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, threshold, value): super().__init__() self.threshold = threshold self.value = value self.greater = ops.Greater() self.fill = ops.Fill() self.select = ops.Select()
def __init__(self, learning_rate, warmup_steps, multi_epochs, steps_per_epoch, factor=10): super(CenterNetMultiEpochsDecayLR, self).__init__() self.warmup_flag = False if warmup_steps > 0: self.warmup_flag = True self.warmup_lr = WarmUpLR(learning_rate, warmup_steps) self.decay_lr = MultiEpochsDecayLR(learning_rate, multi_epochs, steps_per_epoch, factor) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = ops.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = ops.Cast()
def __init__(self, learning_rate, end_learning_rate, warmup_steps, decay_steps, power): super(CenterNetPolynomialDecayLR, self).__init__() self.warmup_flag = False if warmup_steps > 0: self.warmup_flag = True self.warmup_lr = WarmUpLR(learning_rate, warmup_steps) self.decay_lr = PolynomialDecayLR(learning_rate, end_learning_rate, decay_steps, power) self.warmup_steps = Tensor(np.array([warmup_steps]).astype(np.float32)) self.greater = ops.Greater() self.one = Tensor(np.array([1.0]).astype(np.float32)) self.cast = ops.Cast()
def construct(self, x): """construct""" x_averaged = self.avg_pool(x, (2, 3)) y = self.fc1(x_averaged) y = self.relu(y) y = self.fc2(y) mask_before = self.relu(y) mask_before = ops.clip_by_value(mask_before, self.clamp_min, self.clamp_max) tmp = ops.Greater()(mask_before, self.thre) mask = mask_before * tmp return mask
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 hardshrink(input, lambd=0.5): great_lambd = ops.Greater()(input, lambd) less_neg_lambd = ops.Less()(input, lambd) cond = ops.logical_or(great_lambd, less_neg_lambd) return ops.Select()(cond, input, ops.scalar_to_tensor(0.0))