53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
|
|
#
|
|
# 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 __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
import math
|
|
import paddle
|
|
from paddle import nn, ParamAttr
|
|
import paddle.nn.functional as F
|
|
|
|
|
|
class ClsHead(nn.Layer):
|
|
"""
|
|
Class orientation
|
|
|
|
Args:
|
|
|
|
params(dict): super parameters for build Class network
|
|
"""
|
|
|
|
def __init__(self, in_channels, class_dim, **kwargs):
|
|
super(ClsHead, self).__init__()
|
|
self.pool = nn.AdaptiveAvgPool2D(1)
|
|
stdv = 1.0 / math.sqrt(in_channels * 1.0)
|
|
self.fc = nn.Linear(
|
|
in_channels,
|
|
class_dim,
|
|
weight_attr=ParamAttr(
|
|
name="fc_0.w_0",
|
|
initializer=nn.initializer.Uniform(-stdv, stdv)),
|
|
bias_attr=ParamAttr(name="fc_0.b_0"), )
|
|
|
|
def forward(self, x):
|
|
x = self.pool(x)
|
|
x = paddle.reshape(x, shape=[x.shape[0], x.shape[1]])
|
|
x = self.fc(x)
|
|
if not self.training:
|
|
x = F.softmax(x, axis=1)
|
|
return x
|