opcounter.py

from __future__ import absolute_import
from __future__ import unicode_literals
from __future__ import print_function
from __future__ import division

import torch
import torch.nn as nn
from torch.autograd import Variable
from functools import reduce
import operator

count_ops = 0
count_params = 0

conv_ops = 0
relu_ops = 0
linear_ops = 0
avgpool_ops = 0


def get_num_gen(gen):
    return sum(1 for x in gen)


def is_pruned(layer):
    try:
        layer.mask
        return True
    except AttributeError:
        return False


def is_leaf(model):
    return get_num_gen(model.children()) == 0


def convert_model(model, args):
    for m in model._modules:
        child = model._modules[m]
        if is_leaf(child):
            if isinstance(child, nn.Linear):
                model._modules[m] = CondensingLinear(child, 0.5)
                del(child)
        elif is_pruned(child):
            model._modules[m] = CondensingConv(child)
            del(child)
        else:
            convert_model(child, args)


def get_layer_info(layer):
    layer_str = str(layer)
    type_name = layer_str[:layer_str.find('(')].strip()
    return type_name


def get_layer_param(model):
    return sum([reduce(operator.mul, i.size(), 1) for i in model.parameters()])


### The input batch size should be 1 to call this function
def measure_layer(layer, x):
    global count_ops, count_params
    global conv_ops, relu_ops, linear_ops, avgpool_ops
    delta_ops = 0
    delta_params = 0
    multi_add = 1
    type_name = get_layer_info(layer)

    # if type_name in ['ConvNormal', 'ParallelModule']:
    #     print("====================================================== \n======================================================")

    ### ops_conv
    if type_name in ['Conv2d']:
        out_h = int((x.size()[2] + 2 * layer.padding[0] - layer.kernel_size[0]) /
                    layer.stride[0] + 1)
        out_w = int((x.size()[3] + 2 * layer.padding[1] - layer.kernel_size[1]) /
                    layer.stride[1] + 1)
        delta_ops = layer.in_channels * layer.out_channels * layer.kernel_size[0] *  \
                layer.kernel_size[1] * out_h * out_w / layer.groups * multi_add
        delta_params = get_layer_param(layer)

        conv_ops += delta_ops

        # print('Conv2d: in_channels {}, out_channels {}, kernel_size {}, out_h {}, ops {}'.format(layer.in_channels, layer.out_channels, layer.kernel_size[0], out_h, delta_ops))

    ### ops_nonlinearity
    elif type_name in ['ReLU']:
        delta_ops = x.numel()
        # print('ReLU: ops {}'.format(delta_ops))
        # print("+++++++++++++++++++++++++++++++++++++++++++++++++++")
        relu_ops += delta_ops
        delta_params = get_layer_param(layer)

    ### ops_pooling
    elif type_name in ['AvgPool2d', 'MaxPool2d']:
        in_w = x.size()[2]
        kernel_ops = layer.kernel_size * layer.kernel_size
        out_w = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1)
        out_h = int((in_w + 2 * layer.padding - layer.kernel_size) / layer.stride + 1)
        delta_ops = x.size()[0] * x.size()[1] * out_w * out_h * kernel_ops
        # print('AvgPool2d: kernel_size {}, out_h {}, ops {}'.format(layer.kernel_size, out_h, delta_ops))
        avgpool_ops += delta_ops
        delta_params = get_layer_param(layer)

    elif type_name in ['AdaptiveAvgPool2d']:
        delta_ops = x.size()[0] * x.size()[1] * x.size()[2] * x.size()[3]
        delta_params = get_layer_param(layer)

    ### ops_linear
    elif type_name in ['Linear']:
        weight_ops = layer.weight.numel() * multi_add
        bias_ops = layer.bias.numel()
        delta_ops = x.size()[0] * (weight_ops + bias_ops)
        linear_ops += delta_ops
        delta_params = get_layer_param(layer)
        # print('Linear: weight_ops {}, ops {}'.format(weight_ops, delta_ops))

    ### ops_nothing
    elif type_name in ['BatchNorm2d', 'Dropout2d', 'DropChannel', 'Dropout',
                       'ConvBasic', 'ConvBN', 'ParallelModule', 'MSDNet',
                       'MSDNLayer', 'MSDNFirstLayer', 'ConvDownNormal', 'ConvNormal',
                       'ClassifierModule', 'Sequential']:
        delta_params = get_layer_param(layer)

    ### unknown layer type
    else:
        raise TypeError('unknown layer type: %s' % type_name)

    count_ops += delta_ops
    count_params += delta_params
    return


def measure_model(model, H, W):
    global count_ops, count_params
    global conv_ops, relu_ops, linear_ops, avgpool_ops
    count_ops = 0
    count_params = 0
    conv_ops = 0
    data = Variable(torch.zeros(1, 3, H, W))

    def should_measure(x):
        return is_leaf(x) or is_pruned(x)

    def modify_forward(model):
        for child in model.children():
            if should_measure(child):
                def new_forward(m):
                    def lambda_forward(x):
                        measure_layer(m, x)
                        return m.old_forward(x)
                    return lambda_forward
                child.old_forward = child.forward
                child.forward = new_forward(child)
            else:
                modify_forward(child)

                def new_forward(m):
                    def lambda_forward(x):
                        measure_layer(m, x)
                        return m.old_forward(x)
                    return lambda_forward
                child.old_forward = child.forward
                child.forward = new_forward(child)

    def restore_forward(model):
        for child in model.children():
            # leaf node
            if is_leaf(child) and hasattr(child, 'old_forward'):
                child.forward = child.old_forward
                child.old_forward = None
            else:
                restore_forward(child)

    modify_forward(model)
    model.forward(data)
    restore_forward(model)

    # print(conv_ops, relu_ops, linear_ops, avgpool_ops, conv_ops + relu_ops + linear_ops + avgpool_ops)

    return count_ops, count_params