diff --git a/higher/datasets.py b/higher/datasets.py
new file mode 100644
index 0000000..7d0589f
--- /dev/null
+++ b/higher/datasets.py
@@ -0,0 +1,47 @@
+import torch
+from torch.utils.data import SubsetRandomSampler
+import torchvision
+
+BATCH_SIZE = 300
+#TEST_SIZE = 300
+TEST_SIZE = 10000
+
+#ATTENTION : Dataug (Kornia) Expect image in the range of [0, 1]
+#transform_train = torchvision.transforms.Compose([
+#    torchvision.transforms.RandomHorizontalFlip(),
+#    torchvision.transforms.ToTensor(),
+    #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
+#])
+transform = torchvision.transforms.Compose([
+    torchvision.transforms.ToTensor(),
+    #torchvision.transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), #CIFAR10
+])
+'''
+data_train = torchvision.datasets.MNIST(
+    "./data", train=True, download=True, 
+    transform=torchvision.transforms.Compose([
+            #torchvision.transforms.RandomAffine(degrees=180, translate=None, scale=None, shear=None, resample=False, fillcolor=0),
+            torchvision.transforms.ToTensor()
+        ])
+)
+data_test = torchvision.datasets.MNIST(
+    "./data", train=False, download=True, transform=torchvision.transforms.ToTensor()
+)
+'''
+data_train = torchvision.datasets.CIFAR10(
+    "./data", train=True, download=True, transform=transform
+)
+#data_val = torchvision.datasets.CIFAR10(
+#    "./data", train=True, download=True, transform=transform
+#)
+data_test = torchvision.datasets.CIFAR10(
+    "./data", train=False, download=True, transform=transform
+)
+#'''
+train_subset_indices=range(int(len(data_train)/2))
+#train_subset_indices=range(BATCH_SIZE*10)
+val_subset_indices=range(int(len(data_train)/2),len(data_train))
+
+dl_train = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(train_subset_indices))
+dl_val = torch.utils.data.DataLoader(data_train, batch_size=BATCH_SIZE, shuffle=False, sampler=SubsetRandomSampler(val_subset_indices))
+dl_test = torch.utils.data.DataLoader(data_test, batch_size=TEST_SIZE, shuffle=False)
\ No newline at end of file
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
new file mode 100644
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--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,2326 @@
+{
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 0 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..ddc0284
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,1038 @@
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..50edb0f
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 1)-LeNet)-200 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,1705 @@
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\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..afb9565
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,2326 @@
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 0 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..c374dc6
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,1843 @@
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+}
\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..604eb3c
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-200 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,1268 @@
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\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..481ba2a
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,2326 @@
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 0 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..9f89533
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,1061 @@
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\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..1e56c91
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 3)-LeNet)-200 epochs (dataug:0)- 10 in_it.json	
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-100 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-100 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..ed2521b
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-100 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,2326 @@
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 0 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..0b8ac0a
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,1797 @@
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\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..2cdff49
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 4)-LeNet)-200 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,1337 @@
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diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 0 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..c6b54d9
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,1521 @@
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\ No newline at end of file
diff --git a/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 10 in_it.json b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 10 in_it.json
new file mode 100644
index 0000000..43ae8ea
--- /dev/null
+++ b/higher/res/TF_nb_tests/log/Aug_mod(Data_augV4(Uniform-14 TF x 6)-LeNet)-200 epochs (dataug:0)- 10 in_it.json	
@@ -0,0 +1,1337 @@
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diff --git a/higher/res/TF_seq_tests_compare.png b/higher/res/TF_seq_tests_compare.png
new file mode 100644
index 0000000..229885f
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new file mode 100644
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+}
\ No newline at end of file
diff --git a/higher/res/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-3 epochs (dataug:0)- 0 in_it.json b/higher/res/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-3 epochs (dataug:0)- 0 in_it.json
new file mode 100644
index 0000000..17859fd
--- /dev/null
+++ b/higher/res/log/Aug_mod(Data_augV4(Uniform-14 TF x 2)-LeNet)-3 epochs (dataug:0)- 0 in_it.json	
@@ -0,0 +1,95 @@
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+  "Identity",
+  "FlipUD",
+  "FlipLR",
+  "Rotate",
+  "TranslateX",
+  "TranslateY",
+  "ShearX",
+  "ShearY",
+  "Contrast",
+  "Color",
+  "Brightness",
+  "Sharpness",
+  "Posterize",
+  "Solarize"
+ ],
+ "Log": [
+  {
+   "epoch": 1,
+   "train_loss": 2.2571041584014893,
+   "val_loss": 2.212921142578125,
+   "acc": 20.169999999999998,
+   "time": 38.788926192000005,
+   "param": [
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774
+   ]
+  },
+  {
+   "epoch": 2,
+   "train_loss": 2.212834358215332,
+   "val_loss": 2.043567180633545,
+   "acc": 25.009999999999998,
+   "time": 38.117478509,
+   "param": [
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774
+   ]
+  },
+  {
+   "epoch": 3,
+   "train_loss": 2.091825008392334,
+   "val_loss": 1.9359350204467773,
+   "acc": 31.369999999999997,
+   "time": 39.111459771,
+   "param": [
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774,
+    0.0714285746216774
+   ]
+  }
+ ]
+}
\ No newline at end of file
diff --git a/higher/res/log/LeNet-3 epochs.json b/higher/res/log/LeNet-3 epochs.json
new file mode 100644
index 0000000..c5addf9
--- /dev/null
+++ b/higher/res/log/LeNet-3 epochs.json	
@@ -0,0 +1,34 @@
+{
+ "Accuracy": 39.2,
+ "Time": [
+  3.9452463850000012,
+  0.2891758564900622
+ ],
+ "Device": "TITAN RTX",
+ "Log": [
+  {
+   "epoch": 0,
+   "train_loss": 2.109266757965088,
+   "val_loss": 2.1106348037719727,
+   "acc": 22.3,
+   "time": 4.312489993,
+   "param": null
+  },
+  {
+   "epoch": 1,
+   "train_loss": 1.7782783508300781,
+   "val_loss": 1.8776130676269531,
+   "acc": 33.76,
+   "time": 3.605794182000002,
+   "param": null
+  },
+  {
+   "epoch": 2,
+   "train_loss": 1.8152618408203125,
+   "val_loss": 1.6963396072387695,
+   "acc": 39.2,
+   "time": 3.9174549800000023,
+   "param": null
+  }
+ ]
+}
\ No newline at end of file
diff --git a/higher/train_utils.py b/higher/train_utils.py
new file mode 100644
index 0000000..d3bfa3d
--- /dev/null
+++ b/higher/train_utils.py
@@ -0,0 +1,666 @@
+import torch
+#import torch.optim
+import torchvision
+import higher
+
+from datasets import *
+from utils import *
+
+#Variables a definir
+#dl_train = None
+#dl_val = None
+#dl_test = None
+#device = torch.device('cuda')
+
+def test(model):
+    device = next(model.parameters()).device
+    model.eval()
+    for i, (features, labels) in enumerate(dl_test):
+        features,labels = features.to(device), labels.to(device)
+
+        pred = model.forward(features)
+        return pred.argmax(dim=1).eq(labels).sum().item() / dl_test.batch_size * 100
+
+def compute_vaLoss(model, dl_val_it):
+    device = next(model.parameters()).device
+    try:
+        xs_val, ys_val = next(dl_val_it)
+    except StopIteration: #Fin epoch val
+        dl_val_it = iter(dl_val)
+        xs_val, ys_val = next(dl_val_it)
+    xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+    try:
+        model.augment(mode=False) #Validation sans transfornations !
+    except:
+        pass
+    return F.cross_entropy(model(xs_val), ys_val)
+
+def train_classic(model, epochs=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)
+
+    model.train()
+    dl_val_it = iter(dl_val)
+    log = []
+    for epoch in range(epochs):
+        #print_torch_mem("Start epoch")
+        t0 = time.process_time()
+        for i, (features, labels) in enumerate(dl_train):
+            #print_torch_mem("Start iter")
+            features,labels = features.to(device), labels.to(device)
+
+            optim.zero_grad()
+            pred = model.forward(features)
+            loss = F.cross_entropy(pred,labels)
+            loss.backward()
+            optim.step()
+
+        #### Tests ####
+        tf = time.process_time()
+        try:
+            xs_val, ys_val = next(dl_val_it)
+        except StopIteration: #Fin epoch val
+            dl_val_it = iter(dl_val)
+            xs_val, ys_val = next(dl_val_it)
+        xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+        val_loss = F.cross_entropy(model(xs_val), ys_val)
+        accuracy=test(model)
+        model.train()
+        #### Log ####
+        data={
+            "epoch": epoch,
+            "train_loss": loss.item(),
+            "val_loss": val_loss.item(),
+            "acc": accuracy,
+            "time": tf - t0,
+
+            "param": None,
+        }
+        log.append(data)
+
+    return log
+
+def train_classic_higher(model, epochs=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)
+
+    model.train()
+    dl_val_it = iter(dl_val)
+    log = []
+
+    fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
+    diffopt = higher.optim.get_diff_optim(optim, model.parameters(),fmodel=fmodel,track_higher_grads=False)
+    #with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=False) as (fmodel, diffopt):
+
+    for epoch in range(epochs):
+        #print_torch_mem("Start epoch "+str(epoch))
+        #print("Fast param ",len(fmodel._fast_params))
+        t0 = time.process_time()
+        for i, (features, labels) in enumerate(dl_train):
+            #print_torch_mem("Start iter")
+            features,labels = features.to(device), labels.to(device)
+
+            #optim.zero_grad()
+            pred = fmodel.forward(features)
+            loss = F.cross_entropy(pred,labels)
+            #.backward()
+            #optim.step()
+            diffopt.step(loss) #(opt.zero_grad, loss.backward, opt.step)
+
+        model_copy(src=fmodel, dst=model, patch_copy=False)
+        optim_copy(dopt=diffopt, opt=optim)
+        fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
+        diffopt = higher.optim.get_diff_optim(optim, model.parameters(),fmodel=fmodel,track_higher_grads=False)
+
+        #### Tests ####
+        tf = time.process_time()
+        try:
+            xs_val, ys_val = next(dl_val_it)
+        except StopIteration: #Fin epoch val
+            dl_val_it = iter(dl_val)
+            xs_val, ys_val = next(dl_val_it)
+        xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+        val_loss = F.cross_entropy(model(xs_val), ys_val)
+        accuracy=test(model)
+        model.train()
+        #### Log ####
+        data={
+            "epoch": epoch,
+            "train_loss": loss.item(),
+            "val_loss": val_loss.item(),
+            "acc": accuracy,
+            "time": tf - t0,
+
+            "param": None,
+        }
+        log.append(data)
+
+    return log
+
+def train_classic_tests(model, epochs=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)
+
+    countcopy=0
+    model.train()
+    dl_val_it = iter(dl_val)
+    log = []
+
+    fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
+    doptim = higher.optim.get_diff_optim(optim, model.parameters(), fmodel=fmodel, track_higher_grads=False)
+    for epoch in range(epochs):
+        print_torch_mem("Start epoch")
+        print(len(fmodel._fast_params))
+        t0 = time.process_time()
+        #with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=True) as (fmodel, doptim):
+
+        #fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
+        #doptim = higher.optim.get_diff_optim(optim, model.parameters(), track_higher_grads=True)
+
+        for i, (features, labels) in enumerate(dl_train):
+            features,labels = features.to(device), labels.to(device)
+
+            #with higher.innerloop_ctx(model, optim, copy_initial_weights=True, track_higher_grads=False) as (fmodel, doptim):
+
+            
+            #optim.zero_grad()
+            pred = fmodel.forward(features)
+            loss = F.cross_entropy(pred,labels)
+            doptim.step(loss) #(opt.zero_grad, loss.backward, opt.step)
+            #loss.backward()
+            #new_params = doptim.step(loss, params=fmodel.parameters())
+            #fmodel.update_params(new_params)
+
+            
+            #print('Fast param',len(fmodel._fast_params))
+            #print('opt state', type(doptim.state[0][0]['momentum_buffer']), doptim.state[0][2]['momentum_buffer'].shape)
+            
+            if False or (len(fmodel._fast_params)>1):
+                print("fmodel fast param",len(fmodel._fast_params))
+                '''
+                #val_loss = F.cross_entropy(fmodel(features), labels)
+
+                #print_graph(val_loss)
+
+                #val_loss.backward()
+                #print('bip')
+
+                tmp = fmodel.parameters()
+                
+                #print(list(tmp)[1])
+                tmp = [higher.utils._copy_tensor(t,safe_copy=True) if isinstance(t, torch.Tensor) else t for t in tmp]
+                #print(len(tmp))
+
+                #fmodel._fast_params.clear()
+                del fmodel._fast_params
+                fmodel._fast_params=None
+                
+                fmodel.fast_params=tmp # Surcharge la memoire          
+                #fmodel.update_params(tmp) #Meilleur perf / Surcharge la memoire avec trach higher grad
+
+                #optim._fmodel=fmodel
+                '''
+            
+
+                countcopy+=1
+                model_copy(src=fmodel, dst=model, patch_copy=False)
+                fmodel = higher.patch.monkeypatch(model, device=None, copy_initial_weights=True)
+                #doptim.detach_dyn()
+                #tmp = doptim.state
+                #tmp = doptim.state_dict()
+                #for k, v in tmp['state'].items():
+                #    print('dict',k, type(v))
+
+                a = optim.param_groups[0]['params'][0]
+                state = optim.state[a]
+                #state['momentum_buffer'] = None
+                #print('opt state', type(optim.state[a]), len(optim.state[a]))
+                #optim.load_state_dict(tmp)
+
+
+                for group_idx, group in enumerate(optim.param_groups):
+                   # print('gp idx',group_idx)
+                    for p_idx, p in enumerate(group['params']):
+                        optim.state[p]=doptim.state[group_idx][p_idx]
+
+                #print('opt state', type(optim.state[a]['momentum_buffer']), optim.state[a]['momentum_buffer'][0:10])
+                #print('dopt state', type(doptim.state[0][0]['momentum_buffer']), doptim.state[0][0]['momentum_buffer'][0:10])
+                '''
+                for a in tmp:
+                    #print(type(a), len(a))
+                    for nb, b in a.items():
+                        #print(nb, type(b), len(b))
+                        for n, state in b.items():
+                            #print(n, type(states))
+                            #print(state.grad_fn)
+                            state = torch.tensor(state.data).requires_grad_()
+                            #print(state.grad_fn)
+                '''
+
+
+                doptim = higher.optim.get_diff_optim(optim, model.parameters(), track_higher_grads=True)
+                #doptim.state = tmp
+
+
+        countcopy+=1
+        model_copy(src=fmodel, dst=model)
+        optim_copy(dopt=diffopt, opt=inner_opt) 
+
+        #### Tests ####
+        tf = time.process_time()
+        try:
+            xs_val, ys_val = next(dl_val_it)
+        except StopIteration: #Fin epoch val
+            dl_val_it = iter(dl_val)
+            xs_val, ys_val = next(dl_val_it)
+        xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+        val_loss = F.cross_entropy(model(xs_val), ys_val)
+        accuracy=test(model)
+        model.train()
+        #### Log ####
+        data={
+            "epoch": epoch,
+            "train_loss": loss.item(),
+            "val_loss": val_loss.item(),
+            "acc": accuracy,
+            "time": tf - t0,
+
+            "param": None,
+        }
+        log.append(data)
+
+    #countcopy+=1
+    #model_copy(src=fmodel, dst=model, patch_copy=False)
+    #optim.load_state_dict(doptim.state_dict()) #Besoin sauver etat otpim ?
+
+    print("Copy ", countcopy)
+    return log
+
+def run_simple_dataug(inner_it, epochs=1):
+    device = next(model.parameters()).device
+    dl_train_it = iter(dl_train)
+    dl_val_it = iter(dl_val)
+
+    #aug_model = nn.Sequential(
+    #    Data_aug(),
+    #    LeNet(1,10),
+    #    )
+    aug_model = Augmented_model(Data_aug(), LeNet(1,10)).to(device)
+    print(str(aug_model))
+    meta_opt = torch.optim.Adam(aug_model['data_aug'].parameters(), lr=1e-2)
+    inner_opt = torch.optim.SGD(aug_model['model'].parameters(), lr=1e-2, momentum=0.9)
+
+    log = []
+    t0 = time.process_time()
+
+    epoch = 0
+    while epoch < epochs:
+        meta_opt.zero_grad()
+        aug_model.train()
+        with higher.innerloop_ctx(aug_model, inner_opt, copy_initial_weights=True, track_higher_grads=True) as (fmodel, diffopt): #effet copy_initial_weight pas clair...
+
+            for i in range(n_inner_iter):
+                try:
+                    xs, ys = next(dl_train_it)
+                except StopIteration: #Fin epoch train
+                    tf = time.process_time()
+                    epoch +=1
+                    dl_train_it = iter(dl_train)
+                    xs, ys = next(dl_train_it)
+
+                    accuracy=test(aug_model)
+                    aug_model.train()
+
+                    #### Print ####
+                    print('-'*9)
+                    print('Epoch %d/%d'%(epoch,epochs))
+                    print('train loss',loss.item(), '/ val loss', val_loss.item())
+                    print('acc', accuracy)
+                    print('mag', aug_model['data_aug']['mag'].item())
+
+                    #### Log ####
+                    data={
+                        "epoch": epoch,
+                        "train_loss": loss.item(),
+                        "val_loss": val_loss.item(),
+                        "acc": accuracy,
+                        "time": tf - t0,
+
+                        "param": aug_model['data_aug']['mag'].item(),
+                    }
+                    log.append(data)
+                    t0 = time.process_time()
+
+                xs, ys = xs.to(device), ys.to(device)
+
+                logits = fmodel(xs)  # modified `params` can also be passed as a kwarg
+
+                loss = F.cross_entropy(logits, ys)  # no need to call loss.backwards()
+                #loss.backward(retain_graph=True)
+                #print(fmodel['model']._params['b4'].grad)
+                #print('mag', fmodel['data_aug']['mag'].grad)
+
+                diffopt.step(loss)  # note that `step` must take `loss` as an argument!
+                # The line above gets P[t+1] from P[t] and loss[t]. `step` also returns
+                # these new parameters, as an alternative to getting them from
+                # `fmodel.fast_params` or `fmodel.parameters()` after calling
+                # `diffopt.step`.
+
+                # At this point, or at any point in the iteration, you can take the
+                # gradient of `fmodel.parameters()` (or equivalently
+                # `fmodel.fast_params`) w.r.t. `fmodel.parameters(time=0)` (equivalently
+                # `fmodel.init_fast_params`). i.e. `fast_params` will always have
+                # `grad_fn` as an attribute, and be part of the gradient tape.
+            
+            # At the end of your inner loop you can obtain these e.g. ...
+            #grad_of_grads = torch.autograd.grad(
+            #    meta_loss_fn(fmodel.parameters()), fmodel.parameters(time=0))
+            try:
+                xs_val, ys_val = next(dl_val_it)
+            except StopIteration: #Fin epoch val
+                dl_val_it = iter(dl_val)
+                xs_val, ys_val = next(dl_val_it)
+            xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+            fmodel.augment(mode=False)
+            val_logits = fmodel(xs_val) #Validation sans transfornations !
+            val_loss = F.cross_entropy(val_logits, ys_val)
+            #print('val_loss',val_loss.item())
+            val_loss.backward()
+
+            #print('mag', fmodel['data_aug']['mag'], '/', fmodel['data_aug']['mag'].grad)
+
+            #model=copy.deepcopy(fmodel)
+            aug_model.load_state_dict(fmodel.state_dict()) #Do not copy gradient ! 
+            #Copie des gradients
+            for paramName, paramValue, in fmodel.named_parameters():
+              for netCopyName, netCopyValue, in aug_model.named_parameters():
+                if paramName == netCopyName:
+                  netCopyValue.grad = paramValue.grad
+
+            #print('mag', aug_model['data_aug']['mag'], '/', aug_model['data_aug']['mag'].grad)
+            meta_opt.step()
+
+    plot_res(log, fig_name="res/{}-{} epochs- {} in_it".format(str(aug_model),epochs,inner_it))
+    print('-'*9)
+    times = [x["time"] for x in log]
+    print(str(aug_model),": acc", max([x["acc"] for x in log]), "in (ms):", np.mean(times), "+/-", np.std(times))
+
+def run_dist_dataug(model, epochs=1, inner_it=1, dataug_epoch_start=0):
+    device = next(model.parameters()).device
+    dl_train_it = iter(dl_train)
+    dl_val_it = iter(dl_val)
+    
+    meta_opt = torch.optim.Adam(model['data_aug'].parameters(), lr=1e-3)
+    inner_opt = torch.optim.SGD(model['model'].parameters(), lr=1e-2, momentum=0.9)
+
+    high_grad_track = True
+    if dataug_epoch_start>0:
+        model.augment(mode=False)
+        high_grad_track = False
+
+    model.train()
+
+    log = []
+    t0 = time.process_time()
+
+    countcopy=0
+    val_loss=torch.tensor(0)
+    opt_param=None
+
+    epoch = 0
+    while epoch < epochs:
+        meta_opt.zero_grad()
+        with higher.innerloop_ctx(model, inner_opt, copy_initial_weights=True, override=opt_param, track_higher_grads=high_grad_track) as (fmodel, diffopt): #effet copy_initial_weight pas clair...
+
+            for i in range(n_inner_iter):
+                try:
+                    xs, ys = next(dl_train_it)
+                except StopIteration: #Fin epoch train
+                    tf = time.process_time()
+                    epoch +=1
+                    dl_train_it = iter(dl_train)
+                    xs, ys = next(dl_train_it)
+
+                    #viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
+                    #viz_sample_data(imgs=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
+
+                    accuracy=test(model)
+                    model.train()
+
+                    #### Print ####
+                    print('-'*9)
+                    print('Epoch : %d/%d'%(epoch,epochs))
+                    print('Train loss :',loss.item(), '/ val loss', val_loss.item())
+                    print('Accuracy :', accuracy)
+                    print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start))
+                    print('TF Proba :', model['data_aug']['prob'].data)
+                    #print('proba grad',aug_model['data_aug']['prob'].grad)
+                    #############
+                    #### Log ####
+                    data={
+                        "epoch": epoch,
+                        "train_loss": loss.item(),
+                        "val_loss": val_loss.item(),
+                        "acc": accuracy,
+                        "time": tf - t0,
+
+                        "param": [p for p in model['data_aug']['prob']],
+                    }
+                    log.append(data)
+                    #############
+
+                    if epoch == dataug_epoch_start:
+                        print('Starting Data Augmention...')
+                        model.augment(mode=True)
+                        high_grad_track = True
+
+                    t0 = time.process_time()
+
+                xs, ys = xs.to(device), ys.to(device)
+
+                '''
+                #Methode exacte
+                final_loss = 0
+                for tf_idx in range(fmodel['data_aug']._nb_tf):
+                    fmodel['data_aug'].transf_idx=tf_idx
+                    logits = fmodel(xs)
+                    loss = F.cross_entropy(logits, ys)
+                    #loss.backward(retain_graph=True)
+                    #print('idx', tf_idx)
+                    #print(fmodel['data_aug']['prob'][tf_idx], fmodel['data_aug']['prob'][tf_idx].grad)
+                    final_loss += loss*fmodel['data_aug']['prob'][tf_idx] #Take it in the forward function ?
+                
+                loss = final_loss 
+                '''
+                #Methode uniforme
+                logits = fmodel(xs)  # modified `params` can also be passed as a kwarg
+                loss = F.cross_entropy(logits, ys, reduction='none')  # no need to call loss.backwards()
+                if fmodel._data_augmentation: #Weight loss
+                    w_loss = fmodel['data_aug'].loss_weight().to(device)
+                    loss = loss * w_loss
+                loss = loss.mean()
+                #'''
+
+                #to visualize computational graph
+                #print_graph(loss)
+
+                #loss.backward(retain_graph=True)
+                #print(fmodel['model']._params['b4'].grad)
+                #print('prob grad', fmodel['data_aug']['prob'].grad)
+
+                diffopt.step(loss) #(opt.zero_grad, loss.backward, opt.step)
+
+            try:
+                xs_val, ys_val = next(dl_val_it)
+            except StopIteration: #Fin epoch val
+                dl_val_it = iter(dl_val)
+                xs_val, ys_val = next(dl_val_it)
+            xs_val, ys_val = xs_val.to(device), ys_val.to(device)
+
+            fmodel.augment(mode=False) #Validation sans transfornations !
+            val_loss = F.cross_entropy(fmodel(xs_val), ys_val)
+
+            #print_graph(val_loss)
+
+            val_loss.backward()
+            
+            countcopy+=1
+            model_copy(src=fmodel, dst=model)
+            optim_copy(dopt=diffopt, opt=inner_opt)
+            
+            meta_opt.step()
+            model['data_aug'].adjust_prob() #Contrainte sum(proba)=1
+
+    print("Copy ", countcopy)
+    return log
+
+def run_dist_dataugV2(model, epochs=1, inner_it=0, dataug_epoch_start=0, print_freq=1, loss_patience=None):
+    device = next(model.parameters()).device
+    log = []
+    countcopy=0
+    val_loss=torch.tensor(0) #Necessaire si pas de metastep sur une epoch
+    dl_val_it = iter(dl_val)
+
+    meta_opt = torch.optim.Adam(model['data_aug'].parameters(), lr=1e-2)
+    inner_opt = torch.optim.SGD(model['model'].parameters(), lr=1e-2, momentum=0.9)
+
+    high_grad_track = True
+    if inner_it == 0:
+        high_grad_track=False
+    if dataug_epoch_start!=0:
+        model.augment(mode=False)
+        high_grad_track = False
+
+    val_loss_monitor= None
+    if loss_patience != None :
+        if dataug_epoch_start==-1: val_loss_monitor = loss_monitor(patience=loss_patience, end_train=2) #1st limit = dataug start
+        else: val_loss_monitor = loss_monitor(patience=loss_patience) #Val loss monitor
+
+    model.train()
+    
+    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)
+
+    for epoch in range(1, epochs+1):
+        #print_torch_mem("Start epoch "+str(epoch))
+        #print(high_grad_track, fmodel._data_augmentation, len(fmodel._fast_params))
+        t0 = time.process_time()
+        #with higher.innerloop_ctx(model, inner_opt, copy_initial_weights=True, override=opt_param, track_higher_grads=high_grad_track) as (fmodel, diffopt):
+
+        for i, (xs, ys) in enumerate(dl_train):
+            xs, ys = xs.to(device), ys.to(device)
+            '''
+            #Methode exacte
+            final_loss = 0
+            for tf_idx in range(fmodel['data_aug']._nb_tf):
+                fmodel['data_aug'].transf_idx=tf_idx
+                logits = fmodel(xs)
+                loss = F.cross_entropy(logits, ys)
+                #loss.backward(retain_graph=True)
+                #print('idx', tf_idx)
+                #print(fmodel['data_aug']['prob'][tf_idx], fmodel['data_aug']['prob'][tf_idx].grad)
+                final_loss += loss*fmodel['data_aug']['prob'][tf_idx] #Take it in the forward function ?
+            
+            loss = final_loss 
+            '''
+            #Methode uniforme
+            
+            logits = fmodel(xs)  # modified `params` can also be passed as a kwarg
+            loss = F.cross_entropy(logits, ys, reduction='none')  # no need to call loss.backwards()
+            #PAS PONDERE LOSS POUR DIST MIX
+            if fmodel._data_augmentation: # and not fmodel['data_aug']._mix_dist: #Weight loss
+                w_loss = fmodel['data_aug'].loss_weight().to(device)
+                loss = loss * w_loss
+            loss = loss.mean()
+            #'''
+
+            #to visualize computational graph
+            #print_graph(loss)
+
+            #loss.backward(retain_graph=True)
+            #print(fmodel['model']._params['b4'].grad)
+            #print('prob grad', fmodel['data_aug']['prob'].grad)
+
+            diffopt.step(loss) #(opt.zero_grad, loss.backward, opt.step)
+
+            if(high_grad_track and i%inner_it==0): #Perform Meta step
+                #print("meta")
+                #Peu utile si high_grad_track = False
+                val_loss = compute_vaLoss(model=fmodel, dl_val_it=dl_val_it)
+
+                #print_graph(val_loss)
+
+                val_loss.backward()
+            
+                countcopy+=1
+                model_copy(src=fmodel, dst=model)
+                optim_copy(dopt=diffopt, opt=inner_opt)
+
+                meta_opt.step()
+                model['data_aug'].adjust_prob(soft=False) #Contrainte sum(proba)=1
+
+                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)
+
+        tf = time.process_time()
+
+        #viz_sample_data(imgs=xs, labels=ys, fig_name='samples/data_sample_epoch{}_noTF'.format(epoch))
+        #viz_sample_data(imgs=aug_model['data_aug'](xs), labels=ys, fig_name='samples/data_sample_epoch{}'.format(epoch))
+        
+        if(not high_grad_track): 
+            countcopy+=1
+            model_copy(src=fmodel, dst=model)
+            optim_copy(dopt=diffopt, opt=inner_opt)
+            val_loss = compute_vaLoss(model=fmodel, dl_val_it=dl_val_it)
+
+            #Necessaire pour reset higher (Accumule les fast_param meme avec track_higher_grads = False)
+            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)
+
+        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 ms'%(tf - t0))
+            print('Train loss :',loss.item(), '/ val loss', val_loss.item())
+            print('Accuracy :', accuracy)
+            print('Data Augmention : {} (Epoch {})'.format(model._data_augmentation, dataug_epoch_start))
+            print('TF Proba :', model['data_aug']['prob'].data)
+            #print('proba grad',aug_model['data_aug']['prob'].grad)
+        #############
+        #### Log ####
+        data={
+            "epoch": epoch,
+            "train_loss": loss.item(),
+            "val_loss": val_loss.item(),
+            "acc": accuracy,
+            "time": tf - t0,
+
+            "param": [p.item() for p in model['data_aug']['prob']],
+        }
+        log.append(data)
+        #############
+        if val_loss_monitor : 
+            val_loss_monitor.register(val_loss.item())
+            if val_loss_monitor.end_training(): break #Stop training
+
+
+        if not model.is_augmenting() and (epoch == dataug_epoch_start or (val_loss_monitor and val_loss_monitor.limit_reached()==1)):
+            print('Starting Data Augmention...')
+            dataug_epoch_start = epoch
+            model.augment(mode=True)
+            if inner_it != 0: high_grad_track = True
+
+    #print("Copy ", countcopy)
+    return log
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