configs/structured_sparsity/ns.yaml [7:23]:
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  dataset: "cifar10"
  script: "train_indep.py"
  save_dir: "saved_models/structured_sparsity/"
  epochs: 200 # The number of epochs to train for
  batch_size: 128 # Train and test batch sizes
  warmup_budget: 80 # decays topk from 1 to target topk over this percentage of epochs
  test_freq: 20 # Will run test after this many epochs
  learning_rate: 0.1 # Optimizer learning rate
  momentum: 0.9 # Optimizer momentum
  weight_decay: 0.0005 # L2 regularization parameter
  conv_type: "AdaptiveConv2d" # convolution layer for first layer of network
  bn_type: "AdaptiveBN" # [sic] this norm is evaluatable at any point, and is otherwise the same as the AdaptiveIN
  block_conv_type: "AdaptiveConv2d" # convolution layer to use for resnet blocks
  block_bn_type: "AdaptiveBN" # batch norm layer to use for resnet blocks
  topk: 1.0 # the target topk to reach after warmup phase
  random_topk: False # If true, will train with topk=1 during warmup and then use random topk values
  norm_kwargs:
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configs/structured_sparsity/us.yaml [7:23]:
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  dataset: "cifar10"
  script: "train_indep.py"
  save_dir: "saved_models/structured_sparsity/"
  epochs: 200 # The number of epochs to train for
  batch_size: 128 # Train and test batch sizes
  warmup_budget: 80 # decays topk from 1 to target topk over this percentage of epochs
  test_freq: 20 # Will run test after this many epochs
  learning_rate: 0.1 # Optimizer learning rate
  momentum: 0.9 # Optimizer momentum
  weight_decay: 0.0005 # L2 regularization parameter
  conv_type: "AdaptiveConv2d" # convolution layer for first layer of network
  bn_type: "AdaptiveBN" # batch norm layer for first layer of network
  block_conv_type: "AdaptiveConv2d" # convolution layer to use for resnet blocks
  block_bn_type: "AdaptiveBN" # batch norm layer to use for resnet blocks
  topk: 1.0 # the target topk to reach after warmup phase
  random_topk: False # If true, will train with topk=1 during warmup and then use random topk values
  norm_kwargs:
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