factor = 0.5, # The number we multiply learning rate until the milestone. total_iters = 8) # The number of steps that the scheduler decays the learning rate 如果起始因子小于1，那么学习率调度器在训练过程中会提高学习率，而不是
--- 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 ...
# torch.load 需要注意 map_location 的使用# 例子一:defload_model(model_file):model=MobileNetV2()state_dict=torch.load(model_file)model.load_state_dict(state_dict)model.to('cpu')returnmodel# 例子二:net=Net()net.load_state_dict(torch.load(PATH))# 例子三:device=torch.device("cuda")model=...
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,...
device = torch.device("cuda:0")model = torchvision.models.resnet18(weights='IMAGENET1K_V1').cuda(device)criterion = torch.nn.CrossEntropyLoss().cuda(device)optimizer = torch.optim.SGD(model.parameters(), lr=0.001, momentum=0.9)model.train() ...
(data).local_value()# Need to move targets to the device where the output of the# pipeline resides.loss = criterion(output.view(-1, ntokens), targets.cuda(2 * rank + 1))loss.backward()torch.nn.utils.clip_grad_norm_(model.parameters(), 0.5)optimizer.step()total_loss += loss.item...
().mean * 1e6 # Lets define the hyper-parameters of our input batch_size = 32 max_sequence_len = 1024 num_heads = 32 embed_dimension = 32 dtype = torch.float16 query = torch.rand(batch_size, num_heads, max_sequence_len, embed_dimension, device=device, dtype=dtype) key = torch....
# Loss and optimizercriterion = nn.CrossEntropyLoss()optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate) # Train the modeltotal_step = len(train_loader)for epoch in range(num_epochs):for i ,(images, labels) in enumerate(train_loader):images ...
# Define the loss function and optimizercriterion = nn.CrossEntropyLoss()optimizer = optim.AdamW(model.parameters(), lr=5e-6) # Training loopnum_epochs = 25 # Number of epochs to train for for epoch in tqdm(range(num_epochs)): # loop...
optimizer = optim.SGD(net.parameters(), lr=config["lr"], momentum=0.9) # 1个超参数 # 用于存储检查点 if checkpoint_dir: # 模型的状态、优化器的状态 model_state, optimizer_state = torch.load( os.path.join(checkpoint_dir, "checkpoint")) ...