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config.py
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config.py
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import ml_collections
import torch
def get_config(dataset):
if dataset == 'flickr':
return get_config_flickr()
elif dataset == 'cityscapes':
return get_config_cityscapes()
def get_config_flickr():
config = ml_collections.ConfigDict()
# Training
config.training = training = ml_collections.ConfigDict()
training.epochs = 500
training.batch_size = 6
training.log_freq = 12
training.eval_freq = 500
training.save_pred_freq = 1
training.full_eval_freq = 1
training.checkpoint_save_freq = 2
training.sde = 'vesde'
# Model
config.model = model = ml_collections.ConfigDict()
model.sigma_min = 0.01
model.sigma_max = 440
model.num_scales = 2000
model.bilinear = True
model.conditional = True
model.name = 'unet'
# Data
config.data = data = ml_collections.ConfigDict()
data.dataset = 'flickr'
data.n_labels = 182
data.n_channels = 3
# Optimization
config.optim = optim = ml_collections.ConfigDict()
optim.weight_decay = 0
optim.lr = 2e-4
optim.beta1 = 0.9
optim.eps = 1e-8
optim.mixed_prec = True
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
return config
# FCDenseNet
def get_config_cityscapes():
config = ml_collections.ConfigDict()
# Training
config.training = training = ml_collections.ConfigDict()
training.epochs = 5000
training.batch_size = 10
training.log_freq = 20
training.eval_freq = 500
training.save_pred_freq = 1
training.full_eval_freq = 5
training.checkpoint_save_freq = 15
training.sde = 'vesde'
# Model
config.model = model = ml_collections.ConfigDict()
model.sigma_min = 0.01
model.sigma_max = 338
model.num_scales = 2000
model.conditional = True
model.name = 'fcdense'
# Data
config.data = data = ml_collections.ConfigDict()
data.dataset = 'cityscapes256'
data.n_labels = 20
data.n_channels = 3
data.crop = True
# Optimization
config.optim = optim = ml_collections.ConfigDict()
optim.weight_decay = 1e-4
optim.lr = 1e-4
optim.lr_decay = 0.995
optim.step_size = 1
optim.mixed_prec = False
config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
return config
# #Unet
# def get_config_cityscapes():
# config = ml_collections.ConfigDict()
#
# # Training
# config.training = training = ml_collections.ConfigDict()
# training.epochs = 5000
# training.batch_size = 16
# training.log_freq = 12
# training.eval_freq = 500
# training.save_pred_freq = 5
# training.full_eval_freq = 5
# training.checkpoint_save_freq = 15
# training.sde = 'vesde'
#
# # Model
# config.model = model = ml_collections.ConfigDict()
# model.sigma_min = 0.01
# model.sigma_max = 338
# model.num_scales = 2000
# model.bilinear = True
# model.conditional = True
# model.name = 'unet'
#
# # Data
# config.data = data = ml_collections.ConfigDict()
# data.dataset = 'cityscapes256'
# data.n_labels = 20
# data.n_channels = 3
# data.crop = True
#
# # Optimization
# config.optim = optim = ml_collections.ConfigDict()
# optim.weight_decay = 0
# optim.lr = 2e-4
# optim.beta1 = 0.9
# optim.eps = 1e-8
# optim.mixed_prec = True
#
# config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
#
# return config
# #FCN
# def get_config_cityscapes():
# config = ml_collections.ConfigDict()
#
# # Training
# config.training = training = ml_collections.ConfigDict()
# training.epochs = 500
# training.batch_size = 8
# training.log_freq = 25
# training.eval_freq = 500
# training.save_pred_freq = 5
# training.full_eval_freq = 5
# training.checkpoint_save_freq = 5
# training.sde = 'vesde'
#
# # Model
# config.model = model = ml_collections.ConfigDict()
# model.conditional = False
# model.name = 'fcn'
#
# # Data
# config.data = data = ml_collections.ConfigDict()
# data.dataset = 'cityscapes256'
# data.n_labels = 20
# data.n_channels = 3
# data.crop = True
#
# # Optimization
# config.optim = optim = ml_collections.ConfigDict()
# optim.weight_decay = 1e-5
# optim.lr = 1e-4
# optim.momentum = 0.
# optim.gamma = 0.5
# optim.step_size = 30
# optim.mixed_prec = False
#
# config.device = torch.device('cuda:0') if torch.cuda.is_available() else torch.device('cpu')
#
# return config