add comment and exp logger

README.md

@@ -64,6 +64,18 @@ run `python demo.py`, will load the released text2shape model on hugging face an
 * require the vae checkpoint
 * run `bash ./script/train_prior.sh $NGPU` (the released checkpoint is trained with `NGPU=8` with 2 node on V100)
 
+### (Optional) monitor exp 
+* (tested) use comet-ml: need to add a file `.comet_api` under this `LION` folder, example of the `.comet_api` file: 
+```
+{"api_key": "...", "project_name": "lion", "workspace": "..."}
+```
+* (not tested) use wandb: need to add a `.wandb_api` file, and set the env variable `export USE_WB=1` before training 
+```
+{"project": "...", "entity": "..."}
+```
+* (not tested) use tensorboard, set the env variable `export USE_TFB=1` before training
+* see the `utils/utils.py` files for the details of the experiment logger; I usually use comet-ml for my experiments
+
 ### evaluate a trained prior 
 * download the test data (Table 1) from [here](https://drive.google.com/file/d/1uEp0o6UpRqfYwvRXQGZ5ZgT1IYBQvUSV/view?usp=share_link), unzip and put it as `./datasets/test_data/`
 * download the released checkpoint from above

trainers/base_trainer.py

@@ -721,7 +721,7 @@ class BaseTrainer(ABC):
     def eval_nll(self, step, ntest=None, save_file=False):
         loss_dict = {}
         cfg = self.cfg
-        self.swap_vae_param_if_need()
+        self.swap_vae_param_if_need() # if using EMA, load the ema weight
         args = self.args
         device = torch.device('cuda:%d' % args.local_rank)
         tag = exp_helper.get_evalname(self.cfg)
@@ -752,6 +752,8 @@ class BaseTrainer(ABC):
 
             output = self.model.get_loss(
                 val_x, it=step, is_eval_nll=1, noisy_input=inputs, **model_kwargs)
+
+            # book-keeping
             for k, v in output.items():
                 if 'print/' in k:
                     k = k.split('print/')[-1]
@@ -761,7 +763,7 @@ class BaseTrainer(ABC):
                     loss_dict[k].update(v)
 
             gen_x = output['final_pred']
-            if gen_x.shape[1] > val_x.shape[1]:
+            if gen_x.shape[1] > val_x.shape[1]:  # downsample points if needed
                 tr_idxs = np.random.permutation(np.arange(gen_x.shape[1]))[
                     :val_x.shape[1]]
                 gen_x = gen_x[:, tr_idxs]
@@ -813,7 +815,7 @@ class BaseTrainer(ABC):
                 self.writer.add_scalar('eval/%s' % (n), v, step)
             if 'CD' in n:
                 score = v
-        self.swap_vae_param_if_need()
+        self.swap_vae_param_if_need()  # if using EMA, swap back to none-ema weight here
         return score
 
     def prepare_clip_model_data(self):