File size: 8,821 Bytes
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from numpy import ndarray
import os
import psutil
import time
import torch.nn as nn
from datetime import datetime
from datetime import timedelta
try: import wandb
except: print('Didnt import following packages: wandb')
try: from tensorboardX import SummaryWriter
except: print('Didnt import following packages: tensorboardX')
from TorchJaekwon.Util.Util import Util
from TorchJaekwon.Util.UtilAudioSTFT import UtilAudioSTFT
from TorchJaekwon.Util.UtilTorch import UtilTorch
from TorchJaekwon.Util.UtilData import UtilData
from HParams import HParams
class LogWriter():
def __init__(
self,
model:nn.Module
)->None:
self.h_params:HParams = HParams()
self.visualizer_type:str = self.h_params.log.visualizer_type #["tensorboard","wandb"]
self.experiment_start_time:float = time.time()
self.experiment_name:str = "[" +datetime.now().strftime('%y%m%d-%H%M%S') + "] " + self.h_params.mode.config_name if self.h_params.log.use_currenttime_on_experiment_name else self.h_params.mode.config_name
self.log_path:dict[str,str] = {"root":"","console":"","visualizer":""}
self.set_log_path()
self.log_write_init(model=model)
if self.visualizer_type == 'wandb':
if self.h_params.mode.train == 'resume':
try:
wandb_meta_data:dict = UtilData.yaml_load(f'''{self.log_path['root']}/wandb_meta.yaml''')
wandb.init(id=wandb_meta_data['id'], project=self.h_params.log.project_name, resume = 'must')
except:
Util.print("Failed to resume wandb. Please check the wandb_meta.yaml file", type='error')
wandb.init(project=self.h_params.log.project_name)
else:
wandb.init(project=self.h_params.log.project_name)
wandb.config = {"learning_rate": self.h_params.train.lr, "epochs": self.h_params.train.epoch, "batch_size": self.h_params.pytorch_data.dataloader['train']['batch_size'] }
watched_model = model
while not isinstance(watched_model, nn.Module):
watched_model = watched_model[list(watched_model.keys())[0]]
wandb.watch(watched_model)
wandb.run.name = self.experiment_name
wandb.run.save()
UtilData.yaml_save(f'''{self.log_path['root']}/wandb_meta.yaml''', data={
'id': wandb.run.id,
'name': wandb.run.name,
})
elif self.visualizer_type == 'tensorboard':
self.tensorboard_writer = SummaryWriter(log_dir=self.log_path["visualizer"])
else:
print('visualizer should be either wandb or tensorboard')
exit()
def get_time_took(self) -> str:
time_took_second:int = int(time.time() - self.experiment_start_time)
time_took:str = str(timedelta(seconds=time_took_second))
return time_took
def set_log_path(self):
if self.h_params.mode.train == "resume":
self.log_path["root"] = self.h_params.mode.resume_path
else:
self.log_path["root"] = os.path.join(self.h_params.log.class_root_dir,self.experiment_name)
self.log_path["console"] = self.log_path["root"]+ "/log.txt"
self.log_path["visualizer"] = os.path.join(self.log_path["root"],"tb")
os.makedirs(self.log_path["visualizer"],exist_ok=True)
def print_and_log(self, log_message:str) -> None:
log_message_with_time_took:str = f"{log_message} ({self.get_time_took()} took)"
print(log_message_with_time_took)
self.log_write(log_message_with_time_took)
def log_write_init(self, model:nn.Module) -> None:
write_mode:str = 'w' if self.h_params.mode.train != "resume" else 'a'
file = open(self.log_path["console"], write_mode)
file.write("========================================="+'\n')
file.write(f'pid: {os.getpid()} / parent_pid: {psutil.Process(os.getpid()).ppid()} \n')
file.write("========================================="+'\n')
self.log_model_parameters(file, model)
file.write("========================================="+'\n')
file.write("Epoch :" + str(self.h_params.train.epoch)+'\n')
file.write("lr :" + str(self.h_params.train.lr)+'\n')
file.write("Batch :" + str(self.h_params.pytorch_data.dataloader['train']['batch_size'])+'\n')
file.write("========================================="+'\n')
file.close()
def log_model_parameters(self, file, model: Union[nn.Module, dict], model_name:str = ''):
if isinstance(model, nn.Module):
file.write(f'''Model {model_name} Total parameters: {format(UtilTorch.get_param_num(model)['total'], ',d')}'''+'\n')
file.write(f'''Model {model_name} Trainable parameters: {format(UtilTorch.get_param_num(model)['trainable'], ',d')}'''+'\n')
else:
for model_name in model:
self.log_model_parameters(file, model[model_name], model_name)
def log_write(self,log_message:str)->None:
file = open(self.log_path["console"],'a')
file.write(log_message+'\n')
file.close()
def visualizer_log(
self,
x_axis_name:str, #epoch, step, ...
x_axis_value:float,
y_axis_name:str, #metric name
y_axis_value:float
) -> None:
if self.visualizer_type == 'tensorboard':
self.tensorboard_writer.add_scalar(y_axis_name,y_axis_value,x_axis_value)
else:
wandb.log({y_axis_name: y_axis_value, x_axis_name: x_axis_value})
def plot_audio(
self,
name:str, #test case name, you could make structure by using /. ex) 'taskcase_1/test_set_1'
audio_dict:Dict[str,ndarray], #{'audio name': 1d audio array}.
global_step:int,
sample_rate:int = 16000,
is_plot_spec:bool = False,
is_plot_mel:bool = True,
mel_spec_args:Optional[dict] = None
) -> None:
self.plot_wav(name = name + '_audio', audio_dict = audio_dict, sample_rate=sample_rate, global_step=global_step)
if is_plot_mel:
from TorchJaekwon.Util.UtilAudioMelSpec import UtilAudioMelSpec
if mel_spec_args is None:
mel_spec_args = UtilAudioMelSpec.get_default_mel_spec_config(sample_rate=sample_rate)
mel_spec_util = UtilAudioMelSpec(**mel_spec_args)
mel_dict = dict()
for audio_name in audio_dict:
mel_dict[audio_name] = mel_spec_util.get_hifigan_mel_spec(audio=audio_dict[audio_name],return_type='ndarray')
self.plot_spec(name = name + '_mel_spec', spec_dict = mel_dict)
def plot_wav(
self,
name:str, #test case name, you could make structure by using /. ex) 'audio/test_set_1'
audio_dict:Dict[str,ndarray], #{'audio name': 1d audio array},
sample_rate:int,
global_step:int
) -> None:
if self.visualizer_type == 'tensorboard':
for audio_name in audio_dict:
self.tensorboard_writer.add_audio(f'{name}/{audio_name}', audio_dict[audio_name], sample_rate=sample_rate, global_step=global_step)
else:
wandb_audio_list = list()
for audio_name in audio_dict:
wandb_audio_list.append(wandb.Audio(audio_dict[audio_name], caption=audio_name,sample_rate=sample_rate))
wandb.log({name: wandb_audio_list})
def plot_spec(self,
name:str, #test case name, you could make structure by using /. ex) 'mel/test_set_1'
spec_dict:Dict[str,ndarray], #{'name': 2d array},
vmin=-6.0,
vmax=1.5,
transposed=False,
global_step=0):
if self.visualizer_type == 'tensorboard':
for audio_name in spec_dict:
figure = UtilAudioSTFT.spec_to_figure(spec_dict[audio_name], vmin=vmin, vmax=vmax,transposed=transposed)
self.tensorboard_writer.add_figure(f'{name}/{audio_name}',figure,global_step=global_step)
else:
wandb_mel_list = list()
for audio_name in spec_dict:
UtilAudioSTFT.spec_to_figure(spec_dict[audio_name], vmin=vmin, vmax=vmax,transposed=transposed,save_path=f'''{self.log_path['root']}/temp_img_{audio_name}.png''')
wandb_mel_list.append(wandb.Image(f'''{self.log_path['root']}/temp_img_{audio_name}.png''', caption=audio_name))
wandb.log({name: wandb_mel_list})
def log_every_epoch(self,model:nn.Module):
pass |