| import json | |
| import torch | |
| import pandas as pd | |
| from pathlib import Path | |
| from itertools import repeat | |
| from collections import OrderedDict | |
| def ensure_dir(dirname): | |
| dirname = Path(dirname) | |
| if not dirname.is_dir(): | |
| dirname.mkdir(parents=True, exist_ok=False) | |
| def read_json(fname): | |
| fname = Path(fname) | |
| with fname.open('rt') as handle: | |
| return json.load(handle, object_hook=OrderedDict) | |
| def write_json(content, fname): | |
| fname = Path(fname) | |
| with fname.open('wt') as handle: | |
| json.dump(content, handle, indent=4, sort_keys=False) | |
| def inf_loop(data_loader): | |
| ''' wrapper function for endless data loader. ''' | |
| for loader in repeat(data_loader): | |
| yield from loader | |
| def prepare_device(n_gpu_use): | |
| """ | |
| setup GPU device if available. get gpu device indices which are used for DataParallel | |
| """ | |
| n_gpu = torch.cuda.device_count() | |
| if n_gpu_use > 0 and n_gpu == 0: | |
| print("Warning: There\'s no GPU available on this machine," | |
| "training will be performed on CPU.") | |
| n_gpu_use = 0 | |
| if n_gpu_use > n_gpu: | |
| print(f"Warning: The number of GPU\'s configured to use is {n_gpu_use}, but only {n_gpu} are " | |
| "available on this machine.") | |
| n_gpu_use = n_gpu | |
| device = torch.device('cuda:0' if n_gpu_use > 0 else 'cpu') | |
| list_ids = list(range(n_gpu_use)) | |
| return device, list_ids | |
| class MetricTracker: | |
| def __init__(self, *keys, writer=None): | |
| self.writer = writer | |
| self._data = pd.DataFrame(index=keys, columns=['total', 'counts', 'average']) | |
| self.reset() | |
| def reset(self): | |
| for col in self._data.columns: | |
| self._data[col].values[:] = 0 | |
| def update(self, key, value, n=1): | |
| if self.writer is not None: | |
| self.writer.add_scalar(key, value) | |
| self._data.total[key] += value * n | |
| self._data.counts[key] += n | |
| self._data.average[key] = self._data.total[key] / self._data.counts[key] | |
| def avg(self, key): | |
| return self._data.average[key] | |
| def result(self): | |
| return dict(self._data.average) | |