add logical ops

tests/onnx/auto_scan_test.py

@@ -155,6 +155,7 @@ class OPConvertAutoScanTest(unittest.TestCase):
         # max_opset_version is a fixed value
         max_opset_version = 15
         enable_onnx_checker = True
+        run_dynamic = False
 
         self.num_ran_tests += 1
         # add ignore testcases
@@ -165,6 +166,9 @@ class OPConvertAutoScanTest(unittest.TestCase):
         if not isinstance(op_names, (tuple, list)):
             op_names = [op_names]
 
+        if not isinstance(min_opset_version, (tuple, list)):
+            min_opset_version = [min_opset_version]
+
         input_type_list = None
         if len(test_data_types) > 1:
             input_type_list = list(product(*test_data_types))
@@ -186,12 +190,14 @@ class OPConvertAutoScanTest(unittest.TestCase):
             max_opset_version = config["max_opset_version"]
         if "enable_onnx_checker" in config.keys():
             enable_onnx_checker = config["enable_onnx_checker"]
+        if "run_dynamic" in config.keys():
+            run_dynamic = config["run_dynamic"]
 
         for i in range(len(op_names)):
-            obj = ONNXConverter(op_names[i], min_opset_version,
+            obj = ONNXConverter(op_names[i], min_opset_version[i],
                                 max_opset_version, op_names[i], inputs_name,
                                 outputs_name, inputs_shape, delta, rtol, attrs,
-                                enable_onnx_checker)
+                                enable_onnx_checker, run_dynamic)
             for input_type in input_type_list:
                 input_data = list()
                 for j, shape in enumerate(test_data_shapes):

tests/onnx/onnxbase.py

@@ -101,7 +101,8 @@ class ONNXConverter(object):
                  delta=1e-5,
                  rtol=1e-5,
                  attrs=[],
-                 enable_onnx_checker=True):
+                 enable_onnx_checker=True,
+                 run_dynamic=False):
         self.op_type = op_type
         assert isinstance(self.op_type,
                           str), "The dtype of op_type must be string!"
@@ -124,6 +125,7 @@ class ONNXConverter(object):
         self.inputs_shape = inputs_shape
         self.attrs = attrs
         self.enable_onnx_checker = enable_onnx_checker
+        self.run_dynamic = run_dynamic
 
     def set_input_data(self, group_name, *args):
         """
@@ -175,23 +177,36 @@ class ONNXConverter(object):
             onnx_path,
             paddle_path,
             convert_to_lite=False,
-            enable_onnx_checker=self.enable_onnx_checker)
+            enable_onnx_checker=self.enable_onnx_checker,
+            disable_feedback=True)
 
     def _mk_paddle_res(self, ver):
         """
         make paddle res
         """
-        paddle_path = os.path.join(
-            self.pwd, self.name,
-            self.name + '_' + str(ver) + '_paddle/inference_model/model')
-        paddle.disable_static()
-        # run
-        model = paddle.jit.load(paddle_path)
-        paddle_feed = list()
-
+        # input data
+        paddle_numpy_feed = list()
+        paddle_tensor_feed = list()
         for i in range(len(self.input_feed)):
-            paddle_feed.append(self.input_feed[self.inputs_name[i]])
-        result = model(*paddle_feed)
+            paddle_numpy_feed.append(self.input_feed[self.inputs_name[i]])
+            paddle_tensor_feed.append(paddle.to_tensor(self.input_feed[self.inputs_name[i]]))
+
+        if self.run_dynamic:
+            paddle_path = os.path.join(
+                self.pwd, self.name,
+                self.name + '_' + str(ver) + '_paddle/')
+            import sys
+            sys.path.append(paddle_path)
+            from x2paddle_code import main
+            result = main(*paddle_tensor_feed)
+        else:
+            paddle_path = os.path.join(
+                self.pwd, self.name,
+                self.name + '_' + str(ver) + '_paddle/inference_model/model')
+            paddle.disable_static()
+            # run
+            model = paddle.jit.load(paddle_path)
+            result = model(*paddle_numpy_feed)
         # get paddle outputs
         if isinstance(result, (tuple, list)):
             result = tuple(out.numpy() for out in result)

tests/onnx/test_auto_scan_logical_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 = {
+    "And": 7,
+}
+
+
+class TestLogicalopsConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: Logical 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(["bool"]))
+
+        config = {
+            "op_names": ["And"],
+            "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()