def __init__(self, cfg, in_channels):
super(FPNDetNetFeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
pooler = Pooler(cfg.MODEL.ROI_BOX_HEAD)
input_size = in_channels * resolution**2
self.use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
self.pooler = pooler
self.fc6 = nn.Conv2d(
in_channels,
in_channels * 4,
kernel_size=resolution,
stride=resolution,
padding=0,
bias=False if self.use_gn else True,
)
if self.use_gn:
self.gn6 = group_norm(in_channels * 4)
self.fc7 = nn.Conv2d(
in_channels * 4,
in_channels * 4,
kernel_size=1,
stride=1,
padding=0,
bias=False if self.use_gn else True,
)
if self.use_gn:
self.gn7 = group_norm(in_channels * 4)
self.out_channels = in_channels * 4
def __init__(
self,
cfg,
in_channels,
num_backbone_stages,
top_blocks,
):
"""
Arguments:
in_channels_list (list[int]): number of channels for each feature map that
will be fed
out_channels (int): number of channels of the FPN representation
top_blocks (nn.Module or None): if provided, an extra operation will
be performed on the output of the last (smallest resolution)
FPN output, and the result will extend the result list
"""
super(Bottom2UP, self).__init__()
# self.inner_blocks = []
# self.layer_blocks = []
# for idx, in_channels in enumerate(in_channels_list, 1): # 起始索引为1
# inner_block = "fpn_inner{}".format(idx) # 用下表起名: fpn_inner1, fpn_inner2, fpn_inner3, fpn_inner4
# layer_block = "fpn_layer{}".format(idx) # 用下表起名: fpn_layer1, fpn_layer2, fpn_layer3, fpn_layer4
#
# if in_channels == 0:
# continue
# inner_block_module = conv_block(in_channels, out_channels, 1) # 该1*1卷积层主要作用为改变通道数为out_channels
# layer_block_module = conv_block(out_channels, out_channels, 3,
# 1) # 用3*3卷积对融合结果卷积，消除上采样的混叠效(aliasing effect)
# self.add_module(inner_block, inner_block_module)
# self.add_module(layer_block, layer_block_module)
# self.inner_blocks.append(inner_block)
# self.layer_blocks.append(layer_block)
# self.top_blocks = top_blocks # 将top_blocks作为FPN类成员变量，指定最后一层的输出是否需要再经过池化等操作，这里是最大值池化
self.panet_buttomup_conv1_modules = nn.ModuleList()
self.panet_buttomup_conv2_modules = nn.ModuleList()
for i in range(num_backbone_stages):
if cfg.MODEL.FPN.PANET.USE_GN:
self.panet_buttomup_conv1_modules.append(
nn.Sequential(
nn.Conv2d(in_channels, in_channels, 3, 2, 1,
bias=True), # 下采样
group_norm(in_channels),
nn.ReLU(inplace=True)))
self.panet_buttomup_conv2_modules.append(
nn.Sequential(
nn.Conv2d(in_channels, in_channels, 3, 1, 1,
bias=True), # 像素相加后使用
group_norm(in_channels),
nn.ReLU(inplace=True)))
else:
self.panet_buttomup_conv1_modules.append(
nn.Conv2d(in_channels, in_channels, 3, 2, 1))
self.panet_buttomup_conv2_modules.append(
nn.Conv2d(in_channels, in_channels, 3, 1, 1))
self.top_blocks = top_blocks
def __init__(self,
channels,
group=1,
kernel=3,
dilation=(1, 4, 8, 12),
shuffle=False,
deform=None):
super(OneDynamicWeightsCat11, self).__init__()
in_channel = channels // 4
self.scale1 = nn.Sequential(
nn.Conv2d(channels, in_channel, 1, padding=0, bias=False),
group_norm(in_channel), nn.ReLU(inplace=True))
if deform == 'deform':
self.cata = nn.Conv2d(in_channel,
group * kernel * kernel + 18,
3,
padding=dilation[0],
dilation=dilation[0],
bias=False)
self.unfold1 = DeformUnfold(kernel_size=(3, 3),
padding=dilation[0],
dilation=dilation[0])
else:
self.cata = nn.Conv2d(in_channel,
group * kernel * kernel,
3,
padding=dilation[0],
dilation=dilation[0],
bias=False)
self.unfold1 = nn.Unfold(kernel_size=(3, 3),
padding=dilation[0],
dilation=dilation[0])
self.softmax = nn.Softmax(dim=-1)
self.shuffle = shuffle
self.deform = deform
self.group = group
self.K = kernel * kernel
self.scale2 = nn.Sequential(
nn.Conv2d(in_channel * 2, in_channel, 1, padding=0, bias=True),
group_norm(in_channel), nn.ReLU(inplace=True))
self.scale3 = nn.Sequential(
nn.Conv2d(in_channel, channels, 1, padding=0, bias=True),
group_norm(channels), nn.ReLU(inplace=True))
def __init__(self, plane):
super(GCNwithNonlocal, self).__init__()
inter_plane = plane // 2
self.node_k = nn.Conv2d(plane, inter_plane, kernel_size=1)
self.node_v = nn.Conv2d(plane, inter_plane, kernel_size=1)
self.node_q = nn.Conv2d(plane, inter_plane, kernel_size=1)
self.conv_wg = nn.Conv1d(inter_plane,
inter_plane,
kernel_size=1,
bias=False)
self.bn_wg = group_norm(inter_plane)
self.softmax = nn.Softmax(dim=2)
self.out = nn.Sequential(nn.Conv2d(inter_plane, plane, kernel_size=1),
group_norm(plane))
def __init__(self, channels):
super(deformMP, self).__init__()
in_channel = channels // 4
self.scale1 = nn.Sequential(
nn.Conv2d(channels, in_channel, 1, padding=0, bias=False),
group_norm(in_channel), nn.ReLU(inplace=True))
self.off_conva = nn.Conv2d(in_channel, 18, 3, padding=1, bias=False)
self.kernel_conva = DeformConv(in_channel,
in_channel,
kernel_size=3,
padding=1,
bias=False)
self.scale3 = nn.Sequential(
nn.Conv2d(in_channel, channels, 1, padding=0, bias=False),
group_norm(channels), nn.ReLU(inplace=True))
def __init__(self, cfg, in_channels):
super(FPNXconv1fcFeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
pooler = Pooler(output_size=(resolution, resolution),
scales=scales,
sampling_ratio=sampling_ratio,
cfg=cfg)
self.pooler = pooler
use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION
use_ws = cfg.MODEL.USE_WS
xconvs = []
for ix in range(num_stacked_convs):
if use_ws:
xconvs.append(
Conv2dWS(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True))
else:
xconvs.append(
nn.Conv2d(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True))
in_channels = conv_head_dim
if use_gn:
xconvs.append(group_norm(in_channels))
xconvs.append(nn.ReLU(inplace=True))
self.add_module("xconvs", nn.Sequential(*xconvs))
for modules in [
self.xconvs,
]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.normal_(l.weight, std=0.01)
if not use_gn:
torch.nn.init.constant_(l.bias, 0)
input_size = conv_head_dim * resolution**2
representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
self.fc6 = make_fc(input_size, representation_size, use_gn=False)
self.out_channels = representation_size
def conv3x3(in_channels, out_channels, module_name, postfix, stride=1, groups=1, kernel_size=3, padding=1):
"""3x3 convolution with padding"""
return [
(f'{module_name}_{postfix}/conv',
nn.Conv2d(in_channels, out_channels, kernel_size=kernel_size, stride=stride, padding=padding, groups=groups, bias=False)),
(f'{module_name}_{postfix}/norm',
group_norm(out_channels) if _GN else FrozenBatchNorm2d(out_channels)
),
(f'{module_name}_{postfix}/relu', nn.ReLU(inplace=True))
]
def __init__(self, channels):
super(ConvMP, self).__init__()
in_channel = channels // 4
self.scale1 = nn.Sequential(
nn.Conv2d(channels, in_channel, 1, padding=0, bias=False),
group_norm(in_channel), nn.ReLU(inplace=True))
self.cata = nn.Conv2d(in_channel,
in_channel,
3,
padding=1,
groups=in_channel)
self.scale2 = nn.Sequential(
nn.Conv2d(in_channel * 2, in_channel, 1, padding=0, bias=False),
group_norm(in_channel), nn.ReLU(inplace=True))
self.scale3 = nn.Sequential(
nn.Conv2d(in_channel, channels, 1, padding=0, bias=False),
group_norm(channels), nn.ReLU(inplace=True))
def DFConv3x3(in_channels, out_channels, module_name, postfix, stride=1, groups=1, kernel_size=3,
with_modulated_dcn=None, deformable_groups=None):
"""3x3 convolution with padding"""
return [
(f'{module_name}_{postfix}/conv',
DFConv2d(in_channels, out_channels, with_modulated_dcn=with_modulated_dcn,
kernel_size=kernel_size, stride=stride, groups=groups,
deformable_groups=deformable_groups, bias=False)),
(f'{module_name}_{postfix}/norm',
group_norm(out_channels) if _GN else FrozenBatchNorm2d(out_channels)
),
(f'{module_name}_{postfix}/relu', nn.ReLU(inplace=True))
]
def conv1x1(in_channels,
out_channels,
module_name,
postfix,
stride=1,
groups=1,
kernel_size=1,
padding=0):
"""1x1 convolution with padding"""
return [
('{}_{}/conv'.format(module_name, postfix),
nn.Conv2d(in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=groups,
bias=False)),
('{}_{}/norm'.format(module_name, postfix),
group_norm(out_channels) if _GN else FrozenBatchNorm2d(out_channels)),
('{}_{}/relu'.format(module_name, postfix), nn.ReLU(inplace=True))
]
def __init__(self, cfg):
super(FPNXconv1fcFeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
pooler = Pooler(
output_size=(resolution, resolution),
scales=scales,
sampling_ratio=sampling_ratio,
)
self.pooler = pooler
use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
use_gw = cfg.MODEL.ROI_BOX_HEAD.USE_GW
in_channels = cfg.MODEL.BACKBONE.OUT_CHANNELS
conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION
if cfg.MODEL.DECONV.LAYERWISE_NORM:
norm_type = cfg.MODEL.DECONV.BOX_NORM_TYPE
else:
norm_type = 'none'
if cfg.MODEL.DECONV.BOX_NORM_TYPE == 'layernorm':
self.box_norm = LayerNorm(eps=cfg.MODEL.DECONV.EPS)
xconvs = []
for ix in range(num_stacked_convs):
if cfg.MODEL.ROI_BOX_HEAD.USE_DECONV:
xconvs.append(
Deconv(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=True,
block=cfg.MODEL.DECONV.BLOCK,
sampling_stride=cfg.MODEL.DECONV.STRIDE,
sync=cfg.MODEL.DECONV.SYNC,
norm_type=norm_type))
in_channels = conv_head_dim
else:
xconvs.append(
nn.Conv2d(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if (use_gn or use_gw) else True))
in_channels = conv_head_dim
if use_gn or use_gw:
xconvs.append(group_norm(in_channels))
xconvs.append(nn.ReLU(inplace=True))
self.add_module("xconvs", nn.Sequential(*xconvs))
for modules in [
self.xconvs,
]:
for l in modules.modules():
if isinstance(l, nn.Conv2d) or isinstance(l, Deconv):
torch.nn.init.normal_(l.weight, std=0.01)
if not (use_gn or use_gw):
torch.nn.init.constant_(l.bias, 0)
input_size = conv_head_dim * resolution**2
representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
block = 0
use_delinear = cfg.MODEL.ROI_BOX_HEAD.USE_DECONV
if use_delinear:
block = cfg.MODEL.DECONV.BLOCK_FC #check here
self.fc6 = make_fc(input_size,
representation_size,
use_gn=False,
use_gw=False,
use_delinear=use_delinear,
block=block,
sync=cfg.MODEL.DECONV.SYNC,
norm_type=norm_type)
def __init__(self, planes, ratio=4):
super(GloReLocalModule, self).__init__()
self.phi = nn.Conv2d(planes,
planes // ratio * 2,
kernel_size=1,
bias=False)
self.bn_phi = group_norm(planes // ratio * 2)
self.theta = nn.Conv2d(planes,
planes // ratio,
kernel_size=1,
bias=False)
self.bn_theta = group_norm(planes // ratio)
# Interaction Space
# Adjacency Matrix: (-)A_g
self.conv_adj = nn.Conv1d(planes // ratio,
planes // ratio,
kernel_size=1,
bias=False)
self.bn_adj = group_norm(planes // ratio)
# State Update Function: W_g
self.conv_wg = nn.Conv1d(planes // ratio * 2,
planes // ratio * 2,
kernel_size=1,
bias=False)
self.bn_wg = group_norm(planes // ratio * 2)
# last fc
self.conv3 = nn.Conv2d(planes // ratio * 2,
planes,
kernel_size=1,
bias=False)
self.bn3 = group_norm(planes)
self.local = nn.Sequential(
nn.Conv2d(planes,
planes,
3,
groups=planes,
stride=2,
padding=1,
bias=False), group_norm(planes),
nn.Conv2d(planes,
planes,
3,
groups=planes,
stride=2,
padding=1,
bias=False), group_norm(planes))
self.gcn_local_attention = GCNwithNonlocal(planes)
self.sigmoid_spatial = nn.Sigmoid()
self.final = nn.Sequential(
nn.Conv2d(planes * 2, planes, kernel_size=1, bias=False),
group_norm(planes))
self.relu = nn.ReLU(inplace=True)
def __init__(self, cfg, in_channels):
"""
Arguments:
num_classes (int): number of output classes
input_size (int): number of channels of the input once it's flattened
representation_size (int): size of the intermediate representation
"""
super(MaskRCNNPANETFeatureExtractor, self).__init__()
self.cfg = cfg
resolution = cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION # 14
scales = cfg.MODEL.ROI_MASK_HEAD.POOLER_SCALES # (0.25, 0.125, 0.0625, 0.03125)
sampling_ratio = cfg.MODEL.ROI_MASK_HEAD.POOLER_SAMPLING_RATIO # 2
pooler = AdaptivePooler(
output_size=(resolution, resolution),
scales=scales,
sampling_ratio=sampling_ratio,
)
input_size = in_channels
self.pooler = pooler
use_gn = cfg.MODEL.ROI_MASK_HEAD.USE_GN
layers = cfg.MODEL.ROI_MASK_HEAD.CONV_LAYERS # (256, 256, 256, 256)
dilation = cfg.MODEL.ROI_MASK_HEAD.DILATION
next_feature = input_size
self.blocks = []
# for layer_idx, layer_features in enumerate(layers, 1):
# layer_name = "mask_fcn{}".format(layer_idx)
# module = make_conv3x3(
# next_feature, layer_features,
# dilation=dilation, stride=1, use_gn=use_gn
# ) # 这里用到膨胀卷积了
# self.add_module(layer_name, module)
# next_feature = layer_features
# self.blocks.append(layer_name)
self.add_module("mask_fcn1_1",
make_conv3x3(next_feature, layers[0], dilation=dilation, stride=1, use_gn=use_gn))
self.add_module("mask_fcn1_2",
make_conv3x3(next_feature, layers[0], dilation=dilation, stride=1, use_gn=use_gn))
self.add_module("mask_fcn1_3",
make_conv3x3(next_feature, layers[0], dilation=dilation, stride=1, use_gn=use_gn))
self.add_module("mask_fcn1_4",
make_conv3x3(next_feature, layers[0], dilation=dilation, stride=1, use_gn=use_gn))
next_feature = layers[0]
for layer_idx, layer_features in enumerate(layers[1:], 2):
layer_name = "mask_fcn{}".format(layer_idx)
module = make_conv3x3(
next_feature, layer_features,
dilation=dilation, stride=1, use_gn=use_gn
) # 这里用到膨胀卷积了
self.add_module(layer_name, module)
next_feature = layer_features
self.blocks.append(layer_name)
# TODO:区分前后景所需的模块，需要初始化权重！！！
conv4 = nn.Conv2d(layers[2], layers[2], 3, 1, padding=1 * dilation, dilation=dilation, bias=False)
nn.init.kaiming_normal_(
conv4.weight, mode="fan_out", nonlinearity="relu"
)
self.mask_conv4_fc = nn.Sequential(
conv4,
group_norm(layers[2]),
nn.ReLU(inplace=True))
# --------------------------------------------------------------------------------------------------------#
conv5 = nn.Conv2d(layers[2], int(layers[2] / 2), 3, 1, padding=1 * dilation, dilation=dilation, bias=False)
nn.init.kaiming_normal_(
conv5.weight, mode="fan_out", nonlinearity="relu"
)
self.mask_conv5_fc = nn.Sequential(
conv5,
group_norm(int(layers[2] / 2)),
nn.ReLU(inplace=True))
# self.mask_conv5_fc = nn.Sequential(
# nn.Conv2d(layers[2], int(layers[2] / 2), 3, 1, padding=1 * dilation, dilation=dilation, bias=False),
# group_norm(int(layers[2] / 2)),
# nn.ReLU(inplace=True))
# nn.init.kaiming_normal_(
# self.mask_conv5_fc.weight, mode="fan_out", nonlinearity="relu"
# )
#---------------------------------------------------------------------------------------------------------#
fc = nn.Linear(int(layers[2] / 2) * cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION ** 2,
(2 * cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION) ** 2, bias=True)
nn.init.kaiming_normal_(
fc.weight, mode="fan_out", nonlinearity="relu"
)
self.mask_fc = nn.Sequential(
fc,
nn.ReLU(inplace=True))
# self.mask_fc = nn.Sequential(
# nn.Linear(int(layers[2] / 2) * cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION ** 2,
# (2 * cfg.MODEL.ROI_MASK_HEAD.POOLER_RESOLUTION) ** 2, bias=True),
# nn.ReLU(inplace=True))
# nn.init.kaiming_normal_(
# self.mask_fc.weight, mode="fan_out", nonlinearity="relu"
# )
self.out_channels = layer_features
def __init__(self, cfg, in_channels):
super(FPNXconv1fc_panet_FeatureExtractor, self).__init__()
resolution = cfg.MODEL.ROI_BOX_HEAD.POOLER_RESOLUTION
scales = cfg.MODEL.ROI_BOX_HEAD.POOLER_SCALES
sampling_ratio = cfg.MODEL.ROI_BOX_HEAD.POOLER_SAMPLING_RATIO
pooler = Pooler(output_size=(resolution, resolution),
scales=scales,
sampling_ratio=sampling_ratio,
panet=True)
self.pooler = pooler
use_gn = cfg.MODEL.ROI_BOX_HEAD.USE_GN
conv_head_dim = cfg.MODEL.ROI_BOX_HEAD.CONV_HEAD_DIM
num_stacked_convs = cfg.MODEL.ROI_BOX_HEAD.NUM_STACKED_CONVS
dilation = cfg.MODEL.ROI_BOX_HEAD.DILATION
xconvs = []
for ix in range(num_stacked_convs):
xconvs.append(
nn.Conv2d(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True))
in_channels = conv_head_dim
if use_gn:
xconvs.append(group_norm(in_channels))
xconvs.append(nn.ReLU(inplace=True))
self.add_module("xconvs", nn.Sequential(*xconvs))
#tmp use hard-coded #levels
num_levels = 4
self.conv1_head = nn.ModuleList()
for i in range(num_levels):
self.conv1_head.append(
nn.Sequential(
*((nn.Conv2d(in_channels,
conv_head_dim,
kernel_size=3,
stride=1,
padding=dilation,
dilation=dilation,
bias=False if use_gn else True), ) +
((group_norm(in_channels), ) if use_gn else ()) +
(nn.ReLU(inplace=True), ))))
#HACK: use MSRA INIT
for modules in [
self.xconvs,
]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.kaiming_normal_(l.weight,
mode="fan_out",
nonlinearity="relu")
# torch.nn.init.normal_(l.weight, std=0.01)
if not use_gn:
torch.nn.init.constant_(l.bias, 0)
for modules in [
self.conv1_head,
]:
for l in modules.modules():
if isinstance(l, nn.Conv2d):
torch.nn.init.kaiming_normal_(l.weight,
mode="fan_out",
nonlinearity="relu")
if not use_gn:
torch.nn.init.constant_(l.bias, 0)
input_size = conv_head_dim * resolution**2
representation_size = cfg.MODEL.ROI_BOX_HEAD.MLP_HEAD_DIM
self.fc6 = make_fc(input_size, representation_size, use_gn=False)
self.out_channels = representation_size
