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未验证 提交 b3f5876e 编写于 作者: L lujun 提交者： GitHub
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Merge pull request #14 from PaddlePaddle/develop 

merge to local
上级 86511f51 f9061796
展开全部 隐藏空白更改
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Showing with 2910 addition and 510 deletion
paddle/fluid/API.spec
浏览文件 @ b3f5876e
......@@ -255,6 +255,7 @@ paddle.fluid.layers.reverse (ArgSpec(args=['x', 'axis'], varargs=None, keywords=
paddle.fluid.layers.has_inf (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '8f8c0306117ea441f20dcbbdba1f0ecc'))
paddle.fluid.layers.has_nan (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '2e53e83127dbfd86e7098bdfe9a549e8'))
paddle.fluid.layers.isfinite (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '0a437011c3906079fd8947ed3e52d292'))
paddle.fluid.layers.range (ArgSpec(args=['start', 'end', 'step', 'dtype'], varargs=None, keywords=None, defaults=None), ('document', '2ec937ede953ded2fdff2675883900bb'))
paddle.fluid.layers.While.__init__ (ArgSpec(args=['self', 'cond', 'is_test', 'name'], varargs=None, keywords=None, defaults=(False, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.While.block (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.Switch.__init__ (ArgSpec(args=['self', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -376,23 +377,9 @@ paddle.fluid.contrib.Calibrator.__init__ (ArgSpec(args=['self'], varargs='args',
paddle.fluid.contrib.Calibrator.sample_data (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '3b8c85ca1e2cf753cc8c90a6c6992958'))
paddle.fluid.contrib.Calibrator.save_int8_model (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab'))
paddle.fluid.contrib.build_compressor (ArgSpec(args=['place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'config'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.__init__ (ArgSpec(args=['self', 'place', 'data_reader', 'data_feeder', 'scope', 'metrics', 'epoch', 'program_exe'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.CompressPass.add_strategy (ArgSpec(args=['self', 'strategy'], varargs=None, keywords=None, defaults=None), ('document', '3bf6010b6f47d3c86df0ec8957be95e0'))
paddle.fluid.contrib.CompressPass.apply (ArgSpec(args=['self', 'graph'], varargs=None, keywords=None, defaults=None), ('document', 'a92bf85d4b59bd4f2ac1706d7c4899a6'))
paddle.fluid.contrib.ImitationGraph.__init__ (ArgSpec(args=['self', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.ImitationGraph.all_parameters (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.__init__ (ArgSpec(args=['self', 'pruner', 'start_epoch', 'end_epoch', 'delta_rate', 'acc_loss_threshold', 'sensitivities'], varargs=None, keywords=None, defaults=(None, 0, 10, 0.2, 0.2, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_batch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_compress_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_begin (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.SensitivePruneStrategy.on_epoch_end (ArgSpec(args=['self', 'context'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.__init__ (ArgSpec(args=['self', 'threshold'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.MagnitudePruner.prune (ArgSpec(args=['self', 'param', 'threshold'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.contrib.RatioPruner.__init__ (ArgSpec(args=['self', 'ratios'], varargs=None, keywords=None, defaults=(None,)), ('document', 'e7a81a325b296a9ca502ee5adb4fc85d'))
paddle.fluid.contrib.RatioPruner.prune (ArgSpec(args=['self', 'param', 'ratio'], varargs=None, keywords=None, defaults=(None,)), ('document', '358cbf2978c91028fb96a195a9884645'))
paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], './checkpoints', None, None)), ('document', '31ae143830c9bf6b43547dd546c5ba80'))
paddle.fluid.contrib.Compressor.config (ArgSpec(args=['self', 'config_file'], varargs=None, keywords=None, defaults=None), ('document', '780d9c007276ccbb95b292400d7807b0'))
paddle.fluid.contrib.Compressor.run (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'c6e43d6a078d307672283c1f36e04fe9'))
paddle.fluid.contrib.load_persistables_for_increment (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var', 'lookup_table_var_path'], varargs=None, keywords=None, defaults=None), ('document', '2ab36d4f7a564f5f65e455807ad06c67'))
paddle.fluid.contrib.load_persistables_for_inference (ArgSpec(args=['dirname', 'executor', 'program', 'lookup_table_var_name'], varargs=None, keywords=None, defaults=None), ('document', '59066bac9db0ac6ce414d05780b7333f'))
paddle.fluid.contrib.convert_dist_to_sparse_program (ArgSpec(args=['program'], varargs=None, keywords=None, defaults=None), ('document', '74c39c595dc70d6be2f16d8e462d282b'))
......@@ -432,48 +419,59 @@ paddle.fluid.nets.img_conv_group (ArgSpec(args=['input', 'conv_num_filter', 'poo
paddle.fluid.optimizer.SGDOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'regularization', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.SGDOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.SGDOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.SGDOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.MomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'use_nesterov', 'regularization', 'name'], varargs=None, keywords=None, defaults=(False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.MomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.MomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.MomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.AdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'regularization', 'name', 'initial_accumulator_value'], varargs=None, keywords=None, defaults=(1e-06, None, None, 0.0)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.AdamOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name', 'lazy_mode'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdamOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdamOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.AdamaxOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'beta1', 'beta2', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.9, 0.999, 1e-08, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdamaxOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdamaxOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdamaxOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'decay', 'epsilon', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.DecayedAdagradOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.FtrlOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'l1', 'l2', 'lr_power', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.0, 0.0, -0.5, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.FtrlOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.FtrlOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.FtrlOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.RMSPropOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'rho', 'epsilon', 'momentum', 'centered', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.95, 1e-06, 0.0, False, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.RMSPropOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.RMSPropOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.RMSPropOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.AdadeltaOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'epsilon', 'rho', 'regularization', 'name'], varargs=None, keywords=None, defaults=(1e-06, 0.95, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.AdadeltaOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.AdadeltaOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.AdadeltaOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.ModelAverage.__init__ (ArgSpec(args=['self', 'average_window_rate', 'min_average_window', 'max_average_window', 'regularization', 'name'], varargs=None, keywords=None, defaults=(10000, 10000, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.apply (ArgSpec(args=['self', 'executor', 'need_restore'], varargs=None, keywords=None, defaults=(True,)), ('document', '46234a5470590feb336346f70a3db715'))
paddle.fluid.optimizer.ModelAverage.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.ModelAverage.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.ModelAverage.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.ModelAverage.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.optimizer.ModelAverage.restore (ArgSpec(args=['self', 'executor'], varargs=None, keywords=None, defaults=None), ('document', '18db9c70be9c4dd466f9844457b21bfe'))
paddle.fluid.optimizer.LarsMomentumOptimizer.__init__ (ArgSpec(args=['self', 'learning_rate', 'momentum', 'lars_coeff', 'lars_weight_decay', 'regularization', 'name'], varargs=None, keywords=None, defaults=(0.001, 0.0005, None, None)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.apply_gradients (ArgSpec(args=['self', 'params_grads'], varargs=None, keywords=None, defaults=None), ('document', 'bfe7305918552aaecfdaa22411dbe871'))
paddle.fluid.optimizer.LarsMomentumOptimizer.backward (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', 'ba3a113d0229ff7bc9d39bda0a6d947f'))
paddle.fluid.optimizer.LarsMomentumOptimizer.get_opti_var_name_list (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.optimizer.LarsMomentumOptimizer.minimize (ArgSpec(args=['self', 'loss', 'startup_program', 'parameter_list', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '35fd5d3330c97903528c7e0dacc7f6ea'))
paddle.fluid.backward.append_backward (ArgSpec(args=['loss', 'parameter_list', 'no_grad_set', 'callbacks'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '1a79bd7d10ae54ca763ec81bca36ba24'))
paddle.fluid.regularizer.L1DecayRegularizer.__init__ (ArgSpec(args=['self', 'regularization_coeff'], varargs=None, keywords=None, defaults=(0.0,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......
paddle/fluid/operators/range_op.cc 0 → 100644
浏览文件 @ b3f5876e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/range_op.h"
namespace paddle {
namespace operators {
class RangeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext *ctx) const override {
if (ctx->HasInput("Start")) {
auto s_dims = ctx->GetInputDim("Start");
PADDLE_ENFORCE((s_dims.size() == 1) && (s_dims[0] == 1),
"The shape of Input(Start) should be [1].");
}
if (ctx->HasInput("End")) {
auto e_dims = ctx->GetInputDim("End");
PADDLE_ENFORCE((e_dims.size() == 1) && (e_dims[0] == 1),
"The shape of Input(End) should be [1].");
}
if (ctx->HasInput("Step")) {
auto step_dims = ctx->GetInputDim("Step");
PADDLE_ENFORCE((step_dims.size() == 1) && (step_dims[0] == 1),
"The shape of Input(Step) should be [1].");
}
ctx->SetOutputDim("Out", {-1});
}
};
class RangeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
void Make() override {
AddInput("Start",
"Start of interval. The interval includes this value. It is a "
"tensor with shape=[1].");
AddInput("End",
"End of interval. The interval does not include this value, "
"except in some cases where step is not an integer and floating "
"point round-off affects the length of out. It is a tensor with "
"shape=[1].");
AddInput("Step", "Spacing between values. It is a tensor with shape=[1].");
AddOutput("Out", "A sequence of numbers.");
AddComment(R"DOC(
Return evenly spaced values within a given interval. Values are generated within the half-open interval [start, stop) (in other words, the interval including start but excluding stop). Like arange function of numpy.
)DOC");
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_WITHOUT_GRADIENT(range, ops::RangeOp, ops::RangeOpMaker);
REGISTER_OP_CPU_KERNEL(range, ops::CPURangeKernel<int>,
ops::CPURangeKernel<float>, ops::CPURangeKernel<double>,
ops::CPURangeKernel<int64_t>);
paddle/fluid/operators/range_op.cu 0 → 100644
浏览文件 @ b3f5876e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/range_op.h"
#include "paddle/fluid/platform/cuda_primitives.h"
namespace paddle {
namespace operators {
#define CUDA_1D_KERNEL_LOOP(i, n) \
for (int i = blockIdx.x * blockDim.x + threadIdx.x; i < (n); \
i += blockDim.x * gridDim.x)
template <typename T>
__global__ void RangeKernel(T start, T step, int64_t size, T* out) {
CUDA_1D_KERNEL_LOOP(index, size) { out[index] = start + step * index; }
}
template <typename T>
class CUDARangeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* start_t = context.Input<framework::Tensor>("Start");
auto* end_t = context.Input<framework::Tensor>("End");
auto* step_t = context.Input<framework::Tensor>("Step");
auto* out = context.Output<framework::Tensor>("Out");
framework::Tensor n;
framework::TensorCopy(*start_t, platform::CPUPlace(), &n);
T start = n.data<T>()[0];
framework::TensorCopy(*end_t, platform::CPUPlace(), &n);
T end = n.data<T>()[0];
framework::TensorCopy(*step_t, platform::CPUPlace(), &n);
T step = n.data<T>()[0];
int64_t size = 0;
GetSize(start, end, step, &size);
out->Resize(framework::make_ddim({size}));
T* out_data = out->mutable_data<T>(context.GetPlace());
auto stream = context.cuda_device_context().stream();
int block = 512;
int grid = (size + block - 1) / block;
RangeKernel<T><<<grid, block, 0, stream>>>(start, step, size, out_data);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(range, ops::CUDARangeKernel<int>,
ops::CUDARangeKernel<int64_t>,
ops::CUDARangeKernel<float>,
ops::CUDARangeKernel<double>);
paddle/fluid/operators/range_op.h 0 → 100644
浏览文件 @ b3f5876e
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#pragma once
#include <functional>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
template <typename T>
void GetSize(T start, T end, T step, int64_t* size) {
PADDLE_ENFORCE(!std::equal_to<T>()(step, 0),
"The step of range op should not be 0.");
PADDLE_ENFORCE(((start < end) && (step > 0)) || ((start > end) && (step < 0)),
"The step should be greater than 0 while start < end. And the "
"step should be less than 0 while start > end.");
*size = std::is_integral<T>::value
? ((std::abs(end - start) + std::abs(step) - 1) / std::abs(step))
: std::ceil(std::abs((end - start) / step));
}
template <typename T>
class CPURangeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
T start = context.Input<framework::Tensor>("Start")->data<T>()[0];
T end = context.Input<framework::Tensor>("End")->data<T>()[0];
T step = context.Input<framework::Tensor>("Step")->data<T>()[0];
auto* out = context.Output<framework::Tensor>("Out");
int64_t size = 0;
GetSize(start, end, step, &size);
out->Resize(framework::make_ddim({size}));
T* out_data = out->mutable_data<T>(context.GetPlace());
T value = start;
for (int64_t i = 0; i < size; ++i) {
out_data[i] = value;
value += step;
}
}
};
} // namespace operators
} // namespace paddle
python/paddle/fluid/contrib/slim/__init__.py
浏览文件 @ b3f5876e
......@@ -13,13 +13,4 @@
# limitations under the License.
from .core import *
from .graph import *
from .prune import *
__all__ = [
'build_compressor',
'CompressPass',
'ImitationGraph',
'SensitivePruneStrategy',
'MagnitudePruner',
'RatioPruner',
]
__all__ = ['Compressor', ]
python/paddle/fluid/contrib/slim/core/__init__.py
浏览文件 @ b3f5876e
......@@ -14,11 +14,9 @@
from . import config
from .config import *
from . import compress_pass
from .compress_pass import *
from . import compressor
from .compressor import *
from . import strategy
from .strategy import *
from . import pass_builder
from .pass_builder import *
__all__ = config.__all__ + compress_pass.__all__ + strategy.__all__ + pass_builder.__all__
__all__ = config.__all__ + compressor.__all__ + strategy.__all__
python/paddle/fluid/contrib/slim/core/compress_pass.py 已删除 100644 → 0
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from ....core import CPUPlace
from ..graph import get_executor
__all__ = ['Context', 'CompressPass']
class Context(object):
"""
The context in the process of compression.
Args:
exe: The executor used to execute graph.
graph: The graph to be compressed.
scope: The scope used to execute graph.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self, exe, graph, scope, program_exe=None):
# The total number of epoches to be trained.
self.epoch = 0
# Current epoch
self.epoch_id = 0
# Current batch
self.batch_id = 0
self.exe = exe
self.graph = graph
self.scope = scope
self.program_exe = program_exe
class CompressPass(object):
"""
The pass used to compress model.
Args:
place: The device used in compression.
data_reader: The data_reader used to run graph.
data_feeder: The data_feeder used to run graph.
scope: The scope used to run graph.
metrics: The metrics for evaluating model.
epoch: The total epoches of trainning in compression.
program_exe: The program_exe is used to execute the program
created for modifying the variables in scope.
"""
def __init__(self,
place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
program_exe=None):
self.strategies = []
self.place = CPUPlace() if place is None else place
self.data_reader = data_reader
self.data_feeder = data_feeder
self.scope = scope
self.metrics = metrics
self.epoch = epoch
self.program_exe = program_exe
def add_strategy(self, strategy):
"""
Add a strategy to current compress pass.
Args:
strategy: The strategy to be added into current compress pass.
"""
self.strategies.append(strategy)
self.epoch = max(strategy.end_epoch, self.epoch)
def apply(self, graph):
"""
Compress a model.
Args:
graph: The target graph to be compressed.
"""
self.executor = get_executor(graph, self.place)
context = Context(
self.executor, graph, self.scope, program_exe=self.program_exe)
for strategy in self.strategies:
strategy.on_compress_begin(context)
for epoch in range(self.epoch):
for strategy in self.strategies:
strategy.on_epoch_begin(context)
for data in self.data_reader():
for strategy in self.strategies:
strategy.on_batch_begin(context)
fetches = None
if self.metrics:
fetches = self.metrics.values()
feed = None
if self.data_feeder:
feed = self.data_feeder.feed(data)
results = self.executor.run(graph,
fetches=fetches,
scope=self.scope,
feed=feed)
if results:
print("results: {}".format(
zip(self.metrics.keys(), results)))
for strategy in self.strategies:
strategy.on_batch_end(context)
context.batch_id += 1
for strategy in self.strategies:
strategy.on_epoch_end(context)
context.epoch_id += 1
for strategy in self.strategies:
strategy.on_compress_end(context)
python/paddle/fluid/contrib/slim/core/compressor.py 0 → 100644
浏览文件 @ b3f5876e
此差异已折叠。
python/paddle/fluid/contrib/slim/core/config.py
浏览文件 @ b3f5876e
......@@ -17,7 +17,7 @@ import funcsigs
import yaml
from collections import OrderedDict
from ..prune import *
from .compress_pass import *
from ..quantization import *
from .strategy import *
__all__ = ['ConfigFactory']
......@@ -29,15 +29,10 @@ class ConfigFactory(object):
def __init__(self, config):
"""Init a factory from configure file."""
self.instances = {}
self.compressor = {}
self.version = None
self._parse_config(config)
def get_compress_pass(self):
"""
Get compress pass from factory.
"""
return self.instance('compress_pass')
def instance(self, name):
"""
Get instance from factory.
......@@ -59,8 +54,16 @@ class ConfigFactory(object):
args = {}
for key in keys:
value = attrs[key]
if isinstance(value, str) and value.lower() == 'none':
value = None
if isinstance(value, str) and value in self.instances:
value = self.instances[value]
if isinstance(value, list):
for i in range(len(value)):
if isinstance(value[i],
str) and value[i] in self.instances:
value[i] = self.instances[value[i]]
args[key] = value
self.instances[name] = class_(**args)
return self.instances.get(name)
......@@ -76,16 +79,23 @@ class ConfigFactory(object):
assert self.version == int(key_values['version'])
# parse pruners
if key == 'pruners' or key == 'strategies':
if key == 'distillers' or key == 'pruners' or key == 'quantizers' or key == 'strategies':
instances = key_values[key]
for name in instances:
self._new_instance(name, instances[name])
if key == 'compress_pass':
compress_pass = self._new_instance(key, key_values[key])
for name in key_values[key]['strategies']:
strategy = self.instance(name)
compress_pass.add_strategy(strategy)
if key == 'compressor':
self.compressor['strategies'] = []
self.compressor['epoch'] = key_values[key]['epoch']
if 'init_model' in key_values[key]:
self.compressor['init_model'] = key_values[key][
'init_model']
self.compressor['checkpoint_path'] = key_values[key][
'checkpoint_path']
if 'strategies' in key_values[key]:
for name in key_values[key]['strategies']:
strategy = self.instance(name)
self.compressor['strategies'].append(strategy)
if key == 'include':
for config_file in key_values[key]:
......
python/paddle/fluid/contrib/slim/core/pass_builder.py 已删除 100644 → 0
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from .compress_pass import CompressPass
from .config import ConfigFactory
__all__ = ['build_compressor']
def build_compressor(place=None,
data_reader=None,
data_feeder=None,
scope=None,
metrics=None,
epoch=None,
config=None):
if config is not None:
factory = ConfigFactory(config)
comp_pass = factory.get_compress_pass()
else:
comp_pass = CompressPass()
comp_pass.place = place
comp_pass.data_reader = data_reader
comp_pass.data_feeder = data_feeder
comp_pass.scope = scope
comp_pass.metrics = metrics
comp_pass.epoch = epoch
return comp_pass
python/paddle/fluid/contrib/slim/core/strategy.py
浏览文件 @ b3f5876e
......@@ -20,7 +20,7 @@ class Strategy(object):
Base class for all strategies.
"""
def __init__(self, start_epoch=0, end_epoch=10):
def __init__(self, start_epoch=0, end_epoch=0):
"""
Args:
start_epoch: The first epoch to apply the strategy.
......@@ -29,7 +29,7 @@ class Strategy(object):
self.start_epoch = start_epoch
self.end_epoch = end_epoch
def on_compress_begin(self, context):
def on_compression_begin(self, context):
pass
def on_epoch_begin(self, context):
......@@ -44,5 +44,5 @@ class Strategy(object):
def on_batch_end(self, context):
pass
def on_compress_end(self, context):
def on_compression_end(self, context):
pass
python/paddle/fluid/contrib/slim/demo/filter_prune/config.yaml 已删除 100644 → 0
浏览文件 @ 86511f51
version: 1.0
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/demo/filter_prune/demo.py 已删除 100644 → 0
浏览文件 @ 86511f51
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle.fluid as fluid
import paddle
import os
import sys
from paddle.fluid.contrib.slim import CompressPass
from paddle.fluid.contrib.slim import build_compressor
from paddle.fluid.contrib.slim import ImitationGraph
class LinearModel(object):
def __init__(slef):
pass
def train(self):
train_program = fluid.Program()
startup_program = fluid.Program()
startup_program.random_seed = 10
with fluid.program_guard(train_program, startup_program):
x = fluid.layers.data(name='x', shape=[13], dtype='float32')
y = fluid.layers.data(name='y', shape=[1], dtype='float32')
predict = fluid.layers.fc(input=x, size=1, act=None)
cost = fluid.layers.square_error_cost(input=predict, label=y)
avg_cost = fluid.layers.mean(cost)
eval_program = train_program.clone()
sgd_optimizer = fluid.optimizer.SGD(learning_rate=0.001)
sgd_optimizer.minimize(avg_cost)
train_reader = paddle.batch(
paddle.dataset.uci_housing.train(), batch_size=1)
eval_reader = paddle.batch(
paddle.dataset.uci_housing.test(), batch_size=1)
place = fluid.CPUPlace()
train_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
eval_feeder = fluid.DataFeeder(place=place, feed_list=[x, y])
exe = fluid.Executor(place)
exe.run(startup_program)
train_metrics = {"loss": avg_cost.name}
eval_metrics = {"loss": avg_cost.name}
graph = ImitationGraph(train_program)
config = './config.yaml'
comp_pass = build_compressor(
place,
data_reader=train_reader,
data_feeder=train_feeder,
scope=fluid.global_scope(),
metrics=train_metrics,
epoch=1,
config=config)
comp_pass.apply(graph)
if __name__ == "__main__":
model = LinearModel()
model.train()
python/paddle/fluid/contrib/slim/graph/__init__.py
浏览文件 @ b3f5876e
......@@ -14,10 +14,7 @@
from . import executor
from .executor import *
from . import graph
from .graph import *
from . import graph_pass
from .graph_pass import *
from . import graph_wrapper
from .graph_wrapper import *
__all__ = executor.__all__
__all__ += graph.__all__
__all__ += graph_pass.__all__
__all__ += graph_wrapper.__all__
python/paddle/fluid/contrib/slim/graph/executor.py
浏览文件 @ b3f5876e
......@@ -12,51 +12,46 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import abc
from abc import abstractmethod
from ....compiler import CompiledProgram
from ....data_feeder import DataFeeder
from .... import executor
from .graph import IRGraph, ImitationGraph
from .graph_wrapper import GraphWrapper
__all__ = ['get_executor']
__all__ = ['SlimGraphExecutor']
class GraphExecutor(object):
__metaclass__ = abc.ABCMeta
class SlimGraphExecutor(object):
"""
Wrapper of executor used to run GraphWrapper.
"""
def __init__(self, place):
self.place = place
@abstractmethod
def run(self, graph, feches=None, feed=None):
pass
class IRGraphExecutor(GraphExecutor):
def run(self, grah, fetches, feed=None):
pass
class ImitationGraphExecutor(GraphExecutor):
def __init__(self, place):
super(ImitationGraphExecutor, self).__init__(place)
self.exe = executor.Executor(place)
self.place = place
def run(self, graph, scope=None, fetches=None, feed=None):
assert isinstance(graph, ImitationGraph)
fetch_list = None
if fetches:
fetch_list = [
graph.program.global_block().var(name) for name in fetches
]
results = self.exe.run(graph.program,
def run(self, graph, scope, data=None):
"""
Runing a graph with a batch of data.
Args:
graph(GraphWrapper): The graph to be executed.
scope(fluid.core.Scope): The scope to be used.
data(list<tuple>): A batch of data. Each tuple in this list is a sample.
It will feed the items of tuple to the in_nodes of graph.
Returns:
results(list): A list of result with the same order indicated by graph.out_nodes.
"""
assert isinstance(graph, GraphWrapper)
if data is not None:
feeder = DataFeeder(
feed_list=graph.in_nodes.values(),
place=self.place,
program=graph.program)
feed = feeder.feed(data)
fetch_list = graph.out_nodes.values()
program = graph.compiled_graph if graph.compiled_graph else graph.program
results = self.exe.run(program,
scope=scope,
fetch_list=fetch_list,
feed=feed)
return results
def get_executor(graph, place):
if isinstance(graph, ImitationGraph):
return ImitationGraphExecutor(place)
if isinstance(graph, IRGraph):
return IRGraphExecutor(place)
python/paddle/fluid/contrib/slim/graph/graph_wrapper.py 0 → 100644
浏览文件 @ b3f5876e
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections import OrderedDict
from .... import io
from .... import compiler
from ....framework import Program
from ....framework import program_guard
from ....framework import Parameter
from ....framework import Variable
from ....executor import Executor
import copy
from collections import Iterable
from ....io import save_inference_model, load_inference_model, save_persistables
import numpy as np
import pickle
import os
__all__ = ['GraphWrapper', 'VarWrapper', 'OpWrapper']
OPTIMIZER_OPS = [
'momentum',
'lars_momentum',
'adagrad',
'adam',
'adamax',
'decayed_adagrad',
'adadelta',
'rmsprop',
]
class VarWrapper(object):
def __init__(self, var, graph):
assert isinstance(var, Variable)
assert isinstance(graph, GraphWrapper)
self._var = var
self._graph = graph
def __eq__(self, v):
"""
Overwrite this function for ...in... syntax in python.
"""
return self._var.name == v._var.name
def name(self):
"""
Get the name of the variable.
"""
return self._var.name
def shape(self):
"""
Get the shape of the varibale.
"""
return self._var.shape
def set_shape(self, shape):
"""
Set the shape of the variable.
"""
self._var.desc.set_shape(shape)
def inputs(self):
"""
Get all the operators that use this variable as output.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_inputs():
ops.append(op)
return ops
def outputs(self):
"""
Get all the operators that use this variable as input.
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for op in self._graph.ops():
if self in op.all_outputs():
ops.append(op)
return ops
class OpWrapper(object):
def __init__(self, op, graph):
assert isinstance(graph, GraphWrapper)
self._op = op
self._graph = graph
def __eq__(self, op):
"""
Overwrite this function for ...in... syntax in python.
"""
return self.idx() == op.idx()
def all_inputs(self):
"""
Get all the input variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.input_arg_names
]
def all_outputs(self):
"""
Get all the output variables of this operator.
"""
return [
self._graph.var(var_name) for var_name in self._op.output_arg_names
]
def idx(self):
"""
Get the id of this operator.
"""
return self._op.idx
def type(self):
"""
Get the type of this operator.
"""
return self._op.type
def is_bwd_op(self):
"""
Whether this operator is backward op.
"""
return self.type().endswith('_grad')
def is_opt_op(self):
"""
Whether this operator is optimizer op.
"""
return self.type() in OPTIMIZER_OPS
def inputs(self, name):
"""
Get all the varibales by the input name.
"""
return [self._graph.var(var_name) for var_name in self._op.input(name)]
def outputs(self, name):
"""
Get all the varibales by the output name.
"""
return [self._graph.var(var_name) for var_name in self._op.output(name)]
def set_attr(self, key, value):
"""
Set the value of attribute by attribute's name.
Args:
key(str): the attribute name.
value(bool|int|str|float|list): the value of the attribute.
"""
self._op._set_attr(key, value)
def attr(self, name):
"""
Get the attribute by name.
Args:
name(str): the attribute name.
Returns:
bool|int|str|float|list: The attribute value. The return value
can be any valid attribute type.
"""
return self._op.attr(name)
class GraphWrapper(object):
"""
It is a wrapper of paddle.fluid.framework.IrGraph with some special functions
for paddle slim framework.
"""
def __init__(self, program=None, in_nodes=[], out_nodes=[]):
"""
Args:
program(framework.Program): A program with
in_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
out_nodes(dict): A dict to indicate the input nodes of the graph.
The key is user-defined and human-readable name.
The value is the name of Variable.
"""
super(GraphWrapper, self).__init__()
self.program = Program() if program is None else program
self.compiled_graph = None
self.in_nodes = OrderedDict(in_nodes)
self.out_nodes = OrderedDict(out_nodes)
self._attrs = OrderedDict()
def all_parameters(self):
"""
Get all the parameters in this graph.
Returns:
list<VarWrapper>: A list of VarWrapper instances.
"""
params = []
for block in self.program.blocks:
for param in block.all_parameters():
params.append(VarWrapper(param, self))
return params
def is_parameter(self, var):
"""
Whether the given variable is parameter.
Args:
var(VarWrapper): The given varibale.
"""
return isinstance(var._var, Parameter)
def is_persistable(self, var):
"""
Whether the given variable is persistable.
Args:
var(VarWrapper): The given varibale.
"""
return var._var.persistable
def compile(self, for_parallel=True, for_test=False):
"""
Compile the program in this wrapper to framework.CompiledProgram for next running.
This function must be called if the program is modified.
Args:
for_parallel(bool): Whether the program to run in data parallel way. default: True.
for_test(bool): Whether the compiled program is used for test.
"""
target = self.program
if for_test:
loss = None
else:
loss = self.out_nodes['loss']
if for_parallel:
# disable memory optimize for stable training
build_strategy = compiler.BuildStrategy()
build_strategy.enable_inplace = False
build_strategy.memory_optimize = False
self.compiled_graph = compiler.CompiledProgram(
target).with_data_parallel(
loss_name=loss, build_strategy=build_strategy)
else:
self.compiled_graph = compiler.CompiledProgram(target)
def ops(self):
"""
Return all operator nodes included in the graph as a set.
"""
ops = []
for block in self.program.blocks:
for op in block.ops:
ops.append(OpWrapper(op, self))
return ops
def vars(self):
"""
Get all the variables.
"""
return [VarWrapper(var, self) for var in self.program.list_vars()]
def var(self, name):
"""
Get the variable by variable name.
"""
return VarWrapper(self.program.global_block().var(name), self)
def clone(self, for_test=False):
"""
Clone a new graph from current graph.
Returns:
(GraphWrapper): The wrapper of a new graph.
"""
return GraphWrapper(
self.program.clone(for_test),
copy.deepcopy(self.in_nodes), copy.deepcopy(self.out_nodes))
def merge(self, graph):
"""
Merge a graph into current graph.
Args:
graph(GraphWrapper): The graph to be merged by current graph.
"""
for var in graph.program.list_vars():
self.program.global_block()._clone_variable(var)
# TODO: parameters should be cloned
for op in graph.ops():
op = op._op
inputs = {}
outputs = {}
attrs = {}
for input_name in op.input_names:
inputs[input_name] = [
self.var(in_var_name)
for in_var_name in op.inputs(input_name)
]
for output_name in op.output_names:
outputs[output_name] = [
self.var(out_var_name)
for out_var_name in op.output(output_name)
]
for attr_name in op.attr_names:
attrs[attr_name] = op.attr(attr_name)
self.program.global_block().append_op(
type=op.type, inputs=inputs, outputs=outputs, attrs=attrs)
def program(self):
"""
Get the program in current wrapper.
"""
return self.program
def pre_ops(self, op):
"""
Get all the previous operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for in_var in op.all_inputs():
if in_var in p.all_outputs():
ops.append(p)
return ops
def next_ops(self, op):
"""
Get all the next operators of target operator.
Args:
op(OpWrapper): Target operator..
Returns:
list<OpWrapper>: A list of operators.
"""
ops = []
for p in self.ops():
for out_var in op.all_outputs():
if out_var in p.all_inputs():
ops.append(p)
return ops
def get_param_by_op(self, op):
"""
Get the parameters used by target operator.
"""
assert isinstance(op, OpWrapper)
params = []
for var in op.all_inputs():
if isinstance(var._var, Parameter):
params.append(var)
assert len(params) > 0
return params
def numel_params(self):
"""
Get the number of elements in all parameters.
"""
ret = 0
for param in self.all_parameters():
ret += np.product(param.shape())
return ret
def get_optimize_graph(self, optimizer, place, scope, no_grad_var_names=[]):
"""
Get a new graph for training by appending some backward operators and optimization operators.
Args:
optimizer: The optimzier used to generate training graph.
place: The place to run the graph.
scope: The scope used to run the graph. Some new variable will be added into this scope.
no_grad_var_names(list<str>): Names of variables that should be ignored while computing gradients. default: [].
Returns:
(GraphWrapper): The wrapper of new graph with backward ops and optimization ops.
"""
graph = self.clone()
startup_program = Program()
with program_guard(
main_program=graph.program, startup_program=startup_program):
target_name = None
if 'loss' in graph.out_nodes:
target_name = graph.out_nodes['loss']
elif 'cost' in graph.out_nodes:
target_name = graph.out_nodes['cost']
target = graph.var(target_name)._var
optimizer.minimize(target, no_grad_set=no_grad_var_names)
exe = Executor(place)
exe.run(program=startup_program, scope=scope)
return graph
def flops(self, only_conv=False):
"""
Get the flops of current graph.
Args:
only_conv: Only calculating the conv layers. default: False.
Returns:
int: The flops of current graph.
"""
flops = 0
for op in self.ops():
if op.type() in ['conv2d', 'depthwise_conv2d']:
filter_shape = op.inputs("Filter")[0].shape()
input_shape = op.inputs("Input")[0].shape()
output_shape = op.outputs("Output")[0].shape()
c_out, c_in, k_h, k_w = filter_shape
_, _, h_out, w_out = output_shape
groups = op.attr("groups")
kernel_ops = k_h * k_w * (c_in / groups)
if len(op.inputs("Bias")) > 0:
with_bias = 1
else:
with_bias = 0
flops += 2 * h_out * w_out * c_out * (kernel_ops + with_bias)
elif op.type() == 'pool2d' and not only_conv:
input_shape = op.inputs("X")[0].shape()
output_shape = op.outputs("Out")[0].shape()
_, c_out, h_out, w_out = output_shape
k_size = op.attr("ksize")
flops += h_out * w_out * c_out * (k_size[0]**2)
elif op.type() == 'mul' and not only_conv:
x_shape = list(op.inputs("X")[0].shape())
y_shape = op.inputs("Y")[0].shape()
if x_shape[0] == -1:
x_shape[0] = 1
flops += 2 * x_shape[0] * x_shape[1] * y_shape[1]
elif op.type() in ['relu', 'sigmoid', 'batch_norm'
] and not only_conv:
input_shape = list(op.inputs("X")[0].shape())
if input_shape[0] == -1:
input_shape[0] = 1
flops += np.product(input_shape)
return flops
def save_persistables(self, path, exe):
"""
Save all the persistable variables into file.
Args:
path(str): The path to save the persistables.
exe(framework.Executor): The executor used to save the persistables.
"""
io.save_persistables(exe.exe, path, main_program=self.program)
def load_persistables(self, path, exe):
"""
Load the persistable variables from file.
Args:
path(str): The path to load the persistables.
exe(framework.Executor): The executor used to load the persistables.
"""
def if_exist(var):
return os.path.exists(os.path.join(path, var.name))
io.load_vars(
exe.exe, path, main_program=self.program, predicate=if_exist)
def update_param_shape(self, scope):
"""
Update the shape of parameters in the graph according to tensors in scope.
It is used after loading pruned parameters from file.
"""
for param in self.all_parameters():
tensor_shape = np.array(scope.find_var(param.name()).get_tensor(
)).shape
param.set_shape(tensor_shape)
def infer_shape(self):
"""
Update the groups of convolution layer according to current filters.
It is used after loading pruned parameters from file.
"""
for op in self.ops():
if op.type() != 'conditional_block':
op._op.desc.infer_shape(op._op.block.desc)
def update_groups_of_conv(self):
for op in self.ops():
if op.type() == 'depthwise_conv2d':
op.set_attr('groups', op.inputs('Filter')[0].shape()[0])
python/paddle/fluid/contrib/slim/prune/pruner.py
浏览文件 @ b3f5876e
......@@ -13,9 +13,10 @@
# limitations under the License.
import numpy as np
import collections
from .... import layers
__all__ = ['Pruner', 'MagnitudePruner', 'RatioPruner']
__all__ = ['Pruner', 'StructurePruner']
class Pruner(object):
......@@ -30,54 +31,77 @@ class Pruner(object):
pass
class MagnitudePruner(Pruner):
class StructurePruner(Pruner):
"""
Pruner used to pruning a parameter by threshold.
Pruner used to pruning parameters by groups.
"""
def __init__(self, threshold):
self.threshold = threshold
def prune(self, param, threshold=None):
if threshold is None:
thres = layers.fill_constant(
shape=[1], dtype='float32', value=self.threshold)
else:
thres = threshold
zeros_mask = layers.less_than(x=param, y=thres)
return zeros_mask
class RatioPruner(Pruner):
"""
Pruner used to pruning a parameter by ratio.
"""
def __init__(self, pruning_axis, criterions):
"""
Args:
pruning_axis(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the axis to be used. Given a parameter
with shape [3, 4], the result of pruning 50% on aixs 1
is a parameter with shape [3, 2].
criterions(dict): The key is the name of parameter to be pruned,
'*' means all the parameters.
The value is the criterion used to sort groups for pruning.
It only supports 'l1_norm' currently.
"""
self.pruning_axis = pruning_axis
self.criterions = criterions
def __init__(self, ratios=None):
def cal_pruned_idx(self, name, param, ratio, axis=None):
"""
Calculate the index to be pruned on axis by given pruning ratio.
Args:
ratios: dict with pair (paramer_name, pruned_ratio).
name(str): The name of parameter to be pruned.
param(np.array): The data of parameter to be pruned.
ratio(float): The ratio to be pruned.
axis(int): The axis to be used for pruning given parameter.
If it is None, the value in self.pruning_axis will be used.
default: None.
Returns:
list<int>: The indexes to be pruned on axis.
"""
self.ratios = ratios
criterion = self.criterions[
name] if name in self.criterions else self.criterions['*']
if axis is None:
assert self.pruning_axis is not None, "pruning_axis should set if axis is None."
axis = self.pruning_axis[
name] if name in self.pruning_axis else self.pruning_axis['*']
prune_num = int(round(param.shape[axis] * ratio))
reduce_dims = [i for i in range(len(param.shape)) if i != axis]
if criterion == 'l1_norm':
criterions = np.sum(np.abs(param), axis=tuple(reduce_dims))
pruned_idx = criterions.argsort()[:prune_num]
return pruned_idx
def prune(self, param, ratio=None):
def prune_tensor(self, tensor, pruned_idx, pruned_axis, lazy=False):
"""
Pruning a array by indexes on given axis.
Args:
ratio: `ratio=40%` means pruning (1 - 40%) weights to zero.
tensor(numpy.array): The target array to be pruned.
pruned_idx(list<int>): The indexes to be pruned.
pruned_axis(int): The axis of given array to be pruned on.
lazy(bool): True means setting the pruned elements to zero.
False means remove the pruned elements from memory.
default: False.
Returns:
numpy.array: The pruned array.
"""
if ratio is None:
rat = self.ratios[
param.name] if param.name in self.ratios else self.ratios['*']
else:
rat = ratio
if rat < 1.0:
k = max(int(rat * np.prod(param.shape)), 1)
param_vec = layers.reshape(x=param, shape=[1, -1])
param_topk, _ = layers.topk(param_vec, k=k)
threshold = layers.slice(
param_topk, axes=[1], starts=[-1], ends=[k])
threshold = layers.reshape(x=threshold, shape=[1])
zeros_mask = layers.less_than(x=param, y=threshold)
mask = np.zeros(tensor.shape[pruned_axis], dtype=bool)
mask[pruned_idx] = True
def func(data):
return data[~mask]
def lazy_func(data):
data[mask] = 0
return data
if lazy:
return np.apply_along_axis(lazy_func, pruned_axis, tensor)
else:
zeros_mask = layers.ones(param.shape)
return zeros_mask
return np.apply_along_axis(func, pruned_axis, tensor)
python/paddle/fluid/contrib/slim/tests/configs/config.yaml 已删除 100644 → 0
浏览文件 @ 86511f51
version: 1.0
include: ["./configs/pruners.yaml", "./configs/pruners_0.yaml"]
pruners:
pruner_1:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.3
'conv1_2.w': 0.4
'*': 0.9
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
strategies:
strategy_1:
class: 'SensitivePruneStrategy'
pruner: 'pruner_2'
start_epoch: 0
end_epoch: 10
delta_rate: 0.20
acc_loss_threshold: 0.2
sensitivities:
'conv1_1.w': 0.4
compress_pass:
class: 'CompressPass'
epoch: 100
strategies:
- strategy_1
python/paddle/fluid/contrib/slim/tests/configs/filter_pruning.yaml 0 → 100644
浏览文件 @ b3f5876e
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 0
delta_rate: 0.1
target_ratio: 0.3
num_steps: 1
eval_rate: 0.5
pruned_params: '.*_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 120
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/configs/pruners.yaml 已删除 100644 → 0
浏览文件 @ 86511f51
version: 1.0
pruners:
pruner_2:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/tests/configs/pruners_0.yaml 已删除 100644 → 0
浏览文件 @ 86511f51
version: 1.0
pruners:
pruner_3:
class: 'RatioPruner'
ratios:
'conv1_1.w': 0.5
'conv1_2.w': 0.2
'*': 0.7
group_dims:
'*': [1, 2, 3]
criterions:
'*': 'l1-norm'
python/paddle/fluid/contrib/slim/graph/graph_pass.py → python/paddle/fluid/contrib/slim/tests/filter_pruning/__init__.py
浏览文件 @ b3f5876e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,32 +11,3 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
__all__ = ['GraphPass', 'PruneParameterPass']
class GraphPass(object):
"""
Base class for all graph pass.
"""
def __init__(self):
pass
def apply(self, graph):
pass
class PruneParameterPass(GraphPass):
"""
Generate a graph for pruning parameters from target graph.
"""
def __init__(self, pruned_params, thresholds):
super(PruneParameterPass, self).__init__()
self.pruned_params = pruned_params
self.thresholds = thresholds
self.default_threshold = thresholds['*']
def apply(self, graph):
pass
python/paddle/fluid/contrib/slim/tests/filter_pruning/compress.yaml 0 → 100644
浏览文件 @ b3f5876e
#start_epoch: The 'on_epoch_begin' function will be called in start_epoch. default: 0.
#end_epoch: The 'on_epoch_end' function will be called in end_epoch. default: 10.
#delta_rate: The delta used to generate ratios when calculating sensitivities.
#target_ratio: The flops ratio to be pruned from current model.
#metric_name: The metric used to evaluate the model.
#pruned_params: The pattern str to match the parameter names to be pruned.
#sensitivities_file: The sensitivities file.
#num_steps: The number of pruning steps.
#eval_rate: The rate of sampled data used to calculate sensitivities.
version: 1.0
pruners:
pruner_1:
class: 'StructurePruner'
pruning_axis:
'*': 0
criterions:
'*': 'l1_norm'
strategies:
sensitive_pruning_strategy:
class: 'SensitivePruneStrategy'
pruner: 'pruner_1'
start_epoch: 1
delta_rate: 0.2
target_ratio: 0.08
num_steps: 1
eval_rate: 0.5
pruned_params: 'conv6_sep_weights'
sensitivities_file: 'mobilenet_acc_top1_sensitive.data'
metric_name: 'acc_top1'
compressor:
epoch: 2
checkpoint_path: './checkpoints/'
strategies:
- sensitive_pruning_strategy
python/paddle/fluid/contrib/slim/tests/filter_pruning/mobilenet.py 0 → 100644
浏览文件 @ b3f5876e
# Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNet']
train_parameters = {
"input_size": [3, 224, 224],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class MobileNet():
def __init__(self):
self.params = train_parameters
def net(self, input, class_dim=1000, scale=1.0):
# conv1: 112x112
input = self.conv_bn_layer(
input,
filter_size=3,
channels=3,
num_filters=int(32 * scale),
stride=2,
padding=1,
name="conv1")
# 56x56
input = self.depthwise_separable(
input,
num_filters1=32,
num_filters2=64,
num_groups=32,
stride=1,
scale=scale,
name="conv2_1")
input = self.depthwise_separable(
input,
num_filters1=64,
num_filters2=128,
num_groups=64,
stride=2,
scale=scale,
name="conv2_2")
# 28x28
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=128,
num_groups=128,
stride=1,
scale=scale,
name="conv3_1")
input = self.depthwise_separable(
input,
num_filters1=128,
num_filters2=256,
num_groups=128,
stride=2,
scale=scale,
name="conv3_2")
# 14x14
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=256,
num_groups=256,
stride=1,
scale=scale,
name="conv4_1")
input = self.depthwise_separable(
input,
num_filters1=256,
num_filters2=512,
num_groups=256,
stride=2,
scale=scale,
name="conv4_2")
# 14x14
for i in range(5):
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=512,
num_groups=512,
stride=1,
scale=scale,
name="conv5" + "_" + str(i + 1))
# 7x7
input = self.depthwise_separable(
input,
num_filters1=512,
num_filters2=1024,
num_groups=512,
stride=2,
scale=scale,
name="conv5_6")
input = self.depthwise_separable(
input,
num_filters1=1024,
num_filters2=1024,
num_groups=1024,
stride=1,
scale=scale,
name="conv6")
input = fluid.layers.pool2d(
input=input,
pool_size=0,
pool_stride=1,
pool_type='avg',
global_pooling=True)
output = fluid.layers.fc(input=input,
size=class_dim,
act='softmax',
param_attr=ParamAttr(
initializer=MSRA(), name="fc7_weights"),
bias_attr=ParamAttr(name="fc7_offset"))
return output
def conv_bn_layer(self,
input,
filter_size,
num_filters,
stride,
padding,
channels=None,
num_groups=1,
act='relu',
use_cudnn=True,
name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=padding,
groups=num_groups,
act=None,
use_cudnn=use_cudnn,
param_attr=ParamAttr(
initializer=MSRA(), name=name + "_weights"),
bias_attr=False)
bn_name = name + "_bn"
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + "_scale"),
bias_attr=ParamAttr(name=bn_name + "_offset"),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def depthwise_separable(self,
input,
num_filters1,
num_filters2,
num_groups,
stride,
scale,
name=None):
depthwise_conv = self.conv_bn_layer(
input=input,
filter_size=3,
num_filters=int(num_filters1 * scale),
stride=stride,
padding=1,
num_groups=int(num_groups * scale),
use_cudnn=False,
name=name + "_dw")
pointwise_conv = self.conv_bn_layer(
input=depthwise_conv,
filter_size=1,
num_filters=int(num_filters2 * scale),
stride=1,
padding=0,
name=name + "_sep")
return pointwise_conv
python/paddle/fluid/contrib/slim/tests/test_factory.py
浏览文件 @ b3f5876e
......@@ -12,29 +12,25 @@
# See the License for the specific language governing permissions and
# limitations under the License.
from paddle.fluid.contrib.slim import ConfigFactory
from paddle.fluid.contrib.slim.core import ConfigFactory
import unittest
class TestFactory(unittest.TestCase):
def test_parse(self):
factory = ConfigFactory('./configs/config.yaml')
def test_parse_pruning(self):
factory = ConfigFactory('./configs/filter_pruning.yaml')
pruner = factory.instance('pruner_1')
self.assertEquals(pruner.ratios['conv1_1.w'], 0.3)
pruner_1 = factory.instance('pruner_1')
self.assertEquals(pruner_1.pruning_axis['*'], 0)
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
pruner = factory.instance('pruner_2')
self.assertEquals(pruner.ratios['*'], 0.7)
strategy = factory.instance('sensitive_pruning_strategy')
pruner_1 = strategy.pruner
self.assertEquals(pruner_1.criterions['*'], 'l1_norm')
strategy = factory.instance('strategy_1')
pruner = strategy.pruner
self.assertEquals(pruner.ratios['*'], 0.7)
compress_pass = factory.get_compress_pass()
self.assertEquals(compress_pass.epoch, 100)
strategy = compress_pass.strategies[0]
self.assertEquals(strategy.delta_rate, 0.2)
self.assertEquals(strategy.start_epoch, 0)
self.assertEquals(strategy.sensitivities_file,
'mobilenet_acc_top1_sensitive.data')
if __name__ == '__main__':
......
python/paddle/fluid/contrib/slim/tests/test_filter_pruning.py 0 → 100644
浏览文件 @ b3f5876e
# copyright (c) 2019 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
import paddle
import unittest
import paddle.fluid as fluid
from filter_pruning.mobilenet import MobileNet
from paddle.fluid.contrib.slim.core import Compressor
from paddle.fluid.contrib.slim.graph import GraphWrapper
class TestFilterPruning(unittest.TestCase):
def test_compression(self):
"""
Model: mobilenet_v1
data: mnist
step1: Training one epoch
step2: pruning flops
step3: fine-tune one epoch
step4: check top1_acc.
"""
if not fluid.core.is_compiled_with_cuda():
return
class_dim = 10
image_shape = [1, 28, 28]
image = fluid.layers.data(
name='image', shape=image_shape, dtype='float32')
image.stop_gradient = False
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
out = MobileNet().net(input=image, class_dim=class_dim)
acc_top1 = fluid.layers.accuracy(input=out, label=label, k=1)
acc_top5 = fluid.layers.accuracy(input=out, label=label, k=5)
val_program = fluid.default_main_program().clone(for_test=False)
cost = fluid.layers.cross_entropy(input=out, label=label)
avg_cost = fluid.layers.mean(x=cost)
optimizer = fluid.optimizer.Momentum(
momentum=0.9,
learning_rate=0.01,
regularization=fluid.regularizer.L2Decay(4e-5))
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
exe.run(fluid.default_startup_program())
val_reader = paddle.batch(paddle.dataset.mnist.test(), batch_size=128)
val_feed_list = [('img', image.name), ('label', label.name)]
val_fetch_list = [('acc_top1', acc_top1.name), ('acc_top5',
acc_top5.name)]
train_reader = paddle.batch(
paddle.dataset.mnist.train(), batch_size=128)
train_feed_list = [('img', image.name), ('label', label.name)]
train_fetch_list = [('loss', avg_cost.name)]
com_pass = Compressor(
place,
fluid.global_scope(),
fluid.default_main_program(),
train_reader=train_reader,
train_feed_list=train_feed_list,
train_fetch_list=train_fetch_list,
eval_program=val_program,
eval_reader=val_reader,
eval_feed_list=val_feed_list,
eval_fetch_list=val_fetch_list,
train_optimizer=optimizer)
com_pass.config('./filter_pruning/compress.yaml')
eval_graph = com_pass.run()
self.assertTrue(
abs((com_pass.context.eval_results['acc_top1'][-1] - 0.969) / 0.969)
< 0.02)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/slim/tests/test_graph_wrapper.py 0 → 100644
浏览文件 @ b3f5876e
# copyright (c) 2019 paddlepaddle authors. all rights reserved.
#
# licensed under the apache license, version 2.0 (the "license");
# you may not use this file except in compliance with the license.
# you may obtain a copy of the license at
#
# http://www.apache.org/licenses/license-2.0
#
# unless required by applicable law or agreed to in writing, software
# distributed under the license is distributed on an "as is" basis,
# without warranties or conditions of any kind, either express or implied.
# see the license for the specific language governing permissions and
# limitations under the license.
from __future__ import print_function
import unittest
import paddle.fluid as fluid
import six
import numpy as np
from paddle.fluid.contrib.slim.graph import GraphWrapper
from paddle.fluid import core
def residual_block(num):
def conv_bn_layer(input,
ch_out,
filter_size,
stride,
padding,
act='relu',
bias_attr=False):
tmp = fluid.layers.conv2d(
input=input,
filter_size=filter_size,
num_filters=ch_out,
stride=stride,
padding=padding,
act=None,
bias_attr=bias_attr)
return fluid.layers.batch_norm(input=tmp, act=act)
data = fluid.layers.data(name='image', shape=[1, 8, 8], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
data.stop_gradinet = False
hidden = data
for _ in six.moves.xrange(num):
conv = conv_bn_layer(hidden, 16, 3, 1, 1, act=None, bias_attr=True)
short = conv_bn_layer(hidden, 16, 1, 1, 0, act=None)
hidden = fluid.layers.elementwise_add(x=conv, y=short, act='relu')
fc = fluid.layers.fc(input=hidden, size=10)
loss = fluid.layers.cross_entropy(input=fc, label=label)
loss = fluid.layers.mean(loss)
return data, label, loss
class TestGraphWrapper(unittest.TestCase):
def build_program(self):
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
main = fluid.Program()
startup = fluid.Program()
with fluid.program_guard(main, startup):
image, label, self.loss = residual_block(2)
eval_program = main.clone()
opt = fluid.optimizer.SGD(learning_rate=0.001)
opt.minimize(self.loss)
self.scope = core.Scope()
exe = fluid.Executor(place)
exe.run(startup, scope=self.scope)
self.eval_graph = GraphWrapper(
program=eval_program,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
self.train_graph = GraphWrapper(
program=main,
in_nodes={'image': image.name,
'label': label.name},
out_nodes={'loss': self.loss.name})
def test_all_parameters(self):
self.build_program()
self.assertEquals(len(self.train_graph.all_parameters()), 24)
def test_all_vars(self):
self.build_program()
self.assertEquals(len(self.train_graph.vars()), 90)
def test_numel_params(self):
self.build_program()
self.assertEquals(self.train_graph.numel_params(), 13258)
def test_compile(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
exe = fluid.Executor(place)
self.train_graph.compile()
exe.run(self.train_graph.compiled_graph,
scope=self.scope,
feed={
'image':
np.random.randint(0, 40, [16, 1, 8, 8]).astype('float32'),
'label': np.random.randint(0, 10, [16, 1]).astype('int64')
})
def test_pre_and_next_ops(self):
self.build_program()
for op in self.train_graph.ops():
for next_op in self.train_graph.next_ops(op):
self.assertTrue(op in self.train_graph.pre_ops(next_op))
def test_get_optimize_graph(self):
self.build_program()
place = fluid.CPUPlace()
if fluid.core.is_compiled_with_cuda():
place = fluid.CUDAPlace(0)
opt = fluid.optimizer.SGD(learning_rate=0.001)
train_graph = self.eval_graph.get_optimize_graph(
opt, place, self.scope, no_grad_var_names=['image'])
self.assertEquals(len(self.train_graph.ops()), len(train_graph.ops()))
exe = fluid.Executor(place)
train_graph.compile()
image = np.random.randint(0, 225, [16, 1, 8, 8]).astype('float32')
label = np.random.randint(0, 10, [16, 1]).astype('int64')
exe.run(train_graph.compiled_graph,
scope=self.scope,
feed={'image': image,
'label': label})
def test_flops(self):
self.build_program()
self.assertEquals(self.train_graph.flops(), 354624)
if __name__ == '__main__':
unittest.main()
python/paddle/fluid/contrib/tests/test_calibration.py
浏览文件 @ b3f5876e
......@@ -290,7 +290,7 @@ class TestCalibrationForResnet50(unittest.TestCase):
self.model, self.infer_iterations)
(int8_throughput, int8_latency,
int8_acc1) = self.run_program("calibration_out")
delta_value = np.abs(fp32_acc1 - int8_acc1)
delta_value = fp32_acc1 - int8_acc1
self.assertLess(delta_value, 0.01)
print(
"FP32 {0}: batch_size {1}, throughput {2} images/second, latency {3} second, accuracy {4}".
......
python/paddle/fluid/layers/nn.py
浏览文件 @ b3f5876e
......@@ -9768,7 +9768,7 @@ def affine_channel(x,
'Bias': bias},
attrs={"data_layout": data_layout},
outputs={"Out": out})
return helper.append_activation(pre_activation)
return helper.append_activation(out)
def similarity_focus(input, axis, indexes, name=None):
......
python/paddle/fluid/layers/tensor.py
浏览文件 @ b3f5876e
......@@ -25,10 +25,26 @@ from .layer_function_generator import templatedoc
import numpy
__all__ = [
'create_tensor', 'create_parameter', 'create_global_var', 'cast',
'tensor_array_to_tensor', 'concat', 'sums', 'assign',
'fill_constant_batch_size_like', 'fill_constant', 'argmin', 'argmax',
'argsort', 'ones', 'zeros', 'reverse', 'has_inf', 'has_nan', 'isfinite'
'create_tensor',
'create_parameter',
'create_global_var',
'cast',
'tensor_array_to_tensor',
'concat',
'sums',
'assign',
'fill_constant_batch_size_like',
'fill_constant',
'argmin',
'argmax',
'argsort',
'ones',
'zeros',
'reverse',
'has_inf',
'has_nan',
'isfinite',
'range',
]
......@@ -764,3 +780,50 @@ def isfinite(x):
out = helper.create_variable_for_type_inference(dtype=x.dtype)
helper.append_op(type="isfinite", inputs={"X": x}, outputs={"Out": out})
return out
def range(start, end, step, dtype):
"""
Return evenly spaced values within a given interval.
Values are generated within the half-open interval [start, stop) (in other words,
the interval including start but excluding stop).
args:
start(int|float|Variable): Start of interval. The interval includes this value.
end(int|float|Variable): End of interval. The interval does not include this
value, except in some cases where step is not an integer
and floating point round-off affects the length of out.
step(int|float|Variable): Spacing between values. For any output out, this is the
distance between two adjacent values, out[i+1] - out[i].
The default step size is 1.
dtype(string): 'float32'|'int32'|..., the data type of the output tensor.
returns:
Evenly spaced values within a given interval.
examples:
.. code-block:: python
data = fluid.layers.range(0, 10, 2, 'int32')
"""
helper = LayerHelper("range", **locals())
if not isinstance(start, Variable):
start = fill_constant([1], dtype, start)
if not isinstance(end, Variable):
end = fill_constant([1], dtype, end)
if not isinstance(step, Variable):
step = fill_constant([1], dtype, step)
out = helper.create_variable_for_type_inference(dtype=start.dtype)
helper.append_op(
type='range',
inputs={'Start': start,
'End': end,
'Step': step},
outputs={'Out': [out]})
return out
python/paddle/fluid/optimizer.py
浏览文件 @ b3f5876e
......@@ -70,6 +70,10 @@ class Optimizer(object):
# {accum_name : { paramter_name : accumulator_for_parameter, ...}, ...}
self._accumulators = defaultdict(lambda: dict())
self.helper = None
self._opti_name_list = []
def get_opti_var_name_list(self):
return self._opti_name_list
def _create_global_learning_rate(self):
lr = self._global_learning_rate()
......@@ -166,8 +170,13 @@ class Optimizer(object):
if shape == None:
shape = param.shape
assert isinstance(self.helper, LayerHelper)
var_name = param.name + "_" + name
var_name = unique_name.generate(var_name)
self._opti_name_list.append(var_name)
var = self.helper.create_global_variable(
name=unique_name.generate(name),
name=var_name,
persistable=True,
dtype=dtype or param.dtype,
type=param.type,
......
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @ b3f5876e
......@@ -105,7 +105,7 @@ if(WITH_DISTRIBUTE)
# set_tests_properties(test_dist_transformer PROPERTIES TIMEOUT 1000)
set_tests_properties(test_dist_ctr test_dist_mnist test_dist_mnist_batch_merge test_dist_save_load test_dist_se_resnext test_dist_simnet_bow test_dist_text_classification test_dist_train test_dist_word2vec PROPERTIES RUN_SERIAL TRUE)
endif(NOT APPLE)
py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
# py_test_modules(test_dist_transpiler MODULES test_dist_transpiler)
endif()
py_test_modules(test_parallel_executor_crf MODULES test_parallel_executor_crf SERIAL)
py_test_modules(test_parallel_executor_fetch_feed MODULES test_parallel_executor_fetch_feed SERIAL)
......@@ -118,8 +118,8 @@ if(NOT APPLE)
py_test_modules(test_image_classification_resnet MODULES test_image_classification_resnet SERIAL)
endif()
if(CMAKE_BUILD_TYPE STREQUAL "Debug")
# change the timeout from 600 to 1200, because in debug mode, this test need more time.
set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 1200)
# change the timeout from 600 to 2200, because in debug mode, this test need more time.
set_tests_properties(test_parallel_executor_seresnext PROPERTIES TIMEOUT 2200)
endif()
if (WITH_NGRAPH)
......
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @ b3f5876e
......@@ -1240,6 +1240,14 @@ class TestBook(unittest.TestCase):
print(str(program))
def test_range(self):
program = Program()
with program_guard(program):
layers.range(0, 10, 2, 'int32')
layers.range(0.1, 10.0, 0.2, 'float32')
print(str(program))
def test_spectral_norm(self):
program = Program()
with program_guard(program):
......
python/paddle/fluid/contrib/slim/graph/graph.py → python/paddle/fluid/tests/unittests/test_range.py
浏览文件 @ b3f5876e
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
......@@ -11,39 +11,60 @@
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from __future__ import print_function
import os
import subprocess
from ....framework import Program
from ....framework import Block
from .... import core
__all__ = ['Graph', 'ImitationGraph', 'IRGraph']
import unittest
import numpy as np
from op_test import OpTest
class TestRangeOp(OpTest):
def setUp(self):
self.op_type = "range"
self.init_config()
self.inputs = {
'Start': np.array([self.case[0]]).astype(self.dtype),
'End': np.array([self.case[1]]).astype(self.dtype),
'Step': np.array([self.case[2]]).astype(self.dtype)
}
self.outputs = {
'Out': np.arange(self.case[0], self.case[1],
self.case[2]).astype(self.dtype)
}
def init_config(self):
self.dtype = np.float32
self.case = (0, 1, 0.2)
def test_check_output(self):
self.check_output()
class TestFloatRangeOpCase0(TestRangeOp):
def init_config(self):
self.dtype = np.float32
self.case = (0, 5, 1)
class Graph(object):
"""
Base class for all graph.
"""
def __init__(self):
pass
class TestInt32RangeOpCase0(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (0, 5, 2)
def all_parameters(self):
"""
Return all the parameters in current graph.
"""
pass
class TestInt32RangeOpCase1(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (10, 1, -2)
class ImitationGraph(Graph):
def __init__(self, program=None):
super(ImitationGraph, self).__init__()
self.program = Program() if program is None else program
def all_parameters(self):
return self.program.global_block().all_parameters()
class TestInt32RangeOpCase2(TestRangeOp):
def init_config(self):
self.dtype = np.int32
self.case = (-1, -10, -2)
class IRGraph(Graph):
pass
if __name__ == "__main__":
unittest.main()
python/requirements.txt
浏览文件 @ b3f5876e
......@@ -12,3 +12,4 @@ six
funcsigs
pyyaml
decorator
prettytable
tools/manylinux1/Dockerfile.x64
浏览文件 @ b3f5876e
......@@ -52,7 +52,7 @@ RUN LD_LIBRARY_PATH=/opt/_internal/cpython-2.7.11-ucs4/lib:${LD_LIBRARY_PATH} /o
LD_LIBRARY_PATH=/opt/_internal/cpython-3.6.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.6.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python && \
LD_LIBRARY_PATH=/opt/_internal/cpython-3.7.0/lib/:${LD_LIBRARY_PATH} /opt/_internal/cpython-3.7.0/bin/pip3 install pre-commit 'ipython==5.3.0' opencv-python
RUN wget -O /opt/swig-2.0.12.tar.gz https://cytranet.dl.sourceforge.net/project/swig/swig/swig-2.0.12/swig-2.0.12.tar.gz && \
RUN wget -O /opt/swig-2.0.12.tar.gz https://sourceforge.net/projects/swig/files/swig/swig-2.0.12/swig-2.0.12.tar.gz/download && \
cd /opt && tar xzf swig-2.0.12.tar.gz && cd /opt/swig-2.0.12 && ./configure && make && make install && cd /opt && rm swig-2.0.12.tar.gz
CMD ["bash", "/paddle/paddle/scripts/docker/build.sh"]
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