AntoineH/smart_augmentation
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Fixs + Benchmark

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Harle, Antoine (Contracteur) 2020-02-21 15:29:34 -05:00
parent 49472adfab
commit f450fa8201
5 changed files with 60 additions and 16 deletions
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9
higher/smart_aug/benchmark.py
View file

@ -8,7 +8,8 @@ from transformations import TF_loader
import torchvision.models as models
model_list={models.resnet: ['resnet18', 'resnet50','wide_resnet50_2']} #lr=0.1
#model_list={models.resnet: ['resnet18', 'resnet50','wide_resnet50_2']} #lr=0.1
model_list={models.resnet: ['resnet18']}
optim_param={
'Meta':{
@ -28,7 +29,7 @@ optim_param={
res_folder="../res/benchmark/CIFAR10/"
#res_folder="../res/HPsearch/"
epochs= 300
epochs= 400
dataug_epoch_start=0
nb_run= 1
@ -54,8 +55,8 @@ if __name__ == "__main__":
### Benchmark ###
#'''
n_inner_iter = 1
dist_mix = [0.5, 1.0]
n_inner_iter = 1#[0, 1]
dist_mix = [0.5]
N_seq_TF= [3, 4]
mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant)

14
higher/smart_aug/old/compare_TF.py Normal file
View file

@ -0,0 +1,14 @@
import RandAugment as rand
import PIL
import torchvision
import transformations as TF
tpil=torchvision.transforms.ToPILImage()
ttensor=torchvision.transforms.ToTensor()
img,label =data_train[0]
rimg=ttensor(PIL.ImageEnhance.Color(tpil(img)).enhance(1.5))#ttensor(PIL.ImageOps.solarize(tpil(img), 50))#ttensor(tpil(img).transform(tpil(img).size, PIL.Image.AFFINE, (1, -0.1, 0, 0, 1, 0)))#rand.augmentations.FlipUD(tpil(img),1))
timg=TF.color(img.unsqueeze(0),torch.Tensor([1.5])).squeeze(0)
print(torch.allclose(rimg,timg, atol=1e-3))
tpil(rimg).save('rimg.jpg')
tpil(timg).save('timg.jpg')

41
higher/smart_aug/compare_res.py → higher/smart_aug/process_res.py
View file

@ -4,18 +4,20 @@ if __name__ == "__main__":
#'''
files=[
"../res/log/Aug_mod(Data_augV5(Mix0.8-3TFx2-MagFx)-resnet18)-2 epochs (dataug:0)- 1 in_it.json",
"../res/HPsearch/log/Aug_mod(Data_augV5(Mix0.5-14TFx3-Mag)-ResNet)-200 epochs (dataug:0)- 1 in_it-0.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-0.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-1.json",
#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.json",
#"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
]
files = ["../res/benchmark/CIFAR10/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,'wide_resnet50_2', str(run)) for run in range(3)]
files = ["../res/benchmark/CIFAR100/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-Mag)-%s)-200 epochs (dataug:0)- 1 in_it-%s.json"%(0.5,3,'wide_resnet50_2', str(run)) for run in range(3)]
for idx, file in enumerate(files):
#legend+=str(idx)+'-'+file+'\n'
with open(file) as json_file:
data = json.load(json_file)
plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""), param_names=data['Param_names'])
plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""))#, param_names=data['Param_names'])
#plot_TF_influence(data['Log'], param_names=data['Param_names'])
#'''
## Loss , Acc, Proba = f(epoch) ##
@ -95,17 +97,18 @@ if __name__ == "__main__":
'''
'''
#HP search
inner_its = [1]
dist_mix = [0]#[0.5, 0.8, 1.0] #Uniform
N_seq_TF= [4, 3, 2]
dist_mix = [0.3, 0.5, 0.8, 1.0] #Uniform
N_seq_TF= [5]
nb_run= 3
for n_inner_iter in inner_its:
for n_tf in N_seq_TF:
for dist in dist_mix:
#files = ["../res/brutus-tests2/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-MagFxSh)-ResNet18)-150 epochs (dataug:0)- 1 in_it-%s.json"%(dist, n_tf, str(run)) for run in range(nb_run)]
files = ["../res/brutus-tests2/log/Aug_mod(Data_augV5(Uniform-14TFx%d-MagFxSh)-ResNet18)-150 epochs (dataug:0)- 1 in_it-%s.json"%(n_tf, str(run)) for run in range(nb_run)]
#files = ["../res/HPsearch/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-MagFxSh)-ResNet)-200 epochs (dataug:0)- 1 in_it-%s.json"%(dist, n_tf, str(run)) for run in range(nb_run)]
files = ["../res/HPsearch/log/Aug_mod(Data_augV5(Uniform-14TFx%d-MagFxSh)-ResNet)-200 epochs (dataug:0)- 1 in_it-%s.json"%(n_tf, str(run)) for run in range(nb_run)]
accs = []
times = []
for idx, file in enumerate(files):
@ -117,4 +120,30 @@ if __name__ == "__main__":
print(idx, data['Accuracy'])
print(files[0], 'acc', np.mean(accs), '+-',np.std(accs), ',t', np.mean(times))
'''
'''
#Benchmark
model_list=['resnet18', 'resnet50','wide_resnet50_2']
nb_run= 3
for model_name in model_list:
files = ["../res/benchmark/CIFAR100/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,model_name, str(run)) for run in range(nb_run)]
#files = ["../res/benchmark/CIFAR10/log/%s-200 epochs -%s.json"%(model_name, str(run)) for run in range(nb_run)]
accs = []
times = []
mem_alloc = []
mem_cach = []
for idx, file in enumerate(files):
#legend+=str(idx)+'-'+file+'\n'
with open(file) as json_file:
data = json.load(json_file)
accs.append(data['Accuracy'])
times.append(data['Time'][0])
mem_cach.append(data['Memory'])
print(idx, data['Accuracy'])
print(files[0], 'acc', np.mean(accs), '+-',np.std(accs), ',t', np.mean(times), 'Mem', np.mean(mem_cach))
'''

10
higher/smart_aug/test_dataug.py
View file

@ -69,8 +69,8 @@ if __name__ == "__main__":
model = model.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))
print("{} on {} for {} epochs{}".format(model_name, device_name, epochs, postfix))
#print("RandAugment(N{}-M{:.2f})-{} on {} for {} epochs{}".format(rand_aug['N'],rand_aug['M'],model_name, device_name, epochs, postfix))
log= train_classic(model=model, opt_param=optim_param, epochs=epochs, print_freq=10)
#log= train_classic_higher(model=model, epochs=epochs)
@ -88,9 +88,9 @@ 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)
filename = "{}-{} epochs".format(model_name,epochs)+postfix
#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)+'-cosine'
#filename = "RandAugment(N{}-M{:.2f})-{}-{} epochs".format(rand_aug['N'],rand_aug['M'],model_name,epochs)+postfix
with open("../res/log/%s.json" % filename, "w+") as f:
try:
json.dump(out, f, indent=True)
@ -128,7 +128,7 @@ if __name__ == "__main__":
TF_ignore_mag=tf_ignore_mag), 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))
print("{} on {} for {} epochs - {} inner_it{}".format(str(aug_model), device_name, epochs, n_inner_iter, postfix))
log= run_dist_dataugV3(model=aug_model,
epochs=epochs,
inner_it=n_inner_iter,

2
higher/smart_aug/train_utils.py
View file

@ -247,7 +247,6 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
device = next(model.parameters()).device
log = []
dl_val_it = iter(dl_val)
val_loss=None
high_grad_track = True
if inner_it == 0: #No HP optimization
@ -298,6 +297,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
for epoch in range(1, epochs+1):
t0 = time.perf_counter()
val_loss=None
#Cross-Validation
#dl_train, dl_val = cvs.next_split()