def __init__(self, query_vector_dim, input_vector_dim):
super(Attention, self).__init__()
self.dense1 = nn.Dense(input_vector_dim,
query_vector_dim,
has_bias=True,
activation='tanh')
self.dense2 = nn.Dense(query_vector_dim, 1, has_bias=False)
self.softmax = nn.Softmax()
self.sum_keep_dims = ops.ReduceSum(keep_dims=True)
self.sum = ops.ReduceSum(keep_dims=False)
def __init__(self, sparse=False):
super(SoftmaxCrossEntropyExpand, self).__init__()
self.exp = ops.Exp()
self.sum = ops.ReduceSum(keep_dims=True)
self.onehot = ops.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.div = ops.RealDiv()
self.log = ops.Log()
self.sum_cross_entropy = ops.ReduceSum(keep_dims=False)
self.mul = ops.Mul()
self.mul2 = ops.Mul()
self.mean = ops.ReduceMean(keep_dims=False)
self.sparse = sparse
self.max = ops.ReduceMax(keep_dims=True)
self.sub = ops.Sub()
def __init__(self, network, optimizer, scale_update_cell=None):
super(BertTrainOneStepWithLossScaleCell, self).__init__(auto_prefix=False)
self.network = network
self.weights = ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = ops.GradOperation(
get_by_list=True,
sens_param=True)
self.reducer_flag = False
self.allreduce = ops.AllReduce()
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
self.reducer_flag = True
self.grad_reducer = ops.identity
self.degree = 1
if self.reducer_flag:
self.degree = get_group_size()
self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
self.cast = ops.Cast()
self.alloc_status = ops.NPUAllocFloatStatus()
self.get_status = ops.NPUGetFloatStatus()
self.clear_before_grad = ops.NPUClearFloatStatus()
self.reduce_sum = ops.ReduceSum(keep_dims=False)
self.depend_parameter_use = ops.ControlDepend(depend_mode=1)
self.base = Tensor(1, mstype.float32)
self.less_equal = ops.LessEqual()
self.hyper_map = ops.HyperMap()
self.loss_scale = None
self.loss_scaling_manager = scale_update_cell
if scale_update_cell:
self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
def construct(self, s_t_hat, encoder_outputs, encoder_feature,
enc_padding_mask, coverage):
b, t_k, n = encoder_outputs.shape
dec_fea = self.decode_proj(s_t_hat) # (B, 2 * hidden_dim)
dec_fea_expand = P.ExpandDims()(dec_fea, 1)
dec_fea_expand = P.BroadcastTo()(dec_fea_expand, (b, t_k, n))
att_features = encoder_feature + dec_fea_expand
if self.is_coverage:
coverage_input = coverage.view(-1, 1) # (B * t_k, 1)
coverage_feature = self.W_c(
coverage_input) # (B * t_k, 2 * hidden_dim)
att_features = att_features + coverage_feature
e = P.Tanh()(att_features) # (B * t_k, 2 * hidden_dim)
scores = self.v(e) # (B * t_k, 1)
scores = scores.view(-1, t_k) # (B, t_k)
attn_dist_ = P.Softmax(1)(scores) * enc_padding_mask # (B, t_k)
normalization_factor = P.ReduceSum(True)(attn_dist_, 1)
attn_dist = attn_dist_ / normalization_factor
attn_dist = P.ExpandDims()(attn_dist, 1) # (B, 1, t_k)
c_t = P.BatchMatMul(attn_dist, encoder_outputs) # (B, 1, n)
c_t = c_t.view(-1, self.hidden_dim * 2) # (B, 2 * hidden_dim)
attn_dist = attn_dist.view(-1, t_k)
if self.is_coverage:
coverage = coverage.view(-1, t_k)
coverage = coverage + attn_dist
return c_t, attn_dist, coverage
def __init__(self, net_config):
super(CenterNetMultiPoseLossCell, self).__init__()
self.network = GatherMultiPoseFeatureCell(net_config)
self.reduce_sum = ops.ReduceSum()
self.crit = FocalLoss()
self.crit_hm_hp = nn.MSELoss() if net_config.mse_loss else self.crit
self.crit_kp = RegWeightedL1Loss(
) if not net_config.dense_hp else nn.L1Loss(reduction='sum')
self.crit_reg = RegLoss(net_config.reg_loss)
self.hm_weight = net_config.hm_weight
self.hm_hp_weight = net_config.hm_hp_weight
self.hp_weight = net_config.hp_weight
self.wh_weight = net_config.wh_weight
self.off_weight = net_config.off_weight
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
# just used for check
self.print = ops.Print()
self.concat = ops.Concat(axis=1)
self.reshape = ops.Reshape()
def __init__(self):
super(log_sum_exp, self).__init__()
self.maxi = P.ReduceMax()
self.maxi_dim = P.ReduceMax(keep_dims=True)
self.log = P.Log()
self.sums = P.ReduceSum()
self.exp = P.Exp()
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 construct(self, teacher, student, neg):
expand_dims = ops.ExpandDims() # unsqueeze算子
teacher_vgg, student_vgg, neg_vgg = self.vgg(teacher), self.vgg(
student), self.vgg(neg)
loss = 0
for i in range(len(teacher_vgg)):
neg_i = expand_dims(neg_vgg[i], 0) # [8, n_feats, w, h]
# neg_i = neg_i.repeat(student_vgg[i].shape[0], axis=0) #TODO:1.3版本才会支持Tensor.repeat
neg_i = np.repeat(neg_i, student_vgg[i].shape[0],
axis=0) # [16, 8, n_feats, w, h]
neg_i = neg_i.transpose((1, 0, 2, 3, 4)) # [8, 16, n_feats, w, h]
d_ts = self.l1(stop_gradient(teacher_vgg[i]), student_vgg[i])
# d_sn = (stop_gradient(neg_i) - student_vgg[i]).abs().sum(axis=0).mean() #TODO:1.3版本才支持Tensor.sum
d_sn = (stop_gradient(neg_i) -
student_vgg[i]).abs() # [8, 16, n_feats, w, h]
# print(d_sn.shape)
reduceSum = ops.ReduceSum()
d_sn = reduceSum(d_sn, 0).mean()
# print(d_sn)
contrastive = d_ts / (d_sn + 1e-7)
loss += self.weights[i] * contrastive
return self.get_loss(loss)
def __init__(self, network, optimizer, sens=1):
super(CenterNetWithLossScaleCell, self).__init__(auto_prefix=False)
self.image = ImagePreProcess()
self.network = network
self.network.set_grad()
self.weights = optimizer.parameters
self.optimizer = optimizer
self.grad = ops.GradOperation(get_by_list=True, sens_param=True)
self.reducer_flag = False
self.allreduce = ops.AllReduce()
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
self.reducer_flag = True
self.grad_reducer = ops.identity
self.degree = 1
if self.reducer_flag:
self.degree = get_group_size()
self.grad_reducer = DistributedGradReducer(optimizer.parameters, False, self.degree)
self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
self.cast = ops.Cast()
self.alloc_status = ops.NPUAllocFloatStatus()
self.get_status = ops.NPUGetFloatStatus()
self.clear_before_grad = ops.NPUClearFloatStatus()
self.reduce_sum = ops.ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = ops.LessEqual()
self.grad_scale = GradScale()
self.loss_scale = sens
def __init__(self):
super(log_softmax, self).__init__()
self.maxi = P.ReduceMax()
self.log = P.Log()
self.sums = P.ReduceSum()
self.exp = P.Exp()
self.axis = -1
self.concat = P.Concat(-1)
self.expanddims = P.ExpandDims()
def __init__(self, reduction="mean"):
super(CrossEntropyLoss, self).__init__()
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
if reduction == "sum":
self.reduction = P.ReduceSum()
if reduction == "mean":
self.reduction = P.ReduceMean()
self.one_hot = P.OneHot()
self.one = Tensor(1.0, mstype.float32)
self.zero = Tensor(0.0, mstype.float32)
def construct(self, x):
pow = P.Pow()
sum = P.ReduceSum()
div = P.Div()
norm = pow(x, self.power)
norm = sum(x)
norm = pow(norm, 1. / self.power)
out = div(x, norm)
return out
def __init__(self, mixture_size: int, do_layer_norm: bool = False) -> None:
super(Scalar_mix, self).__init__()
self.mixture_size = mixture_size
self.do_layer_norm = do_layer_norm
self.scalar_parameters = ParameterTuple([Parameter(Tensor(np.array([0.0]), mindspore.float32)) \
for _ in range(mixture_size)])
self.gamma = Parameter(Tensor(np.array([0.0]), mindspore.float32))
self.sum = P.ReduceSum()
self.sqrt = P.Sqrt()
self.cat = P.Concat()
self.unsqueeze = P.ExpandDims(0)
def __init__(self, config):
super(BertPretrainingLoss, self).__init__()
self.vocab_size = config.vocab_size
self.onehot = ops.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.reduce_sum = ops.ReduceSum()
self.reduce_mean = ops.ReduceMean()
self.reshape = ops.Reshape()
self.last_idx = (-1,)
self.neg = ops.Neg()
self.cast = ops.Cast()
def __init__(self, mode='l1'):
super(RegLoss, self).__init__()
self.reduce_sum = ops.ReduceSum()
self.cast = ops.Cast()
self.expand_dims = ops.ExpandDims()
self.reshape = ops.Reshape()
self.gather_feature = TransposeGatherFeature()
if mode == 'l1':
self.loss = nn.L1Loss(reduction='sum')
elif mode == 'sl1':
self.loss = nn.SmoothL1Loss()
else:
self.loss = None
def __init__(self, learning_rate, multi_epochs, steps_per_epoch, factor=10):
super(MultiEpochsDecayLR, self).__init__()
if not isinstance(multi_epochs, (list, tuple)):
raise TypeError("multi_epochs must be list or tuple.")
self.multi_epochs = Tensor(np.array(multi_epochs, dtype=np.float32) * steps_per_epoch)
self.num = len(multi_epochs)
self.start_learning_rate = learning_rate
self.steps_per_epoch = steps_per_epoch
self.factor = factor
self.pow = ops.Pow()
self.cast = ops.Cast()
self.less_equal = ops.LessEqual()
self.reduce_sum = ops.ReduceSum()
def __init__(self, alpha=2, beta=4):
super(FocalLoss, self).__init__()
self.alpha = alpha
self.beta = beta
self.pow = ops.Pow()
self.log = ops.Log()
self.select = ops.Select()
self.equal = ops.Equal()
self.less = ops.Less()
self.cast = ops.Cast()
self.fill = ops.Fill()
self.dtype = ops.DType()
self.shape = ops.Shape()
self.reduce_sum = ops.ReduceSum()
def __init__(self, model, config, is_training, dropout_prob=0.0, use_one_hot_embeddings=False):
super(BertPoetry, self).__init__(auto_prefix=False)
self.num_tokens = 3191
self.poetry = model
self.onehot = ops.OneHot()
self.on_value = Tensor(1.0, mstype.float32)
self.off_value = Tensor(0.0, mstype.float32)
self.reduce_sum = ops.ReduceSum()
self.reduce_mean = ops.ReduceMean()
self.reshape = ops.Reshape()
self.neg = ops.Neg()
self.cast = ops.Cast()
self.last_idx = (-1,)
self.log = ops.Log()
self.max = ops.ArgMaxWithValue(axis=-1)
def __init__(self, reduction='mean'):
super(BCEWithLogits, self).__init__()
if reduction is None:
reduction = 'none'
if reduction not in ('mean', 'sum', 'none'):
raise ValueError(
f"reduction method for {reduction.lower()} is not supported")
self.loss = ops.SigmoidCrossEntropyWithLogits()
self.reduce = False
if reduction == 'sum':
self.reduce_mode = ops.ReduceSum()
self.reduce = True
elif reduction == 'mean':
self.reduce_mode = ops.ReduceMean()
self.reduce = True
def __init__(self, net_config):
super(CenterNetLossCell, self).__init__()
self.network = GatherDetectionFeatureCell(net_config)
self.net_config = net_config
self.reduce_sum = ops.ReduceSum()
self.Sigmoid = Sigmoid()
self.FocalLoss = FocalLoss()
self.crit = nn.MSELoss() if net_config.mse_loss else self.FocalLoss
self.crit_reg = RegLoss(net_config.reg_loss)
self.crit_wh = RegLoss(net_config.reg_loss)
self.num_stacks = net_config.num_stacks
self.wh_weight = net_config.wh_weight
self.hm_weight = net_config.hm_weight
self.off_weight = net_config.off_weight
self.reg_offset = net_config.reg_offset
self.not_enable_mse_loss = not net_config.mse_loss
self.Print = ops.Print()
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, network, optimizer, scale_update_cell=None):
super(BertPoetryCell, self).__init__(network, optimizer, scale_update_cell)
self.network = network
self.weights = ParameterTuple(network.trainable_params())
self.optimizer = optimizer
self.grad = ops.GradOperation(
get_by_list=True,
sens_param=True)
self.reducer_flag = False
self.allreduce = ops.AllReduce()
self.parallel_mode = context.get_auto_parallel_context("parallel_mode")
if self.parallel_mode in [ParallelMode.DATA_PARALLEL, ParallelMode.HYBRID_PARALLEL]:
self.reducer_flag = True
self.grad_reducer = None
if self.reducer_flag:
mean = context.get_auto_parallel_context("mirror_mean")
degree = get_group_size()
self.grad_reducer = DistributedGradReducer(optimizer.parameters, mean, degree)
self.is_distributed = (self.parallel_mode != ParallelMode.STAND_ALONE)
self.cast = ops.Cast()
self.gpu_target = False
if context.get_context("device_target") == "GPU":
self.gpu_target = True
self.float_status = ops.FloatStatus()
self.addn = ops.AddN()
self.reshape = ops.Reshape()
else:
self.alloc_status = ops.NPUAllocFloatStatus()
self.get_status = ops.NPUGetFloatStatus()
self.clear_before_grad = ops.NPUClearFloatStatus()
self.reduce_sum = ops.ReduceSum(keep_dims=False)
self.base = Tensor(1, mstype.float32)
self.less_equal = ops.LessEqual()
self.hyper_map = ops.HyperMap()
self.loss_scale = None
self.loss_scaling_manager = scale_update_cell
if scale_update_cell:
self.loss_scale = Parameter(Tensor(scale_update_cell.get_loss_scale(), dtype=mstype.float32),
name="loss_scale")
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 construct(self, img1, img2, label1, label2, modal=0, cpa=False):
feat, feat_att, out, out_att = self._backbone(img1,
x2=img2,
modal=modal,
cpa=False)
op1 = P.Concat()
label = op1((label1, label2))
op2 = P.Cast()
label_ = op2(label, ms.int32)
loss_id = self._ce_loss(out, label_)
# loss_id_att = self._ce_loss(out_att, label_)
sum = P.ReduceSum()
loss_id = sum(loss_id) / label_.shape[0]
# print("loss id is", loss_id)
loss_tri = self._tri_loss(feat, label)
# loss_tri_att = self._tri_loss(feat_att, label)
# print("triplet id is", loss_tri)
return loss_id + loss_tri
def global_norm(x):
sqrt = P.Sqrt()
reduce_sum = P.ReduceSum()
l2 = P.L2Normalize()
x = sqrt(reduce_sum(P.functional.square(l2(x))))
return x
def __init__(self):
super(RegWeightedL1Loss, self).__init__()
self.reduce_sum = ops.ReduceSum()
self.gather_feature = TransposeGatherFeature()
self.cast = ops.Cast()
self.l1_loss = nn.L1Loss(reduction='sum')
def __init__(self):
super().__init__()
self.exp = ops.Exp()
self.reduce_sum = ops.ReduceSum(keep_dims=True)
def construct(self, inputs, targets):
"""
Args:
- inputs: feature matrix with shape (batch_size, feat_dim)
- targets: ground truth labels with shape (num_classes)
"""
n = inputs.shape[0]
# Compute pairwise distance, replace by the official when merged
pow = P.Pow()
sum = P.ReduceSum(keep_dims=True)
expand = P.BroadcastTo((n, n))
transpose = P.Transpose()
mul = P.Mul()
add = P.Add()
sqrt = P.Sqrt()
equal = P.Equal()
cat = P.Concat()
ones_like = P.OnesLike()
dist = pow(inputs, 2)
dist = sum(dist, axis=1)
dist = expand(dist)
dist = dist + transpose(dist, (1, 0))
temp1 = P.matmul(inputs, transpose(inputs, (1, 0)))
temp1 = mul(-2, temp1)
dist = add(dist, temp1)
dist = P.composite.clip_by_value(
dist, clip_value_min=1e-12, clip_value_max=100000000
) # for numerical stability, clip_value_max=? why must set?
dist = sqrt(dist)
# For each anchor, find the hardest positive and negative
targets = expand(targets)
mask = equal(targets, transpose(targets, (1, 0)))
dist_ap = []
dist_an = []
# only for debugging
#####################
# print("dist is")
# print(dist.shape)
# print(dist)
# print("mask is")
# print(mask.shape)
# print(mask)
# print(mask[0])
#####################
for i in range(n):
minval = -1.0
maxval = -1.0
for j in range(n):
if mask[i][j] and dist[i][j] > maxval:
maxval = dist[i][j]
if not mask[i][j] and (dist[i][j] < minval or minval == -1):
minval = dist[i][j]
if (not isinstance(minval, Tensor)
or not isinstance(maxval, Tensor) or minval == -1.0
or maxval == -1.0):
if self.error_msg is not None:
print("Error Msg", file=self.error_msg)
print("mask {} is".format(i), file=self.error_msg)
print(mask[i], file=self.error_msg)
print("dist is:", file=self.error_msg)
print(dist[i], file=self.error_msg)
print(maxval, file=self.error_msg)
print(minval, file=self.error_msg)
print(type(maxval), file=self.error_msg)
print(type(minval), file=self.error_msg)
self.error_msg.flush()
# assert minval != -1.0 and isinstance(minval, Tensor)
# assert maxval != -1.0 and isinstance(maxval, Tensor)
dist_ap.append(maxval.asnumpy())
dist_an.append(minval.asnumpy())
dist_ap = Tensor(dist_ap, ms.float32)
dist_an = Tensor(dist_an, ms.float32)
# only for debugging
#####################
# print(dist_ap)
# print(dist_ap.shape)
# print(dist_an)
#####################
# Compute ranking hinge loss
y = ones_like(dist_an)
loss = self.ranking_loss(dist_an, dist_ap, y)
# # compute accuracy
# correct = torch.ge(dist_an, dist_ap).sum().item()
return loss
# class GradOriTripletLoss(nn.Cell)
# def __init__(self, net):
# super(GradOriTripletLoss, self).__init__()
# self.net = net
# self.grad_op = P.GradOperation(get_all=True)
#
# def construct(self, inputs, targets):
# gradient_function = self.grad_op(self.net)
# return gradient_function(inputs, targets)
