output fixes, I'm not sure why ETA wasn't working but it works in testing

src/utils.py

@@ -638,7 +638,6 @@ class TrainingState():
 
 		self.loss_milestones = [ 1.0, 0.15, 0.05 ]
 
-		self.load_statistics()
 		if keep_x_past_checkpoints > 0:
 			self.cleanup_old(keep=keep_x_past_checkpoints)
 		if start:
@@ -676,7 +675,7 @@ class TrainingState():
 			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'
 			self.it_rates += it_rate
 
-			epoch_rate = self.it_rates / self.it * self.epoch
+			epoch_rate = self.it_rates / self.it * self.steps
 			if epoch_rate > 0:
 				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'
 
@@ -710,6 +709,72 @@ class TrainingState():
 
 		return data
 
+	def get_status(self):
+		message = None
+
+		self.metrics['rate'] = []
+		if self.epoch_rate:
+			self.metrics['rate'].append(self.epoch_rate)
+		if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
+			self.metrics['rate'].append(self.it_rate)
+		self.metrics['rate'] = ", ".join(self.metrics['rate'])
+
+		eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
+
+		self.metrics['loss'] = []
+		if 'lr' in self.info:
+			self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
+
+		if len(self.losses) > 0:
+			self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
+
+		if len(self.losses) >= 2:
+			deriv = 0
+			accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
+			loss_value = self.losses[-1]["value"]
+
+			for i in range(accum_length):
+				d1_loss = self.losses[accum_length-i-1]["value"]
+				d2_loss = self.losses[accum_length-i-2]["value"]
+				dloss = (d2_loss - d1_loss)
+
+				d1_step = self.losses[accum_length-i-1]["epoch"]
+				d2_step = self.losses[accum_length-i-2]["epoch"]
+				dstep = (d2_step - d1_step)
+
+				if dstep == 0:
+					continue
+		
+				inst_deriv = dloss / dstep
+				deriv += inst_deriv
+
+			deriv = deriv / accum_length
+
+			if deriv != 0: # dloss < 0:
+				next_milestone = None
+				for milestone in self.loss_milestones:
+					if loss_value > milestone:
+						next_milestone = milestone
+						break
+						
+				if next_milestone:
+					# tfw can do simple calculus but not basic algebra in my head
+					est_its = (next_milestone - loss_value) / deriv
+					if est_its >= 0:
+						self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
+				else:
+					est_loss = inst_deriv * (self.its - self.it) + loss_value
+					if est_loss >= 0:
+						self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
+
+		self.metrics['loss'] = ", ".join(self.metrics['loss'])
+
+		message = f"[{self.metrics['step']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
+		if self.nan_detected:
+			message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
+
+		return message
+
 	def load_statistics(self, update=False):
 		if not os.path.isdir(f'{self.dataset_dir}/'):
 			return
@@ -720,6 +785,7 @@ class TrainingState():
 		if not update:
 			self.statistics['loss'] = []
 			self.statistics['lr'] = []
+			self.it_rates = 0
 
 		logs = sorted([f'{self.dataset_dir}/{d}' for d in os.listdir(self.dataset_dir) if d[-4:] == ".log" ])
 		if update:
@@ -742,12 +808,13 @@ class TrainingState():
 				if "it" not in data:
 					continue
 
-				step = data['it']
+				it = data['it']
 
-				if update and step <= self.last_info_check_at:
+				if update and it <= self.last_info_check_at:
 					continue
 
 				self.parse_metrics(data)
+				# print(f"Iterations Left: {self.its - self.it} | Elapsed Time: {self.it_rates} | Time Remaining: {self.eta} | Message: {self.get_status()}")
 
 		self.last_info_check_at = highest_step
 
@@ -795,11 +862,10 @@ class TrainingState():
 
 				self.checkpoints = int((self.its - self.it) / self.config['logger']['save_checkpoint_freq'])
 
+				self.load_statistics()
+
 				should_return = True
 		else:
-			message = None
-			data = None
-
 			# 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}
 			if line.find('INFO: Training Metrics:') >= 0:
 				data = json.loads(line.split("INFO: Training Metrics:")[-1])
@@ -809,79 +875,19 @@ class TrainingState():
 				data['mode'] = "validation"
 
 			if data is not None:
+				if ': nan' in line and not self.nan_detected:
+					self.nan_detected = self.it
+				
 				self.parse_metrics( data )
-				should_return = True
+				message = self.get_status()
+				
+				if message:
+					percent = self.it / float(self.its) # self.epoch / float(self.epochs)
+					if progress is not None:
+						progress(percent, message)
 
-			if ': nan' in line and not self.nan_detected:
+					self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
-				self.nan_detected = self.it
+					should_return = True
-
-			self.metrics['rate'] = []
-			if self.epoch_rate:
-				self.metrics['rate'].append(self.epoch_rate)
-			if self.it_rate and self.epoch_rate[:-7] != self.it_rate[:-4]:
-				self.metrics['rate'].append(self.it_rate)
-			self.metrics['rate'] = ", ".join(self.metrics['rate'])
-
-			eta_hhmmss = self.eta_hhmmss if self.eta_hhmmss else "?"
-
-			self.metrics['loss'] = []
-			if 'lr' in self.info:
-				self.metrics['loss'].append(f'LR: {"{:.3e}".format(self.info["lr"])}')
-
-			if len(self.losses) > 0:
-				self.metrics['loss'].append(f'Loss: {"{:.3f}".format(self.losses[-1]["value"])}')
-
-			if len(self.losses) >= 2:
-				deriv = 0
-				accum_length = len(self.losses)//2 # i *guess* this is fine when you think about it
-				loss_value = self.losses[-1]["value"]
-
-				for i in range(accum_length):
-					d1_loss = self.losses[accum_length-i-1]["value"]
-					d2_loss = self.losses[accum_length-i-2]["value"]
-					dloss = (d2_loss - d1_loss)
-
-					d1_step = self.losses[accum_length-i-1]["step"]
-					d2_step = self.losses[accum_length-i-2]["step"]
-					dstep = (d2_step - d1_step)
-
-					if dstep == 0:
-						continue
-			
-					inst_deriv = dloss / dstep
-					deriv += inst_deriv
-
-				deriv = deriv / accum_length
-
-				if deriv != 0: # dloss < 0:
-					next_milestone = None
-					for milestone in self.loss_milestones:
-						if loss_value > milestone:
-							next_milestone = milestone
-							break
-							
-					if next_milestone:
-						# tfw can do simple calculus but not basic algebra in my head
-						est_its = (next_milestone - loss_value) / deriv
-						if est_its >= 0:
-							self.metrics['loss'].append(f'Est. milestone {next_milestone} in: {int(est_its)}its')
-					else:
-						est_loss = inst_deriv * (self.its - self.it) + loss_value
-						if est_loss >= 0:
-							self.metrics['loss'].append(f'Est. final loss: {"{:.3f}".format(est_loss)}')
-
-			self.metrics['loss'] = ", ".join(self.metrics['loss'])
-
-			message = f"[{self.metrics['epoch']}] [{self.metrics['rate']}] [ETA: {eta_hhmmss}]\n[{self.metrics['loss']}]"
-			if self.nan_detected:
-				message = f"[!NaN DETECTED! {self.nan_detected}] {message}"
-
-			if message:
-				percent = self.it / float(self.its) # self.epoch / float(self.epochs)
-				if progress is not None:
-					progress(percent, message)
-
-				self.buffer.append(f'[{"{:.3f}".format(percent*100)}%] {message}')
 
 		if verbose and not self.training_started:
 			should_return = True
@@ -948,6 +954,10 @@ def update_training_dataplot(config_path=None):
 	if not training_state:
 		if config_path:
 			training_state = TrainingState(config_path=config_path, start=False)
+			training_state.load_statistics()
+			message = training_state.get_status()
+			print(message)
+
 			if len(training_state.statistics['loss']) > 0:
 				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,)
 			if len(training_state.statistics['lr']) > 0: