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ppocr/modeling/backbones/rec_hgnet.py
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385
ppocr/modeling/backbones/rec_hgnet.py
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# 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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import paddle
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import paddle.nn as nn
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import paddle.nn.functional as F
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from paddle.nn.initializer import KaimingNormal, Constant
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from paddle.nn import Conv2D, BatchNorm2D, ReLU, AdaptiveAvgPool2D, MaxPool2D
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from paddle.regularizer import L2Decay
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from paddle import ParamAttr
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kaiming_normal_ = KaimingNormal()
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zeros_ = Constant(value=0.0)
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ones_ = Constant(value=1.0)
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class MeanPool2D(nn.Layer):
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def __init__(self, w, h):
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super().__init__()
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self.w = w
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self.h = h
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def forward(self, feat):
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batch_size, channels, _, _ = feat.shape
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feat_flat = paddle.reshape(feat, [batch_size, channels, -1])
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feat_mean = paddle.mean(feat_flat, axis=2)
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feat_mean = paddle.reshape(feat_mean, [batch_size, channels, self.w, self.h])
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return feat_mean
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class ConvBNAct(nn.Layer):
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def __init__(
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self, in_channels, out_channels, kernel_size, stride, groups=1, use_act=True
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):
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super().__init__()
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self.use_act = use_act
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self.conv = Conv2D(
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in_channels,
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out_channels,
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kernel_size,
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stride,
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padding=(kernel_size - 1) // 2,
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groups=groups,
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bias_attr=False,
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)
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self.bn = BatchNorm2D(
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out_channels,
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weight_attr=ParamAttr(regularizer=L2Decay(0.0)),
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bias_attr=ParamAttr(regularizer=L2Decay(0.0)),
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)
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if self.use_act:
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self.act = ReLU()
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def forward(self, x):
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x = self.conv(x)
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x = self.bn(x)
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if self.use_act:
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x = self.act(x)
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return x
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class ESEModule(nn.Layer):
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def __init__(self, channels):
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super().__init__()
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if "npu" in paddle.device.get_device():
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self.avg_pool = MeanPool2D(1, 1)
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else:
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self.avg_pool = AdaptiveAvgPool2D(1)
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self.conv = Conv2D(
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in_channels=channels,
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out_channels=channels,
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kernel_size=1,
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stride=1,
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padding=0,
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)
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self.sigmoid = nn.Sigmoid()
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def forward(self, x):
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identity = x
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x = self.avg_pool(x)
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x = self.conv(x)
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x = self.sigmoid(x)
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return paddle.multiply(x=identity, y=x)
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class HG_Block(nn.Layer):
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def __init__(
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self,
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in_channels,
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mid_channels,
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out_channels,
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layer_num,
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identity=False,
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):
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super().__init__()
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self.identity = identity
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self.layers = nn.LayerList()
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self.layers.append(
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ConvBNAct(
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in_channels=in_channels,
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out_channels=mid_channels,
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kernel_size=3,
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stride=1,
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)
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)
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for _ in range(layer_num - 1):
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self.layers.append(
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ConvBNAct(
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in_channels=mid_channels,
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out_channels=mid_channels,
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kernel_size=3,
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stride=1,
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)
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)
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# feature aggregation
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total_channels = in_channels + layer_num * mid_channels
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self.aggregation_conv = ConvBNAct(
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in_channels=total_channels,
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out_channels=out_channels,
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kernel_size=1,
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stride=1,
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)
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self.att = ESEModule(out_channels)
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def forward(self, x):
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identity = x
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output = []
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output.append(x)
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for layer in self.layers:
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x = layer(x)
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output.append(x)
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x = paddle.concat(output, axis=1)
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x = self.aggregation_conv(x)
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x = self.att(x)
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if self.identity:
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x += identity
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return x
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class HG_Stage(nn.Layer):
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def __init__(
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self,
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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layer_num,
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downsample=True,
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stride=[2, 1],
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):
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super().__init__()
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self.downsample = downsample
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if downsample:
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self.downsample = ConvBNAct(
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in_channels=in_channels,
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out_channels=in_channels,
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kernel_size=3,
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stride=stride,
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groups=in_channels,
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use_act=False,
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)
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blocks_list = []
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blocks_list.append(
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HG_Block(in_channels, mid_channels, out_channels, layer_num, identity=False)
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)
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for _ in range(block_num - 1):
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blocks_list.append(
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HG_Block(
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out_channels, mid_channels, out_channels, layer_num, identity=True
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)
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)
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self.blocks = nn.Sequential(*blocks_list)
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def forward(self, x):
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if self.downsample:
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x = self.downsample(x)
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x = self.blocks(x)
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return x
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class PPHGNet(nn.Layer):
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"""
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PPHGNet
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Args:
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stem_channels: list. Stem channel list of PPHGNet.
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stage_config: dict. The configuration of each stage of PPHGNet. such as the number of channels, stride, etc.
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layer_num: int. Number of layers of HG_Block.
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use_last_conv: boolean. Whether to use a 1x1 convolutional layer before the classification layer.
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class_expand: int=2048. Number of channels for the last 1x1 convolutional layer.
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dropout_prob: float. Parameters of dropout, 0.0 means dropout is not used.
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class_num: int=1000. The number of classes.
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Returns:
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model: nn.Layer. Specific PPHGNet model depends on args.
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"""
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def __init__(
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self,
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stem_channels,
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stage_config,
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layer_num,
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in_channels=3,
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det=False,
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out_indices=None,
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):
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super().__init__()
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self.det = det
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self.out_indices = out_indices if out_indices is not None else [0, 1, 2, 3]
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# stem
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stem_channels.insert(0, in_channels)
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self.stem = nn.Sequential(
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*[
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ConvBNAct(
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in_channels=stem_channels[i],
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out_channels=stem_channels[i + 1],
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kernel_size=3,
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stride=2 if i == 0 else 1,
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)
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for i in range(len(stem_channels) - 1)
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]
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)
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if self.det:
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self.pool = nn.MaxPool2D(kernel_size=3, stride=2, padding=1)
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# stages
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self.stages = nn.LayerList()
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self.out_channels = []
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for block_id, k in enumerate(stage_config):
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(
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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downsample,
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stride,
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) = stage_config[k]
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self.stages.append(
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HG_Stage(
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in_channels,
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mid_channels,
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out_channels,
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block_num,
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layer_num,
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downsample,
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stride,
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)
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)
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if block_id in self.out_indices:
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self.out_channels.append(out_channels)
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if not self.det:
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self.out_channels = stage_config["stage4"][2]
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self._init_weights()
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def _init_weights(self):
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for m in self.sublayers():
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if isinstance(m, nn.Conv2D):
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kaiming_normal_(m.weight)
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elif isinstance(m, (nn.BatchNorm2D)):
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ones_(m.weight)
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zeros_(m.bias)
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elif isinstance(m, nn.Linear):
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zeros_(m.bias)
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def forward(self, x):
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x = self.stem(x)
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if self.det:
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x = self.pool(x)
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out = []
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for i, stage in enumerate(self.stages):
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x = stage(x)
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if self.det and i in self.out_indices:
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out.append(x)
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if self.det:
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return out
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if self.training:
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x = F.adaptive_avg_pool2d(x, [1, 40])
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else:
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x = F.avg_pool2d(x, [3, 2])
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return x
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def PPHGNet_tiny(pretrained=False, use_ssld=False, **kwargs):
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"""
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PPHGNet_tiny
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `PPHGNet_tiny` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [96, 96, 224, 1, False, [2, 1]],
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"stage2": [224, 128, 448, 1, True, [1, 2]],
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"stage3": [448, 160, 512, 2, True, [2, 1]],
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"stage4": [512, 192, 768, 1, True, [2, 1]],
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}
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model = PPHGNet(
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stem_channels=[48, 48, 96], stage_config=stage_config, layer_num=5, **kwargs
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)
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return model
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def PPHGNet_small(pretrained=False, use_ssld=False, det=False, **kwargs):
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"""
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PPHGNet_small
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `PPHGNet_small` model depends on args.
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"""
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stage_config_det = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [128, 128, 256, 1, False, 2],
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"stage2": [256, 160, 512, 1, True, 2],
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"stage3": [512, 192, 768, 2, True, 2],
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"stage4": [768, 224, 1024, 1, True, 2],
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}
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stage_config_rec = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [128, 128, 256, 1, True, [2, 1]],
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"stage2": [256, 160, 512, 1, True, [1, 2]],
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"stage3": [512, 192, 768, 2, True, [2, 1]],
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"stage4": [768, 224, 1024, 1, True, [2, 1]],
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}
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model = PPHGNet(
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stem_channels=[64, 64, 128],
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stage_config=stage_config_det if det else stage_config_rec,
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layer_num=6,
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det=det,
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**kwargs,
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)
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return model
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def PPHGNet_base(pretrained=False, use_ssld=True, **kwargs):
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"""
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PPHGNet_base
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Args:
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pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
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If str, means the path of the pretrained model.
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use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
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Returns:
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model: nn.Layer. Specific `PPHGNet_base` model depends on args.
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"""
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stage_config = {
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# in_channels, mid_channels, out_channels, blocks, downsample
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"stage1": [160, 192, 320, 1, False, [2, 1]],
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"stage2": [320, 224, 640, 2, True, [1, 2]],
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"stage3": [640, 256, 960, 3, True, [2, 1]],
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"stage4": [960, 288, 1280, 2, True, [2, 1]],
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}
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model = PPHGNet(
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stem_channels=[96, 96, 160],
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stage_config=stage_config,
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layer_num=7,
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dropout_prob=0.2,
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**kwargs,
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)
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return model
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