Fix some typos and small bugs in TimeDP

TimeDP/README.md

@@ -68,22 +68,22 @@ Use `main_train.py` for model training and `visualize.py` for domain prompt visu
 ## Usage
 Training TimeDP:
 ```bash
-python main_train.py --base configs/multi_domain_tsgen.yaml --gpus 0, --logdir ./logs/ -sl 168 -up -nl 16 --batch_size 128 -lr 0.0001 -s 0
+python main_train.py --base configs/multi_domain_timedp.yaml --gpus 0, --logdir ./logs/ -sl 168 -up -nl 16 --batch_size 128 -lr 0.0001 -s 0
 ```
 
 Training without PAM:
 ```bash
-python main_train.py --base configs/multi_domain_tsgen.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0
+python main_train.py --base configs/multi_domain_timedp.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0
 ```
 
 Training without domain prompts (unconditional generation model):
 ```bash
-python main_train.py --base configs/multi_domain_tsgen.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0 --uncond
+python main_train.py --base configs/multi_domain_timedp.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0 --uncond
 ```
 
 Visualization of domain prompts:
 ```bash
-python visualize.py --base configs/multi_domain_tsgen.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0 --uncond
+python visualize.py --base configs/multi_domain_timedp.yaml --gpus 0, --logdir ./logs/ -sl 168 --batch_size 128 -lr 0.0001 -s 0 --uncond
 ```
 
 

TimeDP/configs/multi_domain_timedp.yaml

@@ -84,6 +84,7 @@ data:
     normalize: centered_pit
     drop_last: True
     reweight: True
+    input_channels: 1
 
 lightning:
   callbacks:

TimeDP/ldm/data/tsg_dataset.py

@@ -66,7 +66,7 @@ class TSGDataModule(pl.LightningDataModule):
     Data module for unified time series generation task.
     Slicing is also done with this module. So the train/val is i.i.d within train dataset.
     '''
-    def __init__(self, data_path_dict, window=96, val_portion=0.1, as_tensor:bool=True, normalize="centered_pit", batch_size=128, num_workers=0, pin_memory=True, drop_last=False, reweight=False, **kwargs):
+    def __init__(self, data_path_dict, window=96, val_portion=0.1, as_tensor:bool=True, normalize="centered_pit", batch_size=128, num_workers=0, pin_memory=True, drop_last=False, reweight=False, input_channels=1, **kwargs):
         super().__init__()
         self.data_path_dict = data_path_dict  # {data_name: data_path}
         self.data_dict = {}
@@ -88,6 +88,7 @@ def __init__(self, data_path_dict, window=96, val_portion=0.1, as_tensor:bool=Tr
         self.val_dataset = None
         self.test_dataset = None
         self.reweight = reweight
+        self.input_channels = input_channels
         # self.transform = None
         self.kwargs = kwargs
         self.key_list = []

TimeDP/utils/init_utils.py

@@ -116,7 +116,7 @@ def init_model_data_trainer(parser):
 
     # data
     for k, v in config.data.params.data_path_dict.items():
-        config.data.params.data_path_dict[k] = v.replace('{DATA_ROOT}', data_root).replace('{SEQ_LEN}', opt.seq_len)
+        config.data.params.data_path_dict[k] = v.replace('{DATA_ROOT}', data_root).replace('{SEQ_LEN}', str(opt.seq_len))
     data = instantiate_from_config(config.data)
     # NOTE according to https://pytorch-lightning.readthedocs.io/en/latest/datamodules.html
     # calling these ourselves should not be necessary but it is.