def run_noise_test(config):
"""Run noise test on the best scoring model found during training. Make
sure to train the models before calling this function.
:param config: The configuration of the pre-trained model.
:return: dict with noise test results over all experiments
"""
# Load experiment data
name = config["name"]
experiment_path = os.path.join(config["path"], name)
experiment_state = load_ray_tune_experiment(
experiment_path=experiment_path, load_results=True)
# Go through all checkpoints in the experiment
all_checkpoints = experiment_state["checkpoints"]
for checkpoint in all_checkpoints:
results = checkpoint["results"]
if results is None:
continue
# For each checkpoint select the epoch with the best accuracy as the best epoch
best_result = max(results, key=lambda x: x["mean_accuracy"])
best_epoch = best_result["training_iteration"]
best_config = best_result["config"]
print("best_epoch: ", best_epoch)
# Update path
best_config["path"] = config["path"]
best_config["data_dir"] = config["data_dir"]
# Load pre-trained model from checkpoint and run noise test on it
logdir = os.path.join(experiment_path,
os.path.basename(checkpoint["logdir"]))
checkpoint_path = os.path.join(logdir,
"checkpoint_{}".format(best_epoch))
experiment = SparseSpeechExperiment(best_config)
experiment.restore(checkpoint_path)
# Save noise results in checkpoint log dir
noise_test = os.path.join(logdir, "noise.json")
with open(noise_test, "w") as f:
res = experiment.run_noise_tests()
json.dump(res, f)
# Compute total noise score
total_correct = 0
for k, v in res.items():
print(k, v, v["total_correct"])
total_correct += v["total_correct"]
print("Total across all noise values", total_correct)
# Upload results to S3
sync_function = config.get("sync_function", None)
if sync_function is not None:
upload_dir = config["upload_dir"]
final_cmd = sync_function.format(local_dir=experiment_path,
remote_dir=upload_dir)
subprocess.Popen(final_cmd, shell=True)
def run_noise_test(config):
"""
Run noise test on the best scoring model found during training. Make sure to
train the models before calling this function
:param config: The configuration of the pre-trained model.
:return: dict with noise test results over all experiments
"""
# Load experiment data
name = config["name"]
experiment_path = os.path.join(config["path"], name)
experiment_state = load_ray_tune_experiment(
experiment_path=experiment_path, load_results=True)
# Go through all checkpoints in the experiment
all_checkpoints = experiment_state["checkpoints"]
for checkpoint in all_checkpoints:
results = checkpoint["results"]
if results is None:
continue
# For each checkpoint select the epoch with the best accuracy as the best epoch
best_result = max(results, key=lambda x: x["mean_accuracy"])
best_epoch = best_result["training_iteration"]
best_config = best_result["config"]
# Update path
best_config["path"] = config["path"]
best_config["data_dir"] = config["data_dir"]
# Load pre-trained model from checkpoint and run noise test on it
logdir = os.path.join(experiment_path, os.path.basename(checkpoint["logdir"]))
checkpoint_path = os.path.join(logdir, "checkpoint_{}".format(best_epoch))
experiment = SparseSpeechExperiment(best_config)
experiment.restore(checkpoint_path)
# Save noise results in checkpoint log dir
noise_test = os.path.join(logdir, "noise.json")
with open(noise_test, "w") as f:
json.dump(experiment.runNoiseTests(), f)
# Upload results to S3
sync_function = config.get("sync_function", None)
if sync_function is not None:
upload_dir = config["upload_dir"]
final_cmd = sync_function.format(local_dir=experiment_path,
remote_dir=upload_dir)
subprocess.Popen(final_cmd, shell=True)
class SpeechExperimentTune(tune.Trainable):
"""Ray tune trainable wrapping MNISTSparseExperiment."""
def _setup(self, config):
self.experiment = SparseSpeechExperiment(config)
def _train(self):
self.experiment.train(self._iteration)
return self.experiment.test()
def _save(self, checkpoint_dir):
return self.experiment.save(checkpoint_dir)
def _restore(self, checkpoint_dir):
self.experiment.restore(checkpoint_dir)
class SpeechExperimentTune(tune.Trainable):
"""
Ray tune trainable wrapping MNISTSparseExperiment
"""
def _setup(self, config):
self.experiment = SparseSpeechExperiment(config)
def _train(self):
self.experiment.train(self._iteration)
return self.experiment.test()
def _save(self, checkpoint_dir):
return self.experiment.save(checkpoint_dir)
def _restore(self, checkpoint_dir):
self.experiment.restore(checkpoint_dir)
def _setup(self, config):
self.experiment = SparseSpeechExperiment(config)
def _setup(self, config): self.experiment = SparseSpeechExperiment(config)