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]]}),
]
