net=resnet34()#注意:模型内部传参数和不传参数,输出的结果是不一样的#计算网络参数total = sum([param.nelement()forparaminnet.parameters()])#精确地计算:1MB=1024KB=1048576字节print('Number of parameter: % .4fM'% (total / 1e6)) 输出: Number of parameter: 21.7977M 参数量方法二: summary的...
total = sum([param.nelement() for param in net.parameters()]) # 精确地计算:1MB=1024KB=1048576字节 print('Number of parameter: % .4fM' % (total / 1e6)) 1. 2. 3. 4. 5. 6. 7. 输出: Number of parameter: 21.7977M 参数量方法二: summary的使用:来自于torchinfo第三方库 torchinfo ...
一定要先定义模型,然后在model后面插入两句代码OK啦~ fromSimNetimportsimNet#导入模型model=simNet()#定义模型total=sum([param.nelement()forparaminmodel.parameters()])#计算总参数量print("Number of parameter:%.6f"%(total))#输出 调用thop模块中的profile包进行计算 这里需要使用包进行计算,调用方式也很简单,...
seq_len, batch_size,hidden_size= y.shape self.LSTM.flatten_parameters()y= y.view(-1, hidden_size)y= self.reg1(y)y= y.view(seq_len, batch_size, -1) return y, (hn, cn)# 模型训练 device 设置# NPUuse = TrueNPUuse= FalseifNPUuse:device="npu:0"else:device= torch.device('cpu...
print_per_layer_stat=True, verbose=True)print('{:<30} {:<8}'.format('Computational complexity: ', macs))print('{:<30} {:<8}'.format('Number of parameters: ', params))#Computational complexity: 0.05 GMac#Number of parameters: 1.26 M"""torchsummary 用来计算网络的计算参数等信息"""fro...
print(f"{total_trainable_params:,} training parameters.") 学习参数 现在,我们将定义学习/训练参数,其中包括learning rate、epochs、optimizer和loss fuction。 #written and saved in train.py # learning parameters lr = 0.001 epochs = 100 # optimizer ...
num_parameters = sum(torch.numel(parameter) for parameter in model.parameters()) # 总参数量 total_num = sum(p.numel() for p in model.parameters()) # 可训练参数量 trainable_num = sum(p.numel() for p in model.parameters() if p.requires_grad) ...
2. 模型参数量 model = FPN() num_params =sum(p.numel()forpinmodel.parameters())print("num of params: {:.2f}k".format(num_params/1000.0))# torch.numel()返回tensor的元素数目,即number of elements# Returns the total number of elements in the input tensor. ...
world_size: Total number of processes """ # MASTER Node(运行 rank0 进程,多机多卡时的主机)用来协调各个 Node 的所有进程之间的通信 os.environ["MASTER_ADDR"] = "localhost" # 由于这里是单机实验所以直接写 localhost os.environ["MASTER_PORT"] = "12355" # 任意空闲端口 ...
lr_scheduler import StepLR # Import your choice of scheduler hereimport matplotlib.pyplot as pltfrom matplotlib.ticker import MultipleLocatorLEARNING_RATE = 1e-3EPOCHS = 4STEPS_IN_EPOCH = 8# Set model and optimizermodel = torch.nn.Linear(2, 1)optimizer = torch.optim.SGD(model.parameters(),...