[torch] Use try_infer_value for clamp min/max

python/tvm/relay/frontend/pytorch.py

@@ -1688,8 +1688,20 @@ def pad(inputs, input_types):
 
     def clamp(self, inputs, input_types):
         data = inputs[0]
-        amin = inputs[1] if inputs[1] else np.finfo(np.float32).min
-        amax = inputs[2] if inputs[2] else np.finfo(np.float32).max
+
+        def get_v(v, default_v):
+            if isinstance(v, _expr.Constant):
+                return float(v.data.asnumpy())
+            if isinstance(v, _expr.Expr):
+                infer_v, success = try_infer_value(v, lambda ret: float(ret))
+                if success:
+                    return infer_v
+            if v is not None:
+                return v
+            return default_v
+
+        amin = get_v(inputs[1], np.finfo(np.float32).min)
+        amax = get_v(inputs[2], np.finfo(np.float32).max)
         return _op.clip(data, amin, amax)
 
     def to(self, inputs, input_types):

tests/python/frontend/pytorch/test_forward.py

@@ -2622,10 +2622,17 @@ class Clamp3(Module):
         def forward(self, *args):
             return torch.clamp(args[0], max=1.0)
 
+    class Clamp_MinExpr_MaxConstant(Module):
+        def forward(self, *args):
+            h, w = args[0].shape[2:]
+            amin = h / 100.0
+            return torch.clamp(args[0], min=amin, max=w)
+
     input_data = torch.rand(input_shape).float()
     verify_model(Clamp1().float().eval(), input_data=input_data)
     verify_model(Clamp2().float().eval(), input_data=input_data)
     verify_model(Clamp3().float().eval(), input_data=input_data)
+    verify_model(Clamp_MinExpr_MaxConstant().float().eval(), input_data=input_data)
 
 
 @tvm.testing.uses_gpu