Add Reduce_ops and HardSigmoid (#867)

* Update onnx_decoder.py

* Update opset.py

* Update opset.py

* Update opset.py

* Update onnx_decoder.py

* fix gemm and resize

* add_hardsigmoid

* add_reduce_op

* fix

* fix

* add reduceL2

* fix

* remove onnx

* remove onnx

* remove onnx

* remove onnx

* refix

* fix

* test

* retest

tests/onnx/test_auto_scan_conv2d.py

@@ -15,9 +15,6 @@
 from auto_scan_test import OPConvertAutoScanTest
 from hypothesis import reproduce_failure
 import hypothesis.strategies as st
-import onnx
-from onnx import helper
-from onnx import TensorProto
 import numpy as np
 import unittest
 

tests/onnx/test_auto_scan_hardsigmoid.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
+import hypothesis.strategies as st
+import numpy as np
+import unittest
+import random
+
+
+class TestHardSigmoidCovert(OPConvertAutoScanTest):
+    """
+    ONNX op: HardSigmoid
+    OPset version: 7~15
+    """
+
+    def sample_convert_config(self, draw):
+        input_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=2, max_value=6), min_size=2, max_size=5))
+        input_dtype = draw(st.sampled_from(["float32"]))
+
+        alpha = random.random()
+
+        beta = random.random()
+
+        config = {
+            "op_names": ["HardSigmoid"],
+            "test_data_shapes": [input_shape],
+            "test_data_types": [[input_dtype]],
+            "inputs_shape": [input_shape],
+            "min_opset_version": 7,
+            "inputs_name": ["x"],
+            "outputs_name": ["y"],
+            "delta": 1e-4,
+            "rtol": 1e-4
+        }
+
+        attrs = {
+            "alpha": alpha,
+            "beta": beta,
+        }
+
+        return (config, attrs)
+
+    def test(self):
+        self.run_and_statis(max_examples=30)
+
+
+if __name__ == "__main__":
+    unittest.main()

tests/onnx/test_auto_scan_reduce_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
+import hypothesis.strategies as st
+import numpy as np
+import unittest
+import random
+
+
+class TestReduceOpsConvert(OPConvertAutoScanTest):
+    """
+    ONNX op: Reduce Ops
+    OPset version: 7~15
+    """
+
+    def sample_convert_config(self, draw):
+        input_shape = draw(
+            st.lists(
+                st.integers(
+                    min_value=20, max_value=30), min_size=3, max_size=5))
+
+        input_dtype = draw(st.sampled_from(["float32", "int32", "int64"]))
+
+        axes = draw(
+            st.lists(
+                st.integers(
+                    min_value=-len(input_shape), max_value=len(input_shape) -
+                    1),
+                min_size=1,
+                max_size=1))
+
+        keep_dim = draw(st.integers(min_value=0, max_value=1))
+        config = {
+            "op_names": ["ReduceL1", "ReduceL2"],
+            "test_data_shapes": [input_shape],
+            "test_data_types": [input_dtype],
+            "inputs_shape": [input_shape],
+            "min_opset_version": 7,
+            "max_opset_version": 15,
+            "inputs_name": ["x"],
+            "outputs_name": ["y"],
+            "delta": 1e-4,
+            "rtol": 1e-4,
+        }
+
+        attrs = {
+            "axes": axes,
+            "keepdims": keep_dim,
+        }
+
+        return (config, attrs)
+
+    def test(self):
+        self.run_and_statis(max_examples=50)
+
+
+if __name__ == "__main__":
+    unittest.main()

x2paddle/op_mapper/onnx2paddle/opset7.py

@@ -132,3 +132,69 @@ class OpSet7(OpSet):
                 inputs={"x": val_x.name},
                 axis=axes,
                 outputs=[node.name])
+
+    @print_mapping_info
+    def ReduceL1(self, node):
+        output_name = node.name
+        layer_outputs = [output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axes = node.get_attr('axes')
+        keepdims = False if node.get_attr('keepdims') == 0 else True
+        layer_attrs = {'p': 1, 'axis': axes, 'keepdim': keepdims}
+        if val_x.dtype != 'float32' and val_x.dtype != 'float64':
+            indices_cast = val_x.name + '_cast'
+            mid_norm = val_x.name + '_norm'
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": val_x.name},
+                outputs=[indices_cast],
+                dtype=string('float32'))
+            self.paddle_graph.add_layer(
+                "paddle.norm",
+                inputs={"x": indices_cast},
+                outputs=[mid_norm],
+                **layer_attrs)
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": mid_norm},
+                outputs=[node.name],
+                dtype=string(val_x.dtype))
+        else:
+            self.paddle_graph.add_layer(
+                "paddle.norm",
+                inputs={"x": val_x.name},
+                outputs=layer_outputs,
+                **layer_attrs)
+
+    @print_mapping_info
+    def ReduceL2(self, node):
+        output_name = node.name
+        layer_outputs = [output_name]
+        val_x = self.graph.get_input_node(node, idx=0, copy=True)
+        axes = node.get_attr('axes')
+        keepdims = False if node.get_attr('keepdims') == 0 else True
+        layer_attrs = {'p': 2, 'axis': axes, 'keepdim': keepdims}
+        if val_x.dtype != 'float32' and val_x.dtype != 'float64':
+            indices_cast = val_x.name + '_cast'
+            mid_norm = val_x.name + '_norm'
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": val_x.name},
+                outputs=[indices_cast],
+                dtype=string('float32'))
+            self.paddle_graph.add_layer(
+                "paddle.norm",
+                inputs={"x": indices_cast},
+                outputs=[mid_norm],
+                **layer_attrs)
+            self.paddle_graph.add_layer(
+                'paddle.cast',
+                inputs={"x": mid_norm},
+                outputs=[node.name],
+                dtype=string(val_x.dtype))
+        else:
+            self.paddle_graph.add_layer(
+                "paddle.norm",
+                inputs={"x": val_x.name},
+                outputs=layer_outputs,
+                **layer_attrs)