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Test brutus suite
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4 changed files with 90 additions and 32 deletions
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@ -2,10 +2,11 @@ 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/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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]
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for idx, file in enumerate(files):
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@ -14,7 +15,7 @@ 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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@ -72,3 +73,18 @@ if __name__ == "__main__":
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plt.savefig(fig_name, bbox_inches='tight')
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plt.close()
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'''
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#Res print
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nb_run=3
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accs = []
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times = []
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files = ["res/brutus-tests/log/Aug_mod(Data_augV5(Mix1.0-14TFx2-Mag)-LeNet)-150epochs(dataug:0)-1in_it-%s.json"%str(run) for run in range(nb_run)]
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for idx, file in enumerate(files):
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#legend+=str(idx)+'-'+file+'\n'
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with open(file) as json_file:
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data = json.load(json_file)
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accs.append(data['Accuracy'])
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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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@ -531,7 +531,7 @@ class Data_augV4(nn.Module): #Transformations avec mask
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return "Data_augV4(Mix %.1f-%d TF x %d)" % (self._mix_factor, self._nb_tf, self._N_seqTF)
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class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_mag=True, shared_mag=True):
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def __init__(self, TF_dict=TF.TF_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, fixed_mag=True, shared_mag=True):
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super(Data_augV5, self).__init__()
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assert len(TF_dict)>0
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@ -555,6 +555,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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#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
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#Distribution
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self._fixed_prob=fixed_prob
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self._samples = []
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self._mix_dist = False
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if mix_dist != 0.0:
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@ -570,12 +571,12 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=TF.PARAMETER_MAX) #Encourage amplitude max
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def forward(self, x):
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if self._data_augmentation:
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self._samples = []
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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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batch_size, h, w = x.shape[0], x.shape[2], x.shape[3]
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x = copy.deepcopy(x) #Evite de modifier les echantillons par reference (Problematique pour des utilisations paralleles)
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self._samples = []
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for _ in range(self._N_seqTF):
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## Echantillonage ##
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@ -584,7 +585,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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if not self._mix_dist:
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self._distrib = uniforme_dist
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else:
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self._distrib = (self._mix_factor*self._params["prob"]+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
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prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
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self._distrib = (self._mix_factor*prob+(1-self._mix_factor)*uniforme_dist).softmax(dim=1) #Mix distrib reel / uniforme avec mix_factor
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cat_distrib= Categorical(probs=torch.ones((batch_size, self._nb_tf), device=device)*self._distrib)
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sample = cat_distrib.sample()
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@ -622,18 +624,22 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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return x
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def adjust_param(self, soft=False): #Detach from gradient ?
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if not self._fixed_prob:
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if soft :
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self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
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else:
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self._params['prob'].data = F.relu(self._params['prob'].data)
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#self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0)
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self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
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if soft :
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self._params['prob'].data=F.softmax(self._params['prob'].data, dim=0) #Trop 'soft', bloque en dist uniforme si lr trop faible
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else:
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self._params['prob'].data = F.relu(self._params['prob'].data)
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#self._params['prob'].data = self._params['prob'].clamp(min=0.0,max=1.0)
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self._params['prob'].data = self._params['prob']/sum(self._params['prob']) #Contrainte sum(p)=1
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#self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme
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self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
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if not self._fixed_mag:
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#self._params['mag'].data = self._params['mag'].data.clamp(min=0.0,max=TF.PARAMETER_MAX) #Bloque une fois au extreme
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self._params['mag'].data = F.relu(self._params['mag'].data) - F.relu(self._params['mag'].data - TF.PARAMETER_MAX)
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def loss_weight(self):
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if len(self._samples)==0 : return 1 #Pas d'echantillon = pas de ponderation
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prob = self._params["prob"].detach() if self._fixed_prob else self._params["prob"]
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# 1 seule TF
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#self._sample = self._samples[-1]
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#w_loss = torch.zeros((self._sample.shape[0],self._nb_tf), device=self._sample.device)
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@ -648,7 +654,7 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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tmp_w.scatter_(dim=1, index=sample.view(-1,1), value=1/self._N_seqTF)
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w_loss += tmp_w
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w_loss = w_loss * self._params["prob"]/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss)
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w_loss = w_loss * prob/self._distrib #Ponderation par les proba (divisee par la distrib pour pas diminuer la loss)
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w_loss = torch.sum(w_loss,dim=1)
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return w_loss
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@ -676,13 +682,15 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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return self._params[key]
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def __str__(self):
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dist_param=''
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if self._fixed_prob: dist_param+='Fx'
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mag_param='Mag'
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if self._fixed_mag: mag_param+= 'Fx'
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if self._shared_mag: mag_param+= 'Sh'
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if not self._mix_dist:
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return "Data_augV5(Uniform-%dTFx%d-%s)" % (self._nb_tf, self._N_seqTF, mag_param)
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return "Data_augV5(Uniform%s-%dTFx%d-%s)rand0.5" % (dist_param, self._nb_tf, self._N_seqTF, mag_param)
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else:
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return "Data_augV5(Mix%.1f-%dTFx%d-%s)" % (self._mix_factor, self._nb_tf, self._N_seqTF, mag_param)
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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 Augmented_model(nn.Module):
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@ -19,6 +19,10 @@ tf_names = [
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#'BTranslateY',
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#'BShearX',
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#'BShearY',
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#'BadTranslateX',
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#'BadTranslateX_neg',
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#'BadTranslateY',
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#'BadTranslateY_neg',
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast',
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@ -74,11 +78,11 @@ 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_mag=False, shared_mag=False), 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=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=2, mix_dist=0.5, fixed_mag=True, shared_mag=True), WideResNet(num_classes=10, wrn_size=160)).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=1, 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=10, loss_patience=None)
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####
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print('-'*9)
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@ -99,7 +103,7 @@ if __name__ == "__main__":
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#'''
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res_folder="res/brutus-tests/"
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epochs= 150
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inner_its = [0, 1, 10]
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inner_its = [1, 10]
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dist_mix = [0.0, 0.5, 1]
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dataug_epoch_starts= [0]
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tf_dict = {k: TF.TF_dict[k] for k in tf_names}
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@ -28,6 +28,31 @@ TF_dict={ #Dataugv4
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#'Equalize': (lambda mag: None),
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}
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'''
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'''
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TF_dict={ #Dataugv5 #AutoAugment
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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'FlipUD' : (lambda x, mag: flipUD(x)),
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'FlipLR' : (lambda x, mag: flipLR(x)),
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'Rotate': (lambda x, mag: rotate(x, angle=rand_floats(size=x.shape[0], mag=mag, maxval=30))),
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'TranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=0))),
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'TranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=20), zero_pos=1))),
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'ShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=0))),
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'ShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Brightness':(lambda x, mag: brightness(x, brightness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Sharpness':(lambda x, mag: sharpeness(x, sharpness_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Posterize': (lambda x, mag: posterize(x, bits=rand_floats(size=x.shape[0], mag=mag, minval=4., maxval=8.))),#Perte du gradient
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'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] #Pas opti pour des batch
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#Non fonctionnel
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#'Auto_Contrast': (lambda mag: None), #Pas opti pour des batch (Super lent)
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#'Equalize': (lambda mag: None),
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}
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'''
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TF_dict={ #Dataugv5
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## Geometric TF ##
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'Identity' : (lambda x, mag: x),
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@ -45,6 +70,11 @@ TF_dict={ #Dataugv5
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'BShearX': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=0))),
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'BShearY': (lambda x, mag: shear(x, shear=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, maxval=0.3*3), zero_pos=1))),
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'BadTranslateX': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=0))),
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'BadTranslateX_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=0))),
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'BadTranslateY': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=20*2, maxval=20*3), zero_pos=1))),
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'BadTranslateY_neg': (lambda x, mag: translate(x, translation=zero_stack(rand_floats(size=(x.shape[0],), mag=mag, minval=-20*3, maxval=-20*2), zero_pos=1))),
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## Color TF (Expect image in the range of [0, 1]) ##
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'Contrast': (lambda x, mag: contrast(x, contrast_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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'Color':(lambda x, mag: color(x, color_factor=rand_floats(size=x.shape[0], mag=mag, minval=0.1, maxval=1.9))),
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@ -70,15 +100,15 @@ def float_image(int_image):
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#def rand_inverse(value):
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# return value if random.random() < 0.5 else -value
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def rand_int(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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real_max = int_parameter(mag, maxval=maxval)
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if not minval : minval = -real_max
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return random.randint(minval, real_max)
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#def rand_int(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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# real_max = int_parameter(mag, maxval=maxval)
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# if not minval : minval = -real_max
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# return random.randint(minval, real_max)
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def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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real_max = float_parameter(mag, maxval=maxval)
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if not minval : minval = -real_max
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return random.uniform(minval, real_max)
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#def rand_float(mag, maxval, minval=None): #[(-maxval,minval), maxval]
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# real_max = float_parameter(mag, maxval=maxval)
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# if not minval : minval = -real_max
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# return random.uniform(minval, real_max)
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def rand_floats(size, mag, maxval, minval=None): #[(-maxval,minval), maxval]
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real_max = float_parameter(mag, maxval=maxval)
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