text: import os
  import math
  import time
  from copy import deepcopy
  
  import torch
  import torch.nn as nn
  import torch.backends.cudnn as cudnn
  import torch.nn.functional as F
  
  
  def init_seeds(seed=0):
      torch.manual_seed(seed)
      # Speed-reproducibility tradeoff https://pytorch.org/docs/stable/notes/randomness.html
      if seed == 0:  # slower, more reproducible
          cudnn.deterministic = True
          cudnn.benchmark = False
      else:  # faster, less reproducible
          cudnn.deterministic = False
          cudnn.benchmark = True
  
  
  def select_device(device, apex=False, batch_size=None):
      """选择哪个设备进行训练：device = 'cpu' or '0' or '0,1,2,3'"""
      cpu_request = device.lower() == 'cpu'
      if device and not cpu_request:  # if device requested other than 'cpu'
          os.environ['CUDA_VISIBLE_DEVICES'] = device  # set environment variable
          assert torch.cuda.is_available(), 'CUDA unavailable, invalid device %s requested' % device  # check availablity
  
      cuda = False if cpu_request else torch.cuda.is_available()
      if cuda:
          c = 1024 ** 2  # bytes to MB
          ng = torch.cuda.device_count()
          if ng > 1 and batch_size:  # check that batch_size is compatible with device_count
              assert batch_size % ng == 0, 'batch-size %g not multiple of GPU count %g' % (batch_size, ng)
          x = [torch.cuda.get_device_properties(i) for i in range(ng)]
          s = 'Using CUDA ' + ('Apex ' if apex else '')  # apex for mixed precision https://github.com/NVIDIA/apex
          for i in range(0, ng):
              if i == 1:
                  s = ' ' * len(s)
              print("%sdevice%g _CudaDeviceProperties(name='%s', total_memory=%dMB)" %
                    (s, i, x[i].name, x[i].total_memory / c))
      else:
          print('Using CPU')
      print('')  # skip a line
      return torch.device('cuda:0' if cuda else 'cpu')
  
  
  def time_synchronized():
      torch.cuda.synchronize() if torch.cuda.is_available() else None
      return time.time()
  
  
  def initialize_weights(model):
      """模型参数的初始化"""
      for m in model.modules():
          t = type(m)
          if t is nn.Conv2d:
              pass  # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
          elif t is nn.BatchNorm2d:
              m.eps = 1e-4
              m.momentum = 0.03
          elif t in [nn.LeakyReLU, nn.ReLU, nn.ReLU6]:
              m.inplace = True
      return
  
  
  def find_modules(model, mclass=nn.Conv2d):
      """
      寻找模型中，类型为 m_class 的单元
      finds layer indices matching module class 'm_class'
      """
      return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
  
  
  def fuse_conv_and_bn(conv, bn):
      """
      但其实在网络的推理阶段，可以将BN层的运算融合到Conv层中，减少运算量，加速推理。
      本质上是修改了卷积核的参数，在不增加Conv层计算量的同时，略去了BN层的计算量
      """
      # https://tehnokv.com/posts/fusing-batchnorm-and-conv/
      with torch.no_grad():  # 将 conv 和 bn 进行混合计算
          # init
          fusedconv = torch.nn.Conv2d(conv.in_channels, conv.out_channels,
                                      kernel_size=conv.kernel_size, stride=conv.stride,
                                      padding=conv.padding, bias=True)
  
          # prepare filters
          w_conv = conv.weight.clone().view(conv.out_channels, -1)
          w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
          fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.size()))
  
          if conv.bias is not None:  # prepare spatial bias
              b_conv = conv.bias
          else:
              b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device)
          b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
          fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
          return fusedconv
  
  
  def model_info(model, verbose=False):
      """ 打印模型的相关信息
      Plots a line-by-line description of a PyTorch model
      """
      n_p = sum(x.numel() for x in model.parameters())  # number parameters
      n_g = sum(x.numel() for x in model.parameters() if x.requires_grad)  # number gradients
      if verbose:
          print('%5s %40s %9s %12s %20s %10s %10s' % ('layer', 'name', 'gradient', 'parameters', 'shape', 'mu', 'sigma'))
          for i, (name, p) in enumerate(model.named_parameters()):
              name = name.replace('module_list.', '')
              print('%5g %40s %9s %12g %20s %10.3g %10.3g' %
                    (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()))
  
      try:  # FLOPS
          from thop import profile
          macs, _ = profile(model, inputs=(torch.zeros(1, 3, 480, 640),), verbose=False)
          fs = ', %.1f GFLOPS' % (macs / 1E9 * 2)
      except:
          fs = ''
      print('Model Summary: %g layers, %g parameters, %g gradients%s' % (len(list(model.parameters())), n_p, n_g, fs))
  
  
  def load_classifier(name='resnet101', n=2):
      # Loads a pretrained model reshaped to n-class output
      import pretrainedmodels  # https://github.com/Cadene/pretrained-models.pytorch#torchvision
      model = pretrainedmodels.__dict__[name](num_classes=1000, pretrained='imagenet')
  
      # Display model properties
      for x in ['model.input_size', 'model.input_space', 'model.input_range', 'model.mean', 'model.std']:
          print(x + ' =', eval(x))
  
      # Reshape output to n classes
      filters = model.last_linear.weight.shape[1]
      model.last_linear.bias = torch.nn.Parameter(torch.zeros(n))
      model.last_linear.weight = torch.nn.Parameter(torch.zeros(n, filters))
      model.last_linear.out_features = n
      return model
  
  
  def scale_img(img, ratio=1.0, same_shape=False):  # img(16, 3, 256, 416), r=ratio
      h, w = img.shape[2:]  # scales img(bs, 3, y, x) by ratio
      s = (int(h * ratio), int(w * ratio))  # new size
      img = F.interpolate(img, size=s, mode='bilinear', align_corners=False)  # resize
      if not same_shape:  # pad / crop img
          gs = 32  # (pixels) grid size
          h, w = [math.ceil(x * ratio / gs) * gs for x in (h, w)]
      return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447)  # value = imagenet mean
  
  
  class ModelEMA:
      """
      Model Exponential Moving Average from https://github.com/rwightman/pytorch-image-models
      Keep a moving average of everything in the model state_dict (parameters and buffers).
      This is intended to allow functionality like
      https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
      A smoothed version of the weights is necessary for some training schemes to perform well.
      E.g. Google's hyper-params for training MNASNet, MobileNet-V3, EfficientNet, etc that use
      RMSprop with a short 2.4-3 epoch decay period and slow LR decay rate of .96-.99 requires EMA
      smoothing of weights to match results. Pay attention to the decay constant you are using
      relative to your update count per epoch.
      To keep EMA from using GPU resources, set device='cpu'. This will save a bit of memory but
      disable validation of the EMA weights. Validation will have to be done manually in a separate
      process, or after the training stops converging.
      This class is sensitive where it is initialized in the sequence of model init,
      GPU assignment and distributed training wrappers.
      I've tested with the sequence in my own train.py for torch.DataParallel, apex.DDP, and single-GPU.
      """
  
      def __init__(self, model, decay=0.9999, device=''):
          # make a copy of the model for accumulating moving average of weights
          self.ema = deepcopy(model)
          self.ema.eval()
          self.updates = 0  # number of EMA updates
          self.decay = lambda x: decay * (1 - math.exp(-x / 2000))  # decay exponential ramp (to help early epochs)
          self.device = device  # perform ema on different device from model if set
          if device:
              self.ema.to(device=device)
          for p in self.ema.parameters():
              p.requires_grad_(False)
  
      def update(self, model):
          self.updates += 1
          d = self.decay(self.updates)
          with torch.no_grad():
              if type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel):
                  msd, esd = model.module.state_dict(), self.ema.module.state_dict()
              else:
                  msd, esd = model.state_dict(), self.ema.state_dict()
  
              for k, v in esd.items():
                  if v.dtype.is_floating_point:
                      v *= d
                      v += (1. - d) * msd[k].detach()
  
      def update_attr(self, model):
          # Assign attributes (which may change during training)
          for k in model.__dict__.keys():
              if not k.startswith('_'):
                  setattr(self.ema, k, getattr(model, k))
  