def run(method_generateFaceRS='hog'):
# 读取原始图片与索引
ri, index = readRiIndex()
X = extra_feature(ri)
X = np.concatenate([index, X], axis=1)
# 进行HOG特征处理后，划分数据集，并将结果写到目录里
generateFaceRS(X, method_generateFaceRS)
def run(method_generateFaceRS='densenet'):
ri, index = readRiIndex()
densenet201 = models.densenet201(pretrained=True)
densenet201.fc = nn.Linear(2048, 2048)
torch.nn.init.eye(densenet201.fc.weight)
for param in densenet201.parameters():
param.requires_grad = False
use_gpu = torch.cuda.is_available()
X = extra_feature(ri, densenet201, use_gpu)
X = np.concatenate([index, X], axis=1)
# 进行DenseNet特征处理后，划分数据集，并将结果写到目录里
generateFaceRS(X, method_generateFaceRS)
