text: #### https://github.com/warmspringwinds/pytorch-segmentation-detection/blob/master/pytorch_segmentation_detection/utils/flops_benchmark.py
  import torch
  
  
  # ---- Public functions
  
  def add_flops_counting_methods(net_main_module):
      """Adds flops counting functions to an existing model. After that
      the flops count should be activated and the model should be run on an input
      image.
  
      Example:
  
      fcn = add_flops_counting_methods(fcn)
      fcn = fcn.cuda().train()
      fcn.start_flops_count()
  
  
      _ = fcn(batch)
  
      fcn.compute_average_flops_cost() / 1e9 / 2 # Result in GFLOPs per image in batch
  
      Important: dividing by 2 only works for resnet models -- see below for the details
      of flops computation.
  
      Attention: we are counting multiply-add as two flops in this work, because in
      most resnet models convolutions are bias-free (BN layers act as bias there)
      and it makes sense to count muliply and add as separate flops therefore.
      This is why in the above example we divide by 2 in order to be consistent with
      most modern benchmarks. For example in "Spatially Adaptive Computatin Time for Residual
      Networks" by Figurnov et al multiply-add was counted as two flops.
  
      This module computes the average flops which is necessary for dynamic networks which
      have different number of executed layers. For static networks it is enough to run the network
      once and get statistics (above example).
  
      Implementation:
      The module works by adding batch_count to the main module which tracks the sum
      of all batch sizes that were run through the network.
  
      Also each convolutional layer of the network tracks the overall number of flops
      performed.
  
      The parameters are updated with the help of registered hook-functions which
      are being called each time the respective layer is executed.
  
      Parameters
      ----------
      net_main_module : torch.nn.Module
          Main module containing network
  
      Returns
      -------
      net_main_module : torch.nn.Module
          Updated main module with new methods/attributes that are used
          to compute flops.
      """
  
      # adding additional methods to the existing module object,
      # this is done this way so that each function has access to self object
      net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
      net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
      net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
      net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)
  
      net_main_module.reset_flops_count()
  
      # Adding varialbles necessary for masked flops computation
      net_main_module.apply(add_flops_mask_variable_or_reset)
  
      return net_main_module
  
  
  def compute_average_flops_cost(self):
      """
      A method that will be available after add_flops_counting_methods() is called
      on a desired net object.
  
      Returns current mean flops consumption per image.
  
      """
  
      batches_count = self.__batch_counter__
  
      flops_sum = 0
  
      for module in self.modules():
  
          if isinstance(module, torch.nn.Conv2d):
              flops_sum += module.__flops__
  
      return flops_sum / batches_count
  
  
  def start_flops_count(self):
      """
      A method that will be available after add_flops_counting_methods() is called
      on a desired net object.
  
      Activates the computation of mean flops consumption per image.
      Call it before you run the network.
  
      """
  
      add_batch_counter_hook_function(self)
  
      self.apply(add_flops_counter_hook_function)
  
  
  def stop_flops_count(self):
      """
      A method that will be available after add_flops_counting_methods() is called
      on a desired net object.
  
      Stops computing the mean flops consumption per image.
      Call whenever you want to pause the computation.
  
      """
  
      remove_batch_counter_hook_function(self)
  
      self.apply(remove_flops_counter_hook_function)
  
  
  def reset_flops_count(self):
      """
      A method that will be available after add_flops_counting_methods() is called
      on a desired net object.
  
      Resets statistics computed so far.
  
      """
  
      add_batch_counter_variables_or_reset(self)
  
      self.apply(add_flops_counter_variable_or_reset)
  
  
  def add_flops_mask(module, mask):
      def add_flops_mask_func(module):
          if isinstance(module, torch.nn.Conv2d):
              module.__mask__ = mask
  
      module.apply(add_flops_mask_func)
  
  
  def remove_flops_mask(module):
      module.apply(add_flops_mask_variable_or_reset)
  
  
  # ---- Internal functions
  
  
  def conv_flops_counter_hook(conv_module, input, output):
      # Can have multiple inputs, getting the first one
      input = input[0]
  
      batch_size = input.shape[0]
      output_height, output_width = output.shape[2:]
  
      kernel_height, kernel_width = conv_module.kernel_size
      in_channels = conv_module.in_channels
      out_channels = conv_module.out_channels
      groups = conv_module.groups
  
      # We count multiply-add as 2 flops
      conv_per_position_flops = 2 * kernel_height * kernel_width * in_channels * out_channels / groups
  
      active_elements_count = batch_size * output_height * output_width
  
      if conv_module.__mask__ is not None:
          # (b, 1, h, w)
          flops_mask = conv_module.__mask__.expand(batch_size, 1, output_height, output_width)
          active_elements_count = flops_mask.sum()
  
      overall_conv_flops = conv_per_position_flops * active_elements_count
  
      bias_flops = 0
  
      if conv_module.bias is not None:
          bias_flops = out_channels * active_elements_count
  
      overall_flops = overall_conv_flops + bias_flops
  
      conv_module.__flops__ += overall_flops
  
  
  def batch_counter_hook(module, input, output):
      # Can have multiple inputs, getting the first one
      input = input[0]
  
      batch_size = input.shape[0]
  
      module.__batch_counter__ += batch_size
  
  
  def add_batch_counter_variables_or_reset(module):
      module.__batch_counter__ = 0
  
  
  def add_batch_counter_hook_function(module):
      if hasattr(module, '__batch_counter_handle__'):
          return
  
      handle = module.register_forward_hook(batch_counter_hook)
      module.__batch_counter_handle__ = handle
  
  
  def remove_batch_counter_hook_function(module):
      if hasattr(module, '__batch_counter_handle__'):
          module.__batch_counter_handle__.remove()
  
          del module.__batch_counter_handle__
  
  
  def add_flops_counter_variable_or_reset(module):
      if isinstance(module, torch.nn.Conv2d):
          module.__flops__ = 0
  
  
  def add_flops_counter_hook_function(module):
      if isinstance(module, torch.nn.Conv2d):
  
          if hasattr(module, '__flops_handle__'):
              return
  
          handle = module.register_forward_hook(conv_flops_counter_hook)
          module.__flops_handle__ = handle
  
  
  def remove_flops_counter_hook_function(module):
      if isinstance(module, torch.nn.Conv2d):
  
          if hasattr(module, '__flops_handle__'):
              module.__flops_handle__.remove()
  
              del module.__flops_handle__
  
  
  # --- Masked flops counting
  
  
  # Also being run in the initialization
  def add_flops_mask_variable_or_reset(module):
      if isinstance(module, torch.nn.Conv2d):
          module.__mask__ = None
  
  
  def count_flops(model, batch_size, device, dtype, input_size, in_channels, *params):
      net = model(*params, input_size=input_size)
      # print(net)
      net = add_flops_counting_methods(net)
  
      net.to(device=device, dtype=dtype)
      net = net.train()
  
      batch = torch.randn(batch_size, in_channels, input_size, input_size).to(device=device, dtype=dtype)
      net.start_flops_count()
  
      _ = net(batch)
      return net.compute_average_flops_cost() / 2  # Result in FLOPs
  