Updates for lightning 0.10.0

pl_bolts/models/autoencoders/basic_ae/basic_ae_module.py

@@ -106,17 +106,15 @@ def step(self, batch, batch_idx):
 
     def training_step(self, batch, batch_idx):
         loss, logs = self.step(batch, batch_idx)
-        result = pl.TrainResult(minimize=loss)
-        result.log_dict(
+        self.log_dict(
             {f"train_{k}": v for k, v in logs.items()}, on_step=True, on_epoch=False
         )
-        return result
+        return loss
 
     def validation_step(self, batch, batch_idx):
         loss, logs = self.step(batch, batch_idx)
-        result = pl.EvalResult(checkpoint_on=loss)
-        result.log_dict({f"val_{k}": v for k, v in logs.items()})
-        return result
+        self.log_dict({f"val_{k}": v for k, v in logs.items()})
+        return loss
 
     def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=self.lr)

pl_bolts/models/autoencoders/basic_vae/basic_vae_module.py

@@ -139,17 +139,15 @@ def step(self, batch, batch_idx):
 
     def training_step(self, batch, batch_idx):
         loss, logs = self.step(batch, batch_idx)
-        result = pl.TrainResult(minimize=loss)
-        result.log_dict(
+        self.log_dict(
             {f"train_{k}": v for k, v in logs.items()}, on_step=True, on_epoch=False
         )
-        return result
+        return loss
 
     def validation_step(self, batch, batch_idx):
         loss, logs = self.step(batch, batch_idx)
-        result = pl.EvalResult(checkpoint_on=loss)
-        result.log_dict({f"val_{k}": v for k, v in logs.items()})
-        return result
+        self.log_dict({f"val_{k}": v for k, v in logs.items()})
+        return loss
 
     def configure_optimizers(self):
         return torch.optim.Adam(self.parameters(), lr=self.lr)

pl_bolts/models/gans/basic/basic_gan_module.py

@@ -136,18 +136,16 @@ def generator_step(self, x):
 
         # log to prog bar on each step AND for the full epoch
         # use the generator loss for checkpointing
-        result = pl.TrainResult(minimize=g_loss, checkpoint_on=g_loss)
-        result.log('g_loss', g_loss, on_epoch=True, prog_bar=True)
-        return result
+        self.log('g_loss', g_loss, on_epoch=True, prog_bar=True)
+        return g_loss
 
     def discriminator_step(self, x):
         # Measure discriminator's ability to classify real from generated samples
         d_loss = self.discriminator_loss(x)
 
         # log to prog bar on each step AND for the full epoch
-        result = pl.TrainResult(minimize=d_loss)
-        result.log('d_loss', d_loss, on_epoch=True, prog_bar=True)
-        return result
+        self.log('d_loss', d_loss, on_epoch=True, prog_bar=True)
+        return d_loss
 
     def configure_optimizers(self):
         lr = self.hparams.learning_rate

pl_bolts/models/regression/logistic_regression.py

@@ -2,7 +2,7 @@
 
 import pytorch_lightning as pl
 import torch
-from pytorch_lightning.metrics.classification import accuracy
+from pytorch_lightning.metrics.functional import accuracy
 from torch import nn
 from torch.nn import functional as F
 from torch.optim import Adam

pl_bolts/models/self_supervised/byol/byol_module.py

@@ -136,19 +136,17 @@ def training_step(self, batch, batch_idx):
         loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
 
         # log results
-        result = pl.TrainResult(minimize=total_loss)
-        result.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
+        self.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
 
-        return result
+        return total_loss
 
     def validation_step(self, batch, batch_idx):
         loss_a, loss_b, total_loss = self.shared_step(batch, batch_idx)
 
         # log results
-        result = pl.EvalResult(early_stop_on=total_loss, checkpoint_on=total_loss)
-        result.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
+        self.log_dict({'1_2_loss': loss_a, '2_1_loss': loss_b, 'train_loss': total_loss})
 
-        return result
+        return total_loss
 
     def configure_optimizers(self):
         optimizer = Adam(self.parameters(), lr=self.hparams.learning_rate, weight_decay=self.hparams.weight_decay)

pl_bolts/models/self_supervised/cpc/cpc_module.py

@@ -35,7 +35,7 @@ class CPCV2(pl.LightningModule):
     def __init__(
             self,
             datamodule: pl.LightningDataModule = None,
-            encoder: Union[str, torch.nn.Module, pl.LightningModule] = 'cpc_encoder',
+            encoder_name: str = 'cpc_encoder',
             patch_size: int = 8,
             patch_overlap: int = 4,
             online_ft: int = True,
@@ -50,7 +50,7 @@ def __init__(
         """
         Args:
             datamodule: A Datamodule (optional). Otherwise set the dataloaders directly
-            encoder: A string for any of the resnets in torchvision, or the original CPC encoder,
+            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.
@@ -66,28 +66,20 @@ def __init__(
         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
 
-        # link data
-        # if datamodule is None:
-        #     datamodule = CIFAR10DataModule(
-        #         self.hparams.data_dir,
-        #         num_workers=self.hparams.num_workers,
-        #         batch_size=batch_size
-        #     )
-        #     datamodule.train_transforms = CPCTrainTransformsCIFAR10()
-        #     datamodule.val_transforms = CPCEvalTransformsCIFAR10()
         assert datamodule
         self.datamodule = datamodule
 
-        # init encoder
-        self.encoder = encoder
-        if isinstance(encoder, str):
-            self.encoder = self.init_encoder()
+        self.encoder = self.init_encoder()
 
         # info nce loss
         c, h = self.__compute_final_nb_c(self.hparams.patch_size)
@@ -97,20 +89,22 @@ def __init__(
         self.num_classes = self.datamodule.num_classes
 
         if pretrained:
-            self.load_pretrained(encoder)
+            self.load_pretrained(self.hparams.encoder_name)
+
+        print(self.hparams)
 
-    def load_pretrained(self, encoder):
+    def load_pretrained(self, encoder_name):
         available_weights = {'resnet18'}
 
-        if encoder in available_weights:
-            load_pretrained(self, f'CPCV2-{encoder}')
-        elif available_weights not in available_weights:
-            rank_zero_warn(f'{encoder} not yet available')
+        if encoder_name in available_weights:
+            load_pretrained(self, f'CPCV2-{encoder_name}')
+        elif encoder_name not in available_weights:
+            rank_zero_warn(f'{encoder_name} not yet available')
 
     def init_encoder(self):
         dummy_batch = torch.zeros((2, 3, self.hparams.patch_size, self.hparams.patch_size))
 
-        encoder_name = self.hparams.encoder
+        encoder_name = self.hparams.encoder_name
         if encoder_name == 'cpc_encoder':
             return cpc_resnet101(dummy_batch)
         else:
@@ -160,18 +154,16 @@ def training_step(self, batch, batch_nb):
         nce_loss = self.shared_step(batch)
 
         # result
-        result = pl.TrainResult(nce_loss)
-        result.log('train_nce_loss', nce_loss)
-        return result
+        self.log('train_nce_loss', nce_loss)
+        return nce_loss
 
     def validation_step(self, batch, batch_nb):
         # calculate loss
         nce_loss = self.shared_step(batch)
 
         # result
-        result = pl.EvalResult(checkpoint_on=nce_loss)
-        result.log('val_nce', nce_loss, prog_bar=True)
-        return result
+        self.log('val_nce', nce_loss, prog_bar=True)
+        return nce_loss
 
     def shared_step(self, batch):
         try:

pl_bolts/models/self_supervised/simclr/simclr_module.py

@@ -157,16 +157,14 @@ def forward(self, x):
     def training_step(self, batch, batch_idx):
         loss = self.shared_step(batch, batch_idx)
 
-        result = pl.TrainResult(minimize=loss)
-        result.log('train_loss', loss, on_epoch=True)
-        return result
+        self.log('train_loss', loss, on_epoch=True)
+        return loss
 
     def validation_step(self, batch, batch_idx):
         loss = self.shared_step(batch, batch_idx)
 
-        result = pl.EvalResult(checkpoint_on=loss)
-        result.log('avg_val_loss', loss)
-        return result
+        self.log('avg_val_loss', loss)
+        return loss
 
     def shared_step(self, batch, batch_idx):
         (img1, img2), y = batch

pl_bolts/models/self_supervised/ssl_finetuner.py

@@ -59,21 +59,18 @@ def on_train_epoch_start(self) -> None:
 
     def training_step(self, batch, batch_idx):
         loss, acc = self.shared_step(batch)
-        result = pl.TrainResult(loss)
-        result.log('train_acc', acc, prog_bar=True)
-        return result
+        self.log('train_acc', acc, prog_bar=True)
+        return loss
 
     def validation_step(self, batch, batch_idx):
         loss, acc = self.shared_step(batch)
-        result = pl.EvalResult(checkpoint_on=loss, early_stop_on=loss)
-        result.log_dict({'val_acc': acc, 'val_loss': loss}, prog_bar=True)
-        return result
+        self.log_dict({'val_acc': acc, 'val_loss': loss}, prog_bar=True)
+        return loss
 
     def test_step(self, batch, batch_idx):
         loss, acc = self.shared_step(batch)
-        result = pl.EvalResult()
-        result.log_dict({'test_acc': acc, 'test_loss': loss})
-        return result
+        self.log_dict({'test_acc': acc, 'test_loss': loss})
+        return loss
 
     def shared_step(self, batch):
         x, y = batch

requirements/base.txt

@@ -1,2 +1,2 @@
-pytorch-lightning>=0.9.1rc3
+pytorch-lightning>=0.10.0
 torch>=1.6