FCOS官方程式碼詳解（二）：Architecture(head)
阿新 • • 發佈：2021-01-07
https://blog.csdn.net/laizi_laizi/article/details/105519290
FCOS官方程式碼詳解（二）：Architecture[head]
上一篇寫到head部分就感覺太長了，還是分開來寫：FCOS官方程式碼詳解（一）：Architecture(backbone)
這一篇就繼續把architecture中的fcos_head分析一下，腦海中一直要有這圖的印象：
fcos_head
在類GeneralizedRCNN初始化的時候還有這麼一句：self.rpn = build_rpn(cfg, self.backbone.out_channels)
fcos_core/modeling/rpn/fcos/fcos.py然後build_fcos返回的是FCOSModule
def build_fcos(cfg, in_channels):
return FCOSModule(cfg, in_channels)
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看一下FCOSModule()的初始化部分
class FCOSModule(torch.nn.Module):
"""
Module for FCOS computation. Takes feature maps from the backbone and
FCOS outputs and losses. Only Test on FPN now.
"""
def __init__(self, cfg, in_channels):
super(FCOSModule, self).__init__()
head = FCOSHead(cfg, in_channels) # 構造fcos的頭部
box_selector_test = make_fcos_postprocessor(cfg)
loss_evaluator = make_fcos_loss_evaluator(cfg)
self.head = head
self.box_selector_test = box_selector_test
self.loss_evaluator = loss_evaluator
self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES # eg:[8, 16, 32, 64, 128]
def forward(self, images, features, targets=None): # 呼叫的時候:self.rpn(images, features, targets)
pass
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那就轉過去看一下FCOSHead：
class FCOSHead(torch.nn.Module):
def __init__(self, cfg, in_channels):
"""
Arguments:
in_channels (int): number of channels of the input feature
這個就是fpn每層的輸出通道數，根據之前分析，都是一樣的，如256
"""
super(FCOSHead, self).__init__()
# TODO: Implement the sigmoid version first.
num_classes = cfg.MODEL.FCOS.NUM_CLASSES - 1 # eg:80
self.fpn_strides = cfg.MODEL.FCOS.FPN_STRIDES # eg:[8, 16, 32, 64, 128]
self.norm_reg_targets = cfg.MODEL.FCOS.NORM_REG_TARGETS # eg:False 直接回歸還是歸一化後迴歸
self.centerness_on_reg = cfg.MODEL.FCOS.CENTERNESS_ON_REG # eg:False centerness和哪個分支共用特徵
self.use_dcn_in_tower = cfg.MODEL.FCOS.USE_DCN_IN_TOWER # eg:False
cls_tower = []
bbox_tower = []
# eg: cfg.MODEL.FCOS.NUM_CONVS=4頭部共享特徵時(也稱作tower)有4層卷積層
for i in range(cfg.MODEL.FCOS.NUM_CONVS):
if self.use_dcn_in_tower and \
i == cfg.MODEL.FCOS.NUM_CONVS - 1:
conv_func = DFConv2d
else:
conv_func = nn.Conv2d
# cls_tower和bbox_tower都是4層的256通道的3×3的卷積層，後加一些GN和Relu
cls_tower.append(
conv_func(
in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True
)
)
cls_tower.append(nn.GroupNorm(32, in_channels))
cls_tower.append(nn.ReLU())
bbox_tower.append(
conv_func(
in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1,
bias=True
)
)
bbox_tower.append(nn.GroupNorm(32, in_channels))
bbox_tower.append(nn.ReLU())
self.add_module('cls_tower', nn.Sequential(*cls_tower))
self.add_module('bbox_tower', nn.Sequential(*bbox_tower))
# cls_logits就是網路的直接分類輸出結果，shape:[H×W×C]
self.cls_logits = nn.Conv2d(
in_channels, num_classes, kernel_size=3, stride=1,
padding=1
)
# bbox_pred就是網路的迴歸分支輸出結果，shape:[H×W×4]
self.bbox_pred = nn.Conv2d(
in_channels, 4, kernel_size=3, stride=1,
padding=1
)
# centerness就是網路抑制低質量框的分支，shape:[H×W×1]
self.centerness = nn.Conv2d(
in_channels, 1, kernel_size=3, stride=1,
padding=1
)
# initialization 這些層裡面的卷積引數都進行初始化
for modules in [self.cls_tower, self.bbox_tower,
self.cls_logits, self.bbox_pred,
self.centerness]:
for l in modules.modules():
if isinstance(l, nn.Conv2d