Add more algorithms

others/GDAS/lib/scheduler/__init__.py

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+##################################################
+# 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

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+##################################################
+# 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

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+##################################################
+# 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