def setup(file):
# get cfg
cfg = get_cfg()
cfg.merge_from_file(file)
cfg.SOLVER.IMS_PER_BATCH = 2
# get data loader iter
data_loader = build_detection_train_loader(cfg)
data_loader_iter = iter(data_loader)
batched_inputs = next(data_loader_iter)
# build anchors
backbone = build_backbone(cfg).to(device)
images = [x["image"].to(device) for x in batched_inputs]
images = ImageList.from_tensors(images, backbone.size_divisibility)
features = backbone(images.tensor.float())
input_shape = backbone.output_shape()
in_features = cfg.MODEL.RPN.IN_FEATURES
anchor_generator = build_anchor_generator(
cfg, [input_shape[f] for f in in_features])
anchors = anchor_generator([features[f] for f in in_features])
anchors = Boxes.cat(anchors).to(device)
# build matcher
raw_matcher = Matcher(cfg.MODEL.RPN.IOU_THRESHOLDS,
cfg.MODEL.RPN.IOU_LABELS,
allow_low_quality_matches=True)
matcher = TopKMatcher(cfg.MODEL.RPN.IOU_THRESHOLDS,
cfg.MODEL.RPN.IOU_LABELS, 9)
return cfg, data_loader_iter, anchors, matcher, raw_matcher
def setup(args):
cfg = get_cfg()
if args.config_file:
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
return cfg
def test_dataloader():
from slender_det.data import build_detection_test_loader
from slender_det.config import get_cfg
cfg = get_cfg()
cfg.DATASETS.TEST = ("objects365_val", )
data_loader = build_detection_test_loader(cfg, cfg.DATASETS.TEST[0])
data_iter = iter(data_loader)
data = next(data_iter)
ipdb.set_trace()
for data in data_iter:
for dataset_dict in data:
img = dataset_dict['image']
img = convert_image_to_rgb(img.permute(1, 2, 0), cfg.INPUT.FORMAT)
v_gt = Visualizer(img, None)
v_gt = v_gt.overlay_instances(
boxes=dataset_dict["instances"].gt_boxes,
masks=dataset_dict["instances"].gt_masks,
)
v_gt.save('/data/tmp/vis_obj365_val_{}.png'.format(
dataset_dict['image_id']))
ipdb.set_trace()
def setup(file):
# get cfg
cfg = get_cfg()
cfg.merge_from_file(file)
cfg.SOLVER.IMS_PER_BATCH = 2
return cfg
def test_model(cfg_file):
# get cfg
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
cfg.SOLVER.IMS_PER_BATCH = 1
# get model
device = torch.device("cuda")
model = build_model(cfg).to(device)
ipdb.set_trace()
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def test_training(cfg_file):
# get cfg
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
cfg.SOLVER.IMS_PER_BATCH = 2
# get batch data
data_loader = build_detection_train_loader(cfg)
data_loader_iter = iter(data_loader)
data = next(data_loader_iter)
print(len(data))
# get model
device = torch.device("cuda")
model = build_model(cfg).to(device)
model.train()
outs = model(data[:2])
ipdb.set_trace()
import fire
import torch
from detectron2.data import MetadataCatalog, build_detection_train_loader
from detectron2.checkpoint import DetectionCheckpointer
import init_paths
from slender_det.config import get_cfg
from slender_det.data import BorderMaskMapper
from slender_det.modeling.meta_arch.fcos import FCOS, FCOSHead
# get cfg
cfg = get_cfg()
cfg.merge_from_file("configs/fcos/Base-Fcos.yaml")
# get model
device = torch.device("cuda")
model = FCOS(cfg).to(device)
# get batch data
data_loader = build_detection_train_loader(cfg)
data_loader_iter = iter(data_loader)
data = next(data_loader_iter)
def test_training():
model.train()
outs = model(data[:2])
import pdb
def load_cfg(cfg_file):
cfg = get_cfg()
cfg.merge_from_file(cfg_file)
return cfg
