diff --git a/python/tvm/relay/frontend/pytorch.py b/python/tvm/relay/frontend/pytorch.py
index 61478219908c..d534166481e5 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -972,19 +972,21 @@ def convolution(self, inputs, input_types):
             msg = "Data type %s could not be parsed in conv op" % (type(weight))
             raise AssertionError(msg)
 
-        # Transposed convolutions have IOHW layout.
-        if use_transpose:
-            weight_shape[0], weight_shape[1] = weight_shape[1], weight_shape[0]
-
-        channels = weight_shape[0]
         groups = int(inputs[8])
 
+        if use_transpose:
+            channels = weight_shape[1] * groups
+            in_channels = weight_shape[0]
+        else:
+            channels = weight_shape[0]
+            in_channels = weight_shape[1]
+
         # Check if this is depth wise convolution
         # We need to reshape weight so that Relay could recognize this is depth wise
         # weight_shape[1] is always in_channels // groups
         # For depthwise, in_channels == groups, so weight_shape[1] == 1
         # If groups > 1 but weight_shape[1] != 1, this is group convolution
-        if groups > 1 and weight_shape[1] == 1:
+        if groups > 1 and in_channels == 1:
             channel_multiplier = channels // groups
             new_weight_shape = (groups, channel_multiplier) + tuple(weight_shape[2:])
             weight = _op.transform.reshape(weight, new_weight_shape)
diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 97ef08f7b8a9..c240a19c9730 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -1067,6 +1067,32 @@ def test_forward_conv_transpose(
     verify_model(conv1d_transpose, conv1d_input_data)
 
 
+def test_forward_conv2d_transpose_group():
+    # https://github.com/apache/tvm/issues/10223
+
+    class ModulatedConvTranspose2D(torch.nn.Module):
+        def forward(self, x, w, s):
+            B, C, H, W = x.shape
+            I, O, KH, KW = w.shape
+
+            # weight is different for each input in batch (this is why we want grouped conv transpose)
+            w = w.unsqueeze(0) * s.reshape(B, 1, 1, 1, 1)
+            w = w.reshape(B * I, O, KH, KW)
+            x = x.reshape(1, B * C, H, W)
+            x = torch.nn.functional.conv_transpose2d(
+                x, w, stride=(2, 2), padding=(1, 1), output_padding=(1, 1), groups=B
+            )
+            return x.reshape(B, O, H * 2, W * 2)
+
+    b, c, h, w, k = 4, 512, 8, 16, 3
+    inputs = torch.rand(b, c, h, w)
+    weights = torch.rand(c, c // 2, k, k)
+    styles = torch.rand(b)
+
+    # cuda not supported for group > 1 conv2d_transpose
+    verify_trace_model(ModulatedConvTranspose2D().eval(), [inputs, weights, styles], ["llvm"])
+
+
 def test_forward_deform_conv():
     torch.set_grad_enabled(False)
 
@@ -4115,7 +4141,7 @@ def forward(self, x):
 
     x = torch.rand([4, 4, 16, 32]).float()
     script_module = torch.jit.trace(List_tuple(), x, strict=False).eval()
-    mod, params = relay.frontend.from_pytorch(script_module, [("x", x.shape)])
+    relay.frontend.from_pytorch(script_module, [("x", x.shape)])
 
 
 if __name__ == "__main__":