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ppocr/modeling/backbones/rec_densenet.py

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+# 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.
+"""
+This code is refer from:
+https://github.com/LBH1024/CAN/models/densenet.py
+
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import paddle
+import paddle.nn as nn
+import paddle.nn.functional as F
+
+
+class Bottleneck(nn.Layer):
+    def __init__(self, nChannels, growthRate, use_dropout):
+        super(Bottleneck, self).__init__()
+        interChannels = 4 * growthRate
+        self.bn1 = nn.BatchNorm2D(interChannels)
+        self.conv1 = nn.Conv2D(
+            nChannels, interChannels, kernel_size=1, bias_attr=None
+        )  # Xavier initialization
+        self.bn2 = nn.BatchNorm2D(growthRate)
+        self.conv2 = nn.Conv2D(
+            interChannels, growthRate, kernel_size=3, padding=1, bias_attr=None
+        )  # Xavier initialization
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = F.relu(self.bn2(self.conv2(out)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = paddle.concat([x, out], 1)
+        return out
+
+
+class SingleLayer(nn.Layer):
+    def __init__(self, nChannels, growthRate, use_dropout):
+        super(SingleLayer, self).__init__()
+        self.bn1 = nn.BatchNorm2D(nChannels)
+        self.conv1 = nn.Conv2D(
+            nChannels, growthRate, kernel_size=3, padding=1, bias_attr=False
+        )
+
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = self.conv1(F.relu(x))
+        if self.use_dropout:
+            out = self.dropout(out)
+
+        out = paddle.concat([x, out], 1)
+        return out
+
+
+class Transition(nn.Layer):
+    def __init__(self, nChannels, out_channels, use_dropout):
+        super(Transition, self).__init__()
+        self.bn1 = nn.BatchNorm2D(out_channels)
+        self.conv1 = nn.Conv2D(nChannels, out_channels, kernel_size=1, bias_attr=False)
+        self.use_dropout = use_dropout
+        self.dropout = nn.Dropout(p=0.2)
+
+    def forward(self, x):
+        out = F.relu(self.bn1(self.conv1(x)))
+        if self.use_dropout:
+            out = self.dropout(out)
+        out = F.avg_pool2d(out, 2, ceil_mode=True, exclusive=False)
+        return out
+
+
+class DenseNet(nn.Layer):
+    def __init__(
+        self, growthRate, reduction, bottleneck, use_dropout, input_channel, **kwargs
+    ):
+        super(DenseNet, self).__init__()
+
+        nDenseBlocks = 16
+        nChannels = 2 * growthRate
+
+        self.conv1 = nn.Conv2D(
+            input_channel,
+            nChannels,
+            kernel_size=7,
+            padding=3,
+            stride=2,
+            bias_attr=False,
+        )
+        self.dense1 = self._make_dense(
+            nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
+        )
+        nChannels += nDenseBlocks * growthRate
+        out_channels = int(math.floor(nChannels * reduction))
+        self.trans1 = Transition(nChannels, out_channels, use_dropout)
+
+        nChannels = out_channels
+        self.dense2 = self._make_dense(
+            nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
+        )
+        nChannels += nDenseBlocks * growthRate
+        out_channels = int(math.floor(nChannels * reduction))
+        self.trans2 = Transition(nChannels, out_channels, use_dropout)
+
+        nChannels = out_channels
+        self.dense3 = self._make_dense(
+            nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout
+        )
+        self.out_channels = out_channels
+
+    def _make_dense(self, nChannels, growthRate, nDenseBlocks, bottleneck, use_dropout):
+        layers = []
+        for i in range(int(nDenseBlocks)):
+            if bottleneck:
+                layers.append(Bottleneck(nChannels, growthRate, use_dropout))
+            else:
+                layers.append(SingleLayer(nChannels, growthRate, use_dropout))
+            nChannels += growthRate
+        return nn.Sequential(*layers)
+
+    def forward(self, inputs):
+        x, x_m, y = inputs
+        out = self.conv1(x)
+        out = F.relu(out)
+        out = F.max_pool2d(out, 2, ceil_mode=True)
+        out = self.dense1(out)
+        out = self.trans1(out)
+        out = self.dense2(out)
+        out = self.trans2(out)
+        out = self.dense3(out)
+        return out, x_m, y