code: from __future__ import print_function
  import cv2
  import os
  import shutil
  import pickle as pkl
  import time
  import numpy as np
  import hashlib
  
  from IPython import embed
  
  class Logger(object):
      def __init__(self):
          self._logger = None
  
      def init(self, logdir, name='log'):
          if self._logger is None:
              import logging
              if not os.path.exists(logdir):
                  os.makedirs(logdir)
              log_file = os.path.join(logdir, name)
              if os.path.exists(log_file):
                  os.remove(log_file)
              self._logger = logging.getLogger()
              self._logger.setLevel('INFO')
              fh = logging.FileHandler(log_file)
              ch = logging.StreamHandler()
              self._logger.addHandler(fh)
              self._logger.addHandler(ch)
  
      def info(self, str_info):
          self.init('/tmp', 'tmp.log')
          self._logger.info(str_info)
  logger = Logger()
  
  print = logger.info
  def ensure_dir(path, erase=False):
      if os.path.exists(path) and erase:
          print("Removing old folder {}".format(path))
          shutil.rmtree(path)
      if not os.path.exists(path):
          print("Creating folder {}".format(path))
          os.makedirs(path)
  
  def load_pickle(path):
      begin_st = time.time()
      with open(path, 'rb') as f:
          print("Loading pickle object from {}".format(path))
          v = pkl.load(f)
      print("=> Done ({:.4f} s)".format(time.time() - begin_st))
      return v
  
  def dump_pickle(obj, path):
      with open(path, 'wb') as f:
          print("Dumping pickle object to {}".format(path))
          pkl.dump(obj, f, protocol=pkl.HIGHEST_PROTOCOL)
  
  def auto_select_gpu(mem_bound=500, utility_bound=0, gpus=(0, 1, 2, 3, 4, 5, 6, 7), num_gpu=1, selected_gpus=None):
      import sys
      import os
      import subprocess
      import re
      import time
      import numpy as np
      if 'CUDA_VISIBLE_DEVCIES' in os.environ:
          sys.exit(0)
      if selected_gpus is None:
          mem_trace = []
          utility_trace = []
          for i in range(5): # sample 5 times
              info = subprocess.check_output('nvidia-smi', shell=True).decode('utf-8')
              mem = [int(s[:-5]) for s in re.compile('\d+MiB\s/').findall(info)]
              utility = [int(re.compile('\d+').findall(s)[0]) for s in re.compile('\d+%\s+Default').findall(info)]
              mem_trace.append(mem)
              utility_trace.append(utility)
              time.sleep(0.1)
          mem = np.mean(mem_trace, axis=0)
          utility = np.mean(utility_trace, axis=0)
          assert(len(mem) == len(utility))
          nGPU = len(utility)
          ideal_gpus = [i for i in range(nGPU) if mem[i] <= mem_bound and utility[i] <= utility_bound and i in gpus]
  
          if len(ideal_gpus) < num_gpu:
              print("No sufficient resource, available: {}, require {} gpu".format(ideal_gpus, num_gpu))
              sys.exit(0)
          else:
              selected_gpus = list(map(str, ideal_gpus[:num_gpu]))
      else:
          selected_gpus = selected_gpus.split(',')
  
      print("Setting GPU: {}".format(selected_gpus))
      os.environ['CUDA_VISIBLE_DEVICES'] = ','.join(selected_gpus)
      return selected_gpus
  
  def expand_user(path):
      return os.path.abspath(os.path.expanduser(path))
  
  def model_snapshot(model, new_file, old_file=None, verbose=False):
      from collections import OrderedDict
      import torch
      if isinstance(model, torch.nn.DataParallel):
          model = model.module
      if old_file and os.path.exists(expand_user(old_file)):
          if verbose:
              print("Removing old model {}".format(expand_user(old_file)))
          os.remove(expand_user(old_file))
      if verbose:
          print("Saving model to {}".format(expand_user(new_file)))
  
      state_dict = OrderedDict()
      for k, v in model.state_dict().items():
          if v.is_cuda:
              v = v.cpu()
          state_dict[k] = v
      torch.save(state_dict, expand_user(new_file))
  
  
  def load_lmdb(lmdb_file, n_records=None):
      import lmdb
      import numpy as np
      lmdb_file = expand_user(lmdb_file)
      if os.path.exists(lmdb_file):
          data = []
          env = lmdb.open(lmdb_file, readonly=True, max_readers=512)
          with env.begin() as txn:
              cursor = txn.cursor()
              begin_st = time.time()
              print("Loading lmdb file {} into memory".format(lmdb_file))
              for key, value in cursor:
                  _, target, _ = key.decode('ascii').split(':')
                  target = int(target)
                  img = cv2.imdecode(np.fromstring(value, np.uint8), cv2.IMREAD_COLOR)
                  data.append((img, target))
                  if n_records is not None and len(data) >= n_records:
                      break
          env.close()
          print("=> Done ({:.4f} s)".format(time.time() - begin_st))
          return data
      else:
          print("Not found lmdb file".format(lmdb_file))
  
  def str2img(str_b):
      return cv2.imdecode(np.fromstring(str_b, np.uint8), cv2.IMREAD_COLOR)
  
  def img2str(img):
      return cv2.imencode('.jpg', img)[1].tostring()
  
  def md5(s):
      m = hashlib.md5()
      m.update(s)
      return m.hexdigest()
  
  def eval_model(model, ds, n_sample=None, ngpu=1, is_imagenet=False):
      import tqdm
      import torch
      from torch import nn
      from torch.autograd import Variable
  
      class ModelWrapper(nn.Module):
          def __init__(self, model):
              super(ModelWrapper, self).__init__()
              self.model = model
              self.mean = [0.485, 0.456, 0.406]
              self.std = [0.229, 0.224, 0.225]
  
          def forward(self, input):
              input.data.div_(255.)
              input.data[:, 0, :, :].sub_(self.mean[0]).div_(self.std[0])
              input.data[:, 1, :, :].sub_(self.mean[1]).div_(self.std[1])
              input.data[:, 2, :, :].sub_(self.mean[2]).div_(self.std[2])
              return self.model(input)
  
      correct1, correct5 = 0, 0
      n_passed = 0
      if is_imagenet:
          model = ModelWrapper(model)
      model = model.eval()
      model = torch.nn.DataParallel(model, device_ids=range(ngpu)).cuda()
  
      n_sample = len(ds) if n_sample is None else n_sample
      for idx, (data, target) in enumerate(tqdm.tqdm(ds, total=n_sample)):
          n_passed += len(data)
          data =  Variable(torch.FloatTensor(data)).cuda()
          indx_target = torch.LongTensor(target)
          output = model(data)
          bs = output.size(0)
          idx_pred = output.data.sort(1, descending=True)[1]
  
          idx_gt1 = indx_target.expand(1, bs).transpose_(0, 1)
          idx_gt5 = idx_gt1.expand(bs, 5)
  
          correct1 += idx_pred[:, :1].cpu().eq(idx_gt1).sum()
          correct5 += idx_pred[:, :5].cpu().eq(idx_gt5).sum()
  
          if idx >= n_sample - 1:
              break
  
      acc1 = correct1 * 1.0 / n_passed
      acc5 = correct5 * 1.0 / n_passed
      return acc1, acc5
  
  def load_state_dict(model, model_urls, model_root):
      from torch.utils import model_zoo
      from torch import nn
      import re
      from collections import OrderedDict
      own_state_old = model.state_dict()
      own_state = OrderedDict() # remove all 'group' string
      for k, v in own_state_old.items():
          k = re.sub('group\d+\.', '', k)
          own_state[k] = v
  
      state_dict = model_zoo.load_url(model_urls, model_root)
  
      for name, param in state_dict.items():
          if name not in own_state:
              print(own_state.keys())
              raise KeyError('unexpected key "{}" in state_dict'
                             .format(name))
          if isinstance(param, nn.Parameter):
              # backwards compatibility for serialized parameters
              param = param.data
          own_state[name].copy_(param)
  
      missing = set(own_state.keys()) - set(state_dict.keys())
      if len(missing) > 0:
          raise KeyError('missing keys in state_dict: "{}"'.format(missing))
  
  
apis: torch.FloatTensor torch.utils.model_zoo.load_url torch.LongTensor numpy.fromstring numpy.mean