forked from mrq/ai-voice-cloning
output fixes, I'm not sure why ETA wasn't working but it works in testing
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parent
098d7ad635
commit
296129ba9c
150
src/utils.py
150
src/utils.py
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@ -638,7 +638,6 @@ class TrainingState():
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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self.loss_milestones = [ 1.0, 0.15, 0.05 ]
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self.load_statistics()
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if keep_x_past_checkpoints > 0:
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if keep_x_past_checkpoints > 0:
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self.cleanup_old(keep=keep_x_past_checkpoints)
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self.cleanup_old(keep=keep_x_past_checkpoints)
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if start:
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if start:
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@ -676,7 +675,7 @@ class TrainingState():
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self.it_rate = f'{"{:.3f}".format(1/it_rate)}it/s' if 0 < it_rate and it_rate < 1 else f'{"{:.3f}".format(it_rate)}s/it'
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self.it_rate = f'{"{:.3f}".format(1/it_rate)}it/s' if 0 < it_rate and it_rate < 1 else f'{"{:.3f}".format(it_rate)}s/it'
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self.it_rates += it_rate
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self.it_rates += it_rate
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epoch_rate = self.it_rates / self.it * self.epoch
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epoch_rate = self.it_rates / self.it * self.steps
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if epoch_rate > 0:
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if epoch_rate > 0:
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self.epoch_rate = f'{"{:.3f}".format(1/epoch_rate)}epoch/s' if 0 < epoch_rate and epoch_rate < 1 else f'{"{:.3f}".format(epoch_rate)}s/epoch'
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self.epoch_rate = f'{"{:.3f}".format(1/epoch_rate)}epoch/s' if 0 < epoch_rate and epoch_rate < 1 else f'{"{:.3f}".format(epoch_rate)}s/epoch'
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@ -710,6 +709,72 @@ class TrainingState():
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return data
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return data
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def get_status(self):
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message = None
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self.metrics['rate'] = []
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if self.epoch_rate:
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self.metrics['rate'].append(self.epoch_rate)
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if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
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self.metrics['rate'].append(self.it_rate)
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self.metrics['rate'] = ", ".join(self.metrics['rate'])
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eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
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self.metrics['loss'] = []
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if 'lr' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
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if len(self.losses) > 0:
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self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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deriv = 0
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accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
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loss_value = self.losses[-1]["value"]
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for i in range(accum_length):
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d1_loss = self.losses[accum_length-i-1]["value"]
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d2_loss = self.losses[accum_length-i-2]["value"]
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dloss = (d2_loss - d1_loss)
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d1_step = self.losses[accum_length-i-1]["epoch"]
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d2_step = self.losses[accum_length-i-2]["epoch"]
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dstep = (d2_step - d1_step)
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if dstep == 0:
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continue
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inst_deriv = dloss / dstep
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deriv += inst_deriv
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deriv = deriv / accum_length
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if deriv != 0: # dloss < 0:
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next_milestone = None
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for milestone in self.loss_milestones:
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if loss_value > milestone:
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next_milestone = milestone
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break
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if next_milestone:
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# tfw can do simple calculus but not basic algebra in my head
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est_its = (next_milestone - loss_value) / deriv
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if est_its >= 0:
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self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
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else:
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est_loss = inst_deriv * (self.its - self.it) + loss_value
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if est_loss >= 0:
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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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self.metrics['loss'] = ", ".join(self.metrics['loss'])
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message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
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if self.nan_detected:
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message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
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return message
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def load_statistics(self, update=False):
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def load_statistics(self, update=False):
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if not os.path.isdir(f'{self.dataset_dir}/'):
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if not os.path.isdir(f'{self.dataset_dir}/'):
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return
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return
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@ -720,6 +785,7 @@ class TrainingState():
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if not update:
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if not update:
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self.statistics['loss'] = []
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self.statistics['loss'] = []
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self.statistics['lr'] = []
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self.statistics['lr'] = []
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self.it_rates = 0
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
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if update:
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if update:
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@ -742,12 +808,13 @@ class TrainingState():
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if "it" not in data:
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if "it" not in data:
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continue
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continue
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step = data['it']
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it = data['it']
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if update and step <= self.last_info_check_at:
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if update and it <= self.last_info_check_at:
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continue
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continue
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self.parse_metrics(data)
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self.parse_metrics(data)
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# print(f"Iterations Left: {self.its - self.it} | Elapsed Time: {self.it_rates} | Time Remaining: {self.eta} | Message: {self.get_status()}")
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self.last_info_check_at = highest_step
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self.last_info_check_at = highest_step
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@ -795,11 +862,10 @@ class TrainingState():
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
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self.load_statistics()
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should_return = True
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should_return = True
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else:
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else:
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message = None
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data = None
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# INFO: Training Metrics: {"loss_text_ce": 4.308311939239502, "loss_mel_ce": 2.1610655784606934, "loss_gpt_total": 2.204148769378662, "lr": 0.0001, "it": 2, "step": 1, "steps": 1, "epoch": 1, "iteration_rate": 0.10700102965037028}
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# INFO: Training Metrics: {"loss_text_ce": 4.308311939239502, "loss_mel_ce": 2.1610655784606934, "loss_gpt_total": 2.204148769378662, "lr": 0.0001, "it": 2, "step": 1, "steps": 1, "epoch": 1, "iteration_rate": 0.10700102965037028}
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if line.find('INFO: Training Metrics:') >= 0:
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if line.find('INFO: Training Metrics:') >= 0:
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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data = json.loads(line.split("INFO: Training Metrics:")[-1])
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@ -809,72 +875,11 @@ class TrainingState():
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data['mode'] = "validation"
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data['mode'] = "validation"
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if data is not None:
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if data is not None:
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self.parse_metrics( data )
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should_return = True
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if ': nan' in line and not self.nan_detected:
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if ': nan' in line and not self.nan_detected:
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self.nan_detected = self.it
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self.nan_detected = self.it
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self.metrics['rate'] = []
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self.parse_metrics( data )
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if self.epoch_rate:
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message = self.get_status()
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self.metrics['rate'].append(self.epoch_rate)
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if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
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self.metrics['rate'].append(self.it_rate)
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self.metrics['rate'] = ", ".join(self.metrics['rate'])
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eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
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self.metrics['loss'] = []
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if 'lr' in self.info:
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self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
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if len(self.losses) > 0:
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self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
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if len(self.losses) >= 2:
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deriv = 0
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accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
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loss_value = self.losses[-1]["value"]
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for i in range(accum_length):
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d1_loss = self.losses[accum_length-i-1]["value"]
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d2_loss = self.losses[accum_length-i-2]["value"]
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dloss = (d2_loss - d1_loss)
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d1_step = self.losses[accum_length-i-1]["step"]
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d2_step = self.losses[accum_length-i-2]["step"]
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dstep = (d2_step - d1_step)
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if dstep == 0:
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continue
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inst_deriv = dloss / dstep
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deriv += inst_deriv
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deriv = deriv / accum_length
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if deriv != 0: # dloss < 0:
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next_milestone = None
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for milestone in self.loss_milestones:
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if loss_value > milestone:
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next_milestone = milestone
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break
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if next_milestone:
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# tfw can do simple calculus but not basic algebra in my head
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est_its = (next_milestone - loss_value) / deriv
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if est_its >= 0:
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self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
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else:
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est_loss = inst_deriv * (self.its - self.it) + loss_value
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if est_loss >= 0:
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self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
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self.metrics['loss'] = ", ".join(self.metrics['loss'])
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message = f"[{self.metrics['epoch']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
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if self.nan_detected:
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message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
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if message:
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if message:
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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percent = self.it / float(self.its) # self.epoch / float(self.epochs)
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@ -882,6 +887,7 @@ class TrainingState():
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progress(percent, message)
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progress(percent, message)
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self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
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self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
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should_return = True
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if verbose and not self.training_started:
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if verbose and not self.training_started:
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should_return = True
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should_return = True
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@ -948,6 +954,10 @@ def update_training_dataplot(config_path=None):
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if not training_state:
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if not training_state:
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if config_path:
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if config_path:
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training_state = TrainingState(config_path=config_path, start=False)
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training_state = TrainingState(config_path=config_path, start=False)
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training_state.load_statistics()
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message = training_state.get_status()
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print(message)
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if len(training_state.statistics['loss']) > 0:
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if len(training_state.statistics['loss']) > 0:
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'value', 'type'], width=500, height=350,)
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losses = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['loss']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Loss Metrics", color="type", tooltip=['epoch', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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if len(training_state.statistics['lr']) > 0:
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