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https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 12:10:45 +02:00
Modif solarize (Tjrs pas differentiable...)
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5 changed files with 56 additions and 42 deletions
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@ -2,11 +2,12 @@ from utils import *
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if __name__ == "__main__":
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'''
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#'''
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files=[
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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#"res/good_TF_tests/log/Aug_mod(Data_augV5(Uniform-14TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Mix0.5-14TFx1-Mag)-LeNet)-150epochs(dataug:0)-1in_it-0.json",
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"res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json",
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]
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for idx, file in enumerate(files):
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@ -15,7 +16,7 @@ if __name__ == "__main__":
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data = json.load(json_file)
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plot_resV2(data['Log'], fig_name=file.replace('.json','').replace('log/',''), param_names=data['Param_names'])
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#plot_TF_influence(data['Log'], param_names=data['Param_names'])
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'''
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#'''
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## Loss , Acc, Proba = f(epoch) ##
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#plot_compare(filenames=files, fig_name="res/compare")
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@ -75,6 +76,7 @@ if __name__ == "__main__":
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'''
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#Res print
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'''
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nb_run=3
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accs = []
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times = []
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@ -88,3 +90,4 @@ if __name__ == "__main__":
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times.append(data['Time'][0])
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print(files[0], np.mean(accs), np.std(accs), np.mean(times))
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'''
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@ -692,7 +692,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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else:
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return "Data_augV5(Mix%.1f%s-%dTFx%d-%s)" % (self._mix_factor,dist_param, self._nb_tf, self._N_seqTF, mag_param)
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class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh
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class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh + rapide
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
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super(RandAug, self).__init__()
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@ -773,9 +773,9 @@ class RandAug(nn.Module): #RandAugment = UniformFx-MagFxSh
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def __str__(self):
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return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
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class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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class RandAugUDA(nn.Module): #RandAugment from UDA (for DA during training)
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mag=TF.PARAMETER_MAX):
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super(RandAug, self).__init__()
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super(RandAugUDA, self).__init__()
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self._data_augmentation = True
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@ -786,7 +786,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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self.mag=nn.Parameter(torch.tensor(float(mag)))
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self._params = nn.ParameterDict({
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"prob": nn.Parameter(torch.tensor(0.5)),
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"prob": nn.Parameter(torch.tensor(0.5).unsqueeze(dim=0)),
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"mag" : nn.Parameter(torch.tensor(float(TF.PARAMETER_MAX))),
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})
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self._shared_mag = True
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@ -794,12 +794,10 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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self._op_list =[]
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for tf in self._TF:
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for mag in range(0.1, self._params['mag'], 0.1):
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op_list+=[(tf, self._params['prob'], mag)]
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for mag in range(1, int(self._params['mag']*10), 1):
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self._op_list+=[(tf, self._params['prob'].item(), mag/10)]
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self._nb_op = len(self._op_list)
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print(self._op_list)
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def forward(self, x):
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if self._data_augmentation:# and TF.random.random() < 0.5:
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device = x.device
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@ -821,16 +819,16 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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smps_x=[]
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for op_idx in range(self._nb_op):
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mask = sampled_TF==tf_idx #Create selection mask
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mask = sampled_TF==op_idx #Create selection mask
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smp_x = x[mask] #torch.masked_select() ? (Necessite d'expand le mask au meme dim)
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if smp_x.shape[0]!=0: #if there's data to TF
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if TF.random.random() < self.op_list[op_idx][1]:
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magnitude=self.op_list[op_idx][2]
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tf=self.op_list[op_idx][0]
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if TF.random.random() < self._op_list[op_idx][1]:
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magnitude=self._op_list[op_idx][2]
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tf=self._op_list[op_idx][0]
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#In place
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x[mask]=self._TF_dict[tf](x=smp_x, mag=magnitude)
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x[mask]=self._TF_dict[tf](x=smp_x, mag=torch.tensor(magnitude, device=x.device))
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return x
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@ -847,7 +845,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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if mode is None :
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mode=self._data_augmentation
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self.augment(mode=mode) #Inutile si mode=None
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super(RandAug, self).train(mode)
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super(RandAugUDA, self).train(mode)
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def eval(self):
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self.train(mode=False)
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@ -859,7 +857,7 @@ class RandAugUDA(nn.Module): #RandAugment = UniformFx-MagFxSh
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return self._params[key]
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def __str__(self):
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return "RandAug(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
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return "RandAugUDA(%dTFx%d-Mag%d)" % (self._nb_tf, self._N_seqTF, self.mag)
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class Augmented_model(nn.Module):
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def __init__(self, data_augmenter, model):
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@ -5,21 +5,21 @@ from train_utils import *
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tf_names = [
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## Geometric TF ##
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'Identity',
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'FlipUD',
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'FlipLR',
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'Rotate',
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'TranslateX',
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'TranslateY',
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'ShearX',
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'ShearY',
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#'Identity',
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#'FlipUD',
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#'FlipLR',
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#'Rotate',
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#'TranslateX',
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#'TranslateY',
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#'ShearX',
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#'ShearY',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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'Color',
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'Brightness',
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'Sharpness',
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'Posterize',
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#'Contrast',
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#'Color',
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#'Brightness',
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#'Sharpness',
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#'Posterize',
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'Solarize', #=>Image entre [0,1] #Pas opti pour des batch
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#Color TF (Common mag scale)
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@ -48,6 +48,7 @@ tf_names = [
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#'BadSharpness',
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#'BadContrast',
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#'BadBrightness',
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#Non fonctionnel
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#'Auto_Contrast', #Pas opti pour des batch (Super lent)
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#'Equalize',
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@ -63,8 +64,8 @@ else:
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##########################################
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if __name__ == "__main__":
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n_inner_iter = 0
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epochs = 150
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n_inner_iter = 10
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epochs = 1
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dataug_epoch_start=0
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#### Classic ####
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@ -94,12 +95,12 @@ if __name__ == "__main__":
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t0 = time.process_time()
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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#tf_dict = TF.TF_dict
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#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
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aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=True, fixed_mag=False, shared_mag=True), LeNet(3,10)).to(device)
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#aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).to(device)
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
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print(str(aug_model), 'on', device_name)
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#run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
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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=None)
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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=None)
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####
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print('-'*9)
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@ -651,7 +651,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
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print('TF Proba :', model['data_aug']['prob'].data)
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#print('proba grad',model['data_aug']['prob'].grad)
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print('TF Mag :', model['data_aug']['mag'].data)
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#print('Mag grad',model['data_aug']['mag'].grad)
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print('Mag grad',model['data_aug']['mag'].grad)
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#print('Reg loss:', model['data_aug'].reg_loss().item())
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#############
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#### Log ####
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@ -298,12 +298,12 @@ def equalize(x): #PAS OPTIMISE POUR DES BATCH
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def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
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# Optimisation : Mask direct sur toute les donnees (Mask = (B,C,H,W)> (B))
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batch_size, channels, h, w = x.shape
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imgs=[]
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for idx, t in enumerate(thresholds): #Operation par image
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mask = x[idx] > t #Perte du gradient
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#imgs=[]
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#for idx, t in enumerate(thresholds): #Operation par image
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# mask = x[idx] > t #Perte du gradient
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#In place
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inv_x = 1-x[idx][mask]
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x[idx][mask]=inv_x
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# inv_x = 1-x[idx][mask]
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# x[idx][mask]=inv_x
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#
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#Out of place
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@ -316,6 +316,18 @@ def solarize(x, thresholds): #PAS OPTIMISE POUR DES BATCH
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#idxs=idxs.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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#x=x.scatter(dim=0, index=idxs, src=torch.stack(imgs))
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#
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thresholds = thresholds.unsqueeze(dim=1).expand(-1,channels).unsqueeze(dim=2).expand(-1,channels, h).unsqueeze(dim=3).expand(-1,channels, h, w) #Il y a forcement plus simple ...
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#print(thresholds.grad_fn)
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x=torch.where(x>thresholds,1-x, x)
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#print(mask.grad_fn)
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#x=x.min(thresholds)
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#inv_x = 1-x[mask]
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#x=x.where(x<thresholds,1-x)
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#x[mask]=inv_x
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#x=x.masked_scatter(mask, inv_x)
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return x
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#https://github.com/python-pillow/Pillow/blob/9c78c3f97291bd681bc8637922d6a2fa9415916c/src/PIL/Image.py#L2818
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