From b26fbcd2a2a7f99f791493197bd9f7963ccc3cb8 Mon Sep 17 00:00:00 2001
From: "Harle, Antoine (Contracteur)" <Antoine.Harle@Teledyne.com>
Date: Wed, 4 Dec 2019 14:48:11 -0500
Subject: [PATCH] Changement permission fichiers + Simplification utilisation
 Augmented_dataset

---
 .gitignore                                    |     0
 FAR-HO/blue_utils.py                          |     0
 FAR-HO/far_pba_cifar.py                       |     0
 FAR-HO/test.py                                |     0
 FAR-HO/test_cnn.py                            |     0
 FAR-HO/test_cnn_aug.py                        |     0
 FAR-HO/test_fc.py                             |     0
 .../.gitignore                                |     0
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 .../README.md                                 |     0
 .../data_aug.py                               |     0
 .../dataset_aug.py                            |     0
 .../dataset_aug_v2.py                         |     0
 .../graph/graph                               |     0
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 .../hyperopt.py                               |     0
 .../hyperopt_v2.py                            |     0
 .../main.py                                   |     0
 .../requirements.txt                          |     0
 .../tests.py                                  |     0
 PBA/LeNet.py                                  |     0
 UDA/LeNet.py                                  |     0
 UDA/main.py                                   |     0
 UDA/run_cifar10_gpu.sh                        |     0
 higher/augmentation_transforms.py             |     0
 higher/compare_res.py                         |     0
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diff --git a/.gitignore b/.gitignore
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diff --git a/FAR-HO/blue_utils.py b/FAR-HO/blue_utils.py
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diff --git a/FAR-HO/far_pba_cifar.py b/FAR-HO/far_pba_cifar.py
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diff --git a/PBA/LeNet.py b/PBA/LeNet.py
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diff --git a/UDA/LeNet.py b/UDA/LeNet.py
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diff --git a/UDA/main.py b/UDA/main.py
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diff --git a/UDA/run_cifar10_gpu.sh b/UDA/run_cifar10_gpu.sh
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diff --git a/higher/augmentation_transforms.py b/higher/augmentation_transforms.py
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diff --git a/higher/compare_res.py b/higher/compare_res.py
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diff --git a/higher/datasets.py b/higher/datasets.py
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index a04588e..6f06b4c
--- a/higher/datasets.py
+++ b/higher/datasets.py
@@ -125,10 +125,11 @@ class AugmentedDataset(VisionDataset):
                 aug_image = augmentation_transforms.apply_policy(chosen_policy, image, use_mean_std=False) #Cast en float image
                 #aug_image = augmentation_transforms.cutout_numpy(aug_image)
 
-                self.unsup_data+=[aug_image]
+                self.unsup_data+=[(aug_image*255.).astype(self.sup_data.dtype)]#Cast float image to uint8
                 self.unsup_targets+=[self.sup_targets[idx]]
 
-        self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
+        #self.unsup_data=(np.array(self.unsup_data)*255.).astype(self.sup_data.dtype) #Cast float image to uint8
+        self.unsup_data=np.array(self.unsup_data)
         self.data= np.concatenate((self.sup_data, self.unsup_data), axis=0)
         self.targets= np.concatenate((self.sup_targets, self.unsup_targets), axis=0)
 
@@ -159,15 +160,13 @@ train_subset_indices=range(int(len(data_train)/2))
 val_subset_indices=range(int(len(data_train)/2),len(data_train))
 #train_subset_indices=range(BATCH_SIZE*10)
 #val_subset_indices=range(BATCH_SIZE*10, BATCH_SIZE*20)
-
 dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
 
 ### Augmented Dataset ###
-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=1)
-print(data_train_aug)
-
-dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
+#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)
+#print(data_train_aug)
+#dl_train = torch.utils.data.DataLoader(data_train_aug, batch_size=BATCH_SIZE, shuffle=True)
 
 
 dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))
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index e301d48..0000000
--- a/higher/res/log/Aug_mod(Data_augV5(Uniform-14TFx3-Mag)-LeNet)-200 epochs (dataug:0)- 1 in_it.json	
+++ /dev/null
@@ -1,13026 +0,0 @@
-{
- "Accuracy": 75.45,
- "Time": [
-  118.15130038612988,
-  1.3870058325416306
- ],
- "Device": "GeForce GTX 1080 Ti",
- "Param_names": [
-  "Identity",
-  "FlipUD",
-  "FlipLR",
-  "Rotate",
-  "TranslateX",
-  "TranslateY",
-  "ShearX",
-  "ShearY",
-  "Contrast",
-  "Color",
-  "Brightness",
-  "Sharpness",
-  "Posterize",
-  "Solarize"
- ],
- "Log": [
-  {
-   "epoch": 1,
-   "train_loss": 2.4439187049865723,
-   "val_loss": 2.2820303440093994,
-   "acc": 10.41,
-   "time": 114.74723388800001,
-   "param": [
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-  },
-  {
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-   "train_loss": 2.24479603767395,
-   "val_loss": 2.1439127922058105,
-   "acc": 22.97,
-   "time": 118.101620136,
-   "param": [
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-     "m": 1.0
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-    {
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-     "m": 1.0
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-     "m": 0.9942888021469116
-    },
-    {
-     "p": 0.0876631885766983,
-     "m": 1.0
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-15TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-15TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
old mode 100644
new mode 100755
diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-15TFx1-MagFxSh)-LeNet)-100 epochs (dataug:30)- 0 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-15TFx1-MagFxSh)-LeNet)-100 epochs (dataug:30)- 0 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-18TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-18TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-20TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-20TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-3TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-3TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-4TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-4TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV5(Uniform-9TFx1-MagFxSh)rand0.5-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV5(Uniform-9TFx1-MagFxSh)rand0.5-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-14TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-14TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 1 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 1 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 1 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 1 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x3-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TF(3)x3-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TFx1-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(Data_augV6(Uniform-18TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/log/Aug_mod(Data_augV6(Uniform-18TFx2-MagFxSh)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
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diff --git a/higher/res/log/Aug_mod(RandAug(14TFx1-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(RandAug(14TFx1-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json
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diff --git a/higher/res/log/Aug_mod(RandAug(14TFx2-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(RandAug(14TFx2-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json
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diff --git a/higher/res/log/Aug_mod(RandAug(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(RandAug(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json
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diff --git a/higher/res/log/Aug_mod(RandAugUDA(14TFx2-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(RandAugUDA(14TFx2-Mag1)-LeNet)-150 epochs (dataug:0)- 0 in_it.json
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diff --git a/higher/res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(RandAugUDA(18TFx2-Mag1)-LeNet)-100 epochs (dataug:0)- 0 in_it.json
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diff --git a/higher/res/log/LeNet-100 epochs.json b/higher/res/log/LeNet-100 epochs.json
old mode 100644
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diff --git a/higher/res/log/WideResNet (s16-d28)-10 epochs.json b/higher/res/log/WideResNet (s16-d28)-10 epochs.json
old mode 100644
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diff --git a/higher/test_brutus.py b/higher/test_brutus.py
old mode 100644
new mode 100755
diff --git a/higher/test_dataug.py b/higher/test_dataug.py
old mode 100644
new mode 100755
index aee7999..0af56a4
--- a/higher/test_dataug.py
+++ b/higher/test_dataug.py
@@ -66,11 +66,11 @@ if __name__ == "__main__":
 
     tasks={
         #'classic',
-        'aug_dataset',
-        #'aug_model'
+        #'aug_dataset',
+        'aug_model'
     }
     n_inner_iter = 1
-    epochs = 100
+    epochs = 200
     dataug_epoch_start=0
 
 
@@ -83,13 +83,14 @@ if __name__ == "__main__":
         #model.augment(mode=False)
 
         print(str(model), 'on', device_name)
-        log= train_classic(model=model, epochs=epochs)
+        log= train_classic(model=model, epochs=epochs, print_freq=10)
         #log= train_classic_higher(model=model, epochs=epochs)
 
+        exec_time=time.process_time() - t0
         ####
         print('-'*9)
         times = [x["time"] for x in log]
-        out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Log": log}
+        out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Log": log}
         print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
         filename = "{}-{} epochs".format(str(model),epochs)
         with open("res/log/%s.json" % filename, "w+") as f:
@@ -98,29 +99,37 @@ if __name__ == "__main__":
 
         plot_res(log, fig_name="res/"+filename)
 
-        print('Execution Time : %.00f '%(time.process_time() - t0))
+        print('Execution Time : %.00f '%(exec_time))
         print('-'*9)
     
 
     #### Augmented Dataset ####
     if 'aug_dataset' in tasks:
 
-        xs, ys = next(iter(dl_train))
-        viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_{}'.format(str(data_train_aug)))
         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)
+        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))
+        #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)
         #model = Augmented_model(Data_augV3(mix_dist=0.0), LeNet(3,10)).to(device)
         #model.augment(mode=False)
 
         print(str(model), 'on', device_name)
-        log= train_classic(model=model, epochs=epochs)
+        log= train_classic(model=model, epochs=epochs, print_freq=10)
         #log= train_classic_higher(model=model, epochs=epochs)
 
+        exec_time=time.process_time() - t0
         ####
         print('-'*9)
         times = [x["time"] for x in log]
-        out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Log": log}
+        out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times), exec_time), "Device": device_name, "Log": log}
         print(str(model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
         filename = "{}-{}-{} epochs".format(str(data_train_aug),str(model),epochs)
         with open("res/log/%s.json" % filename, "w+") as f:
@@ -129,7 +138,7 @@ if __name__ == "__main__":
 
         plot_res(log, fig_name="res/"+filename)
 
-        print('Execution Time : %.00f '%(time.process_time() - t0))
+        print('Execution Time : %.00f '%(exec_time))
         print('-'*9)
     
 
@@ -147,10 +156,11 @@ if __name__ == "__main__":
         #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)
 
+        exec_time=time.process_time() - t0
         ####
         print('-'*9)
         times = [x["time"] for x in log]
-        out = {"Accuracy": max([x["acc"] for x in log]), "Time": (np.mean(times),np.std(times)), "Device": device_name, "Param_names": aug_model.TF_names(), "Log": 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}
         print(str(aug_model),": acc", out["Accuracy"], "in:", out["Time"][0], "+/-", out["Time"][1])
         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:
@@ -159,5 +169,5 @@ if __name__ == "__main__":
 
         plot_resV2(log, fig_name="res/"+filename, param_names=tf_names)
 
-        print('Execution Time : %.00f '%(time.process_time() - t0))
+        print('Execution Time : %.00f '%(exec_time))
         print('-'*9)
\ No newline at end of file
diff --git a/higher/test_lr.py b/higher/test_lr.py
old mode 100644
new mode 100755
diff --git a/higher/train_utils.py b/higher/train_utils.py
old mode 100644
new mode 100755
index d3451bf..fc8e5e9
--- a/higher/train_utils.py
+++ b/higher/train_utils.py
@@ -47,7 +47,7 @@ def compute_vaLoss(model, dl_it, dl):
 
     return F.cross_entropy(model(xs), ys)
 
-def train_classic(model, epochs=1):
+def train_classic(model, epochs=1, print_freq=1):
     device = next(model.parameters()).device
     #opt = torch.optim.Adam(model.parameters(), lr=1e-3)
     optim = torch.optim.SGD(model.parameters(), lr=1e-2, momentum=0.9)
@@ -80,6 +80,15 @@ def train_classic(model, epochs=1):
         val_loss = F.cross_entropy(model(xs_val), ys_val)
         accuracy, _ =test(model)
         model.train()
+
+        #### Print ####
+        if(print_freq and epoch%print_freq==0):
+            print('-'*9)
+            print('Epoch : %d/%d'%(epoch,epochs))
+            print('Time : %.00f'%(tf - t0))
+            print('Train loss :',loss.item(), '/ val loss', val_loss.item())
+            print('Accuracy :', accuracy)
+
         #### Log ####
         data={
             "epoch": epoch,
@@ -619,7 +628,7 @@ def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_f
                 if epoch>50:
                     meta_opt.step()
                     model['data_aug'].adjust_param(soft=False) #Contrainte sum(proba)=1
-                    #model['data_aug'].next_TF_set()
+                #model['data_aug'].next_TF_set()
 
                 fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
                 diffopt = higher.optim.get_diff_optim(inner_opt, model.parameters(),fmodel=fmodel, track_higher_grads=high_grad_track)
diff --git a/higher/transformations.py b/higher/transformations.py
old mode 100644
new mode 100755
diff --git a/higher/utils.py b/higher/utils.py
old mode 100644
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