Merge remote-tracking branch 'upstream/develop' into support_autoscan_tests

tests/onnx/test_auto_scan_elementwise_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 = {
+    "Add": 7,
+    "Sub": 7,
+    "Div": 7,
+    "Mul": 7,
+    "Pow": 7,
+    "Max": 9,
+    "Min": 9,
+}
+
+
+class TestElementwiseopsConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: elementwise 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
+
+        def generator_data():
+            input_data = randtool("float", -5.0, 5.0, input2_shape)
+            input_data[abs(input_data) < 1.0] = 1.0
+            return input_data
+
+        input_dtype = draw(st.sampled_from(["float32", "float64"]))
+
+        config = {
+            "op_names": ["Add", "Sub", "Div", "Mul", "Pow", "Max", "Min"],
+            "test_data_shapes": [input1_shape, generator_data],
+            "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
+        }
+        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_mod.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 TestModConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: Mod
+    OPset version: 10~15
+    """
+
+    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
+
+        def generator_data():
+            input_data = randtool("float", -5.0, 5.0, input2_shape)
+            input_data[abs(input_data) < 1.0] = 1.0
+            return input_data
+
+        input_dtype = draw(
+            st.sampled_from(["int32", "int64", "float32", "float64"]))
+
+        config = {
+            "op_names": ["Mod"],
+            "test_data_shapes": [input1_shape, generator_data],
+            "test_data_types": [[input_dtype], [input_dtype]],
+            "inputs_shape": [],
+            "min_opset_version": 10,
+            "inputs_name": ["x", "y"],
+            "outputs_name": ["z"],
+            "delta": 1e-4,
+            "rtol": 1e-4
+        }
+
+        attrs = {"fmod": 0 if "int" in input_dtype else 1, }
+
+        return (config, attrs)
+
+    def test(self):
+        self.run_and_statis(max_examples=30)
+
+
+if __name__ == "__main__":
+    unittest.main()

x2paddle/op_mapper/onnx2paddle/opset10.py

@@ -32,3 +32,5 @@ def print_mapping_info(func):
 class OpSet10(OpSet9):
     def __init__(self, decoder, paddle_graph):
         super(OpSet10, self).__init__(decoder, paddle_graph)
+        # Support Mod op Since opset version >= 10
+        self.elementwise_ops.update({"Mod": "paddle.mod"})

x2paddle/utils.py

@@ -83,6 +83,7 @@ class ConverterCheck(threading.Thread):
 
         params.update({"extra": json.dumps(extra)})
         try:
+            logging.basicConfig(level=logging.INFO)
             requests.get(stats_api, params, timeout=2)
         except Exception:
             pass