class_center_sample¶
类别中心采样方法是提出于 PartialFC 论文，目的是从全量的类别中心采样一个子集类别中心参与训练。采样过程也非常简单直观：
首先把所有正类别中心采样；
然后随机采样负类别中心。
具体的过程是，给定一个维度为 [batch_size] 的 label，从 [0, num_classes) 中把所有正类别中心选择出来，然后随机采样负类别中心补够 num_samples。接着用采样出来的类别中心重新映射 label。
更多的细节信息，请参考论文《Partial FC: Training 10 Million Identities on a Single Machine》，arxiv: https://arxiv.org/abs/2010.05222
提示：
如果正类别中心数量大于给定的
num_samples，将保留所有的正类别中心，因此sampled_class_center的维度将是 [num_positive_class_centers]。
参数¶
label (Tensor) - 1-D Tensor，数据类型为 int32 或者 int64，每个元素的取值范围在 [0, num_classes)。
num_classes (int) - 一个正整数，表示当前卡的类别数，注意每张卡的
num_classes可以是不同的值。num_samples (int) - 一个正整数，表示当前卡采样的类别中心数量。
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。
返回¶
Tensor二元组 - (remapped_label,sampled_class_center)，remapped_label是重新映射后的标签，sampled_class_center是所采样的类别中心。
代码示例¶
# CPU or single GPU
import paddle
num_classes = 20
batch_size = 10
num_samples = 6
label = paddle.randint(low=0, high=num_classes, shape=[batch_size], dtype='int64')
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes, num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
# the output is
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [11, 5 , 1 , 3 , 12, 2 , 15, 19, 18, 19])
#Tensor(shape=[10], dtype=int64, place=CPUPlace, stop_gradient=True,
# [4, 3, 0, 2, 5, 1, 6, 8, 7, 8])
#Tensor(shape=[9], dtype=int64, place=CPUPlace, stop_gradient=True,
# [1 , 2 , 3 , 5 , 11, 12, 15, 18, 19])
# required: distributed
# Multi GPU, test_class_center_sample.py
import paddle
import paddle.distributed as dist
strategy = dist.fleet.DistributedStrategy()
dist.fleet.init(is_collective=True, strategy=strategy)
batch_size = 10
num_samples = 6
rank_id = dist.get_rank()
# num_classes of each GPU can be different, e.g num_classes_list = [10, 8]
num_classes_list = [10, 10]
num_classes = paddle.sum(paddle.to_tensor(num_classes_list))
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)
remapped_label, sampled_class_index = paddle.nn.functional.class_center_sample(label, num_classes_list[rank_id], num_samples)
print(label)
print(remapped_label)
print(sampled_class_index)
#python -m paddle.distributed.launch --gpus=0,1 test_class_center_sample.py
# rank 0 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[6], dtype=int64, place=CUDAPlace(0), stop_gradient=True,
# [0, 2, 4, 8, 9, 3])
# rank 1 output:
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [10, 17, 15, 11, 9 , 12, 18, 18, 17, 18, 19, 2 , 8 , 13, 11, 13, 9 , 10, 0 , 4 ])
#Tensor(shape=[20], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [6 , 11, 10, 7 , 4 , 8 , 12, 12, 11, 12, 13, 1 , 3 , 9 , 7 , 9 , 4 , 6 , 0 , 2 ])
#Tensor(shape=[7], dtype=int64, place=CUDAPlace(1), stop_gradient=True,
# [0, 1, 2, 3, 5, 7, 8])