From b989123bd41c7c47e1274713602ca51abf4b5c7a Mon Sep 17 00:00:00 2001
From: mrq <barry.quiggles@protonmail.com>
Date: Wed, 1 Mar 2023 19:32:11 +0000
Subject: [PATCH] leverage tensorboard to parse tb_logger files when starting
training (it seems to give a nicer resolution of training data, need to see
about reading it directly while training)
---
src/utils.py | 91 +++++++++++++++++++++++++++++-----------------------
src/webui.py | 12 +++----
2 files changed, 56 insertions(+), 47 deletions(-)
diff --git a/src/utils.py b/src/utils.py
index b553d6e..6a803a9 100755
--- a/src/utils.py
+++ b/src/utils.py
@@ -498,39 +498,57 @@ class TrainingState():
self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
def load_losses(self):
- if not os.path.isdir(self.dataset_dir):
+ if not os.path.isdir(f'{self.dataset_dir}/tb_logger/'):
return
+ try:
+ from tensorboard.backend.event_processing import event_accumulator
+ use_tensorboard = True
+ except Exception as e:
+ use_tensorboard = False
- logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
- infos = {}
- for log in logs:
- with open(log, 'r', encoding="utf-8") as f:
- lines = f.readlines()
- for line in lines:
- if line.find('INFO: [epoch:') >= 0:
- # easily rip out our stats...
- match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
- if not match or len(match) == 0:
- continue
+ if use_tensorboard:
+ logs = sorted([f'{self.dataset_dir}/tb_logger/{d}' for d in os.listdir(f'{self.dataset_dir}/tb_logger/') if d[:6] == "events" ])
+ infos = {}
+ for log in logs:
+ try:
+ ea = event_accumulator.EventAccumulator(log, size_guidance={event_accumulator.SCALARS: 0})
+ ea.Reload()
- info = {}
- for k, v in match:
- info[k] = float(v.replace(",", ""))
+ keys = ['loss_text_ce', 'loss_mel_ce', 'loss_gpt_total']
+ for key in keys:
+ scalar = ea.Scalars(key)
+ for s in scalar:
+ self.losses.append( { "step": s.step, "value": s.value, "type": key } )
+ except Exception as e:
+ print("Failed to parse event log:", log)
+ pass
- if 'iter' in info:
- it = info['iter']
- infos[it] = info
+ else:
+ logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
+ infos = {}
+ for log in logs:
+ with open(log, 'r', encoding="utf-8") as f:
+ lines = f.readlines()
+ for line in lines:
+ if line.find('INFO: [epoch:') >= 0:
+ # easily rip out our stats...
+ match = re.findall(r'\b([a-z_0-9]+?)\b: +?([0-9]\.[0-9]+?e[+-]\d+|[\d,]+)\b', line)
+ if not match or len(match) == 0:
+ continue
- for k in infos:
- if 'loss_gpt_total' in infos[k]:
- # self.losses.append([ int(k), infos[k]['loss_text_ce'], infos[k]['loss_mel_ce'], infos[k]['loss_gpt_total'] ])
- self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_text_ce'], "type": "text_ce" })
- self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_mel_ce'], "type": "mel_ce" })
- self.losses.append({ "iteration": int(k), "loss": infos[k]['loss_gpt_total'], "type": "gpt_total" })
- """
- self.losses['iteration'].append(int(k))
- self.losses['loss_gpt_total'].append(infos[k]['loss_gpt_total'])
- """
+ info = {}
+ for k, v in match:
+ info[k] = float(v.replace(",", ""))
+
+ if 'iter' in info:
+ it = info['iter']
+ infos[it] = info
+
+ for k in infos:
+ if 'loss_gpt_total' in infos[k]:
+ self.losses.append({ "step": int(k), "value": infos[k]['loss_text_ce'], "type": "text_ce" })
+ self.losses.append({ "step": int(k), "value": infos[k]['loss_mel_ce'], "type": "mel_ce" })
+ self.losses.append({ "step": int(k), "value": infos[k]['loss_gpt_total'], "type": "gpt_total" })
def cleanup_old(self, keep=2):
if keep <= 0:
@@ -606,7 +624,7 @@ class TrainingState():
pass
last_loss = ""
if len(self.losses) > 0:
- last_loss = f'[Loss @ it. {self.losses[-1]["iteration"]}: {self.losses[-1]["loss"]}]'
+ last_loss = f'[Loss @ it. {self.losses[-1]["step"]}: {self.losses[-1]["value"]}]'
message = f'[{self.epoch}/{self.epochs}, {self.it}/{self.its}, {step}/{steps}] [{self.epoch_rate}, {self.it_rate}] {last_loss} [ETA: {self.eta_hhmmss}]'
if lapsed:
@@ -645,18 +663,9 @@ class TrainingState():
if 'loss_gpt_total' in self.info:
self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
- self.losses.append({ "iteration": self.it, "loss": self.info['loss_text_ce'], "type": "text_ce" })
- self.losses.append({ "iteration": self.it, "loss": self.info['loss_mel_ce'], "type": "mel_ce" })
- self.losses.append({ "iteration": self.it, "loss": self.info['loss_gpt_total'], "type": "gpt_total" })
- """
- self.losses.append([int(k), self.info['loss_text_ce'], "loss_text_ce"])
- self.losses.append([int(k), self.info['loss_mel_ce'], "loss_mel_ce"])
- self.losses.append([int(k), self.info['loss_gpt_total'], "loss_gpt_total"])
- """
- """
- self.losses['iteration'].append(self.it)
- self.losses['loss_gpt_total'].append(self.info['loss_gpt_total'])
- """
+ self.losses.append({ "step": self.it, "value": self.info['loss_text_ce'], "type": "text_ce" })
+ self.losses.append({ "step": self.it, "value": self.info['loss_mel_ce'], "type": "mel_ce" })
+ self.losses.append({ "step": self.it, "value": self.info['loss_gpt_total'], "type": "gpt_total" })
should_return = True
elif line.find('Saving models and training states') >= 0:
diff --git a/src/webui.py b/src/webui.py
index 6958034..79153ef 100755
--- a/src/webui.py
+++ b/src/webui.py
@@ -380,7 +380,7 @@ def setup_gradio():
prompt = gr.Textbox(lines=1, label="Custom Emotion + Prompt (if selected)")
voice = gr.Dropdown(choices=voice_list_with_defaults, label="Voice", type="value", value=voice_list_with_defaults[0]) # it'd be very cash money if gradio was able to default to the first value in the list without this shit
mic_audio = gr.Audio( label="Microphone Source", source="microphone", type="filepath" )
- voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=64, value=1, step=1)
+ voice_latents_chunks = gr.Slider(label="Voice Chunks", minimum=1, maximum=128, value=1, step=1)
with gr.Row():
refresh_voices = gr.Button(value="Refresh Voice List")
recompute_voice_latents = gr.Button(value="(Re)Compute Voice Latents")
@@ -538,12 +538,12 @@ def setup_gradio():
training_buffer_size = gr.Slider(label="Console Buffer Size", minimum=4, maximum=32, value=8)
training_keep_x_past_datasets = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
- training_loss_graph = gr.LinePlot(label="Loss Rates",
- x="iteration",
- y="loss",
- title="Loss Rates",
+ training_loss_graph = gr.LinePlot(label="Training Metrics",
+ x="step",
+ y="value",
+ title="Training Metrics",
color="type",
- tooltip=['iteration', 'loss', 'type'],
+ tooltip=['step', 'value', 'type'],
width=600,
height=350
)