#
# 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 paddle.fluid as fluid
import os
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, 'correlation_lib.so'))
from paddle.fluid.layer_helper import LayerHelper
def correlation(input1,
input2,
pad_size,
kernel_size,
max_displacement,
stride1,
stride2,
corr_type_multiply=1):
helper = LayerHelper("correlation", **locals())
output = helper.create_variable_for_type_inference(dtype=input1.dtype)
helper.append_op(type="correlation",
mpc_data_utils_lib = _paddle_enc_lib_path + '/mpc_data_utils.so'
psi_lib = _paddle_enc_lib_path + '/libpsi.so'
set_rpath(
_paddle_enc_lib, paddle_lib_path + ":" + _paddle_enc_lib_path + ":" +
third_party_lib_path)
set_rpath(
mpc_data_utils_lib,
paddle_lib_path + ":" + _paddle_enc_lib_path + ":" + openssl_lib_path)
if os.path.exists(psi_lib):
set_rpath(psi_lib, openssl_lib_path)
sys.path.append(_paddle_enc_lib_path)
set_rpath_to_all_libs()
fluid.load_op_library(_paddle_enc_lib)
from .data import *
from . import layers
from .optimizer import *
from .mpc_init import *
from . import data_utils
from .io import *
from .version import version
from .layers import mpc_math_op_patch
from . import input
from . import initializer
mpc_math_op_patch.monkey_patch_mpc_variable()
import os
import paddle.fluid as fluid
use_cpp = False
file_dir = os.path.dirname(os.path.abspath(__file__))
try:
fluid.load_op_library(os.path.join(file_dir, 'src/cornerpool_lib.so'))
use_cpp = True
except:
print(
'Warning: cornerpool_lib.so not found, use python version instead which may drop the inference speed. Compile in ppdet/ext_op at first if you need cpp version.'
)
from paddle.fluid.layer_helper import LayerHelper
__all__ = [
'bottom_pool',
'top_pool',
'right_pool',
'left_pool',
]
def cornerpool_op(layer_type, input, name):
helper = LayerHelper(layer_type, input=input, name=name)
dtype = helper.input_dtype()
output = helper.create_variable_for_type_inference(dtype)
max_map = helper.create_variable_for_type_inference(dtype)
helper.append_op(type=layer_type,
inputs={"X": input},
# -*- coding: utf-8
import os
import paddle.fluid as fluid
# 调用load_op_library加载动态库
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, 'lib/rerank.so'))
from paddle.fluid.layer_helper import LayerHelper
def vmat(x, name=None):
# vmat的type和在OP中定义的type相同
helper = LayerHelper("vmat", **locals())
# 创建输出Variable
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="vmat", inputs={"X": x}, outputs={"Y": out})
return out
def qe(V, R, S, name=None):
# qe的type和在OP中定义的type相同
helper = LayerHelper("qe", **locals())
# 创建输出Variable
out = helper.create_variable_for_type_inference(dtype=V.dtype)
helper.append_op(type="qe",
inputs={
"V": V,
"R": R,
"S": S
},
outputs={"Y": out})
# 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 os
import sys
import numpy as np
import unittest
import contextlib
import paddle
import paddle.fluid as fluid
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, 'librelu2_op.so'))
from paddle.fluid.layer_helper import LayerHelper
def relu2(x, name=None):
helper = LayerHelper("relu2", **locals())
out = helper.create_variable(type=x.type,
name=name,
dtype=x.dtype,
persistable=False)
helper.append_op(type="relu2", inputs={"X": x}, outputs={"Y": out})
return out
@contextlib.contextmanager
#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 os
import paddle.fluid as fluid
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, 'src/custom_lib.so'))
from paddle.fluid.layer_helper import LayerHelper
__all__ = ['gather_dim']
def gather_dim(input, index):
"""
**Gather Dim Layer**
Output is obtained by gathering entries of X indexed by `index`
and concatenate them together.
.. math::
Out = X[Index]
.. code-block:: text
Given:
#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 os
import paddle.fluid as fluid
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, 'src/pointnet_lib.so'))
from paddle.fluid.layer_helper import LayerHelper
__all__ = [
'three_nn', 'three_interp', 'query_ball', 'gather_point',
'farthest_point_sampling', 'group_points'
]
def three_nn(input, known, eps=1e-10, name=None):
"""
**Three Nearest Neighbor Layer**
This operator samples the top-3 nearest neighbor of each point
coordinates specified by Input(X) between known point coordinates
import os
import paddle
import paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
# 调用load_op_library加载动态库
# os.environ["FLAGS_op_dir"] = "/work/scripts/custom_op"
fluid.load_op_library('relu2_op.so')
def relu2(x, name=None):
# relu2的type和在OP中定义的type相同
helper = LayerHelper("relu2", **locals())
# 创建输出Variable
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="relu2", inputs={"X": [x]}, outputs={"Y": out})
return out
#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 paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.framework import Variable
fluid.load_op_library('models/ext_op/src/rrpn_lib.so')
def rrpn_target_assign(bbox_pred,
cls_logits,
anchor_box,
gt_boxes,
im_info,
rpn_batch_size_per_im=256,
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3,
use_random=True):
"""
**Target Assign Layer for rotated region proposal network (RRPN).**
#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 paddle.fluid as fluid
from paddle.fluid.layer_helper import LayerHelper
from paddle.fluid.framework import Variable
fluid.load_op_library('rrpn_lib.so')
def rrpn_target_assign(bbox_pred,
cls_logits,
anchor_box,
gt_boxes,
im_info,
rpn_batch_size_per_im=256,
rpn_straddle_thresh=0.0,
rpn_fg_fraction=0.5,
rpn_positive_overlap=0.7,
rpn_negative_overlap=0.3,
use_random=True):
"""
**Target Assign Layer for rotated region proposal network (RRPN).**
def load_so(so_name):
"""
Load .so file and parse custom op into OpInfoMap.
"""
file_dir = os.path.dirname(os.path.abspath(__file__))
fluid.load_op_library(os.path.join(file_dir, so_name))
