_C.w_train_binary_txt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label/white_train.txt'
_C.w_val_binary_txt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label/white_val.txt'
#--ckpt path
_C.binary_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/binary_keys_epoch_25_Acc_0.976.pth'
_C.pos_binary_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/with_pos_keys_epoch_28_Acc_0.978.pth'
#----训练的时候要在tensorboard上面观察loss和Acc的变化,可能过拟合或者学习率过大,需要调整学习率
#---较好的模型应该是lr=0.001,然后lr_decay_in_epoch=20训练得到的
# _C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_25_Acc_0.985.pth'
#--这个是lr=0.01,然后lr_decay_in_epoch=5训练得到的,每隔5个epoch学习率下降
_C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_18_Acc_0.982.pth'
# _C.time_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_keys_epoch_26_Acc_0.982.pth'
_C.time_pos_ckpt_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/checkpoints/time_with_pos_keys_epoch_25_Acc_0.981.pth'
#---二分类时的正负样本权重(正样本较少)
_C.ALPHA = {'white': 3.0, 'black': 2.0}
_C.pos_wieghts = [3, 1, 6]
_C.binary_input_size = [112, 32] #---(h,w)
_C.neg_pos_ratio = 2 #---负样本/正样本
_C.neg_img_selece_ratio = 0.7 #---自己选取的负样本/当前帧其他的负样本
#---用以训练单个按键是否被按下
_C.One_key_SAVE_IMG_DIR = '/home/lj/cy/data/piano/new/saved/YouTube/train'
#---用以测试模型的视频图像存储路径
_C.Test_Key_Dir = '/home/lj/cy/data/piano/new/saved/YouTube/'
_C.Test_video = ['level_4_no_02', 'level_2_no_02', 'level_1_no_02', '25', '26']
#---label path(训练二分类)
_C.binary_path = '/home/lj/cy/project/piano/vision-piano-amt-master/backup/multi_label/data/binary_label'
_C.Own_WHITE_TRAIN_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/white_train_add_network.npy.npz'
_C.Own_WHITE_VAL_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/white_val.npy.npz'
_C.Own_BLACK_TRAIN_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/black_train_add_network.npy.npz'
_C.Own_BLACK_VAL_FILE = '/home/data/lj/Piano/KEY_PRESS/data/owndataset/black_val.npy.npz'
_C.SAVE_MODEL_DIR = '/home/data/lj/Piano/KEY_PRESS/checkpoint/experment'
_C.INPUT_SIZE = {
'white':{
'paperdata':[112,32],
'owndata':[112,32]
},
'black':{
'paperdata':[112,32],
'owndata':[112,32]
}
}
_C.ALPHA = {
'white':{
'paperdata':3.0,
'owndata':3.0
},
'black':{
'paperdata':2.0,
'owndata':2.0
}
}
