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from typing import List, Tuple |
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import torch |
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import torch.nn.functional as F |
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from mmcv.cnn import ConvModule |
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from mmengine.model import BaseModule |
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from mmdet.registry import MODELS |
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from mmdet.utils import ConfigType, OptConfigType, OptMultiConfig |
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class SSHContextModule(BaseModule): |
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"""This is an implementation of `SSH context module` described in `SSH: |
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Single Stage Headless Face Detector. |
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<https://arxiv.org/pdf/1708.03979.pdf>`_. |
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Args: |
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in_channels (int): Number of input channels used at each scale. |
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out_channels (int): Number of output channels used at each scale. |
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conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
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convolution layer. Defaults to None. |
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norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
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layer. Defaults to dict(type='BN'). |
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init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
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list[dict], optional): Initialization config dict. |
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Defaults to None. |
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""" |
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def __init__(self, |
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in_channels: int, |
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out_channels: int, |
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conv_cfg: OptConfigType = None, |
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norm_cfg: ConfigType = dict(type='BN'), |
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init_cfg: OptMultiConfig = None): |
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super().__init__(init_cfg=init_cfg) |
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assert out_channels % 4 == 0 |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.conv5x5_1 = ConvModule( |
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self.in_channels, |
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self.out_channels // 4, |
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3, |
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stride=1, |
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padding=1, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg, |
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) |
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self.conv5x5_2 = ConvModule( |
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self.out_channels // 4, |
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self.out_channels // 4, |
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3, |
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stride=1, |
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padding=1, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg, |
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act_cfg=None) |
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self.conv7x7_2 = ConvModule( |
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self.out_channels // 4, |
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self.out_channels // 4, |
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3, |
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stride=1, |
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padding=1, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg, |
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) |
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self.conv7x7_3 = ConvModule( |
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self.out_channels // 4, |
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self.out_channels // 4, |
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3, |
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stride=1, |
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padding=1, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg, |
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act_cfg=None, |
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) |
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def forward(self, x: torch.Tensor) -> tuple: |
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conv5x5_1 = self.conv5x5_1(x) |
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conv5x5 = self.conv5x5_2(conv5x5_1) |
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conv7x7_2 = self.conv7x7_2(conv5x5_1) |
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conv7x7 = self.conv7x7_3(conv7x7_2) |
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return (conv5x5, conv7x7) |
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class SSHDetModule(BaseModule): |
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"""This is an implementation of `SSH detection module` described in `SSH: |
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Single Stage Headless Face Detector. |
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<https://arxiv.org/pdf/1708.03979.pdf>`_. |
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Args: |
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in_channels (int): Number of input channels used at each scale. |
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out_channels (int): Number of output channels used at each scale. |
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conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
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convolution layer. Defaults to None. |
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norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
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layer. Defaults to dict(type='BN'). |
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init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
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list[dict], optional): Initialization config dict. |
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Defaults to None. |
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""" |
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def __init__(self, |
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in_channels: int, |
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out_channels: int, |
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conv_cfg: OptConfigType = None, |
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norm_cfg: ConfigType = dict(type='BN'), |
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init_cfg: OptMultiConfig = None): |
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super().__init__(init_cfg=init_cfg) |
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assert out_channels % 4 == 0 |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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self.conv3x3 = ConvModule( |
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self.in_channels, |
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self.out_channels // 2, |
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3, |
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stride=1, |
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padding=1, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg, |
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act_cfg=None) |
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self.context_module = SSHContextModule( |
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in_channels=self.in_channels, |
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out_channels=self.out_channels, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg) |
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def forward(self, x: torch.Tensor) -> torch.Tensor: |
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conv3x3 = self.conv3x3(x) |
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conv5x5, conv7x7 = self.context_module(x) |
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out = torch.cat([conv3x3, conv5x5, conv7x7], dim=1) |
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out = F.relu(out) |
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return out |
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@MODELS.register_module() |
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class SSH(BaseModule): |
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"""`SSH Neck` used in `SSH: Single Stage Headless Face Detector. |
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<https://arxiv.org/pdf/1708.03979.pdf>`_. |
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Args: |
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num_scales (int): The number of scales / stages. |
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in_channels (list[int]): The number of input channels per scale. |
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out_channels (list[int]): The number of output channels per scale. |
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conv_cfg (:obj:`ConfigDict` or dict, optional): Config dict for |
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convolution layer. Defaults to None. |
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norm_cfg (:obj:`ConfigDict` or dict): Config dict for normalization |
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layer. Defaults to dict(type='BN'). |
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init_cfg (:obj:`ConfigDict` or list[:obj:`ConfigDict`] or dict or |
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list[dict], optional): Initialization config dict. |
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Example: |
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>>> import torch |
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>>> in_channels = [8, 16, 32, 64] |
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>>> out_channels = [16, 32, 64, 128] |
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>>> scales = [340, 170, 84, 43] |
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>>> inputs = [torch.rand(1, c, s, s) |
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... for c, s in zip(in_channels, scales)] |
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>>> self = SSH(num_scales=4, in_channels=in_channels, |
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... out_channels=out_channels) |
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>>> outputs = self.forward(inputs) |
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>>> for i in range(len(outputs)): |
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... print(f'outputs[{i}].shape = {outputs[i].shape}') |
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outputs[0].shape = torch.Size([1, 16, 340, 340]) |
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outputs[1].shape = torch.Size([1, 32, 170, 170]) |
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outputs[2].shape = torch.Size([1, 64, 84, 84]) |
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outputs[3].shape = torch.Size([1, 128, 43, 43]) |
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""" |
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def __init__(self, |
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num_scales: int, |
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in_channels: List[int], |
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out_channels: List[int], |
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conv_cfg: OptConfigType = None, |
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norm_cfg: ConfigType = dict(type='BN'), |
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init_cfg: OptMultiConfig = dict( |
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type='Xavier', layer='Conv2d', distribution='uniform')): |
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super().__init__(init_cfg=init_cfg) |
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assert (num_scales == len(in_channels) == len(out_channels)) |
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self.num_scales = num_scales |
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self.in_channels = in_channels |
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self.out_channels = out_channels |
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for idx in range(self.num_scales): |
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in_c, out_c = self.in_channels[idx], self.out_channels[idx] |
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self.add_module( |
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f'ssh_module{idx}', |
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SSHDetModule( |
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in_channels=in_c, |
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out_channels=out_c, |
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conv_cfg=conv_cfg, |
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norm_cfg=norm_cfg)) |
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def forward(self, inputs: Tuple[torch.Tensor]) -> tuple: |
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assert len(inputs) == self.num_scales |
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outs = [] |
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for idx, x in enumerate(inputs): |
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ssh_module = getattr(self, f'ssh_module{idx}') |
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out = ssh_module(x) |
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outs.append(out) |
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return tuple(outs) |
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