118 lines
3.5 KiB
Python
118 lines
3.5 KiB
Python
import paddle
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from paddle import nn
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# refer from: https://github.com/ViTAE-Transformer/I3CL/blob/736c80237f66d352d488e83b05f3e33c55201317/mmdet/models/detectors/intra_cl_module.py
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class IntraCLBlock(nn.Layer):
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def __init__(self, in_channels=96, reduce_factor=4):
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super(IntraCLBlock, self).__init__()
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self.channels = in_channels
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self.rf = reduce_factor
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weight_attr = paddle.nn.initializer.KaimingUniform()
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self.conv1x1_reduce_channel = nn.Conv2D(
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self.channels,
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self.channels // self.rf,
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kernel_size=1,
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stride=1,
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padding=0)
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self.conv1x1_return_channel = nn.Conv2D(
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self.channels // self.rf,
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self.channels,
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kernel_size=1,
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stride=1,
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padding=0)
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self.v_layer_7x1 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(7, 1),
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stride=(1, 1),
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padding=(3, 0))
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self.v_layer_5x1 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(5, 1),
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stride=(1, 1),
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padding=(2, 0))
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self.v_layer_3x1 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(3, 1),
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stride=(1, 1),
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padding=(1, 0))
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self.q_layer_1x7 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(1, 7),
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stride=(1, 1),
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padding=(0, 3))
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self.q_layer_1x5 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(1, 5),
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stride=(1, 1),
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padding=(0, 2))
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self.q_layer_1x3 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(1, 3),
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stride=(1, 1),
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padding=(0, 1))
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# base
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self.c_layer_7x7 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(7, 7),
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stride=(1, 1),
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padding=(3, 3))
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self.c_layer_5x5 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(5, 5),
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stride=(1, 1),
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padding=(2, 2))
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self.c_layer_3x3 = nn.Conv2D(
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self.channels // self.rf,
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self.channels // self.rf,
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kernel_size=(3, 3),
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stride=(1, 1),
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padding=(1, 1))
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self.bn = nn.BatchNorm2D(self.channels)
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self.relu = nn.ReLU()
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def forward(self, x):
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x_new = self.conv1x1_reduce_channel(x)
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x_7_c = self.c_layer_7x7(x_new)
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x_7_v = self.v_layer_7x1(x_new)
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x_7_q = self.q_layer_1x7(x_new)
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x_7 = x_7_c + x_7_v + x_7_q
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x_5_c = self.c_layer_5x5(x_7)
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x_5_v = self.v_layer_5x1(x_7)
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x_5_q = self.q_layer_1x5(x_7)
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x_5 = x_5_c + x_5_v + x_5_q
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x_3_c = self.c_layer_3x3(x_5)
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x_3_v = self.v_layer_3x1(x_5)
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x_3_q = self.q_layer_1x3(x_5)
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x_3 = x_3_c + x_3_v + x_3_q
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x_relation = self.conv1x1_return_channel(x_3)
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x_relation = self.bn(x_relation)
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x_relation = self.relu(x_relation)
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return x + x_relation
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def build_intraclblock_list(num_block):
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IntraCLBlock_list = nn.LayerList()
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for i in range(num_block):
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IntraCLBlock_list.append(IntraCLBlock())
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return IntraCLBlock_list |