From 129f27178955bfb66185a99733e8794b0017fa95 Mon Sep 17 00:00:00 2001 From: lujun Date: Tue, 25 Jun 2019 03:08:57 +0800 Subject: [PATCH] [cherry pick] dygraph api doc for fluidDoc and api spec (#18302) add dygraph api doc for fluidDoc and api spec --- paddle/fluid/API.spec | 293 ++++++++++++++++++++++++++++++ paddle/fluid/pybind/imperative.cc | 32 ++-- python/paddle/fluid/dygraph/nn.py | 133 ++++++++------ tools/print_signatures.py | 2 +- 4 files changed, 391 insertions(+), 69 deletions(-) diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 9092c5d1527..abc26eb8706 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -429,6 +429,299 @@ paddle.fluid.contrib.multi_upload (ArgSpec(args=['client', 'hdfs_path', 'local_p paddle.fluid.contrib.extend_with_decoupled_weight_decay (ArgSpec(args=['base_optimizer'], varargs=None, keywords=None, defaults=None), ('document', 'a1095dfd4ec725747f662d69cd7659d4')) paddle.fluid.contrib.mixed_precision.decorate (ArgSpec(args=['optimizer', 'init_loss_scaling', 'incr_every_n_steps', 'decr_every_n_nan_or_inf', 'incr_ratio', 'decr_ratio', 'use_dynamic_loss_scaling'], varargs=None, keywords=None, defaults=(1.0, 1000, 2, 2.0, 0.8, False)), ('document', 'bdb8f9dbb0d94b3957272c53eeee9818')) paddle.fluid.contrib.fused_elemwise_activation (ArgSpec(args=['x', 'y', 'functor_list', 'axis', 'scale', 'save_intermediate_out'], varargs=None, keywords=None, defaults=(-1, 0.0, True)), ('document', '1c4b247a2858cea8d9d8750693688270')) +paddle.fluid.dygraph.Layer.__init__ (ArgSpec(args=['self', 'name_scope', 'dtype'], varargs=None, keywords=None, defaults=(VarType.FP32,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Layer.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) +paddle.fluid.dygraph.Layer.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) +paddle.fluid.dygraph.Layer.backward (ArgSpec(args=['self'], varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Layer.clear_gradients (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Layer.create_parameter (ArgSpec(args=['self', 'attr', 'shape', 'dtype', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'a6420ca1455366eaaf972191612de0b6')) +paddle.fluid.dygraph.Layer.create_variable (ArgSpec(args=['self', 'name', 'persistable', 'dtype', 'type'], varargs=None, keywords=None, defaults=(None, None, None, VarType.LOD_TENSOR)), ('document', '171cccfceba636d5bbf7bbae672945d8')) +paddle.fluid.dygraph.Layer.eval (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 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varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.clear_gradients (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.create_parameter (ArgSpec(args=['self', 'attr', 'shape', 'dtype', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'a6420ca1455366eaaf972191612de0b6')) +paddle.fluid.dygraph.Conv3DTranspose.create_variable (ArgSpec(args=['self', 'name', 'persistable', 'dtype', 'type'], varargs=None, keywords=None, defaults=(None, None, None, VarType.LOD_TENSOR)), ('document', '171cccfceba636d5bbf7bbae672945d8')) +paddle.fluid.dygraph.Conv3DTranspose.eval (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.forward (ArgSpec(args=['self', 'input'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.full_name (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '23ce4f961f48ed0f79cadf93a3938ed2')) +paddle.fluid.dygraph.Conv3DTranspose.load_dict (ArgSpec(args=['self', 'stat_dict', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.parameters (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '5aec25a854eb57abc798dccccbb507d5')) +paddle.fluid.dygraph.Conv3DTranspose.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Conv3DTranspose.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) +paddle.fluid.dygraph.Conv3DTranspose.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.__init__ (ArgSpec(args=['self', 'name_scope', 'groups', 'epsilon', 'param_attr', 'bias_attr', 'act', 'data_layout'], varargs=None, keywords=None, defaults=(1e-05, None, None, None, 'NCHW')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) +paddle.fluid.dygraph.GroupNorm.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) +paddle.fluid.dygraph.GroupNorm.backward (ArgSpec(args=['self'], varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.clear_gradients (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.create_parameter (ArgSpec(args=['self', 'attr', 'shape', 'dtype', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'a6420ca1455366eaaf972191612de0b6')) +paddle.fluid.dygraph.GroupNorm.create_variable (ArgSpec(args=['self', 'name', 'persistable', 'dtype', 'type'], varargs=None, keywords=None, defaults=(None, None, None, VarType.LOD_TENSOR)), ('document', '171cccfceba636d5bbf7bbae672945d8')) +paddle.fluid.dygraph.GroupNorm.eval (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.forward (ArgSpec(args=['self', 'input'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.full_name (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '23ce4f961f48ed0f79cadf93a3938ed2')) +paddle.fluid.dygraph.GroupNorm.load_dict (ArgSpec(args=['self', 'stat_dict', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.parameters (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '5aec25a854eb57abc798dccccbb507d5')) +paddle.fluid.dygraph.GroupNorm.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.GroupNorm.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) +paddle.fluid.dygraph.GroupNorm.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.__init__ (ArgSpec(args=['self', 'name_scope', 'dim', 'power_iters', 'eps', 'name'], varargs=None, keywords=None, defaults=(0, 1, 1e-12, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) +paddle.fluid.dygraph.SpectralNorm.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) +paddle.fluid.dygraph.SpectralNorm.backward (ArgSpec(args=['self'], varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.clear_gradients (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.create_parameter (ArgSpec(args=['self', 'attr', 'shape', 'dtype', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'a6420ca1455366eaaf972191612de0b6')) +paddle.fluid.dygraph.SpectralNorm.create_variable (ArgSpec(args=['self', 'name', 'persistable', 'dtype', 'type'], varargs=None, keywords=None, defaults=(None, None, None, VarType.LOD_TENSOR)), ('document', '171cccfceba636d5bbf7bbae672945d8')) +paddle.fluid.dygraph.SpectralNorm.eval (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.forward (ArgSpec(args=['self', 'weight'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.full_name (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '23ce4f961f48ed0f79cadf93a3938ed2')) +paddle.fluid.dygraph.SpectralNorm.load_dict (ArgSpec(args=['self', 'stat_dict', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.parameters (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '5aec25a854eb57abc798dccccbb507d5')) +paddle.fluid.dygraph.SpectralNorm.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.SpectralNorm.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) +paddle.fluid.dygraph.SpectralNorm.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.__init__ (ArgSpec(args=['self', 'name_scope', 'output_size', 'num_filters', 'max_depth', 'act', 'param_attr', 'bias_attr', 'name'], varargs=None, keywords=None, defaults=(1, 2, 'tanh', None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.add_parameter (ArgSpec(args=['self', 'name', 'parameter'], varargs=None, keywords=None, defaults=None), ('document', 'f35ab374c7d5165c3daf3bd64a5a2ec1')) +paddle.fluid.dygraph.TreeConv.add_sublayer (ArgSpec(args=['self', 'name', 'sublayer'], varargs=None, keywords=None, defaults=None), ('document', '839ff3c0534677ba6ad8735c3fd4e995')) +paddle.fluid.dygraph.TreeConv.backward (ArgSpec(args=['self'], varargs='inputs', keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.clear_gradients (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.create_parameter (ArgSpec(args=['self', 'attr', 'shape', 'dtype', 'is_bias', 'default_initializer'], varargs=None, keywords=None, defaults=(False, None)), ('document', 'a6420ca1455366eaaf972191612de0b6')) +paddle.fluid.dygraph.TreeConv.create_variable (ArgSpec(args=['self', 'name', 'persistable', 'dtype', 'type'], varargs=None, keywords=None, defaults=(None, None, None, VarType.LOD_TENSOR)), ('document', '171cccfceba636d5bbf7bbae672945d8')) +paddle.fluid.dygraph.TreeConv.eval (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.forward (ArgSpec(args=['self', 'nodes_vector', 'edge_set'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.full_name (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '23ce4f961f48ed0f79cadf93a3938ed2')) +paddle.fluid.dygraph.TreeConv.load_dict (ArgSpec(args=['self', 'stat_dict', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.parameters (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '5aec25a854eb57abc798dccccbb507d5')) +paddle.fluid.dygraph.TreeConv.state_dict (ArgSpec(args=['self', 'destination', 'include_sublayers'], varargs=None, keywords=None, defaults=(None, True)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.TreeConv.sublayers (ArgSpec(args=['self', 'include_sublayers'], varargs=None, keywords=None, defaults=(True,)), ('document', '00a881005ecbc96578faf94513bf0d62')) +paddle.fluid.dygraph.TreeConv.train (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.__init__ (ArgSpec(args=['self', 'block'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.eval_mode (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.trace 1. trace(self: paddle.fluid.core_avx.Tracer, arg0: paddle.fluid.core_avx.OpBase, arg1: Dict[unicode, handle], arg2: Dict[unicode, handle], arg3: Dict[unicode, Variant], arg4: paddle::platform::CPUPlace, arg5: bool) -> None 2. trace(self: paddle.fluid.core_avx.Tracer, arg0: paddle.fluid.core_avx.OpBase, arg1: Dict[unicode, handle], arg2: Dict[unicode, handle], arg3: Dict[unicode, Variant], arg4: paddle::platform::CUDAPlace, arg5: bool) -> None +paddle.fluid.dygraph.Tracer.trace_op (ArgSpec(args=['self', 'op', 'inputs', 'outputs', 'stop_gradient'], varargs=None, keywords=None, defaults=(False,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.trace_var (ArgSpec(args=['self', 'name', 'var'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.Tracer.train_mode (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.start_gperf_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.stop_gperf_profiler (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.prepare_context (ArgSpec(args=['strategy'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.save_persistables (ArgSpec(args=['model_dict', 'dirname', 'optimizers'], varargs=None, keywords=None, defaults=('save_dir', None)), ('document', 'bdeefe733228f5f2d4a8f8c61a5956cf')) +paddle.fluid.dygraph.load_persistables (ArgSpec(args=['dirname'], varargs=None, keywords=None, defaults=('save_dir',)), ('document', 'fb79b050b5eb52fa9c5fdccefe521aa1')) +paddle.fluid.dygraph.NoamDecay.__init__ (ArgSpec(args=['self', 'd_model', 'warmup_steps', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.NoamDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.NoamDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.PiecewiseDecay.__init__ (ArgSpec(args=['self', 'boundaries', 'values', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.PiecewiseDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.PiecewiseDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.NaturalExpDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.NaturalExpDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.NaturalExpDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.ExponentialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.ExponentialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.ExponentialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.InverseTimeDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'decay_rate', 'staircase', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.InverseTimeDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.InverseTimeDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.PolynomialDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay_steps', 'end_learning_rate', 'power', 'cycle', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0.0001, 1.0, False, 0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.PolynomialDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.PolynomialDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.CosineDecay.__init__ (ArgSpec(args=['self', 'learning_rate', 'step_each_epoch', 'epochs', 'begin', 'step', 'dtype'], varargs=None, keywords=None, defaults=(0, 1, 'float32')), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.CosineDecay.create_lr_var (ArgSpec(args=['self', 'lr'], varargs=None, keywords=None, defaults=None), ('document', '013bc233558149d0757b3df57845b866')) +paddle.fluid.dygraph.CosineDecay.step (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) +paddle.fluid.dygraph.BackwardStrategy.__init__ __init__(self: paddle.fluid.core_avx.BackwardStrategy) -> None paddle.fluid.transpiler.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754')) paddle.fluid.transpiler.DistributeTranspiler.get_pserver_program (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'b1951949c6d21698290aa8ac69afee32')) paddle.fluid.transpiler.DistributeTranspiler.get_pserver_programs (ArgSpec(args=['self', 'endpoint'], varargs=None, keywords=None, defaults=None), ('document', 'c89fc350f975ef827f5448d68af388cf')) diff --git a/paddle/fluid/pybind/imperative.cc b/paddle/fluid/pybind/imperative.cc index 0d15b9a44d8..a4d63351702 100644 --- a/paddle/fluid/pybind/imperative.cc +++ b/paddle/fluid/pybind/imperative.cc @@ -162,22 +162,22 @@ void BindImperative(pybind11::module *m_ptr) { 1. :code:`sort_sum_gradient`, which will sum the gradient by the reverse order of trace. Examples: - - .. code-block:: python - import numpy as np - import paddle.fluid as fluid - from paddle.fluid import FC - - x = np.ones([2, 2], np.float32) - with fluid.dygraph.guard(): - inputs2 = [] - for _ in range(10): - inputs2.append(fluid.dygraph.base.to_variable(x)) - ret2 = fluid.layers.sums(inputs2) - loss2 = fluid.layers.reduce_sum(ret2) - backward_strategy = fluid.dygraph.BackwardStrategy() - backward_strategy.sort_sum_gradient = True - loss2.backward(backward_strategy) + .. code-block:: python + + import numpy as np + import paddle.fluid as fluid + from paddle.fluid import FC + + x = np.ones([2, 2], np.float32) + with fluid.dygraph.guard(): + inputs2 = [] + for _ in range(10): + inputs2.append(fluid.dygraph.base.to_variable(x)) + ret2 = fluid.layers.sums(inputs2) + loss2 = fluid.layers.reduce_sum(ret2) + backward_strategy = fluid.dygraph.BackwardStrategy() + backward_strategy.sort_sum_gradient = True + loss2.backward(backward_strategy) )DOC"); backward_strategy.def(py::init()) .def_property("sort_sum_gradient", diff --git a/python/paddle/fluid/dygraph/nn.py b/python/paddle/fluid/dygraph/nn.py index 4b86ee9b495..a5418257dc4 100644 --- a/python/paddle/fluid/dygraph/nn.py +++ b/python/paddle/fluid/dygraph/nn.py @@ -42,7 +42,7 @@ class Conv2D(layers.Layer): and W is the width of the filter. If the groups is greater than 1, C will equal the number of input image channels divided by the groups. Please refer to UFLDL's `convolution - `_ + ` for more detials. If bias attribution and activation type are provided, bias is added to the output of the convolution, and the corresponding activation function is @@ -124,11 +124,7 @@ class Conv2D(layers.Layer): Examples: .. code-block:: python - - with fluid.dygraph.guard(): - conv2d = Conv2D( "conv2d", 2, 3) - data = to_variable( data ) - conv = conv2d( data ) + from paddle.fluid.dygraph.base import to_variable import paddle.fluid as fluid from paddle.fluid.dygraph import Conv2D @@ -306,6 +302,9 @@ class Conv3D(layers.Layer): W_{out}&= \\frac{(W_{in} + 2 * paddings[2] - (dilations[2] * (W_f - 1) + 1))}{strides[2]} + 1 Args: + input (Variable): The input image with [N, C, D, H, W] format. + num_filters(int): The number of filter. It is as same as the output + image channel. filter_size (int|tuple|None): The filter size. If filter_size is a tuple, it must contain three integers, (filter_size_D, filter_size_H, filter_size_W). Otherwise, the filter will be a square. @@ -356,7 +355,6 @@ class Conv3D(layers.Layer): with fluid.dygraph.guard(): data = numpy.random.random((5, 3, 12, 32, 32)).astype('float32') - conv3d = fluid.dygraph.nn.Conv3D( 'Conv3D', num_filters=2, filter_size=3, act="relu") ret = conv3d(fluid.dygraph.base.to_variable(data)) @@ -508,6 +506,7 @@ class Conv3DTranspose(layers.Layer): W_{out} &= (W_{in} - 1) * strides[2] - 2 * paddings[2] + dilations[2] * (W_f - 1) + 1 Args: + input(Variable): The input image with [N, C, D, H, W] format. num_filters(int): The number of the filter. It is as same as the output image channel. output_size(int|tuple|None): The output image size. If output size is a @@ -677,27 +676,30 @@ class Conv3DTranspose(layers.Layer): class Pool2D(layers.Layer): - # TODO, should delete this class """ - ${comment} + The pooling2d operation calculates the output based on the input, pooling_type and ksize, strides, + paddings parameters.Input(X) and output(Out) are in NCHW format, where N is batch size, C is the number of channels, + H is the height of the feature, and W is the width of the feature. + Parameters(ksize, strides, paddings) are two elements. These two elements represent height and width, respectively. + The input(X) size and output(Out) size may be different. Args: name_scope(str) : The name of this class. pool_size (int|list|tuple): The pool kernel size. If pool kernel size is a tuple or list, it must contain two integers, (pool_size_Height, pool_size_Width). Otherwise, the pool kernel size will be a square of an int. - pool_type: ${pooling_type_comment} + pool_type: (string), pooling type, can be "max" for max-pooling and "avg" for average-pooling pool_stride (int|list|tuple): The pool stride size. If pool stride size is a tuple or list, it must contain two integers, (pool_stride_Height, pool_stride_Width). Otherwise, the pool stride size will be a square of an int. pool_padding (int|list|tuple): The pool padding size. If pool padding size is a tuple, it must contain two integers, (pool_padding_on_Height, pool_padding_on_Width). Otherwise, the pool padding size will be a square of an int. - global_pooling (bool): ${global_pooling_comment} - use_cudnn (bool): ${use_cudnn_comment} - ceil_mode (bool): ${ceil_mode_comment} - name (str|None): A name for this layer(optional). If set None, the - layer will be named automatically. + global_pooling (bool): (bool, default false) Whether to use the global pooling. If global_pooling = true, + kernel size and paddings will be ignored + use_cudnn (bool): (bool, default True) Onlyceil_mode (bool) - (bool, default false) Whether to use the ceil + function to calculate output height and width. False is the default. + If it is set to False, the floor function will be used. exclusive (bool): Whether to exclude padding points in average pooling mode, default is true @@ -713,14 +715,18 @@ class Pool2D(layers.Layer): .. code-block:: python - data = fluid.layers.data( - name='data', shape=[3, 32, 32], dtype='float32') - pool2d = fluid.Pool2D("pool2d",pool_size=2, + import paddle.fluid as fluid + import numpy + + with fluid.dygraph.guard(): + data = numpy.random.random((3, 32, 32)).astype('float32') + + pool2d = fluid.dygraph.Pool2D("pool2d",pool_size=2, pool_type='max', pool_stride=1, global_pooling=False) + pool2d_res = pool2d(data) - pool2d_res = pool2d(data) """ def __init__(self, @@ -836,7 +842,7 @@ class FC(layers.Layer): out.shape = (1, 2) Args: - name(str): The name of this class. + name_scope(str): The name of this class. size(int): The number of output units in this layer. num_flatten_dims (int, default 1): The fc layer can accept an input tensor with more than two dimensions. If this happens, the multidimensional tensor will first be flattened @@ -861,11 +867,12 @@ class FC(layers.Layer): Examples: .. code-block:: python - + from paddle.fluid.dygraph.base import to_variable import paddle.fluid as fluid from paddle.fluid.dygraph import FC import numpy as np + data = np.random.uniform( -1, 1, [30, 10, 32] ).astype('float32') with fluid.dygraph.guard(): fc = FC( "fc", 64, num_flatten_dims=2) @@ -1011,7 +1018,7 @@ class BatchNorm(layers.Layer): y_i &\\gets \\gamma \\hat{x_i} + \\beta Args: - input(variable): The rank of input variable can be 2, 3, 4, 5. + name_scope(str): The name of this class. act(string, Default None): Activation type, linear|relu|prelu|... is_test (bool, Default False): A flag indicating whether it is in test phrase or not. @@ -1032,8 +1039,6 @@ class BatchNorm(layers.Layer): is not set, the bias is initialized zero. Default: None. data_layout(string, default NCHW): NCHW|NHWC in_place(bool, Default False): Make the input and output of batch norm reuse memory. - name(string, Default None): A name for this layer(optional). If set None, the layer - will be named automatically. moving_mean_name(string, Default None): The name of moving_mean which store the global Mean. moving_variance_name(string, Default None): The name of the moving_variance which store the global Variance. do_model_average_for_mean_and_var(bool, Default False): Do model average for mean and variance or not. @@ -1050,12 +1055,12 @@ class BatchNorm(layers.Layer): Variable: A tensor variable which is the result after applying batch normalization on the input. Examples: - .. code-block:: python - fc = fluid.FC('fc', size=200, param_attr='fc1.w') - hidden1 = fc(x) - batch_norm = fluid.BatchNorm("batch_norm", 10) - hidden2 = batch_norm(hidden1) + + fc = fluid.FC('fc', size=200, param_attr='fc1.w') + hidden1 = fc(x) + batch_norm = fluid.BatchNorm("batch_norm", 10) + hidden2 = batch_norm(hidden1) """ def __init__(self, @@ -1193,11 +1198,13 @@ class Embedding(layers.Layer): Args: name_scope: See base class. - size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size of the dictionary of embeddings and the size of each embedding vector respectively. - + size(tuple|list): The shape of the look up table parameter. It should have two elements which indicate the size + of the dictionary of embeddings and the size of each embedding vector respectively. is_sparse(bool): The flag indicating whether to use sparse update. is_distributed(bool): Whether to run lookup table from remote parameter server. - padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`. + padding_idx(int|long|None): If :attr:`None`, it makes no effect to lookup. + Otherwise the given :attr:`padding_idx` indicates padding the output with zeros whenever lookup encounters + it in :attr:`input`. If :math:`padding_idx < 0`, the :attr:`padding_idx` to use in lookup is :math:`size[0] + dim`. param_attr(ParamAttr): Parameters for this layer dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc @@ -1209,15 +1216,19 @@ class Embedding(layers.Layer): .. code-block:: python + import paddle.fluid as fluid + import paddle.fluid.dygraph.base as base + import numpy as np + inp_word = np.array([[[1]]]).astype('int64') dict_size = 20 with fluid.dygraph.guard(): - emb = fluid.Embedding( + emb = fluid.dygraph.Embedding( name_scope='embedding', size=[dict_size, 32], param_attr='emb.w', is_sparse=False) - static_rlt3 = emb2(base.to_variable(inp_word)) + static_rlt3 = emb(base.to_variable(inp_word)) """ def __init__(self, @@ -1228,7 +1239,6 @@ class Embedding(layers.Layer): padding_idx=None, param_attr=None, dtype='float32'): - super(Embedding, self).__init__(name_scope, dtype) self._size = size self._is_sparse = is_sparse @@ -1481,6 +1491,26 @@ class GRUUnit(layers.Layer): Returns: tuple: The hidden value, reset-hidden value and gate values. + + Examples: + + .. code-block:: python + + import paddle.fluid as fluid + import paddle.fluid.dygraph.base as base + import numpy + + lod = [[2, 4, 3]] + D = 5 + T = sum(lod[0]) + + hidden_input = numpy.random.rand(T, D).astype('float32') + with fluid.dygraph.guard(): + x = numpy.random.random((3, 32, 32)).astype('float32') + gru = fluid.dygraph.GRUUnit('gru', size=D * 3) + dy_ret = gru( + base.to_variable(input), base.to_variable(hidden_input)) + """ def __init__(self, @@ -1544,15 +1574,12 @@ class NCE(layers.Layer): Compute and return the noise-contrastive estimation training loss. See `Noise-contrastive estimation: A new estimation principle for unnormalized statistical models - `_. + `. By default this operator uses a uniform distribution for sampling. Args: name_scope (str): See base class. num_total_classes (int): Total number of classes in all samples - sample_weight (Variable|None): A Variable of shape [batch_size, 1] - storing a weight for each sample. The default weight for each - sample is 1.0. param_attr (ParamAttr|None): The parameter attribute for learnable parameters/weights of nce. If it is set to None or one attribute of ParamAttr, nce will create ParamAttr as param_attr. If the Initializer of the param_attr @@ -1563,8 +1590,6 @@ class NCE(layers.Layer): will create ParamAttr as bias_attr. If the Initializer of the bias_attr is not set, the bias is initialized zero. Default: None. num_neg_samples (int): The number of negative classes. The default value is 10. - name (str|None): A name for this layer(optional). If set None, the layer - will be named automatically. Default: None. sampler (str): The sampler used to sample class from negtive classes. It can be 'uniform', 'log_uniform' or 'custom_dist'. default: 'uniform'. @@ -1789,8 +1814,6 @@ class PRelu(layers.Layer): element:each element has a weight param_attr(ParamAttr|None): The parameter attribute for the learnable weight (alpha). - name(str|None): A name for this layer(optional). If set None, the layer - will be named automatically. Returns: Variable: The output tensor with the same shape as input. @@ -1799,14 +1822,18 @@ class PRelu(layers.Layer): .. code-block:: python - inp_np = np.ones([5, 200, 100, 100]).astype('float32') - with fluid.dygraph.guard(): - mode = 'channel' - prelu = fluid.PRelu( - 'prelu', - mode=mode, - param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0))) - dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np)) + import paddle.fluid as fluid + import numpy as np + + inp_np = np.ones([5, 200, 100, 100]).astype('float32') + with fluid.dygraph.guard(): + mode = 'channel' + prelu = fluid.PRelu( + 'prelu', + mode=mode, + param_attr=fluid.ParamAttr(initializer=fluid.initializer.Constant(1.0))) + dy_rlt = prelu(fluid.dygraph.base.to_variable(inp_np)) + """ def __init__(self, name_scope, mode, param_attr=None): @@ -2329,7 +2356,6 @@ class GroupNorm(layers.Layer): If it is set to None, the bias is initialized zero. Default: None. act(str): Activation to be applied to the output of group normalizaiton. data_layout(string|NCHW): Only NCHW is supported. - dtype(np.dtype|core.VarDesc.VarType|str): The type of data : float32, float_16, int etc Returns: Variable: A tensor variable which is the result after applying group normalization on the input. @@ -2536,7 +2562,9 @@ class TreeConv(layers.Layer): out(Variable): (Tensor) The feature vector of subtrees. The shape of the output tensor is [max_tree_node_size, output_size, num_filters]. The output tensor could be a new feature vector for next tree convolution layers Examples: + .. code-block:: python + import paddle.fluid as fluid import numpy @@ -2587,6 +2615,7 @@ class TreeConv(layers.Layer): is_bias=False) def forward(self, nodes_vector, edge_set): + if self._name: out = self.create_variable( name=self._name, dtype=self._dtype, persistable=False) diff --git a/tools/print_signatures.py b/tools/print_signatures.py index 0de2e4f81f0..c387d2535ff 100644 --- a/tools/print_signatures.py +++ b/tools/print_signatures.py @@ -28,7 +28,7 @@ import hashlib member_dict = collections.OrderedDict() -experimental_namespace = {"paddle.fluid.dygraph", "paddle.fluid.LoDTensorset"} +experimental_namespace = {"paddle.fluid.LoDTensorset"} def md5(doc): -- GitLab