def construct(self, grads):
global_norm = self.global_norm(grads)
cond = P.GreaterEqual()(global_norm, self.clip_norm)
global_norm = F.select(cond, global_norm, self.clip_norm)
grads = self.hyper_map(
F.partial(apply_global_norm, self.clip_norm, global_norm), grads)
return grads
def __init__(self, clip_norm=1.0, use_norm=None):
super(_ClipByGlobalNorm, self).__init__()
# Add interface. This parameter is not used at present
if use_norm is not None:
validator.check_number("use_norm", use_norm, 0.0, Rel.GE, self.cls_name)
validator.check_number("clip_norm", clip_norm, 0.0, Rel.GT, self.cls_name)
self.clip_norm = Tensor([clip_norm], mstype.float32)
self.hyper_map = C.HyperMap()
self.greater_equal = P.GreaterEqual()
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 test_float32():
op = P.GreaterEqual()
op_wrapper = OpNetWrapper(op)
input_x = Tensor(np.array([1, 2, -1]).astype(np.float32))
input_y = Tensor(np.array([-3, 2, -1]).astype(np.float32))
outputs = op_wrapper(input_x, input_y)
print(outputs)
assert outputs.shape == (3, )
assert np.allclose(outputs.asnumpy(), [True, True, True])
def __init__(self,
batch_size,
seq_length,
vocab_size,
decoder,
beam_width=4,
length_penalty_weight=1.0,
max_decode_length=128,
sos_id=1,
eos_id=2,
compute_type=mstype.float32):
super(BeamSearchDecoder, self).__init__(auto_prefix=False)
self.seq_length = seq_length
self.batch_size = batch_size
self.vocab_size = vocab_size
self.beam_width = beam_width
self.length_penalty_weight = length_penalty_weight
self.max_decode_length = max_decode_length
self.decoder = decoder
self.add = P.TensorAdd()
self.expand = P.ExpandDims()
self.reshape = P.Reshape()
self.shape_flat = (-1, )
self.shape = P.Shape()
self.zero_tensor = Tensor(np.zeros([batch_size, beam_width]),
mstype.float32)
self.ninf_tensor = Tensor(np.full([batch_size, beam_width], -INF),
mstype.float32)
self.select = P.Select()
self.flat_shape = (batch_size, beam_width * vocab_size)
self.topk = P.TopK(sorted=True)
self.floor_div = P.FloorDiv()
self.vocab_size_tensor = Tensor(self.vocab_size, mstype.int32)
self.real_div = P.RealDiv()
self.mod = Mod()
self.equal = P.Equal()
self.eos_ids = Tensor(np.full([batch_size, beam_width], eos_id),
mstype.int32)
beam_ids = np.tile(
np.arange(beam_width).reshape((1, beam_width)), [batch_size, 1])
self.beam_ids = Tensor(beam_ids, mstype.int32)
batch_ids = np.arange(batch_size * beam_width).reshape(
(batch_size, beam_width)) // beam_width
self.batch_ids = Tensor(batch_ids, mstype.int32)
self.concat = P.Concat(axis=-1)
self.gather_nd = P.GatherNd()
self.greater_equal = P.GreaterEqual()
self.sub = P.Sub()
self.cast = P.Cast()
self.zeroslike = P.ZerosLike()
# init inputs and states
self.start_ids = Tensor(np.full([batch_size * beam_width, 1], sos_id),
mstype.int32)
self.init_seq = Tensor(np.full([batch_size, beam_width, 1], sos_id),
mstype.int32)
init_scores = np.tile(np.array([[0.] + [-INF] * (beam_width - 1)]),
[batch_size, 1])
self.init_scores = Tensor(init_scores, mstype.float32)
self.init_finished = Tensor(
np.zeros([batch_size, beam_width], dtype=np.bool))
self.init_length = Tensor(
np.zeros([batch_size, beam_width], dtype=np.int32))
self.length_penalty = LengthPenalty(weight=length_penalty_weight)
self.one = Tensor(1, mstype.int32)
def __init__(self):
super(NetGreaterEqual, self).__init__()
self.GreaterEqual = P.GreaterEqual()
def __init__(self, alpha=0.2):
super(LeakyReLU, self).__init__()
self.greater_equal = P.GreaterEqual()
self.mul = P.Mul()
self.alpha = alpha
def __init__(self, alpha=0.2):
super(LeakyReLU, self).__init__()
validator.check_value_type('alpha', alpha, [float, int], self.cls_name)
self.greater_equal = P.GreaterEqual()
self.mul = P.Mul()
self.alpha = alpha
'desc_bprop': [Tensor(np.zeros((3, 4, 5), np.bool_))]}),
('NotEqual', {
'block': P.NotEqual(),
'desc_inputs': [[4, 1], [2, 3, 4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))]}),
('NotEqual_0', {
'block': P.NotEqual(),
'desc_inputs': [ 1, [2, 3, 4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))],
'skip': ['backward']}),
('Greater', {
'block': P.Greater(),
'desc_inputs': [[2, 3, 4, 1], [4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))]}),
('GreaterEqual', {
'block': P.GreaterEqual(),
'desc_inputs': [[2, 3, 4, 1], [4, 5]],
'desc_bprop': [Tensor(np.ones((2, 3, 4, 5), np.bool_))]}),
('LogicalNot', {
'block': P.LogicalNot(),
'desc_inputs': [Tensor(np.zeros((3, 4, 5), np.bool_))],
'desc_bprop': [Tensor(np.ones((3, 4, 5), np.bool_))]}),
('LogicalAnd', {
'block': P.LogicalAnd(),
'desc_inputs': [Tensor(np.zeros((2, 3, 4), np.bool_)), Tensor(np.ones((1), np.bool_))],
'desc_bprop': [Tensor(np.zeros((2, 3, 4), np.bool_))]}),
('LogicalOr', {
'block': P.LogicalOr(),
'desc_inputs': [Tensor(np.zeros((3, 4, 5), np.bool_)), Tensor(np.ones((3, 1, 1), np.bool_))],
'desc_bprop': [Tensor(np.zeros((3, 4, 5), np.bool_))]}),
('NpuAllocFloatStatus', {
def __init__(self,
batch_size,
seq_length,
vocab_size,
decoder,
beam_width=4,
decoder_layers_nums=4,
length_penalty_weight=0.6,
cov_penalty_factor=0.1,
hidden_size=1024,
max_decode_length=64,
sos_id=2,
eos_id=3,
compute_type=mstype.float32):
super(BeamSearchDecoder, self).__init__()
self.encoder_length = seq_length
self.hidden_size = hidden_size
self.batch_size = batch_size
self.vocab_size = vocab_size
self.beam_width = beam_width
self.decoder_layers_nums = decoder_layers_nums
self.length_penalty_weight = length_penalty_weight
self.cov_penalty_factor = cov_penalty_factor
self.max_decode_length = max_decode_length
self.decoder = decoder
self.add = P.TensorAdd()
self.expand = P.ExpandDims()
self.reshape = P.Reshape()
self.shape_flat = (-1,)
self.shape = P.Shape()
self.zero_tensor = Tensor(np.zeros([batch_size, beam_width]), mstype.float32)
self.ninf_tensor = Tensor(np.full([batch_size, beam_width], -INF), mstype.float32)
self.select = P.Select()
self.flat_shape = (batch_size, beam_width * vocab_size)
self.topk = P.TopK(sorted=True)
self.floor_div = P.FloorDiv()
self.vocab_size_tensor = Tensor(self.vocab_size, mstype.int32)
self.real_div = P.RealDiv()
self.mod = Mod()
self.equal = P.Equal()
self.eos_ids = Tensor(np.full([batch_size, beam_width], eos_id), mstype.int32)
beam_ids = np.tile(np.arange(beam_width).reshape((1, beam_width)), [batch_size, 1])
self.beam_ids = Tensor(beam_ids, mstype.int32)
batch_ids = np.arange(batch_size * beam_width).reshape((batch_size, beam_width)) // beam_width
self.batch_ids = Tensor(batch_ids, mstype.int32)
self.concat = P.Concat(axis=-1)
self.gather_nd = P.GatherNd()
self.start = Tensor(0, dtype=mstype.int32)
self.start_ids = Tensor(np.full([batch_size * beam_width, 1], sos_id), mstype.int32)
self.init_seq = Tensor(np.full([batch_size, beam_width, self.max_decode_length], sos_id), mstype.int32)
init_scores = np.tile(np.array([[0.] + [-INF] * (beam_width - 1)]), [batch_size, 1])
self.init_scores = Tensor(init_scores, mstype.float32)
self.init_finished = Tensor(np.zeros([batch_size, beam_width], dtype=np.bool))
self.init_length = Tensor(np.zeros([batch_size, beam_width], dtype=np.int32))
self.length_penalty = LengthPenalty(weight=length_penalty_weight)
self.one = Tensor(1, mstype.int32)
self.prob_concat = P.Concat(axis=1)
self.cast = P.Cast()
self.decoder_hidden_state = Tensor(np.zeros([self.decoder_layers_nums, 2,
self.batch_size * self.beam_width,
hidden_size]), mstype.float32)
self.zeros_scores = Tensor(np.zeros([batch_size, beam_width], dtype=np.float))
self.active_index = Tensor(np.ones([batch_size, beam_width], dtype=np.int32))
self.init_zeros = Tensor(np.zeros([batch_size, beam_width], dtype=np.int32))
self.init_ones = Tensor(np.ones([batch_size, beam_width], dtype=np.float32))
self.accu_attn_scores = Tensor(np.zeros([batch_size, beam_width, self.encoder_length], dtype=np.float32))
self.zeros = Tensor([0], mstype.int32)
self.eos_tensor = Tensor(np.full([batch_size, beam_width, beam_width], eos_id), mstype.int32)
self.ones_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], 1), mstype.float32)
self.neg_inf_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], -INF), mstype.float32)
self.zeros_3d = Tensor(np.full([batch_size, beam_width, self.encoder_length], 0), mstype.float32)
self.zeros_2d = Tensor(np.full([batch_size * beam_width, self.encoder_length], 0), mstype.int32)
self.argmin = P.ArgMinWithValue(axis=1)
self.reducesum = P.ReduceSum()
self.div = P.Div()
self.shape_op = P.Shape()
self.mul = P.Mul()
self.log = P.Log()
self.less = P.Less()
self.tile = P.Tile()
self.noteq = P.Neg()
self.zeroslike = P.ZerosLike()
self.greater_equal = P.GreaterEqual()
self.sub = P.Sub()
def __init__(self, config, batch_size, num_bboxes, add_gt_as_proposals):
super(BboxAssignSampleForRcnn, self).__init__()
cfg = config
self.dtype = np.float32
self.ms_type = mstype.float32
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.full(self.num_gts, self.add_gt_as_proposals, 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.full((self.num_gts, 4), -1, dtype=self.dtype))
self.check_anchor_two = Tensor(
np.full((self.num_bboxes, 4), -2, dtype=self.dtype))
# Init tensor
self.assigned_gt_inds = Tensor(np.full(num_bboxes, -1, 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.full(num_bboxes, -1, dtype=np.int32))
self.assigned_pos_ones = Tensor(
np.array(np.ones(self.num_expected_pos), dtype=np.int32))
self.gt_ignores = Tensor(np.full(self.num_gts, -1, dtype=np.int32))
self.range_pos_size = Tensor(
np.arange(self.num_expected_pos).astype(self.dtype))
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=self.dtype))
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=self.ms_type)
self.scalar_neg_iou_thr = Tensor(self.neg_iou_thr, dtype=self.ms_type)
self.scalar_pos_iou_thr = Tensor(self.pos_iou_thr, dtype=self.ms_type)
self.scalar_min_pos_iou = Tensor(self.min_pos_iou, dtype=self.ms_type)
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
('Greater1', {
'block': (P.Greater(), {'exception': TypeError, 'error_keywords': ['Greater']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# shape of x and y not match
('Greater2', {
'block': (P.Greater(), {'exception': ValueError, 'error_keywords': ['Greater']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
'skip': ['backward']}),
# input is not tensor
('GreaterEqual0', {
'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}),
'desc_inputs': [5.0, Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# type of x and y not match
('GreaterEqual1', {
'block': (P.GreaterEqual(), {'exception': TypeError, 'error_keywords': ['GreaterEqual']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.int32)), Tensor(np.ones([3, 4]).astype(np.float32))],
'skip': ['backward']}),
# shape of x and y not match
('GreaterEqual2', {
'block': (P.GreaterEqual(), {'exception': ValueError, 'error_keywords': ['GreaterEqual']}),
'desc_inputs': [Tensor(np.ones([3, 4]).astype(np.float32)), Tensor(np.ones([3, 2]).astype(np.float32))],
'skip': ['backward']}),
# input is not tensor
('Less0', {
# shape of x and y not match
('Greater2', {
'block': (P.Greater(), {
'exception': ValueError,
'error_keywords': ['Greater']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.float32)),
Tensor(np.ones([3, 2]).astype(np.float32))
],
'skip': ['backward']
}),
# shape of x and y not match
('GreaterEqual2', {
'block': (P.GreaterEqual(), {
'exception': ValueError,
'error_keywords': ['GreaterEqual']
}),
'desc_inputs': [
Tensor(np.ones([3, 4]).astype(np.float32)),
Tensor(np.ones([3, 2]).astype(np.float32))
],
'skip': ['backward']
}),
# shape of x and y not match
('Less2', {
'block': (P.Less(), {
'exception': ValueError,
'error_keywords': ['Less']
def __init__(self, strategy1, strategy2):
super().__init__()
self.matmul = P.MatMul().set_strategy(strategy1)
self.greaterEqual = P.GreaterEqual().set_strategy(strategy2)
def __init__(self):
super(GreaterEqualNet, self).__init__()
self.ops = P.GreaterEqual()
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)