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flake8 fixes and new function

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David Scripka 2023-11-21 11:12:22 -05:00
parent 484b2ca17e
commit f6644b8c92
1 changed files with 10 additions and 6 deletions
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16
openwakeword/train.py
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@ -225,7 +225,7 @@ class Model(nn.Module):
def _select_best_model(self, false_positive_validate_data, val_set_hrs=11.3, max_fp_per_hour=0.5, min_recall=0.20):
"""
Select the top model based on the false positive rate on the validation data
Args:
false_positive_validate_data (torch.DataLoader): A dataloader with validation data
n (int): The number of models to select
@ -237,7 +237,8 @@ class Model(nn.Module):
false_positive_rates = [0]*len(self.best_models)
for batch in false_positive_validate_data:
x_val, y_val = batch[0].to(self.device), batch[1].to(self.device)
for mdl_ndx, model in tqdm(enumerate(self.best_models), total=len(self.best_models), desc="Find best checkpoints by false positive rate"):
for mdl_ndx, model in tqdm(enumerate(self.best_models), total=len(self.best_models),
desc="Find best checkpoints by false positive rate"):
with torch.no_grad():
val_ps = model(x_val)
false_positive_rates[mdl_ndx] = false_positive_rates[mdl_ndx] + self.fp(val_ps, y_val[..., None]).detach().cpu().numpy()
@ -251,7 +252,9 @@ class Model(nn.Module):
else:
best_model = self.best_models[candidate_model_ndx[np.argmax(candidate_model_recall)]]
best_model_training_step = self.best_model_scores[candidate_model_ndx[np.argmax(candidate_model_recall)]]["training_step_ndx"]
print(f"Best model from training step {best_model_training_step} out of {len(candidate_model_ndx)} models has recall of {np.max(candidate_model_recall)} and false positive rate of {false_positive_rates[candidate_model_ndx[np.argmax(candidate_model_recall)]]}")
logging.info(f"Best model from training step {best_model_training_step} out of {len(candidate_model_ndx)}"
f"models has recall of {np.max(candidate_model_recall)} and false positive rate of"
f" {false_positive_rates[candidate_model_ndx[np.argmax(candidate_model_recall)]]}")
return best_model
@ -423,7 +426,8 @@ class Model(nn.Module):
# Save ONNX model
logging.info(f"####\nSaving ONNX mode as '{os.path.join(output_dir, model_name + '.onnx')}'")
model_to_save = copy.deepcopy(model)
torch.onnx.export(model_to_save.to("cpu"), torch.rand(self.input_shape)[None, ], os.path.join(output_dir, model_name + ".onnx"), opset_version=13)
torch.onnx.export(model_to_save.to("cpu"), torch.rand(self.input_shape)[None, ],
os.path.join(output_dir, model_name + ".onnx"), opset_version=13)
return None
@ -695,7 +699,7 @@ if __name__ == '__main__':
for target_phrase in config["target_phrase"]:
adversarial_texts.extend(generate_adversarial_texts(
input_text=target_phrase,
N=config["n_samples"],
N=config["n_samples"]//len(config["target_phrase"]),
include_partial_phrase=1.0,
include_input_words=0.2))
generate_samples(text=adversarial_texts, max_samples=config["n_samples"]-n_current_samples,
@ -718,7 +722,7 @@ if __name__ == '__main__':
for target_phrase in config["target_phrase"]:
adversarial_texts.extend(generate_adversarial_texts(
input_text=target_phrase,
N=config["n_samples_val"],
N=config["n_samples_val"]//len(config["target_phrase"]),
include_partial_phrase=1.0,
include_input_words=0.2))
generate_samples(text=adversarial_texts, max_samples=config["n_samples_val"]-n_current_samples,