486 lines
16 KiB
Python
486 lines
16 KiB
Python
import torch.nn as nn
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import torch
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import numpy as np
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'''
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---- 1) FLOPs: floating point operations
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---- 2) #Activations: the number of elements of all ‘Conv2d’ outputs
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---- 3) #Conv2d: the number of ‘Conv2d’ layers
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# --------------------------------------------
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# Kai Zhang (github: https://github.com/cszn)
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# 21/July/2020
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# --------------------------------------------
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# Reference
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https://github.com/sovrasov/flops-counter.pytorch.git
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# If you use this code, please consider the following citation:
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@inproceedings{zhang2020aim, %
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title={AIM 2020 Challenge on Efficient Super-Resolution: Methods and Results},
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author={Kai Zhang and Martin Danelljan and Yawei Li and Radu Timofte and others},
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booktitle={European Conference on Computer Vision Workshops},
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year={2020}
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}
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# --------------------------------------------
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'''
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def get_model_flops(model, input_res, print_per_layer_stat=True,
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input_constructor=None):
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assert type(input_res) is tuple, 'Please provide the size of the input image.'
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assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
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flops_model = add_flops_counting_methods(model)
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flops_model.eval().start_flops_count()
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if input_constructor:
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input = input_constructor(input_res)
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_ = flops_model(**input)
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else:
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device = list(flops_model.parameters())[-1].device
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batch = torch.FloatTensor(1, *input_res).to(device)
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_ = flops_model(batch)
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if print_per_layer_stat:
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print_model_with_flops(flops_model)
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flops_count = flops_model.compute_average_flops_cost()
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flops_model.stop_flops_count()
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return flops_count
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def get_model_activation(model, input_res, input_constructor=None):
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assert type(input_res) is tuple, 'Please provide the size of the input image.'
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assert len(input_res) >= 3, 'Input image should have 3 dimensions.'
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activation_model = add_activation_counting_methods(model)
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activation_model.eval().start_activation_count()
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if input_constructor:
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input = input_constructor(input_res)
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_ = activation_model(**input)
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else:
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device = list(activation_model.parameters())[-1].device
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batch = torch.FloatTensor(1, *input_res).to(device)
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_ = activation_model(batch)
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activation_count, num_conv = activation_model.compute_average_activation_cost()
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activation_model.stop_activation_count()
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return activation_count, num_conv
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def get_model_complexity_info(model, input_res, print_per_layer_stat=True, as_strings=True,
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input_constructor=None):
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assert type(input_res) is tuple
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assert len(input_res) >= 3
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flops_model = add_flops_counting_methods(model)
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flops_model.eval().start_flops_count()
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if input_constructor:
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input = input_constructor(input_res)
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_ = flops_model(**input)
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else:
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batch = torch.FloatTensor(1, *input_res)
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_ = flops_model(batch)
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if print_per_layer_stat:
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print_model_with_flops(flops_model)
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flops_count = flops_model.compute_average_flops_cost()
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params_count = get_model_parameters_number(flops_model)
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flops_model.stop_flops_count()
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if as_strings:
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return flops_to_string(flops_count), params_to_string(params_count)
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return flops_count, params_count
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def flops_to_string(flops, units='GMac', precision=2):
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if units is None:
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if flops // 10**9 > 0:
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return str(round(flops / 10.**9, precision)) + ' GMac'
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elif flops // 10**6 > 0:
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return str(round(flops / 10.**6, precision)) + ' MMac'
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elif flops // 10**3 > 0:
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return str(round(flops / 10.**3, precision)) + ' KMac'
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else:
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return str(flops) + ' Mac'
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else:
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if units == 'GMac':
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return str(round(flops / 10.**9, precision)) + ' ' + units
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elif units == 'MMac':
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return str(round(flops / 10.**6, precision)) + ' ' + units
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elif units == 'KMac':
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return str(round(flops / 10.**3, precision)) + ' ' + units
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else:
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return str(flops) + ' Mac'
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def params_to_string(params_num):
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if params_num // 10 ** 6 > 0:
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return str(round(params_num / 10 ** 6, 2)) + ' M'
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elif params_num // 10 ** 3:
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return str(round(params_num / 10 ** 3, 2)) + ' k'
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else:
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return str(params_num)
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def print_model_with_flops(model, units='GMac', precision=3):
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total_flops = model.compute_average_flops_cost()
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def accumulate_flops(self):
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if is_supported_instance(self):
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return self.__flops__ / model.__batch_counter__
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else:
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sum = 0
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for m in self.children():
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sum += m.accumulate_flops()
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return sum
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def flops_repr(self):
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accumulated_flops_cost = self.accumulate_flops()
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return ', '.join([flops_to_string(accumulated_flops_cost, units=units, precision=precision),
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'{:.3%} MACs'.format(accumulated_flops_cost / total_flops),
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self.original_extra_repr()])
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def add_extra_repr(m):
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m.accumulate_flops = accumulate_flops.__get__(m)
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flops_extra_repr = flops_repr.__get__(m)
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if m.extra_repr != flops_extra_repr:
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m.original_extra_repr = m.extra_repr
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m.extra_repr = flops_extra_repr
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assert m.extra_repr != m.original_extra_repr
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def del_extra_repr(m):
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if hasattr(m, 'original_extra_repr'):
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m.extra_repr = m.original_extra_repr
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del m.original_extra_repr
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if hasattr(m, 'accumulate_flops'):
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del m.accumulate_flops
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model.apply(add_extra_repr)
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print(model)
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model.apply(del_extra_repr)
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def get_model_parameters_number(model):
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params_num = sum(p.numel() for p in model.parameters() if p.requires_grad)
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return params_num
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def add_flops_counting_methods(net_main_module):
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# adding additional methods to the existing module object,
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# this is done this way so that each function has access to self object
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# embed()
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net_main_module.start_flops_count = start_flops_count.__get__(net_main_module)
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net_main_module.stop_flops_count = stop_flops_count.__get__(net_main_module)
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net_main_module.reset_flops_count = reset_flops_count.__get__(net_main_module)
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net_main_module.compute_average_flops_cost = compute_average_flops_cost.__get__(net_main_module)
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net_main_module.reset_flops_count()
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return net_main_module
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def compute_average_flops_cost(self):
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"""
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A method that will be available after add_flops_counting_methods() is called
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on a desired net object.
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Returns current mean flops consumption per image.
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"""
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flops_sum = 0
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for module in self.modules():
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if is_supported_instance(module):
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flops_sum += module.__flops__
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return flops_sum
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def start_flops_count(self):
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"""
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A method that will be available after add_flops_counting_methods() is called
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on a desired net object.
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Activates the computation of mean flops consumption per image.
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Call it before you run the network.
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"""
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self.apply(add_flops_counter_hook_function)
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def stop_flops_count(self):
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"""
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A method that will be available after add_flops_counting_methods() is called
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on a desired net object.
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Stops computing the mean flops consumption per image.
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Call whenever you want to pause the computation.
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"""
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self.apply(remove_flops_counter_hook_function)
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def reset_flops_count(self):
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"""
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A method that will be available after add_flops_counting_methods() is called
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on a desired net object.
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Resets statistics computed so far.
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"""
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self.apply(add_flops_counter_variable_or_reset)
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def add_flops_counter_hook_function(module):
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if is_supported_instance(module):
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if hasattr(module, '__flops_handle__'):
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return
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if isinstance(module, (nn.Conv2d, nn.Conv3d, nn.ConvTranspose2d)):
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handle = module.register_forward_hook(conv_flops_counter_hook)
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elif isinstance(module, (nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6)):
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handle = module.register_forward_hook(relu_flops_counter_hook)
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elif isinstance(module, nn.Linear):
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handle = module.register_forward_hook(linear_flops_counter_hook)
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elif isinstance(module, (nn.BatchNorm2d)):
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handle = module.register_forward_hook(bn_flops_counter_hook)
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else:
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handle = module.register_forward_hook(empty_flops_counter_hook)
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module.__flops_handle__ = handle
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def remove_flops_counter_hook_function(module):
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if is_supported_instance(module):
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if hasattr(module, '__flops_handle__'):
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module.__flops_handle__.remove()
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del module.__flops_handle__
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def add_flops_counter_variable_or_reset(module):
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if is_supported_instance(module):
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module.__flops__ = 0
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# ---- Internal functions
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def is_supported_instance(module):
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if isinstance(module,
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(
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nn.Conv2d, nn.ConvTranspose2d,
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nn.BatchNorm2d,
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nn.Linear,
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nn.ReLU, nn.PReLU, nn.ELU, nn.LeakyReLU, nn.ReLU6,
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)):
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return True
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return False
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def conv_flops_counter_hook(conv_module, input, output):
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# Can have multiple inputs, getting the first one
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# input = input[0]
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batch_size = output.shape[0]
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output_dims = list(output.shape[2:])
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kernel_dims = list(conv_module.kernel_size)
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in_channels = conv_module.in_channels
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out_channels = conv_module.out_channels
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groups = conv_module.groups
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filters_per_channel = out_channels // groups
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conv_per_position_flops = np.prod(kernel_dims) * in_channels * filters_per_channel
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active_elements_count = batch_size * np.prod(output_dims)
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overall_conv_flops = int(conv_per_position_flops) * int(active_elements_count)
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# overall_flops = overall_conv_flops
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conv_module.__flops__ += int(overall_conv_flops)
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# conv_module.__output_dims__ = output_dims
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def relu_flops_counter_hook(module, input, output):
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active_elements_count = output.numel()
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module.__flops__ += int(active_elements_count)
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# print(module.__flops__, id(module))
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# print(module)
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def linear_flops_counter_hook(module, input, output):
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input = input[0]
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if len(input.shape) == 1:
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batch_size = 1
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module.__flops__ += int(batch_size * input.shape[0] * output.shape[0])
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else:
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batch_size = input.shape[0]
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module.__flops__ += int(batch_size * input.shape[1] * output.shape[1])
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def bn_flops_counter_hook(module, input, output):
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# input = input[0]
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# TODO: need to check here
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# batch_flops = np.prod(input.shape)
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# if module.affine:
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# batch_flops *= 2
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# module.__flops__ += int(batch_flops)
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batch = output.shape[0]
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output_dims = output.shape[2:]
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channels = module.num_features
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batch_flops = batch * channels * np.prod(output_dims)
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if module.affine:
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batch_flops *= 2
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module.__flops__ += int(batch_flops)
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# ---- Count the number of convolutional layers and the activation
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def add_activation_counting_methods(net_main_module):
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# adding additional methods to the existing module object,
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# this is done this way so that each function has access to self object
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# embed()
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net_main_module.start_activation_count = start_activation_count.__get__(net_main_module)
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net_main_module.stop_activation_count = stop_activation_count.__get__(net_main_module)
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net_main_module.reset_activation_count = reset_activation_count.__get__(net_main_module)
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net_main_module.compute_average_activation_cost = compute_average_activation_cost.__get__(net_main_module)
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net_main_module.reset_activation_count()
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return net_main_module
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def compute_average_activation_cost(self):
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"""
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A method that will be available after add_activation_counting_methods() is called
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on a desired net object.
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Returns current mean activation consumption per image.
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"""
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activation_sum = 0
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num_conv = 0
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for module in self.modules():
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if is_supported_instance_for_activation(module):
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activation_sum += module.__activation__
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num_conv += module.__num_conv__
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return activation_sum, num_conv
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def start_activation_count(self):
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"""
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A method that will be available after add_activation_counting_methods() is called
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on a desired net object.
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Activates the computation of mean activation consumption per image.
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Call it before you run the network.
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"""
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self.apply(add_activation_counter_hook_function)
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def stop_activation_count(self):
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"""
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A method that will be available after add_activation_counting_methods() is called
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on a desired net object.
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Stops computing the mean activation consumption per image.
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Call whenever you want to pause the computation.
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"""
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self.apply(remove_activation_counter_hook_function)
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def reset_activation_count(self):
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"""
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A method that will be available after add_activation_counting_methods() is called
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on a desired net object.
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Resets statistics computed so far.
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"""
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self.apply(add_activation_counter_variable_or_reset)
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def add_activation_counter_hook_function(module):
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if is_supported_instance_for_activation(module):
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if hasattr(module, '__activation_handle__'):
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return
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if isinstance(module, (nn.Conv2d, nn.ConvTranspose2d)):
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handle = module.register_forward_hook(conv_activation_counter_hook)
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module.__activation_handle__ = handle
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def remove_activation_counter_hook_function(module):
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if is_supported_instance_for_activation(module):
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if hasattr(module, '__activation_handle__'):
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module.__activation_handle__.remove()
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del module.__activation_handle__
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def add_activation_counter_variable_or_reset(module):
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if is_supported_instance_for_activation(module):
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module.__activation__ = 0
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module.__num_conv__ = 0
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def is_supported_instance_for_activation(module):
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if isinstance(module,
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(
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nn.Conv2d, nn.ConvTranspose2d,
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)):
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return True
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return False
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def conv_activation_counter_hook(module, input, output):
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"""
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Calculate the activations in the convolutional operation.
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Reference: Ilija Radosavovic, Raj Prateek Kosaraju, Ross Girshick, Kaiming He, Piotr Dollár, Designing Network Design Spaces.
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:param module:
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:param input:
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:param output:
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:return:
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"""
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module.__activation__ += output.numel()
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module.__num_conv__ += 1
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def empty_flops_counter_hook(module, input, output):
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module.__flops__ += 0
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def upsample_flops_counter_hook(module, input, output):
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output_size = output[0]
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batch_size = output_size.shape[0]
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output_elements_count = batch_size
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for val in output_size.shape[1:]:
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output_elements_count *= val
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module.__flops__ += int(output_elements_count)
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def pool_flops_counter_hook(module, input, output):
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input = input[0]
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module.__flops__ += int(np.prod(input.shape))
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def dconv_flops_counter_hook(dconv_module, input, output):
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input = input[0]
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batch_size = input.shape[0]
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output_dims = list(output.shape[2:])
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m_channels, in_channels, kernel_dim1, _, = dconv_module.weight.shape
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out_channels, _, kernel_dim2, _, = dconv_module.projection.shape
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# groups = dconv_module.groups
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# filters_per_channel = out_channels // groups
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conv_per_position_flops1 = kernel_dim1 ** 2 * in_channels * m_channels
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conv_per_position_flops2 = kernel_dim2 ** 2 * out_channels * m_channels
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active_elements_count = batch_size * np.prod(output_dims)
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overall_conv_flops = (conv_per_position_flops1 + conv_per_position_flops2) * active_elements_count
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overall_flops = overall_conv_flops
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dconv_module.__flops__ += int(overall_flops)
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# dconv_module.__output_dims__ = output_dims
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