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experiments.py
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experiments.py
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import torch
from pathlib import Path
exp_0 = dict(write_access=True,
model_name='unet',
pretrained_on=None,
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=200,
in_channels=3,
num_categories=2,
filter_sizes=(32, 64, 128, 256, 512),
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=3e-3, max_lr=1e-2),
save_model_every_epoch=True)
exp_1 = dict(write_access=True,
model_name='attention_unet',
pretrained_on=None,
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=200,
in_channels=3,
num_categories=2,
filter_sizes=(32, 64, 128, 256, 512),
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=3e-3, max_lr=1e-2),
save_model_every_epoch=True)
exp_2 = dict(write_access=True,
model_name='cbam_unet',
pretrained_on=None,
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=200,
in_channels=3,
num_categories=2,
filter_sizes=(32, 64, 128, 256, 512),
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=3e-3, max_lr=1e-2),
save_model_every_epoch=True)
exp_3 = dict(write_access=True,
model_name='residualattention_unet',
pretrained_on=None,
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=200,
in_channels=3,
num_categories=2,
filter_sizes=(32, 64, 128, 256, 512),
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=2e-3, max_lr=6e-3),
save_model_every_epoch=True)
exp_4 = dict(write_access=True,
model_name='scag_unet',
pretrained_on=None,
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=200,
in_channels=3,
num_categories=2,
filter_sizes=(32, 64, 128, 256, 512),
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=3e-3, max_lr=1e-2),
save_model_every_epoch=True)
exp_5 = dict(write_access=True,
model_name='densenet121_unet',
pretrained_on='Imagenet',
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=60,
in_channels=3,
num_categories=2,
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=1e-2, max_lr=3e-2),
save_model_every_epoch=True)
exp_6 = dict(write_access=True,
model_name='mobilenetv2_unet',
pretrained_on='Imagenet',
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=60,
in_channels=3,
num_categories=2,
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=2e-2, max_lr=6e-2),
save_model_every_epoch=True)
exp_7 = dict(write_access=True,
model_name='resnet34_unet',
pretrained_on='Imagenet',
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=60,
in_channels=3,
num_categories=2,
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=1e-2, max_lr=3e-2),
save_model_every_epoch=True)
exp_8 = dict(write_access=True,
model_name='vgg11_unet',
pretrained_on='Imagenet',
continue_training=False,
continue_model_path=None,
continue_from_epoch=0,
initialize_weights=True,
freeze_encoder=False,
num_epochs=60,
in_channels=3,
num_categories=2,
deep_supervision=True,
dataloader_path=Path('tmp/Dataloader_SN1_Buildings.pkl'),
device=torch.device('cuda' if torch.cuda.is_available() else 'cpu'),
learning_rate_dict=dict(static_lr=3e-4, use_cyclic_learning_rate=True, base_lr=3e-3, max_lr=1e-2),
save_model_every_epoch=True)