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import torch
import torch.nn as nn
import math
from yolov6.layers.common import *
class Detect(nn.Module):
'''Efficient Decoupled Head
With hardware-aware degisn, the decoupled head is optimized with
hybridchannels methods.
'''
def __init__(self, num_classes=80, anchors=1, num_layers=3, inplace=True, head_layers=None): # detection layer
super().__init__()
assert head_layers is not None
self.nc = num_classes # number of classes
self.no = num_classes + 5 # number of outputs per anchor
self.nl = num_layers # number of detection layers
if isinstance(anchors, (list, tuple)):
self.na = len(anchors[0]) // 2
else:
self.na = anchors
self.anchors = anchors
self.grid = [torch.zeros(1)] * num_layers
self.prior_prob = 1e-2
self.inplace = inplace
stride = [8, 16, 32] # strides computed during build
self.stride = torch.tensor(stride)
# Init decouple head
self.cls_convs = nn.ModuleList()
self.reg_convs = nn.ModuleList()
self.cls_preds = nn.ModuleList()
self.reg_preds = nn.ModuleList()
self.obj_preds = nn.ModuleList()
self.stems = nn.ModuleList()
# Efficient decoupled head layers
for i in range(num_layers):
idx = i*6
self.stems.append(head_layers[idx])
self.cls_convs.append(head_layers[idx+1])
self.reg_convs.append(head_layers[idx+2])
self.cls_preds.append(head_layers[idx+3])
self.reg_preds.append(head_layers[idx+4])
self.obj_preds.append(head_layers[idx+5])
def initialize_biases(self):
for conv in self.cls_preds:
b = conv.bias.view(self.na, -1)
b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob))
conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
for conv in self.obj_preds:
b = conv.bias.view(self.na, -1)
b.data.fill_(-math.log((1 - self.prior_prob) / self.prior_prob))
conv.bias = torch.nn.Parameter(b.view(-1), requires_grad=True)
def forward(self, x):
z = []
for i in range(self.nl):
x[i] = self.stems[i](x[i])
cls_x = x[i]
reg_x = x[i]
cls_feat = self.cls_convs[i](cls_x)
cls_output = self.cls_preds[i](cls_feat)
reg_feat = self.reg_convs[i](reg_x)
reg_output = self.reg_preds[i](reg_feat)
obj_output = self.obj_preds[i](reg_feat)
if self.training:
x[i] = torch.cat([reg_output, obj_output, cls_output], 1)
bs, _, ny, nx = x[i].shape
x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
else:
y = torch.cat([reg_output, obj_output.sigmoid(), cls_output.sigmoid()], 1)
bs, _, ny, nx = y.shape
y = y.view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous()
if self.grid[i].shape[2:4] != y.shape[2:4]:
d = self.stride.device
yv, xv = torch.meshgrid([torch.arange(ny).to(d), torch.arange(nx).to(d)])
self.grid[i] = torch.stack((xv, yv), 2).view(1, self.na, ny, nx, 2).float()
if self.inplace:
y[..., 0:2] = (y[..., 0:2] + self.grid[i]) * self.stride[i] # xy
y[..., 2:4] = torch.exp(y[..., 2:4]) * self.stride[i] # wh
else:
xy = (y[..., 0:2] + self.grid[i]) * self.stride[i] # xy
wh = torch.exp(y[..., 2:4]) * self.stride[i] # wh
y = torch.cat((xy, wh, y[..., 4:]), -1)
z.append(y.view(bs, -1, self.no))
return x if self.training else torch.cat(z, 1)
def build_effidehead_layer(channels_list, num_anchors, num_classes):
head_layers = nn.Sequential(
# stem0
Conv(
in_channels=channels_list[6],
out_channels=channels_list[6],
kernel_size=1,
stride=1
),
# cls_conv0
Conv(
in_channels=channels_list[6],
out_channels=channels_list[6],
kernel_size=3,
stride=1
),
# reg_conv0
Conv(
in_channels=channels_list[6],
out_channels=channels_list[6],
kernel_size=3,
stride=1
),
# cls_pred0
nn.Conv2d(
in_channels=channels_list[6],
out_channels=num_classes * num_anchors,
kernel_size=1
),
# reg_pred0
nn.Conv2d(
in_channels=channels_list[6],
out_channels=4 * num_anchors,
kernel_size=1
),
# obj_pred0
nn.Conv2d(
in_channels=channels_list[6],
out_channels=1 * num_anchors,
kernel_size=1
),
# stem1
Conv(
in_channels=channels_list[8],
out_channels=channels_list[8],
kernel_size=1,
stride=1
),
# cls_conv1
Conv(
in_channels=channels_list[8],
out_channels=channels_list[8],
kernel_size=3,
stride=1
),
# reg_conv1
Conv(
in_channels=channels_list[8],
out_channels=channels_list[8],
kernel_size=3,
stride=1
),
# cls_pred1
nn.Conv2d(
in_channels=channels_list[8],
out_channels=num_classes * num_anchors,
kernel_size=1
),
# reg_pred1
nn.Conv2d(
in_channels=channels_list[8],
out_channels=4 * num_anchors,
kernel_size=1
),
# obj_pred1
nn.Conv2d(
in_channels=channels_list[8],
out_channels=1 * num_anchors,
kernel_size=1
),
# stem2
Conv(
in_channels=channels_list[10],
out_channels=channels_list[10],
kernel_size=1,
stride=1
),
# cls_conv2
Conv(
in_channels=channels_list[10],
out_channels=channels_list[10],
kernel_size=3,
stride=1
),
# reg_conv2
Conv(
in_channels=channels_list[10],
out_channels=channels_list[10],
kernel_size=3,
stride=1
),
# cls_pred2
nn.Conv2d(
in_channels=channels_list[10],
out_channels=num_classes * num_anchors,
kernel_size=1
),
# reg_pred2
nn.Conv2d(
in_channels=channels_list[10],
out_channels=4 * num_anchors,
kernel_size=1
),
# obj_pred2
nn.Conv2d(
in_channels=channels_list[10],
out_channels=1 * num_anchors,
kernel_size=1
)
)
return head_layers
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