AntoineH/smart_augmentation
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Brutus

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Harle, Antoine (Contracteur) 2020-01-13 18:02:36 -05:00
parent 53bd421670
commit e291bc2e44
9 changed files with 55 additions and 44 deletions
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17
higher/compare_res.py
View file

@ -2,13 +2,12 @@ from utils import *
if __name__ == "__main__": if __name__ == "__main__":
''' #'''
files=[ files=[
#"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json", "res/log/Aug_mod(Data_augV5(Mix0.8-23TFx4-Mag)-LeNet)-100 epochs (dataug:0)- 1 in_it.json",
#"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.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-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-1.json", #"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.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", #"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
] ]
@ -18,7 +17,7 @@ if __name__ == "__main__":
data = json.load(json_file) data = json.load(json_file)
plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names']) plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
#plot_TF_influence(data['Log'], param_names=data['Param_names']) #plot_TF_influence(data['Log'], param_names=data['Param_names'])
''' #'''
## Loss , Acc, Proba = f(epoch) ## ## Loss , Acc, Proba = f(epoch) ##
#plot_compare(filenames=files, fig_name="res/compare") #plot_compare(filenames=files, fig_name="res/compare")
@ -78,7 +77,7 @@ if __name__ == "__main__":
''' '''
#Res print #Res print
#''' '''
nb_run=3 nb_run=3
accs = [] accs = []
times = [] times = []
@ -93,4 +92,4 @@ if __name__ == "__main__":
print(idx, data['Accuracy']) print(idx, data['Accuracy'])
print(files[0], np.mean(accs), np.std(accs), np.mean(times)) print(files[0], np.mean(accs), np.std(accs), np.mean(times))
#''' '''

8
higher/dataug.py
View file

@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF) return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
class Data_augV5(nn.Module): #Optimisation jointe (mag, proba) class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True): def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True, ):
super(Data_augV5, self).__init__() super(Data_augV5, self).__init__()
assert len(TF_dict)>0 assert len(TF_dict)>0
@ -548,8 +548,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
#self._fixed_mag=5 #[0, PARAMETER_MAX] #self._fixed_mag=5 #[0, PARAMETER_MAX]
self._params = nn.ParameterDict({ self._params = nn.ParameterDict({
"prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme "prob": nn.Parameter(torch.ones(self._nb_tf)/self._nb_tf), #Distribution prob uniforme
"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)) if self._shared_mag "mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX)/2) if self._shared_mag
else torch.tensor(float(TF.PARAMETER_MAX)).expand(self._nb_tf)), #[0, PARAMETER_MAX] else torch.tensor(float(TF.PARAMETER_MAX)/2).expand(self._nb_tf)), #[0, PARAMETER_MAX]
}) })
#for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag #for t in TF.TF_no_mag: self._params['mag'][self._TF.index(t)].data-=self._params['mag'][self._TF.index(t)].data #Mag inutile pour les TF ignore_mag
@ -633,7 +633,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1 self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
if not self._fixed_mag: if not self._fixed_mag:
self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX) #Bloque une fois au extreme self._params['mag'].data = self._params['mag'].data.clamp(min=TF.PARAMETER_MIN, max=TF.PARAMETER_MAX)
#self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX) #self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
def loss_weight(self): def loss_weight(self):

18
higher/test_brutus.py
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@ -93,15 +93,15 @@ if __name__ == "__main__":
json.dump(out, f, indent=True) json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !') print('Log :\"',f.name, '\" saved !')
''' '''
res_folder="res/brutus-tests/" res_folder="res/brutus-tests2/"
epochs= 150 epochs= 150
inner_its = [1] inner_its = [1]
dist_mix = [0.0, 0.5, 0.8, 1.0] dist_mix = [0.0, 0.5, 0.8, 1.0]
dataug_epoch_starts= [0] dataug_epoch_starts= [0]
tf_dict = {k: TF.TF_dict[k] for k in tf_names} tf_dict = {k: TF.TF_dict[k] for k in tf_names}
TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)] TF_nb = [len(tf_dict)] #range(10,len(TF.TF_dict)+1) #[len(TF.TF_dict)]
N_seq_TF= [2, 3] N_seq_TF= [2, 3, 4]
mag_setup = [(True,True), (False, False)] mag_setup = [(True,True), (False, False)] #(Fixed, Shared)
#prob_setup = [True, False] #prob_setup = [True, False]
nb_run= 3 nb_run= 3
@ -118,12 +118,14 @@ if __name__ == "__main__":
#for i in TF_nb: #for i in TF_nb:
for m_setup in mag_setup: for m_setup in mag_setup:
#for p_setup in prob_setup: #for p_setup in prob_setup:
p_setup=True p_setup=False
for run in range(nb_run): for run in range(nb_run):
if n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True): continue #Autres setup inutiles sans meta-opti if (n_inner_iter == 0 and (m_setup!=(True,True) and p_setup!=True)) or (p_setup and dist!=0.0): continue #Autres setup inutiles sans meta-opti
#keys = list(TF.TF_dict.keys())[0:i] #keys = list(TF.TF_dict.keys())[0:i]
#ntf_dict = {k: TF.TF_dict[k] for k in keys} #ntf_dict = {k: TF.TF_dict[k] for k in keys}
t0 = time.process_time()
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) 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)
#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
@ -143,9 +145,9 @@ if __name__ == "__main__":
times = [x["time"] for x in log] 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} 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]) 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, run)
with open("res/log/%s.json" % filename, "w+") as f: with open(res_folder+"log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True) json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !') print('Log :\"',f.name, '\" saved !')
print('-'*9) print('-'*9)
''' #'''

10
higher/test_dataug.py
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@ -19,8 +19,8 @@ tf_names = [
'Color', 'Color',
'Brightness', 'Brightness',
'Sharpness', 'Sharpness',
'Posterize', #'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
#Color TF (Common mag scale) #Color TF (Common mag scale)
#'+Contrast', #'+Contrast',
@ -66,7 +66,7 @@ if __name__ == "__main__":
#'aug_dataset', #'aug_dataset',
'aug_model' 'aug_model'
} }
n_inner_iter = 10 n_inner_iter = 1
epochs = 100 epochs = 100
dataug_epoch_start=0 dataug_epoch_start=0
optim_param={ optim_param={
@ -168,7 +168,7 @@ if __name__ == "__main__":
t0 = time.process_time() t0 = time.process_time()
tf_dict = {k: TF.TF_dict[k] for k in tf_names} tf_dict = {k: TF.TF_dict[k] for k in tf_names}
aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.0, 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) #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))
@ -187,7 +187,7 @@ if __name__ == "__main__":
times = [x["time"] for x in log] 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} 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]) 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)+"demi_mag"
with open("res/log/%s.json" % filename, "w+") as f: with open("res/log/%s.json" % filename, "w+") as f:
json.dump(out, f, indent=True) json.dump(out, f, indent=True)
print('Log :\"',f.name, '\" saved !') print('Log :\"',f.name, '\" saved !')

1
higher/utils.py
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@ -90,6 +90,7 @@ def plot_resV2(log, fig_name='res', param_names=None):
ax[0, 2].set_title('Mag =f(epoch)') ax[0, 2].set_title('Mag =f(epoch)')
ax[0, 2].stackplot(epochs, mag, labels=param_names) ax[0, 2].stackplot(epochs, mag, labels=param_names)
#ax[0, 2].plot(epochs, np.array(mag).T, label=param_names)
ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5)) ax[0, 2].legend(param_names, loc='center left', bbox_to_anchor=(1, 0.5))
ax[1, 2].set_title('Mag =f(TF)') ax[1, 2].set_title('Mag =f(TF)')

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salvador/checkpoint.pt
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31
salvador/train.py
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@ -31,12 +31,12 @@ tf_names = [
'ShearY', 'ShearY',
## Color TF (Expect image in the range of [0, 1]) ## ## Color TF (Expect image in the range of [0, 1]) ##
'Contrast', #'Contrast',
'Color', #'Color',
'Brightness', #'Brightness',
'Sharpness', #'Sharpness',
'Posterize', #'Posterize',
'Solarize', #=>Image entre [0,1] #Pas opti pour des batch #'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
] ]
class Lambda(nn.Module): class Lambda(nn.Module):
@ -95,6 +95,7 @@ def train_one_epoch(model, criterion, optimizer, data_loader, device, epoch, mas
unsupp_coeff = 1 unsupp_coeff = 1
loss = sup_loss + (aug_loss + KL_loss) * unsupp_coeff loss = sup_loss + (aug_loss + KL_loss) * unsupp_coeff
#print(sup_loss.item(), (aug_loss + KL_loss).item())
optimizer.zero_grad() optimizer.zero_grad()
loss.backward() loss.backward()
@ -210,7 +211,7 @@ def get_train_valid_loader(args, augment, random_seed, valid_size=0.1, shuffle=T
split = int(np.floor(valid_size * num_train)) split = int(np.floor(valid_size * num_train))
if shuffle: if shuffle:
#np.random.seed(random_seed) np.random.seed(random_seed)
np.random.shuffle(indices) np.random.shuffle(indices)
train_idx, valid_idx = indices[split:], indices[:split] train_idx, valid_idx = indices[split:], indices[:split]
@ -277,6 +278,8 @@ def main(args):
model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device) model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
if args.augment=='RandKL': Kldiv=True if args.augment=='RandKL': Kldiv=True
model['data_aug']['mag'].data = model['data_aug']['mag'].data * args.magnitude
print("Augmodel") print("Augmodel")
# model.fc = nn.Linear(model.fc.in_features, 2) # model.fc = nn.Linear(model.fc.in_features, 2)
@ -294,7 +297,7 @@ def main(args):
optimizer, optimizer,
lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9) lambda x: (1 - x / (len(data_loader) * args.epochs)) ** 0.9)
es = utils.EarlyStopping() es = utils.EarlyStopping() if not (args.augment=='Rand' or args.augment=='RandKL') else utils.EarlyStopping(augmented_model=True)
if args.test_only: if args.test_only:
model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage)) model.load_state_dict(torch.load('checkpoint.pt', map_location=lambda storage, loc: storage))
@ -324,8 +327,8 @@ def main(args):
# print('Train') # print('Train')
# print(train_confmat) # print(train_confmat)
print('Valid') #print('Valid')
print(valid_confmat) #print(valid_confmat)
# if es.early_stop: # if es.early_stop:
# break # break
@ -339,9 +342,9 @@ def parse_args():
import argparse import argparse
parser = argparse.ArgumentParser(description='PyTorch Classification Training') parser = argparse.ArgumentParser(description='PyTorch Classification Training')
parser.add_argument('--data-path', default='/Salvador', help='dataset') parser.add_argument('--data-path', default='/github/smart_augmentation/salvador/data', help='dataset')
parser.add_argument('--model', default='resnet18', help='model') #'resnet18' parser.add_argument('--model', default='resnet18', help='model') #'resnet18'
parser.add_argument('--device', default='cuda:1', help='device') parser.add_argument('--device', default='cuda:0', help='device')
parser.add_argument('-b', '--batch-size', default=8, type=int) parser.add_argument('-b', '--batch-size', default=8, type=int)
parser.add_argument('--epochs', default=3, type=int, metavar='N', parser.add_argument('--epochs', default=3, type=int, metavar='N',
help='number of total epochs to run') help='number of total epochs to run')
@ -364,6 +367,10 @@ def parse_args():
parser.add_argument('-a', '--augment', default='None', type=str, parser.add_argument('-a', '--augment', default='None', type=str,
metavar='N', help='Data augment', metavar='N', help='Data augment',
dest='augment') dest='augment')
parser.add_argument('-m', '--magnitude', default=1.0, type=float,
metavar='N', help='Augmentation magnitude',
dest='magnitude')
args = parser.parse_args() args = parser.parse_args()

8
salvador/train_dataug.py
View file

@ -549,10 +549,10 @@ def parse_args():
import argparse import argparse
parser = argparse.ArgumentParser(description='PyTorch Classification Training') parser = argparse.ArgumentParser(description='PyTorch Classification Training')
parser.add_argument('--data-path', default='/Salvador', help='dataset') parser.add_argument('--data-path', default='/github/smart_augmentation/salvador/data', help='dataset')
parser.add_argument('--model', default='resnet50', help='model') parser.add_argument('--model', default='resnet18', help='model') #'resnet18'
parser.add_argument('--device', default='cuda:1', help='device') parser.add_argument('--device', default='cuda:0', help='device')
parser.add_argument('-b', '--batch-size', default=4, type=int) parser.add_argument('-b', '--batch-size', default=8, type=int)
parser.add_argument('--epochs', default=3, type=int, metavar='N', parser.add_argument('--epochs', default=3, type=int, metavar='N',
help='number of total epochs to run') help='number of total epochs to run')
parser.add_argument('-j', '--workers', default=0, type=int, metavar='N', parser.add_argument('-j', '--workers', default=0, type=int, metavar='N',

6
salvador/utils.py
View file

@ -157,7 +157,7 @@ def accuracy(output, target, topk=(1,)):
class EarlyStopping: class EarlyStopping:
"""Early stops the training if validation loss doesn't improve after a given patience.""" """Early stops the training if validation loss doesn't improve after a given patience."""
def __init__(self, patience=7, verbose=False, delta=0): def __init__(self, patience=7, verbose=False, delta=0, augmented_model=False):
""" """
Args: Args:
patience (int): How long to wait after last time validation loss improved. patience (int): How long to wait after last time validation loss improved.
@ -175,6 +175,8 @@ class EarlyStopping:
self.val_loss_min = np.Inf self.val_loss_min = np.Inf
self.delta = delta self.delta = delta
self.augmented_model = augmented_model
def __call__(self, val_loss, model): def __call__(self, val_loss, model):
score = -val_loss score = -val_loss
@ -196,5 +198,5 @@ class EarlyStopping:
'''Saves model when validation loss decrease.''' '''Saves model when validation loss decrease.'''
if self.verbose: if self.verbose:
print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...') print(f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
torch.save(model.state_dict(), 'checkpoint.pt') torch.save(model.state_dict(), 'checkpoint.pt') if not self.augmented_model else torch.save(model['model'].state_dict(), 'checkpoint.pt')
self.val_loss_min = val_loss self.val_loss_min = val_loss