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)