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- # logdir = os.path.join(output, task_name)
- output: ./output_dir/
- task_name: base_exp
- dataset:
- train:
- rootA: 'flow_model/data/train/weighted_real'
- rootB: 'flow_model/data/train/maps_sample/subject04_crisp_v_180.npy'
- width: 512
- height: 256
- scale_l: 0.8
- scale_h: 1.0
- transform: [] #['h_flip', 'v_flip', 'crop', 'normalize', 'random_resized_crop']
- random_pair: True
- return_name: False
- batch_size: 1
- test:
- rootA: 'flow_model/data/test/weighted_real'
- rootB: 'flow_model/data/test/maps_sample/subject18_crisp_v_180.npy'
- width: 512
- height: 256
- scale_l: 0.8
- scale_h: 1.0
- transform: [] #['h_flip', 'v_flip', 'crop', 'normalize']
- random_pair: False
- return_name: True
- batch_size: 16
- lr: 0.0001
- epochs: 120
- max_iter: 300000
- print_freq: 450
- save_freq: 450
- resume: True
- load_path: 'flow_model/checkpoint_for_resume/0.ckpt.pth.tar'
- network:
- configurable: False #[True, False]
- pad_size: 10
- in_channel: 3
- out_channels: [30, 120] #[30, 120], [12, 60, 120], [30, 120, 480], [30, 120, 480, 1920]
- weight_type: 'learned' #['fixed', 'sigmoid', 'softmax', 'attention', 'learned']
- loss:
- vgg_encoder: 'flow_model/model/losses/vgg_model/vgg_normalised.pth'
- k: 0.7
- weight: 0.7
- lr_scheduler:
- type: cosine
- eta_min: 0.0000000
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