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)