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 [1]。

代码示例

 # required: gpu
 # Single GPU
 import paddle
 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)
 print(label)
 print(loss)
 print(softmax)

 #Tensor(shape=[2, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[ 0.85204151, -0.55557678,  0.04994566,  0.71986042],
 #        [-0.20198586, -0.35270476, -0.55182702,  0.09749021]])
 #Tensor(shape=[2], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
 #       [2, 3])
 #Tensor(shape=[2, 1], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[82.37059586],
 #        [12.13448420]])
 #Tensor(shape=[2, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[0.99978819, 0.00000000, 0.00000000, 0.00021181],
 #        [0.99992995, 0.00006468, 0.00000000, 0.00000537]])

 # required: distributed
 # Multi GPU, test_margin_cross_entropy.py
 import paddle
 import paddle.distributed as dist
 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 = []
 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 = []
 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 test_margin_cross_entropy.py
 ## for rank0 input
 #Tensor(shape=[4, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[ 0.32888934,  0.02408748, -0.02763289,  0.18173063],
 #        [-0.52893978, -0.10623845, -0.21596515, -0.06432517],
 #        [-0.00536345, -0.03924667,  0.66735314, -0.28640926],
 #        [-0.09907366, -0.48534973, -0.10365338, -0.39472322]])
 #Tensor(shape=[4], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
 #       [11, 1 , 10, 11])

 ## for rank1 input
 #Tensor(shape=[4, 8], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
 #       [[ 0.68654754,  0.28137170,  0.69694954, -0.60923933, -0.57077653,  0.54576703, -0.38709028,  0.56028204],
 #        [-0.80360371, -0.03042448, -0.45107338,  0.49559349,  0.69998950, -0.45411693,  0.61927630, -0.82808600],
 #        [ 0.11457570, -0.34785879, -0.68819499, -0.26189226, -0.48241491, -0.67685711,  0.06510185,  0.49660849],
 #        [ 0.31604851,  0.52087884,  0.53124749, -0.86176582, -0.43426329,  0.34786144, -0.10850784,  0.51566383]])
 #Tensor(shape=[4], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
 #       [11, 1 , 10, 11])

 ## for rank0 output
 #Tensor(shape=[4, 1], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[38.96608230],
 #        [81.28152394],
 #        [69.67229865],
 #        [31.74197251]])
 #Tensor(shape=[4, 4], dtype=float64, place=CUDAPlace(0), stop_gradient=True,
 #       [[0.00000000, 0.00000000, 0.00000000, 0.00000000],
 #        [0.00000000, 0.00000000, 0.00000000, 0.00000000],
 #        [0.00000000, 0.00000000, 0.99998205, 0.00000000],
 #        [0.00000000, 0.00000000, 0.00000000, 0.00000000]])
 ## for rank1 output
 #Tensor(shape=[4, 1], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
 #       [[38.96608230],
 #        [81.28152394],
 #        [69.67229865],
 #        [31.74197251]])
 #Tensor(shape=[4, 8], dtype=float64, place=CUDAPlace(1), stop_gradient=True,
 #       [[0.33943993, 0.00000000, 0.66051859, 0.00000000, 0.00000000, 0.00004148, 0.00000000, 0.00000000],
 #        [0.00000000, 0.00000000, 0.00000000, 0.00000207, 0.99432097, 0.00000000, 0.00567696, 0.00000000],
 #        [0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00000000, 0.00001795],
 #        [0.00000069, 0.33993085, 0.66006319, 0.00000000, 0.00000000, 0.00000528, 0.00000000, 0.00000000]])