请问如何解决调用函数缺少参数（aistudio，paddlenlp）
restore() missing 1 required positional argument: 'trainable'。已经修改参数,但无效果
升级paddlenlp到最新版本，检查你的参数的顺序和类型是否匹配
- 这篇博客也许可以解决你的问题👉 ：【PaddleOCR中文字符检测比赛】三、在AIStudio中使用PaddleOCR进行训练
- 除此之外, 这篇博客: paddlepaddle 38 使用aistudio部署自己训练的动态图模型中的 1、动态图模型构建 部分也许能够解决你的问题, 你可以仔细阅读以下内容或跳转源博客中阅读:
这里博主针对minisit数据集搭建了一个简单的分类网络并进行训练保存，如果各位有自己已经训练好的动态图模型则大可不必进行这个步骤。
import paddle from paddle import nn from paddle.vision.transforms import ToTensor from tqdm import tqdm class MYCONV(nn.Layer): def __init__(self,in_chanel,out_chanel,kernel_size=(3,3),stride=(1,1),padding="SAME"): super(MYCONV, self).__init__() self.conv=nn.Conv2D(in_chanel,out_chanel,kernel_size,stride,padding) self.bn = nn.BatchNorm(out_chanel) self.relu = nn.ReLU() def forward(self, x): x = self.conv(x) x = self.bn(x) x = self.relu(x) return x class Mnist(nn.Layer): def __init__(self): super(Mnist, self).__init__() self.conv1=MYCONV(1,16,kernel_size=(3,3),stride=(1,1),padding="SAME") self.conv2=MYCONV(16,32,kernel_size=(3,3),stride=(1,1),padding="SAME") self.avgpool1=nn.AvgPool2D(kernel_size=2,stride=2, padding=0) self.conv3=MYCONV(32,64,kernel_size=(3,3),stride=(1,1),padding="SAME") self.conv4=MYCONV(64,32,kernel_size=(3,3),stride=(1,1),padding="SAME") self.gap = nn.AdaptiveAvgPool2D(1) self.flatten = nn.Flatten() self.linear_1 = nn.Linear(32, 16) self.relu = nn.ReLU() self.dropout = nn.Dropout(0.2) self.linear_2 = nn.Linear(16, 10) def forward(self, x): x = self.conv1(x) x = self.conv2(x) x = self.avgpool1(x) x = self.conv3(x) x = self.conv4(x) x = self.gap(x) x = self.flatten(x) x = self.linear_1(x) x = self.relu(x) x = self.linear_2(x) return x def train_model(train_loader,model,loss_fn,optim): model.train() total_loss=0 total_acc=0 with tqdm(total=len(train_loader), desc=f'Epoch {epoch + 1}/{Epochs}', postfix=dict, mininterval=0.3) as pbar: for iteration, data in enumerate(train_loader()): x_data = data[0] # 训练数据 y_data = data[1] # 训练数据标签 predicts = model(x_data) # 预测结果 # 计算损失 等价于 prepare 中loss的设置 loss = loss_fn(predicts, y_data) # 计算准确率 等价于 prepare 中metrics的设置 acc = paddle.metric.accuracy(predicts, y_data) # 反向传播 loss.backward() # 更新参数 optim.step() # 梯度清零 optim.clear_grad() #更新进度条 total_loss+=loss.item() total_acc+=acc.item() pbar.set_postfix(**{'total_loss': "%.4f"%(total_loss / (iteration + 1)),'acc':"%.4f"%(total_acc/ (iteration + 1))}) pbar.update(1) def eval_model(test_loader,model,loss_fn): model.eval() total_loss=0 total_acc=0 for iteration, data in enumerate(test_loader()): x_data = data[0] # 测试数据 y_data = data[1] # 测试数据标签 predicts = model(x_data) # 预测结果 # 计算损失与精度 loss = loss_fn(predicts, y_data) acc = paddle.metric.accuracy(predicts, y_data) total_loss+=loss.item() total_acc+=acc.item() print("Test result, loss is: {:.4f}, acc is: {:.4f}".format(total_loss / (iteration + 1), total_acc/ (iteration + 1))) # 加载数据集 train_dataset = paddle.vision.datasets.MNIST(mode='train', transform=ToTensor()) test_dataset = paddle.vision.datasets.MNIST(mode='test', transform=ToTensor()) # 用 DataLoader 实现数据加载 train_loader = paddle.io.DataLoader(train_dataset, batch_size=64, shuffle=True) test_loader = paddle.io.DataLoader(test_dataset, batch_size=64, drop_last=True) mnist = Mnist() paddle.summary(mnist, (64, 1, 28, 28)) # 设置迭代次数 Epochs = 5 # 设置优化器 optim = paddle.optimizer.Adam(parameters=mnist.parameters()) # 设置损失函数 其中内置了softmax和onehot loss_fn = paddle.nn.CrossEntropyLoss( weight=None, ignore_index=-100, reduction='mean', soft_label=False, axis=-1, use_softmax=True,) for epoch in range(Epochs): train_model(train_loader,mnist,loss_fn,optim) eval_model(test_loader,mnist,loss_fn) #保存与加载模型 eval_model(test_loader,mnist,loss_fn) paddle.save(mnist.state_dict(), "mnist.pdparams", protocol=4)
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