python main.py task=flowers model.rot=1
python main.py task=flowers model.rot=3
python main.py task=flowers model.rot=3 model.partial=True
python main.py task=flowers model.rot=1 model.insta=True model.insta_params.num_samples=1
python main.py task=flowers model.rot=1 model.insta=True model.insta_params.num_samples=3
python main.py task=flowers model.rot=1 ada_aug.do=True
python main.py task=flowers model.rot=3 ada_aug.do=True
python main.py task=flowers model.rot=3 ada_aug.do=True model.partial=True
python main.py
task=flowers \
seed=2024 \
model.rot=3 \
model.variational=True \
model.version=v1.2 \
train.lr=2.0e-4 \
train.lr_probs=2.0e-5 \
train.weight_decay=0.01 \
train.lamda=0.01 \
train.lamda2=0 \
train.epochs=700 \
wandb.entity=Your-Wandb-Name \
no_workers=1python main.py task=mnist model.rot=1
python main.py task=mnist model.rot=3
python main.py task=mnist model.rot=3 model.partial=True
python main.py task=mnist model.rot=1 model.insta=True model.insta_params.num_samples=1
python main.py task=mnist model.rot=1 model.insta=True model.insta_params.num_samples=3
python main.py
task=mnist \
seed=2024 \
model.rot=3 \
model.variational=True \
model.version=v1.1 \
train.lr=1.0e-3 \
train.lr_probs=1.0e-4 \
train.weight_decay=1.0e-5 \
train.lamda=1 \
train.lamda2=0 \
train.epochs=1500 \
wandb.entity=Your-Wandb-Name \
no_workers=1python main.py task=cifar_color no_workers=3 model.rot=1
python main.py task=cifar_color no_workers=3 model.rot=3
python main.py task=cifar_color no_workers=3 model.rot=3 model.partial=True
python main.py task=cifar_color no_workers=3 model.rot=1 model.insta=True model.insta_params.num_samples=1
python main.py task=cifar_color no_workers=3 model.rot=1 model.insta=True model.insta_params.num_samples=3
T2
python main.py task=cifar base_group.name=SE2 base_group.no_samples=1 base_group.sample_per_batch_element=False base_group.sample_per_layer=False base_group.sampling_method=deterministic conv.bias=True conv.padding=same conv.partial_equiv=False dataset=CIFAR10 kernel.learn_omega0=False kernel.no_hidden=32 kernel.no_layers=3 kernel.omega0=10 kernel.size=7 kernel.type=SIREN kernel.weight_norm=False net.dropout=0 net.no_blocks=2 net.no_hidden=32 net.norm=BatchNorm net.pool_blocks=[1,2] net.block_width_factors=[1,1,2,1] net.type=CKResNet no_workers=3 seed=0 train.batch_size=64 train.do=True train.epochs=300 train.lr=0.001 train.scheduler=cosine train.scheduler_params.warmup_epochs=5 train.weight_decay=0.0001 train.lr_probs=1e-4
Full
4 rotations
python main.py task=cifar base_group.name=SE2 base_group.no_samples=4 base_group.sample_per_batch_element=False base_group.sample_per_layer=True base_group.sampling_method=random conv.bias=True conv.padding=same conv.partial_equiv=False dataset=CIFAR10 kernel.learn_omega0=False kernel.no_hidden=32 kernel.no_layers=3 kernel.omega0=10 kernel.size=7 kernel.type=SIREN kernel.weight_norm=False net.block_width_factors=[1,1,2,1] net.dropout=0 net.final_spatial_dim=[2,2] net.learnable_final_pooling=True net.no_blocks=2 net.no_hidden=32 net.norm=BatchNorm net.pool_blocks=[1,2] net.type=CKResNet no_workers=3 seed=0 train.batch_size=64 train.do=True train.epochs=300 train.lr=0.001 train.scheduler=cosine train.scheduler_params.warmup_epochs=5 train.weight_decay=0.0001
Partial
4 rotations
python main.py task=cifar base_group.name=SE2 base_group.no_samples=4 base_group.sample_per_batch_element=False base_group.sample_per_layer=True base_group.sampling_method=random conv.bias=True conv.padding=same conv.partial_equiv=True dataset=CIFAR10 kernel.learn_omega0=False kernel.no_hidden=32 kernel.no_layers=3 kernel.omega0=10 kernel.size=7 kernel.type=SIREN kernel.weight_norm=False net.dropout=0 net.no_blocks=2 net.no_hidden=32 net.norm=BatchNorm net.pool_blocks=[1,2] net.block_width_factors=[1,1,2,1] net.type=CKResNet no_workers=3 seed=0 train.batch_size=64 train.do=True train.epochs=300 train.lr=0.001 train.scheduler=cosine train.scheduler_params.warmup_epochs=5 train.weight_decay=0.0001 train.lr_probs=1e-4
InstaAug 1 sample
python main.py task=cifar base_group.name=SE2 base_group.no_samples=1 base_group.sample_per_batch_element=False base_group.sample_per_layer=False base_group.sampling_method=random conv.bias=True conv.padding=same conv.partial_equiv=False dataset=CIFAR10 kernel.learn_omega0=False kernel.no_hidden=32 kernel.no_layers=3 kernel.omega0=10 kernel.size=7 kernel.type=SIREN kernel.weight_norm=False "net.block_width_factors=[1, 1, 2, 1]" net.dropout=0 net.no_blocks=2 net.no_hidden=32 net.norm=BatchNorm "net.pool_blocks=[1, 2]" net.type=InstaCKResNet no_workers=3 seed=0 train.batch_size=64 train.do=True train.epochs=300 train.lr=0.001 train.scheduler=cosine train.scheduler_params.warmup_epochs=5 train.weight_decay=0.0001 train.lamda=0.0001
Change basegroup.no_samples for number of rotations in Full & Partial / number of samples in InstaAug