Another fix

codes/train.py

@@ -260,7 +260,7 @@ class Trainer:
         if len(self.evaluators) != 0 and self.current_step % opt['train']['val_freq'] == 0:
             eval_dict = {}
             for eval in self.evaluators:
-                if eval.uses_all_ddp() or self.rank <= 0:
+                if eval.uses_all_ddp or self.rank <= 0:
                     eval_dict.update(eval.perform_eval())
             if self.rank <= 0:
                 print("Evaluator results: ", eval_dict)

codes/trainer/eval/categorization_loss_eval.py

@@ -13,7 +13,7 @@ from utils.util import opt_get
 
 class CategorizationLossEvaluator(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batch_sz = opt_eval['batch_size']
         assert self.batch_sz is not None
         normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],

codes/trainer/eval/evaluator.py

@@ -7,11 +7,11 @@ import sys
 
 
 class Evaluator:
-    def __init__(self, model, opt_eval, env):
+    def __init__(self, model, opt_eval, env, uses_all_ddp=True):
         self.model = model.module if hasattr(model, 'module') else model
         self.opt = opt_eval
         self.env = env
-        self.uses_all_ddp = opt_get(opt_eval, ['uses_all_ddp'], True)
+        self.uses_all_ddp = uses_all_ddp
 
     def perform_eval(self):
         return {}

codes/trainer/eval/fid.py

@@ -10,7 +10,7 @@ from utils.util import opt_get
 # Evaluator that generates uniform noise to feed into a generator, then calculates a FID score on the results.
 class StyleTransferEvaluator(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batches_per_eval = opt_eval['batches_per_eval']
         self.batch_sz = opt_eval['batch_size']
         self.im_sz = opt_eval['image_size']

codes/trainer/eval/flow_gaussian_nll.py

@@ -11,7 +11,7 @@ from models.srflow.flow import GaussianDiag
 
 class FlowGaussianNll(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batch_sz = opt_eval['batch_size']
         self.dataset = ImageFolderDataset(opt_eval['dataset'])
         self.dataloader = DataLoader(self.dataset, self.batch_sz)

codes/trainer/eval/single_point_pair_contrastive_eval.py

@@ -20,7 +20,7 @@ from utils.util import opt_get
 # dissimilar points remain constant or get further apart.
 class SinglePointPairContrastiveEval(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batch_sz = opt_eval['batch_size']
         self.eval_qty = opt_eval['quantity']
         assert self.eval_qty % self.batch_sz == 0

codes/trainer/eval/sr_fid.py

@@ -16,7 +16,7 @@ from torch.utils.data import DataLoader
 # generator might make from the source image.
 class SrFidEvaluator(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batch_sz = opt_eval['batch_size']
         assert self.batch_sz is not None
         self.dataset = create_dataset(opt_eval['dataset'])

codes/trainer/eval/sr_style.py

@@ -16,7 +16,7 @@ from data.stylegan2_dataset import Stylegan2Dataset
 
 class SrStyleTransferEvaluator(evaluator.Evaluator):
     def __init__(self, model, opt_eval, env):
-        super().__init__(model, opt_eval, env)
+        super().__init__(model, opt_eval, env, uses_all_ddp=False)
         self.batches_per_eval = opt_eval['batches_per_eval']
         self.batch_sz = opt_eval['batch_size']
         self.im_sz = opt_eval['image_size']