load¶
将接口 paddle.jit.save 或者 paddle.static.save_inference_model 存储的模型载入为 paddle.jit.TranslatedLayer ，用于预测推理或者fine-tune训练。
注解
如果载入的模型是通过 paddle.static.save_inference_model 存储的，在使用它进行fine-tune训练时会存在一些局限： 1. 命令式编程模式不支持 LoDTensor ，所有原先输入变量或者参数依赖于LoD信息的模型暂时无法使用； 2. 所有存储模型的feed变量都需要被传入 Translatedlayer 的forward方法； 3. 原模型变量的 stop_gradient 信息已丢失且无法准确恢复； 4. 原模型参数的 trainable 信息已丢失且无法准确恢复。
参数¶
path (str) - 载入模型的路径前缀。格式为
dirname/file_prefix或者file_prefix。**config (dict, 可选) - 其他用于兼容的载入配置选项。这些选项将来可能被移除，如果不是必须使用，不推荐使用这些配置选项。默认为
None。目前支持以下配置选项：(1) model_filename (str) - paddle 1.x版本save_inference_model接口存储格式的预测模型文件名，原默认文件名为__model__； (2) params_filename (str) - paddle 1.x版本save_inference_model接口存储格式的参数文件名，没有默认文件名，默认将各个参数分散存储为单独的文件。
返回¶
TranslatedLayer，一个能够执行存储模型的 Layer 对象。
代码示例¶
载入由接口
paddle.jit.save存储的模型进行预测推理及fine-tune训练。import numpy as np import paddle import paddle.nn as nn import paddle.optimizer as opt BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples class LinearNet(nn.Layer): def __init__(self): super(LinearNet, self).__init__() self._linear = nn.Linear(IMAGE_SIZE, CLASS_NUM) @paddle.jit.to_static def forward(self, x): return self._linear(x) def train(layer, loader, loss_fn, opt): for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = layer(image) loss = loss_fn(out, label) loss.backward() opt.step() opt.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy()))) # 1. train & save model. # create network layer = LinearNet() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=layer.parameters()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) # train train(layer, loader, loss_fn, adam) # save path = "example_model/linear" paddle.jit.save(layer, path) # 2. load model # load loaded_layer = paddle.jit.load(path) # inference loaded_layer.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = loaded_layer(x) # fine-tune loaded_layer.train() adam = opt.Adam(learning_rate=0.001, parameters=loaded_layer.parameters()) train(loaded_layer, loader, loss_fn, adam)
兼容载入由接口
paddle.fluid.io.save_inference_model存储的模型进行预测推理及fine-tune训练。import numpy as np import paddle import paddle.static as static import paddle.nn as nn import paddle.optimizer as opt import paddle.nn.functional as F BATCH_SIZE = 16 BATCH_NUM = 4 EPOCH_NUM = 4 IMAGE_SIZE = 784 CLASS_NUM = 10 # define a random dataset class RandomDataset(paddle.io.Dataset): def __init__(self, num_samples): self.num_samples = num_samples def __getitem__(self, idx): image = np.random.random([IMAGE_SIZE]).astype('float32') label = np.random.randint(0, CLASS_NUM - 1, (1, )).astype('int64') return image, label def __len__(self): return self.num_samples paddle.enable_static() image = static.data(name='image', shape=[None, 784], dtype='float32') label = static.data(name='label', shape=[None, 1], dtype='int64') pred = static.nn.fc(x=image, size=10, activation='softmax') loss = F.cross_entropy(input=pred, label=label) avg_loss = paddle.mean(loss) optimizer = paddle.optimizer.SGD(learning_rate=0.001) optimizer.minimize(avg_loss) place = paddle.CPUPlace() exe = static.Executor(place) exe.run(static.default_startup_program()) # create data loader dataset = RandomDataset(BATCH_NUM * BATCH_SIZE) loader = paddle.io.DataLoader(dataset, feed_list=[image, label], places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, return_list=False, num_workers=2) # 1. train and save inference model for data in loader(): exe.run( static.default_main_program(), feed=data, fetch_list=[avg_loss]) model_path = "fc.example.model" paddle.fluid.io.save_inference_model( model_path, ["image"], [pred], exe) # 2. load model # enable dygraph mode paddle.disable_static(place) # load fc = paddle.jit.load(model_path) # inference fc.eval() x = paddle.randn([1, IMAGE_SIZE], 'float32') pred = fc(x) # fine-tune fc.train() loss_fn = nn.CrossEntropyLoss() adam = opt.Adam(learning_rate=0.001, parameters=fc.parameters()) loader = paddle.io.DataLoader(dataset, places=place, batch_size=BATCH_SIZE, shuffle=True, drop_last=True, num_workers=2) for epoch_id in range(EPOCH_NUM): for batch_id, (image, label) in enumerate(loader()): out = fc(image) loss = loss_fn(out, label) loss.backward() adam.step() adam.clear_grad() print("Epoch {} batch {}: loss = {}".format( epoch_id, batch_id, np.mean(loss.numpy())))