margin_cross_entropy

paddle.nn.functional. margin_cross_entropy ( logits, label, margin1=1.0, margin2=0.5, margin3=0.0, scale=64.0, group=None, return_softmax=False, reduction='mean' ) [源代码]
\[L=-\frac{1}{N}\sum^N_{i=1}\log\frac{e^{s(cos(m_{1}\theta_{y_i}+m_{2})-m_{3})}}{e^{s(cos(m_{1}\theta_{y_i}+m_{2})-m_{3})}+\sum^n_{j=1,j\neq y_i} e^{scos\theta_{y_i}}}\]

其中，\(\theta_{y_i}\) 是特征 \(x\) 与类 \(w_{i}\) 的角度。更详细的介绍请参考 Arcface loss，https://arxiv.org/abs/1801.07698 。

提示：

这个 API 支持单卡，也支持多卡（模型并行），使用模型并行时，logits.shape[-1] 在每张卡上可以不同。

参数

  • logits (Tensor) - 2-D Tensor，维度为 [N, local_num_classes]，logits 为归一化后的 X 与归一化后的 W 矩阵乘得到，数据类型为 float16，float32 或者 float64。如果用了模型并行，则 logits == sahrd_logits。

  • label (Tensor) - 维度为 [N] 或者 [N, 1] 的标签。

  • margin1 (float，可选) - 公式中的 m1。默认值为 1.0。

  • margin2 (float，可选) - 公式中的 m2。默认值为 0.5。

  • margin3 (float，可选) - 公式中的 m3。默认值为 0.0。

  • scale (float，可选) - 公式中的 s。默认值为 64.0。

  • group (Group，可选) - 通信组的抽象描述，具体可以参考 paddle.distributed.collective.Group。默认值为 None。

  • return_softmax (bool，可选) - 是否返回 softmax 概率值。默认值为 None。

  • reduction （str，可选）- 是否对 loss 进行归约。可选值为 'none' | 'mean' | 'sum'。如果 reduction='mean'，则对 loss 进行平均，如果 reduction='sum'，则对 loss 进行求和，reduction='None'，则直接返回 loss。默认值为 'mean'。

返回

  • Tensor (loss) 或者 Tensor 二元组 (loss, softmax) - 如果 return_softmax=False 返回 loss，否则返回 (loss, softmax)。当使用模型并行时 softmax == shard_softmax，否则 softmax 的维度与 logits 相同。如果 reduction == None，loss 的维度为 [N, 1]，否则为 []。

代码示例

 >>> import paddle
 >>> paddle.seed(2023)
 >>> paddle.device.set_device('gpu')
 >>> m1 = 1.0
 >>> m2 = 0.5
 >>> m3 = 0.0
 >>> s = 64.0
 >>> batch_size = 2
 >>> feature_length = 4
 >>> num_classes = 4

 >>> label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')

 >>> X = paddle.randn(
 ...     shape=[batch_size, feature_length],
 ...     dtype='float64')
 >>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
 >>> X = paddle.divide(X, X_l2)

 >>> W = paddle.randn(
 ...     shape=[feature_length, num_classes],
 ...     dtype='float64')
 >>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
 >>> W = paddle.divide(W, W_l2)

 >>> logits = paddle.matmul(X, W)
 >>> loss, softmax = paddle.nn.functional.margin_cross_entropy(
 ...     logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
 >>> print(logits)
 Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
        [[-0.59561850,  0.32797505,  0.80279214,  0.00144975],
         [-0.16265212,  0.84155098,  0.62008629,  0.79126072]])
 >>> print(label)
 Tensor(shape=[2], dtype=int64, place=Place(gpu:0), stop_gradient=True,
        [1, 0])
 >>> print(loss)
 Tensor(shape=[2, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
        [[61.94391901],
         [93.30853839]])
 >>> print(softmax)
 Tensor(shape=[2, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
        [[0.00000000, 0.00000000, 1.        , 0.00000000],
         [0.00000000, 0.96152676, 0.00000067, 0.03847257]])

 >>> # Multi GPU, test_margin_cross_entropy.py
 >>> from typing import List
 >>> import paddle
 >>> import paddle.distributed as dist
 >>> paddle.seed(2023)
 >>> strategy = dist.fleet.DistributedStrategy()
 >>> dist.fleet.init(is_collective=True, strategy=strategy)
 >>> rank_id = dist.get_rank()
 >>> m1 = 1.0
 >>> m2 = 0.5
 >>> m3 = 0.0
 >>> s = 64.0
 >>> batch_size = 2
 >>> feature_length = 4
 >>> num_class_per_card = [4, 8]
 >>> num_classes = paddle.sum(paddle.to_tensor(num_class_per_card))

 >>> label = paddle.randint(low=0, high=num_classes.item(), shape=[batch_size], dtype='int64')
 >>> label_list: List[paddle.Tensor] = []
 >>> dist.all_gather(label_list, label)
 >>> label = paddle.concat(label_list, axis=0)

 >>> X = paddle.randn(
 ...     shape=[batch_size, feature_length],
 ...     dtype='float64'
 ... )
 >>> X_list: List[paddle.Tensor] = []
 >>> dist.all_gather(X_list, X)
 >>> X = paddle.concat(X_list, axis=0)
 >>> X_l2 = paddle.sqrt(paddle.sum(paddle.square(X), axis=1, keepdim=True))
 >>> X = paddle.divide(X, X_l2)

 >>> W = paddle.randn(
 ...     shape=[feature_length, num_class_per_card[rank_id]],
 ...     dtype='float64')
 >>> W_l2 = paddle.sqrt(paddle.sum(paddle.square(W), axis=0, keepdim=True))
 >>> W = paddle.divide(W, W_l2)

 >>> logits = paddle.matmul(X, W)
 >>> loss, softmax = paddle.nn.functional.margin_cross_entropy(
 ...     logits, label, margin1=m1, margin2=m2, margin3=m3, scale=s, return_softmax=True, reduction=None)
 >>> print(logits)
 >>> print(label)
 >>> print(loss)
 >>> print(softmax)

 >>> # python -m paddle.distributed.launch --gpus=0,1 --log_dir log test_margin_cross_entropy.py
 >>> # cat log/workerlog.0
 >>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
 >>> #        [[-0.59561850,  0.32797505,  0.80279214,  0.00144975],
 >>> #         [-0.16265212,  0.84155098,  0.62008629,  0.79126072],
 >>> #         [-0.59561850,  0.32797505,  0.80279214,  0.00144975],
 >>> #         [-0.16265212,  0.84155098,  0.62008629,  0.79126072]])
 >>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True,
 >>> #        [5, 4, 5, 4])
 >>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:0), stop_gradient=True,
 >>> #        [[104.27437027],
 >>> #         [113.40243782],
 >>> #         [104.27437027],
 >>> #         [113.40243782]])
 >>> # Tensor(shape=[4, 4], dtype=float64, place=Place(gpu:0), stop_gradient=True,
 >>> #        [[0.00000000, 0.00000000, 0.01210039, 0.00000000],
 >>> #         [0.00000000, 0.96152674, 0.00000067, 0.03847257],
 >>> #         [0.00000000, 0.00000000, 0.01210039, 0.00000000],
 >>> #         [0.00000000, 0.96152674, 0.00000067, 0.03847257]])
 >>> # cat log/workerlog.1
 >>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True,
 >>> #        [[-0.34913275, -0.35180883, -0.53976657, -0.75234331,  0.70534995,
 >>> #           0.87157838,  0.31064437,  0.19537700],
 >>> #         [-0.63941012, -0.05631600, -0.02561853,  0.09363013,  0.56571130,
 >>> #           0.13611246,  0.08849565,  0.39219619],
 >>> #         [-0.34913275, -0.35180883, -0.53976657, -0.75234331,  0.70534995,
 >>> #           0.87157838,  0.31064437,  0.19537700],
 >>> #         [-0.63941012, -0.05631600, -0.02561853,  0.09363013,  0.56571130,
 >>> #           0.13611246,  0.08849565,  0.39219619]])
 >>> # Tensor(shape=[4], dtype=int64, place=Place(gpu:1), stop_gradient=True,
 >>> #        [5, 4, 5, 4])
 >>> # Tensor(shape=[4, 1], dtype=float64, place=Place(gpu:1), stop_gradient=True,
 >>> #        [[104.27437027],
 >>> #         [113.40243782],
 >>> #         [104.27437027],
 >>> #         [113.40243782]])
 >>> # Tensor(shape=[4, 8], dtype=float64, place=Place(gpu:1), stop_gradient=True,
 >>> #        [[0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593,
 >>> #          0.00000000, 0.00000000],
 >>> #         [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000,
 >>> #          0.00000000, 0.00000000],
 >>> #         [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00002368, 0.98787593,
 >>> #          0.00000000, 0.00000000],
 >>> #         [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000002, 0.00000000,
 >>> #          0.00000000, 0.00000000]])