diff --git a/experiments/util/measurements.py b/experiments/util/measurements.py
index 62747c2..991f0c3 100644
--- a/experiments/util/measurements.py
+++ b/experiments/util/measurements.py
@@ -29,6 +29,7 @@
 from torchvision.models.convnext import LayerNorm2d
 from transformers import Conv1D
 
+from memsave_torch.nn.BatchNorm import MemSaveBatchNorm2d
 from memsave_torch.nn.Conv2d import MemSaveConv2d
 from memsave_torch.nn.Linear import MemSaveLinear
 
@@ -317,6 +318,7 @@ def separate_grad_arguments(
         LayerNorm,
         LayerNorm2d,
         MemSaveBatchNorm2d,
+    )
     embed = Embedding
 
     leafs, no_leafs = [], []
diff --git a/memsave_torch/nn/Dropout.py b/memsave_torch/nn/Dropout.py
index 5d682e5..fd0317d 100644
--- a/memsave_torch/nn/Dropout.py
+++ b/memsave_torch/nn/Dropout.py
@@ -3,7 +3,6 @@
 This is done by not saving the whole input/output `float32` tensor and instead just saving the `bool` mask (8bit).
 """
 
-import torch
 import torch.nn as nn
 
 from memsave_torch.nn.functional import dropoutMemSave
diff --git a/memsave_torch/nn/__init__.py b/memsave_torch/nn/__init__.py
index 5619d8d..e1d65ef 100644
--- a/memsave_torch/nn/__init__.py
+++ b/memsave_torch/nn/__init__.py
@@ -34,7 +34,8 @@ def convert_to_memory_saving(
     model: nn.Module,
     linear=True,
     conv2d=True,
-    conv1d=False,
+    conv1d=True,
+    conv3d=True,
     batchnorm2d=True,
     relu=True,
     maxpool2d=True,
@@ -53,6 +54,7 @@ def convert_to_memory_saving(
         linear (bool, optional): Whether to replace `nn.Linear` layers
         conv2d (bool, optional): Whether to replace `nn.Conv2d` layers
         conv1d (bool, optional): Whether to replace `nn.Conv1d` layers
+        conv3d (bool, optional): Whether to replace `nn.Conv3d` layers
         batchnorm2d (bool, optional): Whether to replace `nn.BatchNorm2d` layers
         relu (bool, optional): Whether to replace `nn.ReLU` layers
         maxpool2d (bool, optional): Whether to replace `nn.MaxPool2d` layers
@@ -78,15 +80,20 @@ def convert_to_memory_saving(
             "cls": nn.MaxPool2d,
             "convert_fn": MemSaveMaxPool2d.from_nn_MaxPool2d,
         },
+        {
+            "allowed": conv1d,
+            "cls": nn.Conv1d,
+            "convert_fn": MemSaveConv1d.from_nn_Conv1d,
+        },
         {
             "allowed": conv2d,
             "cls": nn.Conv2d,
             "convert_fn": MemSaveConv2d.from_nn_Conv2d,
         },
         {
-            "allowed": conv1d,
-            "cls": nn.Conv1d,
-            "convert_fn": MemSaveConv1d.from_nn_Conv1d,
+            "allowed": conv3d,
+            "cls": nn.Conv3d,
+            "convert_fn": MemSaveConv3d.from_nn_Conv3d,
         },
         {
             "allowed": conv1d,