[TFLite] Add support for quantized mirror pad (#16243)

Removes the restriction on converting quantized mirror pad in the front-end and adds tests to support this change. Change-Id: Id513709b38e6bfdcff2bb35554b6799ef5c5cd52
apache · Dec 15, 2023 · b3eec91 · b3eec91
1 parent 37c38ac
commit b3eec91
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Showing 2 changed files with 23 additions and 7 deletions.
diff --git a/python/tvm/relay/frontend/tflite.py b/python/tvm/relay/frontend/tflite.py
@@ -2677,12 +2677,6 @@ def convert_mirror_pad(self, op):
         except ImportError:
             raise ImportError("The tflite package must be installed")
 
-        # the quantized form MirrorPad is not yet implemented in TFLite.
-        if self.is_quantized(op):
-            raise tvm.error.OpNotImplemented(
-                "TFlite quantized MIRROR_PAD operator is not supported yet."
-            )
-
         input_tensors = self.get_input_tensors(op)
         assert len(input_tensors) == 2, "input tensors length should be 2"
 

diff --git a/tests/python/frontend/tflite/test_forward.py b/tests/python/frontend/tflite/test_forward.py
@@ -3436,7 +3436,13 @@ def _test_pad(data, mode="CONSTANT", quantized=False):
                 inq_data[0], ops.convert_to_tensor(data[1], dtype=data[1].dtype), mode=mode
             )
             compare_tflite_with_tvm(
-                [data[0]], ["inq_0:0"], inq_data, [out], quantized=True, input_range=input_range
+                [data[0]],
+                ["inq_0:0"],
+                inq_data,
+                [out],
+                quantized=True,
+                input_range=input_range,
+                experimental_new_converter=True,
             )
         else:
             out = array_ops.pad(
@@ -3506,6 +3512,22 @@ def test_forward_pad():
         ],
         quantized=True,
     )
+    _test_pad(
+        [
+            np.arange(0, 256, dtype=np.uint8).reshape((1, 256)),
+            np.array([[1, 1], [2, 2]], dtype=np.int32),
+        ],
+        quantized=True,
+        mode="SYMMETRIC",
+    )
+    _test_pad(
+        [
+            np.arange(0, 256, dtype=np.uint8).reshape((1, 256)),
+            np.array([[0, 0], [2, 2]], dtype=np.int32),
+        ],
+        quantized=True,
+        mode="REFLECT",
+    )
 
 
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