add choice and mask of units to checkpoint

tests/test_metafiles.py → .dev_scripts/meta_files_test.py

@@ -1,5 +1,6 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import os
+import unittest
 from pathlib import Path
 
 import requests
@@ -8,7 +9,7 @@
 MMRAZOR_ROOT = Path(__file__).absolute().parents[1]
 
 
-class TestMetafiles:
+class TestMetafiles(unittest.TestCase):
 
     def get_metafiles(self, code_path):
         """
@@ -51,3 +52,7 @@ def test_metafiles(self):
                 assert model['Name'] == correct_name, \
                     f'name error in {metafile}, correct name should ' \
                     f'be {correct_name}'
+
+
+if __name__ == '__main__':
+    unittest.main()

mmrazor/models/algorithms/pruning/ite_prune_algorithm.py

@@ -204,23 +204,24 @@ def forward(self,
                 data_samples: Optional[List[BaseDataElement]] = None,
                 mode: str = 'tensor') -> ForwardResults:
         """Forward."""
-        if not hasattr(self, 'prune_config_manager'):
-            # self._iters_per_epoch() only available after initiation
-            self.prune_config_manager = self._init_prune_config_manager()
 
-        if self.prune_config_manager.is_prune_time(self._iter):
+        if self.training:
+            if not hasattr(self, 'prune_config_manager'):
+                # self._iters_per_epoch() only available after initiation
+                self.prune_config_manager = self._init_prune_config_manager()
+            if self.prune_config_manager.is_prune_time(self._iter):
 
-            config = self.prune_config_manager.prune_at(self._iter)
+                config = self.prune_config_manager.prune_at(self._iter)
 
-            self.mutator.set_choices(config)
+                self.mutator.set_choices(config)
 
-            logger = MMLogger.get_current_instance()
-            if (self.by_epoch):
-                logger.info(
-                    f'The model is pruned at {self._epoch}th epoch once.')
-            else:
-                logger.info(
-                    f'The model is pruned at {self._iter}th iter once.')
+                logger = MMLogger.get_current_instance()
+                if (self.by_epoch):
+                    logger.info(
+                        f'The model is pruned at {self._epoch}th epoch once.')
+                else:
+                    logger.info(
+                        f'The model is pruned at {self._iter}th iter once.')
 
         return super().forward(inputs, data_samples, mode)
 

mmrazor/models/mutables/mutable_channel/sequential_mutable_channel.py

@@ -27,7 +27,6 @@ def __init__(self, num_channels: int, choice_mode='number', **kwargs):
         super().__init__(num_channels, **kwargs)
         assert choice_mode in ['ratio', 'number']
         self.choice_mode = choice_mode
-        self.mask = torch.ones([self.num_channels]).bool()
 
     @property
     def is_num_mode(self):
@@ -50,14 +49,13 @@ def current_choice(self, choice: Union[int, float]):
             int_choice = self._ratio2num(choice)
         else:
             int_choice = choice
-        mask = torch.zeros([self.num_channels], device=self.mask.device)
-        mask[0:int_choice] = 1
-        self.mask = mask.bool()
+        self.mask.fill_(0.0)
+        self.mask[0:int_choice] = 1.0
 
     @property
     def current_mask(self) -> torch.Tensor:
         """Return current mask."""
-        return self.mask
+        return self.mask.bool()
 
     # methods for
 

mmrazor/models/mutables/mutable_channel/simple_mutable_channel.py

@@ -20,7 +20,10 @@ class SimpleMutableChannel(BaseMutableChannel):
 
     def __init__(self, num_channels: int, **kwargs) -> None:
         super().__init__(num_channels, **kwargs)
-        self.mask = torch.ones(num_channels).bool()
+        mask = torch.ones([self.num_channels
+                           ])  # save bool as float for dist training
+        self.register_buffer('mask', mask)
+        self.mask: torch.Tensor
 
     # choice
 
@@ -32,7 +35,7 @@ def current_choice(self) -> torch.Tensor:
     @current_choice.setter
     def current_choice(self, choice: torch.Tensor):
         """Set current choice."""
-        self.mask = choice.to(self.mask.device).bool()
+        self.mask = choice.to(self.mask.device).float()
 
     @property
     def current_mask(self) -> torch.Tensor:

requirements/tests.txt

@@ -4,6 +4,7 @@ interrogate
 isort==4.3.21
 nbconvert
 nbformat
+numpy < 1.24.0 # A temporary solution for tests with mmdet.
 pytest
 xdoctest >= 0.10.0
 yapf

tests/test_models/test_algorithms/test_prune_algorithm.py

@@ -261,3 +261,30 @@ def test_dist_init(self):
             algorithm.forward(
                 data['inputs'], data['data_samples'], mode='loss')
             self.assertEqual(algorithm.step_freq, epoch_step * iter_per_epoch)
+
+    def test_resume(self):
+        algorithm: ItePruneAlgorithm = ItePruneAlgorithm(
+            MODEL_CFG,
+            mutator_cfg=MUTATOR_CONFIG_NUM,
+            target_pruning_ratio=None,
+            step_freq=1,
+            prune_times=1,
+        ).to(DEVICE)
+        algorithm.mutator.set_choices(algorithm.mutator.sample_choices())
+        state_dict = algorithm.state_dict()
+        print(state_dict.keys())
+
+        algorithm2: ItePruneAlgorithm = ItePruneAlgorithm(
+            MODEL_CFG,
+            mutator_cfg=MUTATOR_CONFIG_NUM,
+            target_pruning_ratio=None,
+            step_freq=1,
+            prune_times=1,
+        ).to(DEVICE)
+
+        algorithm2.load_state_dict(state_dict)
+
+        print(algorithm.mutator.current_choices)
+        print(algorithm2.mutator.current_choices)
+        self.assertDictEqual(algorithm.mutator.current_choices,
+                             algorithm2.mutator.current_choices)