Dataloader source code_ DataLoader

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import paddle.fluid as fluid

import numpy as np

BATCH_NUM = 10

BATCH_SIZE = 16

EPOCH_NUM = 4

CLASS_NUM = 10

ITERABLE = True # whether the created DataLoader object is iterable

USE_GPU = False # whether to use GPU

DATA_FORMAT = ‘batch_generator’ # data format of data source user provides

def simple_net(image, label):

fc_tmp = fluid.layers.fc(image, size=CLASS_NUM)

cross_entropy = fluid.layers.softmax_with_cross_entropy(image, label)

loss = fluid.layers.reduce_mean(cross_entropy)

sgd = fluid.optimizer.SGD(learning_rate=1e-3)

sgd.minimize(loss)

return loss

def get_random_images_and_labels(image_shape, label_shape):

image = np.random.random(size=image_shape).astype(‘float32’)

label = np.random.random(size=label_shape).astype(‘int64’)

return image, label

# If the data generator yields one sample each time,

# use DataLoader.set_sample_generator to set the data source.

def sample_generator_creator():

def __reader__():

for _ in range(BATCH_NUM * BATCH_SIZE):

image, label = get_random_images_and_labels([784], [1])

yield image, label

return __reader__

# If the data generator yield list of samples each time,

# use DataLoader.set_sample_list_generator to set the data source.

def sample_list_generator_creator():

def __reader__():

for _ in range(BATCH_NUM):

sample_list = []

for _ in range(BATCH_SIZE):

image, label = get_random_images_and_labels([784], [1])

sample_list.append([image, label])

yield sample_list

return __reader__

# If the data generator yields a batch each time,

# use DataLoader.set_batch_generator to set the data source.

def batch_generator_creator():

def __reader__():

for _ in range(BATCH_NUM):

batch_image, batch_label = get_random_images_and_labels([BATCH_SIZE, 784], [BATCH_SIZE, 1])

yield batch_image, batch_label

return __reader__

# If DataLoader is iterable, use for loop to train the network

def train_iterable(exe, prog, loss, loader):

for _ in range(EPOCH_NUM):

for data in loader():

exe.run(prog, feed=data, fetch_list=[loss])

# If DataLoader is not iterable, use start() and reset() method to control the process

def train_non_iterable(exe, prog, loss, loader):

for _ in range(EPOCH_NUM):

loader.start() # call DataLoader.start() before each epoch starts

try:

while True:

exe.run(prog, fetch_list=[loss])

except fluid.core.EOFException:

loader.reset() # call DataLoader.reset() after catching EOFException

def set_data_source(loader, places):

if DATA_FORMAT == ‘sample_generator’:

loader.set_sample_generator(sample_generator_creator(), batch_size=BATCH_SIZE, drop_last=True, places=places)

elif DATA_FORMAT == ‘sample_list_generator’:

loader.set_sample_list_generator(sample_list_generator_creator(), places=places)

elif DATA_FORMAT == ‘batch_generator’:

loader.set_batch_generator(batch_generator_creator(), places=places)

else:

raise ValueError(‘Unsupported data format’)

image = fluid.layers.data(name=’image’, shape=[784], dtype=’float32′)

label = fluid.layers.data(name=’label’, shape=[1], dtype=’int64′)

# Define DataLoader

loader = fluid.io.DataLoader.from_generator(feed_list=[image, label], capacity=16, iterable=ITERABLE)

# Define network

loss = simple_net(image, label)

# Set data source of DataLoader

#

# If DataLoader is iterable, places must be given and the number of places must be the same with device number.

# – If you are using GPU, call `fluid.cuda_places()` to get all GPU places.

# – If you are using CPU, call `fluid.cpu_places()` to get all CPU places.

#

# If DataLoader is not iterable, places can be None.

places = fluid.cuda_places() if USE_GPU else fluid.cpu_places()

set_data_source(loader, places)

exe = fluid.Executor(places[0])

exe.run(fluid.default_startup_program())

prog = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(loss_name=loss.name)

if loader.iterable:

train_iterable(exe, prog, loss, loader)

else:

train_non_iterable(exe, prog, loss, loader)

”’

Users can use return_list = True in dygraph mode.

”’

with fluid.dygraph.guard(places[0]):

loader = fluid.io.DataLoader.from_generator(capacity=2, return_list=True)

set_data_source(loader, places[0])

for image, label in loader():

relu = fluid.layers.relu(image)

assert image.shape == [BATCH_SIZE, 784]

assert label.shape == [BATCH_SIZE, 1]

assert relu.shape == [BATCH_SIZE, 784]

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