[BugFix] Fix optional imports (#535)

Author: vmoens

.circleci/unittest/linux_optdeps/scripts/environment.yml

@@ -14,5 +14,4 @@ dependencies:
     - expecttest
     - pyyaml
     - scipy
-    - hydra-core
     - coverage

test/test_helpers.py

@@ -9,8 +9,14 @@
 import pytest
 import torch
 from _utils_internal import get_available_devices, generate_seeds
-from hydra import initialize, compose
-from hydra.core.config_store import ConfigStore
+
+try:
+    from hydra import initialize, compose
+    from hydra.core.config_store import ConfigStore
+
+    _has_hydra = True
+except ImportError:
+    _has_hydra = False
 from mocking_classes import (
     ContinuousActionConvMockEnvNumpy,
     ContinuousActionVecMockEnv,
@@ -49,6 +55,7 @@ def _assert_keys_match(td, expeceted_keys):
 
 
 @pytest.mark.skipif(not _has_gym, reason="No gym library found")
+@pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("noisy", [tuple(), ("noisy=True",)])
 @pytest.mark.parametrize("distributional", [tuple(), ("distributional=True",)])
@@ -99,6 +106,7 @@ def test_dqn_maker(device, noisy, distributional, from_pixels):
         proof_environment.close()
 
 
+@pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
 @pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("from_pixels", [("from_pixels=True", "catframes=4"), tuple()])
@@ -173,6 +181,7 @@ def test_ddpg_maker(device, from_pixels, gsde, exploration):
         del proof_environment
 
 
+@pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
 @pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("from_pixels", [tuple(), ("from_pixels=True", "catframes=4")])
@@ -287,11 +296,12 @@ def test_ppo_maker(device, from_pixels, shared_mapping, gsde, exploration):
         del proof_environment
 
 
+@pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
+@pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("gsde", [tuple(), ("gSDE=True",)])
 @pytest.mark.parametrize("from_pixels", [tuple()])
 @pytest.mark.parametrize("tanh_loc", [tuple(), ("tanh_loc=True",)])
-@pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("exploration", ["random", "mode"])
 def test_sac_make(device, gsde, tanh_loc, from_pixels, exploration):
     if not gsde and exploration != "random":
@@ -402,10 +412,11 @@ def test_sac_make(device, gsde, tanh_loc, from_pixels, exploration):
         del proof_environment
 
 
+@pytest.mark.skipif(not _has_hydra, reason="No hydra library found")
+@pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("device", get_available_devices())
 @pytest.mark.parametrize("from_pixels", [tuple(), ("from_pixels=True", "catframes=4")])
 @pytest.mark.parametrize("gsde", [tuple(), ("gSDE=True",)])
-@pytest.mark.skipif(not _has_gym, reason="No gym library found")
 @pytest.mark.parametrize("exploration", ["random", "mode"])
 def test_redq_make(device, from_pixels, gsde, exploration):
     if not gsde and exploration != "random":

test/test_loggers.py

@@ -7,12 +7,22 @@
 
 import pytest
 import torch
-import torchvision
 from torchrl.trainers.loggers.csv import CSVLogger
-from torchrl.trainers.loggers.mlflow import MLFlowLogger, _has_mlflow
+from torchrl.trainers.loggers.mlflow import MLFlowLogger, _has_mlflow, _has_tv
 from torchrl.trainers.loggers.tensorboard import TensorboardLogger, _has_tb
 from torchrl.trainers.loggers.wandb import WandbLogger, _has_wandb
 
+if _has_tv:
+    import torchvision
+
+if _has_tb:
+    from tensorboard.backend.event_processing.event_accumulator import (
+        EventAccumulator,
+    )
+
+if _has_mlflow:
+    import mlflow
+
 
 @pytest.mark.skipif(not _has_tb, reason="TensorBoard not installed")
 class TestTensorboard:
@@ -34,9 +44,6 @@ def test_log_scalar(self, steps):
                 )
 
             sleep(0.01)  # wait until events are registered
-            from tensorboard.backend.event_processing.event_accumulator import (
-                EventAccumulator,
-            )
 
             event_acc = EventAccumulator(logger.experiment.get_logdir())
             event_acc.Reload()
@@ -69,9 +76,6 @@ def test_log_video(self, steps):
                 )
 
             sleep(0.01)  # wait until events are registered
-            from tensorboard.backend.event_processing.event_accumulator import (
-                EventAccumulator,
-            )
 
             event_acc = EventAccumulator(logger.experiment.get_logdir())
             event_acc.Reload()
@@ -225,7 +229,6 @@ def test_log_video(self):
 @pytest.fixture
 def mlflow_fixture():
     torch.manual_seed(0)
-    import mlflow
 
     with tempfile.TemporaryDirectory() as log_dir:
         exp_name = "ramala"
@@ -240,7 +243,6 @@ def mlflow_fixture():
 class TestMLFlowLogger:
     @pytest.mark.parametrize("steps", [None, [1, 10, 11]])
     def test_log_scalar(self, steps, mlflow_fixture):
-        import mlflow
 
         logger, client = mlflow_fixture
         values = torch.rand(3)
@@ -259,8 +261,8 @@ def test_log_scalar(self, steps, mlflow_fixture):
             assert metric.value == values[i].item()
 
     @pytest.mark.parametrize("steps", [None, [1, 10, 11]])
+    @pytest.mark.skipif(not _has_tv, reason="torchvision not installed")
     def test_log_video(self, steps, mlflow_fixture):
-        import mlflow
 
         logger, client = mlflow_fixture
         videos = torch.cat(