add __init__ and run

paddleslim/dist/mp_distiller.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.fluid as fluid
+from paddle.fluid.framework import Variable
+from paddle.fluid.reader import DataLoaderBase
+from paddle.fluid.core import EOFException
+from paddle.fluid.incubate.fleet.utils.hdfs import HDFSClient
+from six.moves.queue import Queue
+import numpy as np
+import os
+
+__all__ = ['Knowledge']
+
+
+class Knowledge(object):
+    """
+    The knowledge class describes how to extract and store the dark knowledge
+    of the teacher model, and how the student model learns these dark knowledge.
+    """
+
+    def __init__(self,
+                 path,
+                 items,
+                 reduce_strategy={'type': 'sum',
+                                  'key': 'image'}):
+        """Init a knowledge instance.
+        Args:
+            path(list<str>, str, optional): Specifies the storage path of the knowledge,
+                       supports AFS/HDFS, local file system, and memory.
+            items(list<str>): Save the tensor of the specified name
+            reduce_strategy(dict, optional): The policy for performing the reduce
+                                   operation. If it is set to None,
+                                   the reduce operation is not performed.
+            reduce_strategy.type(str): Type of reduce operation.
+            reduce_strategy.key(str): The key of the reduce operation.
+                                      It is an element in the item.
+        """
+        assert (isinstance(path, list) or isinstance(path, str) or
+                (path is None)), "path type should be list or str or None"
+        assert (isinstance(items, list)), "items should be a list"
+        assert (isinstance(reduce_strategy,
+                           dict)), "reduce_strategy should be a dict"
+        self.path = path
+        if isinstance(self.path, list):
+            self.write_type = 'HDFS/AFS'
+            assert (len(self.path) == 4 and isinstance(self.path[0], str) and
+                    isinstance(self.path[1], str) and
+                    isinstance(self.path[2], str) and
+                    isinstance(self.path[3], str)), "path should contains four \
+            str, ['local hadoop home', 'fs.default.name', 'hadoop.job.ugi', 'FS path']"
+
+            hadoop_home = self.path[0]
+            configs = {
+                "fs.default.name": self.path[1],
+                "hadoop.job.ugi": self.path[2]
+            }
+            self.client = HDFSClient(hadoop_home, configs)
+            assert (self.client.is_exist(self.path[3]) == True
+                    ), "Plese make sure your hadoop \
+                confiuration is correct and FS path exists"
+
+            self.hdfs_local_path = "./teacher_knowledge"
+            if not os.path.exists(self.hdfs_local_path):
+                os.mkdir(self.hdfs_local_path)
+        elif isinstance(self.path, str):
+            self.write_type = "LocalFS"
+            if not os.path.exists(path):
+                raise ValueError("The local path [%s] does not exist." %
+                                 (path))
+        else:
+            self.write_type = "MEM"
+            self.knowledge_queue = Queue(64)
+
+        self.items = items
+        self.reduce_strategy = reduce_strategy
+
+    def _write(self, data):
+        if self.write_type == 'HDFS/AFS':
+            file_name = 'knowledge_' + str(self.file_cnt)
+            file_path = self.hdfs_local_path + file_name
+            np.save(file_path, data)
+            self.file_cnt += 1
+            self.client.upload(self.path[3], self.local_file_path)
+            print('{}.npy pushed to HDFS/AFS: {}'.format(file_name, self.path[
+                3]))
+
+        elif self.write_type == 'LocalFS':
+            file_name = 'knowledge_' + str(self.file_cnt)
+            file_path = os.path.join(self.path, file_name)
+            np.save(file_path, data)
+            print('{}.npy saved'.format(file_name))
+            self.file_cnt += 1
+
+        else:
+            self.knowledge_queue.put(data)
+
+    def run(self, teacher_program, exe, place, scope, reader, inputs, outputs,
+            call_back):
+        """Start teacher model to do information.
+        Args:
+            teacher_program(Program): teacher program.
+            scope(Scope): The scope used to execute the teacher,
+                          which contains the initialized variables.
+            reader(reader): The data reader used by the teacher.
+            inputs(list<str>): The name of variables to feed the teacher program.
+            outputs(list<str>): Need to write to the variable instance's names of
+                                the Knowledge instance, which needs to correspond
+                                to the Knowledge's items.
+            call_back(func, optional): The callback function that handles the
+                          outputs of the teacher, which is none by default,
+                          that is, the output of the teacher is concat directly.
+        Return:
+            (bool): Whether the teacher task was successfully registered and started
+        """
+        assert (isinstance(
+            teacher_program,
+            fluid.Program)), "teacher_program should be a fluid.Program"
+        assert (isinstance(inputs, list)), "inputs should be a list"
+        if len(inputs) > 0:
+            assert (isinstance(inputs[0], str)), "inputs shoud be list<str>"
+        assert (isinstance(outputs, list)), "outputs should be a list"
+        if len(outputs) > 0:
+            assert (isinstance(outputs[0], str)), "outputs should be list<str>"
+        assert (len(self.items) == len(outputs)
+                ), "the length of outputs list should be equal with items list"
+        assert (callable(call_back) or (call_back is None)
+                ), "call_back should be a callable function or NoneType."
+
+        for var in teacher_program.list_vars():
+            var.stop_gradient = True
+
+        compiled_teacher_program = fluid.compiler.CompiledProgram(
+            teacher_program)
+        teacher_knowledge = []
+        self.file_cnt = 0
+        if isinstance(reader, Variable) or (
+                isinstance(reader, DataLoaderBase) and (not reader.iterable)):
+            reader.start()
+            try:
+                batch_id = 0
+                while True:
+                    logits = exe.run(compiled_teacher_program,
+                                     scope=scope,
+                                     fetch_list=outputs,
+                                     feed=None)
+                    knowledge = dict()
+                    for index, array in enumerate(logits):
+                        knowledge[self.items[index]] = array
+                    teacher_knowledge.append(knowledge)
+                    if batch_id % 1 == 0:
+                        print('infer finish iter {}'.format(batch_id))
+                    if len(teacher_knowledge) >= 4:
+                        self._write(teacher_knowledge)
+                        teacher_knowledge = []
+            except EOFException:
+                reader.reset()
+                if len(teacher_knowledge) > 0:
+                    self._write(teacher_knowledge)
+
+        else:
+            feeder = fluid.DataFeeder(
+                feed_list=inputs, place=place, program=teacher_program)
+            for batch_id, data in enumerate(reader()):
+                feed = feeder.feed(data)
+                logits = exe.run(compiled_teacher_program,
+                                 scope=scope,
+                                 fetch_list=outputs,
+                                 feed=feed)
+                knowledge = dict()
+                for index, array in enumerate(logits):
+                    knowledge[self.items[index]] = array
+                teacher_knowledge.append(knowledge)
+                if batch_id % 1 == 0:
+                    print('infer finish iter {}'.format(batch_id))
+                if len(teacher_knowledge) >= 4:
+                    self._write(teacher_knowledge)
+                    teacher_knowledge = []
+            if len(teacher_knowledge) > 0:
+                self._write(teacher_knowledge)
+        return True
+
+    def dist(self, student_program, losses):
+        """Building the distillation network
+        Args:
+            student_program(Program): student program.
+            losses(list<Variable>, optional): The losses need to add. If set to None
+                              does not add any loss.
+        Return:
+            (Program): Program for distillation.
+            (startup_program): Program for initializing distillation network.
+            (reader): Data reader for distillation training.
+            (Variable): Loss of distillation training
+        """
+
+    def loss(self, loss_func, *variables):
+        """User-defined loss
+        Args:
+            loss_func(func): Function used to define loss.
+            *variables(list<str>): Variable name list.
+        Return:
+            (Variable): Distillation loss.
+        """
+        pass
+
+    def fsp_loss(self):
+        """fsp loss
+        """
+        pass
+
+    def l2_loss(self):
+        """l2 loss
+        """
+        pass
+
+    def softlabel_loss(self):
+        """softlabel_loss
+        """
+        pass