diff --git a/doc/fluid/api/dygraph.rst b/doc/fluid/api/dygraph.rst index 095b59804ec569350dd7756b7423f1598f500de2..397419a8bee35b419f7d23b7a7ce47eb764c56fd 100644 --- a/doc/fluid/api/dygraph.rst +++ b/doc/fluid/api/dygraph.rst @@ -31,6 +31,7 @@ fluid.dygraph dygraph/guard.rst dygraph/InstanceNorm.rst dygraph/InverseTimeDecay.rst + dygraph/jit.rst dygraph/Layer.rst dygraph/LayerList.rst dygraph/LayerNorm.rst @@ -55,4 +56,5 @@ fluid.dygraph dygraph/to_variable.rst dygraph/TracedLayer.rst dygraph/Tracer.rst + dygraph/TranslatedLayer.rst dygraph/TreeConv.rst diff --git a/doc/fluid/api/dygraph/TranslatedLayer.rst b/doc/fluid/api/dygraph/TranslatedLayer.rst new file mode 100644 index 0000000000000000000000000000000000000000..a6f7fd9411e5179999a8bda3f1ae197092343a7a --- /dev/null +++ b/doc/fluid/api/dygraph/TranslatedLayer.rst @@ -0,0 +1,8 @@ +.. _api_fluid_dygraph_TranslatedLayer: + +TranslatedLayer +----------------------- + +.. autoclass:: paddle.fluid.dygraph.TranslatedLayer + :members: + :noindex: diff --git a/doc/fluid/api/dygraph/jit.rst b/doc/fluid/api/dygraph/jit.rst new file mode 100644 index 0000000000000000000000000000000000000000..7853a048535c045bae18f71c8b4d7f1e44cc65eb --- /dev/null +++ b/doc/fluid/api/dygraph/jit.rst @@ -0,0 +1,10 @@ +=== +jit +=== + +.. toctree:: + :maxdepth: 1 + + jit/save.rst + jit/load.rst + jit/SaveLoadConfig.rst diff --git a/doc/fluid/api/dygraph/jit/SaveLoadConfig.rst b/doc/fluid/api/dygraph/jit/SaveLoadConfig.rst new file mode 100644 index 0000000000000000000000000000000000000000..e8d1d3bfbc35eca0c05594b540a0cd15c19cebe1 --- /dev/null +++ b/doc/fluid/api/dygraph/jit/SaveLoadConfig.rst @@ -0,0 +1,8 @@ +.. _api_fluid_dygraph_jit_SaveLoadConfig: + +SaveLoadConfig +------------------------------- + +.. autoclass:: paddle.fluid.dygraph.jit.SaveLoadConfig + :members: + :noindex: \ No newline at end of file diff --git a/doc/fluid/api/dygraph/jit/load.rst b/doc/fluid/api/dygraph/jit/load.rst new file mode 100644 index 0000000000000000000000000000000000000000..51f59909873dd46bb43e42bdc2258a990580c24c --- /dev/null +++ b/doc/fluid/api/dygraph/jit/load.rst @@ -0,0 +1,7 @@ +.. _api_fluid_dygraph_jit_load: + +load +------------ + +.. autofunction:: paddle.fluid.dygraph.jit.load + :noindex: diff --git a/doc/fluid/api/dygraph/jit/save.rst b/doc/fluid/api/dygraph/jit/save.rst new file mode 100644 index 0000000000000000000000000000000000000000..fb55029c2870b8c56edd93c4907ae0894036eabe --- /dev/null +++ b/doc/fluid/api/dygraph/jit/save.rst @@ -0,0 +1,7 @@ +.. _api_fluid_dygraph_jit_save: + +save +------------ + +.. autofunction:: paddle.fluid.dygraph.jit.save + :noindex: diff --git a/doc/fluid/api/imperative.rst b/doc/fluid/api/imperative.rst index 48aebcbfddf188d74dfa182559cffa25997bead4..f138e06701b138dc109dab2e3b1c17832658d390 100644 --- a/doc/fluid/api/imperative.rst +++ b/doc/fluid/api/imperative.rst @@ -13,6 +13,7 @@ paddle.imperative imperative/grad.rst imperative/guard.rst imperative/InverseTimeDecay.rst + imperative/jit.rst imperative/load.rst imperative/NaturalExpDecay.rst imperative/no_grad.rst @@ -25,3 +26,4 @@ paddle.imperative imperative/save.rst imperative/to_variable.rst imperative/TracedLayer.rst + imperative/TranslatedLayer.rst diff --git a/doc/fluid/api/imperative/TranslatedLayer.rst b/doc/fluid/api/imperative/TranslatedLayer.rst new file mode 100644 index 0000000000000000000000000000000000000000..0299a9f57392e267ae015947345249784fd929f5 --- /dev/null +++ b/doc/fluid/api/imperative/TranslatedLayer.rst @@ -0,0 +1,5 @@ +.. _api_imperative_TranslatedLayer: + +TranslatedLayer +------------------------------- +:doc_source: paddle.fluid.dygraph.io.TranslatedLayer diff --git a/doc/fluid/api/imperative/jit.rst b/doc/fluid/api/imperative/jit.rst new file mode 100644 index 0000000000000000000000000000000000000000..7853a048535c045bae18f71c8b4d7f1e44cc65eb --- /dev/null +++ b/doc/fluid/api/imperative/jit.rst @@ -0,0 +1,10 @@ +=== +jit +=== + +.. toctree:: + :maxdepth: 1 + + jit/save.rst + jit/load.rst + jit/SaveLoadConfig.rst diff --git a/doc/fluid/api/imperative/jit/SaveLoadConfig.rst b/doc/fluid/api/imperative/jit/SaveLoadConfig.rst new file mode 100644 index 0000000000000000000000000000000000000000..cab85776ec33f9cab2dc788ebbb3081fca1d4035 --- /dev/null +++ b/doc/fluid/api/imperative/jit/SaveLoadConfig.rst @@ -0,0 +1,5 @@ +.. _api_imperative_jit_SaveLoadConfig: + +SaveLoadConfig +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.SaveLoadConfig diff --git a/doc/fluid/api/imperative/jit/load.rst b/doc/fluid/api/imperative/jit/load.rst new file mode 100644 index 0000000000000000000000000000000000000000..723a87936a8f26653eb2b34f361aa35a4b3fd74f --- /dev/null +++ b/doc/fluid/api/imperative/jit/load.rst @@ -0,0 +1,5 @@ +.. _api_imperative_jit_load: + +load +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.load diff --git a/doc/fluid/api/imperative/jit/save.rst b/doc/fluid/api/imperative/jit/save.rst new file mode 100644 index 0000000000000000000000000000000000000000..b809a99166e35edd65af253dffe40053776a68dc --- /dev/null +++ b/doc/fluid/api/imperative/jit/save.rst @@ -0,0 +1,5 @@ +.. _api_imperative_jit_save: + +save +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.save diff --git a/doc/fluid/api_cn/dygraph_cn.rst b/doc/fluid/api_cn/dygraph_cn.rst index 4e7396f3469616fb558cbf6aef7f38fce92dc239..7cf2de04add71995bdb359d5427f2e65f5190946 100644 --- a/doc/fluid/api_cn/dygraph_cn.rst +++ b/doc/fluid/api_cn/dygraph_cn.rst @@ -28,6 +28,7 @@ fluid.dygraph dygraph_cn/guard_cn.rst dygraph_cn/InstanceNorm_cn.rst dygraph_cn/InverseTimeDecay_cn.rst + dygraph_cn/jit_cn.rst dygraph_cn/LambdaDecay_cn.rst dygraph_cn/Layer_cn.rst dygraph_cn/LayerList_cn.rst @@ -55,4 +56,5 @@ fluid.dygraph dygraph_cn/to_variable_cn.rst dygraph_cn/TracedLayer_cn.rst dygraph_cn/Tracer_cn.rst + dygraph_cn/TranslatedLayer_cn.rst dygraph_cn/TreeConv_cn.rst diff --git a/doc/fluid/api_cn/dygraph_cn/TranslatedLayer_cn.rst b/doc/fluid/api_cn/dygraph_cn/TranslatedLayer_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..0924a155a0ffb8861dd0f233cf0b0eff3dd8b169 --- /dev/null +++ b/doc/fluid/api_cn/dygraph_cn/TranslatedLayer_cn.rst @@ -0,0 +1,84 @@ +.. _cn_api_fluid_dygraph_TranslatedLayer: + +TranslatedLayer +------------------------------- + +.. py:class:: paddle.fluid.dygraph.TranslatedLayer(programs, persistable_vars) + +``TranslatedLayer`` 是一个命令式编程模式 :ref:`cn_api_fluid_dygraph_Layer` 的继承类， +通过 :ref:`cn_api_fluid_dygraph_jit_load` 载入构建。能够像一般 ``Layer`` 一样在train或者eval模式下使用。 + +.. note:: + ``TranslatedLayer`` 对象不能够通过构造函数创建，仅能够通过 :ref:`cn_api_fluid_dygraph_jit_load` 接口载入构建。 + +**示例代码：** + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + BATCH_SIZE = 32 + BATCH_NUM = 20 + def random_batch_reader(): + def _get_random_images_and_labels(image_shape, label_shape): + image = np.random.random(size=image_shape).astype('float32') + label = np.random.random(size=label_shape).astype('int64') + return image, label + def __reader__(): + for _ in range(BATCH_NUM): + batch_image, batch_label = _get_random_images_and_labels( + [BATCH_SIZE, 784], [BATCH_SIZE, 1]) + yield batch_image, batch_label + return __reader__ + class LinearNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(LinearNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + return self._linear(x) + # 开启命令式编程模式 + fluid.enable_dygraph() + # 1. 训练存储模型. + # 创建网络 + net = LinearNet(784, 1) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + # 创建DataLoader + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator(random_batch_reader()) + # 训练 + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = net(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + net.clear_gradients() + model_path = "linear.example.model" + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[img]) + # 2. 载入模型构建TranslatedLayer + translated_layer = fluid.dygraph.jit.load(model_path) + # 预测 + translated_layer.eval() + x = fluid.dygraph.to_variable(np.random.random((1, 784)).astype('float32')) + pred = translated_layer(x) + # fine-tune训练 + translated_layer.train() + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=translated_layer.parameters()) + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator(random_batch_reader()) + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = translated_layer(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + translated_layer.clear_gradients() diff --git a/doc/fluid/api_cn/dygraph_cn/jit_cn.rst b/doc/fluid/api_cn/dygraph_cn/jit_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..4a9b90d5cab6ce86a1bd7689676e3ad844eae8ee --- /dev/null +++ b/doc/fluid/api_cn/dygraph_cn/jit_cn.rst @@ -0,0 +1,10 @@ +=== +jit +=== + +.. toctree:: + :maxdepth: 1 + + jit_cn/save_cn.rst + jit_cn/load_cn.rst + jit_cn/SaveLoadConfig_cn.rst diff --git a/doc/fluid/api_cn/dygraph_cn/jit_cn/SaveLoadConfig_cn.rst b/doc/fluid/api_cn/dygraph_cn/jit_cn/SaveLoadConfig_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..cbee1bab234be6f53f83061c52139093513d321b --- /dev/null +++ b/doc/fluid/api_cn/dygraph_cn/jit_cn/SaveLoadConfig_cn.rst @@ -0,0 +1,273 @@ +.. _cn_api_fluid_dygraph_jit_SaveLoadConfig: + +SaveLoadConfig +------------------------------- + +.. py:class:: paddle.fluid.dygraph.jit.SaveLoadConfig() + +用于配置接口 :ref:`cn_api_fluid_dygraph_jit_save` 和 :ref:`cn_api_fluid_dygraph_jit_load` 存储载入 :ref:`cn_api_fluid_dygraph_TranslatedLayer` 时的附加选项。 + +**示例代码：** + + 1. 在存储模型时使用 ``SaveLoadConfig`` + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + class SimpleNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(SimpleNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + y = self._linear(x) + z = self._linear(y) + return z + # 开启命令式编程模式 + fluid.enable_dygraph() + # 训练模型 + net = SimpleNet(8, 8) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + for i in range(10): + out = net(x) + loss = fluid.layers.mean(out) + loss.backward() + adam.minimize(loss) + net.clear_gradients() + # 在存储模型时使用SaveLoadConfig + model_path = "simplenet.example.model" + configs = fluid.dygraph.jit.SaveLoadConfig() + configs.model_filename = "__simplenet__" + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[x], + configs=configs) + + 2. 在载入模型时使用 ``SaveLoadConfig`` + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + # 开启命令式编程模式 + fluid.enable_dygraph() + # 在载入模型时使用SaveLoadconfig + model_path = "simplenet.example.model" + configs = fluid.dygraph.jit.SaveLoadConfig() + configs.model_filename = "__simplenet__" + infer_net = fluid.dygraph.jit.load(model_path, configs=configs) + # 预测 + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + pred = infer_net(x) + +属性 +:::::::::::: + +.. py:attribute:: output_spec + +选择保存模型（ :ref:`cn_api_fluid_dygraph_TranslatedLayer` ）的输出变量，通过指定的这些变量能够使模型仅计算特定的结果。 +默认情况下，原始 :ref:`cn_api_fluid_dygraph_Layer` 的forward方法的所有返回变量都将配置为存储后模型 :ref:`cn_api_fluid_dygraph_TranslatedLayer` 的输出变量。 + +``output_spec`` 属性类型需要是 ``list[Variable]``。如果输入的 ``output_spec`` 列表不是原始 :ref:`cn_api_fluid_dygraph_Layer` 的forward方法的所有返回变量， +将会依据输入的 ``output_spec`` 列表对存储的模型进行裁剪。 + +.. note:: + ``output_spec`` 属性仅在存储模型时使用。 + +**示例代码：** + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + class SimpleNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(SimpleNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + y = self._linear(x) + z = self._linear(y) + loss = fluid.layers.mean(z) + return z, loss + # 开启命令式编程模式 + fluid.enable_dygraph() + # 训练模型 + net = SimpleNet(8, 8) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + for i in range(10): + out, loss = net(x) + loss.backward() + adam.minimize(loss) + net.clear_gradients() + # 使用SaveLoadconfig.output_spec + model_path = "simplenet.example.model.output_spec" + configs = fluid.dygraph.jit.SaveLoadConfig() + # 仅在存储模型中保留预测结果，丢弃loss + configs.output_spec = [out] + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[x], + configs=configs) + infer_net = fluid.dygraph.jit.load(model_path, configs=configs) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + # 仅有预测结果输出 + pred = infer_net(x) + + +.. py:attribute:: model_filename + +存储转写 :ref:`cn_api_fluid_dygraph_Layer` 模型结构 ``Program`` 的文件名称。默认文件名为 ``__model__``。 + +**示例代码** + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + class SimpleNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(SimpleNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + y = self._linear(x) + z = self._linear(y) + return z + # 开启命令式编程模式 + fluid.enable_dygraph() + # 训练模型 + net = SimpleNet(8, 8) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + for i in range(10): + out = net(x) + loss = fluid.layers.mean(out) + loss.backward() + adam.minimize(loss) + net.clear_gradients() + model_path = "simplenet.example.model.model_filename" + configs = fluid.dygraph.jit.SaveLoadConfig() + configs.model_filename = "__simplenet__" + # 配置configs.model_filename存储模型 + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[x], + configs=configs) + # [结果] 存储模型目录文件包括: + # __simplenet__ __variables__ __variables.info__ + # 配置configs.model_filename载入模型 + infer_net = fluid.dygraph.jit.load(model_path, configs=configs) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + pred = infer_net(x) + + +.. py:attribute:: params_filename + +存储转写 :ref:`cn_api_fluid_dygraph_Layer` 所有持久参数（包括 ``Parameters`` 和持久的 ``Buffers``）的文件名称。默认文件名称为 ``__variable__``。 + +**示例代码** + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + class SimpleNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(SimpleNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + y = self._linear(x) + z = self._linear(y) + return z + # 开启命令式编程模式 + fluid.enable_dygraph() + # 训练模型 + net = SimpleNet(8, 8) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + for i in range(10): + out = net(x) + loss = fluid.layers.mean(out) + loss.backward() + adam.minimize(loss) + net.clear_gradients() + model_path = "simplenet.example.model.params_filename" + configs = fluid.dygraph.jit.SaveLoadConfig() + configs.params_filename = "__params__" + # 配置configs.params_filename存储模型 + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[x], + configs=configs) + # [结果] 存储模型目录文件包括: + # __model__ __params__ __variables.info__ + # 配置configs.params_filename载入模型 + infer_net = fluid.dygraph.jit.load(model_path, configs=configs) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + pred = infer_net(x) + + +.. py:attribute:: separate_params + +配置是否将 :ref:`cn_api_fluid_dygraph_Layer` 的参数存储为分散的文件。 +（这是为了兼容接口 :ref:`cn_api_fluid_io_save_inference_model` 的行为） + +如果设置为 ``True`` ，每个参数将会被存储为一个文件，文件名为参数名，同时``SaveLoadConfig.params_filename`` 指定的文件名将不会生效。默认为 ``False``。 + +**示例代码** + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + class SimpleNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(SimpleNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + y = self._linear(x) + z = self._linear(y) + return z + # 开启命令式编程模式 + fluid.enable_dygraph() + # 训练模型 + net = SimpleNet(8, 8) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + for i in range(10): + out = net(x) + loss = fluid.layers.mean(out) + loss.backward() + adam.minimize(loss) + net.clear_gradients() + model_path = "simplenet.example.model.separate_params" + configs = fluid.dygraph.jit.SaveLoadConfig() + configs.separate_params = True + # 配置configs.separate_params存储模型 + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[x], + configs=configs) + # [结果] 存储模型目录文件包括: + # linear_0.b_0 linear_0.w_0 __model__ __variables.info__ + # 配置configs.params_filename载入模型 + infer_net = fluid.dygraph.jit.load(model_path, configs=configs) + x = fluid.dygraph.to_variable(np.random.random((4, 8)).astype('float32')) + pred = infer_net(x) diff --git a/doc/fluid/api_cn/dygraph_cn/jit_cn/load_cn.rst b/doc/fluid/api_cn/dygraph_cn/jit_cn/load_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..f001976971c5e84eb93c62fa5a5d77c8f2a9a335 --- /dev/null +++ b/doc/fluid/api_cn/dygraph_cn/jit_cn/load_cn.rst @@ -0,0 +1,168 @@ +.. _cn_api_fluid_dygraph_jit_load: + +load +----------------- + +.. py:function:: paddle.fluid.dygraph.jit.load(model_path, configs=None) + +:api_attr: 命令式编程模式（动态图) + +将接口 :ref:`cn_api_fluid_dygraph_jit_save` 或者 :ref:`cn_api_fluid_io_save_inference_model` 存储的模型载入为 :ref:`cn_api_fluid_dygraph_TranslatedLayer` ，用于预测推理或者fine-tune训练。 + +.. note:: + 由于一些历史原因，如果载入的模型是通过 :ref:`cn_api_fluid_io_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： + - **model_path** (str) - 存储模型的目录。 + - **configs** (SaveLoadConfig, 可选) - 用于指定额外配置选项的 :ref:`cn_api_fluid_dygraph_jit_SaveLoadConfig` 对象。默认为 ``None``。 + +返回：TranslatedLayer - 一个能够执行存储模型的 ``Layer`` 对象。 + +**示例代码** + +1. 载入由接口 :ref:`cn_api_fluid_dygraph_jit_save` 存储的模型进行预测推理及fine-tune训练。 + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + BATCH_SIZE = 32 + BATCH_NUM = 20 + def random_batch_reader(): + def _get_random_images_and_labels(image_shape, label_shape): + image = np.random.random(size=image_shape).astype('float32') + label = np.random.random(size=label_shape).astype('int64') + return image, label + def __reader__(): + for _ in range(BATCH_NUM): + batch_image, batch_label = _get_random_images_and_labels( + [BATCH_SIZE, 784], [BATCH_SIZE, 1]) + yield batch_image, batch_label + return __reader__ + class LinearNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(LinearNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + return self._linear(x) + # 开启命令式编程模式 + fluid.enable_dygraph() + # 1. 训练存储模型. + # 创建网络 + net = LinearNet(784, 1) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + # 创建DataLoader + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator(random_batch_reader()) + # 训练 + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = net(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + net.clear_gradients() + model_path = "linear.example.model" + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[img]) + # 2. 载入模型 & 预测 + # 载入模型 + infer_net = fluid.dygraph.jit.load(model_path) + # 预测 + x = fluid.dygraph.to_variable(np.random.random((1, 784)).astype('float32')) + pred = infer_net(x) + # 3. 载入模型 & fine-tune训练 + # 载入模型 + train_net = fluid.dygraph.jit.load(model_path) + train_net.train() + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=train_net.parameters()) + # 创建DataLoader + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator(random_batch_reader()) + # fine-tune训练 + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = train_net(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + train_net.clear_gradients() + + +2. 载入由接口 :ref:`cn_api_fluid_io_save_inference_model` 存储的模型进行预测推理及fine-tune训练。 + + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + BATCH_SIZE = 32 + BATCH_NUM = 20 + def random_batch_reader(): + def _get_random_images_and_labels(image_shape, label_shape): + image = np.random.random(size=image_shape).astype('float32') + label = np.random.random(size=label_shape).astype('int64') + return image, label + def __reader__(): + for _ in range(BATCH_NUM): + batch_image, batch_label = _get_random_images_and_labels( + [BATCH_SIZE, 784], [BATCH_SIZE, 1]) + yield batch_image, batch_label + return __reader__ + img = fluid.data(name='img', shape=[None, 784], dtype='float32') + label = fluid.data(name='label', shape=[None, 1], dtype='int64') + pred = fluid.layers.fc(input=img, size=10, act='softmax') + loss = fluid.layers.cross_entropy(input=pred, label=label) + avg_loss = fluid.layers.mean(loss) + optimizer = fluid.optimizer.SGD(learning_rate=0.001) + optimizer.minimize(avg_loss) + place = fluid.CPUPlace() + exe = fluid.Executor(place) + exe.run(fluid.default_startup_program()) + loader = fluid.io.DataLoader.from_generator( + feed_list=[img, label], capacity=5, iterable=True) + loader.set_batch_generator(random_batch_reader(), places=place) + # 1. 训练 & 存储预测模型 + for data in loader(): + exe.run( + fluid.default_main_program(), + feed=data, + fetch_list=[avg_loss]) + model_path = "fc.example.model" + fluid.io.save_inference_model( + model_path, ["img"], [pred], exe) + # 开启命令式编程模式 + fluid.enable_dygraph() + # 2. 载入模型 & 预测 + fc = fluid.dygraph.jit.load(model_path) + x = fluid.dygraph.to_variable(np.random.random((1, 784)).astype('float32')) + pred = fc(x) + # 3. 载入模型 & fine-tune训练 + fc = fluid.dygraph.jit.load(model_path) + fc.train() + sgd = fluid.optimizer.SGD(learning_rate=0.001, + parameter_list=fc.parameters()) + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator( + random_batch_reader(), places=place) + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = fc(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + sgd.minimize(avg_loss) diff --git a/doc/fluid/api_cn/dygraph_cn/jit_cn/save_cn.rst b/doc/fluid/api_cn/dygraph_cn/jit_cn/save_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..f0276316bacd0d7b7cb7ef6df12b1f9ac08b759f --- /dev/null +++ b/doc/fluid/api_cn/dygraph_cn/jit_cn/save_cn.rst @@ -0,0 +1,80 @@ +.. _cn_api_fluid_dygraph_jit_save: + +save +----------------- + +.. py:function:: paddle.fluid.dygraph.jit.save(layer, model_path, input_spec=None, configs=None) + +将输入的经过 ``@declarative`` 装饰的 :ref:`cn_api_fluid_dygraph_Layer` 存储为 :ref:`cn_api_fluid_dygraph_TranslatedLayer` 格式的模型， +载入后可用于预测推理或者fine-tune训练。 + +该接口将会将输入 :ref:`cn_api_fluid_dygraph_Layer` 转写后的模型结构 ``Program`` 和所有必要的持久参数变量存储至输入路径 ``model_path`` 中。 + +默认存储的 ``Program`` 文件名为 ``__model__``， 默认存储持久参数变量的文件名为 ``__variables__``， +同时会将变量的一些描述信息存储至文件 ``__variables.info__``，这些额外的信息将在fine-tune训练中使用。 + +存储的模型能够被以下API载入使用： + - :ref:`cn_api_fluid_dygraph_jit_load` + - :ref:`cn_api_fluid_io_load_inference_model` （需要配置参数 ``params_filename='__variables__'`` ） + - 其他预测库API + +参数： + - **layer** (Layer) - 需要存储的 :ref:`cn_api_fluid_dygraph_Layer` 对象。输入的 ``Layer`` 需要经过 ``@declarative`` 装饰。 + - **model_path** (str) - 存储模型的目录。 + - **input_spec** (list[Variable], 可选) - 描述存储模型的输入。此参数是传入当前存储的 ``TranslatedLayer`` forward方法的一个示例输入。如果为 ``None`` ，所有原 ``Layer`` forward方法的输入变量将都会被配置为存储模型的输入变量。默认为 ``None``。 + - **configs** (SaveLoadConfig, 可选) - 用于指定额外配置选项的 :ref:`cn_api_fluid_dygraph_jit_SaveLoadConfig` 对象。默认为 ``None``。 + +返回：无 + +**示例代码** + +.. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid.dygraph import Linear + from paddle.fluid.dygraph import declarative + BATCH_SIZE = 32 + BATCH_NUM = 20 + def random_batch_reader(): + def _get_random_images_and_labels(image_shape, label_shape): + image = np.random.random(size=image_shape).astype('float32') + label = np.random.random(size=label_shape).astype('int64') + return image, label + def __reader__(): + for _ in range(BATCH_NUM): + batch_image, batch_label = _get_random_images_and_labels( + [BATCH_SIZE, 784], [BATCH_SIZE, 1]) + yield batch_image, batch_label + return __reader__ + class LinearNet(fluid.dygraph.Layer): + def __init__(self, in_size, out_size): + super(LinearNet, self).__init__() + self._linear = Linear(in_size, out_size) + @declarative + def forward(self, x): + return self._linear(x) + # 开启命令式编程模式 + fluid.enable_dygraph() + # 创建网络 + net = LinearNet(784, 1) + adam = fluid.optimizer.AdamOptimizer(learning_rate=0.1, parameter_list=net.parameters()) + # 创建DataLoader + train_loader = fluid.io.DataLoader.from_generator(capacity=5) + train_loader.set_batch_generator(random_batch_reader()) + # 训练 + for data in train_loader(): + img, label = data + label.stop_gradient = True + cost = net(img) + loss = fluid.layers.cross_entropy(cost, label) + avg_loss = fluid.layers.mean(loss) + avg_loss.backward() + adam.minimize(avg_loss) + net.clear_gradients() + # 存储模型 + model_path = "linear.example.model" + fluid.dygraph.jit.save( + layer=net, + model_path=model_path, + input_spec=[img]) diff --git a/doc/fluid/api_cn/imperative_cn.rst b/doc/fluid/api_cn/imperative_cn.rst index a47fbd2dab5417415877130db00f4e0e897289e0..0f99f1c8f7decf504eb675c70b8a81c2715cf6db 100644 --- a/doc/fluid/api_cn/imperative_cn.rst +++ b/doc/fluid/api_cn/imperative_cn.rst @@ -13,6 +13,7 @@ paddle.imperative imperative_cn/grad_cn.rst imperative_cn/guard_cn.rst imperative_cn/InverseTimeDecay_cn.rst + imperative_cn/jit_cn.rst imperative_cn/load_cn.rst imperative_cn/load_dygraph_cn.rst imperative_cn/NaturalExpDecay_cn.rst @@ -27,3 +28,4 @@ paddle.imperative imperative_cn/save_dygraph_cn.rst imperative_cn/to_variable_cn.rst imperative_cn/TracedLayer_cn.rst + imperative_cn/TranslatedLayer_cn.rst diff --git a/doc/fluid/api_cn/imperative_cn/TranslatedLayer_cn.rst b/doc/fluid/api_cn/imperative_cn/TranslatedLayer_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..546094acf65c4fb30341d60ea157576601ae8766 --- /dev/null +++ b/doc/fluid/api_cn/imperative_cn/TranslatedLayer_cn.rst @@ -0,0 +1,7 @@ +.. _cn_api_imperative_TranslatedLayer: + +TranslatedLayer +------------------------------- +:doc_source: paddle.fluid.dygraph.io.TranslatedLayer + + diff --git a/doc/fluid/api_cn/imperative_cn/jit_cn.rst b/doc/fluid/api_cn/imperative_cn/jit_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..491ead2e2f4d51993ca9579301d0e3fbfaba1253 --- /dev/null +++ b/doc/fluid/api_cn/imperative_cn/jit_cn.rst @@ -0,0 +1,10 @@ +=== +jit +=== + +.. toctree:: + :maxdepth: 1 + + jit_cn/save_cn.rst + jit_cn/load_cn.rst + jit_cn/SaveLoadConfig_cn.rst \ No newline at end of file diff --git a/doc/fluid/api_cn/imperative_cn/jit_cn/SaveLoadConfig_cn.rst b/doc/fluid/api_cn/imperative_cn/jit_cn/SaveLoadConfig_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..154e987bdb3ed8d3d86858b13b797b897b8eed62 --- /dev/null +++ b/doc/fluid/api_cn/imperative_cn/jit_cn/SaveLoadConfig_cn.rst @@ -0,0 +1,5 @@ +.. _cn_api_imperative_jit_SaveLoadConfig: + +SaveLoadConfig +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.SaveLoadConfig \ No newline at end of file diff --git a/doc/fluid/api_cn/imperative_cn/jit_cn/load_cn.rst b/doc/fluid/api_cn/imperative_cn/jit_cn/load_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..a326fa58f1bf3634a498113bccac46627df0d8e1 --- /dev/null +++ b/doc/fluid/api_cn/imperative_cn/jit_cn/load_cn.rst @@ -0,0 +1,5 @@ +.. _cn_api_imperative_jit_load: + +load +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.load \ No newline at end of file diff --git a/doc/fluid/api_cn/imperative_cn/jit_cn/save_cn.rst b/doc/fluid/api_cn/imperative_cn/jit_cn/save_cn.rst new file mode 100644 index 0000000000000000000000000000000000000000..0c36588fa37794a81db4c534e2f54ad8aaddd66f --- /dev/null +++ b/doc/fluid/api_cn/imperative_cn/jit_cn/save_cn.rst @@ -0,0 +1,5 @@ +.. _cn_api_imperative_jit_save: + +save +------------------------------- +:doc_source: paddle.fluid.dygraph.jit.save \ No newline at end of file