1.使用torchstat
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pip install torchstat from torchstat import statimport torchvision.models as modelsmodel = models.resnet152()stat(model, (3, 224, 224)) |
關于stat函數(shù)的參數(shù)，第一個應該是模型，第二個則是輸入尺寸，3為通道數(shù)。我沒有調研該函數(shù)的詳細參數(shù)，也不知道為什么使用的時候并不提示相應的參數(shù)。
2.使用torchsummary
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pip install torchsummary from torchsummary import summarysummary(model.cuda(),input_size=(3,32,32),batch_size=-1) |
使用該函數(shù)直接對參數(shù)進行提示，可以發(fā)現(xiàn)直接有顯式輸入batch_size的地方，我自己的感覺好像該函數(shù)更好一些。但是！！！不知道為什么，該函數(shù)在我的機器上一直報錯！！！
TypeError: can't convert CUDA tensor to numpy. Use Tensor.cpu() to copy the tensor to host memory first.
Update：經(jīng)過論壇咨詢，報錯的原因找到了，只需要把
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pip install torchsummary |
修改為
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pip install torch-summary |
補充：Pytorch查看模型參數(shù)并計算模型參數(shù)量與可訓練參數(shù)量
查看模型參數(shù)(以AlexNet為例）
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import torchimport torch.nn as nnimport torchvisionclass AlexNet(nn.Module): def __init__(self,num_classes=1000): super(AlexNet,self).__init__() self.feature_extraction = nn.Sequential( nn.Conv2d(in_channels=3,out_channels=96,kernel_size=11,stride=4,padding=2,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,padding=0), nn.Conv2d(in_channels=96,out_channels=192,kernel_size=5,stride=1,padding=2,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3,stride=2,padding=0), nn.Conv2d(in_channels=192,out_channels=384,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.Conv2d(in_channels=384,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.Conv2d(in_channels=256,out_channels=256,kernel_size=3,stride=1,padding=1,bias=False), nn.ReLU(inplace=True), nn.MaxPool2d(kernel_size=3, stride=2, padding=0), ) self.classifier = nn.Sequential( nn.Dropout(p=0.5), nn.Linear(in_features=256*6*6,out_features=4096), nn.ReLU(inplace=True), nn.Dropout(p=0.5), nn.Linear(in_features=4096, out_features=4096), nn.ReLU(inplace=True), nn.Linear(in_features=4096, out_features=num_classes), ) def forward(self,x): x = self.feature_extraction(x) x = x.view(x.size(0),256*6*6) x = self.classifier(x) return xif __name__ =='__main__': # model = torchvision.models.AlexNet() model = AlexNet() # 打印模型參數(shù) #for param in model.parameters(): #print(param) #打印模型名稱與shape for name,parameters in model.named_parameters(): print(name,':',parameters.size()) |
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feature_extraction.0.weight : torch.Size([96, 3, 11, 11])feature_extraction.3.weight : torch.Size([192, 96, 5, 5])feature_extraction.6.weight : torch.Size([384, 192, 3, 3])feature_extraction.8.weight : torch.Size([256, 384, 3, 3])feature_extraction.10.weight : torch.Size([256, 256, 3, 3])classifier.1.weight : torch.Size([4096, 9216])classifier.1.bias : torch.Size([4096])classifier.4.weight : torch.Size([4096, 4096])classifier.4.bias : torch.Size([4096])classifier.6.weight : torch.Size([1000, 4096])classifier.6.bias : torch.Size([1000]) |
計算參數(shù)量與可訓練參數(shù)量
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def get_parameter_number(model): total_num = sum(p.numel() for p in model.parameters()) trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad) return {'Total': total_num, 'Trainable': trainable_num} |
第三方工具
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from torchstat import statimport torchvision.models as modelsmodel = models.alexnet()stat(model, (3, 224, 224)) |
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from torchvision.models import alexnetimport torchfrom thop import profilemodel = alexnet()input = torch.randn(1, 3, 224, 224)flops, params = profile(model, inputs=(input, ))print(flops, params) |
以上為個人經(jīng)驗，希望能給大家一個參考，也希望大家多多支持服務器之家。如有錯誤或未考慮完全的地方，望不吝賜教。
原文鏈接：https://blog.csdn.net/weixin_45292794/article/details/108227437
