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test_linemod.py
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test_linemod.py
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import argparse
import os, sys
import torch
from lib.utils import gpu_utils, weights, metrics
from lib.utils.config import Config
from lib.models.base_network import BaseFeatureExtractor
from lib.models.network import FeatureExtractor
from lib.datasets.dataloader_utils import init_dataloader
from lib.datasets.linemod.dataloader_query import LINEMOD
from lib.datasets.linemod.dataloader_template import TemplatesLINEMOD
from lib.datasets.linemod import inout
from lib.datasets.linemod import training_utils, testing_utils
parser = argparse.ArgumentParser()
parser.add_argument('--use_slurm', action='store_true')
parser.add_argument('--use_distributed', action='store_true')
parser.add_argument('--ngpu', type=int, default=1)
parser.add_argument('--gpus', type=str, default="0")
parser.add_argument('--local_rank', type=int, default=0)
parser.add_argument('--split', type=str, choices=['split1', 'split2', 'split3'])
parser.add_argument('--config_path', type=str)
parser.add_argument('--checkpoint', type=str)
args = parser.parse_args()
config_global = Config(config_file="./config.json").get_config()
config_run = Config(args.config_path).get_config()
# initialize global config for the training
dir_name = (args.config_path.split('/')[-1]).split('.')[0]
print("config", dir_name)
save_path = os.path.join(config_global.root_path, config_run.log.weights, dir_name)
trainer_dir = os.path.join(os.getcwd(), "logs")
tb_logdir = os.path.join(config_global.root_path, config_run.log.tensorboard, dir_name)
trainer_logger, tb_logger, is_master, world_size, local_rank = gpu_utils.init_gpu(use_slurm=args.use_slurm,
use_distributed=args.use_distributed,
local_rank=args.local_rank,
ngpu=args.ngpu,
gpus=args.gpus,
save_path=save_path,
trainer_dir=trainer_dir,
tb_logdir=tb_logdir,
trainer_logger_name="test")
# initialize network
# load pretrained weight if backbone are ResNet50
if config_run.model.backbone == "resnet50":
model = FeatureExtractor(config_model=config_run.model, threshold=0.2)
model.apply(weights.KaiMingInit)
model.cuda()
print("Loading pretrained weights from MOCO...")
weights.load_pretrained_backbone(prefix="backbone.",
model=model, pth_path=os.path.join(config_global.root_path,
config_run.model.pretrained_weights_resnet50))
else:
model = BaseFeatureExtractor(config_model=config_run.model, threshold=0.2)
model.apply(weights.KaiMingInit)
model.cuda()
# load checkpoint if it's available
if args.checkpoint is not None:
print("Loading checkpoint...")
weights.load_checkpoint(model=model, pth_path=args.checkpoint)
# create dataloader for query wo occlusion: train_loader, (test_seen_loader, test_unseen_loader)
# query with occlusion: (test_seen_occ_loader, test_unseen_occ_loader),
# template: (template_loader, template_unseen_loader)
seen_id_obj, seen_names, seen_occ_id_obj, seen_occ_names, unseen_id_obj, unseen_names, \
unseen_occ_id_obj, unseen_occ_names = inout.get_list_id_obj_from_split_name(config_run.dataset.split)
config_loader = [["seen_test", "seen_test", "LINEMOD", seen_id_obj],
["unseen_test", "test", "LINEMOD", unseen_id_obj],
["seen_template", "test", "templatesLINEMOD", seen_id_obj],
["unseen_template", "test", "templatesLINEMOD", unseen_id_obj],
["seen_occ_test", "test", "occlusionLINEMOD", seen_occ_id_obj],
["unseen_occ_test", "test", "occlusionLINEMOD", unseen_occ_id_obj],
["seen_occ_template", "test", "templatesLINEMOD", seen_occ_id_obj],
["unseen_occ_template", "test", "templatesLINEMOD", unseen_occ_id_obj]]
datasetLoader = {}
for config in config_loader:
print("Dataset", config[0], config[2], config[3])
save_sample_path = os.path.join(config_global.root_path, config_run.dataset.sample_path, dir_name,
config[0])
if config[2] == "templatesLINEMOD":
loader = TemplatesLINEMOD(root_dir=config_global.root_path, dataset=config[2], list_id_obj=config[3],
split=config[1], image_size=config_run.dataset.image_size,
save_path=save_sample_path, is_master=is_master)
else:
loader = LINEMOD(root_dir=config_global.root_path,
dataset=config[2], list_id_obj=config[3], split=config[1],
image_size=config_run.dataset.image_size, save_path=save_sample_path,
is_master=is_master)
datasetLoader[config[0]] = loader
print("---" * 20)
train_sampler, datasetLoader = init_dataloader(dict_dataloader=datasetLoader,
use_distributed=args.use_distributed,
batch_size=config_run.train.batch_size,
num_workers=config_run.train.num_workers)
new_score = {}
for config_split in [["seen", seen_id_obj], ["seen_occ", seen_occ_id_obj],
["unseen", unseen_id_obj], ["unseen_occ", unseen_occ_id_obj]]:
query_name = config_split[0] + "_test"
template_name = config_split[0] + "_template"
testing_score = testing_utils.test(query_data=datasetLoader[query_name],
template_data=datasetLoader[template_name],
model=model, split_name=config_split[0],
list_id_obj=config_split[1].tolist(), epoch=0,
logger=trainer_logger,
tb_logger=tb_logger, is_master=is_master)
new_score[config_split[0] + "_err"] = testing_score[0]
new_score[config_split[0] + "_acc15"] = testing_score[-5]
new_score[config_split[0] + "_acc12"] = testing_score[-4]
new_score[config_split[0] + "_acc9"] = testing_score[-3]
new_score[config_split[0] + "_acc6"] = testing_score[-2]
new_score[config_split[0] + "_acc3"] = testing_score[-1]
print(new_score)