Merge pull request #14 from PaddlePaddle/develop

merge to local

paddle/fluid/API.spec

@@ -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

+/* 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

+/* 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

+/* 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

@@ -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

@@ -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

-#   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

+#   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 .... import compiler
+from .... import io
+from .... import profiler
+from .... import scope_guard
+from ....data_feeder import DataFeeder
+from ..graph import *
+from .config import ConfigFactory
+import numpy as np
+from collections import Iterable
+import time
+import os
+import logging
+import sys
+import pickle
+import functools
+
+__all__ = ['Context', 'Compressor']
+
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+
+def cached_reader(reader, sampled_rate, cache_path, cached_id):
+    """
+    Sample partial data from reader and cache them into local file system.
+    Args:
+        reader: Iterative data source.
+        sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
+        cache_path(str): The path to cache the sampled data.
+        cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
+    """
+    np.random.seed(cached_id)
+    cache_path = os.path.join(cache_path, str(cached_id))
+    _logger.debug('read data from: {}'.format(cache_path))
+
+    def s_reader():
+        if os.path.isdir(cache_path):
+            for file_name in open(os.path.join(cache_path, "list")):
+                yield np.load(os.path.join(cache_path, file_name.strip()))
+        else:
+            os.makedirs(cache_path)
+            list_file = open(os.path.join(cache_path, "list"), 'w')
+            batch = 0
+            dtype = None
+            for data in reader():
+                if batch == 0 or (np.random.uniform() < sampled_rate):
+                    np.save(
+                        os.path.join(cache_path, 'batch' + str(batch)), data)
+                    list_file.write('batch' + str(batch) + '.npy\n')
+                    batch += 1
+                    yield data
+
+    return s_reader
+
+
+class Context(object):
+    """
+    The context in the process of compression.
+    """
+
+    def __init__(self,
+                 place,
+                 scope,
+                 train_graph=None,
+                 train_reader=None,
+                 eval_graph=None,
+                 eval_reader=None,
+                 teacher_graphs=None,
+                 train_optimizer=None,
+                 distiller_optimizer=None):
+        """
+        Args:
+            place: The device place where the compression job running.
+            scope: The scope used in compression job.
+            train_graph: The graph with loss as output node.
+            eval_graph: The graph used for evaluation.
+            eval_reader: The data reader used for evaluation.
+            teacher_graphs: The teacher graphs used in distillation strategies.
+            train_optimizer: The optimizer used to append backward ops and
+                             optimization ops into train_graph.
+            distiller_optimizer: The optimizer used by distillation strategies.
+        """
+        # The total number of epoches to be trained.
+        self.epoch = 0
+        # Current epoch
+        self.epoch_id = 0
+        # Current batch
+        self.batch_id = 0
+
+        self.k_v = {}
+
+        self.place = place
+        self.scope = scope
+        self.train_graph = train_graph
+        self.train_reader = train_reader
+        self.eval_graph = eval_graph
+        self.eval_reader = eval_reader
+        self.executor = None
+        self.teacher_graphs = teacher_graphs
+        self.train_optimizer = train_optimizer
+        self.distiller_optimizer = distiller_optimizer
+        self.optimize_graph = None
+        self.cache_path = './eval_cache'
+        self.eval_results = {}
+
+    def to_file(self, file_name):
+        """
+        Save the context into file.
+        """
+        data = {}
+        data['epoch_id'] = self.epoch_id
+        data['eval_results'] = self.eval_results
+        with open(file_name, 'wb') as context_file:
+            pickle.dump(data, context_file)
+
+    def from_file(self, file_name):
+        """
+        Load the context from file.
+        """
+        with open(file_name) as context_file:
+            if sys.version_info < (3, 0):
+                data = pickle.load(context_file)
+            else:
+                data = pickle.load(context_file, encoding='bytes')
+            self.epoch_id = data['epoch_id']
+            self.eval_results = data['eval_results']
+
+    def eval_converged(self, metric_name, delta=0.001):
+        """
+        Check whether the training has been converged.
+        Args:
+            metric_name(str): The metric used to check convergence.
+            delta(float): '(metric[k] - metric[k-1] / metric[k-1]) < delta'
+                          means that the training has been converged.
+        Returns:
+            bool: True means the training has been converged.
+        """
+        # TODO(wanghaoshuang@baidu.com): enhence this method.
+        if (metric_name not in self.eval_results
+            ) or len(self.eval_results[metric_name]) < 2:
+            return False
+        results = self.eval_results[metric_name][-2:]
+        _logger.info('Latest evaluations: {}'.format(results))
+        return abs(results[1] - results[0]) / results[0] < delta
+
+    def run_eval_graph(self, sampled_rate=None, cached_id=0):
+        """
+        Evaluate the current mode in context.
+        Args:
+            sampled_rate(float): The sampled rate used to sample partial data
+            for evaluation. None means using all data in eval_reader. default: None.
+            cached_id(int): The id of dataset sampled. Evaluations with same
+                            cached_id use the same sampled dataset. default: 0.
+        """
+        _logger.info('Running evaluation')
+        assert self.eval_graph is not None
+        assert self.eval_reader is not None
+        eval_graph = self.eval_graph.clone(for_test=True)
+
+        executor = SlimGraphExecutor(self.place)
+        results = []
+        batch_id = 0
+        s_time = time.time()
+        reader = self.eval_reader
+        if sampled_rate:
+            reader = cached_reader(reader, sampled_rate, self.cache_path,
+                                   cached_id)
+        for data in reader():
+            result = executor.run(eval_graph, self.scope, data=data)
+            result = [np.mean(r) for r in result]
+            results.append(result)
+            if batch_id % 20 == 0:
+                _logger.info("batch-{}; {}={}".format(
+                    batch_id, eval_graph.out_nodes.keys(), result))
+            batch_id += 1
+        result = np.mean(np.array(results), axis=0)
+        _logger.info("Final eval result: {}={}".format(
+            eval_graph.out_nodes.keys(), result))
+        if not isinstance(result, Iterable):
+            result = [result]
+        _logger.info('Finish evaluation')
+        return result, eval_graph.out_nodes.keys()
+
+    def put(self, key, value):
+        self.k_v[key] = value
+
+    def get(self, key):
+        return self.k_v.get(key)
+
+
+class Compressor(object):
+    """
+    The pass used to compress model.
+    """
+
+    def __init__(self,
+                 place,
+                 scope,
+                 train_program,
+                 train_reader=None,
+                 train_feed_list=None,
+                 train_fetch_list=None,
+                 eval_program=None,
+                 eval_reader=None,
+                 eval_feed_list=None,
+                 eval_fetch_list=None,
+                 teacher_programs=[],
+                 checkpoint_path='./checkpoints',
+                 train_optimizer=None,
+                 distiller_optimizer=None):
+        """
+        Args:
+            place(fluid.Place): The device place where the compression job running.
+            scope(fluid.core.Scope): The scope used to run graph.
+            train_program(Program): The main program to be compressed. It must have loss op.
+            train_reader: The data reader used for training.
+            train_feed_list(dict): A dict to indicate the input variable of the training program.
+                                   The key is user-defined and human-readable name.
+                                   The value is the name of Variable.
+            train_fetch_list(dict): A dict to indicate the output variable of the training program.
+                                   The key is user-defined and human-readable name.
+                                   The value is the name of Variable.
+            eval_program(Program): The program used for evaluation.
+            eval_reader: The data reader used for evaluation.
+            eval_feed_list(dict): A dict to indicate the input variable of the evaluation program.
+                                   The key is user-defined and human-readable name.
+                                   The value is the name of Variable.
+            eval_fetch_list(dict): A dict to indicate the output variable of the evaluation program.
+                                   The key is user-defined and human-readable name.
+                                   The value is the name of Variable.
+            teacher_programs: The teacher graphs used in distillation strategies.
+            train_optimizer: The optimizer used to append backward ops and
+                             optimization ops into train_graph.
+            distiller_optimizer: The optimizer used by distillation strategies. In distillation strategy,
+                                 this optimizer is used to minimize the combined loss of student-net and
+                                 teacher-net while train_optimizer is used to minimize loss of
+                                 student-net in fine-tune stage. 
+
+        """
+        assert isinstance(
+            train_feed_list, list
+        ), "train_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
+        assert isinstance(
+            eval_feed_list, list
+        ), "eval_feed_list should be a list of tuple, such as [('image', image.name), ('label', gt.name)]"
+        self.strategies = []
+        self.epoch = 0
+        self.place = CPUPlace() if place is None else place
+        self.scope = scope
+        self.train_graph = GraphWrapper(
+            train_program, in_nodes=train_feed_list, out_nodes=train_fetch_list)
+        self.eval_graph = GraphWrapper(
+            eval_program, in_nodes=eval_feed_list, out_nodes=eval_fetch_list)
+        self.train_reader = train_reader
+        self.eval_reader = eval_reader
+        self.teacher_graphs = []
+        for teacher in teacher_programs:
+            self.teacher_graphs.append(ImitationGraph(teacher, scope=scope))
+
+        self.checkpoint = None
+        self.checkpoint_path = checkpoint_path
+        self.eval_epoch = 1
+
+        self.train_optimizer = train_optimizer
+        self.distiller_optimizer = distiller_optimizer
+        self.init_model = None
+
+    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 config(self, config_file):
+        """
+        Configure the compress pass from file with yaml format.
+        Args:
+            config_file(str): The config file in local file system.
+        """
+        factory = ConfigFactory(config_file)
+        self.epoch = factory.compressor['epoch']
+        for strategy in factory.compressor['strategies']:
+            self._add_strategy(strategy)
+        if 'checkpoint_path' in factory.compressor:
+            self.checkpoint_path = factory.compressor['checkpoint_path']
+
+        if 'init_model' in factory.compressor:
+            self.init_model = factory.compressor['init_model']
+
+    def _init_model(self, context):
+        """
+        Load model that has been compressed. 
+        """
+        if self.init_model and os.path.exists(self.init_model):
+            exe = SlimGraphExecutor(context.place)
+            with scope_guard(context.scope):
+                context.train_graph.load_persistables(self.init_model, exe)
+            flops = context.eval_graph.flops()
+            conv_flops = context.eval_graph.flops(only_conv=True)
+            context.eval_graph.update_param_shape(context.scope)
+            context.eval_graph.update_groups_of_conv()
+            _logger.info("conv flops: -{}".format(1 - float(
+                context.eval_graph.flops(only_conv=True)) / conv_flops))
+            _logger.info("total flops: -{}".format(1 - float(
+                context.eval_graph.flops()) / flops))
+            context.train_graph.update_param_shape(context.scope)
+            context.train_graph.update_groups_of_conv()
+            context.train_graph.infer_shape()
+            _logger.info("Init model from: {}".format(self.init_model))
+
+    def _load_checkpoint(self, context):
+        """
+        Load checkpoints from file.
+        """
+        _logger.debug('_load_checkpoint')
+        strategies = self.strategies
+        if self.checkpoint_path:
+            if not os.path.exists(self.checkpoint_path):
+                _logger.warning("Checkpints path doesn't exist: [{}]".format(
+                    self.checkpoint_path))
+                return context, strategies
+            checkpoints = [
+                dir for dir in os.listdir(self.checkpoint_path)
+                if os.path.isdir(os.path.join(self.checkpoint_path, dir))
+            ]
+            _logger.debug('self.checkpoint_path: {}'.format(
+                self.checkpoint_path))
+            _logger.info('checkpoints: {}'.format(checkpoints))
+            if len(checkpoints) > 0:
+                latest = max([int(ck) for ck in checkpoints])
+                latest_ck_path = os.path.join(self.checkpoint_path, str(latest))
+
+                model_path = os.path.join(latest_ck_path, 'model')
+                context_path = os.path.join(latest_ck_path, 'context')
+                strategy_path = os.path.join(latest_ck_path, 'strategies')
+                if os.path.exists(context_path):
+                    context.from_file(context_path)
+                    context.epoch_id += 1
+                if os.path.exists(strategy_path):
+                    with open(strategy_path, 'rb') as strategy_file:
+                        if sys.version_info < (3, 0):
+                            strategies = pickle.load(strategy_file)
+                        else:
+                            strategies = pickle.load(
+                                strategy_file, encoding='bytes')
+
+                if os.path.exists(model_path):
+                    exe = SlimGraphExecutor(context.place)
+                    with scope_guard(context.scope):
+                        context.optimize_graph.load_persistables(model_path,
+                                                                 exe)
+                    context.optimize_graph.update_param_shape(context.scope)
+                    context.optimize_graph.update_groups_of_conv()
+                    context.eval_graph.update_param_shape(context.scope)
+                    context.eval_graph.update_groups_of_conv()
+                    _logger.info("Loaded params from: {}".format(model_path))
+        return context, strategies
+
+    def _save_checkpoint(self, context):
+        """
+        Save checkpoints to file.
+        """
+        if context.epoch_id % 1 == 0 and self.checkpoint_path:
+            checkpoint_path = os.path.join(self.checkpoint_path,
+                                           str(context.epoch_id))
+            model_path = os.path.join(checkpoint_path, 'model')
+            context_path = os.path.join(checkpoint_path, 'context')
+            strategy_path = os.path.join(checkpoint_path, 'strategies')
+            if not os.path.isdir(model_path):
+                os.makedirs(model_path)
+            exe = SlimGraphExecutor(context.place)
+            with scope_guard(context.scope):
+                context.optimize_graph.save_persistables(model_path, exe)
+            context.to_file(context_path)
+            with open(strategy_path, 'wb') as strategy_file:
+                pickle.dump(self.strategies, strategy_file)
+            _logger.info('Saved checkpoint to: {}'.format(checkpoint_path))
+
+    def _train_one_epoch(self, context):
+        """
+        Train one epoch.
+        """
+
+        executor = SlimGraphExecutor(self.place)
+
+        if context.optimize_graph.compiled_graph is None:
+            context.optimize_graph.compiled_graph = compiler.CompiledProgram(
+                context.optimize_graph.program).with_data_parallel(
+                    loss_name=context.optimize_graph.out_nodes['loss'])
+
+        for data in context.train_reader():
+            for strategy in self.strategies:
+                strategy.on_batch_begin(context)
+            results = executor.run(context.optimize_graph,
+                                   context.scope,
+                                   data=data)
+            results = [float(np.mean(result)) for result in results]
+            if context.batch_id % 20 == 0:
+                _logger.info("epoch:{}; batch_id:{}; {} = {}".format(
+                    context.epoch_id, context.batch_id,
+                    context.optimize_graph.out_nodes.keys(
+                    ), [round(r, 3) for r in results]))
+            for strategy in self.strategies:
+                strategy.on_batch_end(context)
+            context.batch_id += 1
+        context.batch_id = 0
+
+    def _eval(self, context):
+        """
+        Runing evaluation.
+        """
+        results, names = context.run_eval_graph()
+        for name, result in zip(names, results):
+            if name not in context.eval_results:
+                context.eval_results[name] = []
+            context.eval_results[name].append(result)
+
+    def run(self):
+        """
+        Execute compressiong pass.
+        """
+        context = Context(
+            place=self.place,
+            scope=self.scope,
+            train_graph=self.train_graph,
+            train_reader=self.train_reader,
+            eval_graph=self.eval_graph,
+            eval_reader=self.eval_reader,
+            teacher_graphs=self.teacher_graphs,
+            train_optimizer=self.train_optimizer,
+            distiller_optimizer=self.distiller_optimizer)
+        self.context = context
+        if self.teacher_graphs:
+            context.put('teachers', self.teacher_graphs)
+        self._init_model(context)
+        if not context.optimize_graph:
+            if context.train_optimizer:
+                context.train_optimizer._name = 'train_opt'
+                context.optimize_graph = context.train_graph.get_optimize_graph(
+                    context.train_optimizer, context.place, context.scope)
+            else:
+                context.optimize_graph = context.train_graph
+
+        context, self.strategies = self._load_checkpoint(context)
+
+        for strategy in self.strategies:
+            strategy.on_compression_begin(context)
+        start = context.epoch_id
+        self._eval(context)
+        for epoch in range(start, self.epoch):
+            context.epoch_id = epoch
+            for strategy in self.strategies:
+                strategy.on_epoch_begin(context)
+            self._train_one_epoch(context)
+            for strategy in self.strategies:
+                strategy.on_epoch_end(context)
+            if self.eval_epoch and epoch % self.eval_epoch == 0:
+                self._eval(context)
+            self._save_checkpoint(context)
+        for strategy in self.strategies:
+            strategy.on_compression_end(context)
+        return context.eval_graph

python/paddle/fluid/contrib/slim/core/config.py

@@ -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])
+                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)
-                        compress_pass.add_strategy(strategy)
+                            self.compressor['strategies'].append(strategy)
 
                 if key == 'include':
                     for config_file in key_values[key]:

python/paddle/fluid/contrib/slim/core/pass_builder.py

-#   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

@@ -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

-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

-# 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

@@ -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

@@ -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

+# 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/prune_strategy.py

@@ -13,54 +13,919 @@
 # limitations under the License.
 
 from ..core.strategy import Strategy
-from ....framework import Program, program_guard
+from ..graph import VarWrapper, OpWrapper, GraphWrapper
+from ....framework import Program, program_guard, Parameter
 from .... import layers
+import prettytable as pt
 import numpy as np
+from scipy.optimize import leastsq
+import copy
+import re
+import os
+import pickle
+import logging
+import sys
 
-__all__ = ['SensitivePruneStrategy', 'PruneStrategy']
+__all__ = ['SensitivePruneStrategy', 'UniformPruneStrategy']
 
+logging.basicConfig(format='%(asctime)s-%(levelname)s: %(message)s')
+_logger = logging.getLogger(__name__)
+_logger.setLevel(logging.INFO)
+
+
+class PruneStrategy(Strategy):
+    """
+    The base class of all pruning strategies.
+    """
 
-class SensitivePruneStrategy(Strategy):
     def __init__(self,
                  pruner=None,
                  start_epoch=0,
-                 end_epoch=10,
-                 delta_rate=0.20,
-                 acc_loss_threshold=0.2,
-                 sensitivities=None):
-        super(SensitivePruneStrategy, self).__init__(start_epoch, end_epoch)
+                 end_epoch=0,
+                 target_ratio=0.5,
+                 metric_name=None,
+                 pruned_params='conv.*_weights'):
+        """
+        Args:
+            pruner(slim.Pruner): The pruner used to prune the parameters.
+            start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
+            end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
+            target_ratio(float): The flops ratio to be pruned from current model.
+            metric_name(str): The metric used to evaluate the model.
+                         It should be one of keys in out_nodes of graph wrapper.
+            pruned_params(str): The pattern str to match the parameter names to be pruned.
+        """
+        super(PruneStrategy, self).__init__(start_epoch, end_epoch)
         self.pruner = pruner
-        self.delta_rate = delta_rate
-        self.acc_loss_threshold = acc_loss_threshold
-        self.sensitivities = sensitivities
+        self.target_ratio = target_ratio
+        self.metric_name = metric_name
+        self.pruned_params = pruned_params
+        self.pruned_list = []
+        self.backup = {}
+        self.param_shape_backup = {}
 
+    def _eval_graph(self, context, sampled_rate=None, cached_id=0):
+        """
+        Evaluate the current mode in context.
+        Args:
+            context(slim.core.Context): The context storing all information used to evaluate the current model.
+            sampled_rate(float): The sampled rate used to sample partial data for evaluation. None means using all data in eval_reader. default: None.
+            cached_id(int): The id of dataset sampled. Evaluations with same cached_id use the same sampled dataset. default: 0.
+        """
+        results, names = context.run_eval_graph(sampled_rate, cached_id)
+        metric = np.mean(results[list(names).index(self.metric_name)])
+        return metric
 
-class PruneStrategy(Strategy):
+    def _prune_filters_by_ratio(self,
+                                scope,
+                                params,
+                                ratio,
+                                place,
+                                lazy=False,
+                                only_graph=False):
+        """
+        Pruning filters by given ratio.
+        Args:
+            scope(fluid.core.Scope): The scope used to pruning filters.
+            params(list<VarWrapper>): A list of filter parameters.
+            ratio(float): The ratio to be pruned.
+            place(fluid.Place): The device place of filter parameters.
+            lazy(bool): True means setting the pruned elements to zero.
+                        False means cutting down the pruned elements.
+            only_graph(bool): True means only modifying the graph.
+                              False means modifying graph and variables in  scope.
+        """
+        if params[0].name() in self.pruned_list[0]:
+            return
+        param_t = scope.find_var(params[0].name()).get_tensor()
+        pruned_idx = self.pruner.cal_pruned_idx(
+            params[0].name(), np.array(param_t), ratio, axis=0)
+        for param in params:
+            assert isinstance(param, VarWrapper)
+            param_t = scope.find_var(param.name()).get_tensor()
+            if lazy:
+                self.backup[param.name()] = copy.deepcopy(np.array(param_t))
+            pruned_param = self.pruner.prune_tensor(
+                np.array(param_t), pruned_idx, pruned_axis=0, lazy=lazy)
+            if not only_graph:
+                param_t.set(pruned_param, place)
+            ori_shape = param.shape()
+            if param.name() not in self.param_shape_backup:
+                self.param_shape_backup[param.name()] = copy.deepcopy(
+                    param.shape())
+            new_shape = list(param.shape())
+            new_shape[0] = pruned_param.shape[0]
+            param.set_shape(new_shape)
+            _logger.debug(
+                '|----------------------------------------+----+------------------------------+------------------------------|'
+            )
+            _logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
+                str(param.name()), str(0), str(ori_shape), str(param.shape())))
+            self.pruned_list[0].append(param.name())
+        return pruned_idx
+
+    def _prune_parameter_by_idx(self,
+                                scope,
+                                params,
+                                pruned_idx,
+                                pruned_axis,
+                                place,
+                                lazy=False,
+                                only_graph=False):
+        """
+        Pruning parameters in given axis.
+        Args:
+            scope(fluid.core.Scope): The scope storing paramaters to be pruned.
+            params(VarWrapper): The parameter to be pruned.
+            pruned_idx(list): The index of elements to be pruned.
+            pruned_axis(int): The pruning axis.
+            place(fluid.Place): The device place of filter parameters.
+            lazy(bool): True means setting the pruned elements to zero.
+                        False means cutting down the pruned elements.
+            only_graph(bool): True means only modifying the graph.
+                              False means modifying graph and variables in  scope.
+        """
+        if params[0].name() in self.pruned_list[pruned_axis]:
+            return
+        for param in params:
+            assert isinstance(param, VarWrapper)
+            param_t = scope.find_var(param.name()).get_tensor()
+            if lazy:
+                self.backup[param.name()] = copy.deepcopy(np.array(param_t))
+            pruned_param = self.pruner.prune_tensor(
+                np.array(param_t), pruned_idx, pruned_axis, lazy=lazy)
+            if not only_graph:
+                param_t.set(pruned_param, place)
+            ori_shape = param.shape()
+            if param.name() not in self.param_shape_backup:
+                self.param_shape_backup[param.name()] = copy.deepcopy(
+                    param.shape())
+            new_shape = list(param.shape())
+            new_shape[pruned_axis] = pruned_param.shape[pruned_axis]
+            param.set_shape(new_shape)
+            _logger.debug(
+                '|----------------------------------------+----+------------------------------+------------------------------|'
+            )
+            _logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format(
+                str(param.name()),
+                str(pruned_axis), str(ori_shape), str(param.shape())))
+            self.pruned_list[pruned_axis].append(param.name())
+
+    def _forward_search_related_op(self, graph, param):
+        """
+        Forward search operators that will be affected by pruning of param.
+        Args:
+            graph(GraphWrapper): The graph to be searched.
+            param(VarWrapper): The current pruned parameter.
+        Returns:
+            list<OpWrapper>: A list of operators.
+        """
+        assert isinstance(param, VarWrapper)
+        visited = {}
+        for op in graph.ops():
+            visited[op.idx()] = False
+        stack = []
+        for op in graph.ops():
+            if (not op.is_bwd_op()) and (param in op.all_inputs()):
+                stack.append(op)
+        visit_path = []
+        while len(stack) > 0:
+            top_op = stack[len(stack) - 1]
+            if visited[top_op.idx()] == False:
+                visit_path.append(top_op)
+                visited[top_op.idx()] = True
+            next_ops = None
+            if top_op.type() == "conv2d" and param not in top_op.all_inputs():
+                next_ops = None
+            elif top_op.type() == "mul":
+                next_ops = None
+            else:
+                next_ops = self._get_next_unvisited_op(graph, visited, top_op)
+            if next_ops == None:
+                stack.pop()
+            else:
+                stack += next_ops
+        return visit_path
+
+    def _get_next_unvisited_op(self, graph, visited, top_op):
+        """
+        Get next unvisited adjacent operators of given operators.
+        Args:
+            graph(GraphWrapper): The graph used to search. 
+            visited(list): The ids of operators that has been visited.
+            top_op: The given operator.
+        Returns:
+            list<OpWrapper>: A list of operators. 
+        """
+        assert isinstance(top_op, OpWrapper)
+        next_ops = []
+        for op in graph.next_ops(top_op):
+            if (visited[op.idx()] == False) and (not op.is_bwd_op()):
+                next_ops.append(op)
+        return next_ops if len(next_ops) > 0 else None
+
+    def _get_accumulator(self, graph, param):
+        """
+        Get accumulators of given parameter. The accumulator was created by optimizer.
+        Args:
+            graph(GraphWrapper): The graph used to search.
+            param(VarWrapper): The given parameter.
+        Returns:
+            list<VarWrapper>: A list of accumulators which are variables.
+        """
+        assert isinstance(param, VarWrapper)
+        params = []
+        for op in param.outputs():
+            if op.is_opt_op():
+                for out_var in op.all_outputs():
+                    if graph.is_persistable(out_var) and out_var.name(
+                    ) != param.name():
+                        params.append(out_var)
+        return params
+
+    def _forward_pruning_ralated_params(self,
+                                        graph,
+                                        scope,
+                                        param,
+                                        place,
+                                        ratio=None,
+                                        pruned_idxs=None,
+                                        lazy=False,
+                                        only_graph=False):
         """
-    The strategy that pruning weights by threshold or ratio iteratively.
+        Pruning all the parameters affected by the pruning of given parameter.
+        Args:
+            graph(GraphWrapper): The graph to be searched.
+            scope(fluid.core.Scope): The scope storing paramaters to be pruned.
+            param(VarWrapper): The given parameter.
+            place(fluid.Place): The device place of filter parameters.
+            ratio(float): The target ratio to be pruned.
+            pruned_idx(list): The index of elements to be pruned.
+            lazy(bool): True means setting the pruned elements to zero.
+                        False means cutting down the pruned elements.
+            only_graph(bool): True means only modifying the graph.
+                              False means modifying graph and variables in  scope.
+        """
+        assert isinstance(
+            graph,
+            GraphWrapper), "graph must be instance of slim.core.GraphWrapper"
+        assert isinstance(
+            param, VarWrapper), "param must be instance of slim.core.VarWrapper"
+
+        if param.name() in self.pruned_list[0]:
+            return
+        related_ops = self._forward_search_related_op(graph, param)
+
+        if ratio is None:
+            assert pruned_idxs is not None
+            self._prune_parameter_by_idx(
+                scope, [param] + self._get_accumulator(graph, param),
+                pruned_idxs,
+                pruned_axis=0,
+                place=place,
+                lazy=lazy,
+                only_graph=only_graph)
+
+        else:
+            pruned_idxs = self._prune_filters_by_ratio(
+                scope, [param] + self._get_accumulator(graph, param),
+                ratio,
+                place,
+                lazy=lazy,
+                only_graph=only_graph)
+        corrected_idxs = pruned_idxs[:]
+
+        for idx, op in enumerate(related_ops):
+            if op.type() == "conv2d" and (param not in op.all_inputs()):
+                for in_var in op.all_inputs():
+                    if graph.is_parameter(in_var):
+                        conv_param = in_var
+                        self._prune_parameter_by_idx(
+                            scope, [conv_param] + self._get_accumulator(
+                                graph, conv_param),
+                            corrected_idxs,
+                            pruned_axis=1,
+                            place=place,
+                            lazy=lazy,
+                            only_graph=only_graph)
+            if op.type() == "depthwise_conv2d":
+                for in_var in op.all_inputs():
+                    if graph.is_parameter(in_var):
+                        conv_param = in_var
+                        self._prune_parameter_by_idx(
+                            scope, [conv_param] + self._get_accumulator(
+                                graph, conv_param),
+                            corrected_idxs,
+                            pruned_axis=0,
+                            place=place,
+                            lazy=lazy,
+                            only_graph=only_graph)
+            elif op.type() == "elementwise_add":
+                # pruning bias
+                for in_var in op.all_inputs():
+                    if graph.is_parameter(in_var):
+                        bias_param = in_var
+                        self._prune_parameter_by_idx(
+                            scope, [bias_param] + self._get_accumulator(
+                                graph, bias_param),
+                            pruned_idxs,
+                            pruned_axis=0,
+                            place=place,
+                            lazy=lazy,
+                            only_graph=only_graph)
+            elif op.type() == "mul":  # pruning fc layer
+                fc_input = None
+                fc_param = None
+                for in_var in op.all_inputs():
+                    if graph.is_parameter(in_var):
+                        fc_param = in_var
+                    else:
+                        fc_input = in_var
+
+                idx = []
+                feature_map_size = fc_input.shape()[2] * fc_input.shape()[3]
+                range_idx = np.array(range(feature_map_size))
+                for i in corrected_idxs:
+                    idx += list(range_idx + i * feature_map_size)
+                corrected_idxs = idx
+                self._prune_parameter_by_idx(
+                    scope, [fc_param] + self._get_accumulator(graph, fc_param),
+                    corrected_idxs,
+                    pruned_axis=0,
+                    place=place,
+                    lazy=lazy,
+                    only_graph=only_graph)
+
+            elif op.type() == "concat":
+                concat_inputs = op.all_inputs()
+                last_op = related_ops[idx - 1]
+                for out_var in last_op.all_outputs():
+                    if out_var in concat_inputs:
+                        concat_idx = concat_inputs.index(out_var)
+                offset = 0
+                for ci in range(concat_idx):
+                    offset += concat_inputs[ci].shape()[1]
+                corrected_idxs = [x + offset for x in pruned_idxs]
+            elif op.type() == "batch_norm":
+                bn_inputs = op.all_inputs()
+                mean = bn_inputs[2]
+                variance = bn_inputs[3]
+                alpha = bn_inputs[0]
+                beta = bn_inputs[1]
+                self._prune_parameter_by_idx(
+                    scope, [mean] + self._get_accumulator(graph, mean),
+                    corrected_idxs,
+                    pruned_axis=0,
+                    place=place,
+                    lazy=lazy,
+                    only_graph=only_graph)
+                self._prune_parameter_by_idx(
+                    scope, [variance] + self._get_accumulator(graph, variance),
+                    corrected_idxs,
+                    pruned_axis=0,
+                    place=place,
+                    lazy=lazy,
+                    only_graph=only_graph)
+                self._prune_parameter_by_idx(
+                    scope, [alpha] + self._get_accumulator(graph, alpha),
+                    corrected_idxs,
+                    pruned_axis=0,
+                    place=place,
+                    lazy=lazy,
+                    only_graph=only_graph)
+                self._prune_parameter_by_idx(
+                    scope, [beta] + self._get_accumulator(graph, beta),
+                    corrected_idxs,
+                    pruned_axis=0,
+                    place=place,
+                    lazy=lazy,
+                    only_graph=only_graph)
+
+    def _prune_parameters(self,
+                          graph,
+                          scope,
+                          params,
+                          ratios,
+                          place,
+                          lazy=False,
+                          only_graph=False):
+        """
+        Pruning the given parameters.
+        Args:
+            graph(GraphWrapper): The graph to be searched.
+            scope(fluid.core.Scope): The scope storing paramaters to be pruned.
+            params(list<str>): A list of parameter names to be pruned.
+            ratios(list<float>): A list of ratios to be used to pruning parameters.
+            place(fluid.Place): The device place of filter parameters.
+            pruned_idx(list): The index of elements to be pruned.
+            lazy(bool): True means setting the pruned elements to zero.
+                        False means cutting down the pruned elements.
+            only_graph(bool): True means only modifying the graph.
+                              False means modifying graph and variables in  scope.
+
+        """
+        _logger.debug('\n################################')
+        _logger.debug('#       pruning parameters       #')
+        _logger.debug('################################\n')
+        _logger.debug(
+            '|----------------------------------------+----+------------------------------+------------------------------|'
+        )
+        _logger.debug('|{:^40}|{:^4}|{:^30}|{:^30}|'.format('parameter', 'axis',
+                                                            'from', 'to'))
+        assert len(params) == len(ratios)
+        self.pruned_list = [[], []]
+        for param, ratio in zip(params, ratios):
+            assert isinstance(param, str) or isinstance(param, unicode)
+            param = graph.var(param)
+            self._forward_pruning_ralated_params(
+                graph,
+                scope,
+                param,
+                place,
+                ratio=ratio,
+                lazy=lazy,
+                only_graph=only_graph)
+            ops = param.outputs()
+            for op in ops:
+                if op.type() == 'conv2d':
+                    brother_ops = self._search_brother_ops(graph, op)
+                    for broher in brother_ops:
+                        for p in graph.get_param_by_op(broher):
+                            self._forward_pruning_ralated_params(
+                                graph,
+                                scope,
+                                p,
+                                place,
+                                ratio=ratio,
+                                lazy=lazy,
+                                only_graph=only_graph)
+        _logger.debug(
+            '|----------------------------------------+----+------------------------------+------------------------------|'
+        )
+
+    def _search_brother_ops(self, graph, op_node):
+        """
+        Search brother operators that was affected by pruning of given operator.
+        Args:
+            graph(GraphWrapper): The graph to be searched.
+            op_node(OpWrapper): The start node for searching.
+        Returns: 
+            list<VarWrapper>: A list of operators.
+        """
+        visited = [op_node.idx()]
+        stack = []
+        brothers = []
+        for op in graph.next_ops(op_node):
+            if (op.type() != 'conv2d') and (op.type() != 'fc') and (
+                    not op._is_bwd_op()):
+                stack.append(op)
+                visited.append(op.idx())
+        while len(stack) > 0:
+            top_op = stack.pop()
+            for parent in graph.pre_ops(top_op):
+                if parent.idx() not in visited and (not parent._is_bwd_op()):
+                    if ((parent.type == 'conv2d') or (parent.type == 'fc')):
+                        brothers.append(parent)
+                    else:
+                        stack.append(parent)
+                    visited.append(parent.idx())
+
+            for child in graph.next_ops(top_op):
+                if (child.type != 'conv2d') and (child.type != 'fc') and (
+                        child.idx() not in visited) and (
+                            not child._is_bwd_op()):
+                    stack.append(child)
+                    visited.append(child.idx())
+        return brothers
+
+    def _prune_graph(self, graph, target_graph):
+        """
+        Pruning parameters of graph according to target graph.
+        Args:
+            graph(GraphWrapper): The graph to be pruned.
+            target_graph(GraphWrapper): The reference graph.
+        Return: None
+        """
+        count = 1
+        _logger.debug(
+            '|----+----------------------------------------+------------------------------+------------------------------|'
+        )
+        _logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format('id', 'parammeter',
+                                                            'from', 'to'))
+        for param in target_graph.all_parameters():
+            var = graph.var(param.name())
+            ori_shape = var.shape()
+            var.set_shape(param.shape())
+            _logger.debug(
+                '|----+----------------------------------------+------------------------------+------------------------------|'
+            )
+            _logger.debug('|{:^4}|{:^40}|{:^30}|{:^30}|'.format(
+                str(count),
+                str(param.name()), str(ori_shape), str(param.shape())))
+            count += 1
+        _logger.debug(
+            '|----+----------------------------------------+------------------------------+------------------------------|'
+        )
+
+
+class UniformPruneStrategy(PruneStrategy):
+    """
+    The uniform pruning strategy. The parameters will be pruned by uniform ratio.
     """
 
     def __init__(self,
-                 pruner,
-                 mini_batch_pruning_frequency=1,
+                 pruner=None,
                  start_epoch=0,
-                 end_epoch=10):
-        super(PruneStrategy, self).__init__(start_epoch, end_epoch)
-        self.pruner = pruner
-        self.mini_batch_pruning_frequency = mini_batch_pruning_frequency
-
-    def _triger(self, context):
-        return (context.batch_id % self.mini_batch_pruning_frequency == 0 and
-                self.start_epoch <= context.epoch_id < self.end_epoch)
-
-    def on_batch_end(self, context):
-        if self._triger(context):
-            prune_program = Program()
-            with program_guard(prune_program):
-                for param in context.graph.all_parameters():
-                    prune_program.global_block().clone_variable(param)
-                    p = prune_program.global_block().var(param.name)
-                    zeros_mask = self.pruner.prune(p)
-                    pruned_param = p * zeros_mask
-                    layers.assign(input=pruned_param, output=param)
-            context.program_exe.run(prune_program, scope=context.scope)
+                 end_epoch=0,
+                 target_ratio=0.5,
+                 metric_name=None,
+                 pruned_params='conv.*_weights'):
+        """
+        Args:
+            pruner(slim.Pruner): The pruner used to prune the parameters.
+            start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0
+            end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 0
+            target_ratio(float): The flops ratio to be pruned from current model.
+            metric_name(str): The metric used to evaluate the model.
+                         It should be one of keys in out_nodes of graph wrapper.
+            pruned_params(str): The pattern str to match the parameter names to be pruned.
+        """
+        super(UniformPruneStrategy, self).__init__(pruner, start_epoch,
+                                                   end_epoch, target_ratio,
+                                                   metric_name, pruned_params)
+
+    def _get_best_ratios(self, context):
+        """
+        Search a group of ratios for pruning target flops.
+        """
+        _logger.info('_get_best_ratios')
+        pruned_params = []
+        for param in context.eval_graph.all_parameters():
+            if re.match(self.pruned_params, param.name()):
+                pruned_params.append(param.name())
+
+        min_ratio = 0.
+        max_ratio = 1.
+
+        flops = context.eval_graph.flops()
+        model_size = context.eval_graph.numel_params()
+
+        while min_ratio < max_ratio:
+            ratio = (max_ratio + min_ratio) / 2
+            _logger.debug(
+                '-----------Try pruning ratio: {:.2f}-----------'.format(ratio))
+            ratios = [ratio] * len(pruned_params)
+            self._prune_parameters(
+                context.eval_graph,
+                context.scope,
+                pruned_params,
+                ratios,
+                context.place,
+                only_graph=True)
+
+            pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
+            pruned_size = 1 - (float(context.eval_graph.numel_params()) /
+                               model_size)
+            _logger.debug('Pruned flops: {:.2f}'.format(pruned_flops))
+            _logger.debug('Pruned model size: {:.2f}'.format(pruned_size))
+            for param in self.param_shape_backup.keys():
+                context.eval_graph.var(param).set_shape(self.param_shape_backup[
+                    param])
+            self.param_shape_backup = {}
+
+            if abs(pruned_flops - self.target_ratio) < 1e-2:
+                break
+            if pruned_flops > self.target_ratio:
+                max_ratio = ratio
+            else:
+                min_ratio = ratio
+        _logger.info('Get ratios: {}'.format([round(r, 2) for r in ratios]))
+        return pruned_params, ratios
+
+    def on_epoch_begin(self, context):
+        if context.epoch_id == self.start_epoch:
+            params, ratios = self._get_best_ratios(context)
+
+            self._prune_parameters(context.optimize_graph, context.scope,
+                                   params, ratios, context.place)
+
+            model_size = context.eval_graph.numel_params()
+            flops = context.eval_graph.flops()
+            _logger.debug('\n################################')
+            _logger.debug('#          pruning eval graph    #')
+            _logger.debug('################################\n')
+            self._prune_graph(context.eval_graph, context.optimize_graph)
+            context.optimize_graph.update_groups_of_conv()
+            context.eval_graph.update_groups_of_conv()
+
+            _logger.info(
+                '------------------finish pruning--------------------------------'
+            )
+            _logger.info('Pruned size: {:.2f}'.format(1 - (float(
+                context.eval_graph.numel_params()) / model_size)))
+            _logger.info('Pruned flops: {:.2f}'.format(1 - (float(
+                context.eval_graph.flops()) / flops)))
+            #            metric = self._eval_graph(context)
+            #            _logger.info('Metric after pruning: {:.2f}'.format(metric))
+            _logger.info(
+                '------------------UniformPruneStrategy.on_compression_begin finish--------------------------------'
+            )
+
+
+class SensitivePruneStrategy(PruneStrategy):
+    """
+    Sensitive pruning strategy. Different pruned ratio was applied on each layer.
+    """
+
+    def __init__(self,
+                 pruner=None,
+                 start_epoch=0,
+                 end_epoch=0,
+                 delta_rate=0.20,
+                 target_ratio=0.5,
+                 metric_name='top1_acc',
+                 pruned_params='conv.*_weights',
+                 sensitivities_file='./sensitivities.data',
+                 sensitivities={},
+                 num_steps=1,
+                 eval_rate=None):
+        """
+        Args:
+            pruner(slim.Pruner): The pruner used to prune the parameters.
+            start_epoch(int): The 'on_epoch_begin' function will be called in start_epoch. default: 0.
+            end_epoch(int): The 'on_epoch_end' function will be called in end_epoch. default: 10.
+            delta_rate(float): The delta used to generate ratios when calculating sensitivities. default: 0.2
+            target_ratio(float): The flops ratio to be pruned from current model. default: 0.5
+            metric_name(str): The metric used to evaluate the model.
+                         It should be one of keys in out_nodes of graph wrapper. default: 'top1_acc'
+            pruned_params(str): The pattern str to match the parameter names to be pruned. default: 'conv.*_weights'.
+            sensitivities_file(str): The sensitivities file. default: './sensitivities.data'
+            sensitivities(dict): The user-defined sensitivities. default: {}.
+            num_steps(int): The number of pruning steps. default: 1.
+            eval_rate(float): The rate of sampled data used to calculate sensitivities.
+                              None means using all the data. default: None.
+        """
+        super(SensitivePruneStrategy, self).__init__(pruner, start_epoch,
+                                                     end_epoch, target_ratio,
+                                                     metric_name, pruned_params)
+        self.delta_rate = delta_rate
+        self.pruned_list = []
+        self.sensitivities = sensitivities
+        self.sensitivities_file = sensitivities_file
+        self.backup = {}
+        self.param_shape_backup = {}
+        self.num_steps = num_steps
+        self.eval_rate = eval_rate
+        self.pruning_step = 1 - pow((1 - target_ratio), 1.0 / self.num_steps)
+
+    def _save_sensitivities(self, sensitivities, sensitivities_file):
+        """
+        Save sensitivities into file.
+        """
+        with open(sensitivities_file, 'wb') as f:
+            pickle.dump(sensitivities, f)
+
+    def _load_sensitivities(self, sensitivities_file):
+        """
+        Load sensitivities from file.
+        """
+        sensitivities = {}
+        if sensitivities_file and os.path.exists(sensitivities_file):
+            with open(sensitivities_file, 'rb') as f:
+                if sys.version_info < (3, 0):
+                    sensitivities = pickle.load(f)
+                else:
+                    sensitivities = pickle.load(f, encoding='bytes')
+
+        for param in sensitivities:
+            sensitivities[param]['pruned_percent'] = [
+                round(p, 2) for p in sensitivities[param]['pruned_percent']
+            ]
+        self._format_sensitivities(sensitivities)
+        return sensitivities
+
+    def _format_sensitivities(self, sensitivities):
+        """
+        Print formated sensitivities in debug log level.
+        """
+        tb = pt.PrettyTable()
+        tb.field_names = ["parameter", "size"] + [
+            str(round(i, 2))
+            for i in np.arange(self.delta_rate, 1, self.delta_rate)
+        ]
+        for param in sensitivities:
+            if len(sensitivities[param]['loss']) == (len(tb.field_names) - 2):
+                tb.add_row([param, sensitivities[param]['size']] + [
+                    round(loss, 2) for loss in sensitivities[param]['loss']
+                ])
+        _logger.debug('\n################################')
+        _logger.debug('#      sensitivities table     #')
+        _logger.debug('################################\n')
+        _logger.debug(tb)
+
+    def _compute_sensitivities(self, context):
+        """
+        Computing the sensitivities of all parameters.
+        """
+        _logger.info("calling _compute_sensitivities.")
+        self.param_shape_backup = {}
+        self.backup = {}
+        cached_id = np.random.randint(1000)
+        if self.start_epoch == context.epoch_id:
+            sensitivities_file = self.sensitivities_file
+        else:
+            sensitivities_file = self.sensitivities_file + ".epoch" + str(
+                context.epoch_id)
+        sensitivities = self._load_sensitivities(sensitivities_file)
+
+        for param in context.eval_graph.all_parameters():
+            if not re.match(self.pruned_params, param.name()):
+                continue
+            if param.name() not in sensitivities:
+                sensitivities[param.name()] = {
+                    'pruned_percent': [],
+                    'loss': [],
+                    'size': param.shape()[0]
+                }
+
+        metric = None
+
+        for param in sensitivities.keys():
+            ratio = self.delta_rate
+            while ratio < 1:
+                ratio = round(ratio, 2)
+                if ratio in sensitivities[param]['pruned_percent']:
+                    _logger.debug('{}, {} has computed.'.format(param, ratio))
+                    ratio += self.delta_rate
+                    continue
+                if metric is None:
+                    metric = self._eval_graph(context, self.eval_rate,
+                                              cached_id)
+                # prune parameter by ratio
+                self._prune_parameters(
+                    context.eval_graph,
+                    context.scope, [param], [ratio],
+                    context.place,
+                    lazy=True)
+                self.pruned_list[0]
+                # get accuracy after pruning and update self.sensitivities
+                pruned_metric = self._eval_graph(context, self.eval_rate,
+                                                 cached_id)
+                loss = metric - pruned_metric
+                _logger.info("pruned param: {}; {}; loss={}".format(
+                    param, ratio, loss))
+                for brother in self.pruned_list[0]:
+                    if re.match(self.pruned_params, brother):
+                        if brother not in sensitivities:
+                            sensitivities[brother] = {
+                                'pruned_percent': [],
+                                'loss': []
+                            }
+                        sensitivities[brother]['pruned_percent'].append(ratio)
+                        sensitivities[brother]['loss'].append(loss)
+
+                self._save_sensitivities(sensitivities, sensitivities_file)
+
+                # restore pruned parameters
+                for param_name in self.backup.keys():
+                    param_t = context.scope.find_var(param_name).get_tensor()
+                    param_t.set(self.backup[param_name], context.place)
+
+#                pruned_metric = self._eval_graph(context)
+                self.backup = {}
+
+                ratio += self.delta_rate
+        return sensitivities
+
+    def _get_best_ratios(self, context, sensitivities, target_ratio):
+        """
+        Search a group of ratios for pruning target flops.
+        """
+        _logger.info('_get_best_ratios for pruning ratie: {}'.format(
+            target_ratio))
+        self.param_shape_backup = {}
+        self.backup = {}
+
+        def func(params, x):
+            a, b, c, d = params
+            return a * x * x * x + b * x * x + c * x + d
+
+        def error(params, x, y):
+            return func(params, x) - y
+
+        def slove_coefficient(x, y):
+            init_coefficient = [10, 10, 10, 10]
+            coefficient, loss = leastsq(error, init_coefficient, args=(x, y))
+            return coefficient
+
+        min_loss = 0.
+        max_loss = 0.
+
+        # step 1: fit curve by sensitivities
+        coefficients = {}
+        for param in sensitivities:
+            losses = np.array([0] * 5 + sensitivities[param]['loss'])
+            precents = np.array([0] * 5 + sensitivities[param][
+                'pruned_percent'])
+            coefficients[param] = slove_coefficient(precents, losses)
+            loss = np.max(losses)
+            max_loss = np.max([max_loss, loss])
+
+        # step 2: Find a group of ratios by binary searching.
+        flops = context.eval_graph.flops()
+        model_size = context.eval_graph.numel_params()
+        ratios = []
+        while min_loss < max_loss:
+            loss = (max_loss + min_loss) / 2
+            _logger.info(
+                '-----------Try pruned ratios while acc loss={:.4f}-----------'.
+                format(loss))
+            ratios = []
+            # step 2.1: Get ratios according to current loss
+            for param in sensitivities:
+                coefficient = copy.deepcopy(coefficients[param])
+                coefficient[-1] = coefficient[-1] - loss
+                roots = np.roots(coefficient)
+                for root in roots:
+                    min_root = 1
+                    if np.isreal(root) and root > 0 and root < 1:
+                        selected_root = min(root.real, min_root)
+                ratios.append(selected_root)
+            _logger.info('Pruned ratios={}'.format(
+                [round(ratio, 3) for ratio in ratios]))
+            # step 2.2: Pruning by current ratios
+            self._prune_parameters(
+                context.eval_graph,
+                context.scope,
+                sensitivities.keys(),
+                ratios,
+                context.place,
+                only_graph=True)
+
+            pruned_flops = 1 - (float(context.eval_graph.flops()) / flops)
+            pruned_size = 1 - (float(context.eval_graph.numel_params()) /
+                               model_size)
+            _logger.info('Pruned flops: {:.4f}'.format(pruned_flops))
+            _logger.info('Pruned model size: {:.4f}'.format(pruned_size))
+            for param in self.param_shape_backup.keys():
+                context.eval_graph.var(param).set_shape(self.param_shape_backup[
+                    param])
+            self.param_shape_backup = {}
+
+            # step 2.3: Check whether current ratios is enough
+            if abs(pruned_flops - target_ratio) < 0.015:
+                break
+            if pruned_flops > target_ratio:
+                max_loss = loss
+            else:
+                min_loss = loss
+        return sensitivities.keys(), ratios
+
+    def _current_pruning_target(self, context):
+        '''
+        Get the target pruning rate in current epoch.
+        '''
+        _logger.info('Left number of pruning steps: {}'.format(self.num_steps))
+        if self.num_steps <= 0:
+            return None
+        if (self.start_epoch == context.epoch_id) or context.eval_converged(
+                self.metric_name, 0.005):
+            self.num_steps -= 1
+            return self.pruning_step
+
+    def on_epoch_begin(self, context):
+        current_ratio = self._current_pruning_target(context)
+        if current_ratio is not None:
+            sensitivities = self._compute_sensitivities(context)
+            params, ratios = self._get_best_ratios(context, sensitivities,
+                                                   current_ratio)
+            self._prune_parameters(context.optimize_graph, context.scope,
+                                   params, ratios, context.place)
+
+            self.param_shape_backup = {}
+            self.backup = {}
+
+            model_size = context.eval_graph.numel_params()
+            flops = context.eval_graph.flops()
+            _logger.debug('################################')
+            _logger.debug('#          pruning eval graph    #')
+            _logger.debug('################################')
+            self._prune_graph(context.eval_graph, context.optimize_graph)
+            context.optimize_graph.update_groups_of_conv()
+            context.eval_graph.update_groups_of_conv()
+            context.optimize_graph.compile()  # to update the compiled program
+            context.eval_graph.compile(
+                for_parallel=False,
+                for_test=True)  # to update the compiled program
+            _logger.info(
+                '------------------finish pruning--------------------------------'
+            )
+            _logger.info('Pruned size: {:.3f}'.format(1 - (float(
+                context.eval_graph.numel_params()) / model_size)))
+            _logger.info('Pruned flops: {:.3f}'.format(1 - (float(
+                context.eval_graph.flops()) / flops)))
+            metric = self._eval_graph(context)
+            _logger.info('Metric after pruning: {:.2f}'.format(metric))
+            _logger.info(
+                '------------------SensitivePruneStrategy.on_epoch_begin finish--------------------------------'
+            )

python/paddle/fluid/contrib/slim/prune/pruner.py

@@ -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):
+    def __init__(self, pruning_axis, criterions):
         """
-    Pruner used to pruning a parameter by ratio.
+        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

-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

+#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

-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

-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

-#   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

+#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

+# 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

@@ -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

+#   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

+#   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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -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

@@ -12,3 +12,4 @@ six
 funcsigs
 pyyaml
 decorator
+prettytable

tools/manylinux1/Dockerfile.x64

@@ -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"]