Test WRN Brutus

higher/datasets.py

@@ -65,21 +65,26 @@ class AugmentedDataset(VisionDataset):
 
 
         self._TF = [
-            'Invert',
+            ## Geometric TF ##
-            'Cutout',
+            'Rotate',
-            'Sharpness',
-            'AutoContrast',
-            'Posterize',
-            'ShearX', 
             'TranslateX',
             'TranslateY',
+            'ShearX',
             'ShearY',
-            'Rotate',
+
-            'Equalize',
+            'Cutout',
+
+            ## Color TF ##
             'Contrast',
             'Color',
-            'Solarize',
+            'Brightness',
-            'Brightness'
+            'Sharpness',
+            #'Posterize',
+            #'Solarize',
+
+            'Invert',
+            'AutoContrast',
+            'Equalize',
         ]
         self._op_list =[]
         self.prob=0.5
@@ -119,6 +124,7 @@ class AugmentedDataset(VisionDataset):
 
         for idx, image in enumerate(self.sup_data):
             if (idx/self.dataset_info['sup'])%0.2==0: print("Augmenting data... ", idx,"/", self.dataset_info['sup'])
+            #if idx==10000:break
 
             for _ in range(aug_copy):
                 chosen_policy = policies[np.random.choice(len(policies))]

higher/model.py

@@ -94,6 +94,8 @@ class NetworkBlock(nn.Module):
     def forward(self, x):
         return self.layer(x)
 
+
+#wrn_size: 32 = WRN-28-2 ? 160 = WRN-28-10
 class WideResNet(nn.Module):
     #def __init__(self, depth, num_classes, widen_factor=1, dropRate=0.0):
     def __init__(self, num_classes, wrn_size, depth=28, dropRate=0.0):

higher/test_brutus.py

@@ -33,6 +33,49 @@ else:
 ##########################################
 if __name__ == "__main__":
 
+
+    n_inner_iter = 1
+    epochs = 200
+    dataug_epoch_start=0
+
+    tf_dict = {k: TF.TF_dict[k] for k in tf_names}
+
+    t0 = time.process_time()
+
+    aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
+    #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
+    print(str(aug_model), 'on', device_name)
+    #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
+    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=None, loss_patience=None)
+
+    exec_time=time.process_time() - t0
+    ####
+    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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
+    filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
+    with open("res/log/%s.json" % filename, "w+") as f:
+        json.dump(out, f, indent=True)
+        print('Log :\"',f.name, '\" saved !')
+
+    ####
+    t0 = time.process_time()
+
+    #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
+    aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
+    print(str(aug_model), 'on', device_name)
+    #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
+    log= run_dist_dataugV2(model=aug_model, epochs=epochs, inner_it=n_inner_iter, dataug_epoch_start=dataug_epoch_start, print_freq=None, loss_patience=None)
+
+    exec_time=time.process_time() - t0
+    ####
+    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), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": log}
+    filename = "{}-{} epochs (dataug:{})- {} in_it".format(str(aug_model),epochs,dataug_epoch_start,n_inner_iter)
+    with open("res/log/%s.json" % filename, "w+") as f:
+        json.dump(out, f, indent=True)
+        print('Log :\"',f.name, '\" saved !')
+
+    '''
     res_folder="res/brutus-tests/"
     epochs= 150
     inner_its = [1]
@@ -81,3 +124,4 @@ if __name__ == "__main__":
 
                             #plot_resV2(log, fig_name=res_folder+filename, param_names=tf_names)
                             print('-'*9)
+    '''

higher/test_dataug.py

@@ -109,12 +109,12 @@ if __name__ == "__main__":
         t0 = time.process_time()
 
         data_train_aug = AugmentedDataset("./data", train=True, download=download_data, transform=transform, subset=(0,int(len(data_train)/2)))
-        data_train_aug.augement_data(aug_copy=10)
+        data_train_aug.augement_data(aug_copy=30)
         print(data_train_aug)
         dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
 
-        #xs, ys = next(iter(dl_train))
+        xs, ys = next(iter(dl_train))
-        #viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
+        viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
 
         model = LeNet(3,10).to(device)
         #model = WideResNet(num_classes=10, wrn_size=16).to(device)
@@ -149,9 +149,9 @@ if __name__ == "__main__":
         tf_dict = {k: TF.TF_dict[k] for k in tf_names}
 
         #aug_model = Augmented_model(Data_augV6(TF_dict=tf_dict, N_TF=1, mix_dist=0.0, fixed_prob=False, prob_set_size=2, fixed_mag=True, shared_mag=True), LeNet(3,10)).to(device)
-        aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
+        #aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), LeNet(3,10)).to(device)
-        #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)
+        aug_model = Augmented_model(Data_augV5(TF_dict=tf_dict, N_TF=3, mix_dist=0.0, fixed_prob=False, fixed_mag=False, shared_mag=False), WideResNet(num_classes=10, wrn_size=32)).to(device)
-        #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), LeNet(3,10)).to(device)
+        #aug_model = Augmented_model(RandAug(TF_dict=tf_dict, N_TF=2), WideResNet(num_classes=10, wrn_size=32)).to(device)
         print(str(aug_model), 'on', device_name)
         #run_simple_dataug(inner_it=n_inner_iter, epochs=epochs)
         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)

higher/train_utils.py

@@ -625,7 +625,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
                 model_copy(src=fmodel, dst=model)
                 optim_copy(dopt=diffopt, opt=inner_opt)
 
-                if epoch>50:
+                #if epoch>50:
                 meta_opt.step()
                 model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
                 #model['data_aug'].next_TF_set()