Clean unnecessary files

others/GDAS/lib/scheduler/__init__.py

@@ -1,5 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-from .utils import load_config
-from .scheduler import MultiStepLR, obtain_scheduler

others/GDAS/lib/scheduler/scheduler.py

@@ -1,32 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-import torch
-from bisect import bisect_right
-
-
-class MultiStepLR(torch.optim.lr_scheduler._LRScheduler):
-
-  def __init__(self, optimizer, milestones, gammas, last_epoch=-1):
-    if not list(milestones) == sorted(milestones):
-      raise ValueError('Milestones should be a list of'
-                       ' increasing integers. Got {:}', milestones)
-    assert len(milestones) == len(gammas), '{:} vs {:}'.format(milestones, gammas)
-    self.milestones = milestones
-    self.gammas = gammas
-    super(MultiStepLR, self).__init__(optimizer, last_epoch)
-
-  def get_lr(self):
-    LR = 1
-    for x in self.gammas[:bisect_right(self.milestones, self.last_epoch)]: LR = LR * x
-    return [base_lr * LR for base_lr in self.base_lrs]
-
-
-def obtain_scheduler(config, optimizer):
-  if config.type == 'multistep':
-    scheduler = MultiStepLR(optimizer, milestones=config.milestones, gammas=config.gammas)
-  elif config.type == 'cosine':
-    scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, config.epochs)
-  else:
-    raise ValueError('Unknown learning rate scheduler type : {:}'.format(config.type))
-  return scheduler

others/GDAS/lib/scheduler/utils.py

@@ -1,42 +0,0 @@
-##################################################
-# Copyright (c) Xuanyi Dong [GitHub D-X-Y], 2019 #
-##################################################
-import os, sys, json
-from pathlib import Path
-from collections import namedtuple
-
-support_types = ('str', 'int', 'bool', 'float')
-
-def convert_param(original_lists):
-  assert isinstance(original_lists, list), 'The type is not right : {:}'.format(original_lists)
-  ctype, value = original_lists[0], original_lists[1]
-  assert ctype in support_types, 'Ctype={:}, support={:}'.format(ctype, support_types)
-  is_list = isinstance(value, list)
-  if not is_list: value = [value]
-  outs = []
-  for x in value:
-    if ctype == 'int':
-      x = int(x)
-    elif ctype == 'str':
-      x = str(x)
-    elif ctype == 'bool':
-      x = bool(int(x))
-    elif ctype == 'float':
-      x = float(x)
-    else:
-      raise TypeError('Does not know this type : {:}'.format(ctype))
-    outs.append(x)
-  if not is_list: outs = outs[0]
-  return outs
-
-def load_config(path):
-  path = str(path)
-  assert os.path.exists(path), 'Can not find {:}'.format(path)
-  # Reading data back
-  with open(path, 'r') as f:
-    data = json.load(f)
-  f.close()
-  content = { k: convert_param(v) for k,v in data.items()}
-  Arguments = namedtuple('Configure', ' '.join(content.keys()))
-  content = Arguments(**content)
-  return content