def testLevelsPooler(self): with self.test_session() as sess: num_level = 3 features = [] t_features = [] for i in range(num_level): fea = np.ones([1, 10, 10, 1]) * i features.append(fea) t_features.append(np.ones([1, 2, 2, 1]) * (2 - i)) t_features = np.concatenate(t_features, axis=0) boxes = [[[0.0, 0.0, 0.59, 0.9], [0.0, 0.0, 0.3, 0.3], [0.0, 0.0, 0.1, 0.1]]] img_size = [224, 224] cfg = config.get_cfg() config.set_global_cfg(cfg) cfg = cfg.MODEL.ROI_BOX_HEAD cfg.canonical_box_size = 0.3 * 224 cfg.canonical_level = 1 p = wmodule.WModule(cfg) pooler = ROIPooler(cfg, parent=p, output_size=[2, 2]) features = pooler.forward(features, tf.convert_to_tensor(boxes, dtype=tf.float32), img_size=img_size) features = sess.run(features) self.assertAllClose(t_features, features, atol=1e-3)
def setup(args): """ Create configs and perform basic setups. """ cfg = config.get_cfg() print(f"Config file {args.config_file}") cfg.merge_from_file(args.config_file) cfg.merge_from_list(args.opts) cfg.log_dir = args.log_dir cfg.ckpt_dir = args.ckpt_dir return cfg
def setup(args): """ Create configs and perform basic setups. """ cfg = config.get_cfg() if args.gpus is not None: gpus = args.gpus else: gpus = [] gpus_str = "" for g in gpus: gpus_str += str(g) + "," gpus_str = gpus_str[:-1] os.environ['CUDA_VISIBLE_DEVICES'] = gpus_str print(f"Config file {args.config_file}") config_path = get_config_file(args.config_file) cfg.merge_from_file(config_path) cfg.merge_from_list(args.opts) cfg.log_dir = args.log_dir cfg.ckpt_dir = args.ckpt_dir return cfg
from object_detection2.modeling.backbone import * from object_detection2.modeling.backbone.dla import build_any_dla_backbone from object_detection2.config.config import get_cfg import tensorflow as tf from object_detection2.modeling.backbone.mobilenets import * import wml_utils as wmlu import wmodule global_cfg = get_cfg() #global_cfg.MODEL.MOBILENETS.MINOR_VERSION = "SMALL" global_cfg.MODEL.DLA.BACKBONE = "build_hrnet_backbone" global_cfg.MODEL.RESNETS.DEPTH = 34 #global_cfg.MODEL.MOBILENETS.MINOR_VERSION = "LARGE" net = tf.placeholder(tf.float32, [2, 512, 512, 3]) x = {'image': net} parent = wmodule.WRootModule() mn = build_any_dla_backbone(global_cfg, parent=parent) res = mn(x) sess = tf.Session() summary_writer = tf.summary.FileWriter(wmlu.home_dir("ai/tmp/tools_log"), sess.graph)