AntoineH/smart_augmentation
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Amelioration visualisation des proba

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Harle, Antoine (Contracteur) 2019-11-13 16:18:53 -05:00
parent f0c0559e73
commit 93d91815f5
7 changed files with 720 additions and 211 deletions
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28
higher/test_dataug.py
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@ -38,13 +38,13 @@ else:
if __name__ == "__main__":
n_inner_iter = 10
epochs = 200
epochs = 2
dataug_epoch_start=0
#### Classic ####
'''
model = LeNet(3,10).to(device)
#model = torchvision.models.resnet18()
#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)
@ -69,31 +69,32 @@ if __name__ == "__main__":
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), LeNet(3,10)).to(device)
#aug_model = Augmented_model(Data_augV4(TF_dict=tf_dict, N_TF=2, mix_dist=0.0), WideResNet(num_classes=10, wrn_size=160)).to(device)
print(str(aug_model), 'on', device_name)
#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=10, loss_patience=10)
log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=1, loss_patience=10)
####
plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), param_names=tf_names)
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)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in (s ?):", out["Time"][0], "+/-", out["Time"][1])
print(str(aug_model),": acc", out["Accuracy"], "in (s?):", out["Time"][0], "+/-", out["Time"][1])
with open("res/log/%s.json" % "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('Execution Time : %.00f (s ?)'%(time.process_time() - t0))
print('Execution Time : %.00f (s?)'%(time.process_time() - t0))
print('-'*9)
#'''
#### TF number tests ####
'''
res_folder="res/TF_nb_tests/"
epochs= 100
epochs= 200
inner_its = [10]
dataug_epoch_starts= [0]
TF_nb = [len(TF.TF_dict)] #range(1,len(TF.TF_dict)+1)
N_seq_TF= [1, 2, 3, 4]
TF_nb = range(1,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [1] #[1, 2, 3, 4]
try:
os.mkdir(res_folder)
@ -106,7 +107,6 @@ if __name__ == "__main__":
for dataug_epoch_start in dataug_epoch_starts:
print("---Starting dataug", dataug_epoch_start,"---")
for n_tf in N_seq_TF:
print("---Starting N_TF", n_tf,"---")
for i in TF_nb:
keys = list(TF.TF_dict.keys())[0:i]
ntf_dict = {k: TF.TF_dict[k] for k in keys}
@ -114,7 +114,7 @@ if __name__ == "__main__":
aug_model = Augmented_model(Data_augV4(TF_dict=ntf_dict, N_TF=n_tf, mix_dist=0.0), LeNet(3,10)).to(device)
print(str(aug_model), 'on', device_name)
#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=10, 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, loss_patience=10)
####
plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
@ -127,6 +127,4 @@ if __name__ == "__main__":
print('Log :\"',f.name, '\" saved !')
print('-'*9)
'''
'''