66 lines
1.8 KiB
Python
66 lines
1.8 KiB
Python
from typing import List, Tuple
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import torch
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from torch import nn
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from torch.nn import functional as F
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from mmdet.models import NECKS
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__all__ = ["LSSFPN"]
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@NECKS.register_module()
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class LSSFPN(nn.Module):
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def __init__(
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self,
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in_indices: Tuple[int, int],
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in_channels: Tuple[int, int],
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out_channels: int,
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scale_factor: int = 1,
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) -> None:
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super().__init__()
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self.in_indices = in_indices
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self.in_channels = in_channels
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self.out_channels = out_channels
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self.scale_factor = scale_factor
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self.fuse = nn.Sequential(
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nn.Conv2d(in_channels[0] + in_channels[1], out_channels, 1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(True),
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nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(True),
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)
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if scale_factor > 1:
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self.upsample = nn.Sequential(
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nn.Upsample(
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scale_factor=scale_factor,
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mode="bilinear",
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align_corners=True,
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),
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nn.Conv2d(out_channels, out_channels, 3, padding=1, bias=False),
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nn.BatchNorm2d(out_channels),
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nn.ReLU(True),
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)
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def forward(self, x: List[torch.Tensor]) -> torch.Tensor:
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x1 = x[self.in_indices[0]]
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assert x1.shape[1] == self.in_channels[0]
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x2 = x[self.in_indices[1]]
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assert x2.shape[1] == self.in_channels[1]
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x1 = F.interpolate(
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x1,
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size=x2.shape[-2:],
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mode="bilinear",
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align_corners=True,
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)
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x = torch.cat([x1, x2], dim=1)
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x = self.fuse(x)
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if self.scale_factor > 1:
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x = self.upsample(x)
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return x
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