AntoineH/smart_augmentation
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Ajout Augmented_datasetV2+trainUDA

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Harle, Antoine (Contracteur) 2019-12-06 16:54:40 -05:00
parent a859db65c4
commit 48c3925d74
2 changed files with 114 additions and 14 deletions
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43
higher/test_dataug.py
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@ -66,10 +66,10 @@ if __name__ == "__main__":
tasks={
#'classic',
#'aug_dataset',
'aug_model'
'aug_dataset',
#'aug_model'
}
n_inner_iter = 0
n_inner_iter = 1
epochs = 150
dataug_epoch_start=0
@ -108,19 +108,34 @@ if __name__ == "__main__":
t0 = time.process_time()
data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
data_train_aug.augement_data(aug_copy=30)
print(data_train_aug)
dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
#data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
#data_train_aug.augement_data(aug_copy=30)
#print(data_train_aug)
#dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
xs, ys = next(iter(dl_train))
viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
#xs, ys = next(iter(dl_train))
#viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
#model = model.to(device)
#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)
data_train_aug = AugmentedDatasetV2("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
data_train_aug.augement_data(aug_copy=10)
print(data_train_aug)
unsup_ratio = 5
dl_unsup = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE*unsup_ratio, shuffle=True)
unsup_xs, sup_xs, ys = next(iter(dl_unsup))
viz_sample_data(imgs=sup_xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
viz_sample_data(imgs=unsup_xs, labels=ys, fig_name='samples/data_sample_{}_unsup'.format(str(data_train_aug)))
model = model.to(device)
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)
log= train_UDA(model=model, dl_unsup=dl_unsup, epochs=epochs, print_freq=10)
exec_time=time.process_time() - t0
####
@ -145,11 +160,11 @@ if __name__ == "__main__":
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(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
print("{} on {} for {} epochs - {} inner_it".format(str(aug_model), device_name, epochs, n_inner_iter))
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)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=10, KLdiv=False, loss_patience=None)
exec_time=time.process_time() - t0
####
@ -157,7 +172,7 @@ if __name__ == "__main__":
times = [x["time"] for x in log]
out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{} epochs (dataug:{})- {} in_it (KLdiv)".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')