def test_writing_stack(self):
with TemporaryDirectory() as tmp_dir1, TemporaryDirectory(
) as tmp_dir2:
writer1 = SummaryWriter(tmp_dir1)
writer1.add_scalar = MagicMock()
writer2 = SummaryWriter(tmp_dir2)
writer2.add_scalar = MagicMock()
with summary_writer_context(writer1):
with summary_writer_context(writer2):
SummaryWriterContext.add_scalar("test2", torch.ones(1))
SummaryWriterContext.add_scalar("test1", torch.zeros(1))
writer1.add_scalar.assert_called_once_with("test1", torch.zeros(1))
writer2.add_scalar.assert_called_once_with("test2", torch.ones(1))
def test_swallowing_exception(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock(side_effect=NotImplementedError("test"))
writer.exceptions_to_ignore = (NotImplementedError, KeyError)
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
def test_add_custom_scalars(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_custom_scalars = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_custom_scalars_multilinechart(
["a", "b"], category="cat", title="title")
with self.assertRaisesRegex(
AssertionError,
"Title \\(title\\) is already in category \\(cat\\)"):
SummaryWriterContext.add_custom_scalars_multilinechart(
["c", "d"], category="cat", title="title")
SummaryWriterContext.add_custom_scalars_multilinechart(
["e", "f"], category="cat", title="title2")
SummaryWriterContext.add_custom_scalars_multilinechart(
["g", "h"], category="cat2", title="title")
SummaryWriterContext.add_custom_scalars(writer)
writer.add_custom_scalars.assert_called_once_with({
"cat": {
"title": ["Multiline", ["a", "b"]],
"title2": ["Multiline", ["e", "f"]],
},
"cat2": {
"title": ["Multiline", ["g", "h"]]
},
})
def test_writing(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
writer.add_scalar.assert_called_once_with("test", torch.ones(1))
def test_swallowing_exception(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock(side_effect=NotImplementedError("test"))
writer.exceptions_to_ignore = (NotImplementedError, KeyError)
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
def test_add_custom_scalars(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_custom_scalars = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_custom_scalars_multilinechart(
["a", "b"], category="cat", title="title"
)
with self.assertRaisesRegexp(
AssertionError, "Title \(title\) is already in category \(cat\)"
):
SummaryWriterContext.add_custom_scalars_multilinechart(
["c", "d"], category="cat", title="title"
)
SummaryWriterContext.add_custom_scalars_multilinechart(
["e", "f"], category="cat", title="title2"
)
SummaryWriterContext.add_custom_scalars_multilinechart(
["g", "h"], category="cat2", title="title"
)
SummaryWriterContext.add_custom_scalars(writer)
writer.add_custom_scalars.assert_called_once_with(
{
"cat": {
"title": ["Multiline", ["a", "b"]],
"title2": ["Multiline", ["e", "f"]],
},
"cat2": {"title": ["Multiline", ["g", "h"]]},
}
)
def test_writing_stack(self):
with TemporaryDirectory() as tmp_dir1, TemporaryDirectory() as tmp_dir2:
writer1 = SummaryWriter(tmp_dir1)
writer1.add_scalar = MagicMock()
writer2 = SummaryWriter(tmp_dir2)
writer2.add_scalar = MagicMock()
with summary_writer_context(writer1):
with summary_writer_context(writer2):
SummaryWriterContext.add_scalar("test2", torch.ones(1))
SummaryWriterContext.add_scalar("test1", torch.zeros(1))
writer1.add_scalar.assert_called_once_with(
"test1", torch.zeros(1), global_step=0
)
writer2.add_scalar.assert_called_once_with(
"test2", torch.ones(1), global_step=0
)
def test_not_swallowing_exception(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock(side_effect=NotImplementedError("test"))
with self.assertRaisesRegex(
NotImplementedError, "test"
), summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
def test_not_swallowing_exception(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock(side_effect=NotImplementedError("test"))
with self.assertRaisesRegexp(
NotImplementedError, "test"
), summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
def test_writing(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
writer.add_scalar.assert_called_once_with(
"test", torch.ones(1), global_step=0
)
def main(params):
# Set minibatch size based on # of devices being used to train
params["shared_training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = DDPGTrainingParameters(**params["shared_training"])
actor_parameters = DDPGNetworkParameters(**params["actor_training"])
critic_parameters = DDPGNetworkParameters(**params["critic_training"])
model_params = DDPGModelParameters(
rl=rl_parameters,
shared_training=training_parameters,
actor_training=actor_parameters,
critic_training=critic_parameters,
)
state_normalization = BaseWorkflow.read_norm_file(
params["state_norm_data_path"])
action_normalization = BaseWorkflow.read_norm_file(
params["action_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
preprocess_handler = ContinuousPreprocessHandler(
Preprocessor(state_normalization, False),
Preprocessor(action_normalization, False),
PandasSparseToDenseProcessor(),
)
workflow = ContinuousWorkflow(
model_params,
preprocess_handler,
state_normalization,
action_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size)
eval_dataset = JSONDatasetReader(params["eval_data_path"], batch_size=16)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset,
int(params["epochs"]))
return export_trainer_and_predictor(
workflow.trainer,
params["model_output_path"],
exporter=_get_actor_exporter(
trainer=workflow.trainer,
state_normalization=state_normalization,
action_normalization=action_normalization,
),
) # noqa
def main(params):
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
model_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters)
state_normalization = BaseWorkflow.read_norm_file(
params["state_norm_data_path"])
action_normalization = BaseWorkflow.read_norm_file(
params["action_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
preprocess_handler = ParametricDqnPreprocessHandler(
Preprocessor(state_normalization, False),
Preprocessor(action_normalization, False),
PandasSparseToDenseProcessor(),
)
workflow = ParametricDqnWorkflow(
model_params,
preprocess_handler,
state_normalization,
action_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size)
eval_dataset = JSONDatasetReader(params["eval_data_path"], batch_size=16)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset,
int(params["epochs"]))
exporter = ParametricDQNExporter(
workflow.trainer.q_network,
PredictorFeatureExtractor(
state_normalization_parameters=state_normalization,
action_normalization_parameters=action_normalization,
),
ParametricActionOutputTransformer(),
)
return export_trainer_and_predictor(workflow.trainer,
params["model_output_path"],
exporter=exporter) # noqa
def test_global_step(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
SummaryWriterContext.increase_global_step()
SummaryWriterContext.add_scalar("test", torch.zeros(1))
writer.add_scalar.assert_has_calls([
call("test", torch.ones(1), global_step=0),
call("test", torch.zeros(1), global_step=1),
])
self.assertEqual(2, len(writer.add_scalar.mock_calls))
def test_global_step(self):
with TemporaryDirectory() as tmp_dir:
writer = SummaryWriter(tmp_dir)
writer.add_scalar = MagicMock()
with summary_writer_context(writer):
SummaryWriterContext.add_scalar("test", torch.ones(1))
SummaryWriterContext.increase_global_step()
SummaryWriterContext.add_scalar("test", torch.zeros(1))
writer.add_scalar.assert_has_calls(
[
call("test", torch.ones(1), global_step=0),
call("test", torch.zeros(1), global_step=1),
]
)
self.assertEqual(2, len(writer.add_scalar.mock_calls))
def single_process_main(gpu_index, *args):
params = args[0]
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"]
)
action_names = params["actions"]
rl_parameters = RLParameters(**params["rl"])
training_parameters = TrainingParameters(**params["training"])
rainbow_parameters = RainbowDQNParameters(**params["rainbow"])
model_params = DiscreteActionModelParameters(
actions=action_names,
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters,
)
state_normalization = BaseWorkflow.read_norm_file(params["state_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
if params["use_all_avail_gpus"]:
BaseWorkflow.init_multiprocessing(
int(params["num_processes_per_node"]),
int(params["num_nodes"]),
int(params["node_index"]),
gpu_index,
params["init_method"],
)
workflow = DqnWorkflow(
model_params,
state_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
sorted_features, _ = sort_features_by_normalization(state_normalization)
preprocess_handler = DiscreteDqnPreprocessHandler(
action_names, PandasSparseToDenseProcessor(sorted_features)
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size,
preprocess_handler=preprocess_handler,
)
eval_dataset = JSONDatasetReader(
params["eval_data_path"],
batch_size=training_parameters.minibatch_size,
preprocess_handler=preprocess_handler,
)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset, int(params["epochs"]))
exporter = DQNExporter(
workflow.trainer.q_network,
PredictorFeatureExtractor(state_normalization_parameters=state_normalization),
DiscreteActionOutputTransformer(model_params.actions),
)
if int(params["node_index"]) == 0 and gpu_index == 0:
export_trainer_and_predictor(
workflow.trainer, params["model_output_path"], exporter=exporter
) # noqa
def single_process_main(gpu_index, *args):
params = args[0]
# Set minibatch size based on # of devices being used to train
params["training"]["minibatch_size"] *= minibatch_size_multiplier(
params["use_gpu"], params["use_all_avail_gpus"])
rl_parameters = from_json(params["rl"], RLParameters)
training_parameters = from_json(params["training"], TrainingParameters)
rainbow_parameters = from_json(params["rainbow"], RainbowDQNParameters)
model_params = ContinuousActionModelParameters(
rl=rl_parameters,
training=training_parameters,
rainbow=rainbow_parameters)
state_normalization = BaseWorkflow.read_norm_file(
params["state_norm_data_path"])
action_normalization = BaseWorkflow.read_norm_file(
params["action_norm_data_path"])
writer = SummaryWriter(log_dir=params["model_output_path"])
logger.info("TensorBoard logging location is: {}".format(writer.log_dir))
if params["use_all_avail_gpus"]:
BaseWorkflow.init_multiprocessing(
int(params["num_processes_per_node"]),
int(params["num_nodes"]),
int(params["node_index"]),
gpu_index,
params["init_method"],
)
workflow = ParametricDqnWorkflow(
model_params,
state_normalization,
action_normalization,
params["use_gpu"],
params["use_all_avail_gpus"],
)
state_sorted_features, _ = sort_features_by_normalization(
state_normalization)
action_sorted_features, _ = sort_features_by_normalization(
action_normalization)
preprocess_handler = ParametricDqnPreprocessHandler(
PandasSparseToDenseProcessor(state_sorted_features),
PandasSparseToDenseProcessor(action_sorted_features),
)
train_dataset = JSONDatasetReader(
params["training_data_path"],
batch_size=training_parameters.minibatch_size,
preprocess_handler=preprocess_handler,
)
eval_dataset = JSONDatasetReader(params["eval_data_path"],
batch_size=16,
preprocess_handler=preprocess_handler)
with summary_writer_context(writer):
workflow.train_network(train_dataset, eval_dataset,
int(params["epochs"]))
if int(params["node_index"]) == 0 and gpu_index == 0:
workflow.save_models(params["model_output_path"])
def test_with_none(self):
with summary_writer_context(None):
self.assertIsNone(
SummaryWriterContext.add_scalar("test", torch.ones(1)))
def test_with_none(self):
with summary_writer_context(None):
self.assertIsNone(SummaryWriterContext.add_scalar("test", torch.ones(1)))
