Cleanup

docs/misc/changelog.rst

@@ -3,7 +3,7 @@
 Changelog
 ==========
 
-Release 2.4.0a7 (WIP)
+Release 2.4.0a8 (WIP)
 --------------------------
 
 .. note::
@@ -19,6 +19,7 @@ Breaking Changes:
 New Features:
 ^^^^^^^^^^^^^
 - Added support for ``pre_linear_modules`` and ``post_linear_modules`` in ``create_mlp`` (useful for adding normalization layers, like in DroQ or CrossQ)
+- Enabled np.ndarray logging for TensorBoardOutputFormat as histogram (see GH#1634) (@iwishwasaneagle)
 
 Bug Fixes:
 ^^^^^^^^^^
@@ -282,7 +283,6 @@ New Features:
 ^^^^^^^^^^^^^
 - Added Python 3.11 support
 - Added Gymnasium 0.29 support (@pseudo-rnd-thoughts)
-- Enabled np.ndarray logging for TensorBoardOutputFormat as histogram (see GH#1634) (@iwishwasaneagle)
 
 `SB3-Contrib`_
 ^^^^^^^^^^^^^^

stable_baselines3/common/logger.py

@@ -413,17 +413,8 @@ def write(self, key_values: Dict[str, Any], key_excluded: Dict[str, Tuple[str, .
                     self.writer.add_scalar(key, value, step)
 
             if isinstance(value, (th.Tensor, np.ndarray)):
-                try:
-                    self.writer.add_histogram(key, value, step)
-                except TypeError:
-                    warnings.warn(
-                        "A numpy.ndarray was passed to write which threw a "
-                        "TypeError. This is most likely due to an outdated numpy version (<1.24.0) and/or "
-                        "an outdated torch version (<2.0.0). The ndarray will be converted to a torch.Tensor "
-                        "as a workaround. For more information, "
-                        "see https://github.com/DLR-RM/stable-baselines3/pull/1635"
-                    )
-                    self.writer.add_histogram(key, th.from_numpy(value), step)
+                # Convert to Torch so it works with numpy<1.24 and torch<2.0
+                self.writer.add_histogram(key, th.as_tensor(value), step)
 
             if isinstance(value, Video):
                 self.writer.add_video(key, value.frames, step, value.fps)

stable_baselines3/version.txt

@@ -1 +1 @@
-2.4.0a7
+2.4.0a8

tests/test_logger.py

@@ -1,4 +1,3 @@
-import functools
 import importlib.util
 import os
 import sys
@@ -45,6 +44,7 @@
     "f": np.array(1),
     "g": np.array([[[1]]]),
     "h": 'this ", ;is a \n tes:,t',
+    "i": th.ones(3),
 }
 
 KEY_EXCLUDED = {}
@@ -177,6 +177,9 @@ def test_main(tmp_path):
     logger.record_mean("b", -22.5)
     logger.record_mean("b", -44.4)
     logger.record("a", 5.5)
+    # Converted to string:
+    logger.record("hist1", th.ones(2))
+    logger.record("hist2", np.ones(2))
     logger.dump()
 
     logger.record("a", "longasslongasslongasslongasslongasslongassvalue")
@@ -242,7 +245,7 @@ def is_moviepy_installed():
 
 
 @pytest.mark.parametrize("unsupported_format", ["stdout", "log", "json", "csv"])
-def test_report_video_to_unsupported_format_raises_error(tmp_path, unsupported_format):
+def test_unsupported_video_format(tmp_path, unsupported_format):
     writer = make_output_format(unsupported_format, tmp_path)
 
     with pytest.raises(FormatUnsupportedError) as exec_info:
@@ -252,52 +255,41 @@ def test_report_video_to_unsupported_format_raises_error(tmp_path, unsupported_f
     writer.close()
 
 
-_called = None
-
-
-def get_fail_first_then_pass_fn(fn, exception=Exception):
-    _called = False
-
-    @functools.wraps(fn)
-    def _fn(*args, **kwargs):
-        global _called
-        if not _called:
-            _called = True
-            raise exception()
-        return fn(*args, **kwargs)
-
-    return _fn
-
-
 @pytest.mark.parametrize(
-    "histogram,fail_first_write",
+    "histogram",
     [
-        (th.rand(100), False),
-        (np.random.rand(100), False),
-        (np.random.rand(100), True),
+        th.rand(100),
+        np.random.rand(100),
+        np.ones(1),
+        np.ones(1, dtype="int"),
     ],
 )
-def test_report_histogram_to_tensorboard(tmp_path, read_log, fail_first_write, histogram):
+def test_log_histogram(tmp_path, read_log, histogram):
     pytest.importorskip("tensorboard")
 
     writer = make_output_format("tensorboard", tmp_path)
-
-    if fail_first_write:
-        writer.writer.add_histogram = get_fail_first_then_pass_fn(writer.writer.add_histogram, TypeError)
-
     writer.write({"data": histogram}, key_excluded={"data": ()})
 
     log = read_log("tensorboard")
 
     assert not log.empty
-    assert any("data" in f for f in log.lines)
-    assert any("Histogram" in f for f in log.lines)
+    assert any("data" in line for line in log.lines)
+    assert any("Histogram" in line for line in log.lines)
 
     writer.close()
 
 
-@pytest.mark.parametrize("histogram", [list(np.random.rand(100)), tuple(np.random.rand(100)), "1 2 3 4"])
-def test_report_unsupported_type_as_histogram_to_tensorboard(tmp_path, read_log, histogram):
+@pytest.mark.parametrize(
+    "histogram",
+    [
+        list(np.random.rand(100)),
+        tuple(np.random.rand(100)),
+        "1 2 3 4",
+        np.ones(1).item(),
+        th.ones(1).item(),
+    ],
+)
+def test_unsupported_type_histogram(tmp_path, read_log, histogram):
     """
     Check that other types aren't accidentally logged as a Histogram
     """
@@ -306,7 +298,7 @@ def test_report_unsupported_type_as_histogram_to_tensorboard(tmp_path, read_log,
     writer = make_output_format("tensorboard", tmp_path)
     writer.write({"data": histogram}, key_excluded={"data": ()})
 
-    assert all("Histogram" not in f for f in read_log("tensorboard").lines)
+    assert all("Histogram" not in line for line in read_log("tensorboard").lines)
 
     writer.close()
 
@@ -323,7 +315,7 @@ def test_report_image_to_tensorboard(tmp_path, read_log):
 
 
 @pytest.mark.parametrize("unsupported_format", ["stdout", "log", "json", "csv"])
-def test_report_image_to_unsupported_format_raises_error(tmp_path, unsupported_format):
+def test_unsupported_image_format(tmp_path, unsupported_format):
     writer = make_output_format(unsupported_format, tmp_path)
 
     with pytest.raises(FormatUnsupportedError) as exec_info:
@@ -347,7 +339,7 @@ def test_report_figure_to_tensorboard(tmp_path, read_log):
 
 
 @pytest.mark.parametrize("unsupported_format", ["stdout", "log", "json", "csv"])
-def test_report_figure_to_unsupported_format_raises_error(tmp_path, unsupported_format):
+def test_unsupported_figure_format(tmp_path, unsupported_format):
     writer = make_output_format(unsupported_format, tmp_path)
 
     with pytest.raises(FormatUnsupportedError) as exec_info:
@@ -360,7 +352,7 @@ def test_report_figure_to_unsupported_format_raises_error(tmp_path, unsupported_
 
 
 @pytest.mark.parametrize("unsupported_format", ["stdout", "log", "json", "csv"])
-def test_report_hparam_to_unsupported_format_raises_error(tmp_path, unsupported_format):
+def test_unsupported_hparam(tmp_path, unsupported_format):
     writer = make_output_format(unsupported_format, tmp_path)
 
     with pytest.raises(FormatUnsupportedError) as exec_info:
@@ -479,9 +471,9 @@ def test_fps_no_div_zero(algo):
             model.learn(total_timesteps=100)
 
 
-def test_human_output_format_no_crash_on_same_keys_different_tags():
-    o = HumanOutputFormat(sys.stdout, max_length=60)
-    o.write(
+def test_human_output_same_keys_different_tags():
+    human_out = HumanOutputFormat(sys.stdout, max_length=60)
+    human_out.write(
         {"key1/foo": "value1", "key1/bar": "value2", "key2/bizz": "value3", "key2/foo": "value4"},
         {"key1/foo": None, "key2/bizz": None, "key1/bar": None, "key2/foo": None},
     )
@@ -499,7 +491,7 @@ def test_ep_buffers_stats_window_size(algo, stats_window_size):
 
 
 @pytest.mark.parametrize("base_class", [object, TextIOBase])
-def test_human_output_format_custom_test_io(base_class):
+def test_human_out_custom_text_io(base_class):
     class DummyTextIO(base_class):
         def __init__(self) -> None:
             super().__init__()
@@ -591,7 +583,7 @@ def step(self, action):
         return self.observation_space.sample(), 0.0, False, truncated, info
 
 
-def test_rollout_success_rate_on_policy_algorithm(tmp_path):
+def test_rollout_success_rate_onpolicy_algo(tmp_path):
     """
     Test if the rollout/success_rate information is correctly logged with on policy algorithms