flops_counter.py

import torch.nn as nn
import torch
import numpy as np


def get_model_complexity_info(
    model, input_res, print_per_layer_stat=True, as_strings=True, channel=3
):
    assert type(input_res) is tuple
    assert len(input_res) == 2
    batch = torch.FloatTensor(1, channel, *input_res)
    flops_model = add_flops_counting_methods(model)
    flops_model.eval().start_flops_count()
    out = 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 / 10.0**9, precision)) + " GMac"
        elif flops // 10**6 > 0:
            return str(round(flops / 10.0**6, precision)) + " MMac"
        elif flops // 10**3 > 0:
            return str(round(flops / 10.0**3, precision)) + " KMac"
        else:
            return str(flops) + " Mac"
    else:
        if units == "GMac":
            return str(round(flops / 10.0**9, precision)) + " " + units
        elif units == "MMac":
            return str(round(flops / 10.0**6, precision)) + " " + units
        elif units == "KMac":
            return str(round(flops / 10.0**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"


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):
    # 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 variables 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 is_supported_instance(module):
            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 is_supported_instance(module):
    if isinstance(
        module,
        (
            torch.nn.Conv2d,
            torch.nn.ReLU,
            torch.nn.PReLU,
            torch.nn.ELU,
            torch.nn.LeakyReLU,
            torch.nn.ReLU6,
            torch.nn.Linear,
            torch.nn.MaxPool2d,
            torch.nn.AvgPool2d,
            torch.nn.BatchNorm2d,
            torch.nn.Upsample,
            nn.AdaptiveMaxPool2d,
            nn.AdaptiveAvgPool2d,
        ),
    ):
        return True

    return False


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__ += output_elements_count


def relu_flops_counter_hook(module, input, output):
    active_elements_count = output.numel()
    module.__flops__ += active_elements_count


def linear_flops_counter_hook(module, input, output):
    input = input[0]
    batch_size = input.shape[0]
    module.__flops__ += batch_size * input.shape[1] * output.shape[1]


def pool_flops_counter_hook(module, input, output):
    input = input[0]
    module.__flops__ += np.prod(input.shape)


def bn_flops_counter_hook(module, input, output):
    module.affine
    input = input[0]

    batch_flops = np.prod(input.shape)
    if module.affine:
        batch_flops *= 2
    module.__flops__ += batch_flops


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

    filters_per_channel = out_channels // groups
    conv_per_position_flops = (
        kernel_height * kernel_width * in_channels * filters_per_channel
    )

    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 is_supported_instance(module):
        module.__flops__ = 0


def add_flops_counter_hook_function(module):
    if is_supported_instance(module):
        if hasattr(module, "__flops_handle__"):
            return

        if isinstance(module, torch.nn.Conv2d):
            handle = module.register_forward_hook(conv_flops_counter_hook)
        elif isinstance(
            module,
            (
                torch.nn.ReLU,
                torch.nn.PReLU,
                torch.nn.ELU,
                torch.nn.LeakyReLU,
                torch.nn.ReLU6,
            ),
        ):
            handle = module.register_forward_hook(relu_flops_counter_hook)
        elif isinstance(module, torch.nn.Linear):
            handle = module.register_forward_hook(linear_flops_counter_hook)
        elif isinstance(
            module,
            (
                torch.nn.AvgPool2d,
                torch.nn.MaxPool2d,
                nn.AdaptiveMaxPool2d,
                nn.AdaptiveAvgPool2d,
            ),
        ):
            handle = module.register_forward_hook(pool_flops_counter_hook)
        elif isinstance(module, torch.nn.BatchNorm2d):
            handle = module.register_forward_hook(bn_flops_counter_hook)
        elif isinstance(module, torch.nn.Upsample):
            handle = module.register_forward_hook(upsample_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__


# --- Masked flops counting


# Also being run in the initialization
def add_flops_mask_variable_or_reset(module):
    if is_supported_instance(module):
        module.__mask__ = None