def __init__(self, crop_size): super(CropAndResizeNet, self).__init__() self.crop_and_resize = P.CropAndResize() self.crop_size = crop_size
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals): super(BboxAssignSampleForRcnn, self).__init__() cfg = config self.batch_size = batch_size self.neg_iou_thr = cfg.neg_iou_thr_stage2 self.pos_iou_thr = cfg.pos_iou_thr_stage2 self.min_pos_iou = cfg.min_pos_iou_stage2 self.num_gts = cfg.num_gts self.num_bboxes = num_bboxes self.num_expected_pos = cfg.num_expected_pos_stage2 self.num_expected_neg = cfg.num_expected_neg_stage2 self.num_expected_total = cfg.num_expected_total_stage2 self.add_gt_as_proposals = add_gt_as_proposals self.label_inds = Tensor( np.arange(1, self.num_gts + 1).astype(np.int32)) self.add_gt_as_proposals_valid = Tensor( np.array(self.add_gt_as_proposals * np.ones(self.num_gts), dtype=np.int32)) self.concat = P.Concat(axis=0) self.max_gt = P.ArgMaxWithValue(axis=0) self.max_anchor = P.ArgMaxWithValue(axis=1) self.sum_inds = P.ReduceSum() self.iou = P.IOU() self.greaterequal = P.GreaterEqual() self.greater = P.Greater() self.select = P.Select() self.gatherND = P.GatherNd() self.squeeze = P.Squeeze() self.cast = P.Cast() self.logicaland = P.LogicalAnd() self.less = P.Less() self.random_choice_with_mask_pos = P.RandomChoiceWithMask( self.num_expected_pos) self.random_choice_with_mask_neg = P.RandomChoiceWithMask( self.num_expected_neg) self.reshape = P.Reshape() self.equal = P.Equal() self.bounding_box_encode = P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(0.1, 0.1, 0.2, 0.2)) self.concat_axis1 = P.Concat(axis=1) self.logicalnot = P.LogicalNot() self.tile = P.Tile() # Check self.check_gt_one = Tensor( np.array(-1 * np.ones((self.num_gts, 4)), dtype=np.float16)) self.check_anchor_two = Tensor( np.array(-2 * np.ones((self.num_bboxes, 4)), dtype=np.float16)) # Init tensor self.assigned_gt_inds = Tensor( np.array(-1 * np.ones(num_bboxes), dtype=np.int32)) self.assigned_gt_zeros = Tensor( np.array(np.zeros(num_bboxes), dtype=np.int32)) self.assigned_gt_ones = Tensor( np.array(np.ones(num_bboxes), dtype=np.int32)) self.assigned_gt_ignores = Tensor( np.array(-1 * np.ones(num_bboxes), dtype=np.int32)) self.assigned_pos_ones = Tensor( np.array(np.ones(self.num_expected_pos), dtype=np.int32)) self.gt_ignores = Tensor( np.array(-1 * np.ones(self.num_gts), dtype=np.int32)) self.range_pos_size = Tensor( np.arange(self.num_expected_pos).astype(np.float16)) self.check_neg_mask = Tensor( np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool)) self.bboxs_neg_mask = Tensor( np.zeros((self.num_expected_neg, 4), dtype=np.float16)) self.labels_neg_mask = Tensor( np.array(np.zeros(self.num_expected_neg), dtype=np.uint8)) self.reshape_shape_pos = (self.num_expected_pos, 1) self.reshape_shape_neg = (self.num_expected_neg, 1) self.scalar_zero = Tensor(0.0, dtype=mstype.float16) self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=mstype.float16) self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=mstype.float16) self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=mstype.float16) self.expand_dims = P.ExpandDims() self.split = P.Split(axis=1, output_num=4) self.concat_last_axis = P.Concat(axis=-1) self.round = P.Round() self.image_h_w = Tensor( [cfg.img_height, cfg.img_width, cfg.img_height, cfg.img_width], dtype=mstype.float16) self.range = nn.Range(start=0, limit=cfg.num_expected_pos_stage2) self.crop_and_resize = P.CropAndResize(method="bilinear_v2") self.mask_shape = (cfg.mask_shape[0], cfg.mask_shape[1]) self.squeeze_mask_last = P.Squeeze(axis=-1)