from config.defaults import Experiment, SiMVC, CNN, DDC, Fusion, Loss, Dataset, CoMVC, Optimizer
coil = Experiment(
dataset_config=Dataset(name="coil"),
model_config=SiMVC(
backbone_configs=(
CNN(input_size=(1, 128, 128)),
CNN(input_size=(1, 128, 128)),
CNN(input_size=(1, 128, 128)),
),
fusion_config=Fusion(method="weighted_mean", n_views=3),
cm_config=DDC(n_clusters=20),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3",
),
optimizer_config=Optimizer()
),
n_epochs=100,
)
coil_contrast = Experiment(
dataset_config=Dataset(name="coil"),
model_config=CoMVC(
backbone_configs=(
CNN(input_size=(1, 128, 128)),
CNN(input_size=(1, 128, 128)),
CNN(input_size=(1, 128, 128)),
),
fusion_config=Fusion(method="weighted_mean", n_views=3),
projector_config=None,
from config.defaults import Experiment, Dataset, SiMVC, MLP, DDC, Fusion, Loss, CoMVC
blobs_overlap = Experiment(
dataset_config=Dataset(name="blobs_overlap"),
model_config=SiMVC(
backbone_configs=(
MLP(layers=[32, 32, 32], input_size=(2, )),
MLP(layers=[32, 32, 32], input_size=(2, )),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
cm_config=DDC(n_clusters=3),
loss_config=Loss(funcs="ddc_1|ddc_2|ddc_3", ),
),
n_runs=1,
n_epochs=10,
)
blobs_overlap_contrast = Experiment(
dataset_config=Dataset(name="blobs_overlap"),
model_config=CoMVC(backbone_configs=(
MLP(layers=[32, 32, 32], input_size=(2, )),
MLP(layers=[32, 32, 32], input_size=(2, )),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
projector_config=None,
cm_config=DDC(n_clusters=3),
loss_config=Loss(funcs="ddc_1|ddc_2|ddc_3|contrast", )),
n_runs=1,
)
blobs_overlap_5 = Experiment(
from config.defaults import Experiment, Dataset, SiMVC, DDC, Fusion, MLP, Loss, CoMVC, Optimizer
voc = Experiment(
dataset_config=Dataset(name="voc"),
model_config=SiMVC(
backbone_configs=(
MLP(input_size=(512,)),
MLP(input_size=(399,)),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
cm_config=DDC(n_clusters=20),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3",
),
optimizer_config=Optimizer(learning_rate=1e-3, scheduler_step_size=50, scheduler_gamma=0.1)
),
)
voc_contrast = Experiment(
dataset_config=Dataset(name="voc"),
model_config=CoMVC(
backbone_configs=(
MLP(input_size=(512,)),
MLP(input_size=(399,)),
),
projector_config=None,
fusion_config=Fusion(method="weighted_mean", n_views=2),
cm_config=DDC(n_clusters=20),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3|contrast",
),
from config.defaults import Experiment, SiMVC, DDC, Fusion, MLP, Loss, Dataset, CoMVC, Optimizer
ccv = Experiment(
dataset_config=Dataset(name="ccv"),
model_config=SiMVC(backbone_configs=(
MLP(input_size=(5000, )),
MLP(input_size=(5000, )),
MLP(input_size=(4000, )),
),
fusion_config=Fusion(method="weighted_mean", n_views=3),
cm_config=DDC(n_clusters=20),
loss_config=Loss(funcs="ddc_1|ddc_2|ddc_3", ),
optimizer_config=Optimizer()),
)
ccv_contrast = Experiment(
dataset_config=Dataset(name="ccv"),
model_config=CoMVC(backbone_configs=(
MLP(input_size=(5000, )),
MLP(input_size=(5000, )),
MLP(input_size=(4000, )),
),
fusion_config=Fusion(method="weighted_mean", n_views=3),
projector_config=None,
cm_config=DDC(n_clusters=20),
loss_config=Loss(funcs="ddc_1|ddc_2|ddc_3|contrast",
delta=20.0),
optimizer_config=Optimizer(scheduler_step_size=50,
scheduler_gamma=0.1)),
n_epochs=100)
from config.defaults import Experiment, Dataset, SiMVC, DDC, Fusion, MLP, Loss, CoMVC, Optimizer
rgbd = Experiment(
dataset_config=Dataset(name="rgbd"),
model_config=SiMVC(
backbone_configs=(
MLP(input_size=(2048,)),
MLP(input_size=(300,)),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
cm_config=DDC(n_clusters=13),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3",
)
),
)
rgbd_contrast = Experiment(
dataset_config=Dataset(name="rgbd"),
model_config=CoMVC(
backbone_configs=(
MLP(input_size=(2048,)),
MLP(input_size=(300,)),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
projector_config=None,
cm_config=DDC(n_clusters=13),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3|contrast",
),
from config.defaults import Experiment, SiMVC, CNN, DDC, Fusion, Loss, Dataset, CoMVC, Optimizer
mnist = Experiment(
dataset_config=Dataset(name="mnist_mv"),
model_config=SiMVC(
backbone_configs=(
CNN(input_size=(1, 28, 28)),
CNN(input_size=(1, 28, 28)),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
cm_config=DDC(n_clusters=10),
loss_config=Loss(
funcs="ddc_1|ddc_2|ddc_3",
),
optimizer_config=Optimizer()
),
)
mnist_contrast = Experiment(
dataset_config=Dataset(name="mnist_mv"),
model_config=CoMVC(
backbone_configs=(
CNN(input_size=(1, 28, 28)),
CNN(input_size=(1, 28, 28)),
),
fusion_config=Fusion(method="weighted_mean", n_views=2),
projector_config=None,
cm_config=DDC(n_clusters=10),
loss_config=Loss(