def init_param(self, model_config):
self.in_channels = model_config['din']
self.post_nms_topN = model_config['post_nms_topN']
self.pre_nms_topN = model_config['pre_nms_topN']
self.nms_thresh = model_config['nms_thresh']
self.use_focal_loss = model_config['use_focal_loss']
# anchor generator
self.anchor_generator = anchor_generators.build(
model_config['anchor_generator_config'])
self.num_anchors = self.anchor_generator.num_anchors
self.instance = Instance(model_config['instance'])
self.sampler = samplers.build(model_config['sampler_config'])
def generate_anchors():
anchor_generator_config = {
"type": "default",
"anchor_offset": [0, 0],
"anchor_stride": [16, 16],
"aspect_ratios": [0.5, 0.8, 1],
"base_anchor_size": 16,
"scales": [2, 4, 8, 16]
}
anchor_generator = anchor_generators.build(anchor_generator_config)
feature_map_list = [(24, 80)]
input_size = [384, 1280]
anchors = anchor_generator.generate(feature_map_list, input_size)
return anchors
def init_param(self, model_config):
self.in_channels = model_config['din']
self.post_nms_topN = model_config['post_nms_topN']
self.pre_nms_topN = model_config['pre_nms_topN']
self.nms_thresh = model_config['nms_thresh']
self.use_focal_loss = model_config['use_focal_loss']
# anchor generator
self.anchor_generator = anchor_generators.build(
model_config['anchor_generator_config'])
self.num_anchors = self.anchor_generator.num_anchors
self.nc_bbox_out = 4 * self.num_anchors
self.nc_score_out = self.num_anchors * 2
self.target_generators = TargetGenerator(
model_config['target_generator_config'])
def init_param(self, model_config):
# including bg
self.num_classes = len(model_config['classes']) + 1
self.in_channels = model_config.get('in_channels', 128)
self.num_regress = model_config.get('num_regress', 4)
self.feature_extractor_config = model_config[
'feature_extractor_config']
self.target_generators = TargetGenerator(
model_config['target_generator_config'])
self.anchor_generator = anchor_generators.build(
model_config['anchor_generator_config'])
self.num_anchors = self.anchor_generator.num_anchors
input_size = torch.tensor(model_config['input_size']).float()
self.anchors = self.anchor_generator.generate(input_size)
self.use_focal_loss = model_config['use_focal_loss']
def __init__(self, dataset_config, transform=None, training=True):
super().__init__(training)
# import ipdb
# ipdb.set_trace()
self.transforms = transform
self.classes = ['bg'] + dataset_config['classes']
if dataset_config.get('img_dir') is not None:
self.image_dir = dataset_config['img_dir']
# directory
self.sample_names = sorted(
self.load_sample_names_from_image_dir(self.image_dir))
self.imgs = self.sample_names
elif dataset_config.get('demo_file') is not None:
# file
self.sample_names = sorted([dataset_config['demo_file']])
self.imgs = self.sample_names
else:
# val dataset
self.root_path = dataset_config['root_path']
self.data_path = os.path.join(self.root_path,
dataset_config['data_path'])
self.label_path = os.path.join(self.root_path,
dataset_config['label_path'])
self.sample_names = self.make_label_list(
os.path.join(self.label_path, dataset_config['dataset_file']))
self.imgs = self.make_image_list()
self.max_num_boxes = 100
# self.default_boxes = RetinaPriorBox()(dataset_config['anchor_config'])
self.anchor_generator = anchor_generators.build(
dataset_config['anchor_generator_config'])
default_boxes = self.anchor_generator.generate(
dataset_config['input_shape'], normalize=True)
self.default_boxes = geometry_utils.torch_xyxy_to_xywh(
default_boxes)[0]