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| # # This file contains experimental modules | |
| import numpy as np | |
| import torch | |
| from torch import nn | |
| from facelib.detection.yolov5face.models.common import Conv | |
| class CrossConv(nn.Module): | |
| # Cross Convolution Downsample | |
| def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): | |
| # ch_in, ch_out, kernel, stride, groups, expansion, shortcut | |
| super().__init__() | |
| c_ = int(c2 * e) # hidden channels | |
| self.cv1 = Conv(c1, c_, (1, k), (1, s)) | |
| self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) | |
| self.add = shortcut and c1 == c2 | |
| def forward(self, x): | |
| return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) | |
| class MixConv2d(nn.Module): | |
| # Mixed Depthwise Conv https://arxiv.org/abs/1907.09595 | |
| def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): | |
| super().__init__() | |
| groups = len(k) | |
| if equal_ch: # equal c_ per group | |
| i = torch.linspace(0, groups - 1e-6, c2).floor() # c2 indices | |
| c_ = [(i == g).sum() for g in range(groups)] # intermediate channels | |
| else: # equal weight.numel() per group | |
| b = [c2] + [0] * groups | |
| a = np.eye(groups + 1, groups, k=-1) | |
| a -= np.roll(a, 1, axis=1) | |
| a *= np.array(k) ** 2 | |
| a[0] = 1 | |
| c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b | |
| self.m = nn.ModuleList([nn.Conv2d(c1, int(c_[g]), k[g], s, k[g] // 2, bias=False) for g in range(groups)]) | |
| self.bn = nn.BatchNorm2d(c2) | |
| self.act = nn.LeakyReLU(0.1, inplace=True) | |
| def forward(self, x): | |
| return x + self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) | |