pytorch查看模型model参数parameters
示例1：pytorch自带的faster r-cnn模型
import torchimport torchvisionmodel = torchvision.models.detection.fasterrcnn_resnet50_fpn(pretrained=True)for name, p in model.named_parameters(): print(name) print(p.requires_grad) print(...)#或者for p in model.parameters(): print(p) print(...)
示例2：自定义网络模型
class Net(nn.Module): def __init__(self): super(Net, self).__init__() cfg = [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512] self.features = self._vgg_layers(cfg) def _vgg_layers(self, cfg): layers = [] in_channels = 3 for x in cfg: if x == 'M': layers += [nn.MaxPool2d(kernel_size=2, stride=2)] else: layers += [nn.Conv2d(in_channels, x ,kernel_size=3, padding=1), nn.BatchNorm2d(x), nn.ReLU(inplace=True) ] in_channels = x return nn.Sequential(*layers) def forward(self, data): out_map = self.features(data) return out_map Model = Net()for name, p in model.named_parameters(): print(name) print(p.requires_grad) print(...)#或者for p in model.parameters(): print(p) print(...)
在自定义网络中，model.parameters()方法继承自nn.Module
pytorch查看模型参数总结
1：DNN_printer
其中（3, 32, 32）是输入的大小，其他方法中的参数同理
from DNN_printer import DNN_printerbatch_size = 512def train(epoch): print('\nEpoch: %d' % epoch) net.train() train_loss = 0 correct = 0 total = 0 // put the code here and you can get the result DNN_printer(net, (3, 32, 32),batch_size)结果
2：parameters
def cnn_paras_count(net): """cnn参数量统计, 使用方式cnn_paras_count(net)""" # Find total parameters and trainable parameters total_params = sum(p.numel() for p in net.parameters()) print(f'{total_params:,} total parameters.') total_trainable_params = sum(p.numel() for p in net.parameters() if p.requires_grad) print(f'{total_trainable_params:,} training parameters.') return total_params, total_trainable_paramscnn_paras_count(net)直接输出参数量，然后自己计算
需要注意的是，一般模型中参数是以float32保存的，也就是一个参数由4个bytes表示，那么就可以将参数量转化为存储大小。
例如：
- 44426个参数*4 / 1024 ≈ 174KB
3：get_model_complexity_info()
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)
4：torchstat
from torchstat import statimport torchvision.models as modelsmodel = models.resnet152()stat(model, (3, 224, 224))
输出
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。
原文地址：https://blog.csdn.net/qq_38600065/article/details/105552816