def setup_cfg(args):
cfg = get_cfg()
cfg = add_export_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def _test_model(self, config_path, device="cpu"):
# requires extra dependencies
from detectron2.export import (
Caffe2Model,
add_export_config,
export_caffe2_model,
)
cfg = get_cfg()
cfg.merge_from_file(model_zoo.get_config_file(config_path))
cfg = add_export_config(cfg)
cfg.MODEL.DEVICE = device
model = build_model(cfg)
DetectionCheckpointer(model).load(
model_zoo.get_checkpoint_url(config_path))
inputs = [{"image": self._get_test_image()}]
c2_model = export_caffe2_model(cfg, model, copy.deepcopy(inputs))
with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
c2_model.save_protobuf(d)
c2_model.save_graph(os.path.join(d, "test.svg"),
inputs=copy.deepcopy(inputs))
c2_model = Caffe2Model.load_protobuf(d)
c2_model(inputs)[0]["instances"]
def setup_cfg(args):
cfg = get_cfg()
# cuda context is initialized before creating dataloader, so we don't fork anymore
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def setup_cfg(args):
cfg = get_cfg()
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
add_pointrend_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def _test_model(self, config_path, device="cpu"):
# requires extra dependencies
from detectron2.export import Caffe2Model, add_export_config, Caffe2Tracer
cfg = model_zoo.get_config(config_path)
add_export_config(cfg)
cfg.MODEL.DEVICE = device
model = model_zoo.get(config_path, trained=True, device=device)
inputs = [{"image": get_sample_coco_image()}]
c2_model = Caffe2Tracer(cfg, model,
copy.deepcopy(inputs)).export_caffe2()
with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
c2_model.save_protobuf(d)
c2_model.save_graph(os.path.join(d, "test.svg"),
inputs=copy.deepcopy(inputs))
c2_model = Caffe2Model.load_protobuf(d)
c2_model(inputs)[0]["instances"]
def setup_cfg(args):
cfg = get_cfg()
# cuda context is initialized before creating dataloader, so we don't fork anymore
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
if cfg.MODEL.DEVICE != "cpu":
assert TORCH_VERSION >= (1, 5), "PyTorch>=1.5 required for GPU conversion!"
return cfg
def _test_model(self, config_path, device="cpu"):
cfg = model_zoo.get_config(config_path)
add_export_config(cfg)
cfg.MODEL.DEVICE = device
model = model_zoo.get(config_path, trained=True, device=device)
inputs = [{"image": get_sample_coco_image()}]
tracer = Caffe2Tracer(cfg, model, copy.deepcopy(inputs))
with tempfile.TemporaryDirectory(prefix="detectron2_unittest") as d:
if not os.environ.get("CI"):
# This requires onnx, which is not yet available on public CI
c2_model = tracer.export_caffe2()
c2_model.save_protobuf(d)
c2_model.save_graph(os.path.join(d, "test.svg"),
inputs=copy.deepcopy(inputs))
c2_model = Caffe2Model.load_protobuf(d)
c2_model(inputs)[0]["instances"]
ts_model = tracer.export_torchscript()
ts_model.save(os.path.join(d, "model.ts"))
def setup_cfg(args):
MetadataCatalog.get("custom")
cfg = get_cfg()
# cuda context is initialized before creating dataloader, so we don't fork anymore
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
# cfg.merge_from_file(args.config_file)
cfg.merge_from_file(
# "../detectron2-master/configs/COCO-Detection/faster_rcnn_R_50_FPN_3x.yaml" # COCO
"../../detectron2-master/configs/PascalVOC-Detection/faster_rcnn_R_50_FPN.yaml" # VOC
)
cfg.merge_from_list(args.opts)
cfg.MODEL.WEIGHTS = "../output/model_final.pth"
cfg.DATASETS.TRAIN = ("custom",)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 4 # num classes
cfg.freeze()
if cfg.MODEL.DEVICE != "cpu":
assert TORCH_VERSION >= (1, 5), "PyTorch>=1.5 required for GPU conversion!"
return cfg
def setup_cfg(args):
DatasetCatalog.clear()
MetadataCatalog.clear()
keypoint_names = [
"c1", "c2", "c3", "c4", "c5", "c6", "c7", "c8", "c9", "c10"
]
keypoint_connection_rules = [[1, 2, [255, 175, 100]],
[2, 3, [255, 175, 100]],
[3, 4, [255, 175, 100]],
[4, 5, [255, 175, 100]],
[5, 6, [255, 175, 100]],
[6, 7, [255, 175, 100]],
[7, 8, [255, 175, 100]],
[8, 9, [255, 175, 100]],
[9, 10, [255, 175, 100]]]
keypoint_flip_map = [('c1', 'c10'), ('c2', 'c9'), ('c3', 'c8'),
('c4', 'c7'), ('c5', 'c6')]
register_coco_instances("my_dataset_train", {},
"/content/keypointsl/annotations.json",
"/content/VOC2007/JPEGImages")
my_md = MetadataCatalog.get("my_dataset_train").set(
thing_classes=["table", "r"],
keypoint_names=keypoint_names,
keypoint_connection_rules=keypoint_connection_rules,
keypoint_flip_map=keypoint_flip_map)
cfg = get_cfg()
# cuda context is initialized before creating dataloader, so we don't fork anymore
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.DATASETS.my_dataset_train = ("my_dataset_train", )
cfg.DATASETS.TEST = ("my_dataset_train", )
cfg.DATASETS.TRAIN = ("my_dataset_train", )
cfg.freeze()
if cfg.MODEL.DEVICE != "cpu":
assert TORCH_VERSION >= (
1, 5), "PyTorch>=1.5 required for GPU conversion!"
return cfg
metadata.thing_classes = class_ids
# metadata.thing_dataset_id_to_contiguous_id = id_map
# log.info("metadata: {}".format(metadata))
return metadata
name = "hmd"
for subset in ["train", "val"]:
metadata = load_and_register_dataset(name, subset, appcfg)
cfg = config.get_cfg()
# cfg.merge_from_file("/codehub/external/detectron2/configs/COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml")
cfg.DATALOADER.NUM_WORKERS = 0
cfg = add_export_config(cfg)
cfg.merge_from_file(
model_zoo.get_config_file(
"COCO-InstanceSegmentation/mask_rcnn_R_50_FPN_3x.yaml"))
cfg.DATASETS.TRAIN = ("hmd_train", )
cfg.DATASETS.TEST = ("hmd_val", )
# cfg.DATASETS.TRAIN = ("balloon_train",)
# cfg.DATASETS.TEST = ("balloon_val",)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = 3
# cfg.MODEL.WEIGHTS = "/codehub/tmp/model_final_balloon.pth"
# cfg.MODEL.WEIGHTS = "/codehub/apps/detectron2/temp/model_final_balloon.pth"
cfg.MODEL.WEIGHTS = "/codehub/apps/detectron2/release/model_final.pth"
