chore: import upstream snapshot with attribution

test/cinn/conv2d_utils.py

@@ -0,0 +1,97 @@
+#!/usr/bin/env python3
+
+# Copyright (c) 2021 CINN 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.
+
+import numpy as np
+
+import paddle
+from paddle import static
+
+
+def conv2d_native(inputs_data, input_shape, filter_size, attrs, is_depthwise):
+    main_program = static.Program()
+    paddle.enable_static()
+
+    with static.program_guard(main_program, static.Program()):
+        padding = [0, 0]
+        stride = [1, 1]
+        dilation = [1, 1]
+        data_format = "NCHW"
+        groups = 1
+        for key in attrs.attr_store:
+            if key == "stride":
+                stride = attrs.get_attr("stride")
+            elif key == "padding":
+                padding = attrs.get_attr("padding")
+            elif key == "dilation":
+                dilation = attrs.get_attr("dilation")
+            elif key == "groups":
+                groups = attrs.get_attr("groups")
+            elif key == "data_format":
+                data_format = attrs.get_attr("data_format")
+            else:
+                raise ValueError(f"attr_store {key} is not supported")
+
+        img = static.data(name='img', shape=input_shape[1:], dtype='float32')
+        if is_depthwise:
+            if data_format == "NCHW":
+                cin_index = 1
+            else:
+                cin_index = 3
+            filter_size_new = [
+                filter_size[1] * input_shape[cin_index],
+                filter_size[0] // groups,
+                filter_size[2],
+                filter_size[3],
+            ]
+        else:
+            filter_size_new = filter_size
+        param = paddle.nn.initializer.NumpyArrayInitializer(
+            np.array(inputs_data[1]).reshape(filter_size_new).astype("float32")
+        )
+        # filter: (c_out, c_in // group, kernel_h, kernel_w)
+        filter_hw = list(filter_size_new[2:4])
+        if data_format == "NHWC":
+            filter_hw = list(filter_size_new[1:3])
+        if isinstance(stride, int):
+            stride = [stride, stride]
+        if isinstance(padding, int):
+            padding = [padding, padding]
+        if isinstance(dilation, int):
+            dilation = [dilation, dilation]
+
+        c_index = 1 if data_format == "NCHW" else 3
+        res = paddle.nn.Conv2D(
+            in_channels=input_shape[c_index],
+            out_channels=filter_size_new[0],
+            kernel_size=filter_hw,
+            stride=stride,
+            padding=padding,
+            dilation=dilation,
+            groups=groups,
+            data_format=data_format,
+            weight_attr=param,
+        )(img)
+        exe = static.Executor(paddle.CPUPlace())
+        exe.run(static.default_startup_program())
+
+        x = np.array(inputs_data[0]).reshape(input_shape).astype("float32")
+        output = exe.run(feed={"img": x}, fetch_list=[res])
+        output = np.array(output)
+        print("output's shape is:", output.shape)
+
+    res_shape = output.shape[1:]
+
+    return output, [res_shape]