diff --git a/memsave_torch/nn/BatchNorm.py b/memsave_torch/nn/BatchNorm.py
index a8de50c..79a1507 100644
--- a/memsave_torch/nn/BatchNorm.py
+++ b/memsave_torch/nn/BatchNorm.py
@@ -79,4 +79,5 @@ def from_nn_BatchNorm2d(cls, bn2d: nn.BatchNorm2d):
         obj.bias = bn2d.bias
         obj.running_mean = bn2d.running_mean
         obj.running_var = bn2d.running_var
+        obj.training = bn2d.training
         return obj
diff --git a/memsave_torch/nn/functional/BatchNorm.py b/memsave_torch/nn/functional/BatchNorm.py
index 33efcf0..934b6ef 100644
--- a/memsave_torch/nn/functional/BatchNorm.py
+++ b/memsave_torch/nn/functional/BatchNorm.py
@@ -38,8 +38,12 @@ def forward(
         need_grad = []  # save_mean and save_invstd
         if ctx.needs_input_grad[0]:
             need_grad.append(weight)
-        if any(ctx.needs_input_grad):
-            need_grad.append(x)
+        if ctx.training:
+            if any(ctx.needs_input_grad):
+                need_grad.append(x)
+        else:
+            if ctx.needs_input_grad[3]:
+                need_grad.append(x)
         # bias doesnt need anything for calc
 
         ctx.save_for_backward(*need_grad)
@@ -54,7 +58,8 @@ def backward(ctx, grad_output):
         if ctx.needs_input_grad[0]:
             weight = ctx.saved_tensors[current_idx]
             current_idx += 1
-            x = ctx.saved_tensors[current_idx]
+            if ctx.training:
+                x = ctx.saved_tensors[current_idx]
         if ctx.needs_input_grad[3]:
             x = ctx.saved_tensors[current_idx]