arc_loss训练手写数字分类

arc_loss

import torch as t
import torch.nn as nn
import torch.nn.functional as F


class ArcLoss(nn.Module):
    def __init__(self, class_num, feature_num, s=10, m=0.1):
        super().__init__()
        self.class_num = class_num
        self.feature_num = feature_num
        self.s = s
        self.m = t.tensor(m)
        self.w = nn.Parameter(t.rand(feature_num, class_num))  # 2*10

    def forward(self, feature):
        feature = F.normalize(feature, dim=1)  # 128*2
        w = F.normalize(self.w, dim=0)  # 2*10

        cos_theat = t.matmul(feature, w) / 10
        sin_theat = t.sqrt(1.0 - t.pow(cos_theat, 2))
        cos_theat_m = cos_theat * t.cos(self.m) - sin_theat * t.sin(self.m)
        cos_theat_ = t.exp(cos_theat * self.s)
        sum_cos_theat = t.sum(t.exp(cos_theat * self.s), dim=1, keepdim=True) - cos_theat_
        top = t.exp(cos_theat_m * self.s)
        divide = (top / (top + sum_cos_theat))

        # a = torch.acos(cos_theat)
        # top = torch.exp(( torch.cos(a + 0.1)) * 10)
        # _top = torch.exp(( torch.cos(a)) * 10)
        # bottom = torch.sum(torch.exp(cos_theat * 10), dim=1).view(-1, 1)
        #
        # divide = (top / (bottom - _top + top)) + 1e-10  ##n,10

        return divide
    # 以上两种写法逻辑上是一样的，但试验效果不同（反函数求出theat然后直接代入公式的收敛效果略优）

new_net

import torch as t
import torchvision as tv
import torch.nn as nn
import torch.nn.functional as F
import torch.utils.data as data
import matplotlib.pyplot as plt
from tensorboardX import SummaryWriter
import torch.optim.lr_scheduler as lr_scheduler
import os

Batch_Size = 128
train_data = tv.datasets.MNIST(
    root="./mnist",
    train=True,
    download=False,
    transform=tv.transforms.Compose([tv.transforms.ToTensor(),
                                     tv.transforms.Normalize((0.1307,), (0.3081,))]))

train_loader = data.DataLoader(train_data, batch_size=Batch_Size, shuffle=True, drop_last=True, num_workers=8)


class TrainNet(nn.Module):
    def __init__(self):
        super().__init__()
        self.hidden_layer = nn.Sequential(
            nn.Conv2d(1, 64, 3, 2, 1),
            nn.BatchNorm2d(64),
            nn.PReLU(),
            nn.Conv2d(64, 256, 3, 2, 1),
            nn.BatchNorm2d(256),
            nn.PReLU(),
            nn.Conv2d(256, 256, 3, 1, 1),
            nn.BatchNorm2d(256),
            nn.PReLU(),
            nn.Conv2d(256, 64, 3, 1, 1),
            nn.BatchNorm2d(64),
            nn.PReLU(),
            nn.Conv2d(64, 16, 3, 2, 1),
            nn.PReLU())
        self.linear_layer = nn.Linear(16 * 4 * 4, 2)
        self.output_layer = nn.Linear(2, 10, bias=False)

    def forward(self, xs):
        feat = self.hidden_layer(xs)
        # print(feature.shape)
        fc = feat.reshape(-1, 16 * 4 * 4)
        # print(fc.data.size())
        feature = self.linear_layer(fc)
        output = self.output_layer(feature)
        return feature, F.log_softmax(output, dim=1)


def decet(feature, targets, epoch, save_path):
    color = ["red", "black", "yellow", "green", "pink", "gray", "lightgreen", "orange", "blue", "teal"]
    cls = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
    plt.ion()
    plt.clf()
    for j in cls:

        mask = [targets == j]
        feature_ = feature[mask].numpy()
        x = feature_[:, 1]
        y = feature_[:, 0]
        label = cls
        plt.plot(x, y, ".", color=color[j])
        # print(x,y)
        plt.legend(label, loc="upper right")  # 如果写在plot上面，则标签内容不能显示完整
        plt.title("epoch={}".format(str(epoch + 1)))

    plt.savefig('{}/{}.jpg'.format(save_path, epoch + 1))
    plt.draw()
    plt.pause(0.01)

train

from new_net import *
from arc_loss import ArcLoss
weight = 1
save_path = r".\model_data\{}\train{}.pth"
save_pic_path = "img17"
if __name__ == '__main__':

    net = TrainNet()
    device = t.device("cuda:0" if t.cuda.is_available() else "cpu")
    arcloss = ArcLoss(10, 2).to(device)
    # crossloss = nn.CrossEntropyLoss().to(device)
    nllloss = nn.NLLLoss(reduction="sum").to(device)  # 如果reduction="mean"则效果略差
    optmizer = t.optim.SGD(net.parameters(), lr=0.0001, momentum=0.9, weight_decay=0.0005)
    scheduler = lr_scheduler.StepLR(optmizer, 20, gamma=0.8)
    optmizerarc = t.optim.Adam(arcloss.parameters())

    # if os.path.exists(save_path):
    #     net.load_state_dict(t.load(save_path))
    net = net.to(device)
    for epoch in range(15000):
        scheduler.step()
        feat = []
        target = []
        for i, (x, y) in enumerate(train_loader):
            x, y = x.to(device), y.to(device)
            xs, ys = net(x)
            value = t.argmax(ys, dim=1)
            arc_loss = t.log(arcloss(xs))
            nll_loss = nllloss(ys, y)
            arcface_loss = nllloss(arc_loss, y)
            loss = nll_loss + arcface_loss
            acc = t.sum((value == y).float()) / len(y)
            # loss = crossloss(arc_loss,y)
            optmizer.zero_grad()
            optmizerarc.zero_grad()
            loss.backward()
            optmizer.step()
            optmizerarc.step()

            feat.append(xs)  # 为画图预加载数据,提速
            target.append(y)
            if i % 100 == 0:
                print(epoch, i, loss.item())
                print("acc", acc.item())
                if acc.item()==1:
                    print("dd")
                    t.save(net,"newt.pth")
                # print(value[0].item(), "========>", y[0].item())
        # if (epoch + 1) % 1 == 0:
        #     t.save(net.state_dict(), save_path.format(r"D:\PycharmProjects\center_loss\data", str(epoch)))
        features = t.cat(feat, 0)
        targets = t.cat(target, 0)
        decet(features.data.cpu(), targets.data.cpu(), epoch, save_pic_path)
        #     write.add_histogram("loss",loss.item(),count)