mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 04:00:46 +02:00
77 lines
1.8 KiB
Python
77 lines
1.8 KiB
Python
""" Example use of smart augmentation.
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"""
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from model import *
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from dataug import *
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from train_utils import *
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# Use available TF (see transformations.py)
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tf_names = [
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## Geometric TF ##
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'Identity',
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'FlipUD',
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'FlipLR',
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'Rotate',
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'TranslateX',
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'TranslateY',
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'ShearX',
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'ShearY',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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'Color',
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'Brightness',
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'Sharpness',
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'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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]
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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
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'momentum':0.9, #0.9
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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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#model = ResNet(num_classes=10)
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#model = MobileNetV2(num_classes=10)
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#model = WideResNet(num_classes=10, wrn_size=32)
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#Smart_aug initialisation
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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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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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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