def __init__(self, feature_extractor, head, norm_layer=nn.BatchNorm2d, use_rgb_conv=True):
super(DistMapsModel, self).__init__()
if use_rgb_conv:
self.rgb_conv = nn.Sequential(
nn.Conv2d(in_channels=5, out_channels=8, kernel_size=1),
nn.LeakyReLU(negative_slope=0.2),
norm_layer(8),
nn.Conv2d(in_channels=8, out_channels=3, kernel_size=1),
)
else:
self.rgb_conv = None
self.dist_maps = DistMaps(norm_radius=260, spatial_scale=1.0)
self.feature_extractor = feature_extractor
self.head = head
def __init__(self, feature_extractor, max_interactive_points=10, use_rgb_conv=True, with_aux_output=False,
norm_layer=nn.BatchNorm2d):
super(DistMapsHRNetModel, self).__init__()
self.with_aux_output = with_aux_output
if use_rgb_conv:
self.rgb_conv = nn.Sequential(
nn.Conv2d(in_channels=5, out_channels=8, kernel_size=1),
nn.LeakyReLU(negative_slope=0.2),
norm_layer(8),
nn.Conv2d(in_channels=8, out_channels=3, kernel_size=1),
)
else:
self.rgb_conv = None
self.dist_maps = DistMaps(norm_radius=260, max_interactive_points=max_interactive_points,
spatial_scale=1.0)
self.feature_extractor = feature_extractor
def __init__(self,
use_rgb_conv=True,
with_aux_output=False,
norm_radius=260,
use_disks=False,
cpu_dist_maps=False,
clicks_groups=None,
with_prev_mask=False,
use_leaky_relu=False,
binary_prev_mask=False,
conv_extend=False,
norm_layer=nn.BatchNorm2d,
norm_mean_std=([.485, .456, .406], [.229, .224, .225])):
super().__init__()
self.with_aux_output = with_aux_output
self.clicks_groups = clicks_groups
self.with_prev_mask = with_prev_mask
self.binary_prev_mask = binary_prev_mask
self.normalization = BatchImageNormalize(norm_mean_std[0],
norm_mean_std[1])
self.coord_feature_ch = 2
if clicks_groups is not None:
self.coord_feature_ch *= len(clicks_groups)
if self.with_prev_mask:
self.coord_feature_ch += 1
if use_rgb_conv:
rgb_conv_layers = [
nn.Conv2d(in_channels=3 + self.coord_feature_ch,
out_channels=6 + self.coord_feature_ch,
kernel_size=1),
norm_layer(6 + self.coord_feature_ch),
nn.LeakyReLU(negative_slope=0.2)
if use_leaky_relu else nn.ReLU(inplace=True),
nn.Conv2d(in_channels=6 + self.coord_feature_ch,
out_channels=3,
kernel_size=1)
]
self.rgb_conv = nn.Sequential(*rgb_conv_layers)
elif conv_extend:
self.rgb_conv = None
self.maps_transform = nn.Conv2d(in_channels=self.coord_feature_ch,
out_channels=64,
kernel_size=3,
stride=2,
padding=1)
self.maps_transform.apply(LRMult(0.1))
else:
self.rgb_conv = None
mt_layers = [
nn.Conv2d(in_channels=self.coord_feature_ch,
out_channels=16,
kernel_size=1),
nn.LeakyReLU(negative_slope=0.2)
if use_leaky_relu else nn.ReLU(inplace=True),
nn.Conv2d(in_channels=16,
out_channels=64,
kernel_size=3,
stride=2,
padding=1),
ScaleLayer(init_value=0.05, lr_mult=1)
]
self.maps_transform = nn.Sequential(*mt_layers)
if self.clicks_groups is not None:
self.dist_maps = nn.ModuleList()
for click_radius in self.clicks_groups:
self.dist_maps.append(
DistMaps(norm_radius=click_radius,
spatial_scale=1.0,
cpu_mode=cpu_dist_maps,
use_disks=use_disks))
else:
self.dist_maps = DistMaps(norm_radius=norm_radius,
spatial_scale=1.0,
cpu_mode=cpu_dist_maps,
use_disks=use_disks)