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57 lines
1.3 KiB
57 lines
1.3 KiB
"""Params for DANN."""
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import os
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# params for path
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dataset_root = os.path.expanduser(os.path.join('~', 'Datasets'))
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model_root = os.path.expanduser(os.path.join('~', 'Models', 'pytorch-DANN'))
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# params for datasets and data loader
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dataset_mean_value = 0.5
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dataset_std_value = 0.5
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dataset_mean = (dataset_mean_value, dataset_mean_value, dataset_mean_value)
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dataset_std = (dataset_std_value, dataset_std_value, dataset_std_value)
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imagenet_dataset_mean = (0.485, 0.456, 0.406)
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imagenet_dataset_std = (0.229, 0.224, 0.225)
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batch_size = 64
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digit_image_size = 28
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office_image_size = 227
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# params for source dataset
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src_dataset = "amazon31"
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src_model_trained = True
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src_classifier_restore = os.path.join(model_root,src_dataset + '-source-classifier-final.pt')
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class_num_src = 31
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# params for target dataset
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tgt_dataset = "webcam31"
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tgt_model_trained = True
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dann_restore = os.path.join(model_root , src_dataset + '-' + tgt_dataset + '-dann-final.pt')
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# params for pretrain
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num_epochs_src = 100
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log_step_src = 10
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save_step_src = 20
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eval_step_src = 20
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# params for training dann
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## for digit
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# num_epochs = 400
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# log_step = 100
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# save_step = 20
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# eval_step = 20
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## for office
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num_epochs = 1000
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log_step = 10 # iters
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save_step = 500
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eval_step = 5 # epochs
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manual_seed = 8888
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alpha = 0
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# params for optimizing models
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lr = 2e-4
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