def setup(args):
"""
Create configs and perform basic setups.
"""
cfg_file = args['config']
print('Det2 config file : {}'.format(cfg_file))
model_weights = args['weights']
positive_thresh = args['thresh']
cfg = get_cfg()
add_tridentnet_config(cfg)
cfg.merge_from_file(cfg_file)
# This is the way we set the thresh, and model weights. TODO: take in as params from args
print('Det2 threshold : {}'.format(positive_thresh))
cfg.MODEL.ROI_HEADS.SCORE_THRESH_TEST = positive_thresh
print('Det2 model loaded from : {}'.format(model_weights))
cfg.MODEL.WEIGHTS = model_weights
cfg.freeze()
# register_coco_instances("Ships", {},"ships-lite.json","")
# register_coco_instances("Ships", {},"ships-lite.json","Ships")
# register_coco_instances("Ships", {},"config/ships-lite.json","Ships")
# metadata = MetadataCatalog.get("Ships")
classes_path = args['classes_path']
with open(classes_path) as f:
class_names = f.readlines()
class_names = [c.strip() for c in class_names]
return cfg, class_names
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_tridentnet_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_tridentnet_config(cfg)
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
# if args.eval_only:
# cfg.MODEL.WEIGHTS = "/root/detectron2/projects/TridentNet/log_80_20/model_0029999.pth"
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
cfg = get_cfg()
add_tridentnet_config(cfg)
cfg.merge_from_file(
"/data/mostertrij/tridentnet/detectron2/projects/TridentNet/configs/my_tridentnet_fast_R_101_C4_3x.yaml"
)
DATASET_NAME = "LGZ_v10"
cfg.DATASETS.TRAIN = (f"{DATASET_NAME}_train", )
cfg.DATASETS.VAL = (f"{DATASET_NAME}_val", )
cfg.DATASETS.TEST = (f"{DATASET_NAME}_test", )
#cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.freeze()
default_setup(cfg, args)
return cfg
def setup(args):
"""
Create configs and perform basic setups.
"""
# cfg = get_cfg()
dataset_path = '/raid/cesar_workspace/cesar_workspace/Object_Detection/Detectron2/detectron2/detectron2/data/Datasets/'
train = dataset_path + "up_trees_train_2021"
val = dataset_path + "up_trees_val_2021"
train_dataset = train # cfg.DATASETS.TRAIN
val_dataset = val # cfg.DATASETS.TEST
# print(cfg.DATASETS.TRAIN, 'eee')
dic_marks_path = dataset_path + "up_trees_labels.json"
datasets_dic = {'train': train_dataset, 'val': val_dataset}
dic_marks = {'0': 'up_tree'}
# with open(dic_marks_path, 'w') as out:
# json.dump(dic_marks, out)
with open(dic_marks_path, 'r') as out:
dic_marks = json.load(out)
# cat_ids = {label:key for key, label in dic_marks.items()}
classes = [label for key, label in dic_marks.items()]
def get_board_dicts(imgdir):
json_file = imgdir + '.json' # Fetch the json file
print(json_file)
with open(json_file) as f:
dataset_dicts = json.load(f)
for i in dataset_dicts:
filename = i["file_name"]
for j in i["annotations"]:
# Setting the required Box Mode
j["bbox_mode"] = BoxMode.XYWH_ABS
j["category_id"] = int(j["category_id"])
return dataset_dicts
# Registering the Dataset
for d in ['val', 'train']:
# print(datasets_dic[d])
dataset_name = os.path.basename(datasets_dic[d])
print(dataset_name)
DatasetCatalog.register(dataset_name,
lambda d=d: get_board_dicts(datasets_dic[d]))
MetadataCatalog.get(dataset_name).set(thing_classes=classes)
train_name = os.path.basename(datasets_dic['train'])
val_name = os.path.basename(datasets_dic['val'])
print(train_name, val_name)
board_metadata = MetadataCatalog.get(train_name)
dataset_dicts = get_board_dicts(train_dataset)
n_imgs = len(dataset_dicts)
dataset_dicts = get_board_dicts(val_dataset)
n_imgs_val = len(dataset_dicts)
print('Number of images on training data is :', n_imgs)
cfg = get_cfg()
add_tridentnet_config(cfg)
# cfg.DATALOADER.NUM_WORKERS = 2
cfg.merge_from_file(args.config_file)
cfg.merge_from_list(args.opts)
cfg.DATASETS.TRAIN = (train_name, )
cfg.DATASETS.TEST = (val_name, )
cfg.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 128
# No. of iterations after which the Validation Set is evaluated.
cfg.TEST.EVAL_PERIOD = (n_imgs // cfg.SOLVER.IMS_PER_BATCH)
cfg.MODEL.ROI_HEADS.NUM_CLASSES = len(classes)
path = '/raid/cesar_workspace/cesar_workspace/Object_Detection/Detectron/detectron2/ImageNetPretrained/MSRA/'
os.makedirs(path, exist_ok=True)
backbone = os.path.basename(cfg.MODEL.WEIGHTS)
print('Number of images on training data is :', n_imgs, n_imgs_val)
backbone += '.pkl' if '.pkl' not in backbone else ''
weight = path + backbone
print(weight)
if not os.path.isfile(weight):
print("Downloading ImageNet weights")
url_weights = weights_catalog[backbone] + backbone
urllib.request.urlretrieve(url_weights, weight)
cfg.MODEL.WEIGHTS = weight
print(weight)
# cfg.OUTPUT_DIR = './output_%s_X-101_b/'%accr
print(cfg, '~~ I dedicate this to Shadow Moon ~~')
default_setup(cfg, args)
cfg.freeze()
return cfg