--- a
+++ b/monai/configs/segres20_all_round2.py
@@ -0,0 +1,159 @@
+import numpy as np
+import torch
+from monai.transforms import (
+    Compose,
+    LoadImaged,
+    RandSpatialCropd,
+    EnsureTyped,
+    CastToTyped,
+    NormalizeIntensityd,
+    RandFlipd,
+    CenterSpatialCropd,
+    ScaleIntensityRanged,
+    RandAffined,
+    RandScaleIntensityd,
+    RandShiftIntensityd,
+    RandCoarseDropoutd,
+    Rand2DElasticd,
+    Lambdad,
+    Resized,
+    AddChanneld,
+    RandGaussianNoised,
+    RandGridDistortiond,
+    RepeatChanneld,
+    Transposed,
+    OneOf,
+    EnsureChannelFirstd,
+    RandLambdad,
+    Spacingd,
+    FgBgToIndicesd,
+    CropForegroundd,
+    RandCropByPosNegLabeld,
+    ToDeviced,
+    SpatialPadd,
+
+)
+
+from default_config import basic_cfg
+
+cfg = basic_cfg
+
+# train
+cfg.train = True
+cfg.eval = False
+cfg.start_eval_epoch = 0  # when use large lr, can set a large num
+cfg.run_org_eval = False
+cfg.run_tta_val = False
+cfg.load_best_weights = False
+cfg.amp = False
+cfg.val_amp = False
+cfg.num_workers = 8
+cfg.model_type = "segres20"
+
+# device
+cfg.gpu = 0
+cfg.device = "cuda:%d" % cfg.gpu
+
+# lr
+# warmup_restart, cosine
+cfg.lr_mode = "cosine"
+cfg.lr = 1e-4
+cfg.min_lr = 1e-6
+cfg.weight_decay = 1e-6
+cfg.epochs = 20
+cfg.restart_epoch = 2  # only for warmup_restart
+cfg.eval_epochs = 1
+
+cfg.finetune_lb = -1
+
+# dataset
+cfg.img_size = (224, 224, 80)
+cfg.spacing = (1.5, 1.5, 1.5)
+cfg.batch_size = 4
+cfg.val_batch_size = 1
+cfg.train_cache_rate = 0.0
+cfg.val_cache_rate = 0.0
+cfg.gpu_cache = False
+cfg.val_gpu_cache = False
+
+# val
+cfg.roi_size = (224, 224, 80)
+cfg.sw_batch_size = 4
+
+# model
+
+# loss
+cfg.w_dice = 1.0
+
+cfg.output_dir = "./output/segres20_all_round2"
+        
+# transforms
+cfg.train_transforms = Compose(
+    [
+        LoadImaged(keys=["image", "mask"]),
+        EnsureChannelFirstd(keys=["image", "mask"]),
+        # Spacingd(keys=["image", "mask"], pixdim=cfg.spacing, mode=("bilinear", "nearest")),
+        RandSpatialCropd(
+            keys=("image", "mask"),
+            roi_size=cfg.img_size,
+            random_size=False,
+        ),
+       
+        Lambdad(keys="image", func=lambda x: x / x.max()),
+
+        RandFlipd(keys=("image", "mask"), prob=0.5, spatial_axis=[0]),
+        RandFlipd(keys=("image", "mask"), prob=0.5, spatial_axis=[1]),
+        # RandFlipd(keys=("image", "mask"), prob=0.5, spatial_axis=[2]),
+        RandAffined(
+            keys=("image", "mask"),
+            prob=0.5,
+            rotate_range=np.pi / 12,
+            translate_range=(cfg.img_size[0]*0.0625, cfg.img_size[1]*0.0625),
+            scale_range=(0.1, 0.1),
+            mode="nearest",
+            padding_mode="reflection",
+        ),
+        OneOf(
+            [
+                RandGridDistortiond(keys=("image", "mask"), prob=0.5, distort_limit=(-0.05, 0.05), mode="nearest", padding_mode="reflection"),
+                RandCoarseDropoutd(
+                    keys=("image", "mask"),
+                    holes=5,
+                    max_holes=8,
+                    spatial_size=(1, 1, 1),
+                    max_spatial_size=(12, 12, 12),
+                    fill_value=0.0,
+                    prob=0.5,
+                ),
+            ]
+        ),
+        RandScaleIntensityd(keys="image", factors=(-0.2, 0.2), prob=0.5),
+        RandShiftIntensityd(keys="image", offsets=(-0.1, 0.1), prob=0.5),
+        EnsureTyped(keys=("image", "mask"), dtype=torch.float32),
+    ]
+)
+
+cfg.val_transforms = Compose(
+    [
+        LoadImaged(keys=["image", "mask"]),
+        EnsureChannelFirstd(keys=["image", "mask"]),
+        # Spacingd(keys=["image", "mask"], pixdim=cfg.spacing, mode=("bilinear", "nearest")),
+        Lambdad(keys="image", func=lambda x: x / x.max()),
+       
+        EnsureTyped(keys=("image", "mask"), dtype=torch.float32),
+        # ToDeviced(keys=["image", "mask"], device="cuda:0"),
+    ]
+)
+
+cfg.org_val_transforms = Compose(
+    [
+        LoadImaged(keys="image"),
+        EnsureChannelFirstd(keys="image"),
+        # Spacingd(keys="image", pixdim=cfg.spacing, mode="bilinear"),
+        Lambdad(keys="image", func=lambda x: x / x.max()),
+        # SpatialPadd(keys="image", spatial_size=cfg.img_size),
+        EnsureTyped(keys="image", dtype=torch.float32),
+    ]
+)
+
+