Add logical and 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 |  |  |  |  |  |  |
+| 93 | CumSum | 94 | Or | 95 | Xor | 96 | Mod |
 
 ## PyTorch
 

tests/onnx/auto_scan_test.py

@@ -48,7 +48,7 @@
     derandomize=True,
     report_multiple_bugs=False)
 if float(os.getenv('TEST_NUM_PERCENT_CASES', default='1.0')) < 1 or \
-    os.getenv('HYPOTHESIS_TEST_PROFILE', 'dev') == 'ci':
+        os.getenv('HYPOTHESIS_TEST_PROFILE', 'dev') == 'ci':
     settings.load_profile("ci")
 else:
     settings.load_profile("dev")
@@ -155,6 +155,7 @@ def run_test(self, configs):
         # 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
@@ -189,12 +190,14 @@ def run_test(self, configs):
             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[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

@@ -60,7 +60,7 @@ def compare(result, expect, delta=1e-10, rtol=1e-10):
             result.shape, expect.shape)
         assert result.dtype == expect.dtype, "result.dtype: {} != expect.dtype: {}".format(
             result.dtype, expect.dtype)
-    elif isinstance(result, (list, tuple)) and len(result) > 1:
+    elif isinstance(result, (list, tuple)):
         for i in range(len(result)):
             if isinstance(result[i], (np.generic, np.ndarray)):
                 compare(result[i], expect[i], delta, rtol)
@@ -69,6 +69,8 @@ def compare(result, expect, delta=1e-10, rtol=1e-10):
     # deal with scalar tensor
     elif len(expect) == 1:
         compare(result, expect[0], delta, rtol)
+    else:
+        raise Exception("Compare diff wrong!!!!!!")
 
 
 def randtool(dtype, low, high, shape):
@@ -101,7 +103,8 @@ def __init__(self,
                  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 +127,7 @@ def __init__(self,
         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):
         """
@@ -182,17 +186,29 @@ 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_compare_ops.py

@@ -0,0 +1,78 @@
+# 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

@@ -0,0 +1,67 @@
+# 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()

tests/onnx/test_auto_scan_logical_ops.py

@@ -0,0 +1,77 @@
+# 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,
+    "Or": 7,
+    "Xor": 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", "Or", "Xor"],
+            "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()

x2paddle/op_mapper/onnx2paddle/opset7.py

@@ -33,6 +33,26 @@ class OpSet7(OpSet):
     def __init__(self, decoder, paddle_graph):
         super(OpSet7, self).__init__(decoder, paddle_graph)
 
+    @print_mapping_info
+    def Or(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        self.paddle_graph.add_layer(
+            "paddle.logical_or",
+            inputs={"x": val_x.name,
+                    "y": val_y.name},
+            outputs=[node.name])
+
+    @print_mapping_info
+    def Xor(self, node):
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        val_y = self.graph.get_input_node(node, idx=1, copy=True)
+        self.paddle_graph.add_layer(
+            "paddle.logical_xor",
+            inputs={"x": val_x.name,
+                    "y": val_y.name},
+            outputs=[node.name])
+
     @print_mapping_info
     def Unsqueeze(self, node):
         val_x = self.graph.get_input_node(node, idx=0, copy=True)