这篇教程pytorch如何获得模型的计算量和参数量写得很实用，希望能帮到您。
方法1 自带pytorch自带方法，计算模型参数总量 total = sum([param.nelement() for param in model.parameters()])print("Number of parameter: %.2fM" % (total/1e6))或者 total = sum(p.numel() for p in model.parameters())print("Total params: %.2fM" % (total/1e6))方法2 编写代码计算模型参数总量和模型计算量 def count_params(model, input_size=224): # param_sum = 0 with open('models.txt', 'w') as fm: fm.write(str(model)) # 计算模型的计算量 calc_flops(model, input_size) # 计算模型的参数总量 model_parameters = filter(lambda p: p.requires_grad, model.parameters()) params = sum([np.prod(p.size()) for p in model_parameters]) print('The network has {} params.'.format(params)) # 计算模型的计算量def calc_flops(model, input_size): def conv_hook(self, input, output): batch_size, input_channels, input_height, input_width = input[0].size() output_channels, output_height, output_width = output[0].size() kernel_ops = self.kernel_size[0] * self.kernel_size[1] * (self.in_channels / self.groups) * ( 2 if multiply_adds else 1) bias_ops = 1 if self.bias is not None else 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_height * output_width list_conv.append(flops) def linear_hook(self, input, output): batch_size = input[0].size(0) if input[0].dim() == 2 else 1 weight_ops = self.weight.nelement() * (2 if multiply_adds else 1) bias_ops = self.bias.nelement() flops = batch_size * (weight_ops + bias_ops) list_linear.append(flops) def bn_hook(self, input, output): list_bn.append(input[0].nelement()) def relu_hook(self, input, output): list_relu.append(input[0].nelement()) def pooling_hook(self, input, output): batch_size, input_channels, input_height, input_width = input[0].size() output_channels, output_height, output_width = output[0].size() kernel_ops = self.kernel_size * self.kernel_size bias_ops = 0 params = output_channels * (kernel_ops + bias_ops) flops = batch_size * params * output_height * output_width list_pooling.append(flops) def foo(net): childrens = list(net.children()) if not childrens: if isinstance(net, torch.nn.Conv2d): net.register_forward_hook(conv_hook) if isinstance(net, torch.nn.Linear): net.register_forward_hook(linear_hook) if isinstance(net, torch.nn.BatchNorm2d): net.register_forward_hook(bn_hook) if isinstance(net, torch.nn.ReLU): net.register_forward_hook(relu_hook) if isinstance(net, torch.nn.MaxPool2d) or isinstance(net, torch.nn.AvgPool2d): net.register_forward_hook(pooling_hook) return for c in childrens: foo(c) multiply_adds = False list_conv, list_bn, list_relu, list_linear, list_pooling = [], [], [], [], [] foo(model) if '0.4.' in torch.__version__: if assets.USE_GPU: input = torch.cuda.FloatTensor(torch.rand(2, 3, input_size, input_size).cuda()) else: input = torch.FloatTensor(torch.rand(2, 3, input_size, input_size)) else: input = Variable(torch.rand(2, 3, input_size, input_size), requires_grad=True) _ = model(input) total_flops = (sum(list_conv) + sum(list_linear) + sum(list_bn) + sum(list_relu) + sum(list_pooling)) print(' + Number of FLOPs: %.2fM' % (total_flops / 1e6 / 2))方法3 thop需要安装thop 调用方法：计算模型参数总量和模型计算量，而且会打印每一层网络的具体信息 from thop import profile input = torch.randn(1, 3, 224, 224)flops, params = profile(model, inputs=(input,))print(flops)print(params) 或者 from torchvision.models import resnet50from thop import profile # model = resnet50()checkpoints = '模型path'model = torch.load(checkpoints)model_name = 'yolov3 cut asff'input = torch.randn(1, 3, 224, 224)flops, params = profile(model, inputs=(input, ),verbose=True)print("%s | %.2f | %.2f" % (model_name, params / (1000 ** 2), flops / (1000 ** 3)))#这里除以1000的平方，是为了化成M的单位，注意:输入必须是四维的 提高输出可读性, 加入一下代码。 from thop import clever_formatmacs, params = clever_format([flops, params], "%.3f") 方法4 torchstatfrom torchstat import statfrom torchvision.models import resnet50, resnet101, resnet152, resnext101_32x8d model = resnet50()stat(model, (3, 224, 224)) # (3,224,224)表示输入图片的尺寸 使用torchstat这个库来查看网络模型的一些信息，包括总的参数量params、MAdd、显卡内存占用量和FLOPs等。需要安装torchstat: 方法5 ptflops作用：计算模型参数总量和模型计算量 安装方法：pip install ptflops 或者 pip install --upgrade git+https://github.com/sovrasov/flops-counter.pytorch.git 使用方法 import torchvision.models as modelsimport torchfrom ptflops import get_model_complexity_infowith torch.cuda.device(0): net = models.resnet18() flops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True) #不用写batch_size大小，默认batch_size=1 print('Flops: ' + flops) print('Params: ' + params)或者 from torchvision.models import resnet50import torchimport torchvision.models as models# import torchfrom ptflops import get_model_complexity_info # model = models.resnet50() #调用官方的模型，checkpoints = '自己模型的path'model = torch.load(checkpoints)model_name = 'yolov3 cut'flops, params = get_model_complexity_info(model, (3,320,320),as_strings=True,print_per_layer_stat=True)print("%s |%s |%s" % (model_name,flops,params))注意，这里输入一定是要tuple类型，且不需要输入batch，直接输入输入通道数量与尺寸，如（3,320,320） 320为网络输入尺寸。 输出为网络模型的总参数量（单位M，即百万）与计算量（单位G，即十亿） 方法6 torchsummary安装：pip install torchsummary 使用方法： from torchsummary import summary...summary(your_model, input_size=(channels, H, W)) 作用： 1、每一层的类型、shape 和 参数量 2、模型整体的参数量 3、模型大小，和 fp/bp 一次需要的内存大小，可以用来估计最佳 batch_size 补充：pytorch计算模型算力与参数大小 ptflops介绍官方链接 这个脚本设计用于计算卷积神经网络中乘法-加法操作的理论数量。它还可以计算参数的数量和打印给定网络的每层计算成本。 支持layer：Conv1d/2d/3d，ConvTranspose2d，BatchNorm1d/2d/3d，激活(ReLU, PReLU, ELU, ReLU6, LeakyReLU)，Linear，Upsample，Poolings (AvgPool1d/2d/3d、MaxPool1d/2d/3d、adaptive ones) 安装要求:Pytorch >= 0.4.1, torchvision >= 0.2.1 get_model_complexity_info()get_model_complexity_info是ptflops下的一个方法，可以计算出网络的算力与模型参数大小，并且可以输出每层的算力消耗。 栗子以输出Mobilenet_v2算力信息为例： from ptflops import get_model_complexity_infofrom torchvision import modelsnet = models.mobilenet_v2()ops, params = get_model_complexity_info(net, (3, 224, 224), as_strings=True, print_per_layer_stat=True, verbose=True) 
从图中可以看到，MobileNetV2在输入图像尺寸为(3, 224, 224)的情况下将会产生3.505MB的参数，算力消耗为0.32G，同时还打印出了每个层所占用的算力，权重参数数量。当然，整个模型的算力大小与模型大小也被存到了变量ops与params中。 以上为个人经验，希望能给大家一个参考，也希望大家多多支持51zixue.net。
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