| |
| import torch |
| import torch.nn as nn |
| import torch.nn.functional as F |
| from torch import Tensor |
|
|
|
|
| class DeployFocus(nn.Module): |
|
|
| def __init__(self, orin_Focus: nn.Module): |
| super().__init__() |
| self.__dict__.update(orin_Focus.__dict__) |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| batch_size, channel, height, width = x.shape |
| x = x.reshape(batch_size, channel, -1, 2, width) |
| x = x.reshape(batch_size, channel, x.shape[2], 2, -1, 2) |
| half_h = x.shape[2] |
| half_w = x.shape[4] |
| x = x.permute(0, 5, 3, 1, 2, 4) |
| x = x.reshape(batch_size, channel * 4, half_h, half_w) |
|
|
| return self.conv(x) |
|
|
|
|
| class NcnnFocus(nn.Module): |
|
|
| def __init__(self, orin_Focus: nn.Module): |
| super().__init__() |
| self.__dict__.update(orin_Focus.__dict__) |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| batch_size, c, h, w = x.shape |
| assert h % 2 == 0 and w % 2 == 0, f'focus for yolox needs even feature\ |
| height and width, got {(h, w)}.' |
|
|
| x = x.reshape(batch_size, c * h, 1, w) |
| _b, _c, _h, _w = x.shape |
| g = _c // 2 |
| |
| x = x.view(_b, g, 2, _h, _w) |
| x = torch.transpose(x, 1, 2).contiguous() |
| x = x.view(_b, -1, _h, _w) |
|
|
| x = x.reshape(_b, c * h * w, 1, 1) |
|
|
| _b, _c, _h, _w = x.shape |
| g = _c // 2 |
| |
| x = x.view(_b, g, 2, _h, _w) |
| x = torch.transpose(x, 1, 2).contiguous() |
| x = x.view(_b, -1, _h, _w) |
|
|
| x = x.reshape(_b, c * 4, h // 2, w // 2) |
|
|
| return self.conv(x) |
|
|
|
|
| class GConvFocus(nn.Module): |
|
|
| def __init__(self, orin_Focus: nn.Module): |
| super().__init__() |
| device = next(orin_Focus.parameters()).device |
| self.weight1 = torch.tensor([[1., 0], [0, 0]]).expand(3, 1, 2, |
| 2).to(device) |
| self.weight2 = torch.tensor([[0, 0], [1., 0]]).expand(3, 1, 2, |
| 2).to(device) |
| self.weight3 = torch.tensor([[0, 1.], [0, 0]]).expand(3, 1, 2, |
| 2).to(device) |
| self.weight4 = torch.tensor([[0, 0], [0, 1.]]).expand(3, 1, 2, |
| 2).to(device) |
| self.__dict__.update(orin_Focus.__dict__) |
|
|
| def forward(self, x: Tensor) -> Tensor: |
| conv1 = F.conv2d(x, self.weight1, stride=2, groups=3) |
| conv2 = F.conv2d(x, self.weight2, stride=2, groups=3) |
| conv3 = F.conv2d(x, self.weight3, stride=2, groups=3) |
| conv4 = F.conv2d(x, self.weight4, stride=2, groups=3) |
| return self.conv(torch.cat([conv1, conv2, conv3, conv4], dim=1)) |
|
|
