import torch.backends.cudnn as cudnn
import torch.optim
import torch.utils.data
import torchvision.transforms as transforms
import torchvision.datasets as datasets
H5_address = '/home/lyq/caffe-master/data_gaze/gaze_detection/h5/'
Save_model_address = '/disks/disk0/linyuqi/model/gaze_demo/'
gaze_model = model.gaze_model()
#gaze_model = nn.DataParallel(gaze_model)
gaze_model.cuda()
img_list = dataset.load_all_h5(H5_address)
train_Dataset = dataset.train_gaze_dataset(img_list)
test_Dataset = dataset.test_gaze_dataset(img_list)
train_loader = torch.utils.data.DataLoader(train_Dataset,
batch_size=128,
num_workers=6)
test_loader = torch.utils.data.DataLoader(test_Dataset,
batch_size=128,
num_workers=6)
l1_loss = nn.SmoothL1Loss().cuda()
optimizer = torch.optim.Adam(gaze_model.parameters(), lr=0.001)
def train():
gaze_model.train()
for i, (image, head_pose, label) in enumerate(train_loader):
image = image.squeeze(1)
import torchvision.transforms as transforms
import torchvision.datasets as datasets
H5_address = '/home/lyq/caffe-master/data_gaze/gaze_detection/h5/'
Save_model_address = '/disks/disk0/linyuqi/model/gaze_2eye_AR/'
BatchSize = 64
gaze_model = model.gaze_model()
gaze_model = nn.DataParallel(gaze_model)
gaze_model.cuda()
img_list_left, img_list_right = dataset.load_all_h5(H5_address)
#print(img_list_left[1])
#print(img_list_right[1])
train_Dataset = dataset.train_gaze_dataset(img_list_left, img_list_right)
test_Dataset = dataset.test_gaze_dataset(img_list_left, img_list_right)
train_loader = torch.utils.data.DataLoader(train_Dataset,
batch_size=BatchSize,
num_workers=6)
test_loader = torch.utils.data.DataLoader(test_Dataset,
batch_size=BatchSize,
num_workers=6)
l1_loss = nn.SmoothL1Loss().cuda()
#l1_loss = nn.MSELoss().cuda()
#optimizer = torch.optim.Adam(gaze_model.parameters(),lr=0.01)
optimizer = torch.optim.SGD(gaze_model.parameters(), lr=0.001, momentum=0.9)
def accuracy_text(result, label):