model.eval() # Set the model to evaluation mode # Hook setup activations = {} def get_activation(name): def hook(model, input, output): activations[name] = output.detach() return hook # Register hooks model.laye
model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = (device) labels = (device) # zero the parameter gradients optimizer.zero_grad(...
model.train() # Set model to training mode else: model.eval() # Set model to evaluate mode (BN,dropout。。。) running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = (device) labels = (device) # zero the parameter gradients...
model.train()# Set model to training modeelse: model.eval()# Set model to evaluate moderunning_loss =0.0running_corrects =0# 迭代数据forinputs, labelsindataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device)# 零参数梯度optimizer.zero_grad()# 前向# 如果只在训练时则...
model.train() # Set model to training mode,设置为训练模式 else: model.eval() # Set model to evaluate mode,设置为测试模式 running_loss = 0.0 running_corrects = 0 # Iterate over data. for inputs, labels in dataloaders[phase]: inputs = inputs.to(device) labels = labels.to(device) #...
很多人仍然习惯dropout与bn同时使用，在eval mode下dropout会导致方差偏移，相较于train mode，大家可以...
model_fp32 = M() # model must be set to eval mode for static quantization logic to work model_fp32.eval() 2、设置qconfig # Use 'fbgemm' for server inference and 'qnnpack' for mobile inference. Other quantization configurations such ...
for epoch in range(EPOCHS): print('EPOCH {}:'.format(epoch_number + 1)) # Make sure gradient tracking is on, and do a pass over the data model.train(True) avg_loss = train_one_epoch(epoch_number, writer) running_vloss = 0.0 # Set the model to evaluation mode, disabling dropout ...
deftrain(model, data_loader, optimizer):# Use GPU if available, otherwise CPUdevice = torch.device('cuda'iftorch.cuda.is_available()else'cpu') model.to(device)# Set the model to training mode (to enable backpropagation)model.train() train_loss =0# Feed the batches of data forward throu...
importtorch.onnx#Function to Convert to ONNXdefConvert_ONNX():# set the model to inference modemodel.eval()# Let's create a dummy input tensordummy_input = torch.randn(1, input_size, requires_grad=True)# Export the modeltorch.onnx.export(model,# model being rundummy_input,# model in...