def test_vis():
from lib.utils.config import cfg
# TODO make this an API
dataset = VOCDetection(cfg.DATASET.DATASET_DIR, [('2007', 'test')],
SSDAugmentation((300, 500), dataset_mean),
VOCAnnotationTransform())
from lib.utils.visualize_utils import TBWriter
tb_writer = TBWriter(log_dir, {'vis_list': [3, 4, 5, 6, 8]})
# import random
# img_idx = random.randint(0, len(data_loader.dataset)-1)
# image, target = dataset.pull_item(img_index, tb_writer)
for img_idx in range(len(dataset)):
if img_idx > 5:
break
tb_writer.cfg['steps'] = img_idx
image, target, extra = dataset.pull_item(img_idx, tb_writer)
print(image.shape)
def test_loader():
from lib.utils.config import cfg
# TODO: a strange bug: datasets loader hangs in cpu mode
os.environ["CUDA_VISIBLE_DEVICES"] = "0" # Specified GPUs range
dataset = VOCDetection(
cfg.DATASET.DATASET_DIR, cfg.DATASET.TEST_SETS,
SSDAugmentation(cfg.DATASET.IMAGE_SIZE,
cfg.DATASET.PIXEL_MEANS,
use_base=True), VOCAnnotationTransform())
data_loader = data.DataLoader(dataset,
batch_size=cfg.DATASET.TRAIN_BATCH_SIZE,
num_workers=cfg.DATASET.NUM_WORKERS,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True)
for i, (images, targets, extra) in enumerate(data_loader):
print(i)
def test_vis():
dataset = COCODetection(
COCO_ROOT, ('valminusminival2014', ),
SSDAugmentation((300, 300), dataset_mean, use_base=True),
COCOAnnotationTransform())
from lib.utils.visualize_utils import TBWriter
tb_writer = TBWriter(log_dir, {'epoch': 50, 'vis_list': [3, 4, 5, 6, 8]})
# import random
# img_idx = random.randint(0, len(dataset)-1)
# image, target = dataset.pull_item(img_idx, tb_writer)
for img_idx in range(len(dataset)):
if img_idx > 5:
break
tb_writer.cfg['steps'] = img_idx
image, target = dataset.pull_item(img_idx, tb_writer)
print(image.shape)
def test_loader():
# TODO: a strange bug: datasets loader hangs in cpu mode
os.environ["CUDA_VISIBLE_DEVICES"] = cfg.CUDA_VISIBLE_DEVICES
cfg_path = osp.join(cfg.CFG_ROOT, 'coco.yml')
merge_cfg_from_file(cfg_path)
dataset = COCODetection(
cfg.DATASET.DATASET_DIR, cfg.DATASET.TEST_SETS,
SSDAugmentation(cfg.DATASET.IMAGE_SIZE, dataset_mean, use_base=True),
COCOAnnotationTransform())
loader = data.DataLoader(dataset,
batch_size=cfg.DATASET.EVAL_BATCH_SIZE,
num_workers=cfg.DATASET.NUM_WORKERS,
shuffle=False,
collate_fn=detection_collate,
pin_memory=True)
print(len(dataset))
for i, (images, targets, extra) in enumerate(loader):
print(i)