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https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-03 11:40:46 +02:00
+controle mag reg + Tests fonction mag reg
This commit is contained in:
Harle, Antoine (Contracteur)
parent
fc0fb25148
commit
534e244307
6 changed files with 45 additions and 22 deletions
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@ -29,11 +29,11 @@ optim_param={
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res_folder="../res/benchmark/CIFAR10/"
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#res_folder="../res/HPsearch/"
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epochs= 400
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epochs= 200
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dataug_epoch_start=0
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nb_run= 1
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tf_config='../config/base_tf_config.json'
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tf_config='../config/wide_tf_config.json' #'../config/wide_tf_config.json'#'../config/base_tf_config.json'
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TF_loader=TF_loader()
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
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@ -55,15 +55,16 @@ if __name__ == "__main__":
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### Benchmark ###
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#'''
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n_inner_iter = 1#[0, 1]
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inner_its = [3]
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dist_mix = [0.5]
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N_seq_TF= [3, 4]
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N_seq_TF= [3]
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mag_setup = [(False, False)] #[(True, True), (False, False)] #(FxSh, Independant)
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for model_type in model_list.keys():
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for model_name in model_list[model_type]:
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for run in range(nb_run):
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for n_inner_iter in inner_its:
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for n_tf in N_seq_TF:
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for dist in dist_mix:
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for m_setup in mag_setup:
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@ -115,8 +115,17 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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if self._shared_mag :
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self._reg_tgt = torch.tensor(TF.PARAMETER_MAX, dtype=torch.float) #Encourage amplitude max
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else:
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self._reg_mask=[self._TF.index(t) for t in self._TF if t not in self._TF_ignore_mag]
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TF_mag=[t for t in self._TF if t not in self._TF_ignore_mag] #TF w/ differentiable mag
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self._reg_mask=[self._TF.index(t) for t in TF_mag]
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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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#Prevent Identity
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#print(TF.TF_identity)
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#self._reg_tgt=torch.full(size=(len(self._reg_mask),), fill_value=0.0)
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#for val in TF.TF_identity.keys():
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# idx=[self._reg_mask.index(self._TF.index(t)) for t in TF_mag if t in TF.TF_identity[val]]
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# self._reg_tgt[idx]=val
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#print(TF_mag, self._reg_tgt)
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def forward(self, x):
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""" Main method of the Data augmentation module.
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@ -247,7 +256,8 @@ class Data_augV5(nn.Module): #Optimisation jointe (mag, proba)
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else:
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#return reg_factor * F.l1_loss(self._params['mag'][self._reg_mask], target=self._reg_tgt, reduction='mean')
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mags = self._params['mag'] if self._params['mag'].shape==torch.Size([]) else self._params['mag'][self._reg_mask]
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max_mag_reg = reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean')
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max_mag_reg = reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean') #Close to target ?
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#max_mag_reg = - reg_factor * F.mse_loss(mags, target=self._reg_tgt.to(mags.device), reduction='mean') #Far from target ?
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return max_mag_reg
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def train(self, mode=True):
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@ -10,14 +10,15 @@ if __name__ == "__main__":
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#"res/brutus-tests/log/Aug_mod(Data_augV5(Uniform-14TFx3-MagFxSh)-LeNet)-150epochs(dataug:0)-10in_it-2.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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files = ["../res/benchmark/CIFAR10/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,'wide_resnet50_2', str(run)) for run in range(3)]
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files = ["../res/benchmark/CIFAR100/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-Mag)-%s)-200 epochs (dataug:0)- 1 in_it-%s.json"%(0.5,3,'wide_resnet50_2', str(run)) for run in range(3)]
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#files = ["../res/benchmark/CIFAR10/log/RandAugment(N%d-M%.2f)-%s-200 epochs -%s.json"%(3,0.17,'wide_resnet50_2', str(run)) for run in range(3)]
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#files = ["../res/benchmark/CIFAR10/log/Aug_mod(RandAug(14TFx%d-Mag%d)-%s)-200 epochs (dataug:0)- 0 in_it-%s.json"%(2,1,'resnet18', str(run)) for run in range(1)]
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files = ["../res/benchmark/CIFAR10/log/Aug_mod(Data_augV5(Mix%.1f-14TFx%d-Mag)-%s)-200 epochs (dataug:0)- 3 in_it-%s.json"%(0.5,3,'resnet18', str(run)) for run in range(1)]
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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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plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""))#, param_names=data['Param_names'])
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plot_resV2(data['Log'], fig_name=file.replace("/log","").replace(".json",""), param_names=data['Param_names'], f1=True)
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#plot_TF_influence(data['Log'], param_names=data['Param_names'])
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#'''
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## Loss , Acc, Proba = f(epoch) ##
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@ -34,13 +34,15 @@ if __name__ == "__main__":
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}
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#Parameters
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n_inner_iter = 1
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epochs = 2
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epochs = 200
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dataug_epoch_start=0
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Nb_TF_seq=3
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optim_param={
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'Meta':{
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'optim':'Adam',
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'lr':1e-2, #1e-2
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'epoch_start': 2, #0 / 2 (Resnet?)
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'reg_factor': 0.001,
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},
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'Inner':{
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'optim': 'SGD',
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@ -110,7 +112,7 @@ if __name__ == "__main__":
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#### Augmented Model ####
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if 'aug_model' in tasks:
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tf_config='../config/base_tf_config.json'
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tf_config='../config/invScale_wide_tf_config.json'#'../config/base_tf_config.json'
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tf_dict, tf_ignore_mag =TF_loader.load_TF_dict(tf_config)
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torch.cuda.reset_max_memory_allocated() #reset_peak_stats
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@ -118,15 +120,17 @@ if __name__ == "__main__":
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t0 = time.perf_counter()
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model = Higher_model(model, model_name) #run_dist_dataugV3
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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N_TF=1,
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mix_dist=0.5,
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fixed_prob=False,
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fixed_mag=False,
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shared_mag=False,
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TF_ignore_mag=tf_ignore_mag), model).to(device)
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#aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), model).to(device)
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if n_inner_iter !=0:
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aug_model = Augmented_model(
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Data_augV5(TF_dict=tf_dict,
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N_TF=Nb_TF_seq,
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mix_dist=0.5,
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fixed_prob=False,
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fixed_mag=False,
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shared_mag=False,
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TF_ignore_mag=tf_ignore_mag), model).to(device)
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else:
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aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=Nb_TF_seq), model).to(device)
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print("{} on {} for {} epochs - {} inner_it{}".format(str(aug_model), device_name, epochs, n_inner_iter, postfix))
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log= run_dist_dataugV3(model=aug_model,
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@ -134,7 +138,7 @@ if __name__ == "__main__":
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inner_it=n_inner_iter,
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dataug_epoch_start=dataug_epoch_start,
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opt_param=optim_param,
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print_freq=20,
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print_freq=10,
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unsup_loss=1,
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hp_opt=False,
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save_sample_freq=None)
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@ -329,7 +329,7 @@ def run_dist_dataugV3(model, opt_param, epochs=1, inner_it=1, dataug_epoch_start
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if(high_grad_track and i>0 and i%inner_it==0 and epoch>=opt_param['Meta']['epoch_start']): #Perform Meta step
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#print("meta")
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val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss()
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val_loss = compute_vaLoss(model=model, dl_it=dl_val_it, dl=dl_val) + model['data_aug'].reg_loss(opt_param['Meta']['reg_factor'])
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#print_graph(val_loss) #to visualize computational graph
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val_loss.backward()
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@ -31,6 +31,13 @@ PARAMETER_MAX = 1
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# What is the min 'level' a transform could be predicted
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PARAMETER_MIN = 0.1
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#Dict containing the value for wich TF are closer to identity
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#TF_identity={
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# PARAMETER_MAX:{'Solarize', 'Posterize'},
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# PARAMETER_MAX/2:{'Contrast','Color','Brightness','Sharpness'},
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# PARAMETER_MIN:{'Rotate','TranslateX','TranslateY','ShearX','ShearY'},
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#}
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class TF_loader(object):
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""" Transformations builder.
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