wehub-resource-sync
98 lines
3.4 KiB
Python
98 lines
3.4 KiB
Python
#!/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]
|