Using Tensorflow DALI plugin: using various readers¶
Overview¶
This example shows how different readers could be used to interact with Tensorflow. It shows how flexible DALI is.
The following readers are used in this example:
MXNetReader
CaffeReader
FileReader
TFRecordReader
For details on how to use them please see other examples.
Let us start with defining some global constants
[1]:
# MXNet RecordIO
db_folder = "/data/imagenet/train-480-val-256-recordio/"
# Caffe LMDB
lmdb_folder = "/data/imagenet/train-lmdb-256x256"
# image dir with plain jpeg files
image_dir = "../images"
# TFRecord
tfrecord = "/data/imagenet/train-val-tfrecord-480/train-00001-of-01024"
tfrecord_idx = "idx_files/train-00001-of-01024.idx"
tfrecord2idx_script = "tfrecord2idx"
N = 8 # number of GPUs
BATCH_SIZE = 128 # batch size per GPU
ITERATIONS = 32
IMAGE_SIZE = 3
Create idx file by calling tfrecord2idx
script
[2]:
from subprocess import call
import os.path
if not os.path.exists("idx_files"):
os.mkdir("idx_files")
if not os.path.isfile(tfrecord_idx):
call([tfrecord2idx_script, tfrecord, tfrecord_idx])
Let us define: - common part of pipeline, other pipelines will inherit it
[3]:
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.types as types
class CommonPipeline(Pipeline):
def __init__(self, batch_size, num_threads, device_id):
super(CommonPipeline, self).__init__(batch_size, num_threads, device_id)
self.decode = ops.nvJPEGDecoder(device = "mixed", output_type = types.RGB)
self.resize = ops.Resize(device = "gpu",
image_type = types.RGB,
interp_type = types.INTERP_LINEAR)
self.cmn = ops.CropMirrorNormalize(device = "gpu",
output_dtype = types.FLOAT,
crop = (227, 227),
image_type = types.RGB,
mean = [128., 128., 128.],
std = [1., 1., 1.])
self.uniform = ops.Uniform(range = (0.0, 1.0))
self.resize_rng = ops.Uniform(range = (256, 480))
def base_define_graph(self, inputs, labels):
images = self.decode(inputs)
images = self.resize(images, resize_shorter = self.resize_rng())
output = self.cmn(images, crop_pos_x = self.uniform(),
crop_pos_y = self.uniform())
return (output, labels.gpu())
MXNetReaderPipeline
[4]:
from nvidia.dali.pipeline import Pipeline
import nvidia.dali.ops as ops
import nvidia.dali.types as types
class MXNetReaderPipeline(CommonPipeline):
def __init__(self, batch_size, num_threads, device_id, num_gpus):
super(MXNetReaderPipeline, self).__init__(batch_size, num_threads, device_id)
self.input = ops.MXNetReader(path = [db_folder+"train.rec"], index_path=[db_folder+"train.idx"],
random_shuffle = True, shard_id = device_id, num_shards = num_gpus)
def define_graph(self):
images, labels = self.input(name="Reader")
return self.base_define_graph(images, labels)
CaffeReadPipeline
[5]:
class CaffeReadPipeline(CommonPipeline):
def __init__(self, batch_size, num_threads, device_id, num_gpus):
super(CaffeReadPipeline, self).__init__(batch_size, num_threads, device_id)
self.input = ops.CaffeReader(path = lmdb_folder,
random_shuffle = True, shard_id = device_id, num_shards = num_gpus)
def define_graph(self):
images, labels = self.input()
return self.base_define_graph(images, labels)
FileReadPipeline
[6]:
class FileReadPipeline(CommonPipeline):
def __init__(self, batch_size, num_threads, device_id, num_gpus):
super(FileReadPipeline, self).__init__(batch_size, num_threads, device_id)
self.input = ops.FileReader(file_root = image_dir)
def define_graph(self):
images, labels = self.input()
return self.base_define_graph(images, labels)
TFRecordPipeline
[7]:
import nvidia.dali.tfrecord as tfrec
class TFRecordPipeline(CommonPipeline):
def __init__(self, batch_size, num_threads, device_id, num_gpus):
super(TFRecordPipeline, self).__init__(batch_size, num_threads, device_id)
self.input = ops.TFRecordReader(path = tfrecord,
index_path = tfrecord_idx,
features = {"image/encoded" : tfrec.FixedLenFeature((), tfrec.string, ""),
"image/class/label": tfrec.FixedLenFeature([1], tfrec.int64, -1)
})
def define_graph(self):
inputs = self.input()
images = inputs["image/encoded"]
labels = inputs["image/class/label"]
return self.base_define_graph(images, labels)
Now let us create function which builds pipeline on demand:
[8]:
import tensorflow as tf
import nvidia.dali.plugin.tf as dali_tf
def get_batch_test_dali(batch_size, pipe_type):
pipe_name, label_type, _ = pipe_type
pipes = [pipe_name(batch_size=batch_size, num_threads=2, device_id = device_id, num_gpus = N) for device_id in range(N)]
daliop = dali_tf.DALIIterator()
images = []
labels = []
for d in range(N):
with tf.device('/gpu:%i' % d):
image, label = daliop(pipeline = pipes[d],
shapes = [(BATCH_SIZE, 3, 227, 227), ()],
dtypes = [tf.int32, label_type],
device_id = d)
images.append(image)
labels.append(label)
return [images, labels]
At the end let us test if all pipelines have been correctly built and run with TF session
[9]:
from __future__ import print_function
import numpy as np
pipe_types = [[MXNetReaderPipeline, tf.float32, (0, 999)],
[CaffeReadPipeline, tf.int32, (0, 999)],
[FileReadPipeline, tf.int32, (0, 1)],
[TFRecordPipeline, tf.int64, (1, 1000)]]
for pipe_name in pipe_types:
print ("RUN: " + pipe_name[0].__name__)
test_batch = get_batch_test_dali(BATCH_SIZE, pipe_name)
x = tf.placeholder(tf.float32, shape=[BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, 3], name='x')
gpu_options = tf.GPUOptions(per_process_gpu_memory_fraction=0.8)
config = tf.ConfigProto(gpu_options=gpu_options)
with tf.Session(config=config) as sess:
for i in range(ITERATIONS):
imgs, labels = sess.run(test_batch)
# Testing correctness of labels
for label in labels:
## labels need to be integers
assert(np.equal(np.mod(label, 1), 0).all())
## labels need to be in range pipe_name[2]
assert((label >= pipe_name[2][0]).all())
assert((label <= pipe_name[2][1]).all())
print("OK : " + pipe_name[0].__name__)
RUN: MXNetReaderPipeline
OK : MXNetReaderPipeline
RUN: CaffeReadPipeline
OK : CaffeReadPipeline
RUN: FileReadPipeline
OK : FileReadPipeline
RUN: TFRecordPipeline
OK : TFRecordPipeline