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import os
import sys
import collections
sys.path.append('..')
import torchtask
import proxy
config = collections.OrderedDict(
[
('exp_id', os.path.basename(__file__).split(".")[0]),
('trainer', 'harmonizer_trainer'),
# arguments - Task Proxy
('short_ep', False),
# arguments - exp
('resume', ''),
('validation', False),
('out_path', 'result'),
('visualize', False),
('debug', False),
('val_freq', 1),
('log_freq', 100),
('visual_freq', 100),
('checkpoint_freq', 1),
# arguments - dataset / dataloader
('im_size', 256),
('num_workers', 4),
('ignore_additional', False),
('trainset', {
'harmonizer_iharmony4': [
'./dataset/iHarmony4/HAdobe5k/train',
'./dataset/iHarmony4/HCOCO/train',
'./dataset/iHarmony4/Hday2night/train',
'./dataset/iHarmony4/HFlickr/train',
]
}),
('additionalset', {
'original_iharmony4': [
'./dataset/iHarmony4/HAdobe5k/train',
'./dataset/iHarmony4/HCOCO/train',
'./dataset/iHarmony4/Hday2night/train',
'./dataset/iHarmony4/HFlickr/train',
],
}),
('valset', {
'original_iharmony4': [
'./dataset/iHarmony4/HAdobe5k/test',
'./dataset/iHarmony4/HCOCO/test',
'./dataset/iHarmony4/Hday2night/test',
'./dataset/iHarmony4/HFlickr/test',
]
}),
# arguments - task specific components
('models', {'model': 'harmonizer'}),
('optimizers', {'model': 'adam'}),
('lrers', {'model': 'multisteplr'}),
('criterions', {'model': 'harmonizer_loss'}),
# arguments - task specific optimizer / lr scheduler
('lr', 0.0003),
('milestones', [25, 50]),
('gamma', 0.1),
# arguments - training details
('epochs', 60),
('batch_size', 16),
('additional_batch_size', 8),
]
)
if __name__ == '__main__':
torchtask.run_script(config, proxy, proxy.HarmonizerProxy)
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