提交 478b0c8a 编写于 作者: H Helin Wang

correct data reader and data reader creator usage

上级 9b3cdb12
# Python Data Reading Design Doc
# Python Data Reader Design Doc
Paddle reads data from *data reader* during training. *data reader creator* (or *reader creator*) creates a *data reader* when invoked. *reader creator* will be passed into `paddle.train` as a parameter.
Paddle reads data from *data reader* during training. *data reader* will be passed into `paddle.train` as a parameter.
## Data Reader Creator Interface
## Data Reader Interface
Data reader creator is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
Data reader is a function with no parameter that creates a iterable (anything can be used in `for x in iterable`):
```
iterable = data_reader_creator()
iterable = data_reader()
```
Element produced for the iterable should be a **single** entry of data, **not** a mini batch. That entry of data could be a single item, or a tuple of items. Item should be of [supported type](http://www.paddlepaddle.org/doc/ui/data_provider/pydataprovider2.html?highlight=dense_vector#input-types) (e.g., numpy 1d array of float32, int, list of int)
An example implementation for single item data reader creator:
An example implementation for single item data reader:
```python
def data_reader_creator_fake_image():
def data_reader_fake_image():
while True:
yield numpy.random.uniform(-1, 1, size=20*20)
```
An example implementation for multiple item data reader creator:
An example implementation for multiple item data reader:
```python
def data_reader_creator_fake_image_and_label():
def data_reader_fake_image_and_label():
while True:
yield numpy.random.uniform(-1, 1, size=20*20), False
```
## Usage
data reader creator, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
data reader, mapping from item(s) read to data layer, batch size and number of total pass will be passed into `paddle.train`:
```python
# two data layer is created:
......@@ -41,11 +41,11 @@ label_layer = paddle.layer.data("label", ...)
paddle.train(paddle.dataset.mnist, {"image":0, "label":1}, 128, 10, ...)
```
## Data Reader Creator Decorator
## Data Reader Decorator
*Data reader creator decorator* (or *reader creator decorator*) takes a single or multiple data reader creator, returns a new data reader creator. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
*Data reader decorator* takes a single or multiple data reader, returns a new data reader. It is similar to a [python decorator](https://wiki.python.org/moin/PythonDecorators), but it does not use `@` syntax.
Since we have a strict interface for data readers creators (no parameter, return a single data item). Data reader creators can be used flexiable via data reader creator decorators. Following are a few examples:
Since we have a strict interface for data readers (no parameter, return a single data item). Data reader can be used flexiable via data reader decorators. Following are a few examples:
### Prefetch Data
......@@ -54,63 +54,63 @@ Since reading data may take time and training can not proceed without data. It i
Use `paddle.reader.buffered` to prefetch data:
```python
buffered_reader_creator = paddle.reader.buffered(paddle.dataset.mnist, 100)
buffered_reader = paddle.reader.buffered(paddle.dataset.mnist, 100)
```
`buffered_reader_creator` will try to buffer (prefetch) `100` data entries.
`buffered_reader` will try to buffer (prefetch) `100` data entries.
### Compose Multiple Data Reader Creators
### Compose Multiple Data Readers
For example, we want to use a source of real images (reusing mnist dataset), and a source of fake images as input for [Generative Adversarial Networks](https://arxiv.org/abs/1406.2661).
We can do:
```python
def data_reader_creator_fake_image():
def data_reader_fake_image():
while True:
yield numpy.random.uniform(-1, 1, size=20*20)
def data_reader_creator_bool(t):
def creator:
def reader:
while True:
yield t
return creator
return reader
true_reader_creator = data_reader_creator_bool(True)
false_reade_creatorr = data_reader_creator_bool(False)
true_reader = data_reader_creator_bool(True)
false_reader = data_reader_creator_bool(False)
reader_creator = paddle.reader.compose(paddle.dataset.mnist, data_reader_creator_fake_image, true_reader_creator, false_reader_creator)
reader = paddle.reader.compose(paddle.dataset.mnist, data_reader_fake_image, true_reader, false_reader)
# Skipped 1 because paddle.dataset.mnist produces two items per data entry.
# And we don't care second item at this time.
paddle.train(reader_creator, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
paddle.train(reader, {"true_image":0, "fake_image": 2, "true_label": 3, "false_label": 4}, ...)
```
### Shuffle
Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader creator that buffers `n` data entries and shuffle them before a data entry is read.
Given shuffle buffer size `n`, `paddle.reader.shuffle` will return a data reader that buffers `n` data entries and shuffle them before a data entry is read.
Example:
```python
reader_creator = paddle.reader.shuffle(paddle.dataset.mnist, 512)
reader = paddle.reader.shuffle(paddle.dataset.mnist, 512)
```
## Q & A
### Why return only a single entry, but not a mini batch?
If a mini batch is returned, data reader creator need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
If a mini batch is returned, data reader need to take care of batch size. But batch size is a concept for training, it makes more sense for user to specify batch size as a parameter for `train`.
Practically, always return a single entry make reusing existing data reader creators much easier (e.g., if existing reader creator return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
Practically, always return a single entry make reusing existing data readers much easier (e.g., if existing reader return not a single entry but 3 entries, training code will be more complex because it need to handle cases like batch size 2).
### Why use a dictionary but not a list to provide mapping?
We decided to use dictionary (`{"image":0, "label":1}`) instead of list (`["image", "label"]`) is because that user can easily resue item (e.g., using `{"image_a":0, "image_b":0, "label":1}`) or skip item (e.g., using `{"image_a":0, "label":2}`).
### How to create custom data reader creator
### How to create custom data reader
```python
def image_reader_creator(image_path, label_path, n):
def reader_creator():
def reader():
f = open(image_path)
l = open(label_path)
images = numpy.fromfile(
......@@ -121,10 +121,11 @@ def image_reader_creator(image_path, label_path, n):
yield images[i, :], labels[i] # a single entry of data is created each time
f.close()
l.close()
return reader_creator
return reader
reader_creator = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
paddle.train(reader_creator, {"image":0, "label":1}, ...)
# images_reader_creator creates a reader
reader = image_reader_creator("/path/to/image_file", "/path/to/label_file", 1024)
paddle.train(reader, {"image":0, "label":1}, ...)
```
### How is `paddle.train` implemented
......@@ -132,17 +133,17 @@ paddle.train(reader_creator, {"image":0, "label":1}, ...)
An example implementation of paddle.train could be:
```python
def minibatch_decorater(reader_creator, minibatch_size):
def minibatch_decorater(reader, minibatch_size):
def ret():
r = reader_creator()
r = reader()
buf = [r.next() for x in xrange(minibatch_size)]
while len(buf) > 0:
yield buf
buf = [r.next() for x in xrange(minibatch_size)]
return ret
def train(reader_creator, mapping, batch_size, total_pass):
def train(reader, mapping, batch_size, total_pass):
for pass_idx in range(total_pass):
for mini_batch in minibatch_decorater(reader_creator): # this loop will never end in online learning.
for mini_batch in minibatch_decorater(reader): # this loop will never end in online learning.
do_forward_backward(mini_batch, mapping)
```
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