# 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. import paddle import paddle.nn as nn import paddle.nn.functional as F class L2Norm(nn.Layer): def __init__(self, n_channels, scale=1.0): super(L2Norm, self).__init__() self.n_channels = n_channels self.scale = scale self.eps = 1e-10 self.weight = paddle.create_parameter(shape=[self.n_channels], dtype='float32') self.weight.set_value(paddle.zeros([self.n_channels]) + self.scale) def forward(self, x): norm = x.pow(2).sum(axis=1, keepdim=True).sqrt() + self.eps x = x / norm * self.weight.reshape([1, -1, 1, 1]) return x class s3fd(nn.Layer): def __init__(self): super(s3fd, self).__init__() self.conv1_1 = nn.Conv2D(3, 64, kernel_size=3, stride=1, padding=1) self.conv1_2 = nn.Conv2D(64, 64, kernel_size=3, stride=1, padding=1) self.conv2_1 = nn.Conv2D(64, 128, kernel_size=3, stride=1, padding=1) self.conv2_2 = nn.Conv2D(128, 128, kernel_size=3, stride=1, padding=1) self.conv3_1 = nn.Conv2D(128, 256, kernel_size=3, stride=1, padding=1) self.conv3_2 = nn.Conv2D(256, 256, kernel_size=3, stride=1, padding=1) self.conv3_3 = nn.Conv2D(256, 256, kernel_size=3, stride=1, padding=1) self.conv4_1 = nn.Conv2D(256, 512, kernel_size=3, stride=1, padding=1) self.conv4_2 = nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1) self.conv4_3 = nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1) self.conv5_1 = nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1) self.conv5_2 = nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1) self.conv5_3 = nn.Conv2D(512, 512, kernel_size=3, stride=1, padding=1) self.fc6 = nn.Conv2D(512, 1024, kernel_size=3, stride=1, padding=3) self.fc7 = nn.Conv2D(1024, 1024, kernel_size=1, stride=1, padding=0) self.conv6_1 = nn.Conv2D(1024, 256, kernel_size=1, stride=1, padding=0) self.conv6_2 = nn.Conv2D(256, 512, kernel_size=3, stride=2, padding=1) self.conv7_1 = nn.Conv2D(512, 128, kernel_size=1, stride=1, padding=0) self.conv7_2 = nn.Conv2D(128, 256, kernel_size=3, stride=2, padding=1) self.conv3_3_norm = L2Norm(256, scale=10) self.conv4_3_norm = L2Norm(512, scale=8) self.conv5_3_norm = L2Norm(512, scale=5) self.conv3_3_norm_mbox_conf = nn.Conv2D(256, 4, kernel_size=3, stride=1, padding=1) self.conv3_3_norm_mbox_loc = nn.Conv2D(256, 4, kernel_size=3, stride=1, padding=1) self.conv4_3_norm_mbox_conf = nn.Conv2D(512, 2, kernel_size=3, stride=1, padding=1) self.conv4_3_norm_mbox_loc = nn.Conv2D(512, 4, kernel_size=3, stride=1, padding=1) self.conv5_3_norm_mbox_conf = nn.Conv2D(512, 2, kernel_size=3, stride=1, padding=1) self.conv5_3_norm_mbox_loc = nn.Conv2D(512, 4, kernel_size=3, stride=1, padding=1) self.fc7_mbox_conf = nn.Conv2D(1024, 2, kernel_size=3, stride=1, padding=1) self.fc7_mbox_loc = nn.Conv2D(1024, 4, kernel_size=3, stride=1, padding=1) self.conv6_2_mbox_conf = nn.Conv2D(512, 2, kernel_size=3, stride=1, padding=1) self.conv6_2_mbox_loc = nn.Conv2D(512, 4, kernel_size=3, stride=1, padding=1) self.conv7_2_mbox_conf = nn.Conv2D(256, 2, kernel_size=3, stride=1, padding=1) self.conv7_2_mbox_loc = nn.Conv2D(256, 4, kernel_size=3, stride=1, padding=1) def forward(self, x): h = F.relu(self.conv1_1(x)) h = F.relu(self.conv1_2(h)) h = F.max_pool2d(h, 2, 2) h = F.relu(self.conv2_1(h)) h = F.relu(self.conv2_2(h)) h = F.max_pool2d(h, 2, 2) h = F.relu(self.conv3_1(h)) h = F.relu(self.conv3_2(h)) h = F.relu(self.conv3_3(h)) f3_3 = h h = F.max_pool2d(h, 2, 2) h = F.relu(self.conv4_1(h)) h = F.relu(self.conv4_2(h)) h = F.relu(self.conv4_3(h)) f4_3 = h h = F.max_pool2d(h, 2, 2) h = F.relu(self.conv5_1(h)) h = F.relu(self.conv5_2(h)) h = F.relu(self.conv5_3(h)) f5_3 = h h = F.max_pool2d(h, 2, 2) h = F.relu(self.fc6(h)) h = F.relu(self.fc7(h)) ffc7 = h h = F.relu(self.conv6_1(h)) h = F.relu(self.conv6_2(h)) f6_2 = h h = F.relu(self.conv7_1(h)) h = F.relu(self.conv7_2(h)) f7_2 = h f3_3 = self.conv3_3_norm(f3_3) f4_3 = self.conv4_3_norm(f4_3) f5_3 = self.conv5_3_norm(f5_3) cls1 = self.conv3_3_norm_mbox_conf(f3_3) reg1 = self.conv3_3_norm_mbox_loc(f3_3) cls2 = self.conv4_3_norm_mbox_conf(f4_3) reg2 = self.conv4_3_norm_mbox_loc(f4_3) cls3 = self.conv5_3_norm_mbox_conf(f5_3) reg3 = self.conv5_3_norm_mbox_loc(f5_3) cls4 = self.fc7_mbox_conf(ffc7) reg4 = self.fc7_mbox_loc(ffc7) cls5 = self.conv6_2_mbox_conf(f6_2) reg5 = self.conv6_2_mbox_loc(f6_2) cls6 = self.conv7_2_mbox_conf(f7_2) reg6 = self.conv7_2_mbox_loc(f7_2) # max-out background label chunk = paddle.chunk(cls1, 4, 1) tmp_max = paddle.where(chunk[0] > chunk[1], chunk[0], chunk[1]) bmax = paddle.where(tmp_max > chunk[2], tmp_max, chunk[2]) cls1 = paddle.concat([bmax, chunk[3]], axis=1) return [ cls1, reg1, cls2, reg2, cls3, reg3, cls4, reg4, cls5, reg5, cls6, reg6 ]