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hubconf.py
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hubconf.py
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# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import torch
from models.backbone import Backbone, Joiner
from models.detr import DETR, PostProcess
from models.position_encoding import PositionEmbeddingSine
from models.segmentation import DETRsegm, PostProcessPanoptic
from models.transformer import Transformer
dependencies = ["torch", "torchvision"]
def _make_detr(backbone_name: str, dilation=False, num_classes=91, mask=False):
hidden_dim = 256
backbone = Backbone(backbone_name, train_backbone=True, return_interm_layers=mask, dilation=dilation)
pos_enc = PositionEmbeddingSine(hidden_dim // 2, normalize=True)
backbone_with_pos_enc = Joiner(backbone, pos_enc)
backbone_with_pos_enc.num_channels = backbone.num_channels
transformer = Transformer(d_model=hidden_dim, return_intermediate_dec=True)
detr = DETR(backbone_with_pos_enc, transformer, num_classes=num_classes, num_queries=100)
if mask:
return DETRsegm(detr)
return detr
def detr_resnet50(pretrained=False, num_classes=91, return_postprocessor=False):
"""
DETR R50 with 6 encoder and 6 decoder layers.
Achieves 42/62.4 AP/AP50 on COCO val5k.
"""
model = _make_detr("resnet50", dilation=False, num_classes=num_classes)
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/detr/detr-r50-e632da11.pth", map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcess()
return model
def detr_resnet50_dc5(pretrained=False, num_classes=91, 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 43.3/63.1 AP/AP50 on COCO val5k.
"""
model = _make_detr("resnet50", dilation=True, num_classes=num_classes)
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/detr/detr-r50-dc5-f0fb7ef5.pth", map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcess()
return model
def detr_resnet101(pretrained=False, num_classes=91, return_postprocessor=False):
"""
DETR-DC5 R101 with 6 encoder and 6 decoder layers.
Achieves 43.5/63.8 AP/AP50 on COCO val5k.
"""
model = _make_detr("resnet101", dilation=False, num_classes=num_classes)
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/detr/detr-r101-2c7b67e5.pth", map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcess()
return model
def detr_resnet101_dc5(pretrained=False, num_classes=91, return_postprocessor=False):
"""
DETR-DC5 R101 with 6 encoder and 6 decoder layers.
The last block of ResNet-101 has dilation to increase
output resolution.
Achieves 44.9/64.7 AP/AP50 on COCO val5k.
"""
model = _make_detr("resnet101", dilation=True, num_classes=num_classes)
if pretrained:
checkpoint = torch.hub.load_state_dict_from_url(
url="https://dl.fbaipublicfiles.com/detr/detr-r101-dc5-a2e86def.pth", map_location="cpu", check_hash=True
)
model.load_state_dict(checkpoint["model"])
if return_postprocessor:
return model, PostProcess()
return model
def detr_resnet50_panoptic(
pretrained=False, num_classes=250, threshold=0.85, return_postprocessor=False
):
"""
DETR R50 with 6 encoder and 6 decoder layers.
Achieves 43.4 PQ on COCO val5k.
threshold is the minimum confidence required for keeping segments in the prediction
"""
model = _make_detr("resnet50", 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-r50-panoptic-00ce5173.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
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