class RepNCSP_AKConv(RepNCSP):# CSP Bottleneck with 3 convolutionsdef __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__(c1, c2, n, shortcut, g, e)c_ = int(c2 * e)  # hidden channelsself.cv1 = AKConv(c1, c_)self.cv2 = AKConv(c1, c_)self.cv3 = AKConv(2 * c_, c2)  # optional act=FReLU(c2)self.m = nn.Sequential(*(RepNBottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n)))class RepNCSPELAN4AKConv1(RepNCSPELAN4):def __init__(self, c1, c2, c3, c4, c5=1):  # ch_in, ch_out, number, shortcut, groups, expansionsuper().__init__(c1, c2, c3, c4, c5)self.cv1 = AKConv(c1, c3)self.cv2 = nn.Sequential(RepNCSP_AKConv(c3//2, c4, c5), Conv(c4, c4, 3, 1))self.cv3 = nn.Sequential(RepNCSP_AKConv(c4, c4, c5), Conv(c4, c4, 3, 1))self.cv4 = AKConv(c3+(2*c4), c2)from einops import rearrangeclass AKConv(nn.Module):def __init__(self, inc, outc, num_param=5, stride=1):"""初始化參數說明：inc: 輸入通道數, outc: 輸出通道數, num_param:（卷積核）參數量, stride = 1:卷積步長默認為1, bias = None：默認無偏執"""super(AKConv, self).__init__()self.num_param = num_paramself.stride = strideself.conv = Conv(inc, outc, k=(num_param, 1), s=(num_param, 1) )self.p_conv = nn.Conv2d(inc, 2 * num_param, kernel_size=3, padding=1, stride=stride)nn.init.constant_(self.p_conv.weight, 0)self.p_conv.register_full_backward_hook(self._set_lr)@staticmethoddef _set_lr(module, grad_input, grad_output):grad_input = (grad_input[i] * 0.1 for i in range(len(grad_input)))grad_output = (grad_output[i] * 0.1 for i in range(len(grad_output)))def forward(self, x):# N is num_param.offset = self.p_conv(x)dtype = offset.data.type()N = offset.size(1) // 2# (b, 2N, h, w)p = self._get_p(offset, dtype)# (b, h, w, 2N)p = p.contiguous().permute(0, 2, 3, 1)q_lt = p.detach().floor()q_rb = q_lt + 1q_lt = torch.cat([torch.clamp(q_lt[..., :N], 0, x.size(2) - 1), torch.clamp(q_lt[..., N:], 0, x.size(3) - 1)],dim=-1).long()q_rb = torch.cat([torch.clamp(q_rb[..., :N], 0, x.size(2) - 1), torch.clamp(q_rb[..., N:], 0, x.size(3) - 1)],dim=-1).long()q_lb = torch.cat([q_lt[..., :N], q_rb[..., N:]], dim=-1)q_rt = torch.cat([q_rb[..., :N], q_lt[..., N:]], dim=-1)# clip pp = torch.cat([torch.clamp(p[..., :N], 0, x.size(2) - 1), torch.clamp(p[..., N:], 0, x.size(3) - 1)], dim=-1)# bilinear kernel (b, h, w, N)g_lt = (1 + (q_lt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_lt[..., N:].type_as(p) - p[..., N:]))g_rb = (1 - (q_rb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_rb[..., N:].type_as(p) - p[..., N:]))g_lb = (1 + (q_lb[..., :N].type_as(p) - p[..., :N])) * (1 - (q_lb[..., N:].type_as(p) - p[..., N:]))g_rt = (1 - (q_rt[..., :N].type_as(p) - p[..., :N])) * (1 + (q_rt[..., N:].type_as(p) - p[..., N:]))# resampling the features based on the modified coordinates.x_q_lt = self._get_x_q(x, q_lt, N)x_q_rb = self._get_x_q(x, q_rb, N)x_q_lb = self._get_x_q(x, q_lb, N)x_q_rt = self._get_x_q(x, q_rt, N)# bilinearx_offset = g_lt.unsqueeze(dim=1) * x_q_lt + \g_rb.unsqueeze(dim=1) * x_q_rb + \g_lb.unsqueeze(dim=1) * x_q_lb + \g_rt.unsqueeze(dim=1) * x_q_rtx_offset = self._reshape_x_offset(x_offset, self.num_param)out = self.conv(x_offset)return out# generating the inital sampled shapes for the AKConv with different sizes.def _get_p_n(self, N, dtype):base_int = round(math.sqrt(self.num_param))row_number = self.num_param // base_intmod_number = self.num_param % base_intp_n_x, p_n_y = torch.meshgrid(torch.arange(0, row_number),torch.arange(0, base_int), indexing='xy')p_n_x = torch.flatten(p_n_x)p_n_y = torch.flatten(p_n_y)if mod_number > 0:mod_p_n_x, mod_p_n_y = torch.meshgrid(torch.arange(row_number, row_number + 1),torch.arange(0, mod_number), indexing='xy')mod_p_n_x = torch.flatten(mod_p_n_x)mod_p_n_y = torch.flatten(mod_p_n_y)p_n_x, p_n_y = torch.cat((p_n_x, mod_p_n_x)), torch.cat((p_n_y, mod_p_n_y))p_n = torch.cat([p_n_x, p_n_y], 0)p_n = p_n.view(1, 2 * N, 1, 1).type(dtype)return p_n# no zero-paddingdef _get_p_0(self, h, w, N, dtype):p_0_x, p_0_y = torch.meshgrid(torch.arange(0, h * self.stride, self.stride),torch.arange(0, w * self.stride, self.stride), indexing='xy')p_0_x = torch.flatten(p_0_x).view(1, 1, h, w).repeat(1, N, 1, 1)p_0_y = torch.flatten(p_0_y).view(1, 1, h, w).repeat(1, N, 1, 1)p_0 = torch.cat([p_0_x, p_0_y], 1).type(dtype)return p_0def _get_p(self, offset, dtype):N, h, w = offset.size(1) // 2, offset.size(2), offset.size(3)# (1, 2N, 1, 1)p_n = self._get_p_n(N, dtype)# (1, 2N, h, w)p_0 = self._get_p_0(h, w, N, dtype)p = p_0 + p_n + offsetreturn pdef _get_x_q(self, x, q, N):b, h, w, _ = q.size()padded_w = x.size(3)c = x.size(1)# (b, c, h*w)x = x.contiguous().view(b, c, -1)# (b, h, w, N)index = q[..., :N] * padded_w + q[..., N:]  # offset_x*w + offset_y# (b, c, h*w*N)index = index.contiguous().unsqueeze(dim=1).expand(-1, c, -1, -1, -1).contiguous().view(b, c, -1)x_offset = x.gather(dim=-1, index=index).contiguous().view(b, c, h, w, N)return x_offset#  Stacking resampled features in the row direction.@staticmethoddef _reshape_x_offset(x_offset, num_param):b, c, h, w, n = x_offset.size()x_offset = rearrange(x_offset, 'b c h w n -> b c (h n) w')return x_offset

            RepNCSPELAN4, SPPELAN, RepNCSPELAN4AKConv1}:

# YOLOv9
# Powered bu https://blog.csdn.net/StopAndGoyyy# parameters
nc: 80  # number of classes
#depth_multiple: 0.33  # model depth multiple
depth_multiple: 1  # model depth multiple
#width_multiple: 0.25  # layer channel multiple
width_multiple: 1  # layer channel multiple
#activation: nn.LeakyReLU(0.1)
#activation: nn.ReLU()# anchors
anchors: 3# YOLOv9 backbone
backbone:[[-1, 1, Silence, []],  # conv down[-1, 1, Conv, [64, 3, 2]],  # 1-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 2-P2/4# elan-1 block[-1, 1, RepNCSPELAN4AKConv1, [256, 128, 64, 1]],  # 3# avg-conv down[-1, 1, ADown, [256]],  # 4-P3/8# elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 5# avg-conv down[-1, 1, ADown, [512]],  # 6-P4/16# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 7# avg-conv down[-1, 1, ADown, [512]],  # 8-P5/32# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 9]# YOLOv9 head
head:[# elan-spp block[-1, 1, SPPELAN, [512, 256]],  # 10# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 7], 1, Concat, [1]],  # cat backbone P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 13# up-concat merge[-1, 1, nn.Upsample, [None, 2, 'nearest']],[[-1, 5], 1, Concat, [1]],  # cat backbone P3# elan-2 block[-1, 1, RepNCSPELAN4, [256, 256, 128, 1]],  # 16 (P3/8-small)# avg-conv-down merge[-1, 1, ADown, [256]],[[-1, 13], 1, Concat, [1]],  # cat head P4# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 19 (P4/16-medium)# avg-conv-down merge[-1, 1, ADown, [512]],[[-1, 10], 1, Concat, [1]],  # cat head P5# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 22 (P5/32-large)# multi-level reversible auxiliary branch# routing[5, 1, CBLinear, [[256]]], # 23[7, 1, CBLinear, [[256, 512]]], # 24[9, 1, CBLinear, [[256, 512, 512]]], # 25# conv down[0, 1, Conv, [64, 3, 2]],  # 26-P1/2# conv down[-1, 1, Conv, [128, 3, 2]],  # 27-P2/4# elan-1 block[-1, 1, RepNCSPELAN4, [256, 128, 64, 1]],  # 28# avg-conv down fuse[-1, 1, ADown, [256]],  # 29-P3/8[[23, 24, 25, -1], 1, CBFuse, [[0, 0, 0]]], # 30  # elan-2 block[-1, 1, RepNCSPELAN4, [512, 256, 128, 1]],  # 31# avg-conv down fuse[-1, 1, ADown, [512]],  # 32-P4/16[[24, 25, -1], 1, CBFuse, [[1, 1]]], # 33 # elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 34# avg-conv down fuse[-1, 1, ADown, [512]],  # 35-P5/32[[25, -1], 1, CBFuse, [[2]]], # 36# elan-2 block[-1, 1, RepNCSPELAN4, [512, 512, 256, 1]],  # 37# detection head# detect[[31, 34, 37, 16, 19, 22], 1, DualDDetect, [nc]],  # DualDDetect(A3, A4, A5, P3, P4, P5)]