mirror of
https://github.com/AntoineHX/smart_augmentation.git
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59 lines
1.6 KiB
Python
Executable file
59 lines
1.6 KiB
Python
Executable file
""" Example use of smart augmentation.
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"""
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from LeNet import *
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from dataug import *
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from train_utils import *
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tf_config='../config/base_tf_config.json'
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TF_loader=TF_loader()
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device = torch.device('cuda') #Select device to use
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if device == torch.device('cpu'):
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device_name = 'CPU'
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else:
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device_name = torch.cuda.get_device_name(device)
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##########################################
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if __name__ == "__main__":
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#Parameters
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n_inner_iter = 1
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epochs = 150
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optim_param={
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'Meta':{
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'optim':'Adam',
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'lr':1e-2, #1e-2
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},
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'Inner':{
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'optim': 'SGD',
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'lr':1e-2, #1e-2/1e-1 (ResNet)
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'momentum':0.9, #0.9
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'decay':0.0005, #0.0005
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'nesterov':False, #False (True: Bad behavior w/ Data_aug)
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'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential'
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}
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}
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#Models
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model = LeNet(3,10)
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#Smart_aug initialisation
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
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model = Higher_model(model) #run_dist_dataugV3
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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N_TF=3,
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mix_dist=0.8,
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fixed_prob=False,
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fixed_mag=False,
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shared_mag=False,
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TF_ignore_mag=tf_ignore_mag),
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model).to(device)
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print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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# Training
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trained_model = run_simple_smartaug(model=aug_model, epochs=epochs, inner_it=n_inner_iter, opt_param=optim_param)
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