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138 lines
5.1 KiB
Python
138 lines
5.1 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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"""
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This code is refer from:
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https://github.com/hikopensource/DAVAR-Lab-OCR/blob/main/davarocr/davar_rcg/models/connects/single_block/RFAdaptor.py
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"""
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import paddle
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import paddle.nn as nn
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from paddle.nn.initializer import TruncatedNormal, Constant, Normal, KaimingNormal
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kaiming_init_ = KaimingNormal()
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zeros_ = Constant(value=0.)
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ones_ = Constant(value=1.)
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class S2VAdaptor(nn.Layer):
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""" Semantic to Visual adaptation module"""
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def __init__(self, in_channels=512):
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super(S2VAdaptor, self).__init__()
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self.in_channels = in_channels # 512
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# feature strengthen module, channel attention
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self.channel_inter = nn.Linear(
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self.in_channels, self.in_channels, bias_attr=False)
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self.channel_bn = nn.BatchNorm1D(self.in_channels)
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self.channel_act = nn.ReLU()
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self.apply(self.init_weights)
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def init_weights(self, m):
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if isinstance(m, nn.Conv2D):
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kaiming_init_(m.weight)
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if isinstance(m, nn.Conv2D) and m.bias is not None:
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zeros_(m.bias)
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elif isinstance(m, (nn.BatchNorm, nn.BatchNorm2D, nn.BatchNorm1D)):
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zeros_(m.bias)
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ones_(m.weight)
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def forward(self, semantic):
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semantic_source = semantic # batch, channel, height, width
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# feature transformation
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semantic = semantic.squeeze(2).transpose(
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[0, 2, 1]) # batch, width, channel
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channel_att = self.channel_inter(semantic) # batch, width, channel
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channel_att = channel_att.transpose([0, 2, 1]) # batch, channel, width
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channel_bn = self.channel_bn(channel_att) # batch, channel, width
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channel_att = self.channel_act(channel_bn) # batch, channel, width
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# Feature enhancement
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channel_output = semantic_source * channel_att.unsqueeze(
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-2) # batch, channel, 1, width
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return channel_output
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class V2SAdaptor(nn.Layer):
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""" Visual to Semantic adaptation module"""
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def __init__(self, in_channels=512, return_mask=False):
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super(V2SAdaptor, self).__init__()
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# parameter initialization
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self.in_channels = in_channels
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self.return_mask = return_mask
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# output transformation
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self.channel_inter = nn.Linear(
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self.in_channels, self.in_channels, bias_attr=False)
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self.channel_bn = nn.BatchNorm1D(self.in_channels)
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self.channel_act = nn.ReLU()
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def forward(self, visual):
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# Feature enhancement
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visual = visual.squeeze(2).transpose([0, 2, 1]) # batch, width, channel
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channel_att = self.channel_inter(visual) # batch, width, channel
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channel_att = channel_att.transpose([0, 2, 1]) # batch, channel, width
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channel_bn = self.channel_bn(channel_att) # batch, channel, width
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channel_att = self.channel_act(channel_bn) # batch, channel, width
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# size alignment
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channel_output = channel_att.unsqueeze(-2) # batch, width, channel
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if self.return_mask:
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return channel_output, channel_att
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return channel_output
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class RFAdaptor(nn.Layer):
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def __init__(self, in_channels=512, use_v2s=True, use_s2v=True, **kwargs):
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super(RFAdaptor, self).__init__()
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if use_v2s is True:
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self.neck_v2s = V2SAdaptor(in_channels=in_channels, **kwargs)
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else:
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self.neck_v2s = None
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if use_s2v is True:
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self.neck_s2v = S2VAdaptor(in_channels=in_channels, **kwargs)
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else:
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self.neck_s2v = None
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self.out_channels = in_channels
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def forward(self, x):
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visual_feature, rcg_feature = x
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if visual_feature is not None:
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batch, source_channels, v_source_height, v_source_width = visual_feature.shape
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visual_feature = visual_feature.reshape(
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[batch, source_channels, 1, v_source_height * v_source_width])
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if self.neck_v2s is not None:
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v_rcg_feature = rcg_feature * self.neck_v2s(visual_feature)
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else:
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v_rcg_feature = rcg_feature
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if self.neck_s2v is not None:
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v_visual_feature = visual_feature + self.neck_s2v(rcg_feature)
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else:
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v_visual_feature = visual_feature
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if v_rcg_feature is not None:
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batch, source_channels, source_height, source_width = v_rcg_feature.shape
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v_rcg_feature = v_rcg_feature.reshape(
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[batch, source_channels, 1, source_height * source_width])
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v_rcg_feature = v_rcg_feature.squeeze(2).transpose([0, 2, 1])
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return v_visual_feature, v_rcg_feature
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