pytorch计算网络模型flops的代码

可以使用下面的代码计算PyTorch模型的FLOPs（浮点操作次数）：

import torch
from torch.autograd import Variable

def print_model_parm_flops(model, input_size, custom_layers):
    multiply_adds = 1
    params = 0
    flops = 0
    input = Variable(torch.rand(1, *input_size))

    def register_hook(module):
        def hook(module, input, output):
            class_name = str(module.__class__).split(".")[-1].split("'")[0]
            if class_name == 'Conv2d':
                out_h, out_w = output.size()[2:]
                kernel_h, kernel_w = module.kernel_size
                in_channels = module.in_channels
                out_channels = module.out_channels
                if isinstance(module.padding, int):
                    pad_h = pad_w = module.padding
                else:
                    pad_h, pad_w = module.padding
                if isinstance(module.stride, int):
                    stride_h = stride_w = module.stride
                else:
                    stride_h, stride_w = module.stride
                params += out_channels * (in_channels // module.groups) * kernel_h * kernel_w
                flops += out_channels * (in_channels // module.groups) * kernel_h * kernel_w * out_h * out_w / (stride_h * stride_w)
            elif class_name == 'Linear':
                weight_flops = module.weight.nelement() * input[0].nelement() // module.weight.size(1)
                bias_flops = module.bias.nelement()
                flops = weight_flops + bias_flops
                params = weight_flops + bias_flops
            elif class_name in custom_layers:
                custom_flops, custom_params = custom_layers[class_name](module, input, output)
                flops += custom_flops
                params += custom_params
            else:
                print(f"Warning: module {class_name} not implemented")

        if not isinstance(module, torch.nn.Sequential) and \
           not isinstance(module, torch.nn.ModuleList) and \
           not (module == model):
            hooks.append(module.register_forward_hook(hook))

    # loop through the model architecture and register hooks for each layer
    hooks = []
    model.apply(register_hook)

    # run the input through the model
    model(input)

    # remove the hooks
    for hook in hooks:
        hook.remove()

    print(f"Number of parameters: {params}")
    print(f"Number of FLOPs: {flops}")

    return flops, params

调用这个函数需要传入模型、输入大小和一个自定义图层字典，其中字典的键是自定义层的名称，值是一个函数，该函数接受模块，输入和输出，返回FLOPs和参数数量。例如，如果您的模型包含一个名为MyLayer的自定义层，则可以将以下内容添加到字典中：

def my_layer_impl(module, input, output):
    # compute FLOPs and params for MyLayer
    flops = ...
    params = ...
    return flops, params

custom_layers = {'MyLayer': my_layer_impl}

使用示例：

import torchvision.models as models

model = models.resnet18()
input_size = (3, 224, 224)
custom_layers = {}

flops, params = print_model_parm_flops(model, input_size, custom_layers)

该函数将打印出模型的参数数量和FLOPs。