def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
# the anchor_scales are the edge length, not the area of the anchor. In order to get the
# area of the anchor, take the square of it (8^2, 16^2, 32^2).
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
# first conv layer before send into RPN
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
# the 1 by 1 conv layer of score
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0)
# the 1 by 1 conv layer of bbox regression
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels,
mid_channels,
kernel_size=3,
stride=1,
padding=1)
self.score = nn.Conv2d(mid_channels,
n_anchor * 2,
kernel_size=1,
stride=1,
padding=0)
self.loc = nn.Conv2d(mid_channels,
n_anchor * 4,
kernel_size=1,
stride=1,
padding=0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
# 在reshaped image的尺度上，以feature map上一个点对应的一个16*16的左上角点为原点，计算得到的所有anchorbox的角点的相对坐标
# 为了后边计算reshaped image上的所有anchor box做准备
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(
self, **proposal_creator_params
) # parent_model = instance of RegionProposalNetwork, and use other default parameters
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0)
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(self):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base()
self.conv = nn.Conv2d(512, 512, 3, 1, 1)
k = self.anchor_base.shape[0]
self.score = nn.Conv2d(512, 2 * k, 1)
self.loc = nn.Conv2d(512, 4 * k, 1)
self.proposal_layer = ProposalCreator(self)
normal_init(self.conv, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32], feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base( #生成anchor_base
anchor_scales=anchor_scales, ratios=ratios)
self.feat_stride = feat_stride #下采样倍数
self.proposal_layer = ProposalCreator(self, **proposal_creator_params) #实例化生成roi函数
n_anchor = self.anchor_base.shape[0] #anchor_base的数量
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) #3x3卷积核
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0) #rpn分类层
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0) #rpn回归层
normal_init(self.conv1, 0, 0.01) #初始化3x3卷积核参数
normal_init(self.score, 0, 0.01) #初始化rpn分类层参数
normal_init(self.loc, 0, 0.01) #初始化rpn回归层参数
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5,1,2],
anchor_scales=[8,16,32], feat_stride=16,
proposal_creator_params=dict()):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
num_anchor_base = self.anchor_base.shape[0] #9
self.conv1 = torch.nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = torch.nn.Conv2d(mid_channels, num_anchor_base*2, 1, 1, 0) #二分类，obj or nobj
self.loc = torch.nn.Conv2d(mid_channels, num_anchor_base*4, 1, 1, 0) #坐标回归
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32], feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
# anchor_baseは(9,4)のndarray。 anchor_scales * rations = 3 * 3なので9。x,y,w,hで4
self.anchor_base = generate_anchor_base(
anchor_scales=anchor_scales, ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params) # これはなんだろうなぁ。
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0) # 2k 論文でいうところのcls_layer
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0) # 4k 論文でいうところのreg_layer
normal_init(self.conv1, 0, 0.01) # 初期化
normal_init(self.score, 0, 0.01) # 初期化
normal_init(self.loc, 0, 0.01) # 初期化
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32], feat_stride=16,
proposal_creator_params=dict(),
):
super(fixed_RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(
anchor_scales=anchor_scales, ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
self.act_quant = qt.activation_quantization(8, qt.Quant.linear)
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0)
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self, in_channels=512, mid_channels=512, ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32], feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(
anchor_scales=anchor_scales, ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0] # 9
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) # 首先是加pad的512个3*3大小卷积核，输出仍为（N，512，h，w）
#然后左右两边各有一个1 * 1卷积。
# 左路为18个1 * 1卷积，输出为（N，18，h，w），即所有anchor的0 - 1类别概率（h * w约为2400，h * w * 9约为20000）。
# 右路为36个1 * 1卷积，输出为（N，36，h，w），即所有anchor的回归位置参数。
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0) # 18，分类得分
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0) # 36，回归参数
normal_init(self.conv1, 0, 0.01) # 归一初始化
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_base_anchor(
anchor_scales=anchor_scales,
ratios=ratios) # 调用generate_anchor_base（）函数，生成左上角9个anchor_base
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0] # 9
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0)
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict()):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
#TODO:为什么要传入这个self变量
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0] #9
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1) #卷积核3*3
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1,
0) #卷积核 1*1,是不是物体二分类
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1,
0) #卷积核1*1,回归框的4个坐标
#TODO:为什么要这样初始化,以及bais要置零
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base( # 创建 9 个锚框的 以 cell 为中心的相对坐标（9,4）
anchor_scales=anchor_scales,
ratios=ratios)
n_anchor = self.anchor_base.shape[0] # 每个点对应着 9 个锚框
self.feat_stride = feat_stride # 缩小后是原图的 1/16
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0) # 前景后景特征提取
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0) # 回归特征提取
self.proposal_layer = ProposalCreator(
self, **proposal_creator_params) # 输出2000个roi
normal_init(self.conv1, 0, 0.01) # 归一化处理
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(self,
feature_channels=512,
mid_channels=512,
ratios=OPT.ratios,
anchor_scales=OPT.anchor_scales,
sub_sample=OPT.sub_sample,
proposal_creator_params=dict()):
super(RegionProposalNetwork, self).__init__()
# 第一个锚点处生成的锚点框
self.anchor_base = generate_anchor_base(ratios=ratios,
anchor_scales=anchor_scales)
self.sub_sample = sub_sample # 下采样的倍数，由特征提取网络决定
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
# 一个锚点处的锚点框的个数
n_anchor = self.anchor_base.shape[0]
# rpn网络中的第一个卷积层
self.conv1 = nn.Conv2d(in_channels=feature_channels,
out_channels=mid_channels,
kernel_size=3,
stride=1,
padding=1)
# rpn网络背景和前景的分类层
self.score = nn.Conv2d(in_channels=mid_channels,
out_channels=n_anchor * 2,
kernel_size=1,
stride=1,
padding=0)
# rpn网络预测的锚点框偏移量
self.loc = nn.Conv2d(in_channels=mid_channels,
out_channels=n_anchor * 4,
kernel_size=1,
stride=1,
padding=0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1,
1) # Head layer of RPN 3*3*512
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1,
0) # classification layer 1*1*18
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1,
0) # regression layer 1*1*36
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01) # ([1*18*50*50])
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
# 生成一个(R, 4),R = len(anchor_scales)*len(ratios)=9,[y_min, x_min, y_max, x_max]
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0) # 2为背景和前景
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0) # 4为坐标
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)
def __init__(
self,
in_channels=512,
mid_channels=512,
ratios=[0.5, 1, 2],
anchor_scales=[8, 16, 32],
feat_stride=16,
proposal_creator_params=dict(),
):
super(RegionProposalNetwork, self).__init__()
# 生成面积为[128,256,512],比例为[0.5,1,2]的9个base anchor，[9,4]
self.anchor_base = generate_anchor_base(anchor_scales=anchor_scales,
ratios=ratios)
self.feat_stride = feat_stride
# proposal_layer用于生成ROI
self.proposal_layer = ProposalCreator(self, **proposal_creator_params)
n_anchor = self.anchor_base.shape[0]
self.conv1 = nn.Conv2d(in_channels, mid_channels, 3, 1, 1)
self.score = nn.Conv2d(mid_channels, n_anchor * 2, 1, 1, 0)
self.loc = nn.Conv2d(mid_channels, n_anchor * 4, 1, 1, 0)
normal_init(self.conv1, 0, 0.01)
normal_init(self.score, 0, 0.01)
normal_init(self.loc, 0, 0.01)