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ppocr/modeling/backbones/rec_shallow_cnn.py

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+# copyright (c) 2022 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/open-mmlab/mmocr/blob/1.x/mmocr/models/textrecog/backbones/shallow_cnn.py
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import math
+import numpy as np
+import paddle
+from paddle import ParamAttr
+import paddle.nn as nn
+import paddle.nn.functional as F
+from paddle.nn import MaxPool2D
+from paddle.nn.initializer import KaimingNormal, Uniform, Constant
+
+
+class ConvBNLayer(nn.Layer):
+    def __init__(
+        self, num_channels, filter_size, num_filters, stride, padding, num_groups=1
+    ):
+        super(ConvBNLayer, self).__init__()
+
+        self.conv = nn.Conv2D(
+            in_channels=num_channels,
+            out_channels=num_filters,
+            kernel_size=filter_size,
+            stride=stride,
+            padding=padding,
+            groups=num_groups,
+            weight_attr=ParamAttr(initializer=KaimingNormal()),
+            bias_attr=False,
+        )
+
+        self.bn = nn.BatchNorm2D(
+            num_filters,
+            weight_attr=ParamAttr(initializer=Uniform(0, 1)),
+            bias_attr=ParamAttr(initializer=Constant(0)),
+        )
+        self.relu = nn.ReLU()
+
+    def forward(self, inputs):
+        y = self.conv(inputs)
+        y = self.bn(y)
+        y = self.relu(y)
+        return y
+
+
+class ShallowCNN(nn.Layer):
+    def __init__(self, in_channels=1, hidden_dim=512):
+        super().__init__()
+        assert isinstance(in_channels, int)
+        assert isinstance(hidden_dim, int)
+
+        self.conv1 = ConvBNLayer(in_channels, 3, hidden_dim // 2, stride=1, padding=1)
+        self.conv2 = ConvBNLayer(hidden_dim // 2, 3, hidden_dim, stride=1, padding=1)
+        self.pool = nn.MaxPool2D(kernel_size=2, stride=2, padding=0)
+        self.out_channels = hidden_dim
+
+    def forward(self, x):
+        x = self.conv1(x)
+        x = self.pool(x)
+
+        x = self.conv2(x)
+        x = self.pool(x)
+
+        return x