def __init__(self): super(LocalizationLoss, self).__init__() self.reduce_sum = P.ReduceSum() self.reduce_mean = P.ReduceMean() self.loss = nn.SmoothL1Loss() self.expand_dims = P.ExpandDims() self.less = P.Less()
def smoothl1loss(beta): np.random.seed(42) prediction = np.random.randn(20).astype(np.float32) target = np.random.randn(20).astype(np.float32) net = nn.SmoothL1Loss(beta) return net(Tensor(prediction), Tensor(target))
def __init__(self): super(SmoothL1LossNew, self).__init__() self.transpose = P.Transpose() self.smooth_l1_loss = nn.SmoothL1Loss() self.shape = P.Shape() self.expand_dims = P.ExpandDims() self.sum = P.ReduceSum() self.cast = P.Cast()
def smoothl1loss_grad(beta): np.random.seed(42) prediction = np.random.randn(20).astype(np.float32) target = np.random.randn(20).astype(np.float32) sens = np.random.randn(20).astype(np.float32) net = nn.SmoothL1Loss(beta) grad = Grad(net) return grad(Tensor(prediction), Tensor(target), Tensor(sens))
def __init__(self, network, config): super(SSDWithLossCell, self).__init__() self.network = network self.less = P.Less() self.tile = P.Tile() self.reduce_sum = P.ReduceSum() self.expand_dims = P.ExpandDims() self.class_loss = SigmoidFocalClassificationLoss( config.gamma, config.alpha) self.loc_loss = nn.SmoothL1Loss()
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 test_smoothl1loss(): np.random.seed(42) prediction = np.random.randn(20).astype(np.float32) target = np.random.randn(20).astype(np.float32) sigma = 1.0 net = nn.SmoothL1Loss(sigma) loss = net(Tensor(prediction), Tensor(target)) expect = [0.46941718, 0.00382918, 0.16829303, 2.447778, 0.04812113, 0.05953304, 2.2302065, 0.07672881, 0.00860204, 0.34798968, 0.00956192, 1.818008, 0.03262977, 0.36599946, 2.047463, 0.2168481, 0.7216947, 1.7739174, 0.08826803, 1.109165] assert np.allclose(loss.asnumpy(), expect)
def __init__(self, network, config): super(retinanetWithLossCell, self).__init__() self.network = network self.less = P.Less() self.tile = P.Tile() self.reduce_sum = P.ReduceSum() self.reduce_mean = P.ReduceMean() self.expand_dims = P.ExpandDims() self.class_loss = SigmoidFocalClassificationLoss( config.gamma, config.alpha) self.loc_loss = nn.SmoothL1Loss() self.cast = P.Cast() self.network.to_float(mstype.float16)
def test_smoothl1loss_grad(): np.random.seed(42) prediction = np.random.randn(20).astype(np.float32) target = np.random.randn(20).astype(np.float32) sens = np.random.randn(20).astype(np.float32) sigma = 1.0 net = nn.SmoothL1Loss(sigma) grad = Grad(net) dx = grad(Tensor(prediction), Tensor(target), Tensor(sens)) dx1_expect = [-0.71552587, 0.01499678, -0.06709455, -0.30110368, -0.45868093, 0.24838912, -0.46063876, 0.41411355, 0.04507046, -1.4708229, 0.04481723, 0.38508227, -0.17292616, -0.52333146, -1.0309995, 0.61330026, 0.83921754, -0.3092124, 0.1391843, -0.9755451] dx2_expect = [0.71552587, -0.01499678, 0.06709455, 0.30110368, 0.45868093, -0.24838912, 0.46063876, -0.41411355, -0.04507046, 1.4708229, -0.04481723, -0.38508227, 0.17292616, 0.52333146, 1.0309995, -0.61330026, -0.83921754, 0.3092124, -0.1391843, 0.9755451] assert np.allclose(dx[0].asnumpy(), dx1_expect) assert np.allclose(dx[1].asnumpy(), dx2_expect)