def __init__(
self,
feat_channels,
stacked_convs,
la_down_rate=8,
norm_type='gn',
norm_groups=32,
):
super(TaskDecomposition, self).__init__()
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.norm_type = norm_type
self.norm_groups = norm_groups
self.in_channels = self.feat_channels * self.stacked_convs
self.la_conv1 = nn.Conv2D(self.in_channels,
self.in_channels // la_down_rate, 1)
self.la_conv2 = nn.Conv2D(self.in_channels // la_down_rate,
self.stacked_convs, 1)
self.reduction_conv = ConvNormLayer(self.in_channels,
self.feat_channels,
filter_size=1,
stride=1,
norm_type=self.norm_type,
norm_groups=self.norm_groups)
self._init_weights()
def __init__(self, ch_in, ch_out, stride=1, base_width=64, cardinality=1):
super(Bottleneck, self).__init__()
self.stride = stride
mid_planes = int(
math.floor(ch_out * (base_width / 64)) * cardinality
)
mid_planes = mid_planes // self.expansion
self.conv1 = ConvNormLayer(
ch_in,
mid_planes,
filter_size=1,
stride=1,
bias_on=False,
norm_decay=None)
self.conv2 = ConvNormLayer(
mid_planes,
mid_planes,
filter_size=3,
stride=stride,
bias_on=False,
norm_decay=None)
self.conv3 = ConvNormLayer(
mid_planes,
ch_out,
filter_size=1,
stride=1,
bias_on=False,
norm_decay=None)
def __init__(self, ch_in, ch_out, norm_type='bn'):
super(DeConv, self).__init__()
self.deconv = nn.Sequential()
conv1 = ConvNormLayer(ch_in=ch_in,
ch_out=ch_out,
stride=1,
filter_size=1,
norm_type=norm_type,
initializer=XavierUniform())
conv2 = nn.Conv2DTranspose(
in_channels=ch_out,
out_channels=ch_out,
kernel_size=4,
padding=1,
stride=2,
groups=ch_out,
weight_attr=ParamAttr(initializer=XavierUniform()),
bias_attr=False)
bn = batch_norm(ch_out, norm_type=norm_type, norm_decay=0.)
conv3 = ConvNormLayer(ch_in=ch_out,
ch_out=ch_out,
stride=1,
filter_size=1,
norm_type=norm_type,
initializer=XavierUniform())
self.deconv.add_sublayer('conv1', conv1)
self.deconv.add_sublayer('relu6_1', nn.ReLU6())
self.deconv.add_sublayer('conv2', conv2)
self.deconv.add_sublayer('bn', bn)
self.deconv.add_sublayer('relu6_2', nn.ReLU6())
self.deconv.add_sublayer('conv3', conv3)
self.deconv.add_sublayer('relu6_3', nn.ReLU6())
def __init__(self,
in_channels=(512, 1024, 2048),
out_channel=256,
num_extra_levels=2,
fpn_strides=[8, 16, 32, 64, 128],
num_stacks=1,
use_weighted_fusion=True,
norm_type='bn',
norm_groups=32,
act='swish'):
super(BiFPN, self).__init__()
assert num_stacks > 0, "The number of stacks of BiFPN is at least 1."
assert norm_type in ['bn', 'sync_bn', 'gn', None]
assert act in ['swish', 'relu', None]
assert num_extra_levels >= 0, \
"The `num_extra_levels` must be non negative(>=0)."
self.in_channels = in_channels
self.out_channel = out_channel
self.num_extra_levels = num_extra_levels
self.num_stacks = num_stacks
self.use_weighted_fusion = use_weighted_fusion
self.norm_type = norm_type
self.norm_groups = norm_groups
self.act = act
self.num_levels = len(self.in_channels) + self.num_extra_levels
if len(fpn_strides) != self.num_levels:
for i in range(self.num_extra_levels):
fpn_strides += [fpn_strides[-1] * 2]
self.fpn_strides = fpn_strides
self.lateral_convs = nn.LayerList()
for in_c in in_channels:
self.lateral_convs.append(
ConvNormLayer(in_c, self.out_channel, 1, 1))
if self.num_extra_levels > 0:
self.extra_convs = nn.LayerList()
for i in range(self.num_extra_levels):
if i == 0:
self.extra_convs.append(
ConvNormLayer(self.in_channels[-1], self.out_channel,
3, 2))
else:
self.extra_convs.append(nn.MaxPool2D(3, 2, 1))
self.bifpn_cells = nn.LayerList()
for i in range(self.num_stacks):
self.bifpn_cells.append(
BiFPNCell(self.out_channel,
self.num_levels,
use_weighted_fusion=self.use_weighted_fusion,
norm_type=self.norm_type,
norm_groups=self.norm_groups,
act=self.act))
def __init__(self, ch_in, ch_out, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = ConvNormLayer(
ch_in,
ch_out,
filter_size=3,
stride=stride,
bias_on=False,
norm_decay=None)
self.conv2 = ConvNormLayer(
ch_out,
ch_out,
filter_size=3,
stride=1,
bias_on=False,
norm_decay=None)
def __init__(self, ch_ins, ch_out, up_strides, dcn_v2=True):
super(IDAUp, self).__init__()
for i in range(1, len(ch_ins)):
ch_in = ch_ins[i]
up_s = int(up_strides[i])
fan_in = ch_in * 3 * 3
stdv = 1. / math.sqrt(fan_in)
proj = nn.Sequential(
ConvNormLayer(ch_in,
ch_out,
filter_size=3,
stride=1,
use_dcn=dcn_v2,
bias_on=dcn_v2,
norm_decay=None,
dcn_lr_scale=1.,
dcn_regularizer=None,
initializer=Uniform(-stdv, stdv)), nn.ReLU())
node = nn.Sequential(
ConvNormLayer(ch_out,
ch_out,
filter_size=3,
stride=1,
use_dcn=dcn_v2,
bias_on=dcn_v2,
norm_decay=None,
dcn_lr_scale=1.,
dcn_regularizer=None,
initializer=Uniform(-stdv, stdv)), nn.ReLU())
kernel_size = up_s * 2
fan_in = ch_out * kernel_size * kernel_size
stdv = 1. / math.sqrt(fan_in)
up = nn.Conv2DTranspose(
ch_out,
ch_out,
kernel_size=up_s * 2,
stride=up_s,
padding=up_s // 2,
groups=ch_out,
weight_attr=ParamAttr(initializer=Uniform(-stdv, stdv)),
bias_attr=False)
fill_up_weights(up)
setattr(self, 'proj_' + str(i), proj)
setattr(self, 'up_' + str(i), up)
setattr(self, 'node_' + str(i), node)
class TaskDecomposition(nn.Layer):
"""This code is based on
https://github.com/fcjian/TOOD/blob/master/mmdet/models/dense_heads/tood_head.py
"""
def __init__(
self,
feat_channels,
stacked_convs,
la_down_rate=8,
norm_type='gn',
norm_groups=32,
):
super(TaskDecomposition, self).__init__()
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.norm_type = norm_type
self.norm_groups = norm_groups
self.in_channels = self.feat_channels * self.stacked_convs
self.la_conv1 = nn.Conv2D(self.in_channels,
self.in_channels // la_down_rate, 1)
self.la_conv2 = nn.Conv2D(self.in_channels // la_down_rate,
self.stacked_convs, 1)
self.reduction_conv = ConvNormLayer(self.in_channels,
self.feat_channels,
filter_size=1,
stride=1,
norm_type=self.norm_type,
norm_groups=self.norm_groups)
self._init_weights()
def _init_weights(self):
normal_(self.la_conv1.weight, std=0.001)
normal_(self.la_conv2.weight, std=0.001)
def forward(self, feat, avg_feat=None):
b, _, h, w = feat.shape
if avg_feat is None:
avg_feat = F.adaptive_avg_pool2d(feat, (1, 1))
weight = F.relu(self.la_conv1(avg_feat))
weight = F.sigmoid(self.la_conv2(weight))
# here new_conv_weight = layer_attention_weight * conv_weight
# in order to save memory and FLOPs.
conv_weight = weight.reshape([b, 1, self.stacked_convs, 1]) * \
self.reduction_conv.conv.weight.reshape(
[1, self.feat_channels, self.stacked_convs, self.feat_channels])
conv_weight = conv_weight.reshape(
[b, self.feat_channels, self.in_channels])
feat = feat.reshape([b, self.in_channels, h * w])
feat = paddle.bmm(conv_weight,
feat).reshape([b, self.feat_channels, h, w])
if self.norm_type is not None:
feat = self.reduction_conv.norm(feat)
feat = F.relu(feat)
return feat
def __init__(self, ch_in, ch_out, kernel_size, residual):
super(Root, self).__init__()
self.conv = ConvNormLayer(
ch_in,
ch_out,
filter_size=1,
stride=1,
bias_on=False,
norm_decay=None)
self.residual = residual
def __init__(self,
feat_in=256,
feat_out=256,
num_convs=4,
norm_type='bn',
use_dcn=False):
super(FCOSFeat, self).__init__()
self.num_convs = num_convs
self.norm_type = norm_type
self.cls_subnet_convs = []
self.reg_subnet_convs = []
for i in range(self.num_convs):
in_c = feat_in if i == 0 else feat_out
cls_conv_name = 'fcos_head_cls_tower_conv_{}'.format(i)
cls_conv = self.add_sublayer(
cls_conv_name,
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=3,
stride=1,
norm_type=norm_type,
use_dcn=use_dcn,
norm_name=cls_conv_name + '_norm',
bias_on=True,
lr_scale=2.,
name=cls_conv_name))
self.cls_subnet_convs.append(cls_conv)
reg_conv_name = 'fcos_head_reg_tower_conv_{}'.format(i)
reg_conv = self.add_sublayer(
reg_conv_name,
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=3,
stride=1,
norm_type=norm_type,
use_dcn=use_dcn,
norm_name=reg_conv_name + '_norm',
bias_on=True,
lr_scale=2.,
name=reg_conv_name))
self.reg_subnet_convs.append(reg_conv)
def __init__(self,
level,
block,
ch_in,
ch_out,
stride=1,
level_root=False,
root_dim=0,
root_kernel_size=1,
root_residual=False):
super(Tree, self).__init__()
if root_dim == 0:
root_dim = 2 * ch_out
if level_root:
root_dim += ch_in
if level == 1:
self.tree1 = block(ch_in, ch_out, stride)
self.tree2 = block(ch_out, ch_out, 1)
else:
self.tree1 = Tree(
level - 1,
block,
ch_in,
ch_out,
stride,
root_dim=0,
root_kernel_size=root_kernel_size,
root_residual=root_residual)
self.tree2 = Tree(
level - 1,
block,
ch_out,
ch_out,
1,
root_dim=root_dim + ch_out,
root_kernel_size=root_kernel_size,
root_residual=root_residual)
if level == 1:
self.root = Root(root_dim, ch_out, root_kernel_size, root_residual)
self.level_root = level_root
self.root_dim = root_dim
self.downsample = None
self.project = None
self.level = level
if stride > 1:
self.downsample = nn.MaxPool2D(stride, stride=stride)
if ch_in != ch_out:
self.project = ConvNormLayer(
ch_in,
ch_out,
filter_size=1,
stride=1,
bias_on=False,
norm_decay=None)
def __init__(self,
num_convs=4,
in_channels=256,
out_channels=256,
norm_type=None):
super(MaskFeat, self).__init__()
self.num_convs = num_convs
self.in_channels = in_channels
self.out_channels = out_channels
self.norm_type = norm_type
fan_conv = out_channels * 3 * 3
fan_deconv = out_channels * 2 * 2
mask_conv = nn.Sequential()
if norm_type == 'gn':
for i in range(self.num_convs):
conv_name = 'mask_inter_feat_{}'.format(i + 1)
mask_conv.add_sublayer(
conv_name,
ConvNormLayer(
ch_in=in_channels if i == 0 else out_channels,
ch_out=out_channels,
filter_size=3,
stride=1,
norm_type=self.norm_type,
norm_name=conv_name + '_norm',
initializer=KaimingNormal(fan_in=fan_conv),
name=conv_name))
mask_conv.add_sublayer(conv_name + 'act', nn.ReLU())
else:
for i in range(self.num_convs):
conv_name = 'mask_inter_feat_{}'.format(i + 1)
mask_conv.add_sublayer(
conv_name,
nn.Conv2D(
in_channels=in_channels if i == 0 else out_channels,
out_channels=out_channels,
kernel_size=3,
padding=1,
weight_attr=paddle.ParamAttr(initializer=KaimingNormal(
fan_in=fan_conv))))
mask_conv.add_sublayer(conv_name + 'act', nn.ReLU())
mask_conv.add_sublayer(
'conv5_mask',
nn.Conv2DTranspose(
in_channels=self.in_channels,
out_channels=self.out_channels,
kernel_size=2,
stride=2,
weight_attr=paddle.ParamAttr(initializer=KaimingNormal(
fan_in=fan_deconv))))
mask_conv.add_sublayer('conv5_mask' + 'act', nn.ReLU())
self.upsample = mask_conv
def __init__(self, ch_ins, ch_out, up_strides, dcn_v2=True):
super(IDAUp, self).__init__()
for i in range(1, len(ch_ins)):
ch_in = ch_ins[i]
up_s = int(up_strides[i])
proj = nn.Sequential(
ConvNormLayer(ch_in,
ch_out,
filter_size=3,
stride=1,
use_dcn=dcn_v2,
bias_on=dcn_v2,
norm_decay=None,
dcn_lr_scale=1.,
dcn_regularizer=None), nn.ReLU())
node = nn.Sequential(
ConvNormLayer(ch_out,
ch_out,
filter_size=3,
stride=1,
use_dcn=dcn_v2,
bias_on=dcn_v2,
norm_decay=None,
dcn_lr_scale=1.,
dcn_regularizer=None), nn.ReLU())
param_attr = paddle.ParamAttr(initializer=KaimingUniform())
up = nn.Conv2DTranspose(ch_out,
ch_out,
kernel_size=up_s * 2,
weight_attr=param_attr,
stride=up_s,
padding=up_s // 2,
groups=ch_out,
bias_attr=False)
# TODO: uncomment fill_up_weights
#fill_up_weights(up)
setattr(self, 'proj_' + str(i), proj)
setattr(self, 'up_' + str(i), up)
setattr(self, 'node_' + str(i), node)
def __init__(self, depth=34, residual_root=False):
super(DLA, self).__init__()
levels, channels = DLA_cfg[depth]
if depth == 34:
block = BasicBlock
if depth == 46 or depth == 60 or depth == 102:
block = Bottleneck
self.channels = channels
self.base_layer = nn.Sequential(
ConvNormLayer(
3,
channels[0],
filter_size=7,
stride=1,
bias_on=False,
norm_decay=None),
nn.ReLU())
self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
self.level1 = self._make_conv_level(
channels[0], channels[1], levels[1], stride=2)
self.level2 = Tree(
levels[2],
block,
channels[1],
channels[2],
2,
level_root=False,
root_residual=residual_root)
self.level3 = Tree(
levels[3],
block,
channels[2],
channels[3],
2,
level_root=True,
root_residual=residual_root)
self.level4 = Tree(
levels[4],
block,
channels[3],
channels[4],
2,
level_root=True,
root_residual=residual_root)
self.level5 = Tree(
levels[5],
block,
channels[4],
channels[5],
2,
level_root=True,
root_residual=residual_root)
def _make_conv_level(self, ch_in, ch_out, conv_num, stride=1):
modules = []
for i in range(conv_num):
modules.extend([
ConvNormLayer(
ch_in,
ch_out,
filter_size=3,
stride=stride if i == 0 else 1,
bias_on=False,
norm_decay=None), nn.ReLU()
])
ch_in = ch_out
return nn.Sequential(*modules)
def __init__(self,
in_dim=256,
num_convs=4,
conv_dim=256,
mlp_dim=1024,
resolution=7,
norm_type='gn',
freeze_norm=False,
stage_name=''):
super(XConvNormHead, self).__init__()
self.in_dim = in_dim
self.num_convs = num_convs
self.conv_dim = conv_dim
self.mlp_dim = mlp_dim
self.norm_type = norm_type
self.freeze_norm = freeze_norm
self.bbox_head_convs = []
fan = conv_dim * 3 * 3
initializer = KaimingNormal(fan_in=fan)
for i in range(self.num_convs):
in_c = in_dim if i == 0 else conv_dim
head_conv_name = stage_name + 'bbox_head_conv{}'.format(i)
head_conv = self.add_sublayer(
head_conv_name,
ConvNormLayer(ch_in=in_c,
ch_out=conv_dim,
filter_size=3,
stride=1,
norm_type=self.norm_type,
norm_name=head_conv_name + '_norm',
freeze_norm=self.freeze_norm,
initializer=initializer,
name=head_conv_name))
self.bbox_head_convs.append(head_conv)
fan = conv_dim * resolution * resolution
self.fc6 = nn.Linear(conv_dim * resolution * resolution,
mlp_dim,
weight_attr=paddle.ParamAttr(
initializer=XavierUniform(fan_out=fan)),
bias_attr=paddle.ParamAttr(
learning_rate=2., regularizer=L2Decay(0.)))
def __init__(self,
feat_in=256,
feat_out=96,
num_fpn_stride=3,
num_convs=2,
norm_type='bn',
share_cls_reg=False,
act='hard_swish'):
super(PicoFeat, self).__init__()
self.num_convs = num_convs
self.norm_type = norm_type
self.share_cls_reg = share_cls_reg
self.act = act
self.cls_convs = []
self.reg_convs = []
for stage_idx in range(num_fpn_stride):
cls_subnet_convs = []
reg_subnet_convs = []
for i in range(self.num_convs):
in_c = feat_in if i == 0 else feat_out
cls_conv_dw = self.add_sublayer(
'cls_conv_dw{}.{}'.format(stage_idx, i),
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=5,
stride=1,
groups=feat_out,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
cls_subnet_convs.append(cls_conv_dw)
cls_conv_pw = self.add_sublayer(
'cls_conv_pw{}.{}'.format(stage_idx, i),
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=1,
stride=1,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
cls_subnet_convs.append(cls_conv_pw)
if not self.share_cls_reg:
reg_conv_dw = self.add_sublayer(
'reg_conv_dw{}.{}'.format(stage_idx, i),
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=5,
stride=1,
groups=feat_out,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
reg_subnet_convs.append(reg_conv_dw)
reg_conv_pw = self.add_sublayer(
'reg_conv_pw{}.{}'.format(stage_idx, i),
ConvNormLayer(ch_in=in_c,
ch_out=feat_out,
filter_size=1,
stride=1,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
reg_subnet_convs.append(reg_conv_pw)
self.cls_convs.append(cls_subnet_convs)
self.reg_convs.append(reg_subnet_convs)
def __init__(self, feat_channels):
super(PicoSE, self).__init__()
self.fc = nn.Conv2D(feat_channels, feat_channels, 1)
self.conv = ConvNormLayer(feat_channels, feat_channels, 1, 1)
self._init_weights()
def __init__(self,
in_channels=256,
mid_channels=128,
out_channels=256,
start_level=0,
end_level=3,
use_dcn_in_tower=False,
norm_type='gn'):
super(SOLOv2MaskHead, self).__init__()
assert start_level >= 0 and end_level >= start_level
self.in_channels = in_channels
self.out_channels = out_channels
self.mid_channels = mid_channels
self.use_dcn_in_tower = use_dcn_in_tower
self.range_level = end_level - start_level + 1
self.use_dcn = True if self.use_dcn_in_tower else False
self.convs_all_levels = []
self.norm_type = norm_type
for i in range(start_level, end_level + 1):
conv_feat_name = 'mask_feat_head.convs_all_levels.{}'.format(i)
conv_pre_feat = nn.Sequential()
if i == start_level:
conv_pre_feat.add_sublayer(
conv_feat_name + '.conv' + str(i),
ConvNormLayer(
ch_in=self.in_channels,
ch_out=self.mid_channels,
filter_size=3,
stride=1,
use_dcn=self.use_dcn,
norm_type=self.norm_type))
self.add_sublayer('conv_pre_feat' + str(i), conv_pre_feat)
self.convs_all_levels.append(conv_pre_feat)
else:
for j in range(i):
ch_in = 0
if j == 0:
ch_in = self.in_channels + 2 if i == end_level else self.in_channels
else:
ch_in = self.mid_channels
conv_pre_feat.add_sublayer(
conv_feat_name + '.conv' + str(j),
ConvNormLayer(
ch_in=ch_in,
ch_out=self.mid_channels,
filter_size=3,
stride=1,
use_dcn=self.use_dcn,
norm_type=self.norm_type))
conv_pre_feat.add_sublayer(
conv_feat_name + '.conv' + str(j) + 'act', nn.ReLU())
conv_pre_feat.add_sublayer(
'upsample' + str(i) + str(j),
nn.Upsample(
scale_factor=2, mode='bilinear'))
self.add_sublayer('conv_pre_feat' + str(i), conv_pre_feat)
self.convs_all_levels.append(conv_pre_feat)
conv_pred_name = 'mask_feat_head.conv_pred.0'
self.conv_pred = self.add_sublayer(
conv_pred_name,
ConvNormLayer(
ch_in=self.mid_channels,
ch_out=self.out_channels,
filter_size=1,
stride=1,
use_dcn=self.use_dcn,
norm_type=self.norm_type))
def __init__(self,
num_classes=80,
in_channels=256,
seg_feat_channels=256,
stacked_convs=4,
num_grids=[40, 36, 24, 16, 12],
kernel_out_channels=256,
dcn_v2_stages=[],
segm_strides=[8, 8, 16, 32, 32],
solov2_loss=None,
score_threshold=0.1,
mask_threshold=0.5,
mask_nms=None,
norm_type='gn',
drop_block=False):
super(SOLOv2Head, self).__init__()
self.num_classes = num_classes
self.in_channels = in_channels
self.seg_num_grids = num_grids
self.cate_out_channels = self.num_classes
self.seg_feat_channels = seg_feat_channels
self.stacked_convs = stacked_convs
self.kernel_out_channels = kernel_out_channels
self.dcn_v2_stages = dcn_v2_stages
self.segm_strides = segm_strides
self.solov2_loss = solov2_loss
self.mask_nms = mask_nms
self.score_threshold = score_threshold
self.mask_threshold = mask_threshold
self.norm_type = norm_type
self.drop_block = drop_block
self.kernel_pred_convs = []
self.cate_pred_convs = []
for i in range(self.stacked_convs):
use_dcn = True if i in self.dcn_v2_stages else False
ch_in = self.in_channels + 2 if i == 0 else self.seg_feat_channels
kernel_conv = self.add_sublayer(
'bbox_head.kernel_convs.' + str(i),
ConvNormLayer(
ch_in=ch_in,
ch_out=self.seg_feat_channels,
filter_size=3,
stride=1,
use_dcn=use_dcn,
norm_type=self.norm_type))
self.kernel_pred_convs.append(kernel_conv)
ch_in = self.in_channels if i == 0 else self.seg_feat_channels
cate_conv = self.add_sublayer(
'bbox_head.cate_convs.' + str(i),
ConvNormLayer(
ch_in=ch_in,
ch_out=self.seg_feat_channels,
filter_size=3,
stride=1,
use_dcn=use_dcn,
norm_type=self.norm_type))
self.cate_pred_convs.append(cate_conv)
self.solo_kernel = self.add_sublayer(
'bbox_head.solo_kernel',
nn.Conv2D(
self.seg_feat_channels,
self.kernel_out_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=True))
self.solo_cate = self.add_sublayer(
'bbox_head.solo_cate',
nn.Conv2D(
self.seg_feat_channels,
self.cate_out_channels,
kernel_size=3,
stride=1,
padding=1,
weight_attr=ParamAttr(initializer=Normal(
mean=0., std=0.01)),
bias_attr=ParamAttr(initializer=Constant(
value=float(-np.log((1 - 0.01) / 0.01))))))
if self.drop_block and self.training:
self.drop_block_fun = DropBlock(
block_size=3, keep_prob=0.9, name='solo_cate.dropblock')
def __init__(self,
feat_in=256,
feat_out=96,
num_fpn_stride=3,
num_convs=2,
norm_type='bn',
share_cls_reg=False,
act='hard_swish',
use_se=False):
super(PicoFeat, self).__init__()
self.num_convs = num_convs
self.norm_type = norm_type
self.share_cls_reg = share_cls_reg
self.act = act
self.use_se = use_se
self.cls_convs = []
self.reg_convs = []
if use_se:
assert share_cls_reg == True, \
'In the case of using se, share_cls_reg is not supported'
self.se = nn.LayerList()
for stage_idx in range(num_fpn_stride):
cls_subnet_convs = []
reg_subnet_convs = []
for i in range(self.num_convs):
in_c = feat_in if i == 0 else feat_out
cls_conv_dw = self.add_sublayer(
'cls_conv_dw{}.{}'.format(stage_idx, i),
ConvNormLayer(
ch_in=in_c,
ch_out=feat_out,
filter_size=5,
stride=1,
groups=feat_out,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
cls_subnet_convs.append(cls_conv_dw)
cls_conv_pw = self.add_sublayer(
'cls_conv_pw{}.{}'.format(stage_idx, i),
ConvNormLayer(
ch_in=in_c,
ch_out=feat_out,
filter_size=1,
stride=1,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
cls_subnet_convs.append(cls_conv_pw)
if not self.share_cls_reg:
reg_conv_dw = self.add_sublayer(
'reg_conv_dw{}.{}'.format(stage_idx, i),
ConvNormLayer(
ch_in=in_c,
ch_out=feat_out,
filter_size=5,
stride=1,
groups=feat_out,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
reg_subnet_convs.append(reg_conv_dw)
reg_conv_pw = self.add_sublayer(
'reg_conv_pw{}.{}'.format(stage_idx, i),
ConvNormLayer(
ch_in=in_c,
ch_out=feat_out,
filter_size=1,
stride=1,
norm_type=norm_type,
bias_on=False,
lr_scale=2.))
reg_subnet_convs.append(reg_conv_pw)
self.cls_convs.append(cls_subnet_convs)
self.reg_convs.append(reg_subnet_convs)
if use_se:
self.se.append(PicoSE(feat_out))
def __init__(self,
in_channels,
out_channel,
spatial_scales=[0.25, 0.125, 0.0625, 0.03125],
has_extra_convs=False,
extra_stage=1,
use_c5=True,
norm_type=None,
norm_decay=0.,
freeze_norm=False,
relu_before_extra_convs=True):
super(FPN, self).__init__()
self.out_channel = out_channel
for s in range(extra_stage):
spatial_scales = spatial_scales + [spatial_scales[-1] / 2.]
self.spatial_scales = spatial_scales
self.has_extra_convs = has_extra_convs
self.extra_stage = extra_stage
self.use_c5 = use_c5
self.relu_before_extra_convs = relu_before_extra_convs
self.norm_type = norm_type
self.norm_decay = norm_decay
self.freeze_norm = freeze_norm
self.lateral_convs = []
self.fpn_convs = []
fan = out_channel * 3 * 3
# stage index 0,1,2,3 stands for res2,res3,res4,res5 on ResNet Backbone
# 0 <= st_stage < ed_stage <= 3
st_stage = 4 - len(in_channels)
ed_stage = st_stage + len(in_channels) - 1
for i in range(st_stage, ed_stage + 1):
if i == 3:
lateral_name = 'fpn_inner_res5_sum'
else:
lateral_name = 'fpn_inner_res{}_sum_lateral'.format(i + 2)
in_c = in_channels[i - st_stage]
if self.norm_type == 'gn':
lateral = self.add_sublayer(
lateral_name,
ConvNormLayer(ch_in=in_c,
ch_out=out_channel,
filter_size=1,
stride=1,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
norm_name=lateral_name + '_norm',
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=in_c),
name=lateral_name))
else:
lateral = self.add_sublayer(
lateral_name,
nn.Conv2D(in_channels=in_c,
out_channels=out_channel,
kernel_size=1,
weight_attr=ParamAttr(initializer=XavierUniform(
fan_out=in_c))))
self.lateral_convs.append(lateral)
fpn_name = 'fpn_res{}_sum'.format(i + 2)
if self.norm_type == 'gn':
fpn_conv = self.add_sublayer(
fpn_name,
ConvNormLayer(ch_in=out_channel,
ch_out=out_channel,
filter_size=3,
stride=1,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
norm_name=fpn_name + '_norm',
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=fan),
name=fpn_name))
else:
fpn_conv = self.add_sublayer(
fpn_name,
nn.Conv2D(in_channels=out_channel,
out_channels=out_channel,
kernel_size=3,
padding=1,
weight_attr=ParamAttr(initializer=XavierUniform(
fan_out=fan))))
self.fpn_convs.append(fpn_conv)
# add extra conv levels for RetinaNet(use_c5)/FCOS(use_p5)
if self.has_extra_convs:
for i in range(self.extra_stage):
lvl = ed_stage + 1 + i
if i == 0 and self.use_c5:
in_c = in_channels[-1]
else:
in_c = out_channel
extra_fpn_name = 'fpn_{}'.format(lvl + 2)
if self.norm_type == 'gn':
extra_fpn_conv = self.add_sublayer(
extra_fpn_name,
ConvNormLayer(ch_in=in_c,
ch_out=out_channel,
filter_size=3,
stride=2,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
norm_name=extra_fpn_name + '_norm',
freeze_norm=self.freeze_norm,
initializer=XavierUniform(fan_out=fan),
name=extra_fpn_name))
else:
extra_fpn_conv = self.add_sublayer(
extra_fpn_name,
nn.Conv2D(in_channels=in_c,
out_channels=out_channel,
kernel_size=3,
stride=2,
padding=1,
weight_attr=ParamAttr(
initializer=XavierUniform(fan_out=fan))))
self.fpn_convs.append(extra_fpn_conv)
def __init__(self,
num_classes=80,
feat_channels=256,
stacked_convs=6,
fpn_strides=(8, 16, 32, 64, 128),
grid_cell_scale=8,
grid_cell_offset=0.5,
norm_type='gn',
norm_groups=32,
static_assigner_epoch=4,
use_align_head=True,
loss_weight={
'class': 1.0,
'bbox': 1.0,
'iou': 2.0,
},
nms='MultiClassNMS',
static_assigner='ATSSAssigner',
assigner='TaskAlignedAssigner'):
super(TOODHead, self).__init__()
self.num_classes = num_classes
self.feat_channels = feat_channels
self.stacked_convs = stacked_convs
self.fpn_strides = fpn_strides
self.grid_cell_scale = grid_cell_scale
self.grid_cell_offset = grid_cell_offset
self.static_assigner_epoch = static_assigner_epoch
self.use_align_head = use_align_head
self.nms = nms
self.static_assigner = static_assigner
self.assigner = assigner
self.loss_weight = loss_weight
self.giou_loss = GIoULoss()
self.inter_convs = nn.LayerList()
for i in range(self.stacked_convs):
self.inter_convs.append(
ConvNormLayer(self.feat_channels,
self.feat_channels,
filter_size=3,
stride=1,
norm_type=norm_type,
norm_groups=norm_groups))
self.cls_decomp = TaskDecomposition(self.feat_channels,
self.stacked_convs,
self.stacked_convs * 8,
norm_type=norm_type,
norm_groups=norm_groups)
self.reg_decomp = TaskDecomposition(self.feat_channels,
self.stacked_convs,
self.stacked_convs * 8,
norm_type=norm_type,
norm_groups=norm_groups)
self.tood_cls = nn.Conv2D(self.feat_channels,
self.num_classes,
3,
padding=1)
self.tood_reg = nn.Conv2D(self.feat_channels, 4, 3, padding=1)
if self.use_align_head:
self.cls_prob_conv1 = nn.Conv2D(
self.feat_channels * self.stacked_convs,
self.feat_channels // 4, 1)
self.cls_prob_conv2 = nn.Conv2D(self.feat_channels // 4,
1,
3,
padding=1)
self.reg_offset_conv1 = nn.Conv2D(
self.feat_channels * self.stacked_convs,
self.feat_channels // 4, 1)
self.reg_offset_conv2 = nn.Conv2D(self.feat_channels // 4,
4 * 2,
3,
padding=1)
self.scales_regs = nn.LayerList([ScaleReg() for _ in self.fpn_strides])
self._init_weights()
