pass loggers to trainer (#166)

diffusion/evaluation/clean_fid_eval.py

@@ -9,11 +9,10 @@
 
 import clip
 import torch
-import wandb
 from cleanfid import fid
 from composer import ComposerModel, Trainer
 from composer.core import get_precision_context
-from composer.loggers import LoggerDestination, WandBLogger
+from composer.loggers import LoggerDestination
 from composer.utils import dist
 from torch.utils.data import DataLoader
 from torchmetrics.multimodal import CLIPScore
@@ -91,19 +90,15 @@ def __init__(self,
         self.additional_generate_kwargs = additional_generate_kwargs if additional_generate_kwargs is not None else {}
         self.sdxl = model.sdxl
 
-        # Init loggers
-        if self.loggers and dist.get_local_rank() == 0:
-            for logger in self.loggers:
-                if isinstance(logger, WandBLogger):
-                    wandb.init(**logger._init_kwargs)
-
         # Load the model
-        Trainer(model=self.model,
-                load_path=self.load_path,
-                load_weights_only=True,
-                load_strict_model_weights=load_strict_model_weights,
-                eval_dataloader=self.eval_dataloader,
-                seed=self.seed)
+        trainer = Trainer(model=self.model,
+                          load_path=self.load_path,
+                          load_weights_only=True,
+                          load_strict_model_weights=load_strict_model_weights,
+                          eval_dataloader=self.eval_dataloader,
+                          seed=self.seed,
+                          loggers=self.loggers)
+        self.trainer = trainer
 
         # Move CLIP metric to device
         self.device = dist.get_local_rank()