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lunwen_ccfc_cifar10_noiid_2.log
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nohup: ignoring input
cuda:4
Namespace(batch_size=1500, classes_per_user=4, data_root='./datasets', exp_dir='./save/CCFC/cifar10-noiid', global_lr=1, image_size=224, k=10, latent_dim=256, lbd=0.1, lr=0.0005, mini_bs=125, 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=0.8, seed=66, test_image_size=256, trial='v0')
save/CCFC/cifar10-noiid/v0/model_pretrain_0_79.pt
Global NMI = 0.3519 ARI = 0.2288 F = 0.3337 ACC = 0.4350
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 32
Round: 0 Train Loss: -1.933
count 32
Round: 1 Train Loss: -1.933
count 32
Round: 2 Train Loss: -1.927
count 32
Round: 3 Train Loss: -1.936
count 32
Round: 4 Train Loss: -1.941
Global NMI = 0.3622 ARI = 0.2340 F = 0.3339 ACC = 0.4658
count 32
Round: 5 Train Loss: -1.934
count 32
Round: 6 Train Loss: -1.933
count 32
Round: 7 Train Loss: -1.935
count 32
Round: 8 Train Loss: -1.937
count 32
Round: 9 Train Loss: -1.934
Global NMI = 0.3426 ARI = 0.1922 F = 0.3166 ACC = 0.4095
count 32
Round: 10 Train Loss: -1.934
count 32
Round: 11 Train Loss: -1.930
count 32
Round: 12 Train Loss: -1.935
count 32
Round: 13 Train Loss: -1.938
count 32
Round: 14 Train Loss: -1.929
Global NMI = 0.3443 ARI = 0.1940 F = 0.3254 ACC = 0.3991
count 32
Round: 15 Train Loss: -1.936
count 32
Round: 16 Train Loss: -1.935
count 32
Round: 17 Train Loss: -1.935
count 32
Round: 18 Train Loss: -1.940
count 32
Round: 19 Train Loss: -1.939
Global NMI = 0.3437 ARI = 0.2031 F = 0.3308 ACC = 0.4074
count 32
Round: 20 Train Loss: -1.938
count 32
Round: 21 Train Loss: -1.938
count 32
Round: 22 Train Loss: -1.935
count 32
Round: 23 Train Loss: -1.936
count 32
Round: 24 Train Loss: -1.935
Global NMI = 0.3297 ARI = 0.1755 F = 0.3177 ACC = 0.3738
count 32
Round: 25 Train Loss: -1.936
count 32
Round: 26 Train Loss: -1.940
count 32
Round: 27 Train Loss: -1.943
count 32
Round: 28 Train Loss: -1.934
count 32
Round: 29 Train Loss: -1.943
Global NMI = 0.3365 ARI = 0.1791 F = 0.3221 ACC = 0.3772
count 32
Round: 30 Train Loss: -1.939
count 32
Round: 31 Train Loss: -1.935
count 32
Round: 32 Train Loss: -1.936
count 32
Round: 33 Train Loss: -1.936
count 32
Round: 34 Train Loss: -1.941
Global NMI = 0.3240 ARI = 0.1737 F = 0.3205 ACC = 0.3593
count 32
Round: 35 Train Loss: -1.939
count 32
Round: 36 Train Loss: -1.948
count 32
Round: 37 Train Loss: -1.935
count 32
Round: 38 Train Loss: -1.939
count 32
Round: 39 Train Loss: -1.938
Global NMI = 0.3498 ARI = 0.2175 F = 0.3408 ACC = 0.4228
count 32
Round: 40 Train Loss: -1.946
count 32
Round: 41 Train Loss: -1.939
count 32
Round: 42 Train Loss: -1.943
count 32
Round: 43 Train Loss: -1.949
count 32
Round: 44 Train Loss: -1.939
Global NMI = 0.3364 ARI = 0.1951 F = 0.3246 ACC = 0.3992