diff --git a/paddle/fluid/API.spec b/paddle/fluid/API.spec index 55f90fcf550a55c7e56078ac4a7abfd740136ceb..b1db03d505d3ed73515b72ad3712697161addc67 100644 --- a/paddle/fluid/API.spec +++ b/paddle/fluid/API.spec @@ -22,9 +22,6 @@ paddle.fluid.Operator.rename_input ArgSpec(args=['self', 'old_name', 'new_name'] paddle.fluid.Operator.rename_output ArgSpec(args=['self', 'old_name', 'new_name'], varargs=None, keywords=None, defaults=None) paddle.fluid.Operator.set_attr ArgSpec(args=['self', 'name', 'val'], varargs=None, keywords=None, defaults=None) paddle.fluid.Operator.to_string ArgSpec(args=['self', 'throw_on_error'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Parameter.__init__ ArgSpec(args=['self', 'block', 'shape', 'dtype'], varargs=None, keywords='kwargs', defaults=None) -paddle.fluid.Parameter.astype ArgSpec(args=['self', 'dtype'], varargs=None, keywords=None, defaults=None) -paddle.fluid.Parameter.to_string ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)) paddle.fluid.default_startup_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None) paddle.fluid.default_main_program ArgSpec(args=[], varargs=None, keywords=None, defaults=None) paddle.fluid.program_guard ArgSpec(args=[], varargs='args', keywords='kwds', defaults=None) @@ -44,7 +41,7 @@ paddle.fluid.DistributeTranspiler.transpile ArgSpec(args=['self', 'trainer_id', paddle.fluid.memory_optimize ArgSpec(args=['input_program', 'skip_opt_set', 'print_log', 'level'], varargs=None, keywords=None, defaults=(None, False, 0)) paddle.fluid.release_memory ArgSpec(args=['input_program', 'skip_opt_set'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.DistributeTranspilerConfig.__init__ -paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords='kwargs', defaults=(None, None, None, None, None, 1, 0, None)) +paddle.fluid.ParallelExecutor.__init__ ArgSpec(args=['self', 'use_cuda', 'loss_name', 'main_program', 'share_vars_from', 'exec_strategy', 'build_strategy', 'num_trainers', 'trainer_id', 'scope'], varargs=None, keywords=None, defaults=(None, None, None, None, None, 1, 0, None)) paddle.fluid.ParallelExecutor.run ArgSpec(args=['self', 'fetch_list', 'feed', 'feed_dict', 'return_numpy'], varargs=None, keywords=None, defaults=(None, None, True)) paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ExecutionStrategy) -> None paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.GradientScaleStrategy, arg0: int) -> None @@ -180,6 +177,14 @@ paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn' paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None)) paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None)) paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(-1, False, None, None)) +paddle.fluid.layers.scale ArgSpec(args=['x', 'scale', 'bias', 'bias_after_scale', 'out', 'act', 'name'], varargs=None, keywords=None, defaults=(1.0, 0.0, True, None, None, None)) +paddle.fluid.layers.elementwise_add ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_div ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_sub ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_mul ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_max ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_min ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) +paddle.fluid.layers.elementwise_pow ArgSpec(args=['x', 'y', 'out', 'axis', 'use_mkldnn', 'act', 'name'], varargs=None, keywords=None, defaults=(None, -1, False, None, None)) paddle.fluid.layers.data ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)) paddle.fluid.layers.open_files ArgSpec(args=['filenames', 'shapes', 'lod_levels', 'dtypes', 'thread_num', 'buffer_size', 'pass_num', 'is_test'], varargs=None, keywords=None, defaults=(None, None, 1, None)) paddle.fluid.layers.read_file ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None) @@ -377,7 +382,7 @@ paddle.fluid.CPUPlace.__init__ __init__(self: paddle.fluid.core.CPUPlace) -> Non paddle.fluid.CUDAPlace.__init__ __init__(self: paddle.fluid.core.CUDAPlace, arg0: int) -> None paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinnedPlace) -> None paddle.fluid.ParamAttr.__init__ ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)) -paddle.fluid.WeightNormParamAttr.__init__ ArgSpec(args=['self', 'dim'], varargs=None, keywords='kwargs', defaults=(None,)) +paddle.fluid.WeightNormParamAttr.__init__ ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)) paddle.fluid.DataFeeder.__init__ ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)) paddle.fluid.DataFeeder.decorate_reader ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)) paddle.fluid.DataFeeder.feed ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None) diff --git a/paddle/fluid/inference/api/api_impl.cc b/paddle/fluid/inference/api/api_impl.cc index c57fc64bb6bfeebc7935f19d0e977e8fccd4c9a0..dca4386b21b4a064c21b52218682321258f368c4 100644 --- a/paddle/fluid/inference/api/api_impl.cc +++ b/paddle/fluid/inference/api/api_impl.cc @@ -22,6 +22,7 @@ limitations under the License. */ #include "paddle/fluid/framework/feed_fetch_method.h" #include "paddle/fluid/inference/api/api_impl.h" +#include "paddle/fluid/inference/api/helper.h" #include "paddle/fluid/inference/api/timer.h" #include "paddle/fluid/platform/profiler.h" @@ -215,57 +216,20 @@ bool NativePaddlePredictor::SetFeed(const std::vector &inputs, template void NativePaddlePredictor::GetFetchOne(const framework::LoDTensor &fetch, PaddleTensor *output) { - std::vector shape; - auto dims_i = fetch.dims(); - auto lod = fetch.lod(); - const T *output_ptr = fetch.data(); - auto num = fetch.numel(); - std::vector data; - if (0 == lod.size()) { - std::copy(output_ptr, output_ptr + num, std::back_inserter(data)); - for (int j = 0; j < dims_i.size(); ++j) { - shape.push_back(dims_i[j]); - } - } else { - // for batch detection - // image[0] -> output[0] shape {145, 6} - // image[1] -> output[1] shape {176, 6} - // then, - // the batch output shape {321, 6} - // the lod {{0, 145, 321}} - // so we should append output[0] to {176, 6} - size_t max_dim = 0; - for (size_t j = 1; j < lod[0].size(); j++) { - max_dim = std::max(max_dim, lod[0][j] - lod[0][j - 1]); - } - size_t common_dim = lod[0].back() == 0 ? 0 : num / lod[0].back(); - if (max_dim > 0) { - data.resize((lod[0].size() - 1) * max_dim * common_dim, 0); - } - for (size_t j = 1; j < lod[0].size(); j++) { - size_t start = lod[0][j - 1] * common_dim; - size_t end = lod[0][j] * common_dim; - if (end > start) { - std::copy(output_ptr + start, output_ptr + end, - data.begin() + (j - 1) * max_dim * common_dim); - } - } - shape.push_back(lod[0].size() - 1); - shape.push_back(max_dim); - for (int j = 1; j < dims_i.size(); ++j) { - shape.push_back(dims_i[j]); - } - } - - output->shape = shape; - auto &buffer = output->data; - if (buffer.empty() || buffer.length() < sizeof(T) * data.size()) { - buffer.Resize(sizeof(T) * data.size()); - } - std::memcpy(buffer.data(), data.data(), sizeof(T) * data.size()); - // copy LoD - for (const auto &level : fetch.lod()) { - output->lod.emplace_back(level); + // set shape. + auto shape = framework::vectorize(fetch.dims()); + output->shape.assign(shape.begin(), shape.end()); + // set data. + const T *data = fetch.data(); + int num_elems = inference::VecReduceToInt(shape); + output->data.Resize(num_elems * sizeof(T)); + // The fetched tensor output by fetch op, should always in CPU memory, so just + // copy. + memcpy(output->data.data(), data, num_elems * sizeof(T)); + // set lod + output->lod.clear(); + for (auto &level : fetch.lod()) { + output->lod.emplace_back(level.begin(), level.end()); } } diff --git a/paddle/fluid/inference/api/helper.h b/paddle/fluid/inference/api/helper.h index 8e359a67738c0df180933421b45f15b39fd0e78c..1fec2f96da0f9d978a3537b2d78e4ce5ef628c81 100644 --- a/paddle/fluid/inference/api/helper.h +++ b/paddle/fluid/inference/api/helper.h @@ -74,13 +74,17 @@ template <> std::string to_string>>( const std::vector>> &vec); +template +int VecReduceToInt(const std::vector &v) { + return std::accumulate(v.begin(), v.end(), 1, [](T a, T b) { return a * b; }); +} + template static void TensorAssignData(PaddleTensor *tensor, const std::vector> &data) { // Assign buffer - int dim = std::accumulate(tensor->shape.begin(), tensor->shape.end(), 1, - [](int a, int b) { return a * b; }); - tensor->data.Resize(sizeof(T) * dim); + int num_elems = VecReduceToInt(tensor->shape); + tensor->data.Resize(sizeof(T) * num_elems); int c = 0; for (const auto &f : data) { for (T v : f) { @@ -89,7 +93,7 @@ static void TensorAssignData(PaddleTensor *tensor, } } -std::string DescribeTensor(const PaddleTensor &tensor) { +static std::string DescribeTensor(const PaddleTensor &tensor) { std::stringstream os; os << "Tensor [" << tensor.name << "]\n"; os << " - type: "; @@ -113,8 +117,7 @@ std::string DescribeTensor(const PaddleTensor &tensor) { os << "\n"; os << " - data: "; - int dim = std::accumulate(tensor.shape.begin(), tensor.shape.end(), 1, - [](int a, int b) { return a * b; }); + int dim = VecReduceToInt(tensor.shape); for (int i = 0; i < dim; i++) { os << static_cast(tensor.data.data())[i] << " "; } @@ -122,8 +125,8 @@ std::string DescribeTensor(const PaddleTensor &tensor) { return os.str(); } -void PrintTime(int batch_size, int repeat, int num_threads, int tid, - double latency, int epoch = 1) { +static void PrintTime(int batch_size, int repeat, int num_threads, int tid, + double latency, int epoch = 1) { LOG(INFO) << "====== batch_size: " << batch_size << ", repeat: " << repeat << ", threads: " << num_threads << ", thread id: " << tid << ", latency: " << latency << "ms ======"; diff --git a/paddle/fluid/inference/tests/api/tester_helper.h b/paddle/fluid/inference/tests/api/tester_helper.h index 7189df775227680726a9d4840386280c5ad44c23..9fcb5129d268a7730c11e5910077ad233050484e 100644 --- a/paddle/fluid/inference/tests/api/tester_helper.h +++ b/paddle/fluid/inference/tests/api/tester_helper.h @@ -47,11 +47,8 @@ void CompareResult(const std::vector &outputs, for (size_t i = 0; i < outputs.size(); i++) { auto &out = outputs[i]; auto &ref_out = ref_outputs[i]; - size_t size = std::accumulate(out.shape.begin(), out.shape.end(), 1, - [](int a, int b) { return a * b; }); - size_t ref_size = - std::accumulate(ref_out.shape.begin(), ref_out.shape.end(), 1, - [](int a, int b) { return a * b; }); + size_t size = VecReduceToInt(out.shape); + size_t ref_size = VecReduceToInt(ref_out.shape); EXPECT_GT(size, 0); EXPECT_EQ(size, ref_size); EXPECT_EQ(out.dtype, ref_out.dtype); @@ -87,10 +84,7 @@ std::unique_ptr CreateTestPredictor( } } -size_t GetSize(const PaddleTensor &out) { - return std::accumulate(out.shape.begin(), out.shape.end(), 1, - [](int a, int b) { return a * b; }); -} +size_t GetSize(const PaddleTensor &out) { return VecReduceToInt(out.shape); } std::unordered_map GetFuseStatis(AnalysisConfig config, int *num_ops) { diff --git a/paddle/scripts/paddle_build.sh b/paddle/scripts/paddle_build.sh index f50a68c54114d5cce15418ad22f38c83163ba866..e6a9524382be219e550017ed4f1a6070dca22fbf 100755 --- a/paddle/scripts/paddle_build.sh +++ b/paddle/scripts/paddle_build.sh @@ -147,6 +147,7 @@ function cmake_gen() { -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} -DPY_VERSION=${PY_VERSION:-2.7} + -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build} ======================================== EOF # Disable UNITTEST_USE_VIRTUALENV in docker because @@ -178,7 +179,8 @@ EOF -DWITH_INFERENCE_API_TEST=${WITH_INFERENCE_API_TEST:-ON} \ -DINFERENCE_DEMO_INSTALL_DIR=${INFERENCE_DEMO_INSTALL_DIR} \ -DWITH_ANAKIN=${WITH_ANAKIN:-OFF} \ - -DPY_VERSION=${PY_VERSION:-2.7} + -DPY_VERSION=${PY_VERSION:-2.7} \ + -DCMAKE_INSTALL_PREFIX=${INSTALL_PREFIX:-/paddle/build} } @@ -361,7 +363,7 @@ EOF ctest --output-on-failure # make install should also be test when unittest make install -j `nproc` - pip install /usr/local/opt/paddle/share/wheels/*.whl + pip install ${INSTALL_PREFIX:-/paddle/build}/opt/paddle/share/wheels/*.whl if [[ ${WITH_FLUID_ONLY:-OFF} == "OFF" ]] ; then paddle version fi diff --git a/python/paddle/fluid/framework.py b/python/paddle/fluid/framework.py index 1d3c94229048ef568dfa651cc20731190beee3b8..fc61bcbea66de07350ee778abb16e81f8f8bc8db 100644 --- a/python/paddle/fluid/framework.py +++ b/python/paddle/fluid/framework.py @@ -38,7 +38,6 @@ from . import unique_name __all__ = [ 'Program', 'Operator', - 'Parameter', 'default_startup_program', 'default_main_program', 'program_guard', diff --git a/python/paddle/fluid/layers/nn.py b/python/paddle/fluid/layers/nn.py index a6e15909e2d3ff95684cf742e26cec27895365a1..208a3427e0500a52405ba614dc40b22cfa8f1415 100644 --- a/python/paddle/fluid/layers/nn.py +++ b/python/paddle/fluid/layers/nn.py @@ -6669,12 +6669,14 @@ def _elementwise_op(helper): assert y is not None, 'y cannot be None in {}'.format(op_type) axis = helper.kwargs.get('axis', -1) use_mkldnn = helper.kwargs.get('use_mkldnn', False) - name = helper.kwargs.get('name', None) - if name is None: - out = helper.create_tmp_variable(dtype=x.dtype) - else: - out = helper.create_variable( - name=name, dtype=x.dtype, persistable=False) + out = helper.kwargs.get('out', None) + if out is None: + name = helper.kwargs.get('name', None) + if name is None: + out = helper.create_tmp_variable(dtype=x.dtype) + else: + out = helper.create_variable( + name=name, dtype=x.dtype, persistable=False) helper.append_op( type=op_type, @@ -6687,7 +6689,13 @@ def _elementwise_op(helper): @templatedoc() -def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): +def scale(x, + scale=1.0, + bias=0.0, + bias_after_scale=True, + out=None, + act=None, + name=None): """ ${comment} @@ -6696,6 +6704,7 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): scale(${scale_type}): ${scale_comment} bias(${bias_type}): ${bias_comment} bias_after_scale(${bias_after_scale_type}): ${bias_after_scale_comment} + out(Tensor): Output tensor. act(basestring|None): Activation applied to the output. name(basestring|None): Name of the output. @@ -6704,11 +6713,12 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): """ helper = LayerHelper('scale', **locals()) - if name is None: - out = helper.create_tmp_variable(dtype=x.dtype) - else: - out = helper.create_variable( - name=name, dtype=x.dtype, persistable=False) + if out is None: + if name is None: + out = helper.create_tmp_variable(dtype=x.dtype) + else: + out = helper.create_variable( + name=name, dtype=x.dtype, persistable=False) helper.append_op( type='scale', @@ -6722,31 +6732,73 @@ def scale(x, scale=1.0, bias=0.0, bias_after_scale=True, act=None, name=None): return helper.append_activation(out) -def elementwise_add(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_add(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_add', **locals())) -def elementwise_div(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_div(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_div', **locals())) -def elementwise_sub(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_sub(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_sub', **locals())) -def elementwise_mul(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_mul(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_mul', **locals())) -def elementwise_max(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_max(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_max', **locals())) -def elementwise_min(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_min(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_min', **locals())) -def elementwise_pow(x, y, axis=-1, use_mkldnn=False, act=None, name=None): +def elementwise_pow(x, + y, + out=None, + axis=-1, + use_mkldnn=False, + act=None, + name=None): return _elementwise_op(LayerHelper('elementwise_pow', **locals())) @@ -6758,6 +6810,7 @@ for func in [ func.__doc__ = _generate_doc_string_( op_proto, additional_args_lines=[ + "out (Tensor): The output tensor of elementwise op.", "act (basestring|None): Activation applied to the output.", "name (basestring|None): Name of the output." ]) diff --git a/python/paddle/fluid/parallel_executor.py b/python/paddle/fluid/parallel_executor.py index 44af29d3390e35129d0ee65b31eacad6b28a9d60..57d272cbfb948840679e80e8db40379c57603113 100644 --- a/python/paddle/fluid/parallel_executor.py +++ b/python/paddle/fluid/parallel_executor.py @@ -74,28 +74,7 @@ class ParallelExecutor(object): build_strategy=None, num_trainers=1, trainer_id=0, - scope=None, - **kwargs): - if len(kwargs) != 0: - err_msg = "" - for key in kwargs: - if key in dir(ExecutionStrategy): - err_msg += \ - "Setting {0} by constructor is deprecated. Use " \ - "strategy=ExecutionStrategy(); strategy.{0}=xxx; " \ - "pe=ParallelExecutor(exec_strategy=strategy) " \ - "instead.\n ".format(key) - elif key in dir(BuildStrategy): - err_msg += \ - "Setting {0} by constructor is deprecated. Use " \ - "strategy=BuildStrategy(); See help(" \ - "paddle.fluid.ParallelExecutor.BuildStrategy) \n".format( - key) - else: - err_msg += "Setting {0} by constructor is deprecated. Use strategy.\n".format( - key) - raise ValueError(err_msg) - + scope=None): self._places = [] self._act_places = [] if use_cuda: diff --git a/python/paddle/fluid/param_attr.py b/python/paddle/fluid/param_attr.py index f0be794327f51cbbc4202b8b7b401b712b6d66a3..a51607bfdb1dde3d25f490770cc2ba368ceb27ff 100644 --- a/python/paddle/fluid/param_attr.py +++ b/python/paddle/fluid/param_attr.py @@ -185,7 +185,17 @@ class WeightNormParamAttr(ParamAttr): Args: dim(list): The parameter's name. Default None. - kwargs: Any field in ParamAttr. Default None. + name(str): The parameter's name. Default None. + initializer(Initializer): The method to initial this parameter. Default None. + learning_rate(float): The parameter's learning rate. The learning rate when + optimize is :math:`global\_lr * parameter\_lr * scheduler\_factor`. + Default 1.0. + regularizer(WeightDecayRegularizer): Regularization factor. Default None. + trainable(bool): Whether this parameter is trainable. Default True. + gradient_clip(BaseGradientClipAttr): The method to clip this parameter's + gradient. Default None. + do_model_average(bool): Whether this parameter should do model average. + Default False. Examples: .. code-block:: python @@ -204,6 +214,21 @@ class WeightNormParamAttr(ParamAttr): # these paramters for inference. params_with_weight_norm = [] - def __init__(self, dim=None, **kwargs): - super(WeightNormParamAttr, self).__init__(**kwargs) + def __init__(self, + dim=None, + name=None, + initializer=None, + learning_rate=1.0, + regularizer=None, + trainable=True, + gradient_clip=None, + do_model_average=False): + super(WeightNormParamAttr, self).__init__( + name=name, + initializer=initializer, + learning_rate=learning_rate, + regularizer=regularizer, + trainable=trainable, + gradient_clip=gradient_clip, + do_model_average=do_model_average) self.dim = dim