def get_config():
conf = edict()
conf.data = edict()
conf.model = edict()
conf.model.network = edict()
conf.train = edict()
conf.train.optimizer = edict()
conf.train.criterion = edict()
conf.job_name = 'test-20200301-4@1-ir-01'
conf.train_list = 'anno/IR/4@1_train.txt'
conf.val_list = [
'anno/IR/4@1_dev.txt', 'anno/IR/4@2_dev.txt', 'anno/IR/4@3_dev.txt'
]
conf.test_list = ['anno/IR/4@1_dev.txt', 'anno/IR/4@1_test.txt']
conf.data.folder = '/mnt/cephfs/smartauto/users/guoli.wang/jiachen.xue/anti_spoofing/data/CASIA-CeFA/phase1'
conf.data.crop_size = [256, 256]
conf.data.input_size = [224, 224]
conf.data.expand_ratio = 1.0
conf.data.use_multi_color = False
if conf.data.use_multi_color:
conf.data.in_data_format = ['L', 'RGB', 'YCbCr']
conf.data.in_plane = sum(
[color_channel_index[x] for x in conf.data.in_data_format])
else:
conf.data.in_data_format = 'RGB'
conf.data.in_plane = color_channel_index[conf.data.in_data_format]
conf.data.pin_memory = True
conf.data.num_workers = 4
conf.data.train_transform = trans.Compose([
trans.RandomCrop(conf.data.input_size),
# trans.RandomResizedCrop(conf.crop_size),
trans.RandomHorizontalFlip(p=0.5),
trans.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=(-0.1, 0.1)),
trans.ToTensor(),
])
conf.data.test_transform = trans.Compose([
trans.CenterCrop(conf.data.input_size),
trans.ToTensor(),
])
conf.model.save_path = './snapshots'
conf.model.batch_size = 128
conf.model.use_mixup = True
conf.model.mixup_alpha = 0.5
conf.model.use_center_loss = False
conf.model.center_loss_weight = 0.01
conf.model.network.use_senet = True
conf.model.network.se_reduction = 16
conf.model.network.drop_out = 0.
conf.model.network.embedding_size = 512
# --------------------Training Config ------------------------
# if training:
conf.train.epoches = 50
conf.train.optimizer.lr = 0.001
conf.train.optimizer.gamma = 0.1
conf.train.optimizer.milestones = [20, 35, 45]
conf.train.optimizer.momentum = 0.9
conf.train.optimizer.weight_decay = 1e-4
conf.train.criterion.sl1 = nn.SmoothL1Loss()
conf.train.criterion.ce = nn.CrossEntropyLoss()
conf.train.criterion.cent = CenterLoss(
num_classes=2, feat_dim=conf.model.network.embedding_size)
conf.test = edict()
conf.test.save_name = [
'4@1-dev-single-best-acc-loss', '4@1-test-single-best-acc-loss'
]
conf.test.epochs = range(50)
conf.test.pred_path = './result/single_frame'
return conf
def get_config(training=True):
conf = edict()
conf.model = edict()
conf.train = edict()
conf.eval = edict()
# data root for training, and testing, you should change is according to your setting
conf.data_folder = '/mnt/cephfs/smartauto/users/guoli.wang/jiachen.xue/anti_spoofing/data/CASIA-CeFA/phase1'
# path for save model in training process, you should change it according to your setting
conf.save_path = './snapshots'
# path where training list is, you should change it according to your setting
conf.train_list = 'anno/4@2_train.txt'
# path where validation list is, you should change it according to your setting
conf.val_list = 'anno/4@2_dev_test_res_label.txt'
# path where test list is, you should change it according to your setting
# conf.test_list = 'anno/4@2_dev_test_res_label.txt'
conf.test_list = 'anno/phase2/4@2_test_res.txt'
conf.batch_size = 128
# model is saved in conf.save_path/conf.exp, if you want to train different models, you can distinguish them according to this parameter
conf.exp = 'test-20200228-4@2-05'
conf.model.crop_size = [256, 256] # the crop size of our model
conf.model.input_size = [224, 224] # the input size of our model
conf.model.expand_ratio = 1.0 # the expand_ratio of bbox
conf.model.random_offset = [16, 16] # for random crop
conf.model.use_senet = True # senet is adopted in our resnet18 model
conf.model.se_reduction = 16 # parameter concerning senet
conf.model.drop_out = 0. # we add dropout layer in our resnet18 model
conf.model.embedding_size = 512 # feature size of our resnet18 model
conf.model.use_multi_color = False
if conf.model.use_multi_color:
conf.model.in_data_format = ['L', 'RGB', 'YCbCr']
conf.model.in_plane = sum(
[color_channel_index[x] for x in conf.model.in_data_format])
else:
conf.model.in_data_format = 'RGB'
conf.model.in_plane = color_channel_index[conf.model.in_data_format]
conf.model.use_mixup = True
conf.model.mixup_alpha = 1.0
conf.pin_memory = True
conf.num_workers = 4
# --------------------Training Config ------------------------
# if training:
conf.train.lr = 0.01 # the initial learning rate
# epoch milestones decreased by a factor of 10
conf.train.milestones = [20, 35, 45]
conf.train.epoches = 50 # we trained our model for 200 epoches
conf.train.momentum = 0.9 # parameter in setting SGD
conf.train.weight_decay = 1e-4 # parameter in setting SGD
conf.train.gamma = 0.1 # parameter in setting lr_scheduler
conf.train.criterion_SL1 = nn.SmoothL1Loss()
conf.train.criterion_ce = nn.CrossEntropyLoss(
) # we use CE in training stage
conf.train.use_center_loss = False
conf.train.center_loss_weight = 0.01
conf.train.criterion_cent = CenterLoss(num_classes=2,
feat_dim=conf.model.embedding_size)
mean_ = [0.5] * conf.model.in_plane
std_ = [0.5] * conf.model.in_plane
conf.train.transform = trans.Compose(
[ # convert input from PIL.Image to Tensor and normalized
trans.RandomCrop(conf.model.input_size),
# trans.RandomResizedCrop(conf.crop_size),
trans.RandomHorizontalFlip(p=0.5),
trans.ColorJitter(brightness=0.3,
contrast=0.3,
saturation=0.3,
hue=(-0.1, 0.1)),
trans.ToTensor(),
])
# --------------------Inference Config ------------------------
conf.test = edict()
# conf.test.save_name = '4@2-dev-single-best-acc-loss'
conf.test.save_name = '4@2-test-single-best-acc-loss'
conf.test.epoch = 33
conf.test.pred_path = './result/single_frame'
conf.test.transform = trans.Compose(
[ # convert input from PIL.Image to Tensor and normalized
trans.CenterCrop(conf.model.input_size),
trans.ToTensor(),
])
return conf