AntoineH/smart_augmentation
1
0
Fork 0
mirror of https://github.com/AntoineHX/smart_augmentation.git synced 2025-05-04 12:10:45 +02:00
Code Issues Projects Releases Packages Wiki Activity

Fix Translate + TF loader

Browse source
This commit is contained in:
Harle, Antoine (Contracteur) 2020-02-14 13:57:17 -05:00
parent 79de0191a8
commit b170af076f
9 changed files with 674 additions and 40 deletions
Download patch file Download diff file Expand all files Collapse all files

31
higher/smart_aug/benchmark.py
View file

@ -16,8 +16,11 @@ optim_param={
},
'Inner':{
'optim': 'SGD',
'lr':1e-2, #1e-2 #1e-1 for ResNet
'lr':1e-1, #1e-2/1e-1 (ResNet)
'momentum':0.9, #0.9
'decay':0.0005, #0.0005
'nesterov':True,
'scheduler':'cosine', #None, 'cosine', 'multiStep', 'exponential'
}
}
@ -28,6 +31,7 @@ dataug_epoch_start=0
nb_run= 3
# Use available TF (see transformations.py)
'''
tf_names = [
## Geometric TF ##
'Identity',
@ -63,7 +67,10 @@ tf_names = [
#'RandBlend'
]
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
'''
tf_config='../config/base_tf_config.json'
TF_loader=TF_loader()
tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
device = torch.device('cuda')
@ -82,11 +89,11 @@ np.random.seed(0)
if __name__ == "__main__":
### Benchmark ###
'''
#'''
n_inner_iter = 1
dist_mix = [0.5]#[0.5, 1.0]
N_seq_TF= [3, 4]
mag_setup = [(True, True), (False, False)] #(FxSh, Independant)
mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant)
for model_type in model_list.keys():
for model_name in model_list[model_type]:
@ -100,7 +107,7 @@ if __name__ == "__main__":
torch.cuda.reset_max_memory_cached() #reset_peak_stats
t0 = time.perf_counter()
model = getattr(model_type, model_name)(pretrained=False)
model = getattr(model_type, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes))
model = Higher_model(model, model_name) #run_dist_dataugV3
if n_inner_iter!=0:
@ -137,6 +144,7 @@ if __name__ == "__main__":
'Optimizer': optim_param,
"Device": device_name,
"Memory": [max_allocated, max_cached],
"TF_config": tf_config,
"Param_names": aug_model.TF_names(),
"Log": log}
print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
@ -150,9 +158,9 @@ if __name__ == "__main__":
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
'''
### Benchmark - RandAugment/Vanilla ###
#'''
### Benchmark - RandAugment/Vanilla ###
'''
for model_type in model_list.keys():
for model_name in model_list[model_type]:
for run in range(nb_run):
@ -160,7 +168,7 @@ if __name__ == "__main__":
torch.cuda.reset_max_memory_cached() #reset_peak_stats
t0 = time.perf_counter()
model = getattr(model_type, model_name)(pretrained=False).to(device)
model = getattr(model_type, model_name)(pretrained=False, num_classes=len(dl_train.dataset.classes)).to(device)
print("{} on {} for {} epochs".format(model_name, device_name, epochs))
#print("RandAugment(N{}-M{:.2f})-{} on {} for {} epochs".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs))
@ -180,7 +188,7 @@ if __name__ == "__main__":
#"Rand_Aug": rand_aug,
"Log": log}
print(model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
filename = "{} epochs -{}".format(model_name,epochs, run)
filename = "{}-{} epochs -{}".format(model_name,epochs, run)
#print("RandAugment-",model_name,": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
#filename = "RandAugment(N{}-M{:.2f})-{}-{} epochs -{}".format(rand_aug['N'],rand_aug['M'],model_name,epochs, run)
with open(res_folder+"log/%s.json" % filename, "w+") as f:
@ -189,12 +197,13 @@ if __name__ == "__main__":
print('Log :\"',f.name, '\" saved !')
except:
print("Failed to save logs :",f.name)
print(sys.exc_info()[1])
#plot_resV2(log, fig_name=res_folder+filename)
print('Execution Time : %.00f '%(exec_time))
print('-'*9)
#'''
'''
### HP Search ###
'''
from LeNet import *
@ -221,7 +230,7 @@ if __name__ == "__main__":
t0 = time.perf_counter()
model = getattr(models.resnet, 'resnet18')(pretrained=False)
model = getattr(models.resnet, 'resnet18')(pretrained=False, num_classes=len(dl_train.dataset.classes))
#model = LeNet(3,10)
model = Higher_model(model) #run_dist_dataugV3
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=n_tf, mix_dist=dist, fixed_prob=p_setup, fixed_mag=m_setup[0], shared_mag=m_setup[1]), model).to(device)