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Tests consomation memoire/temps + methode KL divergence (UDA)

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Harle, Antoine (Contracteur) 2019-12-06 14:13:28 -05:00
parent b60610d9a7
commit d68034eec1
5 changed files with 214 additions and 37 deletions
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34
higher/test_dataug.py
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@ -69,21 +69,21 @@ if __name__ == "__main__":
#'aug_dataset',
'aug_model'
}
n_inner_iter = 1
epochs = 100
n_inner_iter = 0
epochs = 150
dataug_epoch_start=0
model = LeNet(3,10)
#model = MobileNetV2(num_classes=10)
#model = WideResNet(num_classes=10, wrn_size=32)
#### Classic ####
if 'classic' in tasks:
t0 = time.process_time()
model = LeNet(3,10).to(device)
#model = WideResNet(num_classes=10, wrn_size=16).to(device)
#model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
#model.augment(mode=False)
model = model.to(device)
print(str(model), 'on', device_name)
log= train_classic(model=model, epochs=epochs, print_freq=10)
print("{} on {} for {} epochs".format(str(model), device_name, epochs))
log= train_classic(model=model, epochs=epochs, print_freq=1)
#log= train_classic_higher(model=model, epochs=epochs)
exec_time=time.process_time() - t0
@ -116,12 +116,9 @@ if __name__ == "__main__":
xs, ys = next(iter(dl_train))
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
model = LeNet(3,10).to(device)
#model = WideResNet(num_classes=10, wrn_size=16).to(device)
#model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
#model.augment(mode=False)
model = model.to(device)
print(str(model), 'on', device_name)
print("{} on {} for {} epochs".format(str(model), device_name, epochs))
log= train_classic(model=model, epochs=epochs, print_freq=10)
#log= train_classic_higher(model=model, epochs=epochs)
@ -147,14 +144,13 @@ if __name__ == "__main__":
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), model).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, fixed_prob=True, fixed_mag=True, shared_mag=True), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
#aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
print(str(aug_model), 'on', device_name)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, KLdiv=True, loss_patience=None)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=True, loss_patience=None)
exec_time=time.process_time() - t0
####