def detr_resnet101_panoptic(pretrained=False,
num_classes=250,
threshold=0.85,
return_postprocessor=False):
"""
DETR-DC5 R101 with 6 encoder and 6 decoder layers.
Achieves 45.1 PQ on COCO val5k.
threshold is the minimum confidence required for keeping segments in the prediction
"""
model = _make_detr("resnet101",
dilation=False,
num_classes=num_classes,
mask=True)
is_thing_map = {i: i <= 90 for i in range(250)}
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url=
"https://dl.fbaipublicfiles.com/detr/detr-r101-panoptic-40021d53.pth",
map_location="cpu",
check_hash=True,
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcessPanoptic(is_thing_map, threshold=threshold)
return model
def detr_resnet50_dc5_panoptic(pretrained=False,
num_classes=250,
threshold=0.85,
return_postprocessor=False):
"""
DETR-DC5 R50 with 6 encoder and 6 decoder layers.
The last block of ResNet-50 has dilation to increase
output resolution.
Achieves 44.6 on COCO val5k.
threshold is the minimum confidence required for keeping segments in the prediction
"""
model = _make_detr("resnet50",
dilation=True,
num_classes=num_classes,
mask=True)
is_thing_map = {i: i <= 90 for i in range(250)}
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url=
"https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-panoptic-da08f1b1.pth",
map_location="cpu",
check_hash=True,
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcessPanoptic(is_thing_map, threshold=threshold)
return model