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Suite de test brutus

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Harle, Antoine (Contracteur) 2019-11-20 16:06:27 -05:00
parent cc737b7997
commit 0e7ec8b5b0
6 changed files with 65 additions and 47 deletions
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71
higher/test_dataug.py
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@ -38,7 +38,7 @@ else:
if __name__ == "__main__":
n_inner_iter = 10
epochs = 2
epochs = 200
dataug_epoch_start=0
#### Classic ####
@ -64,39 +64,43 @@ if __name__ == "__main__":
print('-'*9)
'''
#### Augmented Model ####
#'''
'''
t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=False, shared_mag=True), 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=1, loss_patience=10)
####
plot_resV2(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:", 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:
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 !')
plot_TF_influence(log, param_names=tf_names)
plot_resV2(log, fig_name="res/"+filename, param_names=tf_names)
print('Execution Time : %.00f '%(time.process_time() - t0))
print('-'*9)
#'''
#### TF number tests ####
'''
res_folder="res/TF_nb_tests/"
epochs= 100
#### TF tests ####
#'''
res_folder="res/brutus-tests/"
epochs= 150
inner_its = [0, 1, 10]
dist_mix = [0.0, 0.5]
dataug_epoch_starts= [0]
TF_nb = [len(TF.TF_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [2, 3, 4, 6]
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [1,2,3,4]#[2, 3, 4, 6]
mag_setup = [(True,True), (False,True), (False, False)]
nb_run= 3
try:
os.mkdir(res_folder)
@ -105,28 +109,31 @@ if __name__ == "__main__":
pass
for n_inner_iter in inner_its:
print("---Starting inner_it", n_inner_iter,"---")
for dataug_epoch_start in dataug_epoch_starts:
print("---Starting dataug", dataug_epoch_start,"---")
for n_tf in N_seq_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}
for dist in dist_mix:
#for i in TF_nb:
for m_setup in mag_setup:
for run in range(nb_run):
#keys = list(TF.TF_dict.keys())[0:i]
#ntf_dict = {k: TF.TF_dict[k] for k in keys}
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)
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_mag=m_setup[0], shared_mag=m_setup[1]), 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=20, loss_patience=None)
####
plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter), param_names=keys)
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])
with open(res_folder+"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('-'*9)
####
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])
filename = "{}-{}epochs(dataug:{})-{}in_it-{}".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter,run)
with open(res_folder+"log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
'''
#plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
print('-'*9)
#'''