From 9c4399fb7a30a2767dc8b823d1e89d3821fab40d Mon Sep 17 00:00:00 2001 From: Brandon Thomas Date: Tue, 2 May 2023 01:39:59 -0700 Subject: [PATCH] more logging --- .../src/so_vits_svc_fork/utils.py | 16 ++++++++++++++++ 1 file changed, 16 insertions(+) diff --git a/voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/utils.py b/voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/utils.py index 576c32d..2ce8810 100644 --- a/voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/utils.py +++ b/voice_conversion/so-vits-svc-paul/src/so_vits_svc_fork/utils.py @@ -394,7 +394,13 @@ def load_checkpoint( ): if not Path(checkpoint_path).is_file(): raise FileNotFoundError(f"File {checkpoint_path} not found") + + LOG.info(f"load_checkpoint() - torch.load() ... ") + checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") + + LOG.info(f"load_checkpoint() - torch.load() - DONE") + iteration = checkpoint_dict["iteration"] learning_rate = checkpoint_dict["learning_rate"] if ( @@ -402,12 +408,19 @@ def load_checkpoint( and not skip_optimizer and checkpoint_dict["optimizer"] is not None ): + LOG.info(f"load_checkpoint() - optimizer.load_state_dict() ...") optimizer.load_state_dict(checkpoint_dict["optimizer"]) + LOG.info(f"load_checkpoint() - optimizer.load_state_dict() - DONE") + saved_state_dict = checkpoint_dict["model"] if hasattr(model, "module"): + LOG.info(f"load_checkpoint() - model.module.state_dict() ...") state_dict = model.module.state_dict() + LOG.info(f"load_checkpoint() - model.module.state_dict() - DONE") else: + LOG.info(f"load_checkpoint() - model.state_dict() ...") state_dict = model.state_dict() + LOG.info(f"load_checkpoint() - model.state_dict() - DONE") new_state_dict = {} for k, v in state_dict.items(): try: @@ -423,9 +436,12 @@ def load_checkpoint( LOG.error("%s is not in the checkpoint" % k) new_state_dict[k] = v if hasattr(model, "module"): + LOG.info(f"load_checkpoint() - [2] model.module.load_state_dict() ...") model.module.load_state_dict(new_state_dict) else: + LOG.info(f"load_checkpoint() - [2] model.load_state_dict() ...") model.load_state_dict(new_state_dict) + LOG.info(f"Loaded checkpoint '{checkpoint_path}' (iteration {iteration})") return model, optimizer, learning_rate, iteration