def test_tfrecord_reader_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
tfrecord = sorted(glob.glob(os.path.join(tfrecord_dir, '*[!i][!d][!x]')))
tfrecord_idx = sorted(glob.glob(os.path.join(tfrecord_dir, '*idx')))
input = fn.tfrecord_reader(path=tfrecord,
index_path=tfrecord_idx,
shard_id=0,
num_shards=1,
features={
"image/encoded":
tfrec.FixedLenFeature((), tfrec.string, ""),
"image/class/label":
tfrec.FixedLenFeature([1], tfrec.int64, -1)
})
out = input["image/encoded"]
pipe.set_outputs(out)
pipe.build()
for _ in range(3):
pipe.run()
def get_dali_pipeline(tfrec_filenames,
tfrec_idx_filenames,
height,
width,
batch_size,
num_threads,
device_id,
shard_id,
num_gpus,
deterministic=False,
dali_cpu=True,
training=True):
kwargs = dict()
if deterministic:
kwargs['seed'] = 7 * (1 + hvd.rank())
pipeline = Pipeline(batch_size, num_threads, device_id, **kwargs)
with pipeline:
inputs = fn.tfrecord_reader(
path=tfrec_filenames,
index_path=tfrec_idx_filenames,
random_shuffle=training,
shard_id=shard_id,
num_shards=num_gpus,
initial_fill=10000,
features={
'image/encoded': tfrec.FixedLenFeature((), tfrec.string, ""),
'image/class/label': tfrec.FixedLenFeature([1], tfrec.int64,
-1),
'image/class/text': tfrec.FixedLenFeature([], tfrec.string,
''),
'image/object/bbox/xmin':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/ymin':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/xmax':
tfrec.VarLenFeature(tfrec.float32, 0.0),
'image/object/bbox/ymax':
tfrec.VarLenFeature(tfrec.float32, 0.0)
})
decode_device = "cpu" if dali_cpu else "mixed"
resize_device = "cpu" if dali_cpu else "gpu"
if training:
images = fn.image_decoder_random_crop(
inputs["image/encoded"],
device=decode_device,
output_type=types.RGB,
random_aspect_ratio=[0.75, 1.25],
random_area=[0.05, 1.0],
num_attempts=100)
images = fn.resize(images,
device=resize_device,
resize_x=width,
resize_y=height)
else:
images = fn.image_decoder(inputs["image/encoded"],
device=decode_device,
output_type=types.RGB)
# Make sure that every image > 224 for CropMirrorNormalize
images = fn.resize(images,
device=resize_device,
resize_shorter=256)
images = fn.crop_mirror_normalize(images.gpu(),
dtype=types.FLOAT,
crop=(height, width),
mean=[123.68, 116.78, 103.94],
std=[58.4, 57.12, 57.3],
output_layout="HWC",
mirror=fn.random.coin_flip())
labels = inputs["image/class/label"].gpu()
labels -= 1 # Change to 0-based (don't use background class)
pipeline.set_outputs(images, labels)
return pipeline
def load_tfrecord(directory, batch_size, training):
tfrecord = []
tfrecord_idx = []
for f in os.listdir(directory):
fullpath = os.path.join(directory, f)
if not os.path.isfile(fullpath):
continue
if f.endswith(".tfrecord"):
tfrecord.append(fullpath)
if f.endswith(".idx"):
tfrecord_idx.append(fullpath)
tfrecord.sort()
tfrecord_idx.sort()
pipe = Pipeline(batch_size=batch_size, num_threads=32, device_id=0)
with pipe:
inputs = fn.tfrecord_reader(
path=tfrecord,
index_path=tfrecord_idx,
features={
"frame_one":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_two":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_three":
tfrec.FixedLenFeature((), tfrec.string, ""),
"frame_four":
tfrec.FixedLenFeature((), tfrec.string, ""),
"plus_one_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_one_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_two_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_two_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_three_position":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"plus_three_orientation":
tfrec.FixedLenFeature([3], tfrec.float32, 0.0),
"speed":
tfrec.FixedLenFeature([], tfrec.float32, 0.0),
})
frame1 = inputs["frame_one"]
frame1 = fn.image_decoder(frame1,
device="mixed",
output_type=types.RGB)
# frame1 = fn.resize(frame1, device="gpu", resize_shorter=256.)
frame1 = fn.crop_mirror_normalize(frame1,
device="gpu",
dtype=types.FLOAT,
mean=[0., 0., 0.],
std=[1., 1., 1.])
frame1 = fn.transpose(frame1, device="gpu", perm=[1, 2, 0])
frame2 = inputs["frame_two"]
frame2 = fn.image_decoder(frame2,
device="mixed",
output_type=types.RGB)
# frame2 = fn.resize(frame2, device="gpu", resize_shorter=256.)
frame2 = fn.crop_mirror_normalize(frame2,
device="gpu",
dtype=types.FLOAT,
mean=[0., 0., 0.],
std=[1., 1., 1.])
frame2 = fn.transpose(frame2, device="gpu", perm=[1, 2, 0])
position = inputs["plus_one_position"].gpu()
orientation = inputs["plus_one_orientation"].gpu()
speed = inputs["speed"].gpu()
image = fn.cat(frame1, frame2, device="gpu", axis=2)
pose = fn.cat(position, orientation, device="gpu", axis=0)
pipe.set_outputs(image, pose, speed)
# Define shapes and types of the outputs
shapes = ((batch_size, 480, 640), (batch_size, 6), (batch_size))
dtypes = (tf.float32, tf.float32)
# Create dataset
return dali_tf.DALIDataset(pipeline=pipe,
batch_size=batch_size,
output_shapes=shapes,
output_dtypes=dtypes,
device_id=0)