model.eval()负责改变batchnorm、dropout的工作方式,如在eval()模式下,dropout是不工作的。 torch.no_grad()负责关掉梯度计算,节省eval的时间。 只进行inference时,model.eval()是必须使用的,否则会影响结果准确性。 而torch.no_grad()并不是强制的,只影响运行效率。 一般写为: # evaluate model: model.eval()...
并且,通常在实际代码中,在预测阶段,也会加上torch.no_grad()来关闭梯度的计算。那么,预测阶段的代码基本要这样写: # evaluate model:model.eval()withtorch.no_grad():...out_data=model(data)... 相应的,在训练之前,我们也要记得将这些特殊的层设置到训练模式: model.train() 还有好奇的小伙伴可能会问,...
https://github.com/Laicheng0830/Pytorch_Model_Quantization 代码下载后如上图,把下载的MSCOC数据集选100张放在data目录,把下载的模型文件coco_pose_iter_440000.pth.tar放在models目录。 pth_to_int.py是对Pytorch的float32模型转成int8模型。 evaluate_model.py里加载int8模型进行推理。 3. 模型静态量化 模型...
accuracy_expert1 = evaluate(expert1, x_test, y_test) accuracy_expert2 = evaluate(expert2, x_test, y_test) accuracy_expert3 = evaluate(expert3, x_test, y_test) accuracy_moe = evaluate(moe_model, x_test, y_test) print("Expert 1 Accuracy:", accuracy_expert1) print("Expert 2 Accura...
# Evaluate all models def evaluate(model, x, y): with torch.no_grad():outputs = model(x) _, predicted = torch.max(outputs, 1) correct = (predicted == y).sum().item()accuracy = correct / len(y) return accuracy evaluate函数计算模型在给定...
def evaluate(model, iterator, criterion): epoch_loss = 0 epoch_acc = 0 model.eval() with torch.no_grad(): for batch in iterator: predictions = model(batch.text).squeeze(1) loss = criterion(predictions, batch.label) acc = binary_accuracy(predictions, bath.label) ...
outputs = model(x) _, predicted = torch.max(outputs, 1) correct = (predicted == y).sum().item() accuracy = correct / len(y) return accuracy evaluate函数计算模型在给定数据上的精度(x代表样本,y代表预期标签)。准确度计算为正确预测数与预测总数之比。
SimpleModel+forward(x)Optimizer+step()+zero_grad()Trainer+train(model, optimizer, data)+evaluate(model, data) 性能对比 我们还可以使用饼状图来展示训练和加载模型后的性能对比,以及模型是否存在过拟合的情况。 75%25%模型性能对比训练效果加载效果 ...
inputs = torch.randn(sequence_length,batch_size,model_dimension) # hidden is actually is a tuple of the initial hidden state and the initial cell state hidden = (torch.randn(lstm_depth,batch_size,model_dimension), torch.randn(lstm_depth,batch_size,model_dimension)) ...
outputs =model(x) _, predicted = torch.max(outputs,1) correct = (predicted == y).sum().item() accuracy = correct /len(y) return accuracy evaluate函数计算模型在给定数据上的精度(x代表样本,y代表预期标签)。准确度计算为正确预测数与预测总数之比。