def __init__(self, data): super(Net, self).__init__() self.start = Tensor(0, dtype=mstype.int32) self.end = Tensor(2, dtype=mstype.int32) self.max_output = Parameter(data, "output_x") self.upd = P.ScatterNdUpdate() self.zero = Tensor(np.ones([1], dtype=np.int32))
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals): super(BboxAssignSample, self).__init__() cfg = config self.batch_size = batch_size self.neg_iou_thr = Tensor(cfg.neg_iou_thr, mstype.float16) self.pos_iou_thr = Tensor(cfg.pos_iou_thr, mstype.float16) self.min_pos_iou = Tensor(cfg.min_pos_iou, mstype.float16) self.zero_thr = Tensor(0.0, mstype.float16) self.num_bboxes = num_bboxes self.num_gts = cfg.num_gts self.num_expected_pos = cfg.num_expected_pos self.num_expected_neg = cfg.num_expected_neg self.add_gt_as_proposals = add_gt_as_proposals if self.add_gt_as_proposals: self.label_inds = Tensor(np.arange(1, self.num_gts + 1)) 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 = BoundingBoxEncode() self.scatterNdUpdate = P.ScatterNdUpdate() self.scatterNd = P.ScatterNd() self.logicalnot = P.LogicalNot() self.tile = P.Tile() self.zeros_like = P.ZerosLike() 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.check_neg_mask = Tensor(np.array(np.ones(self.num_expected_neg - self.num_expected_pos), dtype=np.bool)) self.range_pos_size = Tensor(np.arange(self.num_expected_pos).astype(np.float16)) 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)) self.print = P.Print()
def __init__(self): super(NetScatterNdUpdate, self).__init__() self.b = Parameter(initializer('ones', [5, 5]), name='b') self.scatter = P.ScatterNdUpdate()
def __init__(self): super(ScatterNdUpdate3, self).__init__() self.scatter_nd_update = P.ScatterNdUpdate() self.x = Parameter(Tensor(np.zeros((4, 4, 4)), mstype.float32), name="x")
def __init__(self): super(ScatterNdUpdate2, self).__init__() self.scatter_nd_update = P.ScatterNdUpdate() self.x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mstype.float32), name="x")
def __init__(self): super(ScatterNdUpdate1, self).__init__() self.scatter_nd_update = P.ScatterNdUpdate() self.x = Parameter(Tensor( np.array([[-0.1, 0.3, 3.6], [0.4, 0.5, -3.2]]), mstype.float32), name="x")
'block': P.BoundingBoxEncode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0)), 'desc_inputs': [[256, 4], [256, 4]], 'desc_bprop': [[256, 4]], 'skip': ['backward']}), ('BoundingBoxDecode', { 'block': P.BoundingBoxDecode(means=(0.0, 0.0, 0.0, 0.0), stds=(1.0, 1.0, 1.0, 1.0), max_shape=(768, 1280)), 'desc_inputs': [[256, 4], [256, 4]], 'desc_bprop': [[256, 4]], 'skip': ['backward']}), ('GatherNd', { 'block': P.GatherNd(), 'desc_inputs': (Tensor(np.ones((1, 3, 6, 6), np.float32)), Tensor(np.ones((2, 4), np.int32))), 'desc_bprop': [[2]]}), ('ScatterNdUpdate', { 'block': P.ScatterNdUpdate(), 'desc_inputs': (Tensor(np.ones((2, 3), np.float32)), Tensor(np.ones((2, 2), np.int32)), Tensor(np.ones((2,), np.float32))), 'desc_bprop': [[2, 3]]}), ('ScatterNd', { 'block': P.ScatterNd(), 'desc_const': [(3, 3)], 'desc_inputs': (Tensor(np.ones((2, 2), np.int32)), Tensor(np.ones((2,), np.int32))), 'desc_bprop': [[3, 3]]}), ('SmoothL1Loss', { 'block': P.SmoothL1Loss(), 'desc_inputs': [[256, 4], [256, 4]], 'desc_bprop': [[256, 4]]}), ('IOU', {
def __init__(self, input_x): super(ScatterNdUpdateNet, self).__init__() self.input_x = Parameter(input_x, name="para") self.scatter_nd_update = P.ScatterNdUpdate()
stds=(1.0, 1.0, 1.0, 1.0), max_shape=(768, 1280)), 'desc_inputs': [[256, 4], [256, 4]], 'desc_bprop': [[256, 4]], 'skip': ['backward'] }), ('GatherNd', { 'block': P.GatherNd(), 'desc_inputs': (Tensor(np.ones( (1, 3, 6, 6), np.float32)), Tensor(np.ones((2, 4), np.int32))), 'desc_bprop': [[2]] }), ('ScatterNdUpdate', { 'block': P.ScatterNdUpdate(), 'desc_inputs': (Tensor(np.ones( (2, 3), np.float32)), Tensor(np.ones( (2, 2), np.int32)), Tensor(np.ones((2, ), np.float32))), 'desc_bprop': [[2, 3]] }), ('ScatterNd', { 'block': P.ScatterNd(), 'desc_const': [(3, 3)], 'desc_inputs': (Tensor(np.ones( (2, 2), np.int32)), Tensor(np.ones((2, ), np.int32))), 'desc_bprop': [[3, 3]] }), ('SmoothL1Loss', { 'block': P.SmoothL1Loss(),
'desc_bprop': [Tensor(np.ones((2, 3, 3, 5)).astype(np.int32))]}), ('Maximum_Error', { 'block': (P.Maximum(), {'exception': TypeError}), 'desc_const': [(1, 2, 3)], 'desc_inputs': [[2, 3, 3, 5]], 'desc_bprop': [[2, 3, 3, 5]]}), ('Shape_error', { 'block': (P.Shape(), {'exception': TypeError}), 'desc_inputs': [(64, 1)], 'desc_bprop': [[64]]}), ('Flatten_Error', { 'block': (NetForFlatten0D(), {'exception': ValueError}), 'desc_inputs': [Tensor(np.array(0).astype(np.int32))], 'desc_bprop': [Tensor(np.array(0).astype(np.int32))]}), ('ScatterNdUpdate', { 'block': (P.ScatterNdUpdate(), {'exception': TypeError}), 'desc_inputs': (Tensor(np.ones((2, 3), np.float32)), Tensor(np.ones((2, 2), np.int32)), Tensor(np.ones((2,), np.float32))), 'desc_bprop': [[2, 3]]}), ('Pack', { 'block': (NetForPackInput(P.Pack()), {'exception': ValueError}), 'desc_inputs': [[2, 2]], 'desc_bprop': [[1, 2, 2]]}), ('PReLU', { 'block': (P.PReLU(), {'exception': ValueError}), 'desc_inputs': [[2], [1]], 'desc_bprop': [[1]]}), ]