def __init__(self,
roidb,
config,
test_scale,
batch_size=4,
threads=8,
nGPUs=1,
pad_rois_to=400,
crop_size=(512, 512),
num_classes=None):
self.crop_size = crop_size
self.num_classes = num_classes if num_classes else roidb[0][
'gt_overlaps'].shape[1]
self.data_name = ['data', 'im_info', 'im_ids']
self.label_name = None
self.label = []
self.context_size = 320
self.im_worker = im_worker(
crop_size=None if not self.crop_size else self.crop_size[0],
cfg=config,
target_size=test_scale)
self.test_scale = test_scale
super(MNIteratorTest, self).__init__(roidb, config, batch_size,
threads, nGPUs, pad_rois_to, True)
self.reset()
def __init__(self,
roidb,
config,
batch_size=4,
threads=8,
nGPUs=1,
pad_rois_to=400,
crop_size=(512, 512)):
self.crop_size = crop_size
self.num_classes = roidb[0]['gt_overlaps'].shape[1]
self.bbox_means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
(self.num_classes, 1))
self.bbox_stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
(self.num_classes, 1))
self.data_name = ['data', 'valid_ranges', 'im_info']
self.label_name = ['label', 'bbox_target', 'bbox_weight', 'gt_boxes']
if config.TRAIN.WITH_MASK:
self.label_name.append('gt_masks')
self.pool = Pool(config.TRAIN.NUM_PROCESS)
self.epiter = 0
self.im_worker = im_worker(crop_size=self.crop_size[0], cfg=config)
self.chip_worker = chip_worker(chip_size=self.crop_size[0], cfg=config)
self.anchor_worker = anchor_worker(chip_size=self.crop_size[0],
cfg=config)
super(MNIteratorE2E, self).__init__(roidb, config, batch_size, threads,
nGPUs, pad_rois_to, False)
def __init__(self,
roidb,
config,
test_scale,
batch_size=4,
threads=8,
nGPUs=1,
pad_rois_to=400,
crop_size=None,
num_classes=None):
self.crop_size = crop_size
self.roidb = roidb
self.batch_size = batch_size
self.num_classes = num_classes if num_classes else roidb[0][
'gt_overlaps'].shape[1]
self.data_name = ['data', 'im_info', 'im_ids']
self.label_name = None
self.cur_i = 0
self.label = []
self.context_size = 320
self.thread_pool = ThreadPool(threads)
self.im_worker = im_worker(
crop_size=None if not self.crop_size else self.crop_size[0],
cfg=config,
target_size=test_scale)
self.test_scale = test_scale
self.reset()
def __init__(self, roidb, config, test_scale, batch_size=4, threads=8, nGPUs=1, pad_rois_to=400, crop_size=(512, 512),
num_classes=None):
self.crop_size = crop_size
self.num_classes = num_classes if num_classes else roidb[0]['gt_overlaps'].shape[1]
self.data_name = ['data', 'im_info', 'im_ids']
self.label_name = None
self.label = []
self.context_size = 320
self.im_worker = im_worker(crop_size=None if not self.crop_size else self.crop_size[0], cfg=config, target_size=test_scale)
self.test_scale = test_scale
super(MNIteratorTest, self).__init__(roidb, config, batch_size, threads, nGPUs, pad_rois_to, True)
self.reset()
def __init__(self, roidb, config, batch_size=4, threads=8, nGPUs=1, pad_rois_to=400, crop_size=(512, 512)):
self.crop_size = crop_size
self.num_classes = roidb[0]['gt_overlaps'].shape[1]
self.bbox_means = np.tile(np.array(config.TRAIN.BBOX_MEANS), (self.num_classes, 1))
self.bbox_stds = np.tile(np.array(config.TRAIN.BBOX_STDS), (self.num_classes, 1))
self.data_name = ['data', 'valid_ranges', 'im_info']
self.label_name = ['label', 'bbox_target', 'bbox_weight', 'gt_boxes']
if config.TRAIN.WITH_MASK:
self.label_name.append('gt_masks')
self.pool = Pool(config.TRAIN.NUM_PROCESS)
self.epiter = 0
self.im_worker = im_worker(crop_size=self.crop_size[0], cfg=config)
self.chip_worker = chip_worker(chip_size=self.crop_size[0], cfg=config)
self.anchor_worker = anchor_worker(chip_size=self.crop_size[0] ,cfg=config)
super(MNIteratorE2E, self).__init__(roidb, config, batch_size, threads, nGPUs, pad_rois_to, False)
def __init__(self,
roidb,
config,
batch_size=4,
threads=8,
nGPUs=1,
pad_rois_to=400,
crop_size=(512, 512),
single_size_change=False):
self.cur_i = 0
self.roidb = roidb
self.batch_size = batch_size
self.pixel_mean = config.network.PIXEL_MEANS
self.thread_pool = ThreadPool(threads)
# self.executor_pool = ThreadPoolExecutor(threads)
self.n_per_gpu = batch_size / nGPUs
self.batch = None
self.cfg = config
self.n_expected_roi = pad_rois_to
self.pad_label = np.array(-1)
self.pad_weights = np.zeros((1, 8))
self.pad_targets = np.zeros((1, 8))
self.pad_roi = np.array([[0, 0, 100, 100]])
self.single_size_change = single_size_change
self.crop_size = crop_size
self.num_classes = roidb[0]['gt_overlaps'].shape[1]
self.bbox_means = np.tile(np.array(config.TRAIN.BBOX_MEANS),
(self.num_classes, 1))
self.bbox_stds = np.tile(np.array(config.TRAIN.BBOX_STDS),
(self.num_classes, 1))
if config.TRAIN.WITH_MASK:
self.label_name.append('gt_masks')
self.pool = Pool(config.TRAIN.NUM_PROCESS)
self.epiter = 0
self.im_worker = im_worker(crop_size=self.crop_size[0], cfg=config)
self.chip_worker = chip_worker(chip_size=self.crop_size[0], cfg=config)
self.anchor_worker = anchor_worker(chip_size=self.crop_size[0],
cfg=config)
self.get_chip()
def set_scale(self, scale):
self.test_scale = scale
self.im_worker = im_worker(crop_size=None if not self.crop_size else self.crop_size[0], cfg=self.cfg,
target_size=scale)
def set_scale(self, scale):
self.test_scale = scale
self.im_worker = im_worker(
crop_size=None if not self.crop_size else self.crop_size[0],
cfg=self.cfg,
target_size=scale)
