Fix the test case; disable assert error suppression

veritable-tech · Apr 9, 2021 · c69c5a1 · c69c5a1
1 parent e35192d
commit c69c5a1
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Showing 2 changed files with 9 additions and 9 deletions.
diff --git a/pytorch_lightning/trainer/trainer.py b/pytorch_lightning/trainer/trainer.py
@@ -633,11 +633,11 @@ def run_train(self) -> None:
             if not self.interrupted:
                 self.state = TrainerState.INTERRUPTED
                 self.on_keyboard_interrupt()
-        except (RuntimeError, AssertionError):
-            # if an exception is raised, the finally block is executed and can hide the actual exception
-            # that was initially raised if `on_train_end` also raises an exception. we want to avoid that
-            # for assertions and other runtime errors so we aren't misled while debugging
-            print_exc()
+        # except (RuntimeError, AssertionError):
+        #     # if an exception is raised, the finally block is executed and can hide the actual exception
+        #     # that was initially raised if `on_train_end` also raises an exception. we want to avoid that
+        #     # for assertions and other runtime errors so we aren't misled while debugging
+        #     print_exc()
         finally:
             # hook
             self.train_loop.on_train_end()

diff --git a/tests/trainer/test_trainer.py b/tests/trainer/test_trainer.py
@@ -918,13 +918,13 @@ def test_gradient_clipping_by_value(tmpdir):
 
     model = BoringModel()
 
-    grad_clip_val = 0.0001
+    grad_clip_val = 1e-5
     trainer = Trainer(
         max_steps=10,
         max_epochs=1,
         gradient_clip_val=grad_clip_val,
         gradient_clip_algorithm='value',
-        default_root_dir=tmpdir,
+        default_root_dir=tmpdir
     )
 
     trainer.train_loop.old_training_step_and_backward = trainer.train_loop.training_step_and_backward
@@ -938,8 +938,8 @@ def training_step_and_backward(split_batch, batch_idx, opt_idx, optimizer, hidde
         parameters = model.parameters()
         grad_max_list = [torch.max(p.grad.detach().abs()) for p in parameters]
         grad_max = torch.max(torch.stack(grad_max_list))
-        assert round(grad_max.item(), 6) <= grad_clip_val, \
-            f"Gradient max value {grad_max} > grad_clip_val {grad_clip_val} ."
+        assert abs(round(grad_max.item(), 6) - grad_clip_val) < 1e-6, \
+            f"Gradient max value {grad_max} != grad_clip_val {grad_clip_val} ."
 
         return ret_val