diff --git a/imperative/python/megengine/utils/module_stats.py b/imperative/python/megengine/utils/module_stats.py
index c7b2a37ca9a2b2fa4cce4b2952f64635e61d063d..690cda07ef9854bdb9594342b0ba834b9c55016a 100644
--- a/imperative/python/megengine/utils/module_stats.py
+++ b/imperative/python/megengine/utils/module_stats.py
@@ -5,6 +5,7 @@
 # Unless required by applicable law or agreed to in writing,
 # software distributed under the License is distributed on an
 # "AS IS" BASIS, WITHOUT ARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+import contextlib
 from functools import partial
 
 import numpy as np
@@ -87,30 +88,20 @@ def disable_receptive_field():
 
 
 @register_flops(
-    m.Conv1d, m.Conv2d, m.Conv3d,
+    m.Conv1d, m.Conv2d, m.Conv3d, m.ConvTranspose2d, m.LocalConv2d, m.DeformableConv2d
 )
 def flops_convNd(module: m.Conv2d, inputs, outputs):
     bias = 1 if module.bias is not None else 0
-    group = module.groups
-    ic = inputs[0].shape[1]
-    oc = outputs[0].shape[1]
-    goc = oc // group
-    gic = ic // group
-    N = outputs[0].shape[0]
-    HW = np.prod(outputs[0].shape[2:])
     # N x Cout x H x W x  (Cin x Kw x Kh + bias)
-    return N * HW * goc * (gic * np.prod(module.kernel_size) + bias)
-
-
-@register_flops(m.ConvTranspose2d)
-def flops_deconvNd(module: m.ConvTranspose2d, inputs, outputs):
-    return np.prod(inputs[0].shape) * outputs[0].shape[1] * np.prod(module.kernel_size)
+    return np.prod(outputs[0].shape) * (
+        module.in_channels // module.groups * np.prod(module.kernel_size) + bias
+    )
 
 
 @register_flops(m.Linear)
 def flops_linear(module: m.Linear, inputs, outputs):
-    bias = 1 if module.bias is not None else 0
-    return np.prod(outputs[0].shape) * module.in_features
+    bias = module.out_features if module.bias is not None else 0
+    return np.prod(outputs[0].shape) * module.in_features + bias
 
 
 @register_flops(m.BatchMatMulActivation)
@@ -340,6 +331,31 @@ def module_stats(
             param_stats["name"] = name + "-b"
             params.append(param_stats)
 
+    @contextlib.contextmanager
+    def adjust_stats(module, training=False):
+        """Adjust module to training/eval mode temporarily.
+
+        Args:
+            module (M.Module): used module.
+            training (bool): training mode. True for train mode, False fro eval mode.
+        """
+
+        def recursive_backup_stats(module, mode):
+            for m in module.modules():
+                # save prev status to _prev_training
+                m._prev_training = m.training
+                m.train(mode, recursive=False)
+
+        def recursive_recover_stats(module):
+            for m in module.modules():
+                # recover prev status and delete attribute
+                m.training = m._prev_training
+                delattr(m, "_prev_training")
+
+        recursive_backup_stats(module, mode=training)
+        yield module
+        recursive_recover_stats(module)
+
     # multiple inputs to the network
     if not isinstance(input_size[0], tuple):
         input_size = [input_size]
@@ -355,8 +371,9 @@ def module_stats(
             )
 
     inputs = [zeros(in_size, dtype=np.float32) for in_size in input_size]
-    model.eval()
-    model(*inputs)
+    with adjust_stats(model, training=False) as model:
+        model(*inputs)
+
     for h in hooks:
         h.remove()