AntoineH/smart_augmentation
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Harle, Antoine (Contracteur) 2020-01-29 06:36:12 -05:00
parent 96ed9fe2ae
commit 5cd50ca9f3
4 changed files with 198 additions and 103 deletions
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89
higher/smart_aug/test_dataug.py
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@ -73,12 +73,12 @@ if __name__ == "__main__":
#Task to perform
tasks={
#'classic',
#'aug_dataset', #Moved to old code
'aug_model'
#'aug_dataset', #Moved to old code
}
#Parameters
n_inner_iter = 1
epochs = 200
epochs = 1
dataug_epoch_start=0
optim_param={
'Meta':{
@ -123,7 +123,47 @@ if __name__ == "__main__":
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
#### Augmented Model ####
if 'aug_model' in tasks:
t0 = time.process_time()
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=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))
log= run_simple_smartaug(model=aug_model, epochs=epochs, inner_it=n_inner_iter, opt_param=optim_param)
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=1,
unsup_loss=1,
hp_opt=False)
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, "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".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
except:
print("Failed to save logs :",f.name)
try:
plot_resV2(log, fig_name="../res/"+filename, param_names=aug_model.TF_names())
except:
print("Failed to plot res")
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
#### Augmented Dataset ####
'''
@ -175,45 +215,4 @@ if __name__ == "__main__":
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
'''
#### Augmented Model ####
if 'aug_model' in tasks:
t0 = time.process_time()
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=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))
log= run_simple_smartaug(model=aug_model, opt_param=optim_param)
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,
dataug_epoch_start=dataug_epoch_start,
opt_param=optim_param,
print_freq=1,
unsup_loss=1,
hp_opt=False)
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, "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".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
with open("res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
except:
print("Failed to save logs :",f.name)
try:
plot_resV2(log, fig_name="res/"+filename, param_names=aug_model.TF_names())
except:
print("Failed to plot res")
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
'''