forked from mrq/ai-voice-cloning
x_lim and y_lim for graph
This commit is contained in:
parent
9856db5900
commit
fd9b2e082c
88
src/utils.py
88
src/utils.py
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@ -726,24 +726,13 @@ class TrainingState():
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else:
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return
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if args.tts_backend == "vall-e":
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it = data['global_step']
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if self.valle_last_it == it:
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self.valle_steps += 1
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return
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else:
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self.valle_last_it = it
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self.valle_steps = 0
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data['it'] = it
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data['steps'] = self.valle_steps
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self.info = data
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if 'epoch' in self.info:
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self.epoch = int(self.info['epoch'])
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if 'it' in self.info:
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self.it = int(self.info['it'])
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if 'iteration' in self.info:
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self.it = int(self.info['iteration'])
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if 'step' in self.info:
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self.step = int(self.info['step'])
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if 'steps' in self.info:
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@ -776,7 +765,7 @@ class TrainingState():
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if args.tts_backend == "tortoise":
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epoch = self.epoch + (self.step / self.steps)
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else:
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epoch = self.it
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epoch = self.info['epoch'] if 'epoch' in self.info else self.it
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if self.it > 0:
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# probably can double for-loop but whatever
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@ -892,6 +881,8 @@ class TrainingState():
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self.statistics['grad_norm'] = []
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self.it_rates = 0
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unq = {}
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for log in logs:
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with open(log, 'r', encoding="utf-8") as f:
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lines = f.readlines()
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@ -919,14 +910,18 @@ class TrainingState():
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continue
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it = data['it']
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else:
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if "global_step" not in data:
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if "iteration" not in data:
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continue
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it = data['global_step']
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it = data['iteration']
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# this method should have it at least
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unq[f'{it}'] = data
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if update and it <= self.last_info_check_at:
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continue
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self.parse_metrics(data)
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for it in unq:
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self.parse_metrics(unq[it])
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self.last_info_check_at = highest_step
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@ -954,18 +949,6 @@ class TrainingState():
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print("Removing", path)
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os.remove(path)
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def parse_valle_metrics(self, data):
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res = {}
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res['mode'] = "training"
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res['loss'] = data['model.loss']
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res['lr'] = data['model.lr']
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res['it'] = data['global_step']
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res['step'] = res['it'] % self.dataset_size
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res['steps'] = self.steps
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res['epoch'] = int(res['it'] / self.dataset_size)
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res['iteration_rate'] = data['elapsed_time']
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return res
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def parse(self, line, verbose=False, keep_x_past_checkpoints=0, buffer_size=8, progress=None ):
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self.buffer.append(f'{line}')
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@ -1086,33 +1069,56 @@ def run_training(config_path, verbose=False, keep_x_past_checkpoints=0, progress
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return_code = training_state.process.wait()
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training_state = None
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def update_training_dataplot(config_path=None):
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def update_training_dataplot(x_lim=None, y_lim=None, config_path=None):
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global training_state
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losses = None
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lrs = None
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grad_norms = None
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x_lim = [ 0, x_lim ]
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y_lim = [ 0, y_lim ]
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if not training_state:
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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.load_statistics()
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message = training_state.get_status()
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if training_state:
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if not x_lim[-1]:
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x_lim[-1] = training_state.epochs
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if not y_lim[-1]:
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y_lim = None
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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', 'it', 'value', 'type'], width=500, height=350,)
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losses = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['loss']),
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x_lim=x_lim, y_lim=y_lim,
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x="epoch", y="value",
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title="Loss Metrics", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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lrs = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['lr']),
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x_lim=x_lim, y_lim=y_lim,
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x="epoch", y="value",
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title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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if len(training_state.statistics['grad_norm']) > 0:
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grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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grad_norms = gr.LinePlot.update(
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value = pd.DataFrame(training_state.statistics['grad_norm']),
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x_lim=x_lim, y_lim=y_lim,
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x="epoch", y="value",
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title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'],
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width=500, height=350
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)
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if config_path:
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del training_state
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training_state = None
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else:
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# training_state.load_statistics()
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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', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['lr']) > 0:
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lrs = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['lr']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Learning Rate", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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if len(training_state.statistics['grad_norm']) > 0:
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grad_norms = gr.LinePlot.update(value=pd.DataFrame(training_state.statistics['grad_norm']), x_lim=[0,training_state.epochs], x="epoch", y="value", title="Gradient Normals", color="type", tooltip=['epoch', 'it', 'value', 'type'], width=500, height=350,)
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return (losses, lrs, grad_norms)
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16
src/webui.py
16
src/webui.py
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@ -527,11 +527,17 @@ def setup_gradio():
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verbose_training = gr.Checkbox(label="Verbose Console Output", value=True)
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keep_x_past_checkpoints = gr.Slider(label="Keep X Previous States", minimum=0, maximum=8, value=0, step=1)
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with gr.Row():
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training_graph_x_lim = gr.Number(label="X Limit", precision=0, value=0)
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training_graph_y_lim = gr.Number(label="Y Limit", precision=0, value=0)
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with gr.Row():
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start_training_button = gr.Button(value="Train")
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stop_training_button = gr.Button(value="Stop")
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reconnect_training_button = gr.Button(value="Reconnect")
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with gr.Column():
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training_loss_graph = gr.LinePlot(label="Training Metrics",
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x="epoch",
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@ -562,6 +568,7 @@ def setup_gradio():
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visible=args.tts_backend=="vall-e"
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)
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view_losses = gr.Button(value="View Losses")
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with gr.Tab("Settings"):
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with gr.Row():
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exec_inputs = []
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@ -787,7 +794,10 @@ def setup_gradio():
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)
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training_output.change(
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fn=update_training_dataplot,
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inputs=None,
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inputs=[
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training_graph_x_lim,
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training_graph_y_lim,
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],
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outputs=[
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training_loss_graph,
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training_lr_graph,
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@ -799,7 +809,9 @@ def setup_gradio():
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view_losses.click(
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fn=update_training_dataplot,
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inputs=[
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training_configs
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training_graph_x_lim,
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training_graph_y_lim,
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training_configs,
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],
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outputs=[
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training_loss_graph,
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