def __init__(self, bins=10, momentum=0.0, mu=0.02):
super(GHMRLoss, self).__init__()
self.bins = bins
self.momentum = momentum
self.mu = mu
edges_left = np.array([float(x) / bins for x in range(bins)], dtype=np.float32)
self.edges_left = Tensor(edges_left.reshape((bins, 1, 1, 1, 1)))
edges_right = np.array([float(x) / bins for x in range(1, bins + 1)], dtype=np.float32)
edges_right[-1] += 1e-4
self.edges_right = Tensor(edges_right.reshape((bins, 1, 1, 1, 1)))
if momentum >= 0:
self.acc_sum = Parameter(initializer(0, [bins], mstype.float32))
self.abs = ops.Abs()
self.sqrt = ops.Sqrt()
self.cast = ops.Cast()
self.select = ops.Select()
self.reshape = ops.Reshape()
self.reduce_sum = ops.ReduceSum()
self.max = ops.Maximum()
self.less = ops.Less()
self.equal = ops.Equal()
self.greater = ops.Greater()
self.logical_and = ops.LogicalAnd()
self.greater_equal = ops.GreaterEqual()
self.zeros_like = ops.ZerosLike()
self.expand_dims = ops.ExpandDims()
def __init__(self, src_type=mstype.float32, dst_type=mstype.float32):
super(SaturateCast, self).__init__()
np_type = mstype.dtype_to_nptype(dst_type)
self.tensor_min_type = float(np.finfo(np_type).min)
self.tensor_max_type = float(np.finfo(np_type).max)
self.min_op = P.Minimum()
self.max_op = P.Maximum()
self.cast = P.Cast()
self.dst_type = dst_type
def __init__(self, src_type=mstype.float32, dst_type=mstype.float32):
super(SaturateCast, self).__init__()
np_type = mstype.dtype_to_nptype(dst_type)
min_type = np.finfo(np_type).min
max_type = np.finfo(np_type).max
self.tensor_min_type = Tensor([min_type], dtype=src_type)
self.tensor_max_type = Tensor([max_type], dtype=src_type)
self.min_op = ops.Minimum()
self.max_op = ops.Maximum()
self.cast = ops.Cast()
self.dst_type = dst_type
def __init__(self, log_scale_min=-7.0, reduce=True):
super(mix_gaussian_loss, self).__init__()
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.maximum = P.Maximum()
self.tile = P.Tile()
self.exp = P.Exp()
self.logsoftmax = P.LogSoftmax(-1)
self.expand_dims = P.ExpandDims()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.const = P.ScalarToArray()
self.log = P.Log()
def __init__(self, num_classes=256, log_scale_min=-7.0, reduce=True):
super(discretized_mix_logistic_loss, self).__init__()
self.num_classes = num_classes
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.exp = P.Exp()
self.sigmoid = P.Sigmoid()
self.softplus = Stable_softplus()
self.log = P.Log()
self.cast = P.Cast()
self.logsoftmax = P.LogSoftmax(-1)
self.expand_dims = P.ExpandDims()
self.tile = P.Tile()
self.maximum = P.Maximum()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.reshape = P.Reshape()
self.factor = self.log(Tensor((self.num_classes - 1) / 2, ms.float32))
def __init__(self, log_scale_min=-7.0, reduce=True):
super(mix_gaussian_loss, self).__init__()
self.log_scale_min = log_scale_min
self.reduce = reduce
self.transpose_op = P.Transpose()
self.maximum = P.Maximum()
self.tile = P.Tile()
self.exp = P.Exp()
self.expand_dims = P.ExpandDims()
self.sums = P.ReduceSum()
self.lse = log_sum_exp()
self.sq = P.Square()
self.sqrt = P.Sqrt()
self.const = P.ScalarToArray()
self.log = P.Log()
self.tensor_one = Tensor(1., ms.float32)
if context.get_context("device_target") == "CPU":
self.logsoftmax = log_softmax()
else:
self.logsoftmax = P.LogSoftmax(-1)
def __init__(self, net_config, K=100, enable_nms_fp16=True):
super(MultiPoseDecode, self).__init__()
self.K = K
self.nms = NMS(enable_nms_fp16=enable_nms_fp16)
self.shape = ops.Shape()
self.gather_topk = GatherTopK()
self.gather_topk_channel = GatherTopKChannel()
self.gather_by_ind = GatherFeatureByInd()
self.half = ops.Split(axis=-1, output_num=2)
self.half_first = ops.Split(axis=0, output_num=2)
self.split = ops.Split(axis=-1, output_num=4)
self.flip_lr = FlipLR()
self.flip_lr_off = FlipLROff()
self.flip_tensor = FlipTensor()
self.concat = ops.Concat(axis=1)
self.concat_a2 = ops.Concat(axis=2)
self.concat_a3 = ops.Concat(axis=3)
self.trans_gather_feature = TransposeGatherFeature()
self.expand_dims = ops.ExpandDims()
self.reshape = ops.Reshape()
self.add = ops.TensorAdd()
self.dtype = ops.DType()
self.cast = ops.Cast()
self.thresh = 0.1
self.transpose = ops.Transpose()
self.perm_list = (0, 2, 1, 3)
self.tile = ops.Tile()
self.greater = ops.Greater()
self.square = ops.Square()
self.sqrt = ops.Sqrt()
self.reduce_sum = ops.ReduceSum()
self.min = ops.ArgMinWithValue(axis=3)
self.max = ops.Maximum()
self.hm_hp = net_config.hm_hp
self.dense_hp = net_config.dense_hp
self.reg_offset = net_config.reg_offset
self.reg_hp_offset = net_config.reg_hp_offset
self.hm_hp_ind = 3 if self.hm_hp else 2
self.reg_ind = self.hm_hp_ind + 1 if self.reg_offset else self.hm_hp_ind
self.reg_hp_ind = self.reg_ind + 1 if self.reg_hp_offset else self.reg_ind
def __init__(self):
super(ClipByValue, self).__init__()
self.min = ops.Minimum()
self.max = ops.Maximum()
def __init__(self, min_val=-1.0, max_val=1.0):
super().__init__()
self.min_val = min_val
self.max_val = max_val
self.max = ops.Maximum()
self.min = ops.Minimum()
def __init__(self, alpha=1.0):
super().__init__()
self.alpha = alpha
self.exp = ops.Exp()
self.max = ops.Maximum()
self, min = ops.Minimum()