Add np.ndarray as a recognized type for TB histograms. (#1635)

* Add np.ndarray as a recognized type for TB histograms.

Torch histograms allow th.Tensor, np.ndarray, and caffe2 formatted strings. This commits expands the TensorBoardOutputFormat's capabilities to log the two former types.

* Update changelog to reflect bug fix

* fix: try/catch for if either np or torch aren't at the required versions. See #1635 for more details

* fix: Add comment describing the test for when add_histogram should not have been called

* Cleanup

---------

Co-authored-by: Antonin RAFFIN <[email protected]>

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:
 ^^^^^^^^^^

stable_baselines3/common/logger.py

@@ -412,8 +412,9 @@ def write(self, key_values: Dict[str, Any], key_excluded: Dict[str, Tuple[str, .
                 else:
                     self.writer.add_scalar(key, value, step)
 
-            if isinstance(value, th.Tensor):
-                self.writer.add_histogram(key, value, step)
+            if isinstance(value, (th.Tensor, np.ndarray)):
+                # 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

@@ -44,6 +44,7 @@
     "f": np.array(1),
     "g": np.array([[[1]]]),
     "h": 'this ", ;is a \n tes:,t',
+    "i": th.ones(3),
 }
 
 KEY_EXCLUDED = {}
@@ -176,6 +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")
@@ -241,7 +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:
@@ -251,6 +255,54 @@ def test_report_video_to_unsupported_format_raises_error(tmp_path, unsupported_f
     writer.close()
 
 
+@pytest.mark.parametrize(
+    "histogram",
+    [
+        th.rand(100),
+        np.random.rand(100),
+        np.ones(1),
+        np.ones(1, dtype="int"),
+    ],
+)
+def test_log_histogram(tmp_path, read_log, histogram):
+    pytest.importorskip("tensorboard")
+
+    writer = make_output_format("tensorboard", tmp_path)
+    writer.write({"data": histogram}, key_excluded={"data": ()})
+
+    log = read_log("tensorboard")
+
+    assert not log.empty
+    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",
+        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
+    """
+    pytest.importorskip("tensorboard")
+
+    writer = make_output_format("tensorboard", tmp_path)
+    writer.write({"data": histogram}, key_excluded={"data": ()})
+
+    assert all("Histogram" not in line for line in read_log("tensorboard").lines)
+
+    writer.close()
+
+
 def test_report_image_to_tensorboard(tmp_path, read_log):
     pytest.importorskip("tensorboard")
 
@@ -263,7 +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:
@@ -287,7 +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:
@@ -300,7 +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:
@@ -419,9 +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},
     )
@@ -439,7 +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__()
@@ -531,7 +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