diff --git a/upscale_test.py b/upscale_test.py
index 2e4d158..2900c20 100644
--- a/upscale_test.py
+++ b/upscale_test.py
@@ -36,14 +36,14 @@ from models.upscaler import UpscalerBasicConv as Upscaler
 # !!!!!!!!!! CHANGE SKIRT OR NOT
 
 
-low_res = 64
+low_res = 128
 is_skirt = True
 is_cape = False
 skirt_modifier = '_skirt' if is_skirt else ''
 smpl_res = 3638 if is_cape else 14475
 
 
-name=f'iccv_{low_res}_skirt'
+name=f'iccv_{low_res}_2' + skirt_modifier
 def init_wandb():
     run = wandb.init(project='upscale', name=name,
             notes='\
@@ -112,7 +112,7 @@ def plot_3d(mesh_gt, mesh_pred_unposed, faces):
 def train_step(batch):
     with tf.GradientTape() as tape:
         pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=True)
-        pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
+        pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
 
         mesh_pred = uv2mesh(pred)
         mesh_orig = batch['mesh_unposed']
@@ -135,7 +135,7 @@ def train_step(batch):
 @tf.function
 def valid_step(batch):
     pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
-    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
+    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
 
     mesh_pred = uv2mesh(pred)
     mesh_orig = batch['mesh_unposed']
@@ -156,7 +156,7 @@ def valid_step(batch):
 def plot_everything(batch: dict, type: str):
 
     pred = model([batch['uv_unposed' + low_resolution_modifier], batch['uv_mask' + low_resolution_modifier]], training=False)
-    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.LANCZOS5)
+    pred = tf.image.resize(pred, size=(high_res, high_res//2), method=tf.image.ResizeMethod.BILINEAR)
     
     mesh_pred = uv2mesh(pred)
     mesh_orig = batch['mesh_unposed']
@@ -185,9 +185,10 @@ def train():
         batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
         train_loss = train_step(batch)
 
-        data_valid = valid_iterator.get_next()
-        data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
-        valid_loss = valid_step(data_valid)
+        if step % 100 == 0:
+            data_valid = valid_iterator.get_next()
+            data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
+            valid_loss = valid_step(data_valid)
 
         to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
         if step % 10000 == 0 or (step < 2000 and step % 500 == 0):
@@ -241,15 +242,23 @@ if __name__ == '__main__':
                          'uv_mask' + low_resolution_modifier: (low_res, low_res//2,),
                         }
 
-    dataset_train = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-    static_dataset_train = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-    dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid_skirt', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-    static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+    if not is_skirt:
+        dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+        dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
 
-    # dataset_train = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-    # static_dataset_train = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
-    # dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/test/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
-    # static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/cape_processed/test/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+    if is_skirt:
+        dataset_train = factory.get_dataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_train = StaticDataset(folder='/home/workspace/data/dataset/local-cache/train_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+        dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/valid_skirt', batch_size=8, signature=signature_dynamic,resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/valid_skirt', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+
+    if is_cape:
+        dataset_train = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/train/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_train = StaticDataset(folder='/home/workspace/local-data/cape_processed/train/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
+        dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/cape_processed/test/male', batch_size=8, signature=signature_dynamic, resolutions=[high_res, low_res], in_memory=False, is_cape=is_cape)
+        static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/cape_processed/test/male', signature=signature_static, resolutions=[high_res, low_res], in_memory=True, is_cape=is_cape)
 
     if low_res == 32 or low_res == 64:
         model = Upscaler(low_res=low_res, kernel_size=3)
@@ -264,5 +273,6 @@ if __name__ == '__main__':
 
     ckpt = tf.train.Checkpoint(model=model)
     manager = tf.train.CheckpointManager(ckpt, 'checkpoints/iccv/' + name, max_to_keep=20)
+    # load_model(model, f'checkpoints/iccv/iccv_{low_res}' + skirt_modifier)
 
     train()
\ No newline at end of file