[Relay to onnx conversion fixes][Pool, Pad] (apache#8435)

* [Relay to Onnx conversion][Pool]

* added missing ceil_mode in average pool and max pool conversion

* [Relay to Onnx conversion][Pad]

* Fixed issue in Pad conversion: changed pad_value to input instead of attrs
* Refer to PR: apache#7860
* Updated unit test for Pad
* Fixed some formatting errors

python/tvm/contrib/target/onnx.py

@@ -147,6 +147,7 @@ def convert_attributes(cls, attrs):
             "pads": attrs.get_int_tuple("padding"),
             "strides": attrs.get_int_tuple("strides"),
             "kernel_shape": attrs.get_int_tuple("pool_size"),
+            "ceil_mode": 1 if attrs.ceil_mode else 0,
         }
 
 
@@ -330,7 +331,10 @@ def convert_attributes(cls, attrs):
             after.append(axis_pads[1])
         pads = before + after
         pads = numpy.asarray(pads, dtype=pads[0].dtype)
-        return {"pads": pads, "mode": attrs.get_str("pad_mode"), "constant_value": attrs.pad_value}
+        return {
+            "pads": pads,
+            "mode": attrs.get_str("pad_mode"),
+        }
 
     @classmethod
     def convert(cls, node_entry, model_container, node_dict):
@@ -341,16 +345,17 @@ def convert(cls, node_entry, model_container, node_dict):
         attrs = cls.convert_attributes(node_entry["relay_node"].attrs)
 
         name = node_entry["name"]
-        data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)
-        value = numpy.dtype(node_entry["types"][0].dtype).type(attrs["constant_value"])
+        pad_data = numpy.asarray(attrs["pads"], dtype=attrs["pads"][0].dtype).astype(numpy.int64)
 
         input_names = [
             node_entry["input_names"][0],
-            add_input(data, name, "pads", model_container),
-            add_input(value, name, "value", model_container),
+            add_input(pad_data, name, "pads", model_container),
+            node_entry["input_names"][1],
         ]
 
-        node = onnx.helper.make_node(cls.__name__, input_names, node_entry["output_names"])
+        node = onnx.helper.make_node(
+            cls.__name__, input_names, node_entry["output_names"], mode=attrs["mode"]
+        )
         model_container.add_nodes([node])
 
 

tests/python/contrib/test_onnx.py

@@ -27,6 +27,7 @@
 import tvm
 from tvm import relay
 from tvm.contrib.target.onnx import to_onnx
+from tvm.relay.testing import run_infer_type
 
 
 def func_to_onnx(func, name):
@@ -270,14 +271,16 @@ def verify_batch_norm(axis=1):
 
 
 def test_pad():
+    """Pad unit test."""
+
     def verify_pad():
-        for dtype in ["float16", "float32"]:
-            dshape = (4, 10, 7, 7)
-            x = relay.var("x", shape=dshape, dtype=dtype)
-            y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
-            func = relay.Function([x], y)
-            x_data = np.random.uniform(size=dshape).astype(dtype)
-            verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
+        dshape = (4, 10, 7, 7)
+        x = relay.var("x", shape=dshape, dtype="int32")
+        y = relay.nn.pad(x, ((1, 1), (2, 2), (3, 3), (4, 4)))
+        func = relay.Function([x], y)
+        func = run_infer_type(func)
+        x_data = np.random.randint(low=-255, high=255, size=dshape).astype(np.int32)
+        verify_results(func, [x_data], "test_pad", rtol=1e-5, atol=1e-5)
 
     verify_pad()