Decouple DataModules from Models - CPCV2 (#386)

* Decouple dms from CPCV2

* Update tests

pl_bolts/models/self_supervised/cpc/cpc_module.py

@@ -33,65 +33,52 @@ class CPCV2(pl.LightningModule):
 
     def __init__(
             self,
-            datamodule: Optional[pl.LightningDataModule] = None,
             encoder_name: str = 'cpc_encoder',
             patch_size: int = 8,
             patch_overlap: int = 4,
-            online_ft: int = True,
+            online_ft: bool = True,
             task: str = 'cpc',
             num_workers: int = 4,
-            learning_rate: int = 1e-4,
-            data_dir: str = '',
-            batch_size: int = 32,
+            num_classes: int = 10,
+            learning_rate: float = 1e-4,
             pretrained: Optional[str] = None,
             **kwargs,
     ):
         """
         Args:
-            datamodule: A Datamodule (optional). Otherwise set the dataloaders directly
             encoder_name: A string for any of the resnets in torchvision, or the original CPC encoder,
                 or a custon nn.Module encoder
             patch_size: How big to make the image patches
-            patch_overlap: How much overlap should each patch have.
-            online_ft: Enable a 1024-unit MLP to fine-tune online
+            patch_overlap: How much overlap each patch should have
+            online_ft: If True, enables a 1024-unit MLP to fine-tune online
             task: Which self-supervised task to use ('cpc', 'amdim', etc...)
-            num_workers: num dataloader worksers
-            learning_rate: what learning rate to use
-            data_dir: where to store data
-            batch_size: batch size
+            num_workers: number of dataloader workers
+            num_classes: number of classes
+            learning_rate: learning rate
             pretrained: If true, will use the weights pretrained (using CPC) on Imagenet
         """
 
         super().__init__()
         self.save_hyperparameters()
 
-        # HACK - datamodule not pickleable so we remove it from hparams.
-        # TODO - remove datamodule from init. data should be decoupled from models.
-        del self.hparams['datamodule']
-
         self.online_evaluator = self.hparams.online_ft
 
         if pretrained:
             self.hparams.dataset = pretrained
             self.online_evaluator = True
 
-        assert datamodule
-        self.datamodule = datamodule
-
         self.encoder = self.init_encoder()
 
         # info nce loss
         c, h = self.__compute_final_nb_c(self.hparams.patch_size)
         self.contrastive_task = CPCTask(num_input_channels=c, target_dim=64, embed_scale=0.1)
 
         self.z_dim = c * h * h
-        self.num_classes = self.datamodule.num_classes
+        self.num_classes = num_classes
 
         if pretrained:
             self.load_pretrained(self.hparams.encoder_name)
 
-        print(self.hparams)
-
     def load_pretrained(self, encoder_name):
         available_weights = {'resnet18'}
 
@@ -212,19 +199,9 @@ def add_model_specific_args(parent_parser):
             'resnext50_32x4d', 'resnext101_32x8d', 'wide_resnet50_2', 'wide_resnet101_2'
         ]
         parser.add_argument('--encoder', default='cpc_encoder', type=str, choices=possible_resnets)
-
-        # training params
-        parser.add_argument('--batch_size', type=int, default=128)
-
         # cifar10: 1e-5, stl10: 3e-5, imagenet: 4e-4
         parser.add_argument('--learning_rate', type=float, default=1e-5)
 
-        # data
-        parser.add_argument('--dataset', default='cifar10', type=str)
-        parser.add_argument('--data_dir', default='.', type=str)
-        parser.add_argument('--meta_dir', default='.', type=str, help='path to meta.bin for imagenet')
-        parser.add_argument('--num_workers', default=8, type=int)
-
         return parser
 
 
@@ -237,9 +214,13 @@ def cli_main():
     parser = ArgumentParser()
     parser = pl.Trainer.add_argparse_args(parser)
     parser = CPCV2.add_model_specific_args(parser)
+    parser.add_argument('--dataset', default='cifar10', type=str)
+    parser.add_argument('--data_dir', default='.', type=str)
+    parser.add_argument('--meta_dir', default='.', type=str, help='path to meta.bin for imagenet')
+    parser.add_argument('--num_workers', default=8, type=int)
+    parser.add_argument('--batch_size', type=int, default=128)
 
     args = parser.parse_args()
-    args.online_ft = True
 
     datamodule = None
 
@@ -276,9 +257,9 @@ def to_device(batch, device):
         datamodule.val_transforms = CPCEvalTransformsImageNet128()
         args.patch_size = 32
 
-    model = CPCV2(**vars(args), datamodule=datamodule)
+    model = CPCV2(**vars(args))
     trainer = pl.Trainer.from_argparse_args(args, callbacks=[online_evaluator])
-    trainer.fit(model)
+    trainer.fit(model, datamodule)
 
 
 if __name__ == '__main__':

tests/models/self_supervised/test_models.py

@@ -22,9 +22,9 @@ def test_cpcv2(tmpdir, datadir):
     datamodule.train_transforms = CPCTrainTransformsCIFAR10()
     datamodule.val_transforms = CPCEvalTransformsCIFAR10()
 
-    model = CPCV2(encoder='resnet18', data_dir=datadir, batch_size=2, online_ft=True, datamodule=datamodule)
+    model = CPCV2(encoder='resnet18', online_ft=True, num_classes=datamodule.num_classes)
     trainer = pl.Trainer(fast_dev_run=True, max_epochs=1, default_root_dir=tmpdir)
-    trainer.fit(model)
+    trainer.fit(model, datamodule)
     loss = trainer.progress_bar_dict['val_nce']
 
     assert float(loss) > 0