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from typing import Optional |
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import torch.nn as nn |
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from mmengine.model import BaseModule |
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from torch import Tensor |
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from mmdet.registry import MODELS |
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from mmdet.utils import MultiConfig |
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@MODELS.register_module() |
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class FeatureRelayHead(BaseModule): |
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"""Feature Relay Head used in `SCNet <https://arxiv.org/abs/2012.10150>`_. |
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Args: |
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in_channels (int): number of input channels. Defaults to 256. |
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conv_out_channels (int): number of output channels before |
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classification layer. Defaults to 256. |
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roi_feat_size (int): roi feat size at box head. Default: 7. |
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scale_factor (int): scale factor to match roi feat size |
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at mask head. Defaults to 2. |
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init_cfg (:obj:`ConfigDict` or dict or list[dict] or |
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list[:obj:`ConfigDict`]): Initialization config dict. Defaults to |
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dict(type='Kaiming', layer='Linear'). |
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""" |
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def __init__( |
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self, |
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in_channels: int = 1024, |
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out_conv_channels: int = 256, |
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roi_feat_size: int = 7, |
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scale_factor: int = 2, |
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init_cfg: MultiConfig = dict(type='Kaiming', layer='Linear') |
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) -> None: |
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super().__init__(init_cfg=init_cfg) |
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assert isinstance(roi_feat_size, int) |
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self.in_channels = in_channels |
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self.out_conv_channels = out_conv_channels |
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self.roi_feat_size = roi_feat_size |
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self.out_channels = (roi_feat_size**2) * out_conv_channels |
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self.scale_factor = scale_factor |
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self.fp16_enabled = False |
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self.fc = nn.Linear(self.in_channels, self.out_channels) |
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self.upsample = nn.Upsample( |
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scale_factor=scale_factor, mode='bilinear', align_corners=True) |
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def forward(self, x: Tensor) -> Optional[Tensor]: |
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"""Forward function. |
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Args: |
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x (Tensor): Input feature. |
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Returns: |
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Optional[Tensor]: Output feature. When the first dim of input is |
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0, None is returned. |
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""" |
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N, _ = x.shape |
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if N > 0: |
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out_C = self.out_conv_channels |
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out_HW = self.roi_feat_size |
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x = self.fc(x) |
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x = x.reshape(N, out_C, out_HW, out_HW) |
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x = self.upsample(x) |
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return x |
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return None |
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