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...
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"""fromtorchsummaryimportsummary
--- DeepSpeed Flops Profiler --- Profile Summary at step 10: Notations: data parallel size (dp_size), model parallel size(mp_size), number of parameters (params), number of multiply-accumulate operations(MACs), number of floating-point operations (flops), floating-point operations per second ...
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. 3. 打印模型 model = FPN() num_params =sum...
Adam(cnn.parameters(), lr = learning_rate) # define train function that trains the model using a CIFAR10 dataset def train(model, epoch, num_epochs): model.train() total_batch = len(train_dataset) // batch_size for i, (images, labels) in enumerate(train_loader): X = images.to(...
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(),...
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 ...
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,原理是初始化一个图片丢进去计算,当然这个初始化的图...
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 ...
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 ...