PyTorch Hub updates

models/common.py

@@ -1,8 +1,6 @@
 # This file contains modules common to various models
 
 
-import torch.nn.functional as F
-
 from utils.utils import *
 
 
@@ -58,17 +56,6 @@ class BottleneckCSP(nn.Module):
         return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1))))
 
 
-class ConvPlus(nn.Module):
-    # Plus-shaped convolution
-    def __init__(self, c1, c2, k=3, s=1, g=1, bias=True):  # ch_in, ch_out, kernel, stride, groups
-        super(ConvPlus, self).__init__()
-        self.cv1 = nn.Conv2d(c1, c2, (k, 1), s, (k // 2, 0), groups=g, bias=bias)
-        self.cv2 = nn.Conv2d(c1, c2, (1, k), s, (0, k // 2), groups=g, bias=bias)
-
-    def forward(self, x):
-        return self.cv1(x) + self.cv2(x)
-
-
 class SPP(nn.Module):
     # Spatial pyramid pooling layer used in YOLOv3-SPP
     def __init__(self, c1, c2, k=(5, 9, 13)):
@@ -107,27 +94,3 @@ class Concat(nn.Module):
 
     def forward(self, x):
         return torch.cat(x, self.d)
-
-
-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(MixConv2d, self).__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)))