def __init__(self,
inplanes,
outplanes,
innerplanes,
stride=1,
dilation=1,
group=1,
downsample=None):
super().__init__()
# In original resnet, stride=2 is on 1x1.
# In fb.torch resnet, stride=2 is on 3x3.
(str1x1, str3x3) = (stride, 1) if cfg.RESNETS.STRIDE_1X1 else (1,
stride)
self.stride = stride
self.conv1 = nn.Conv2d(inplanes,
innerplanes,
kernel_size=1,
stride=str1x1,
bias=False)
self.sn1 = mynn.SwitchNorm(
innerplanes,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.RESNETS.SN.USE_BN)
self.conv2 = nn.Conv2d(innerplanes,
innerplanes,
kernel_size=3,
stride=str3x3,
bias=False,
padding=1 * dilation,
dilation=dilation,
groups=group)
self.sn2 = mynn.SwitchNorm(
innerplanes,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.RESNETS.SN.USE_BN)
self.conv3 = nn.Conv2d(innerplanes,
outplanes,
kernel_size=1,
stride=1,
bias=False)
self.sn3 = mynn.SwitchNorm(
outplanes,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.RESNETS.SN.USE_BN)
self.downsample = downsample
self.relu = nn.ReLU(inplace=True)
def __init__(self, dim_in, roi_xform_func, spatial_scale):
super().__init__()
self.dim_in = dim_in
self.roi_xform = roi_xform_func
self.spatial_scale = spatial_scale
hidden_dim = cfg.FAST_RCNN.CONV_HEAD_DIM
module_list = []
for i in range(cfg.FAST_RCNN.NUM_STACKED_CONVS):
module_list.extend([
nn.Conv2d(dim_in, hidden_dim, 3, 1, 1, bias=False),
mynn.SwitchNorm(
hidden_dim,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.FAST_RCNN.SN.USE_BN),
nn.ReLU(inplace=True)
])
dim_in = hidden_dim
self.convs = nn.Sequential(*module_list)
self.dim_out = fc_dim = cfg.FAST_RCNN.MLP_HEAD_DIM
roi_size = cfg.FAST_RCNN.ROI_XFORM_RESOLUTION
self.fc = nn.Linear(dim_in * roi_size * roi_size, fc_dim)
self._init_weights()
def __init__(self, dim_in, roi_xform_func, spatial_scale, num_convs):
super().__init__()
self.dim_in = dim_in
self.roi_xform = roi_xform_func
self.spatial_scale = spatial_scale
self.num_convs = num_convs
dilation = cfg.MRCNN.DILATION
dim_inner = cfg.MRCNN.DIM_REDUCED
self.dim_out = dim_inner
module_list = []
for i in range(num_convs):
module_list.extend([
nn.Conv2d(dim_in,
dim_inner,
3,
1,
padding=1 * dilation,
dilation=dilation,
bias=False),
mynn.SwitchNorm(
dim_inner,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.MRCNN.SN.USE_BN),
nn.ReLU(inplace=True)
])
dim_in = dim_inner
self.conv_fcn = nn.Sequential(*module_list)
# upsample layer
self.upconv = nn.ConvTranspose2d(dim_inner, dim_inner, 2, 2, 0)
self.apply(self._init_weights)
def basic_sn_shortcut(inplanes, outplanes, stride):
return nn.Sequential(
nn.Conv2d(inplanes,
outplanes,
kernel_size=1,
stride=stride,
bias=False),
mynn.SwitchNorm(outplanes,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.RESNETS.SN.USE_BN))
def basic_sn_stem():
return nn.Sequential(
OrderedDict([
('conv1', nn.Conv2d(3, 64, 7, stride=2, padding=3, bias=False)),
('sn1',
mynn.SwitchNorm(64,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.RESNETS.SN.USE_BN)),
('relu', nn.ReLU(inplace=True)),
('maxpool', nn.MaxPool2d(kernel_size=3, stride=2, padding=1))
]))
def __init__(self, dim_in_top, dim_in_lateral):
super().__init__()
self.dim_in_top = dim_in_top
self.dim_in_lateral = dim_in_lateral
self.dim_out = dim_in_top
if cfg.FPN.USE_GN:
self.conv_lateral = nn.Sequential(
nn.Conv2d(dim_in_lateral, self.dim_out, 1, 1, 0, bias=False),
nn.GroupNorm(net_utils.get_group_gn(self.dim_out),
self.dim_out,
eps=cfg.GROUP_NORM.EPSILON))
elif cfg.FPN.USE_SN:
self.conv_lateral = nn.Sequential(
nn.Conv2d(dim_in_lateral, self.dim_out, 1, 1, 0, bias=False),
mynn.SwitchNorm(
self.dim_out,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.FPN.SN.USE_BN))
else:
self.conv_lateral = nn.Conv2d(dim_in_lateral, self.dim_out, 1, 1,
0)
self._init_weights()
def __init__(self, conv_body_func, fpn_level_info, P2only=False):
super().__init__()
self.fpn_level_info = fpn_level_info
self.P2only = P2only
self.dim_out = fpn_dim = cfg.FPN.DIM
min_level, max_level = get_min_max_levels()
self.num_backbone_stages = len(
fpn_level_info.blobs) - (min_level - LOWEST_BACKBONE_LVL)
fpn_dim_lateral = fpn_level_info.dims
self.spatial_scale = [] # a list of scales for FPN outputs
#
# Step 1: recursively build down starting from the coarsest backbone level
#
# For the coarest backbone level: 1x1 conv only seeds recursion
self.conv_top = nn.Conv2d(fpn_dim_lateral[0], fpn_dim, 1, 1, 0)
if cfg.FPN.USE_GN:
self.conv_top = nn.Sequential(
nn.Conv2d(fpn_dim_lateral[0], fpn_dim, 1, 1, 0, bias=False),
nn.GroupNorm(net_utils.get_group_gn(fpn_dim),
fpn_dim,
eps=cfg.GROUP_NORM.EPSILON))
elif cfg.FPN.USE_SN:
self.conv_top = nn.Sequential(
nn.Conv2d(fpn_dim_lateral[0], fpn_dim, 1, 1, 0, bias=False),
mynn.SwitchNorm(
fpn_dim,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.FPN.SN.USE_BN))
else:
self.conv_top = nn.Conv2d(fpn_dim_lateral[0], fpn_dim, 1, 1, 0)
self.topdown_lateral_modules = nn.ModuleList()
self.posthoc_modules = nn.ModuleList()
# For other levels add top-down and lateral connections
for i in range(self.num_backbone_stages - 1):
self.topdown_lateral_modules.append(
topdown_lateral_module(fpn_dim, fpn_dim_lateral[i + 1]))
# Post-hoc scale-specific 3x3 convs
for i in range(self.num_backbone_stages):
if cfg.FPN.USE_GN:
self.posthoc_modules.append(
nn.Sequential(
nn.Conv2d(fpn_dim, fpn_dim, 3, 1, 1, bias=False),
nn.GroupNorm(net_utils.get_group_gn(fpn_dim),
fpn_dim,
eps=cfg.GROUP_NORM.EPSILON)))
elif cfg.FPN.USE_SN:
self.posthoc_modules.append(
nn.Sequential(
nn.Conv2d(fpn_dim, fpn_dim, 3, 1, 1, bias=False),
mynn.SwitchNorm(
fpn_dim,
using_moving_average=(not cfg.TEST.USE_BATCH_AVG),
using_bn=cfg.FPN.SN.USE_BN)))
else:
self.posthoc_modules.append(
nn.Conv2d(fpn_dim, fpn_dim, 3, 1, 1))
self.spatial_scale.append(fpn_level_info.spatial_scales[i])
#
# Step 2: build up starting from the coarsest backbone level
#
# Check if we need the P6 feature map
if not cfg.FPN.EXTRA_CONV_LEVELS and max_level == HIGHEST_BACKBONE_LVL + 1:
# Original FPN P6 level implementation from our CVPR'17 FPN paper
# Use max pooling to simulate stride 2 subsampling
self.maxpool_p6 = nn.MaxPool2d(kernel_size=1, stride=2, padding=0)
self.spatial_scale.insert(0, self.spatial_scale[0] * 0.5)
# Coarser FPN levels introduced for RetinaNet
if cfg.FPN.EXTRA_CONV_LEVELS and max_level > HIGHEST_BACKBONE_LVL:
self.extra_pyramid_modules = nn.ModuleList()
dim_in = fpn_level_info.dims[0]
for i in range(HIGHEST_BACKBONE_LVL + 1, max_level + 1):
self.extra_pyramid_modules(nn.Conv2d(dim_in, fpn_dim, 3, 2, 1))
dim_in = fpn_dim
self.spatial_scale.insert(0, self.spatial_scale[0] * 0.5)
if self.P2only:
# use only the finest level
self.spatial_scale = self.spatial_scale[-1]
self._init_weights()
# Deliberately add conv_body after _init_weights.
# conv_body has its own _init_weights function
self.conv_body = conv_body_func() # e.g resnet