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| import paddle | |
| from paddle import nn | |
| # refer from: https://github.com/ViTAE-Transformer/I3CL/blob/736c80237f66d352d488e83b05f3e33c55201317/mmdet/models/detectors/intra_cl_module.py | |
| class IntraCLBlock(nn.Layer): | |
| def __init__(self, in_channels=96, reduce_factor=4): | |
| super(IntraCLBlock, self).__init__() | |
| self.channels = in_channels | |
| self.rf = reduce_factor | |
| weight_attr = paddle.nn.initializer.KaimingUniform() | |
| 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.LayerList() | |
| for i in range(num_block): | |
| IntraCLBlock_list.append(IntraCLBlock()) | |
| return IntraCLBlock_list |