | 2 | | | | | 2 | 3| | 1 | | | |60 7| 1 | | 1 | | 1 1|
26: 1 | | 7 | | 2 | | | | | | 2 | | | 1 | 2 | | 4 | | 9 | 3| 7 | | | | |13 |46 22| | | 1 | 5 |
27: | | | | 2 | 2 | | 5 | 2 | | 1 | | 3 | | 2 | 2| 3 8| | | | 4| | 2 1| 1 | | | |73 13| | 1 | 2 2|
28: | | 1 | | 3 | | | 1 | | | | | | | 7| |10 | | 4 | 2 | 1 | 3 | |16 8| | | 2 | 5|40 2|13 2| 3 |
29: 1 | | 9 | | 4 1| | 1 | | | | 5 | | | 2 | | | 7 3| 1 | 5 | | 8 | 1 | 2| 1 1| 2 | | 1 | 2 3| 1 |36 13| 9 |
30: 1 | | | |31 4| | 1 | 2 | 2 | | 2 | | | 3 | | | 1 | 8 3| 1 1| | 2| | | | 9 6| | | | | | 3 5|
Ending...
2023-05-20 16:56:14 Dequeue 2023-05-20_16:56:01_002001.Process