ppdate tests for early stop (Lightning-AI#657)

tests/test_cpu_models.py

@@ -21,6 +21,7 @@ def test_early_stopping_cpu_model(tmpdir):
     stopping = EarlyStopping(monitor='val_loss', min_delta=0.1)
     trainer_options = dict(
         default_save_path=tmpdir,
+        min_epochs=2,
         early_stop_callback=stopping,
         gradient_clip_val=1.0,
         overfit_pct=0.20,
@@ -33,7 +34,7 @@ def test_early_stopping_cpu_model(tmpdir):
     )
 
     model, hparams = tutils.get_model()
-    tutils.run_model_test(trainer_options, model, on_gpu=False)
+    tutils.run_model_test(trainer_options, model, on_gpu=False, early_stop=True)
 
     # test freeze on cpu
     model.freeze()

tests/utils.py

@@ -45,7 +45,7 @@ def run_model_test_no_loggers(trainer_options, model, min_acc=0.50):
         trainer.optimizers, trainer.lr_schedulers = pretrained_model.configure_optimizers()
 
 
-def run_model_test(trainer_options, model, on_gpu=True):
+def run_model_test(trainer_options, model, on_gpu=True, early_stop=False):
     save_dir = trainer_options['default_save_path']
 
     # logger file to get meta
@@ -65,6 +65,10 @@ def run_model_test(trainer_options, model, on_gpu=True):
     # correct result and ok accuracy
     assert result == 1, 'amp + ddp model failed to complete'
 
+    if (early_stop):
+        assert trainer.current_epoch >= trainer.min_epochs, 'amp + ddp model failed to complete'
+        assert trainer.current_epoch < trainer.max_epochs-1, 'amp + ddp model failed to stop early'
+
     # test model loading
     pretrained_model = load_model(logger, trainer.checkpoint_callback.filepath)