.

codes/train.py

@@ -111,11 +111,6 @@ class Trainer:
                 self.dataset_debugger = get_dataset_debugger(dataset_opt)
                 if self.dataset_debugger is not None and resume_state is not None:
                     self.dataset_debugger.load_state(opt_get(resume_state, ['dataset_debugger_state'], {}))
-                # it will indefinitely try to train if your batch size is larger than your dataset
-                # could just whine when generating the YAML rather than assert here
-                if len(self.train_set) < dataset_opt['batch_size']:
-                    dataset_opt['batch_size'] = len(self.train_set)
-                    print("dataset size is less than batch size, consider reducing your batch size, or increasing your dataset.")
                 train_size = int(math.ceil(len(self.train_set) / dataset_opt['batch_size']))
                 total_iters = int(opt['train']['niter'])
                 self.total_epochs = int(math.ceil(total_iters / train_size))
@@ -133,6 +128,9 @@ class Trainer:
                     self.logger.info('Total epochs needed: {:d} for iters {:,d}'.format(
                         self.total_epochs, total_iters))
             elif phase == 'val':
+                if not opt_get(opt, ['eval', 'pure'], False):
+                    continue
+
                 self.val_set, collate_fn = create_dataset(dataset_opt, return_collate=True)
                 self.val_loader = create_dataloader(self.val_set, dataset_opt, opt, None, collate_fn=collate_fn)
                 if self.rank <= 0:

codes/utils/options.py

@@ -39,9 +39,9 @@ def parse(opt_path, is_train=True):
                 opt['path'][key] = osp.expanduser(path)
     else:
         opt['path'] = {}
-    opt['path']['root'] = osp.abspath(os.getcwd()) #osp.join(__file__, osp.pardir, osp.pardir, osp.pardir))
+    opt['path']['root'] = "./"
     if is_train:
-        experiments_root = osp.join(opt['path']['root'], 'training', opt['name'])
+        experiments_root = osp.join(opt['path']['root'], 'training', opt['name'], "finetune")
         opt['path']['experiments_root'] = experiments_root
         opt['path']['models'] = osp.join(experiments_root, 'models')
         opt['path']['training_state'] = osp.join(experiments_root, 'training_state')