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import torch.nn as nn |
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import torch.nn.functional as F |
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from annotator.mmpkg.mmcv.cnn import ConvModule |
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from ..builder import NECKS |
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@NECKS.register_module() |
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class MultiLevelNeck(nn.Module): |
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"""MultiLevelNeck. |
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A neck structure connect vit backbone and decoder_heads. |
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Args: |
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in_channels (List[int]): Number of input channels per scale. |
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out_channels (int): Number of output channels (used at each scale). |
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scales (List[int]): Scale factors for each input feature map. |
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norm_cfg (dict): Config dict for normalization layer. Default: None. |
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act_cfg (dict): Config dict for activation layer in ConvModule. |
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Default: None. |
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""" |
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def __init__(self, |
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in_channels, |
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out_channels, |
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scales=[0.5, 1, 2, 4], |
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norm_cfg=None, |
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act_cfg=None): |
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super(MultiLevelNeck, self).__init__() |
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assert isinstance(in_channels, list) |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.scales = scales |
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self.num_outs = len(scales) |
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self.lateral_convs = nn.ModuleList() |
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self.convs = nn.ModuleList() |
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for in_channel in in_channels: |
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self.lateral_convs.append( |
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ConvModule( |
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in_channel, |
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out_channels, |
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kernel_size=1, |
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norm_cfg=norm_cfg, |
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act_cfg=act_cfg)) |
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for _ in range(self.num_outs): |
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self.convs.append( |
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ConvModule( |
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out_channels, |
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out_channels, |
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kernel_size=3, |
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padding=1, |
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stride=1, |
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norm_cfg=norm_cfg, |
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act_cfg=act_cfg)) |
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def forward(self, inputs): |
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assert len(inputs) == len(self.in_channels) |
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print(inputs[0].shape) |
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inputs = [ |
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lateral_conv(inputs[i]) |
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for i, lateral_conv in enumerate(self.lateral_convs) |
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] |
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if len(inputs) == 1: |
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inputs = [inputs[0] for _ in range(self.num_outs)] |
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outs = [] |
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for i in range(self.num_outs): |
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x_resize = F.interpolate( |
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inputs[i], scale_factor=self.scales[i], mode='bilinear') |
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outs.append(self.convs[i](x_resize)) |
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return tuple(outs) |
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