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_score = model_config['use_score']
# self.rpn_batch_size = model_config['rpn_batch_size']
self.num_cls_samples = model_config['num_cls_samples']
self.num_reg_samples = model_config['num_reg_samples']
self.use_focal_loss = model_config['use_focal_loss']
# sampler
# self.sampler = HardNegativeSampler({"fg_fraction": 1.0})
# self.sampler = BalancedSampler(model_config['sampler_config'])
self.sampler = DetectionSampler({"fg_fraction": 1.0})
# anchor generator
self.anchor_generator = AnchorGenerator(
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
# target assigner
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# bbox coder
self.bbox_coder = self.target_assigner.bbox_coder
self.use_iou = model_config.get('use_iou')
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_score = model_config['use_score']
self.rpn_batch_size = model_config['rpn_batch_size']
self.use_focal_loss = model_config['use_focal_loss']
self.iou_criterion = model_config['iou_criterion']
self.use_iox = model_config['use_iox']
self.theta = 1.0
self.alpha = 0.6
# sampler
# self.sampler = HardNegativeSampler(model_config['sampler_config'])
# self.sampler = BalancedSampler(model_config['sampler_config'])
self.sampler = DetectionSampler(model_config['sampler_config'])
# anchor generator
self.anchor_generator = AnchorGenerator(
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
# target assigner
self.target_assigner = LEDTargetAssigner(
model_config['target_assigner_config'])
# bbox coder
self.bbox_coder = self.target_assigner.bbox_coder
def init_param(self, model_config):
self.outside_window_filter = model_config.get('outside_window_filter',
False)
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_score = model_config['use_score']
self.rpn_batch_size = model_config['rpn_batch_size']
self.use_focal_loss = model_config['use_focal_loss']
self.clip_boxes = model_config.get('clip_boxes', True)
# sampler
# self.sampler = HardNegativeSampler(model_config['sampler_config'])
# self.sampler = BalancedSampler(model_config['sampler_config'])
self.sampler = DetectionSampler(model_config['sampler_config'])
# anchor generator
self.anchor_generator = AnchorGenerator(
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
# target assigner
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# bbox coder
self.bbox_coder = self.target_assigner.bbox_coder
self.use_iou = model_config.get('use_iou')
def read_anchors():
anchor_generator_config = {
"base_anchor_size": 16,
"scales": [4, 8, 16],
"aspect_ratios": [0.5, 0.8, 1],
"anchor_stride": [16, 16],
"anchor_offset": [0, 0]
}
anchor_generator = AnchorGenerator(anchor_generator_config)
anchors = anchor_generator.generate([[1, 1]])
return anchors[0].cpu().numpy()
def init_param(self, model_config):
self.feature_extractor_config = model_config['feature_extractor_config']
self.multibox_cfg = [3, 3, 3, 3, 3, 3]
self.n_classes = len(model_config['classes'])
self.sampler = DetectionSampler(model_config['sampler_config'])
self.batch_size = model_config['batch_size']
self.use_focal_loss = model_config['use_focal_loss']
# self.multibox_cfg = model_config['multibox_config']
self.target_assigner = TargetAssigner(
model_config['target_assigner_config'])
# import ipdb
# ipdb.set_trace()
self.anchor_generator = AnchorGenerator(
model_config['anchor_generator_config'])
self.bbox_coder = self.target_assigner.bbox_coder
import numpy as np
import sys
sys.path.append('.')
from core.anchor_generators.anchor_generator import AnchorGenerator
from lib.model.rpn.generate_anchors import generate_anchors
from utils.visualize import visualize_bbox, read_img, shift_bbox
anchor_generator_config = {
"base_anchor_size": 1,
"scales": [4],
"aspect_ratios": [1],
"anchor_stride": [16, 16],
"anchor_offset": [0, 0]
}
anchor_generator = AnchorGenerator(anchor_generator_config)
anchors = anchor_generator.generate([[24, 80]])
# print(anchors)
expect_anchors = generate_anchors(
base_size=anchor_generator_config['base_anchor_size'],
ratios=np.array(anchor_generator_config['aspect_ratios']),
scales=np.array(anchor_generator_config['scales']))
img = read_img('/data/object/training/image_2/000117.png')
def vis_help(anchors, expect_anchors):
# shift_bbox(anchors, translation=(200, 200))
# shift_bbox(expect_anchors, translation=(800, 200))
