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| # Copyright (c) OpenMMLab. All rights reserved. | |
| 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 | |
| # fuse to ncnn's shufflechannel | |
| 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 | |
| # fuse to ncnn's shufflechannel | |
| 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)) | |