add Compare ops

docs/inference_model_convertor/op_list.md

@@ -76,7 +76,7 @@
 | 81 | Add | 82 | Concat | 83 | Max | 84 | Min |
 | 85 | GreaterOrEqual | 86 | GatherND | 87 | And | 88 | cos |
 | 89 | Neg | 90 | SpaceToDepth | 91 | GatherElement | 92 | Sin |
-| 93 | CumSum | 94 | Or | 95 | Xor |  |  |
+| 93 | CumSum | 94 | Or | 95 | Xor | 96 | Mod |
 
 ## PyTorch
 

tests/onnx/test_auto_scan_compare_ops.py

+# Copyright (c) 2022  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from auto_scan_test import OPConvertAutoScanTest
+from hypothesis import reproduce_failure
+from onnxbase import randtool
+import hypothesis.strategies as st
+import numpy as np
+import unittest
+
+min_opset_version_map = {
+    "Greater": 7,
+    "Less": 7,
+    "GreaterOrEqual": 12,
+    "LessOrEqual": 12,
+}
+
+
+class TestCompareopsConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: Compare ops
+    OPset version: 7~15
+    """
+
+    def sample_convert_config(self, draw):
+        input1_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=10, max_value=20), min_size=2, max_size=4))
+
+        if draw(st.booleans()):
+            input2_shape = [input1_shape[-1]]
+        else:
+            input2_shape = input1_shape
+
+        if draw(st.booleans()):
+            input2_shape = [1]
+
+        input_dtype = draw(st.sampled_from(["float32", "float64"]))
+
+        config = {
+            "op_names": ["Greater", "Less", "GreaterOrEqual", "LessOrEqual"],
+            "test_data_shapes": [input1_shape, input2_shape],
+            "test_data_types": [[input_dtype], [input_dtype]],
+            "inputs_shape": [],
+            "min_opset_version": 7,
+            "inputs_name": ["x", "y"],
+            "outputs_name": ["z"],
+            "delta": 1e-4,
+            "rtol": 1e-4,
+            "run_dynamic": True,
+        }
+        min_opset_versions = list()
+        for op_name in config["op_names"]:
+            min_opset_versions.append(min_opset_version_map[op_name])
+        config["min_opset_version"] = min_opset_versions
+
+        attrs = {}
+
+        return (config, attrs)
+
+    def test(self):
+        self.run_and_statis(max_examples=30)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/onnx/test_auto_scan_equal.py

+# Copyright (c) 2022  PaddlePaddle Authors. All Rights Reserved.
+#
+# Licensed under the Apache License, Version 2.0 (the "License"
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+from auto_scan_test import OPConvertAutoScanTest
+from hypothesis import reproduce_failure
+from onnxbase import randtool
+import hypothesis.strategies as st
+import numpy as np
+import unittest
+
+
+class TestEqualConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: Equal
+    OPset version: 7~15
+    """
+
+    def sample_convert_config(self, draw):
+        input1_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=10, max_value=20), min_size=2, max_size=4))
+
+        if draw(st.booleans()):
+            input2_shape = [input1_shape[-1]]
+        else:
+            input2_shape = input1_shape
+
+        if draw(st.booleans()):
+            input2_shape = [1]
+
+        input_dtype = draw(st.sampled_from(["int32", "int64", "bool"]))
+
+        config = {
+            "op_names": ["Equal"],
+            "test_data_shapes": [input1_shape, input2_shape],
+            "test_data_types": [[input_dtype], [input_dtype]],
+            "inputs_shape": [],
+            "min_opset_version": 7,
+            "inputs_name": ["x", "y"],
+            "outputs_name": ["z"],
+            "delta": 1e-4,
+            "rtol": 1e-4,
+            "run_dynamic": True,
+        }
+
+        attrs = {}
+
+        return (config, attrs)
+
+    def test(self):
+        self.run_and_statis(max_examples=30)
+
+
+if __name__ == "__main__":
+    unittest.main()