forked from mrq/ai-voice-cloning
fixed the brain worm discrepancy between epochs, iterations, and steps
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parent
1cbcf14cff
commit
487f2ebf32
40
src/utils.py
40
src/utils.py
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@ -445,9 +445,16 @@ class TrainingState():
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with open(config_path, 'r') as file:
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self.config = yaml.safe_load(file)
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self.dataset_path = self.config['datasets']['train']['path']
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with open(self.dataset_path, 'r', encoding="utf-8") as f:
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self.dataset_size = len(f.readlines())
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self.it = 0
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self.its = self.config['train']['niter']
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self.epoch = 0
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self.epochs = int(self.its/self.dataset_size)
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self.checkpoint = 0
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self.checkpoints = int(self.its / self.config['logger']['save_checkpoint_freq'])
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@ -459,10 +466,11 @@ class TrainingState():
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self.info = {}
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self.status = ""
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self.it_rate = ""
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self.it_time_start = 0
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self.it_time_end = 0
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self.epoch_rate = ""
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self.epoch_time_start = 0
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self.epoch_time_end = 0
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self.eta = "?"
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self.eta_hhmmss = "?"
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print("Spawning process: ", " ".join(self.cmd))
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self.process = subprocess.Popen(self.cmd, stdout=subprocess.PIPE, stderr=subprocess.STDOUT, universal_newlines=True)
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@ -473,27 +481,30 @@ class TrainingState():
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# rip out iteration info
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if not self.training_started:
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if line.find('Start training from epoch') >= 0:
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self.it_time_start = time.time()
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self.epoch_time_start = time.time()
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self.training_started = True # could just leverage the above variable, but this is python, and there's no point in these aggressive microoptimizations
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match = re.findall(r'epoch: ([\d,]+)', line)
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if match and len(match) > 0:
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self.epoch = int(match[0].replace(",", ""))
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match = re.findall(r'iter: ([\d,]+)', line)
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if match and len(match) > 0:
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self.it = int(match[0].replace(",", ""))
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elif progress is not None:
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if line.find(' 0%|') == 0:
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if line.find('%|') > 0 and not self.open_state:
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self.open_state = True
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elif line.find('100%|') == 0 and self.open_state:
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self.open_state = False
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self.it = self.it + 1
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self.epoch = self.epoch + 1
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self.it_time_end = time.time()
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self.it_time_delta = self.it_time_end-self.it_time_start
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self.it_time_start = time.time()
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self.it_rate = f'[{"{:.3f}".format(self.it_time_delta)}s/it]' if self.it_time_delta >= 1 else f'[{"{:.3f}".format(1/self.it_time_delta)}it/s]' # I doubt anyone will have it/s rates, but its here
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self.eta = (self.its - self.it) * self.it_time_delta
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self.epoch_time_end = time.time()
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self.epoch_time_delta = self.epoch_time_end-self.epoch_time_start
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self.epoch_time_start = time.time()
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self.epoch_rate = f'[{"{:.3f}".format(self.epoch_time_delta)}s/epoch]' if self.epoch_time_delta >= 1 else f'[{"{:.3f}".format(1/self.epoch_time_delta)}epoch/s]' # I doubt anyone will have it/s rates, but its here
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self.eta = (self.epochs - self.epoch) * self.epoch_time_delta
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self.eta_hhmmss = str(timedelta(seconds=int(self.eta)))
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progress(self.it / float(self.its), f'[{self.it}/{self.its}] [ETA: {self.eta_hhmmss}] {self.it_rate} Training... {self.status}')
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progress(self.epoch / float(self.epochs), f'[{self.epoch}/{self.epochs}] [ETA: {self.eta_hhmmss}] {self.epoch_rate} Training... {self.status}')
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if line.find('INFO: [epoch:') >= 0:
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# easily rip out our stats...
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@ -502,11 +513,8 @@ class TrainingState():
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for k, v in match:
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self.info[k] = float(v)
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# ...and returns our loss rate
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# it would be nice for losses to be shown at every step
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if 'loss_gpt_total' in self.info:
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# self.info['step'] returns the steps, not iterations, so we won't even bother ripping the reported step count, as iteration count won't get ripped from the regex
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self.status = f"Total loss at iteration {self.it}: {self.info['loss_gpt_total']}"
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self.status = f"Total loss at epoch {self.epoch}: {self.info['loss_gpt_total']}"
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elif line.find('Saving models and training states') >= 0:
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self.checkpoint = self.checkpoint + 1
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progress(self.checkpoint / float(self.checkpoints), f'[{self.checkpoint}/{self.checkpoints}] Saving checkpoint...')
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