Ajout meta-learning differee

higher/smart_aug/benchmark.py

@@ -4,6 +4,7 @@
 from dataug import *
 #from utils import *
 from train_utils import *
+from transformations import TF_loader
 
 import torchvision.models as models
 
@@ -13,6 +14,7 @@ optim_param={
     'Meta':{
         'optim':'Adam',
         'lr':1e-2, #1e-2
+        'epoch_start': 2, #0 / 2 (Resnet?)
     },
     'Inner':{
         'optim': 'SGD',
@@ -26,9 +28,9 @@ optim_param={
 
 res_folder="../res/benchmark/CIFAR10/"
 #res_folder="../res/HPsearch/"
-epochs= 200
+epochs= 300
 dataug_epoch_start=0
-nb_run= 3
+nb_run= 1
 
 tf_config='../config/base_tf_config.json'
 TF_loader=TF_loader()

higher/smart_aug/dataug.py

@@ -964,7 +964,7 @@ class Augmented_model(nn.Module):
 
                 model.step(loss)
                 
-            Does not support LR scheduler.
+            Lacking epoch informations, this does not support LR scheduler and delayed meta-optimisation(Meta-optimizer: epoch_start>1).
             
             See ''run_simple_smartaug'' for a complete example.
 

higher/smart_aug/test_dataug.py

@@ -6,10 +6,10 @@ from LeNet import *
 from dataug import *
 #from utils import *
 from train_utils import *
-#from transformations import TF_loader
+from transformations import TF_loader
 
 postfix=''
-TF_loader=TF.TF_loader()
+TF_loader=TF_loader()
 
 device = torch.device('cuda') #Select device to use
 
@@ -40,6 +40,7 @@ if __name__ == "__main__":
         'Meta':{
             'optim':'Adam',
             'lr':1e-2, #1e-2
+            'epoch_start': 2, #0 / 2 (Resnet?)
         },
         'Inner':{
             'optim': 'SGD',

higher/smart_aug/transformations.py

@@ -31,64 +31,6 @@ PARAMETER_MAX = 1
 # What is the min 'level' a transform could be predicted
 PARAMETER_MIN = 0.1 
 
-'''
-# Dictionnary mapping tranformations identifiers to their function.
-# Each value of the dict should be a lambda function taking a (batch of data, magnitude of transformations) tuple as input and returns a batch of data.
-TF_dict={ #Dataugv5+
-    ## Geometric TF ##
-    'Identity' : (lambda x, mag: x),
-    'FlipUD' : (lambda x, mag: flipUD(x)),
-    'FlipLR' : (lambda x, mag: flipLR(x)),
-    'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
-    'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[2]*0.33), zero_pos=0))),
-    'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[3]*0.33), zero_pos=1))),
-    'TranslateXabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
-    'TranslateYabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
-    'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
-    'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
-
-    ## Color TF (Expect image in the range of [0, 1]) ##
-    'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
-    'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
-    'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
-    'Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
-    'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
-    'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
-
-    #Color TF (Common mag scale)
-    '+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
-    '+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
-    '+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
-    '+Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
-    '-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
-    '-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
-    '-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
-    '-Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
-    '=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
-    '=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
-
-    ## Bad Tranformations ##
-    # Bad Geometric TF #
-    'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=0))),
-    'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=1))),
-    'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=0))),
-    'BTranslateX-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=0))),
-    'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=1))),
-    'BTranslateY-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=1))),
-
-    # Bad Color TF #
-    'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9*2, maxval=2*4))),
-    'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
-
-    # Random TF #
-    'Random':(lambda x, mag: torch.rand_like(x)),
-    'RandBlend': (lambda x, mag: blend(x,torch.rand_like(x), alpha=torch.tensor(0.7,device=mag.device).expand(x.shape[0]))),
-
-    #Not ready for use
-    #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
-    #'Equalize': (lambda mag: None),
-}
-'''
 class TF_loader(object):
     """ Transformations builder.
 
@@ -155,6 +97,8 @@ class TF_loader(object):
     def build_lambda(self, fct_name, rand_fct_name, minval, maxval, absolute=True, axis=None):
         """ Build a lambda function performing transformations.
 
+            Force different context for creation of each lambda function.
+
             Args:
                 fct_name (str): Name of the transformations to use (see transformations.py).
                 rand_fct_name (str): Name of the random mapping function to use (see transformations.py).
@@ -620,4 +564,63 @@ def equalize(x):
             #print(chan.shape)
             hist = torch.histc(chan, bins=256, min=0, max=255) #PAS DIFFERENTIABLE
 
-    return float_image(x)
+    return float_image(x)
+
+'''
+# Dictionnary mapping tranformations identifiers to their function.
+# Each value of the dict should be a lambda function taking a (batch of data, magnitude of transformations) tuple as input and returns a batch of data.
+TF_dict={ #Dataugv5+
+    ## Geometric TF ##
+    'Identity' : (lambda x, mag: x),
+    'FlipUD' : (lambda x, mag: flipUD(x)),
+    'FlipLR' : (lambda x, mag: flipLR(x)),
+    'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
+    'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[2]*0.33), zero_pos=0))),
+    'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=x.shape[3]*0.33), zero_pos=1))),
+    'TranslateXabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
+    'TranslateYabs': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
+    'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
+    'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
+
+    ## Color TF (Expect image in the range of [0, 1]) ##
+    'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
+    'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
+    'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
+    'Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
+    'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
+    'Solarize': (lambda x, mag: solarize(x, thresholds=rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
+
+    #Color TF (Common mag scale)
+    '+Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
+    '+Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
+    '+Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
+    '+Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.0, maxval=1.9))),
+    '-Contrast': (lambda x, mag: contrast(x, contrast_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
+    '-Color':(lambda x, mag: color(x, color_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
+    '-Brightness':(lambda x, mag: brightness(x, brightness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
+    '-Sharpness':(lambda x, mag: sharpness(x, sharpness_factor=invScale_rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.0))),
+    '=Posterize': (lambda x, mag: posterize(x, bits=invScale_rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
+    '=Solarize': (lambda x, mag: solarize(x, thresholds=invScale_rand_floats(size=x.shape[0], mag=mag, minval=1/256., maxval=256/256.))), #Perte du gradient #=>Image entre [0,1]
+
+    ## Bad Tranformations ##
+    # Bad Geometric TF #
+    'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=0))),
+    'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=0.3*3, maxval=0.3*4), zero_pos=1))),
+    'BTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=0))),
+    'BTranslateX-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=0))),
+    'BTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=25, maxval=30), zero_pos=1))),
+    'BTranslateY-': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-25, maxval=-30), zero_pos=1))),
+
+    # Bad Color TF #
+    'BadContrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9*2, maxval=2*4))),
+    'BadBrightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=1.9, maxval=2*3))),
+
+    # Random TF #
+    'Random':(lambda x, mag: torch.rand_like(x)),
+    'RandBlend': (lambda x, mag: blend(x,torch.rand_like(x), alpha=torch.tensor(0.7,device=mag.device).expand(x.shape[0]))),
+
+    #Not ready for use
+    #'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
+    #'Equalize': (lambda mag: None),
+}
+'''