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pep8 format all

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Eren G
2018-08-02 16:34:17 +02:00
parent 3238ffa3e6
commit f5537dc48f
32 changed files with 766 additions and 599 deletions
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utils/generic_utils.py
+35 -27
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@@ -28,10 +28,13 @@ def load_config(config_path):
def get_commit_hash():
"""https://stackoverflow.com/questions/14989858/get-the-current-git-hash-in-a-python-script"""
try:
subprocess.check_output(['git', 'diff-index', '--quiet', 'HEAD']) # Verify client is clean
subprocess.check_output(['git', 'diff-index', '--quiet',
'HEAD']) # Verify client is clean
except:
raise RuntimeError(" !! Commit before training to get the commit hash.")
commit = subprocess.check_output(['git', 'rev-parse', '--short', 'HEAD']).decode().strip()
raise RuntimeError(
" !! Commit before training to get the commit hash.")
commit = subprocess.check_output(['git', 'rev-parse', '--short',
'HEAD']).decode().strip()
print(' > Git Hash: {}'.format(commit))
return commit
@@ -43,7 +46,8 @@ def create_experiment_folder(root_path, model_name, debug):
commit_hash = 'debug'
else:
commit_hash = get_commit_hash()
output_folder = os.path.join(root_path, date_str + '-' + model_name + '-' + commit_hash)
output_folder = os.path.join(
root_path, date_str + '-' + model_name + '-' + commit_hash)
os.makedirs(output_folder, exist_ok=True)
print(" > Experiment folder: {}".format(output_folder))
return output_folder
@@ -52,7 +56,7 @@ def create_experiment_folder(root_path, model_name, debug):
def remove_experiment_folder(experiment_path):
"""Check folder if there is a checkpoint, otherwise remove the folder"""
checkpoint_files = glob.glob(experiment_path+"/*.pth.tar")
checkpoint_files = glob.glob(experiment_path + "/*.pth.tar")
if len(checkpoint_files) < 1:
if os.path.exists(experiment_path):
shutil.rmtree(experiment_path)
@@ -86,13 +90,15 @@ def save_checkpoint(model, optimizer, optimizer_st, model_loss, out_path,
print(" | | > Checkpoint saving : {}".format(checkpoint_path))
new_state_dict = _trim_model_state_dict(model.state_dict())
state = {'model': new_state_dict,
'optimizer': optimizer.state_dict(),
'optimizer_st': optimizer_st.state_dict(),
'step': current_step,
'epoch': epoch,
'linear_loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y")}
state = {
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
'optimizer_st': optimizer_st.state_dict(),
'step': current_step,
'epoch': epoch,
'linear_loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y")
}
torch.save(state, checkpoint_path)
@@ -100,12 +106,14 @@ def save_best_model(model, optimizer, model_loss, best_loss, out_path,
current_step, epoch):
if model_loss < best_loss:
new_state_dict = _trim_model_state_dict(model.state_dict())
state = {'model': new_state_dict,
'optimizer': optimizer.state_dict(),
'step': current_step,
'epoch': epoch,
'linear_loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y")}
state = {
'model': new_state_dict,
'optimizer': optimizer.state_dict(),
'step': current_step,
'epoch': epoch,
'linear_loss': model_loss,
'date': datetime.date.today().strftime("%B %d, %Y")
}
best_loss = model_loss
bestmodel_path = 'best_model.pth.tar'
bestmodel_path = os.path.join(out_path, bestmodel_path)
@@ -161,12 +169,12 @@ def sequence_mask(sequence_length, max_len=None):
def synthesis(model, ap, text, use_cuda, text_cleaner):
text_cleaner = [text_cleaner]
seq = np.array(text_to_sequence(text, text_cleaner))
chars_var = torch.from_numpy(seq).unsqueeze(0)
if use_cuda:
chars_var = chars_var.cuda().long()
_, linear_out, alignments, _ = model.forward(chars_var)
linear_out = linear_out[0].data.cpu().numpy()
wav = ap.inv_spectrogram(linear_out.T)
return wav, linear_out, alignments
text_cleaner = [text_cleaner]
seq = np.array(text_to_sequence(text, text_cleaner))
chars_var = torch.from_numpy(seq).unsqueeze(0)
if use_cuda:
chars_var = chars_var.cuda().long()
_, linear_out, alignments, _ = model.forward(chars_var)
linear_out = linear_out[0].data.cpu().numpy()
wav = ap.inv_spectrogram(linear_out.T)
return wav, linear_out, alignments