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Resultats experience effet nbTF + outil comparaison resultat

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Harle, Antoine (Contracteur) 2019-11-11 14:33:40 -05:00
parent aef002891e
commit fd38bcbdf5
2 changed files with 51 additions and 13 deletions
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64
higher/test_dataug.py
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@ -11,7 +11,36 @@ BATCH_SIZE = 300
#TEST_SIZE = 300
TEST_SIZE = 10000
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',
]
#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
#transform_train = torchvision.transforms.Compose([
# torchvision.transforms.RandomHorizontalFlip(),
# torchvision.transforms.ToTensor(),
#torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
#])
transform = torchvision.transforms.Compose([
torchvision.transforms.ToTensor(),
#torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
@ -31,6 +60,9 @@ data_test = torchvision.datasets.MNIST(
data_train = torchvision.datasets.CIFAR10(
"./data", train=True, download=True, transform=transform
)
#data_val = torchvision.datasets.CIFAR10(
# "./data", train=True, download=True, transform=transform
#)
data_test = torchvision.datasets.CIFAR10(
"./data", train=False, download=True, transform=transform
)
@ -81,7 +113,7 @@ def train_classic(model, epochs=1):
dl_val_it = iter(dl_val)
log = []
for epoch in range(epochs):
print_torch_mem("Start epoch")
#print_torch_mem("Start epoch")
t0 = time.process_time()
for i, (features, labels) in enumerate(dl_train):
#print_torch_mem("Start iter")
@ -132,8 +164,8 @@ def train_classic_higher(model, epochs=1):
#with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=False) as (fmodel, diffopt):
for epoch in range(epochs):
print_torch_mem("Start epoch "+str(epoch))
print("Fast param ",len(fmodel._fast_params))
#print_torch_mem("Start epoch "+str(epoch))
#print("Fast param ",len(fmodel._fast_params))
t0 = time.process_time()
for i, (features, labels) in enumerate(dl_train):
#print_torch_mem("Start iter")
@ -702,8 +734,8 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
##########################################
if __name__ == "__main__":
n_inner_iter = 0
epochs = 2
n_inner_iter = 10
epochs = 100
dataug_epoch_start=0
#### Classic ####
@ -714,7 +746,8 @@ if __name__ == "__main__":
#model.augment(mode=False)
print(str(model), 'on', device_name)
log= train_classic_higher(model=model, epochs=epochs)
log= train_classic(model=model, epochs=epochs)
#log= train_classic_higher(model=model, epochs=epochs)
####
plot_res(log, fig_name="res/{}-{} epochs".format(str(model),epochs))
@ -728,11 +761,13 @@ if __name__ == "__main__":
print('-'*9)
'''
#### Augmented Model ####
'''
#'''
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.TF_dict, 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=1, 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".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter))
@ -744,13 +779,14 @@ if __name__ == "__main__":
json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !')
print('-'*9)
'''
#'''
## TF number tests ##
'''
res_folder="res/TF_nb_tests/"
epochs= 200
inner_its = [0, 10]
dataug_epoch_starts= [0, -1]
max_TF_nb = len(TF.TF_dict)
TF_nb = [14] #range(1,len(TF.TF_dict)+1)
try:
os.mkdir(res_folder)
@ -762,14 +798,14 @@ if __name__ == "__main__":
print("---Starting inner_it", n_inner_iter,"---")
for dataug_epoch_start in dataug_epoch_starts:
print("---Starting dataug", dataug_epoch_start,"---")
for i in range(1,max_TF_nb):
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)
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)
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))
@ -780,4 +816,6 @@ if __name__ == "__main__":
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)
'''