diff --git a/tests/python/frontend/pytorch/test_forward.py b/tests/python/frontend/pytorch/test_forward.py
index 588ef2524502f..e74832a78a727 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -702,28 +702,34 @@ def forward(self, x):
 
 
 def test_adaptive_pool3d():
-    inp = torch.rand((1, 32, 16, 16, 16))
-    verify_model(torch.nn.AdaptiveMaxPool3d((1, 1, 1)).eval(), inp)
-    verify_model(torch.nn.AdaptiveMaxPool3d((2, 2, 2)).eval(), inp)
-    verify_model(torch.nn.AdaptiveAvgPool3d((1, 1, 1)).eval(), inp)
-    verify_model(torch.nn.AdaptiveAvgPool3d((2, 2, 2)).eval(), inp)
-    verify_model(torch.nn.AdaptiveAvgPool3d((4, 8, 8)).eval(), inp)
-    verify_model(torch.nn.AdaptiveMaxPool3d((7, 8, 9)).eval(), inp)
+    for ishape in [(1, 32, 16, 16, 16),
+                   (1, 32, 9, 15, 15),
+                   (1, 32, 13, 7, 7)]:
+        inp = torch.rand(ishape)
+        verify_model(torch.nn.AdaptiveMaxPool3d((1, 1, 1)).eval(), inp)
+        verify_model(torch.nn.AdaptiveMaxPool3d((2, 2, 2)).eval(), inp)
+        verify_model(torch.nn.AdaptiveAvgPool3d((1, 1, 1)).eval(), inp)
+        verify_model(torch.nn.AdaptiveAvgPool3d((2, 2, 2)).eval(), inp)
+        verify_model(torch.nn.AdaptiveAvgPool3d((4, 8, 8)).eval(), inp)
+        verify_model(torch.nn.AdaptiveMaxPool3d((7, 8, 9)).eval(), inp)
 
 
 def test_conv3d():
-    inp = torch.rand((1, 32, 16, 16, 16))
-    verify_model(torch.nn.Conv3d(32, 16, (3, 3, 3),
-                                 padding=(1, 1, 1)).eval(),
-                 inp),
-    verify_model(torch.nn.Conv3d(32, 16, (5, 5, 5),
-                                 padding=(2, 2, 2)).eval(),
-                 inp),
-    verify_model(torch.nn.Conv3d(32, 16, kernel_size=1).eval(),
-                 inp)
-    # downsample
-    verify_model(torch.nn.Conv3d(32, 16, kernel_size=1, stride=2).eval(),
-                 inp)
+    for ishape in [(1, 32, 16, 16, 16),
+                   (1, 32, 9, 15, 15),
+                   (1, 32, 13, 7, 7)]:
+        inp = torch.rand(ishape)
+        verify_model(torch.nn.Conv3d(32, 16, (3, 3, 3),
+                                     padding=(1, 1, 1)).eval(),
+                     inp),
+        verify_model(torch.nn.Conv3d(32, 16, (5, 5, 5),
+                                     padding=(2, 2, 2)).eval(),
+                     inp),
+        verify_model(torch.nn.Conv3d(32, 16, kernel_size=1).eval(),
+                     inp)
+        # downsample
+        verify_model(torch.nn.Conv3d(32, 16, kernel_size=1, stride=2).eval(),
+                     inp)
 
 
 # Model tests