（原）人脸姿态时别HYBRID COARSE-FINE CLASSIFICATION FOR HEAD POSE ESTIMATION

转载请注明出处：

https://www.cnblogs.com/darkknightzh/p/12150128.html

论文：

HYBRID COARSE-FINE CLASSIFICATION FOR HEAD POSE ESTIMATION

论文网址：

https://arxiv.org/abs/1901.06778

官方pytorch代码：

https://github.com/haofanwang/accurate-head-pose

 

该论文提出了coarse-fine的分类方式。

1. 网络结构

论文网络结构如下图所示。输入图像通过骨干网络得到特征后，分别连接到不同的fc层。这些fc层将输入特征映射到-99度至102度之内不同间隔的角度区间（间隔分别为1,3,11,33,99），而后通过softmax得到归一化特征，并分2支，一方面计算期望及期望与真值的MSE loss，另一方面计算交叉熵损失。而后求和，得到最终的损失。

1)     MSE loss和deep head pose中接近（区别是此处使用198个类别的分类结果计算期望，deep head pose使用66个类别）。

2)     其他角度区间（除198个类别的角度区间之外）只用于计算交叉熵损失（如下图所示）。

3)     不同角度区间的交叉熵损失权重不同。

4)     本文MSE损失的权重较大（为2）

5)     训练时使用softmax计算概率。测试时使用带temperature的softmax计算概率（由于代码中T=1，实际上等效于softmax）。

6)     从https://arxiv.org/abs/1503.02531可知，给定输入logit ${{z}_{i}}$，其softmax temperature的输出${{q}_{i}}$计算如下：

${{q}_{i}}=frac{exp ({{z}_{i}}/T)}{sum olimits_{j}{exp ({{z}_{j}}/T)}}$

其中T为temperature。通常设置为1（即为softmax）。T越大，输出概率的差异性越小；T越小（越接近0），输出概率的差异性越大。

因而，感觉上图变成下面这样，会更容易理解：

本文损失函数如下：

$Loss=alpha centerdot MSE(y,{{y}^{*}})+sumlimits_{i=1}^{num}{{{eta }_{i}}centerdot H({{y}_{i}},y_{i}^{*})}$

其中H代表交叉熵损失。${{eta }_{i}}$为不同角度区间时交叉熵损失的权重（具体权重可参见代码）。

2. 代码

2.1 网络结构

class Multinet(nn.Module):
    # Hopenet with 3 output layers for yaw, pitch and roll
    # Predicts Euler angles by binning and regression with the expected value
    def __init__(self, block, layers, num_bins):
        self.inplanes = 64
        super(Multinet, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7)    # 至此为Resnet的骨干网络
        self.fc_yaw = nn.Linear(512 * block.expansion, num_bins)     #  和hopenet类似，只是num_bins=198
        self.fc_pitch = nn.Linear(512 * block.expansion, num_bins)   #  和hopenet类似，只是num_bins=198
        self.fc_roll = nn.Linear(512 * block.expansion, num_bins)    #  和hopenet类似，只是num_bins=198
        
        self.fc_yaw_1 = nn.Linear(512 * block.expansion, 66)   # 66和deep head pose一致
        self.fc_yaw_2 = nn.Linear(512 * block.expansion, 18)   # 其他为新的fc层
        self.fc_yaw_3 = nn.Linear(512 * block.expansion, 6)
        self.fc_yaw_4 = nn.Linear(512 * block.expansion, 2)
        
        self.fc_pitch_1 = nn.Linear(512 * block.expansion, 66)
        self.fc_pitch_2 = nn.Linear(512 * block.expansion, 18)
        self.fc_pitch_3 = nn.Linear(512 * block.expansion, 6)
        self.fc_pitch_4 = nn.Linear(512 * block.expansion, 2)
        
        self.fc_roll_1 = nn.Linear(512 * block.expansion, 66)
        self.fc_roll_2 = nn.Linear(512 * block.expansion, 18)
        self.fc_roll_3 = nn.Linear(512 * block.expansion, 6)
        self.fc_roll_4 = nn.Linear(512 * block.expansion, 2)

        # Vestigial layer from previous experiments
        self.fc_finetune = nn.Linear(512 * block.expansion + 3, 3)  # 未使用

        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)  # 得到骨干网络的特征
        pre_yaw = self.fc_yaw(x)     # 以下得到yaw、pitch、roll等的其他特征
        pre_pitch = self.fc_pitch(x)
        pre_roll = self.fc_roll(x)
        
        pre_yaw_1 = self.fc_yaw_1(x)
        pre_pitch_1 = self.fc_pitch_1(x)
        pre_roll_1 = self.fc_roll_1(x)
        
        pre_yaw_2 = self.fc_yaw_2(x)
        pre_pitch_2 = self.fc_pitch_2(x)
        pre_roll_2 = self.fc_roll_2(x)
        
        pre_yaw_3 = self.fc_yaw_3(x)
        pre_pitch_3 = self.fc_pitch_3(x)
        pre_roll_3 = self.fc_roll_3(x)
        
        pre_yaw_4 = self.fc_yaw_4(x)
        pre_pitch_4 = self.fc_pitch_4(x)
        pre_roll_4 = self.fc_roll_4(x)

        return pre_yaw,pre_yaw_1,pre_yaw_2,pre_yaw_3,pre_yaw_4, pre_pitch,pre_pitch_1,pre_pitch_2,pre_pitch_3,pre_pitch_4, pre_roll,pre_roll_1,pre_roll_2,pre_roll_3,pre_roll_4

2.2 训练代码

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', default=0, type=int)
    parser.add_argument('--num_epochs', dest='num_epochs', help='Maximum number of training epochs.', default=25, type=int)
    parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', default=32, type=int)
    parser.add_argument('--lr', dest='lr', help='Base learning rate.', default=0.000001, type=float)
    parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW_multi', type=str)
    parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.', default='', type=str)
    parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.', default='/tools/AFLW_train.txt', type=str)
    parser.add_argument('--output_string', dest='output_string', help='String appended to output snapshots.', default = '', type=str)
    parser.add_argument('--alpha', dest='alpha', help='Regression loss coefficient.', default=2, type=float)
    parser.add_argument('--snapshot', dest='snapshot', help='Path of model snapshot.', default='', type=str)

    args = parser.parse_args()
    return args

def get_ignored_params(model):
    # Generator function that yields ignored params.
    b = [model.conv1, model.bn1, model.fc_finetune]
    for i in range(len(b)):
        for module_name, module in b[i].named_modules():
            if 'bn' in module_name:
                module.eval()
            for name, param in module.named_parameters():
                yield param

def get_non_ignored_params(model):
    # Generator function that yields params that will be optimized.
    b = [model.layer1, model.layer2, model.layer3, model.layer4]
    for i in range(len(b)):
        for module_name, module in b[i].named_modules():
            if 'bn' in module_name:
                module.eval()
            for name, param in module.named_parameters():
                yield param

def get_fc_params(model):
    # Generator function that yields fc layer params.
    b = [model.fc_yaw, model.fc_pitch, model.fc_roll,
         model.fc_yaw_1, model.fc_pitch_1, model.fc_roll_1,
         model.fc_yaw_2, model.fc_pitch_2, model.fc_roll_2,
         model.fc_yaw_3, model.fc_pitch_3, model.fc_roll_3]
    for i in range(len(b)):
        for module_name, module in b[i].named_modules():
            for name, param in module.named_parameters():
                yield param

def load_filtered_state_dict(model, snapshot):
    # By user apaszke from discuss.pytorch.org
    model_dict = model.state_dict()
    snapshot = {k: v for k, v in snapshot.items() if k in model_dict}
    model_dict.update(snapshot)
    model.load_state_dict(model_dict)

if __name__ == '__main__':
    args = parse_args()

    cudnn.enabled = True
    num_epochs = args.num_epochs
    batch_size = args.batch_size
    gpu = args.gpu_id

    if not os.path.exists('output/snapshots'):
        os.makedirs('output/snapshots')

    # ResNet50 structure
    model = hopenet.Multinet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 198)   # 载入模型

    if args.snapshot == '':
        load_filtered_state_dict(model, model_zoo.load_url('https://download.pytorch.org/models/resnet50-19c8e357.pth'))
    else:
        saved_state_dict = torch.load(args.snapshot)
        model.load_state_dict(saved_state_dict)

    print('Loading data.')

    transformations = transforms.Compose([transforms.Resize(240),
    transforms.RandomCrop(224), transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

    if args.dataset == 'Pose_300W_LP':
        pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Pose_300W_LP_multi':
        pose_dataset = datasets.Pose_300W_LP_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Pose_300W_LP_random_ds':
        pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Synhead':
        pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW2000':
        pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'BIWI':
        pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'BIWI_multi':
        pose_dataset = datasets.BIWI_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW':
        pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW_multi':        # 载入数据的dataset
        pose_dataset = datasets.AFLW_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW_aug':
        pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFW':
        pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
    else:
        print('Error: not a valid dataset name')
        sys.exit()

    train_loader = torch.utils.data.DataLoader(dataset=pose_dataset, batch_size=batch_size, shuffle=True, num_workers=2)
    
    model.cuda(gpu)
    criterion = nn.CrossEntropyLoss().cuda(gpu)
    reg_criterion = nn.MSELoss().cuda(gpu)
    # Regression loss coefficient
    alpha = args.alpha

    softmax = nn.Softmax(dim=1).cuda(gpu)
    idx_tensor = [idx for idx in range(198)]
    idx_tensor = Variable(torch.FloatTensor(idx_tensor)).cuda(gpu)

    optimizer = torch.optim.Adam([{'params': get_ignored_params(model), 'lr': 0},
                                  {'params': get_non_ignored_params(model), 'lr': args.lr},
                                  {'params': get_fc_params(model), 'lr': args.lr * 5}],
                                   lr = args.lr)

    print('Ready to train network.')
    for epoch in range(num_epochs):
        for i, (images, labels, labels_0, labels_1, labels_2, labels_3, cont_labels, name) in enumerate(train_loader):
            images = Variable(images).cuda(gpu)
            
            # Binned labels
            label_yaw = Variable(labels[:,0]).cuda(gpu)
            label_pitch = Variable(labels[:,1]).cuda(gpu)
            label_roll = Variable(labels[:,2]).cuda(gpu)
            
            label_yaw_1 = Variable(labels_0[:,0]).cuda(gpu)
            label_pitch_1 = Variable(labels_0[:,1]).cuda(gpu)
            label_roll_1 = Variable(labels_0[:,2]).cuda(gpu)
            
            label_yaw_2 = Variable(labels_1[:,0]).cuda(gpu)
            label_pitch_2 = Variable(labels_1[:,1]).cuda(gpu)
            label_roll_2 = Variable(labels_1[:,2]).cuda(gpu)
            
            label_yaw_3 = Variable(labels_2[:,0]).cuda(gpu)
            label_pitch_3 = Variable(labels_2[:,1]).cuda(gpu)
            label_roll_3 = Variable(labels_2[:,2]).cuda(gpu)
            
            label_yaw_4 = Variable(labels_3[:,0]).cuda(gpu)
            label_pitch_4 = Variable(labels_3[:,1]).cuda(gpu)
            label_roll_4 = Variable(labels_3[:,2]).cuda(gpu)
                        
            # Continuous labels
            label_yaw_cont = Variable(cont_labels[:,0]).cuda(gpu)
            label_pitch_cont = Variable(cont_labels[:,1]).cuda(gpu)
            label_roll_cont = Variable(cont_labels[:,2]).cuda(gpu)

            # Forward pass
            yaw,yaw_1,yaw_2,yaw_3,yaw_4, pitch,pitch_1,pitch_2,pitch_3,pitch_4, roll,roll_1,roll_2,roll_3,roll_4 = model(images)     # 得到各个特征

            # Cross entropy loss  # 各个交叉熵损失
            loss_yaw,loss_yaw_1,loss_yaw_2,loss_yaw_3,loss_yaw_4 = criterion(yaw, label_yaw),criterion(yaw_1, label_yaw_1),criterion(yaw_2, label_yaw_2),criterion(yaw_3, label_yaw_3),criterion(yaw_4, label_yaw_4)
            loss_pitch,loss_pitch_1,loss_pitch_2,loss_pitch_3,loss_pitch_4 = criterion(pitch, label_pitch),criterion(pitch_1, label_pitch_1),criterion(pitch_2, label_pitch_2),criterion(pitch_3, label_pitch_3),criterion(pitch_4, label_pitch_4)
            loss_roll,loss_roll_1,loss_roll_2,loss_roll_3,loss_roll_4 = criterion(roll, label_roll),criterion(roll_1, label_roll_1),criterion(roll_2, label_roll_2),criterion(roll_3, label_roll_3),criterion(roll_4, label_roll_4)

            # MSE loss  # 归一化特征
            yaw_predicted = softmax(yaw)
            pitch_predicted = softmax(pitch)
            roll_predicted = softmax(roll)

            yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1) - 99  # 此部分和deep head pose计算一致
            pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1) - 99
            roll_predicted = torch.sum(roll_predicted * idx_tensor, 1) - 99
                        
            loss_reg_yaw = reg_criterion(yaw_predicted, label_yaw_cont)  # 此部分和deep head pose计算一致
            loss_reg_pitch = reg_criterion(pitch_predicted, label_pitch_cont)
            loss_reg_roll = reg_criterion(roll_predicted, label_roll_cont)

            # Total loss
            total_loss_yaw = alpha * loss_reg_yaw + 7*loss_yaw + 5*loss_yaw_1 + 3*loss_yaw_2 + 1*loss_yaw_3 + 1*loss_yaw_4  # 各个角度区间的加权总损失
            total_loss_pitch = alpha * loss_reg_pitch + 7*loss_pitch + 5*loss_pitch_1 + 3*loss_pitch_2 + 1*loss_pitch_3 + 1*loss_pitch_4
            total_loss_roll = alpha * loss_reg_roll + 7*loss_roll + 5*loss_roll_1 + 3*loss_roll_2 + 1*loss_roll_3 + 1*loss_pitch_4
            
            loss_seq = [total_loss_yaw, total_loss_pitch, total_loss_roll]
            grad_seq = [torch.tensor(1.0).cuda(gpu) for _ in range(len(loss_seq))]
            optimizer.zero_grad()
            torch.autograd.backward(loss_seq, grad_seq)
            optimizer.step()
            
            if (i+1) % 100 == 0:
                print ('Epoch [%d/%d], Iter [%d/%d] Losses: Yaw %.4f, Pitch %.4f, Roll %.4f'
                       %(epoch+1, num_epochs, i+1, len(pose_dataset)//batch_size, total_loss_yaw.item(), total_loss_pitch.item(), total_loss_roll.item()))
        # Save models at numbered epochs.
        if epoch % 1 == 0 and epoch < num_epochs:
            print('Taking snapshot...')
            torch.save(model.state_dict(),
            'output/snapshots/' + args.output_string + '_epoch_'+ str(epoch+1) + '.pkl')

2.3 测试代码

def parse_args():
    """Parse input arguments."""
    parser = argparse.ArgumentParser(description='Head pose estimation using the Hopenet network.')
    parser.add_argument('--gpu', dest='gpu_id', help='GPU device id to use [0]', default=0, type=int)
    parser.add_argument('--data_dir', dest='data_dir', help='Directory path for data.', default='/AFLW2000/', type=str)
    parser.add_argument('--filename_list', dest='filename_list', help='Path to text file containing relative paths for every example.',
          default='/tools/AFLW2000_filename_filtered.txt', type=str)
    parser.add_argument('--snapshot', dest='snapshot', help='Name of model snapshot.',
          default='/output/snapshots/AFLW2000/_epoch_9.pkl', type=str)
    parser.add_argument('--batch_size', dest='batch_size', help='Batch size.', default=1, type=int)
    parser.add_argument('--save_viz', dest='save_viz', help='Save images with pose cube.', default=False, type=bool)
    parser.add_argument('--dataset', dest='dataset', help='Dataset type.', default='AFLW2000', type=str)

    args = parser.parse_args()

    return args

if __name__ == '__main__':
    args = parse_args()

    cudnn.enabled = True
    gpu = args.gpu_id
    snapshot_path = args.snapshot

    # ResNet50 structure
    model = hopenet.Multinet(torchvision.models.resnet.Bottleneck, [3, 4, 6, 3], 198)

    print('Loading snapshot.')
    # Load snapshot
    saved_state_dict = torch.load(snapshot_path)
    model.load_state_dict(saved_state_dict)

    print('Loading data.')

    transformations = transforms.Compose([transforms.Resize(224),
    transforms.CenterCrop(224), transforms.ToTensor(),
    transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])

    if args.dataset == 'Pose_300W_LP':
        pose_dataset = datasets.Pose_300W_LP(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Pose_300W_LP_multi':
        pose_dataset = datasets.Pose_300W_LP_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Pose_300W_LP_random_ds':
        pose_dataset = datasets.Pose_300W_LP_random_ds(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'Synhead':
        pose_dataset = datasets.Synhead(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW2000':
        pose_dataset = datasets.AFLW2000(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'BIWI':
        pose_dataset = datasets.BIWI(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'BIWI_multi':
        pose_dataset = datasets.BIWI_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW':
        pose_dataset = datasets.AFLW(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW_multi':
        pose_dataset = datasets.AFLW_multi(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFLW_aug':
        pose_dataset = datasets.AFLW_aug(args.data_dir, args.filename_list, transformations)
    elif args.dataset == 'AFW':
        pose_dataset = datasets.AFW(args.data_dir, args.filename_list, transformations)
    else:
        print('Error: not a valid dataset name')
        sys.exit()
    test_loader = torch.utils.data.DataLoader(dataset=pose_dataset,
                                               batch_size=args.batch_size,
                                               num_workers=2)

    model.cuda(gpu)

    print('Ready to test network.')

    # Test the Model
    model.eval()  # Change model to 'eval' mode (BN uses moving mean/var).
    total = 0

    idx_tensor = [idx for idx in range(198)]
    idx_tensor = torch.FloatTensor(idx_tensor).cuda(gpu)

    yaw_error = .0
    pitch_error = .0
    roll_error = .0

    l1loss = torch.nn.L1Loss(size_average=False)
    for i, (images, labels, cont_labels, name) in enumerate(test_loader):
    #for i, (images, labels, labels_0, labels_1, labels_2, labels_3, cont_labels, name) in enumerate(test_loader):
        images = Variable(images).cuda(gpu)
        total += cont_labels.size(0)

        label_yaw = cont_labels[:,0].float()
        label_pitch = cont_labels[:,1].float()
        label_roll = cont_labels[:,2].float()
        
        yaw,yaw_1,yaw_2,yaw_3,yaw_4, pitch,pitch_1,pitch_2,pitch_3,pitch_4, roll,roll_1,roll_2,roll_3,roll_4 = model(images)  # 得到特征

        # Binned predictions
        _, yaw_bpred = torch.max(yaw.data, 1)
        _, pitch_bpred = torch.max(pitch.data, 1)
        _, roll_bpred = torch.max(roll.data, 1)

        # Continuous predictions
        yaw_predicted = utils.softmax_temperature(yaw.data, 1)  # 带temperature的softmax
        pitch_predicted = utils.softmax_temperature(pitch.data, 1)
        roll_predicted = utils.softmax_temperature(roll.data, 1)

        yaw_predicted = torch.sum(yaw_predicted * idx_tensor, 1).cpu() - 99     # 计算期望
        pitch_predicted = torch.sum(pitch_predicted * idx_tensor, 1).cpu() - 99
        roll_predicted = torch.sum(roll_predicted * idx_tensor, 1).cpu() - 99

        # Mean absolute error
        yaw_error += torch.sum(torch.abs(yaw_predicted - label_yaw))
        pitch_error += torch.sum(torch.abs(pitch_predicted - label_pitch))
        roll_error += torch.sum(torch.abs(roll_predicted - label_roll))

        # Save first image in batch with pose cube or axis.
        if args.save_viz:
            name = name[0]
            if args.dataset == 'BIWI':
                cv2_img = cv2.imread(os.path.join(args.data_dir, name + '_rgb.png'))
            else:
                cv2_img = cv2.imread(os.path.join(args.data_dir, name + '.jpg'))
            if args.batch_size == 1:
                error_string = 'y %.2f, p %.2f, r %.2f' % (torch.sum(torch.abs(yaw_predicted - label_yaw)), torch.sum(torch.abs(pitch_predicted - label_pitch)), torch.sum(torch.abs(roll_predicted - label_roll)))
                cv2.putText(cv2_img, error_string, (30, cv2_img.shape[0]- 30), fontFace=1, fontScale=1, color=(0,0,255), thickness=2)
            # utils.plot_pose_cube(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], size=100)
            utils.draw_axis(cv2_img, yaw_predicted[0], pitch_predicted[0], roll_predicted[0], tdx = 200, tdy= 200, size=100)
            cv2.imwrite(os.path.join('output/images', name + '.jpg'), cv2_img)

    print('Test error in degrees of the model on the ' + str(total) +
    ' test images. Yaw: %.4f, Pitch: %.4f, Roll: %.4f, MAE: %.4f' % (yaw_error / total,
    pitch_error / total, roll_error / total, (yaw_error+pitch_error+roll_error)/(3.0*total)))

2.4 softmax_temperature代码

def softmax_temperature(tensor, temperature):
    result = torch.exp(tensor / temperature)
    result = torch.div(result, torch.sum(result, 1).unsqueeze(1).expand_as(result))  # 带temperature的softmax
    return result