Merge pull request #2062 from myhloli/dev

feat: support 3.10~3.12 & remove paddle

magic_pdf/model/sub_modules/ocr/paddleocr2pytorch/pytorchocr/modeling/necks/intracl.py

+from torch import nn
+
+
+class IntraCLBlock(nn.Module):
+    def __init__(self, in_channels=96, reduce_factor=4):
+        super(IntraCLBlock, self).__init__()
+        self.channels = in_channels
+        self.rf = reduce_factor
+        self.conv1x1_reduce_channel = nn.Conv2d(
+            self.channels, self.channels // self.rf, kernel_size=1, stride=1, padding=0
+        )
+        self.conv1x1_return_channel = nn.Conv2d(
+            self.channels // self.rf, self.channels, kernel_size=1, stride=1, padding=0
+        )
+
+        self.v_layer_7x1 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(7, 1),
+            stride=(1, 1),
+            padding=(3, 0),
+        )
+        self.v_layer_5x1 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(5, 1),
+            stride=(1, 1),
+            padding=(2, 0),
+        )
+        self.v_layer_3x1 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(3, 1),
+            stride=(1, 1),
+            padding=(1, 0),
+        )
+
+        self.q_layer_1x7 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(1, 7),
+            stride=(1, 1),
+            padding=(0, 3),
+        )
+        self.q_layer_1x5 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(1, 5),
+            stride=(1, 1),
+            padding=(0, 2),
+        )
+        self.q_layer_1x3 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(1, 3),
+            stride=(1, 1),
+            padding=(0, 1),
+        )
+
+        # base
+        self.c_layer_7x7 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(7, 7),
+            stride=(1, 1),
+            padding=(3, 3),
+        )
+        self.c_layer_5x5 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(5, 5),
+            stride=(1, 1),
+            padding=(2, 2),
+        )
+        self.c_layer_3x3 = nn.Conv2d(
+            self.channels // self.rf,
+            self.channels // self.rf,
+            kernel_size=(3, 3),
+            stride=(1, 1),
+            padding=(1, 1),
+        )
+
+        self.bn = nn.BatchNorm2d(self.channels)
+        self.relu = nn.ReLU()
+
+    def forward(self, x):
+        x_new = self.conv1x1_reduce_channel(x)
+
+        x_7_c = self.c_layer_7x7(x_new)
+        x_7_v = self.v_layer_7x1(x_new)
+        x_7_q = self.q_layer_1x7(x_new)
+        x_7 = x_7_c + x_7_v + x_7_q
+
+        x_5_c = self.c_layer_5x5(x_7)
+        x_5_v = self.v_layer_5x1(x_7)
+        x_5_q = self.q_layer_1x5(x_7)
+        x_5 = x_5_c + x_5_v + x_5_q
+
+        x_3_c = self.c_layer_3x3(x_5)
+        x_3_v = self.v_layer_3x1(x_5)
+        x_3_q = self.q_layer_1x3(x_5)
+        x_3 = x_3_c + x_3_v + x_3_q
+
+        x_relation = self.conv1x1_return_channel(x_3)
+
+        x_relation = self.bn(x_relation)
+        x_relation = self.relu(x_relation)
+
+        return x + x_relation
+
+
+def build_intraclblock_list(num_block):
+    IntraCLBlock_list = nn.ModuleList()
+    for i in range(num_block):
+        IntraCLBlock_list.append(IntraCLBlock())
+
+    return IntraCLBlock_list