AntoineH/smart_augmentation
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1er resultats experience TF sequentiels

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Harle, Antoine (Contracteur) 2019-11-12 18:14:10 -05:00
parent ddac630548
commit 3cffac9852
2 changed files with 37 additions and 53 deletions
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37
higher/compare_res.py
View file

@ -1,29 +1,5 @@
from utils import * from utils import *
tf_names = [
## Geometric TF ##
'Identity',
'FlipUD',
'FlipLR',
'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
#Non fonctionnel
#'Auto_Contrast', #Pas opti pour des batch (Super lent)
#'Equalize',
]
if __name__ == "__main__": if __name__ == "__main__":
#### Comparison #### #### Comparison ####
@ -44,15 +20,17 @@ if __name__ == "__main__":
#plot_compare(filenames=files, fig_name="res/compare") #plot_compare(filenames=files, fig_name="res/compare")
## Acc, Time, Epochs = f(n_tf) ## ## Acc, Time, Epochs = f(n_tf) ##
fig_name="res/TF_nb_tests_compare" fig_name="res/TF_seq_tests_compare"
inner_its = [0, 10] inner_its = [0, 10]
dataug_epoch_starts= [0, -1] dataug_epoch_starts= [0]
TF_nb = range(1,14+1) TF_nb = 14 #range(1,14+1)
N_seq_TF= [1, 2, 3, 4, 6]
fig, ax = plt.subplots(ncols=3, figsize=(30, 8)) fig, ax = plt.subplots(ncols=3, figsize=(30, 8))
for in_it in inner_its: for in_it in inner_its:
for dataug in dataug_epoch_starts: for dataug in dataug_epoch_starts:
filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF)-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(n_tf, dataug, in_it) for n_tf in TF_nb] #filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF)-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(n_tf, dataug, in_it) for n_tf in TF_nb]
filenames =["res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-{} TF x {})-LeNet)-200 epochs (dataug:{})- {} in_it.json".format(TF_nb, n_tf, dataug, in_it) for n_tf in N_seq_TF]
all_data=[] all_data=[]
#legend="" #legend=""
@ -62,7 +40,8 @@ if __name__ == "__main__":
data = json.load(json_file) data = json.load(json_file)
all_data.append(data) all_data.append(data)
n_tf = [len(x["Param_names"]) for x in all_data] n_tf = N_seq_TF
#n_tf = [len(x["Param_names"]) for x in all_data]
acc = [x["Accuracy"] for x in all_data] acc = [x["Accuracy"] for x in all_data]
epochs = [len(x["Log"]) for x in all_data] epochs = [len(x["Log"]) for x in all_data]
time = [x["Time"][0] for x in all_data] time = [x["Time"][0] for x in all_data]

53
higher/test_dataug.py
View file

@ -761,7 +761,7 @@ if __name__ == "__main__":
print('-'*9) print('-'*9)
''' '''
#### Augmented Model #### #### Augmented Model ####
#''' '''
tf_dict = {k: TF.TF_dict[k] for k in tf_names} tf_dict = {k: TF.TF_dict[k] for k in tf_names}
#tf_dict = TF.TF_dict #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), LeNet(3,10)).to(device)
@ -770,7 +770,7 @@ if __name__ == "__main__":
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=10, loss_patience=10)
#### ####
plot_res(log, fig_name="res/{}-{} epochs (dataug:{})- {} in_it (SOFT)".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))
print('-'*9) print('-'*9)
times = [x["time"] for x in log] 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} 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}
@ -779,14 +779,15 @@ if __name__ == "__main__":
json.dump(out, f, indent=True) json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !') print('Log :\"',f.name, '\" saved !')
print('-'*9) print('-'*9)
#'''
## TF number tests ##
''' '''
#### TF number tests ####
#'''
res_folder="res/TF_nb_tests/" res_folder="res/TF_nb_tests/"
epochs= 200 epochs= 200
inner_its = [0, 10] inner_its = [0, 10]
dataug_epoch_starts= [0, -1] dataug_epoch_starts= [0]
TF_nb = [14] #range(1,len(TF.TF_dict)+1) TF_nb = [len(TF.TF_dict)] #range(1,len(TF.TF_dict)+1)
N_seq_TF= [1, 2, 3, 4]
try: try:
os.mkdir(res_folder) os.mkdir(res_folder)
@ -798,24 +799,28 @@ if __name__ == "__main__":
print("---Starting inner_it", n_inner_iter,"---") print("---Starting inner_it", n_inner_iter,"---")
for dataug_epoch_start in dataug_epoch_starts: for dataug_epoch_start in dataug_epoch_starts:
print("---Starting dataug", dataug_epoch_start,"---") print("---Starting dataug", dataug_epoch_start,"---")
for i in TF_nb: for n_tf in N_seq_TF:
keys = list(TF.TF_dict.keys())[0:i] print("---Starting N_TF", n_tf,"---")
ntf_dict = {k: TF.TF_dict[k] for k in keys} for i in TF_nb:
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, mix_dist=0.0), LeNet(3,10)).to(device) 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) print(str(aug_model), 'on', device_name)
#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs) #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=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))
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 (ms):", 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)
#'''
####
plot_res(log, fig_name=res_folder+"{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
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 (ms):", 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)
'''