mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-03 11:40:46 +02:00
Cross Validation splits + New mesure process time (train utils)
This commit is contained in:
Harle, Antoine (Contracteur)
parent
bce882de38
commit
385bc9977c
3 changed files with 51 additions and 30 deletions
|
|
@ -19,6 +19,8 @@ download_data=False
|
|||
num_workers=2 #4
|
||||
#Pin GPU memory
|
||||
pin_memory=False #True :+ GPU memory / + Lent
|
||||
#Data storage folder
|
||||
dataroot="../data"
|
||||
|
||||
#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
|
||||
#transform_train = torchvision.transforms.Compose([
|
||||
|
|
@ -41,7 +43,6 @@ transform_train = torchvision.transforms.Compose([
|
|||
#transform_train.transforms.insert(0, RandAugment(n=2, m=30))
|
||||
|
||||
### Classic Dataset ###
|
||||
dataroot="../data"
|
||||
|
||||
#MNIST
|
||||
#data_train = torchvision.datasets.MNIST(dataroot, train=True, download=True, transform=transform_train)
|
||||
|
|
@ -70,11 +71,27 @@ data_test = torchvision.datasets.CIFAR10(dataroot, train=False, download=downloa
|
|||
#data_test = torchvision.datasets.ImageNet(root=os.path.join(dataroot, 'imagenet-pytorch'), split='val', transform=transform_test)
|
||||
|
||||
|
||||
train_subset_indices=range(int(len(data_train)/2))
|
||||
val_subset_indices=range(int(len(data_train)/2),len(data_train))
|
||||
#Validation set size [0, 1]
|
||||
#valid_size=0.1
|
||||
#train_subset_indices=range(int(len(data_train)*(1-valid_size)))
|
||||
#val_subset_indices=range(int(len(data_train)*(1-valid_size)),len(data_train))
|
||||
#train_subset_indices=range(BATCH_SIZE*10)
|
||||
#val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
|
||||
|
||||
dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
|
||||
dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
|
||||
#dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
|
||||
#dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices), num_workers=num_workers, pin_memory=pin_memory)
|
||||
dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False, num_workers=num_workers, pin_memory=pin_memory)
|
||||
|
||||
#Cross Validation
|
||||
from skorch.dataset import CVSplit
|
||||
cvs = CVSplit(cv=5)
|
||||
|
||||
def next_CVSplit():
|
||||
|
||||
train_subset, val_subset = cvs(data_train)
|
||||
dl_train = torch.utils.data.DataLoader(train_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
|
||||
dl_val = torch.utils.data.DataLoader(val_subset, batch_size=BATCH_SIZE, shuffle=True, num_workers=num_workers, pin_memory=pin_memory)
|
||||
|
||||
return dl_train, dl_val
|
||||
|
||||
dl_train, dl_val = next_CVSplit()
|
||||
|
|
@ -13,19 +13,19 @@ tf_names = [
|
|||
'Identity',
|
||||
'FlipUD',
|
||||
'FlipLR',
|
||||
#'Rotate',
|
||||
#'TranslateX',
|
||||
#'TranslateY',
|
||||
#'ShearX',
|
||||
#'ShearY',
|
||||
'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
|
||||
'Contrast',
|
||||
'Color',
|
||||
'Brightness',
|
||||
'Sharpness',
|
||||
'Posterize',
|
||||
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
|
||||
|
||||
#Color TF (Common mag scale)
|
||||
#'+Contrast',
|
||||
|
|
@ -74,12 +74,12 @@ if __name__ == "__main__":
|
|||
|
||||
#Task to perform
|
||||
tasks={
|
||||
'classic',
|
||||
#'aug_model'
|
||||
#'classic',
|
||||
'aug_model'
|
||||
}
|
||||
#Parameters
|
||||
n_inner_iter = 1
|
||||
epochs = 2
|
||||
epochs = 150
|
||||
dataug_epoch_start=0
|
||||
optim_param={
|
||||
'Meta':{
|
||||
|
|
@ -147,7 +147,7 @@ if __name__ == "__main__":
|
|||
|
||||
tf_dict = {k: TF.TF_dict[k] for k in tf_names}
|
||||
model = Higher_model(model, model_name) #run_dist_dataugV3
|
||||
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.8, fixed_prob=False, fixed_mag=False, shared_mag=False), model).to(device)
|
||||
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))
|
||||
|
|
@ -156,7 +156,7 @@ if __name__ == "__main__":
|
|||
inner_it=n_inner_iter,
|
||||
dataug_epoch_start=dataug_epoch_start,
|
||||
opt_param=optim_param,
|
||||
print_freq=1,
|
||||
print_freq=20,
|
||||
unsup_loss=1,
|
||||
hp_opt=False,
|
||||
save_sample_freq=None)
|
||||
|
|
@ -174,7 +174,7 @@ if __name__ == "__main__":
|
|||
"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)
|
||||
filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)+"(CV)"
|
||||
with open("../res/log/%s.json" % filename, "w+") as f:
|
||||
try:
|
||||
json.dump(out, f, indent=True)
|
||||
|
|
|
|||
|
|
@ -150,7 +150,7 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
|
|||
log = []
|
||||
for epoch in range(epochs):
|
||||
#print_torch_mem("Start epoch")
|
||||
t0 = time.process_time()
|
||||
t0 = time.perf_counter()
|
||||
for i, (features, labels) in enumerate(dl_train):
|
||||
#viz_sample_data(imgs=features, labels=labels, fig_name='../samples/data_sample_epoch{}_noTF'.format(epoch))
|
||||
#print_torch_mem("Start iter")
|
||||
|
|
@ -164,7 +164,7 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
|
|||
optim.step()
|
||||
|
||||
#### Tests ####
|
||||
tf = time.process_time()
|
||||
tf = time.perf_counter()
|
||||
|
||||
val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val)
|
||||
accuracy, f1 =test(model)
|
||||
|
|
@ -176,8 +176,8 @@ def train_classic(model, opt_param, epochs=1, print_freq=1):
|
|||
print('Epoch : %d/%d'%(epoch,epochs))
|
||||
print('Time : %.00f'%(tf - t0))
|
||||
print('Train loss :',loss.item(), '/ val loss', val_loss.item())
|
||||
print('Accuracy :', accuracy)
|
||||
print('F1 :', f1.data)
|
||||
print('Accuracy max:', accuracy)
|
||||
print('F1 :', f1)
|
||||
|
||||
#### Log ####
|
||||
data={
|
||||
|
|
@ -219,7 +219,7 @@ 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)
|
||||
#dl_val_it = iter(dl_val)
|
||||
val_loss=None
|
||||
|
||||
high_grad_track = True
|
||||
|
|
@ -251,8 +251,11 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
|
|||
meta_opt.zero_grad()
|
||||
|
||||
for epoch in range(1, epochs+1):
|
||||
t0 = time.process_time()
|
||||
t0 = time.perf_counter()
|
||||
|
||||
dl_train, dl_val = next_CVSplit()
|
||||
dl_val_it = iter(dl_val)
|
||||
|
||||
for i, (xs, ys) in enumerate(dl_train):
|
||||
xs, ys = xs.to(device), ys.to(device)
|
||||
|
||||
|
|
@ -303,7 +306,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
|
|||
#diffopt.detach_()
|
||||
model['model'].detach_()
|
||||
|
||||
tf = time.process_time()
|
||||
tf = time.perf_counter()
|
||||
|
||||
if (save_sample_freq and epoch%save_sample_freq==0): #Data sample saving
|
||||
try:
|
||||
|
|
@ -345,7 +348,8 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
|
|||
print('Epoch : %d/%d'%(epoch,epochs))
|
||||
print('Time : %.00f'%(tf - t0))
|
||||
print('Train loss :',loss.item(), '/ val loss', val_loss.item())
|
||||
print('Accuracy :', max([x["acc"] for x in log]))
|
||||
print('Accuracy max:', max([x["acc"] for x in log]))
|
||||
print('F1 :', f1)
|
||||
print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start))
|
||||
if not model['data_aug']._fixed_prob: print('TF Proba :', model['data_aug']['prob'].data)
|
||||
#print('proba grad',model['data_aug']['prob'].grad)
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue