def __init__(self):
super(NetOneHot, self).__init__()
self.on_value = 2.0
self.off_value = 3.0
self.depth_1 = 6
self.one_hot_1 = nn.OneHot(-1, self.depth_1, self.on_value, self.off_value)
self.depth_2 = 4
self.one_hot_2 = nn.OneHot(0, self.depth_1, self.on_value, self.off_value)
self.one_hot_3 = nn.OneHot(0, self.depth_2, self.on_value, self.off_value)
self.one_hot_4 = nn.OneHot(1, self.depth_1, self.on_value, self.off_value)
def __init__(self, num_classes):
super(Encoder, self).__init__()
self.fc1 = nn.Dense(1024 + num_classes, 400)
self.relu = nn.ReLU()
self.flatten = nn.Flatten()
self.concat = P.Concat(axis=1)
self.one_hot = nn.OneHot(depth=num_classes)
def __init__(self, backbone, config):
super(WithLossCell, self).__init__(auto_prefix=False)
self._backbone = backbone
self.batch_size = config.batch_size
self.onehot = nn.OneHot(depth=config.ch_vocab_size)
self._loss_fn = NLLLoss()
self.max_len = config.max_seq_length
self.squeeze = P.Squeeze()
self.cast = P.Cast()
self.argmax = P.ArgMaxWithValue(axis=1, keep_dims=True)
self.print = P.Print()
def __init__(self, num, ignore_label):
super(OhemLoss, self).__init__()
self.mul = P.Mul()
self.shape = P.Shape()
self.one_hot = nn.OneHot(-1, num, 1.0, 0.0)
self.squeeze = P.Squeeze()
self.num = num
self.cross_entropy = P.SoftmaxCrossEntropyWithLogits()
self.mean = P.ReduceMean()
self.select = P.Select()
self.reshape = P.Reshape()
self.cast = P.Cast()
self.not_equal = P.NotEqual()
self.equal = P.Equal()
self.reduce_sum = P.ReduceSum(keep_dims=False)
self.fill = P.Fill()
self.transpose = P.Transpose()
self.ignore_label = ignore_label
self.loss_weight = 1.0
def __init__(self,
length,
depth,
max_relative_position,
initializer_range,
use_one_hot_embeddings=False):
super(RelaPosEmbeddingsGenerator, self).__init__()
self.depth = depth
self.vocab_size = max_relative_position * 2 + 1
self.use_one_hot_embeddings = use_one_hot_embeddings
self.embeddings_table = Parameter(initializer(
TruncatedNormal(initializer_range), [self.vocab_size, self.depth]),
name='embeddings_for_position')
self.relative_positions_matrix = RelaPosMatrixGenerator(
length=length, max_relative_position=max_relative_position)
self.reshape = P.Reshape()
self.one_hot = nn.OneHot(depth=self.vocab_size)
self.shape = P.Shape()
self.gather = P.GatherV2() # index_select
self.matmul = P.BatchMatMul()
def __init__(self, num_classes, anchors, anchors_mask, reduction=32, seen=0, coord_scale=1.0, no_object_scale=1.0,
object_scale=1.0, class_scale=1.0, thresh=0.5, head_idx=0.0):
super(YoloLoss, self).__init__()
self.num_classes = num_classes
self.num_anchors = len(anchors_mask)
self.anchor_step = len(anchors[0]) # each scale has step anchors
self.anchors = np.array(anchors, dtype=np.float32) / reduction # scale every anchor for every scale
self.tensor_anchors = Tensor(self.anchors, mstype.float32)
self.anchors_mask = anchors_mask
anchors_w = []
anchors_h = []
for i in range(len(anchors_mask)):
anchors_w.append(self.anchors[self.anchors_mask[i]][0])
anchors_h.append(self.anchors[self.anchors_mask[i]][1])
self.anchors_w = Tensor(np.array(anchors_w).reshape(len(self.anchors_mask), 1))
self.anchors_h = Tensor(np.array(anchors_h).reshape(len(self.anchors_mask), 1))
self.reduction = reduction
self.seen = seen
self.head_idx = head_idx
self.zero = Tensor(0)
self.coord_scale = coord_scale
self.no_object_scale = no_object_scale
self.object_scale = object_scale
self.class_scale = class_scale
self.thresh = thresh
self.info = {'avg_iou': 0, 'class': 0, 'obj': 0, 'no_obj': 0,
'recall50': 0, 'recall75': 0, 'obj_cur': 0, 'obj_all': 0,
'coord_xy': 0, 'coord_wh': 0}
self.shape = P.Shape()
self.reshape = P.Reshape()
self.sigmoid = P.Sigmoid()
self.zeros_like = P.ZerosLike()
self.concat0 = P.Concat(0)
self.concat0_2 = P.Concat(0)
self.concat0_3 = P.Concat(0)
self.concat0_4 = P.Concat(0)
self.concat1 = P.Concat(1)
self.concat1_2 = P.Concat(1)
self.concat1_3 = P.Concat(1)
self.concat1_4 = P.Concat(1)
self.concat2 = P.Concat(2)
self.concat2_2 = P.Concat(2)
self.concat2_3 = P.Concat(2)
self.concat2_4 = P.Concat(2)
self.tile = P.Tile()
self.transpose = P.Transpose()
self.cast = P.Cast()
self.exp = P.Exp()
self.sum = P.ReduceSum()
self.smooth_l1_loss = P.SmoothL1Loss()
self.bce = P.SigmoidCrossEntropyWithLogits()
self.ce = P.SoftmaxCrossEntropyWithLogits()
self.pt_linspace = PtLinspace()
self.one_hot = nn.OneHot(-1, self.num_classes, 1.0, 0.0)
self.squeeze_2 = P.Squeeze(2)
self.reduce_sum = P.ReduceSum()
self.select = P.Select()
self.iou = P.IOU()
