# This file contains modules common to various models import torch.nn as nn import torch import torch.nn.functional as F def autopad(k, p=None): # kernel, padding # Pad to 'same' if p is None: p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad return p def DWConv(c1, c2, k=1, s=1, act=True): # Depthwise convolution return Conv(c1, c2, k, s, g=math.gcd(c1, c2), act=act) class Conv(nn.Module): # Standard convolution def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Conv, self).__init__() self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) self.bn = nn.BatchNorm2d(c2) self.act = nn.LeakyReLU(0.1, inplace=True) if act else nn.Identity() def forward(self, x): return self.act(self.bn(self.conv(x))) def fuseforward(self, x): return self.act(self.conv(x)) class Bottleneck(nn.Module): # Standard bottleneck def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): # ch_in, ch_out, shortcut, groups, expansion super(Bottleneck, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_, c2, 3, 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 BottleneckCSP(nn.Module): # CSP Bottleneck https://github.com/WongKinYiu/CrossStagePartialNetworks def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(BottleneckCSP, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) self.cv4 = Conv(2 * c_, c2, 1, 1) self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) self.act = nn.LeakyReLU(0.1, inplace=True) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): y1 = self.cv3(self.m(self.cv1(x))) y2 = self.cv2(x) return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) class SPP(nn.Module): # Spatial pyramid pooling layer used in YOLOv3-SPP def __init__(self, c1, c2, k=(5, 9, 13)): super(SPP, self).__init__() c_ = c1 // 2 # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) def forward(self, x): x = self.cv1(x) return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) class Focus(nn.Module):# # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Focus, self).__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) w1_1 = torch.tensor([[[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w1_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w1_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[1., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w3_1 = torch.tensor([[[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w3_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w3_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 1., 0.],[0., 0., 0.],[0., 0., 0.]]]) w2_1 = torch.tensor([[[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w2_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w2_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[1., 0., 0.],[0., 0., 0.]]]) w4_1 = torch.tensor([[[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w4_2 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]]]) w4_3 = torch.tensor([[[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 0., 0.],[0., 0., 0.]], [[0., 0., 0.],[0., 1., 0.],[0., 0., 0.]]]) w1_1 = w1_1.view(1, 3, 3, 3) w1_2 = w1_2.view(1, 3, 3, 3) w1_3 = w1_3.view(1, 3, 3, 3) w2_1 = w2_1.view(1, 3, 3, 3) w2_2 = w2_2.view(1, 3, 3, 3) w2_3 = w2_3.view(1, 3, 3, 3) w3_1 = w3_1.view(1, 3, 3, 3) w3_2 = w3_2.view(1, 3, 3, 3) w3_3 = w3_3.view(1, 3, 3, 3) w4_1 = w4_1.view(1, 3, 3, 3) w4_2 = w4_2.view(1, 3, 3, 3) w4_3 = w4_3.view(1, 3, 3, 3) self.w_cat = torch.cat([w1_1, w1_2,w1_3, w2_1,w2_2,w2_3, w3_1,w3_2,w3_3, w4_1,w4_2,w4_3], 0) self.p2d = (0, 2, 0, 2) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) # x = x.type(torch.cuda.FloatTensor) #x_gt = self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) x_pad = F.pad(x, self.p2d, 'constant', 0) xx = F.conv2d(x_pad, self.w_cat.to(x.device),stride=2) xx = self.conv(xx) #print(torch.sum(x_gt - xx)) return xx class Focus_ori(nn.Module):# # Focus wh information into c-space def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): # ch_in, ch_out, kernel, stride, padding, groups super(Focus, self).__init__() self.conv = Conv(c1 * 4, c2, k, s, p, g, act) def forward(self, x): # x(b,c,w,h) -> y(b,4c,w/2,h/2) x = x.type(torch.cuda.FloatTensor) return self.conv(torch.cat([x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]], 1)) class Concat(nn.Module): # Concatenate a list of tensors along dimension def __init__(self, dimension=1): super(Concat, self).__init__() self.d = dimension def forward(self, x): return torch.cat(x, self.d) class Flatten(nn.Module): # Use after nn.AdaptiveAvgPool2d(1) to remove last 2 dimensions @staticmethod def forward(x): return x.view(x.size(0), -1) class Classify(nn.Module): # Classification head, i.e. x(b,c1,20,20) to x(b,c2) def __init__(self, c1, c2, k=1, s=1, p=None, g=1): # ch_in, ch_out, kernel, stride, padding, groups super(Classify, self).__init__() self.aap = nn.AdaptiveAvgPool2d(1) # to x(b,c1,1,1) self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p), groups=g, bias=False) # to x(b,c2,1,1) self.flat = Flatten() def forward(self, x): z = torch.cat([self.aap(y) for y in (x if isinstance(x, list) else [x])], 1) # cat if list return self.flat(self.conv(z)) # flatten to x(b,c2) 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))) 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(CrossConv, self).__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 C3(nn.Module): # # Cross Convolution CSP # def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion # super(C3, self).__init__() # c_ = int(c2 * e) # hidden channels # self.cv1 = Conv(c1, c_, 1, 1) # self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) # self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) # self.cv4 = Conv(2 * c_, c2, 1, 1) # self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) # self.act = nn.LeakyReLU(0.1, inplace=True) # self.m = nn.Sequential(*[CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n)]) # def forward(self, x): # y1 = self.cv3(self.m(self.cv1(x))) # y2 = self.cv2(x) # return self.cv4(self.act(self.bn(torch.cat((y1, y2), dim=1)))) class C3(nn.Module): # CSP Bottleneck with 3 convolutions def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): # ch_in, ch_out, number, shortcut, groups, expansion super(C3, self).__init__() c_ = int(c2 * e) # hidden channels self.cv1 = Conv(c1, c_, 1, 1) self.cv2 = Conv(c1, c_, 1, 1) self.cv3 = Conv(2 * c_, c2, 1) # act=FReLU(c2) self.m = nn.Sequential(*[Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)]) def forward(self, x): return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), dim=1))