# 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. """Pyreader based Dataset""" import sys import numpy as np import logging import paddle.fluid as F import paddle.fluid.layers as L from propeller.data.functional import Dataset as DatasetBase log = logging.getLogger(__name__) class Dataset(DatasetBase): """Pyreader based Dataset""" def placeholders(self): """doc""" if self.name is None: raise ValueError('can not get feature from unnamed Dataset') ret = [] for i, (shape, types) in enumerate(zip(self.data_shapes, self.data_types)): ret.append( L.data( '%s_placeholder_%d' % (self.name, i), shape=shape, append_batch_size=False, dtype=types)) return ret def features(self): """start point of net building. call this in a program scope""" if self.name is None: raise ValueError('can not get feature from unnamed Dataset') if len(self.data_shapes) != len(self.data_types): raise ValueError( 'Dataset shapes and types not match: shape:%s types%s' % (repr(self._data_shapes), repr(self._data_types))) return self.placeholders() def start(self, places=None): """start Pyreader""" if places is None: places = F.cuda_places() if F.core.is_compiled_with_cuda( ) else F.cpu_places() #assert self.pyreader is not None, 'use Dataset.features to build net first, then start dataset' def _gen(): try: for idx, i in enumerate(self.generator()): yield i except Exception as e: log.exception(e) raise e r = F.io.PyReader( feed_list=self.placeholders(), capacity=50, iterable=True, return_list=F.in_dygraph_mode()) r.decorate_batch_generator(_gen, places=places) return r()