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lunwen_ccfc_cifar100_noiid_3.log
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nohup: ignoring input
cuda:3
Namespace(batch_size=1496, classes_per_user=8, data_root='./datasets', exp_dir='./save/CCFC/cifar100-noiid', global_lr=1, image_size=224, k=20, latent_dim=256, lbd=0.1, lr=0.0005, mini_bs=136, n_clients=40, num_proj_layers=2, num_workers=6, p=0.0, pre_hidden_dim=64, proj_hidden_dim=512, resnet='ResNet18', sample_ratio=0.9, seed=66, test_image_size=256, trial='v1')
59840
save/CCFC/Cifar100-fed/v2/model_pretrain_0_39.pt
Global NMI = 0.1244 ARI = 0.0521 F = 0.1055 ACC = 0.1728
backbone.conv1.weight 9408 torch.Size([64, 3, 7, 7])
backbone.bn1.weight 64 torch.Size([64])
backbone.bn1.bias 64 torch.Size([64])
backbone.layer1.0.conv1.weight 36864 torch.Size([64, 64, 3, 3])
backbone.layer1.0.bn1.weight 64 torch.Size([64])
backbone.layer1.0.bn1.bias 64 torch.Size([64])
backbone.layer1.0.conv2.weight 36864 torch.Size([64, 64, 3, 3])
backbone.layer1.0.bn2.weight 64 torch.Size([64])
backbone.layer1.0.bn2.bias 64 torch.Size([64])
backbone.layer1.1.conv1.weight 36864 torch.Size([64, 64, 3, 3])
backbone.layer1.1.bn1.weight 64 torch.Size([64])
backbone.layer1.1.bn1.bias 64 torch.Size([64])
backbone.layer1.1.conv2.weight 36864 torch.Size([64, 64, 3, 3])
backbone.layer1.1.bn2.weight 64 torch.Size([64])
backbone.layer1.1.bn2.bias 64 torch.Size([64])
backbone.layer2.0.conv1.weight 73728 torch.Size([128, 64, 3, 3])
backbone.layer2.0.bn1.weight 128 torch.Size([128])
backbone.layer2.0.bn1.bias 128 torch.Size([128])
backbone.layer2.0.conv2.weight 147456 torch.Size([128, 128, 3, 3])
backbone.layer2.0.bn2.weight 128 torch.Size([128])
backbone.layer2.0.bn2.bias 128 torch.Size([128])
backbone.layer2.0.downsample.0.weight 8192 torch.Size([128, 64, 1, 1])
backbone.layer2.0.downsample.1.weight 128 torch.Size([128])
backbone.layer2.0.downsample.1.bias 128 torch.Size([128])
backbone.layer2.1.conv1.weight 147456 torch.Size([128, 128, 3, 3])
backbone.layer2.1.bn1.weight 128 torch.Size([128])
backbone.layer2.1.bn1.bias 128 torch.Size([128])
backbone.layer2.1.conv2.weight 147456 torch.Size([128, 128, 3, 3])
backbone.layer2.1.bn2.weight 128 torch.Size([128])
backbone.layer2.1.bn2.bias 128 torch.Size([128])
backbone.layer3.0.conv1.weight 294912 torch.Size([256, 128, 3, 3])
backbone.layer3.0.bn1.weight 256 torch.Size([256])
backbone.layer3.0.bn1.bias 256 torch.Size([256])
backbone.layer3.0.conv2.weight 589824 torch.Size([256, 256, 3, 3])
backbone.layer3.0.bn2.weight 256 torch.Size([256])
backbone.layer3.0.bn2.bias 256 torch.Size([256])
backbone.layer3.0.downsample.0.weight 32768 torch.Size([256, 128, 1, 1])
backbone.layer3.0.downsample.1.weight 256 torch.Size([256])
backbone.layer3.0.downsample.1.bias 256 torch.Size([256])
backbone.layer3.1.conv1.weight 589824 torch.Size([256, 256, 3, 3])
backbone.layer3.1.bn1.weight 256 torch.Size([256])
backbone.layer3.1.bn1.bias 256 torch.Size([256])
backbone.layer3.1.conv2.weight 589824 torch.Size([256, 256, 3, 3])
backbone.layer3.1.bn2.weight 256 torch.Size([256])
backbone.layer3.1.bn2.bias 256 torch.Size([256])
backbone.layer4.0.conv1.weight 1179648 torch.Size([512, 256, 3, 3])
backbone.layer4.0.bn1.weight 512 torch.Size([512])
backbone.layer4.0.bn1.bias 512 torch.Size([512])
backbone.layer4.0.conv2.weight 2359296 torch.Size([512, 512, 3, 3])
backbone.layer4.0.bn2.weight 512 torch.Size([512])
backbone.layer4.0.bn2.bias 512 torch.Size([512])
backbone.layer4.0.downsample.0.weight 131072 torch.Size([512, 256, 1, 1])
backbone.layer4.0.downsample.1.weight 512 torch.Size([512])
backbone.layer4.0.downsample.1.bias 512 torch.Size([512])
backbone.layer4.1.conv1.weight 2359296 torch.Size([512, 512, 3, 3])
backbone.layer4.1.bn1.weight 512 torch.Size([512])
backbone.layer4.1.bn1.bias 512 torch.Size([512])
backbone.layer4.1.conv2.weight 2359296 torch.Size([512, 512, 3, 3])
backbone.layer4.1.bn2.weight 512 torch.Size([512])
backbone.layer4.1.bn2.bias 512 torch.Size([512])
projector.layer1.0.weight 262144 torch.Size([512, 512])
projector.layer1.0.bias 512 torch.Size([512])
projector.layer1.1.weight 512 torch.Size([512])
projector.layer1.1.bias 512 torch.Size([512])
projector.layer2.0.weight 262144 torch.Size([512, 512])
projector.layer2.0.bias 512 torch.Size([512])
projector.layer2.1.weight 512 torch.Size([512])
projector.layer2.1.bias 512 torch.Size([512])
projector.layer3.0.weight 131072 torch.Size([256, 512])
projector.layer3.0.bias 256 torch.Size([256])
predictor.layer1.0.weight 16384 torch.Size([64, 256])
predictor.layer1.0.bias 64 torch.Size([64])
predictor.layer1.1.weight 64 torch.Size([64])
predictor.layer1.1.bias 64 torch.Size([64])
predictor.layer2.weight 16384 torch.Size([256, 64])
predictor.layer2.bias 256 torch.Size([256])
count 36
Round: 0 Train Loss: -1.871
count 36
Round: 1 Train Loss: -1.877
count 36
Round: 2 Train Loss: -1.885
count 36
Round: 3 Train Loss: -1.887
count 36
Round: 4 Train Loss: -1.885
centeral clustering
Global NMI = 0.1358 ARI = 0.0551 F = 0.1048 ACC = 0.1791
count 36
Round: 5 Train Loss: -1.881
count 36
Round: 6 Train Loss: -1.883
count 36
Round: 7 Train Loss: -1.890
count 36
Round: 8 Train Loss: -1.889
count 36
Round: 9 Train Loss: -1.888
centeral clustering
Global NMI = 0.1338 ARI = 0.0546 F = 0.1048 ACC = 0.1776
Global NMI = 0.1136 ARI = 0.0500 F = 0.1161 ACC = 0.1607
count 36
Round: 10 Train Loss: -1.891
count 36
Round: 11 Train Loss: -1.891
count 36
Round: 12 Train Loss: -1.890
count 36
Round: 13 Train Loss: -1.893
count 36
Round: 14 Train Loss: -1.893
centeral clustering
Global NMI = 0.1358 ARI = 0.0545 F = 0.1047 ACC = 0.1775
count 36
Round: 15 Train Loss: -1.890
count 36
Round: 16 Train Loss: -1.894
count 36
Round: 17 Train Loss: -1.892
count 36
Round: 18 Train Loss: -1.893
count 36
Round: 19 Train Loss: -1.895
centeral clustering
Global NMI = 0.1338 ARI = 0.0543 F = 0.1042 ACC = 0.1795
Global NMI = 0.1149 ARI = 0.0501 F = 0.1135 ACC = 0.1553
count 36
Round: 20 Train Loss: -1.895
count 36
Round: 21 Train Loss: -1.894
count 36
Round: 22 Train Loss: -1.900
count 36
Round: 23 Train Loss: -1.897
count 36
Round: 24 Train Loss: -1.899
centeral clustering
Global NMI = 0.1354 ARI = 0.0549 F = 0.1048 ACC = 0.1803
count 36
Round: 25 Train Loss: -1.902
count 36
Round: 26 Train Loss: -1.899
count 36
Round: 27 Train Loss: -1.901
count 36
Round: 28 Train Loss: -1.902
count 36
Round: 29 Train Loss: -1.905
centeral clustering
Global NMI = 0.1287 ARI = 0.0522 F = 0.1025 ACC = 0.1643
Global NMI = 0.1167 ARI = 0.0513 F = 0.1104 ACC = 0.1664
count 36
Round: 30 Train Loss: -1.907
count 36
Round: 31 Train Loss: -1.903
count 36
Round: 32 Train Loss: -1.908
count 36
Round: 33 Train Loss: -1.907
count 36
Round: 34 Train Loss: -1.908
centeral clustering
Global NMI = 0.1356 ARI = 0.0548 F = 0.1049 ACC = 0.1812
count 36
Round: 35 Train Loss: -1.908
count 36
Round: 36 Train Loss: -1.908
count 36
Round: 37 Train Loss: -1.908
count 36
Round: 38 Train Loss: -1.907
count 36
Round: 39 Train Loss: -1.907
centeral clustering
Global NMI = 0.1358 ARI = 0.0547 F = 0.1045 ACC = 0.1806
Global NMI = 0.1205 ARI = 0.0511 F = 0.1109 ACC = 0.1661
count 36
Round: 40 Train Loss: -1.911
count 36
Round: 41 Train Loss: -1.913
count 36
Round: 42 Train Loss: -1.910
count 36
Round: 43 Train Loss: -1.909
count 36
Round: 44 Train Loss: -1.913
centeral clustering
Global NMI = 0.1322 ARI = 0.0531 F = 0.1029 ACC = 0.1717
count 36
Round: 45 Train Loss: -1.913
count 36
Round: 46 Train Loss: -1.908
count 36
Round: 47 Train Loss: -1.913
count 36
Round: 48 Train Loss: -1.911
count 36
Round: 49 Train Loss: -1.913
centeral clustering
Global NMI = 0.1365 ARI = 0.0562 F = 0.1063 ACC = 0.1804
Global NMI = 0.1199 ARI = 0.0490 F = 0.1045 ACC = 0.1661
count 36
Round: 50 Train Loss: -1.912
count 36
Round: 51 Train Loss: -1.912
count 36
Round: 52 Train Loss: -1.913
count 36
Round: 53 Train Loss: -1.913
count 36
Round: 54 Train Loss: -1.914
centeral clustering
Global NMI = 0.1341 ARI = 0.0547 F = 0.1044 ACC = 0.1814
count 36
Round: 55 Train Loss: -1.917
count 36
Round: 56 Train Loss: -1.915
count 36
Round: 57 Train Loss: -1.914
count 36
Round: 58 Train Loss: -1.916
count 36
Round: 59 Train Loss: -1.917
centeral clustering
Global NMI = 0.1341 ARI = 0.0548 F = 0.1046 ACC = 0.1826
Global NMI = 0.1136 ARI = 0.0446 F = 0.1007 ACC = 0.1638
count 36
Round: 60 Train Loss: -1.915
count 36
Round: 61 Train Loss: -1.914
count 36
Round: 62 Train Loss: -1.917
count 36
Round: 63 Train Loss: -1.916
count 36
Round: 64 Train Loss: -1.918
centeral clustering
Global NMI = 0.1321 ARI = 0.0529 F = 0.1024 ACC = 0.1711
count 36
Round: 65 Train Loss: -1.920
count 36
Round: 66 Train Loss: -1.919
count 36
Round: 67 Train Loss: -1.920
count 36
Round: 68 Train Loss: -1.917
count 36
Round: 69 Train Loss: -1.919
centeral clustering
Global NMI = 0.1318 ARI = 0.0520 F = 0.1017 ACC = 0.1716
Global NMI = 0.1160 ARI = 0.0515 F = 0.1124 ACC = 0.1538
count 36
Round: 70 Train Loss: -1.918
count 36
Round: 71 Train Loss: -1.925
count 36
Round: 72 Train Loss: -1.921
count 36
Round: 73 Train Loss: -1.922
count 36
Round: 74 Train Loss: -1.919
centeral clustering
Global NMI = 0.1309 ARI = 0.0525 F = 0.1027 ACC = 0.1700
count 36
Round: 75 Train Loss: -1.918
count 36
Round: 76 Train Loss: -1.918
count 36
Round: 77 Train Loss: -1.921
count 36
Round: 78 Train Loss: -1.920
count 36
Round: 79 Train Loss: -1.925
centeral clustering
Global NMI = 0.1303 ARI = 0.0516 F = 0.1013 ACC = 0.1682
Global NMI = 0.1199 ARI = 0.0499 F = 0.1100 ACC = 0.1606
count 36
Round: 80 Train Loss: -1.921
count 36
Round: 81 Train Loss: -1.922
count 36
Round: 82 Train Loss: -1.919
count 36
Round: 83 Train Loss: -1.924
count 36
Round: 84 Train Loss: -1.927
centeral clustering
Global NMI = 0.1270 ARI = 0.0489 F = 0.0986 ACC = 0.1613
count 36
Round: 85 Train Loss: -1.921
count 36
Round: 86 Train Loss: -1.923
count 36
Round: 87 Train Loss: -1.921
count 36
Round: 88 Train Loss: -1.920
count 36
Round: 89 Train Loss: -1.921
centeral clustering
Global NMI = 0.1272 ARI = 0.0497 F = 0.0997 ACC = 0.1659
Global NMI = 0.1158 ARI = 0.0473 F = 0.1027 ACC = 0.1638
count 36
Round: 90 Train Loss: -1.921
count 36
Round: 91 Train Loss: -1.923
count 36
Round: 92 Train Loss: -1.923
count 36
Round: 93 Train Loss: -1.923
count 36
Round: 94 Train Loss: -1.921
centeral clustering
Global NMI = 0.1334 ARI = 0.0522 F = 0.1019 ACC = 0.1724
count 36
Round: 95 Train Loss: -1.921
count 36
Round: 96 Train Loss: -1.916
count 36
Round: 97 Train Loss: -1.923
count 36
Round: 98 Train Loss: -1.923
count 36
Round: 99 Train Loss: -1.923
centeral clustering
Global NMI = 0.1329 ARI = 0.0520 F = 0.1019 ACC = 0.1717
Global NMI = 0.1189 ARI = 0.0501 F = 0.1056 ACC = 0.1641
count 36
Round: 100 Train Loss: -1.925
count 36
Round: 101 Train Loss: -1.925
count 36
Round: 102 Train Loss: -1.923
count 36
Round: 103 Train Loss: -1.924
count 36
Round: 104 Train Loss: -1.923
centeral clustering
Global NMI = 0.1308 ARI = 0.0500 F = 0.1002 ACC = 0.1687
count 36
Round: 105 Train Loss: -1.923
count 36
Round: 106 Train Loss: -1.924
count 36
Round: 107 Train Loss: -1.925
count 36
Round: 108 Train Loss: -1.923
count 36
Round: 109 Train Loss: -1.922
centeral clustering
Global NMI = 0.1305 ARI = 0.0496 F = 0.0996 ACC = 0.1675
Global NMI = 0.1174 ARI = 0.0510 F = 0.1101 ACC = 0.1658
count 36
Round: 110 Train Loss: -1.929
count 36
Round: 111 Train Loss: -1.926
count 36
Round: 112 Train Loss: -1.928
count 36
Round: 113 Train Loss: -1.921
count 36
Round: 114 Train Loss: -1.928
centeral clustering
Global NMI = 0.1302 ARI = 0.0496 F = 0.0996 ACC = 0.1666
count 36
Round: 115 Train Loss: -1.925
count 36
Round: 116 Train Loss: -1.927
count 36
Round: 117 Train Loss: -1.926
count 36
Round: 118 Train Loss: -1.928
count 36
Round: 119 Train Loss: -1.927
centeral clustering
Global NMI = 0.1329 ARI = 0.0513 F = 0.1012 ACC = 0.1698
Global NMI = 0.1199 ARI = 0.0522 F = 0.1152 ACC = 0.1713
count 36
Round: 120 Train Loss: -1.924
count 36
Round: 121 Train Loss: -1.926
count 36
Round: 122 Train Loss: -1.928
count 36
Round: 123 Train Loss: -1.929
count 36
Round: 124 Train Loss: -1.929
centeral clustering
Global NMI = 0.1313 ARI = 0.0500 F = 0.0999 ACC = 0.1689
count 36
Round: 125 Train Loss: -1.930