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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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#!/usr/bin/env python |
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# -*- coding: utf-8 -*- |
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import types |
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from . import Dataset |
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from deepy.utils import FakeGenerator, StreamPickler, global_rand |
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import logging as loggers |
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logging = loggers.getLogger(__name__) |
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class OnDiskDataset(Dataset): |
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""" |
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Load large on-disk dataset. |
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The data should be dumped with deepy.utils.StreamPickler. |
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You must convert the data to mini-batches before dump it to a file. |
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""" |
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def __init__(self, train_path, valid_path=None, test_path=None, train_size=None, |
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cached=False, post_processing=None, shuffle_memory=False, curriculum=None): |
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self._train_path = train_path |
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self._valid_path = valid_path |
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self._test_path = test_path |
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self._train_size = train_size |
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self._cache_on_memory = cached |
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self._cached_train_data = None |
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self._post_processing = post_processing if post_processing else lambda x: x |
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self._shuffle_memory = shuffle_memory |
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self._curriculum = curriculum |
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self._curriculum_count = 0 |
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if curriculum and not callable(curriculum): |
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raise Exception("curriculum function must be callable") |
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if curriculum and not cached: |
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raise Exception("curriculum learning needs training data to be cached") |
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if self._cache_on_memory: |
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logging.info("Cache on memory") |
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self._cached_train_data = list(map(self._post_processing, StreamPickler.load(open(self._train_path)))) |
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self._train_size = len(self._cached_train_data) |
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if self._shuffle_memory: |
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logging.info("Shuffle on-memory data") |
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global_rand.shuffle(self._cached_train_data) |
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def curriculum_train_data(self): |
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self._curriculum_count += 1 |
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logging.info("curriculum learning: round {}".format(self._curriculum_count)) |
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return self._curriculum(self._cached_train_data, self._curriculum_count) |
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def generate_train_data(self): |
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for data in StreamPickler.load(open(self._train_path)): |
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yield self._post_processing(data) |
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def generate_valid_data(self): |
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for data in StreamPickler.load(open(self._valid_path)): |
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yield self._post_processing(data) |
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def generate_test_data(self): |
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for data in StreamPickler.load(open(self._test_path)): |
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yield self._post_processing(data) |
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def train_set(self): |
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if self._cache_on_memory: |
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if self._curriculum: |
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if not isinstance(self._curriculum(self._cached_train_data, 1), types.GeneratorType): |
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raise Exception("Curriculum function must be a generator.") |
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return FakeGenerator(self, "curriculum_train_data") |
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else: |
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return self._cached_train_data |
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if not self._train_path: |
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return None |
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return FakeGenerator(self, "generate_train_data") |
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def valid_set(self): |
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if not self._valid_path: |
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return None |
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return FakeGenerator(self, "generate_valid_data") |
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def test_set(self): |
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if not self._test_path: |
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return None |
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return FakeGenerator(self, "generate_test_data") |
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def train_size(self): |
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return self._train_size |
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zomux /
deepy
