| import torch.nn as nn
|
| import torch
|
| import numpy as np
|
|
|
| '''
|
| ---- 1) FLOPs: floating point operations
|
| ---- 2) #Activations: the number of elements of all ‘Conv2d’ outputs
|
| ---- 3) #Conv2d: the number of ‘Conv2d’ layers
|
| '''
|
|
|
| def get_model_flops(model, input_res, print_per_layer_stat=True,
|
| input_constructor=None):
|
| assert type(input_res) is tuple, 'Please provide the size of the input image.'
|
| assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
|
| flops_model = add_flops_counting_methods(model)
|
| flops_model.eval().start_flops_count()
|
| if input_constructor:
|
| input = input_constructor(input_res)
|
| _ = flops_model(**input)
|
| else:
|
| device = list(flops_model.parameters())[-1].device
|
| batch = torch.FloatTensor(1, *input_res).to(device)
|
| _ = flops_model(batch)
|
|
|
| if print_per_layer_stat:
|
| print_model_with_flops(flops_model)
|
| flops_count = flops_model.compute_average_flops_cost()
|
| flops_model.stop_flops_count()
|
|
|
| return flops_count
|
|
|
| def get_model_activation(model, input_res, input_constructor=None):
|
| assert type(input_res) is tuple, 'Please provide the size of the input image.'
|
| assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
|
| activation_model = add_activation_counting_methods(model)
|
| activation_model.eval().start_activation_count()
|
| if input_constructor:
|
| input = input_constructor(input_res)
|
| _ = activation_model(**input)
|
| else:
|
| device = list(activation_model.parameters())[-1].device
|
| batch = torch.FloatTensor(1, *input_res).to(device)
|
| _ = activation_model(batch)
|
|
|
| activation_count, num_conv = activation_model.compute_average_activation_cost()
|
| activation_model.stop_activation_count()
|
|
|
| return activation_count, num_conv
|
|
|
|
|
| def get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True,
|
| input_constructor=None):
|
| assert type(input_res) is tuple
|
| assert len(input_res) >= 3
|
| flops_model = add_flops_counting_methods(model)
|
| flops_model.eval().start_flops_count()
|
| if input_constructor:
|
| input = input_constructor(input_res)
|
| _ = flops_model(**input)
|
| else:
|
| batch = torch.FloatTensor(1, *input_res)
|
| _ = flops_model(batch)
|
|
|
| if print_per_layer_stat:
|
| print_model_with_flops(flops_model)
|
| flops_count = flops_model.compute_average_flops_cost()
|
| params_count = get_model_parameters_number(flops_model)
|
| flops_model.stop_flops_count()
|
|
|
| if as_strings:
|
| return flops_to_string(flops_count), params_to_string(params_count)
|
|
|
| return flops_count, params_count
|
|
|
|
|
| def flops_to_string(flops, units='GMac', precision=2):
|
| if units is None:
|
| if flops // 10**9 > 0:
|
| return str(round(flops / 10.**9, precision)) + ' GMac'
|
| elif flops // 10**6 > 0:
|
| return str(round(flops / 10.**6, precision)) + ' MMac'
|
| elif flops // 10**3 > 0:
|
| return str(round(flops / 10.**3, precision)) + ' KMac'
|
| else:
|
| return str(flops) + ' Mac'
|
| else:
|
| if units == 'GMac':
|
| return str(round(flops / 10.**9, precision)) + ' ' + units
|
| elif units == 'MMac':
|
| return str(round(flops / 10.**6, precision)) + ' ' + units
|
| elif units == 'KMac':
|
| return str(round(flops / 10.**3, precision)) + ' ' + units
|
| else:
|
| return str(flops) + ' Mac'
|
|
|
|
|
| def params_to_string(params_num):
|
| if params_num // 10 ** 6 > 0:
|
| return str(round(params_num / 10 ** 6, 2)) + ' M'
|
| elif params_num // 10 ** 3:
|
| return str(round(params_num / 10 ** 3, 2)) + ' k'
|
| else:
|
| return str(params_num)
|
|
|
|
|
| def print_model_with_flops(model, units='GMac', precision=3):
|
| total_flops = model.compute_average_flops_cost()
|
|
|
| def accumulate_flops(self):
|
| if is_supported_instance(self):
|
| return self.__flops__ / model.__batch_counter__
|
| else:
|
| sum = 0
|
| for m in self.children():
|
| sum += m.accumulate_flops()
|
| return sum
|
|
|
| def flops_repr(self):
|
| accumulated_flops_cost = self.accumulate_flops()
|
| return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision),
|
| '{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
|
| self.original_extra_repr()])
|
|
|
| def add_extra_repr(m):
|
| m.accumulate_flops = accumulate_flops.__get__(m)
|
| flops_extra_repr = flops_repr.__get__(m)
|
| if m.extra_repr != flops_extra_repr:
|
| m.original_extra_repr = m.extra_repr
|
| m.extra_repr = flops_extra_repr
|
| assert m.extra_repr != m.original_extra_repr
|
|
|
| def del_extra_repr(m):
|
| if hasattr(m, 'original_extra_repr'):
|
| m.extra_repr = m.original_extra_repr
|
| del m.original_extra_repr
|
| if hasattr(m, 'accumulate_flops'):
|
| del m.accumulate_flops
|
|
|
| model.apply(add_extra_repr)
|
| print(model)
|
| model.apply(del_extra_repr)
|
|
|
|
|
| def get_model_parameters_number(model):
|
| params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
|
| return params_num
|
|
|
|
|
| def add_flops_counting_methods(net_main_module):
|
|
|
|
|
|
|
| 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()
|
| 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.
|
|
|
| """
|
|
|
| flops_sum = 0
|
| for module in self.modules():
|
| if is_supported_instance(module):
|
| flops_sum += module.__flops__
|
|
|
| return flops_sum
|
|
|
|
|
| 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.
|
|
|
| """
|
| 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.
|
|
|
| """
|
| 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.
|
|
|
| """
|
| self.apply(add_flops_counter_variable_or_reset)
|
|
|
|
|
| def add_flops_counter_hook_function(module):
|
| if is_supported_instance(module):
|
| if hasattr(module, '__flops_handle__'):
|
| return
|
|
|
| if isinstance(module, (nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d)):
|
| handle = module.register_forward_hook(conv_flops_counter_hook)
|
| elif isinstance(module, (nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6)):
|
| handle = module.register_forward_hook(relu_flops_counter_hook)
|
| elif isinstance(module, nn.Linear):
|
| handle = module.register_forward_hook(linear_flops_counter_hook)
|
| elif isinstance(module, (nn.BatchNorm2d)):
|
| handle = module.register_forward_hook(bn_flops_counter_hook)
|
| else:
|
| handle = module.register_forward_hook(empty_flops_counter_hook)
|
| module.__flops_handle__ = handle
|
|
|
|
|
| def remove_flops_counter_hook_function(module):
|
| if is_supported_instance(module):
|
| if hasattr(module, '__flops_handle__'):
|
| module.__flops_handle__.remove()
|
| del module.__flops_handle__
|
|
|
|
|
| def add_flops_counter_variable_or_reset(module):
|
| if is_supported_instance(module):
|
| module.__flops__ = 0
|
|
|
|
|
|
|
| def is_supported_instance(module):
|
| if isinstance(module,
|
| (
|
| nn.Conv2d, nn.ConvTranspose2d,
|
| nn.BatchNorm2d,
|
| nn.Linear,
|
| nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6,
|
| )):
|
| return True
|
|
|
| return False
|
|
|
|
|
| def conv_flops_counter_hook(conv_module, input, output):
|
|
|
|
|
|
|
| batch_size = output.shape[0]
|
| output_dims = list(output.shape[2:])
|
|
|
| kernel_dims = list(conv_module.kernel_size)
|
| in_channels = conv_module.in_channels
|
| out_channels = conv_module.out_channels
|
| groups = conv_module.groups
|
|
|
| filters_per_channel = out_channels // groups
|
| conv_per_position_flops = np.prod(kernel_dims) * in_channels * filters_per_channel
|
|
|
| active_elements_count = batch_size * np.prod(output_dims)
|
| overall_conv_flops = int(conv_per_position_flops) * int(active_elements_count)
|
|
|
|
|
|
|
| conv_module.__flops__ += int(overall_conv_flops)
|
|
|
|
|
|
|
| def relu_flops_counter_hook(module, input, output):
|
| active_elements_count = output.numel()
|
| module.__flops__ += int(active_elements_count)
|
|
|
|
|
|
|
|
|
| def linear_flops_counter_hook(module, input, output):
|
| input = input[0]
|
| if len(input.shape) == 1:
|
| batch_size = 1
|
| module.__flops__ += int(batch_size * input.shape[0] * output.shape[0])
|
| else:
|
| batch_size = input.shape[0]
|
| module.__flops__ += int(batch_size * input.shape[1] * output.shape[1])
|
|
|
|
|
| def bn_flops_counter_hook(module, input, output):
|
|
|
|
|
|
|
|
|
|
|
|
|
| batch = output.shape[0]
|
| output_dims = output.shape[2:]
|
| channels = module.num_features
|
| batch_flops = batch * channels * np.prod(output_dims)
|
| if module.affine:
|
| batch_flops *= 2
|
| module.__flops__ += int(batch_flops)
|
|
|
|
|
|
|
| def add_activation_counting_methods(net_main_module):
|
|
|
|
|
|
|
| net_main_module.start_activation_count = start_activation_count.__get__(net_main_module)
|
| net_main_module.stop_activation_count = stop_activation_count.__get__(net_main_module)
|
| net_main_module.reset_activation_count = reset_activation_count.__get__(net_main_module)
|
| net_main_module.compute_average_activation_cost = compute_average_activation_cost.__get__(net_main_module)
|
|
|
| net_main_module.reset_activation_count()
|
| return net_main_module
|
|
|
|
|
| def compute_average_activation_cost(self):
|
| """
|
| A method that will be available after add_activation_counting_methods() is called
|
| on a desired net object.
|
|
|
| Returns current mean activation consumption per image.
|
|
|
| """
|
|
|
| activation_sum = 0
|
| num_conv = 0
|
| for module in self.modules():
|
| if is_supported_instance_for_activation(module):
|
| activation_sum += module.__activation__
|
| num_conv += module.__num_conv__
|
| return activation_sum, num_conv
|
|
|
|
|
| def start_activation_count(self):
|
| """
|
| A method that will be available after add_activation_counting_methods() is called
|
| on a desired net object.
|
|
|
| Activates the computation of mean activation consumption per image.
|
| Call it before you run the network.
|
|
|
| """
|
| self.apply(add_activation_counter_hook_function)
|
|
|
|
|
| def stop_activation_count(self):
|
| """
|
| A method that will be available after add_activation_counting_methods() is called
|
| on a desired net object.
|
|
|
| Stops computing the mean activation consumption per image.
|
| Call whenever you want to pause the computation.
|
|
|
| """
|
| self.apply(remove_activation_counter_hook_function)
|
|
|
|
|
| def reset_activation_count(self):
|
| """
|
| A method that will be available after add_activation_counting_methods() is called
|
| on a desired net object.
|
|
|
| Resets statistics computed so far.
|
|
|
| """
|
| self.apply(add_activation_counter_variable_or_reset)
|
|
|
|
|
| def add_activation_counter_hook_function(module):
|
| if is_supported_instance_for_activation(module):
|
| if hasattr(module, '__activation_handle__'):
|
| return
|
|
|
| if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
|
| handle = module.register_forward_hook(conv_activation_counter_hook)
|
| module.__activation_handle__ = handle
|
|
|
|
|
| def remove_activation_counter_hook_function(module):
|
| if is_supported_instance_for_activation(module):
|
| if hasattr(module, '__activation_handle__'):
|
| module.__activation_handle__.remove()
|
| del module.__activation_handle__
|
|
|
|
|
| def add_activation_counter_variable_or_reset(module):
|
| if is_supported_instance_for_activation(module):
|
| module.__activation__ = 0
|
| module.__num_conv__ = 0
|
|
|
|
|
| def is_supported_instance_for_activation(module):
|
| if isinstance(module,
|
| (
|
| nn.Conv2d, nn.ConvTranspose2d, nn.Conv1d, nn.Linear, nn.ConvTranspose1d
|
| )):
|
| return True
|
|
|
| return False
|
|
|
| def conv_activation_counter_hook(module, input, output):
|
| """
|
| Calculate the activations in the convolutional operation.
|
| Reference: Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár, Designing Network Design Spaces.
|
| :param module:
|
| :param input:
|
| :param output:
|
| :return:
|
| """
|
| module.__activation__ += output.numel()
|
| module.__num_conv__ += 1
|
|
|
|
|
| def empty_flops_counter_hook(module, input, output):
|
| module.__flops__ += 0
|
|
|
|
|
| def upsample_flops_counter_hook(module, input, output):
|
| output_size = output[0]
|
| batch_size = output_size.shape[0]
|
| output_elements_count = batch_size
|
| for val in output_size.shape[1:]:
|
| output_elements_count *= val
|
| module.__flops__ += int(output_elements_count)
|
|
|
|
|
| def pool_flops_counter_hook(module, input, output):
|
| input = input[0]
|
| module.__flops__ += int(np.prod(input.shape))
|
|
|
|
|
| def dconv_flops_counter_hook(dconv_module, input, output):
|
| input = input[0]
|
|
|
| batch_size = input.shape[0]
|
| output_dims = list(output.shape[2:])
|
|
|
| m_channels, in_channels, kernel_dim1, _, = dconv_module.weight.shape
|
| out_channels, _, kernel_dim2, _, = dconv_module.projection.shape
|
|
|
|
|
|
|
| conv_per_position_flops1 = kernel_dim1 ** 2 * in_channels * m_channels
|
| conv_per_position_flops2 = kernel_dim2 ** 2 * out_channels * m_channels
|
| active_elements_count = batch_size * np.prod(output_dims)
|
|
|
| overall_conv_flops = (conv_per_position_flops1 + conv_per_position_flops2) * active_elements_count
|
| overall_flops = overall_conv_flops
|
|
|
| dconv_module.__flops__ += int(overall_flops)
|
|
|
| |
