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
# --------------------------------------------
# Kai Zhang (github: https://github.com/cszn)
# 21/July/2020
# --------------------------------------------
# Reference
https://github.com/sovrasov/flops-counter.pytorch.git
# If you use this code, please consider the following citation:
@inproceedings{zhang2020aim, %
title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},
author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},
booktitle={European Conference on Computer Vision Workshops},
year={2020}
}
# --------------------------------------------
'''
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,
)):
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)