import os import glob import sys import argparse import logging import json import subprocess import numpy as np from scipy.io.wavfile import read import torch MATPLOTLIB_FLAG = False logging.basicConfig(stream=sys.stdout, level=logging.DEBUG) logger = logging def load_checkpoint(checkpoint_path, model, optimizer=None): assert os.path.isfile(checkpoint_path) checkpoint_dict = torch.load(checkpoint_path, map_location="cpu") iteration = checkpoint_dict["iteration"] learning_rate = checkpoint_dict["learning_rate"] if optimizer is not None: optimizer.load_state_dict(checkpoint_dict["optimizer"]) saved_state_dict = checkpoint_dict["model"] if hasattr(model, "module"): state_dict = model.module.state_dict() else: state_dict = model.state_dict() new_state_dict = {} for k, v in state_dict.items(): try: new_state_dict[k] = saved_state_dict[k] except: logger.info("%s is not in the checkpoint" % k) new_state_dict[k] = v if hasattr(model, "module"): model.module.load_state_dict(new_state_dict) else: model.load_state_dict(new_state_dict) logger.info( "Loaded checkpoint '{}' (iteration {})".format(checkpoint_path, iteration) ) return model, optimizer, learning_rate, iteration def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path): logger.info( "Saving model and optimizer state at iteration {} to {}".format( iteration, checkpoint_path ) ) if hasattr(model, "module"): state_dict = model.module.state_dict() else: state_dict = model.state_dict() torch.save( { "model": state_dict, "iteration": iteration, "optimizer": optimizer.state_dict(), "learning_rate": learning_rate, }, checkpoint_path, ) def summarize( writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050, ): for k, v in scalars.items(): writer.add_scalar(k, v, global_step) for k, v in histograms.items(): writer.add_histogram(k, v, global_step) for k, v in images.items(): writer.add_image(k, v, global_step, dataformats="HWC") for k, v in audios.items(): writer.add_audio(k, v, global_step, audio_sampling_rate) def scan_checkpoint(dir_path, regex): f_list = glob.glob(os.path.join(dir_path, regex)) f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f)))) if len(f_list) == 0: return None return f_list def latest_checkpoint_path(dir_path, regex="G_*.pth"): f_list = scan_checkpoint(dir_path, regex) if not f_list: return None x = f_list[-1] print(x) return x def remove_old_checkpoints(cp_dir, prefixes=['G_*.pth', 'D_*.pth', 'DUR_*.pth']): for prefix in prefixes: sorted_ckpts = scan_checkpoint(cp_dir, prefix) if sorted_ckpts and len(sorted_ckpts) > 3: for ckpt_path in sorted_ckpts[:-3]: os.remove(ckpt_path) print("removed {}".format(ckpt_path)) def plot_spectrogram_to_numpy(spectrogram): global MATPLOTLIB_FLAG if not MATPLOTLIB_FLAG: import matplotlib matplotlib.use("Agg") MATPLOTLIB_FLAG = True mpl_logger = logging.getLogger("matplotlib") mpl_logger.setLevel(logging.WARNING) import matplotlib.pylab as plt import numpy as np fig, ax = plt.subplots(figsize=(10, 2)) im = ax.imshow(spectrogram, aspect="auto", origin="lower", interpolation="none") plt.colorbar(im, ax=ax) plt.xlabel("Frames") plt.ylabel("Channels") plt.tight_layout() fig.canvas.draw() data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) plt.close() return data def plot_alignment_to_numpy(alignment, info=None): global MATPLOTLIB_FLAG if not MATPLOTLIB_FLAG: import matplotlib matplotlib.use("Agg") MATPLOTLIB_FLAG = True mpl_logger = logging.getLogger("matplotlib") mpl_logger.setLevel(logging.WARNING) import matplotlib.pylab as plt import numpy as np fig, ax = plt.subplots(figsize=(6, 4)) im = ax.imshow( alignment.transpose(), aspect="auto", origin="lower", interpolation="none" ) fig.colorbar(im, ax=ax) xlabel = "Decoder timestep" if info is not None: xlabel += "\n\n" + info plt.xlabel(xlabel) plt.ylabel("Encoder timestep") plt.tight_layout() fig.canvas.draw() data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep="") data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,)) plt.close() return data def load_wav_to_torch(full_path): sampling_rate, data = read(full_path) return torch.FloatTensor(data.astype(np.float32)), sampling_rate def load_filepaths_and_text(filename, split="|"): filepaths_and_text = [] with open(filename, encoding="utf-8") as f: for line in f: line = line.rstrip().strip().split(split) filepaths_and_text.append(line) return filepaths_and_text def get_hparams(init=True): parser = argparse.ArgumentParser() parser.add_argument( "-c", "--config", type=str, default="./configs/base.json", help="JSON file for configuration", ) parser.add_argument("-m", "--model", type=str, required=True, help="Model name") args = parser.parse_args() model_dir = os.path.join("./logs", args.model) if not os.path.exists(model_dir): os.makedirs(model_dir) config_path = args.config config_save_path = os.path.join(model_dir, "config.json") if init: with open(config_path, "r") as f: data = f.read() with open(config_save_path, "w") as f: f.write(data) else: with open(config_save_path, "r") as f: data = f.read() config = json.loads(data) hparams = HParams(**config) hparams.model_dir = model_dir return hparams def get_hparams_from_dir(model_dir): config_save_path = os.path.join(model_dir, "config.json") with open(config_save_path, "r") as f: data = f.read() config = json.loads(data) hparams = HParams(**config) hparams.model_dir = model_dir return hparams def get_hparams_from_file(config_path): with open(config_path, "r") as f: data = f.read() config = json.loads(data) hparams = HParams(**config) return hparams def check_git_hash(model_dir): source_dir = os.path.dirname(os.path.realpath(__file__)) if not os.path.exists(os.path.join(source_dir, ".git")): logger.warn( "{} is not a git repository, therefore hash value comparison will be ignored.".format( source_dir ) ) return cur_hash = subprocess.getoutput("git rev-parse HEAD") path = os.path.join(model_dir, "githash") if os.path.exists(path): saved_hash = open(path).read() if saved_hash != cur_hash: logger.warn( "git hash values are different. {}(saved) != {}(current)".format( saved_hash[:8], cur_hash[:8] ) ) else: open(path, "w").write(cur_hash) def get_logger(model_dir, filename="train.log"): global logger logger = logging.getLogger(os.path.basename(model_dir)) logger.setLevel(logging.DEBUG) formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s") if not os.path.exists(model_dir): os.makedirs(model_dir) h = logging.FileHandler(os.path.join(model_dir, filename)) h.setLevel(logging.DEBUG) h.setFormatter(formatter) logger.addHandler(h) return logger class HParams: def __init__(self, **kwargs): for k, v in kwargs.items(): if type(v) == dict: v = HParams(**v) self[k] = v def keys(self): return self.__dict__.keys() def items(self): return self.__dict__.items() def values(self): return self.__dict__.values() def __len__(self): return len(self.__dict__) def __getitem__(self, key): return getattr(self, key) def __setitem__(self, key, value): return setattr(self, key, value) def __contains__(self, key): return key in self.__dict__ def __repr__(self): return self.__dict__.__repr__()