mirror of
https://github.com/babysor/MockingBird.git
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218 lines
8.3 KiB
Python
218 lines
8.3 KiB
Python
import os
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import sys
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import abc
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import math
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import yaml
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import torch
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from torch.utils.tensorboard import SummaryWriter
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from .option import default_hparas
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from utils.util import human_format, Timer
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from utils.load_yaml import HpsYaml
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class BaseSolver():
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'''
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Prototype Solver for all kinds of tasks
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Arguments
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config - yaml-styled config
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paras - argparse outcome
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mode - "train"/"test"
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'''
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def __init__(self, config, paras, mode="train"):
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# General Settings
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self.config = config # load from yaml file
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self.paras = paras # command line args
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self.mode = mode # 'train' or 'test'
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for k, v in default_hparas.items():
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setattr(self, k, v)
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self.device = torch.device('cuda') if self.paras.gpu and torch.cuda.is_available() \
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else torch.device('cpu')
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# Name experiment
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self.exp_name = paras.name
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if self.exp_name is None:
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if 'exp_name' in self.config:
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self.exp_name = self.config.exp_name
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else:
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# By default, exp is named after config file
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self.exp_name = paras.config.split('/')[-1].replace('.yaml', '')
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if mode == 'train':
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self.exp_name += '_seed{}'.format(paras.seed)
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if mode == 'train':
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# Filepath setup
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os.makedirs(paras.ckpdir, exist_ok=True)
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self.ckpdir = os.path.join(paras.ckpdir, self.exp_name)
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os.makedirs(self.ckpdir, exist_ok=True)
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# Logger settings
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self.logdir = os.path.join(paras.logdir, self.exp_name)
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self.log = SummaryWriter(
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self.logdir, flush_secs=self.TB_FLUSH_FREQ)
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self.timer = Timer()
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# Hyper-parameters
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self.step = 0
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self.valid_step = config.hparas.valid_step
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self.max_step = config.hparas.max_step
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self.verbose('Exp. name : {}'.format(self.exp_name))
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self.verbose('Loading data... large corpus may took a while.')
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# elif mode == 'test':
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# # Output path
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# os.makedirs(paras.outdir, exist_ok=True)
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# self.ckpdir = os.path.join(paras.outdir, self.exp_name)
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# Load training config to get acoustic feat and build model
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# self.src_config = HpsYaml(config.src.config)
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# self.paras.load = config.src.ckpt
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# self.verbose('Evaluating result of tr. config @ {}'.format(
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# config.src.config))
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def backward(self, loss):
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'''
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Standard backward step with self.timer and debugger
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Arguments
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loss - the loss to perform loss.backward()
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'''
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self.timer.set()
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loss.backward()
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grad_norm = torch.nn.utils.clip_grad_norm_(
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self.model.parameters(), self.GRAD_CLIP)
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if math.isnan(grad_norm):
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self.verbose('Error : grad norm is NaN @ step '+str(self.step))
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else:
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self.optimizer.step()
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self.timer.cnt('bw')
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return grad_norm
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def load_ckpt(self):
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''' Load ckpt if --load option is specified '''
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print(self.paras)
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if self.paras.load is not None:
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if self.paras.warm_start:
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self.verbose(f"Warm starting model from checkpoint {self.paras.load}.")
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ckpt = torch.load(
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self.paras.load, map_location=self.device if self.mode == 'train'
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else 'cpu')
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model_dict = ckpt['model']
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if "ignore_layers" in self.config.model and len(self.config.model.ignore_layers) > 0:
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model_dict = {k:v for k, v in model_dict.items()
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if k not in self.config.model.ignore_layers}
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dummy_dict = self.model.state_dict()
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dummy_dict.update(model_dict)
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model_dict = dummy_dict
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self.model.load_state_dict(model_dict)
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else:
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# Load weights
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ckpt = torch.load(
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self.paras.load, map_location=self.device if self.mode == 'train'
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else 'cpu')
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self.model.load_state_dict(ckpt['model'])
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# Load task-dependent items
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if self.mode == 'train':
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self.step = ckpt['global_step']
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self.optimizer.load_opt_state_dict(ckpt['optimizer'])
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self.verbose('Load ckpt from {}, restarting at step {}'.format(
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self.paras.load, self.step))
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else:
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for k, v in ckpt.items():
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if type(v) is float:
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metric, score = k, v
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self.model.eval()
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self.verbose('Evaluation target = {} (recorded {} = {:.2f} %)'.format(
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self.paras.load, metric, score))
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def verbose(self, msg):
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''' Verbose function for print information to stdout'''
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if self.paras.verbose:
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if type(msg) == list:
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for m in msg:
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print('[INFO]', m.ljust(100))
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else:
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print('[INFO]', msg.ljust(100))
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def progress(self, msg):
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''' Verbose function for updating progress on stdout (do not include newline) '''
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if self.paras.verbose:
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sys.stdout.write("\033[K") # Clear line
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print('[{}] {}'.format(human_format(self.step), msg), end='\r')
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def write_log(self, log_name, log_dict):
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'''
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Write log to TensorBoard
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log_name - <str> Name of tensorboard variable
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log_value - <dict>/<array> Value of variable (e.g. dict of losses), passed if value = None
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'''
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if type(log_dict) is dict:
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log_dict = {key: val for key, val in log_dict.items() if (
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val is not None and not math.isnan(val))}
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if log_dict is None:
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pass
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elif len(log_dict) > 0:
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if 'align' in log_name or 'spec' in log_name:
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img, form = log_dict
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self.log.add_image(
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log_name, img, global_step=self.step, dataformats=form)
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elif 'text' in log_name or 'hyp' in log_name:
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self.log.add_text(log_name, log_dict, self.step)
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else:
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self.log.add_scalars(log_name, log_dict, self.step)
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def save_checkpoint(self, f_name, metric, score, show_msg=True):
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''''
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Ckpt saver
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f_name - <str> the name of ckpt file (w/o prefix) to store, overwrite if existed
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score - <float> The value of metric used to evaluate model
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'''
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ckpt_path = os.path.join(self.ckpdir, f_name)
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full_dict = {
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"model": self.model.state_dict(),
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"optimizer": self.optimizer.get_opt_state_dict(),
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"global_step": self.step,
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metric: score
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}
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torch.save(full_dict, ckpt_path)
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if show_msg:
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self.verbose("Saved checkpoint (step = {}, {} = {:.2f}) and status @ {}".
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format(human_format(self.step), metric, score, ckpt_path))
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# ----------------------------------- Abtract Methods ------------------------------------------ #
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@abc.abstractmethod
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def load_data(self):
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'''
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Called by main to load all data
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After this call, data related attributes should be setup (e.g. self.tr_set, self.dev_set)
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No return value
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'''
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raise NotImplementedError
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@abc.abstractmethod
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def set_model(self):
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'''
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Called by main to set models
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After this call, model related attributes should be setup (e.g. self.l2_loss)
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The followings MUST be setup
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- self.model (torch.nn.Module)
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- self.optimizer (src.Optimizer),
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init. w/ self.optimizer = src.Optimizer(self.model.parameters(),**self.config['hparas'])
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Loading pre-trained model should also be performed here
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No return value
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'''
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raise NotImplementedError
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@abc.abstractmethod
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def exec(self):
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'''
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Called by main to execute training/inference
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'''
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raise NotImplementedError
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