Merge branch 'python3.8' into python3.7

1 files changed, 9 insertions, 5 deletions

diff --git a/models/model.py b/models/model.py
index 0eb9823..e924f41 100644
--- a/models/model.py
+++ b/models/model.py
@@ -1,4 +1,5 @@
 import os
+import random
 from datetime import datetime
 from typing import Union, Optional, Tuple, List, Dict, Set
 
@@ -196,14 +197,17 @@ class Model:
 
         self.writer = SummaryWriter(self._log_name)
 
+        # Set seeds for reproducibility
+        random.seed(0)
+        torch.manual_seed(0)
         self.rgb_pn.train()
         # Init weights at first iter
         if self.curr_iter == 0:
             self.rgb_pn.apply(self.init_weights)
         else:  # Load saved state dicts
             checkpoint = torch.load(self._checkpoint_name)
-            iter_, loss = checkpoint['iter'], checkpoint['loss']
-            print('{0:5d} loss: {1:.3f}'.format(iter_, loss))
+            random.setstate(checkpoint['rand_states'][0])
+            torch.set_rng_state(checkpoint['rand_states'][1])
             self.rgb_pn.load_state_dict(checkpoint['model_state_dict'])
             self.optimizer.load_state_dict(checkpoint['optim_state_dict'])
             self.scheduler.load_state_dict(checkpoint['sched_state_dict'])
@@ -317,11 +321,10 @@ class Model:
 
             if self.curr_iter % 1000 == 0:
                 torch.save({
-                    'iter': self.curr_iter,
+                    'rand_states': (random.getstate(), torch.get_rng_state()),
                     'model_state_dict': self.rgb_pn.state_dict(),
                     'optim_state_dict': self.optimizer.state_dict(),
                     'sched_state_dict': self.scheduler.state_dict(),
-                    'loss': loss,
                 }, self._checkpoint_name)
 
             if self.curr_iter == self.total_iter:
@@ -417,7 +420,8 @@ class Model:
     def _get_eval_sample(self, sample: Dict[str, Union[List, torch.Tensor]]):
         label = sample.pop('label').item()
         clip = sample.pop('clip').to(self.device)
-        feature = self.rgb_pn(clip).detach()
+        with torch.no_grad():
+            feature = self.rgb_pn(clip)
         return {
             **{'label': label},
             **sample,