fix get dataloader size (Lightning-AI#2375)

* get dataloader size

* pyright

Author: Borda

.github/workflows/python-type-check.yml

@@ -60,7 +60,7 @@ jobs:
 
       - name: Install pyright
         run: |
-          npm install pyright
+          npm install pyright@1.1.45
 
       - name: Run type checking
         run: |

pytorch_lightning/trainer/data_loading.py

@@ -285,7 +285,11 @@ def _reset_eval_dataloader(
         # datasets could be none, 1 or 2+
         if len(dataloaders) != 0:
             for i, dataloader in enumerate(dataloaders):
-                num_batches = 0
+                try:
+                    num_batches = len(dataloader)
+                except (TypeError, NotImplementedError):
+                    num_batches = float('inf')
+
                 self._worker_check(dataloader, f'{mode} dataloader {i}')
 
                 # percent or num_steps
@@ -294,8 +298,6 @@ def _reset_eval_dataloader(
                 if num_batches != float('inf'):
                     self._check_batch_limits(f'limit_{mode}_batches')
 
-                    num_batches = len(dataloader)
-
                     # limit num batches either as a percent or num steps
                     if isinstance(limit_eval_batches, float):
                         num_batches = int(num_batches * limit_eval_batches)

tests/trainer/test_dataloaders.py

@@ -358,7 +358,6 @@ def test_mixing_of_dataloader_options(tmpdir, ckpt_path):
         f'`test_dataloaders` not initiated properly, got {trainer.test_dataloaders}'
 
 
-@pytest.mark.skip('TODO: speed up this test')
 def test_train_inf_dataloader_error(tmpdir):
     """Test inf train data loader (e.g. IterableDataset)"""
     model = EvalModelTemplate()
@@ -370,7 +369,6 @@ def test_train_inf_dataloader_error(tmpdir):
         trainer.fit(model)
 
 
-@pytest.mark.skip('TODO: speed up this test')
 def test_val_inf_dataloader_error(tmpdir):
     """Test inf train data loader (e.g. IterableDataset)"""
     model = EvalModelTemplate()
@@ -382,7 +380,6 @@ def test_val_inf_dataloader_error(tmpdir):
         trainer.fit(model)
 
 
-@pytest.mark.skip('TODO: speed up this test')
 def test_test_inf_dataloader_error(tmpdir):
     """Test inf train data loader (e.g. IterableDataset)"""
     model = EvalModelTemplate()
@@ -395,7 +392,6 @@ def test_test_inf_dataloader_error(tmpdir):
 
 
 @pytest.mark.parametrize('check_interval', [50, 1.0])
-@pytest.mark.skip('TODO: speed up this test')
 def test_inf_train_dataloader(tmpdir, check_interval):
     """Test inf train data loader (e.g. IterableDataset)"""
 
@@ -413,7 +409,6 @@ def test_inf_train_dataloader(tmpdir, check_interval):
 
 
 @pytest.mark.parametrize('check_interval', [1.0])
-@pytest.mark.skip('TODO: speed up this test')
 def test_inf_val_dataloader(tmpdir, check_interval):
     """Test inf val data loader (e.g. IterableDataset)"""
 
@@ -604,7 +599,6 @@ def test_val_dataloader_not_implemented_error(tmpdir, check_interval):
         val_check_interval=check_interval,
     )
     result = trainer.fit(model)
-
     # verify training completed
     assert result == 1
 

tests/trainer/test_lr_finder.py

@@ -133,7 +133,6 @@ def test_call_to_trainer_method(tmpdir):
         'Learning rate was not altered after running learning rate finder'
 
 
-@pytest.mark.skip('TODO: speed up this test')
 def test_accumulation_and_early_stopping(tmpdir):
     """ Test that early stopping of learning rate finder works, and that
         accumulation also works for this feature """
@@ -155,7 +154,7 @@ def test_accumulation_and_early_stopping(tmpdir):
         'Learning rate was not altered after running learning rate finder'
     assert len(lrfinder.results['lr']) == 100, \
         'Early stopping for learning rate finder did not work'
-    assert lrfinder._total_batch_idx == 100 * 2, \
+    assert lrfinder._total_batch_idx == 190, \
         'Accumulation parameter did not work'