AntoineH/smart_augmentation
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Harle, Antoine (Contracteur) 2020-01-31 10:34:44 -05:00
parent bf29d4fb6d
commit cd6e159b77
6 changed files with 59 additions and 95 deletions
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higher/smart_aug/test_dataug.py
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@ -2,7 +2,7 @@
"""
from model import *
from LeNet import *
from dataug import *
#from utils import *
from train_utils import *
@ -13,19 +13,19 @@ tf_names = [
'Identity',
'FlipUD',
'FlipLR',
'Rotate',
'TranslateX',
'TranslateY',
'ShearX',
'ShearY',
#'Rotate',
#'TranslateX',
#'TranslateY',
#'ShearX',
#'ShearY',
## Color TF (Expect image in the range of [0, 1]) ##
'Contrast',
'Color',
'Brightness',
'Sharpness',
'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#'Contrast',
#'Color',
#'Brightness',
#'Sharpness',
#'Posterize',
#'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#Color TF (Common mag scale)
#'+Contrast',
@ -76,7 +76,6 @@ if __name__ == "__main__":
tasks={
#'classic',
'aug_model'
#'aug_dataset', #Moved to old code
}
#Parameters
n_inner_iter = 1
@ -131,7 +130,7 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
model = Higher_model(model) #run_dist_dataugV3
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.8, 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))
@ -140,7 +139,7 @@ if __name__ == "__main__":
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=1,
print_freq=20,
unsup_loss=1,
hp_opt=False,
save_sample_freq=None)
@ -164,56 +163,4 @@ if __name__ == "__main__":
print("Failed to plot res")
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
#### Augmented Dataset ####
'''
if 'aug_dataset' in tasks:
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)
#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=1)
print(data_train_aug)
unsup_ratio = 5
dl_unsup = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE*unsup_ratio, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
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_UDA(model=model, dl_unsup=dl_unsup, epochs=epochs, opt_param=optim_param, print_freq=10)
exec_time=time.process_time() - t0
####
print('-'*9)
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), 'Optimizer': optim_param['Inner'], "Device": device_name, "Param_names": data_train_aug._TF, "Log": log}
print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{}-{}-{} epochs".format(str(data_train_aug),str(model),epochs)
with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
plot_res(log, fig_name="res/"+filename)
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
'''
print('-'*9)