train_mean = [0.485, 0.456, 0.406, 0.5]
train_std = [0.229, 0.224, 0.225, 0.25]
test_mean = [0.485, 0.456, 0.406, 0.5]
test_std = [0.229, 0.224, 0.225, 0.25]
# 配置transform
# 注意：train和val涉及到label，需要用带_DL后缀的transform
# test不涉及label，用原来的transform
prob = 0.5
train_transform = T.Compose([
T_DL.ToTensor_DL(), # 转为tensor
T_DL.RandomFlip_DL(p=prob), # 概率p水平或者垂直翻转
T_DL.RandomRotation_DL(p=prob), # 概率p发生随机旋转(只会90，180，270)
# T_DL.RandomColorJitter_DL(p=prob, brightness=1, contrast=1, saturation=1, hue=0.5), # 概率p调整rgb
T_DL.Normalized_DL(mean=train_mean[:input_channel], std=train_std[:input_channel]), # 归一化
])
val_transform = T.Compose([
T_DL.ToTensor_DL(), # 转为tensor
T_DL.Normalized_DL(mean=train_mean[:input_channel],
std=train_std[:input_channel]), # 归一化
])
test_transform = T.Compose([
# T.ToTensor(),
T.Normalize(mean=test_mean[:input_channel], std=test_std[:input_channel]),
])
dataset_cfg = dict(
# dir全都改成list
train_mean = [0.485, 0.456, 0.406, 0.5]
train_std = [0.229, 0.224, 0.225, 0.25]
test_mean = [0.485, 0.456, 0.406, 0.5]
test_std = [0.229, 0.224, 0.225, 0.25]
# 配置transform
# 注意：train和val涉及到label，需要用带_DL后缀的transform
# test不涉及label，用原来的transform
prob = 0.25
train_transform = T.Compose([
T_DL.ToTensor_DL(), # 转为tensor
T_DL.RandomFlip_DL(p=prob), # 概率p水平或者垂直翻转
# T_DL.RandomRotation_DL(p=prob), # 概率p发生随机旋转(只会90，180，270)
T_DL.RandomColorJitter_DL(p=prob, brightness=0.5, contrast=0.5, saturation=0.5, hue=0.5), # 概率p调整rgb
T_DL.Normalized_DL(mean=train_mean[:input_channel if input_channel != 4 else -1], std=train_std[:input_channel if input_channel != 4 else -1]), # 归一化
])
val_transform = T.Compose([
T_DL.ToTensor_DL(), # 转为tensor
T_DL.Normalized_DL(mean=train_mean[:input_channel if input_channel != 4 else -1],
std=train_std[:input_channel if input_channel != 4 else -1]), # 归一化
])
test_transform = T.Compose([
T.ToTensor(),
T.Normalize(mean=test_mean[:input_channel if input_channel != 4 else -1], std=test_std[:input_channel if input_channel != 4 else -1]),
])
dataset_cfg = dict(
# train_dir=root_dir + '/tcdata/suichang_round1_train_210120',