def test_mxnet_reader_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
out, _ = fn.mxnet_reader(path=os.path.join(recordio_dir, "train.rec"),
index_path=os.path.join(recordio_dir,
"train.idx"),
shard_id=0,
num_shards=1)
pipe.set_outputs(out)
pipe.build()
for _ in range(3):
pipe.run()
def test_mfcc_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
data = fn.external_source(source=get_audio_data)
spectrum = fn.spectrogram(data, nfft=60, window_length=50, window_step=25)
mel = fn.mel_filter_bank(spectrum)
dec = fn.to_decibels(mel)
processed = fn.mfcc(dec)
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
def check_no_input(op, get_data=get_data, **kwargs):
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
with pipe:
processed = op(**kwargs)
if isinstance(processed, Iterable):
pipe.set_outputs(*processed)
else:
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
def test_image_decoder_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
with pipe:
input, _ = fn.readers.file(file_root=images_dir,
shard_id=0,
num_shards=1)
decoded = fn.decoders.image(input, output_type=types.RGB)
pipe.set_outputs(decoded)
pipe.build()
for _ in range(3):
pipe.run()
def test_combine_transforms_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
with pipe:
t = fn.transforms.translation(offset=(1, 2))
r = fn.transforms.rotation(angle=30.0)
s = fn.transforms.scale(scale=(2, 3))
out = fn.transforms.combine(t, r, s)
pipe.set_outputs(out)
pipe.build()
for _ in range(3):
pipe.run()
def audio_decoder_pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
encoded = fn.external_source(source=input_data,
cycle=False,
device='cpu')
decoded, _ = fn.decoders.audio(encoded,
downmix=True,
sample_rate=12345,
device=device)
pipe.set_outputs(decoded)
return pipe
def test_external_source_collection():
pipe = Pipeline(1, 3, 0)
batches = [
[np.array([1.5,2.5], dtype= np.float32)],
[np.array([-1, 3.5,4.5], dtype= np.float32)]
]
pipe.set_outputs(fn.external_source(batches))
pipe.build()
run_and_check(pipe, batches)
def test_operator_coco_reader_same_images():
file_root = os.path.join(test_data_root, 'db', 'coco_pixelwise', 'images')
train_annotations = os.path.join(test_data_root, 'db', 'coco_pixelwise', 'instances.json')
coco_dir = os.path.join(test_data_root, 'db', 'coco')
coco_dir_imgs = os.path.join(coco_dir, 'images')
coco_pixelwise_dir = os.path.join(test_data_root, 'db', 'coco_pixelwise')
coco_pixelwise_dir_imgs = os.path.join(coco_pixelwise_dir, 'images')
for file_root, annotations_file in [ \
(coco_dir_imgs, os.path.join(coco_dir, 'instances.json')),
(coco_pixelwise_dir_imgs, os.path.join(coco_pixelwise_dir, 'instances.json')),
(coco_pixelwise_dir_imgs, os.path.join(coco_pixelwise_dir, 'instances_rle_counts.json'))]:
pipe = Pipeline(batch_size=1, num_threads=4, device_id=0)
with pipe:
inputs1, boxes1, labels1, *other = fn.coco_reader(
file_root=file_root,
annotations_file=train_annotations,
name="reader1",
seed=1234
)
inputs2, boxes2, labels2, *other = fn.coco_reader(
file_root=file_root,
annotations_file=train_annotations,
polygon_masks=True,
name="reader2"
)
inputs3, boxes3, labels3, *other = fn.coco_reader(
file_root=file_root,
annotations_file=train_annotations,
pixelwise_masks=True,
name="reader3"
)
pipe.set_outputs(
inputs1, boxes1, labels1,
inputs2, boxes2, labels2,
inputs3, boxes3, labels3
)
pipe.build()
epoch_sz = pipe.epoch_size("reader1")
assert epoch_sz == pipe.epoch_size("reader2")
assert epoch_sz == pipe.epoch_size("reader3")
for i in range(epoch_sz):
inputs1, boxes1, labels1, inputs2, boxes2, labels2, inputs3, boxes3, labels3 = \
pipe.run()
np.testing.assert_array_equal(inputs1.at(0), inputs2.at(0))
np.testing.assert_array_equal(inputs1.at(0), inputs3.at(0))
np.testing.assert_array_equal(labels1.at(0), labels2.at(0))
np.testing.assert_array_equal(labels1.at(0), labels3.at(0))
np.testing.assert_array_equal(boxes1.at(0), boxes2.at(0))
np.testing.assert_array_equal(boxes1.at(0), boxes3.at(0))
def _test_permute_batch_out_of_range(device):
batch_size = 10
pipe = Pipeline(batch_size, 4, 0)
data = fn.external_source(source=lambda: gen_data(batch_size, np.int32),
device=device,
layout="abc")
perm = fn.batch_permutation()
pipe.set_outputs(
data, fn.permute_batch(data, indices=[0, 1, 2, 3, 4, 5, 10, 7, 8, 9]),
perm)
pipe.build()
pipe.run()
def check_per_sample_gaussian_blur(batch_size,
sigma_dim,
window_size_dim,
shape,
layout,
axes,
op_type="cpu"):
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
data = RandomlyShapedDataIterator(batch_size, max_shape=shape)
with pipe:
if sigma_dim is not None:
sigma = fn.uniform(range=[0.5, 3], shape=[sigma_dim])
sigma_arg = sigma
else:
# placeholder, so we can return something
sigma = fn.coin_flip(probability=0)
sigma_arg = None
if window_size_dim is not None:
window_radius = fn.uniform(range=[5, 10], shape=[window_size_dim])
window_size = fn.cast(window_radius, dtype=types.INT32) * 2 + 1
window_arg = window_size
else:
window_size = fn.coin_flip(probability=0)
window_arg = None
input = fn.external_source(data, layout=layout)
if op_type == "gpu":
input = input.gpu()
blurred = fn.gaussian_blur(input,
device=op_type,
sigma=sigma_arg,
window_size=window_arg)
pipe.set_outputs(blurred, input, sigma, window_size)
pipe.build()
for _ in range(test_iters):
result, input, sigma, window_size = pipe.run()
if op_type == "gpu":
result = result.as_cpu()
input = input.as_cpu()
input = to_batch(input, batch_size)
sigma = to_batch(sigma, batch_size)
window_size = to_batch(window_size, batch_size)
baseline = []
for i in range(batch_size):
sigma_arg = sigma[i] if sigma is not None else None
window_arg = window_size[i] if window_size_dim is not None else None
skip_axes = count_skip_axes(layout)
baseline.append(
gaussian_baseline(input[i], sigma_arg, window_arg, axes,
skip_axes))
check_batch(result, baseline, batch_size, max_allowed_error=1)
def check_coin_flip(device='cpu',
batch_size=32,
max_shape=[1e5],
p=None,
use_shape_like_input=False):
pipe = Pipeline(batch_size=batch_size,
device_id=0,
num_threads=3,
seed=123456)
with pipe:
shape_gen_f = lambda: random_shape(max_shape)
shape_arg = None
inputs = []
shape_out = None
if max_shape is not None:
if use_shape_like_input:
shape_like_in = dali.fn.external_source(
lambda: np.zeros(shape_gen_f()),
device=device,
batch=False)
inputs += [shape_like_in]
shape_out = dali.fn.shapes(shape_like_in)
else:
shape_arg = dali.fn.external_source(shape_gen_f, batch=False)
shape_out = shape_arg
outputs = [
dali.fn.random.coin_flip(*inputs,
device=device,
probability=p,
shape=shape_arg)
]
if shape_out is not None:
outputs += [shape_out]
pipe.set_outputs(*outputs)
pipe.build()
outputs = pipe.run()
data_out = outputs[0].as_cpu() \
if isinstance(outputs[0], TensorListGPU) else outputs[0]
shapes_out = None
if max_shape is not None:
shapes_out = outputs[1].as_cpu() \
if isinstance(outputs[1], TensorListGPU) else outputs[1]
p = p if p is not None else 0.5
for i in range(batch_size):
data = np.array(data_out[i])
assert np.logical_or(data == 0, data == 1).all()
if max_shape is not None:
sample_shape = np.array(shapes_out[i])
assert (data.shape == sample_shape).all()
total = len(data)
positive = np.count_nonzero(data)
np.testing.assert_allclose(p, positive / total,
atol=0.005) # +/- -.5%
def test_changing_dtype():
batch_size = 2
src_data = [
[np.ones((120, 120, 3), dtype=np.float32)]*batch_size,
[np.ones((120, 120, 3), dtype=np.uint8)]*batch_size
]
src_pipe = Pipeline(batch_size, 1, 0)
src_ext = fn.external_source(source=src_data, device='cpu')
src_pipe.set_outputs(src_ext)
src_pipe.build()
src_pipe.run()
src_pipe.run()
def get_pipeline(batch_size, tile, ratio, angle):
pipe = Pipeline(batch_size, 4, None)
with pipe:
input, _ = fn.file_reader(file_root=img_dir)
decoded = fn.image_decoder(input, device='cpu', output_type=types.RGB)
grided = fn.grid_mask(decoded,
device='cpu',
tile=tile,
ratio=ratio,
angle=angle)
pipe.set_outputs(grided, decoded)
return pipe
def check_generic_gaussian_blur(batch_size,
sigma,
window_size,
shape,
layout,
axes,
op_type="cpu",
in_dtype=np.uint8,
out_dtype=types.NO_TYPE):
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
data = RandomlyShapedDataIterator(batch_size,
max_shape=shape,
dtype=in_dtype)
# Extract the numpy type from DALI, we can have float32 or the same as input
if out_dtype == types.NO_TYPE:
result_type = in_dtype
elif dali_type(in_dtype) == out_dtype:
result_type = in_dtype
else:
result_type = np.float32
with pipe:
input = fn.external_source(data, layout=layout)
if op_type == "gpu":
input = input.gpu()
blurred = fn.gaussian_blur(input,
device=op_type,
sigma=sigma,
window_size=window_size,
dtype=out_dtype)
pipe.set_outputs(blurred, input)
pipe.build()
for _ in range(test_iters):
result, input = pipe.run()
if op_type == "gpu":
result = result.as_cpu()
input = input.as_cpu()
input = to_batch(input, batch_size)
skip_axes = count_skip_axes(layout)
baseline = [
gaussian_baseline(img,
sigma,
window_size,
axes,
skip_axes,
dtype=result_type) for img in input
]
max_error = 1 if result_type != np.float32 else 1e-04
check_batch(result,
baseline,
batch_size,
max_allowed_error=max_error,
expected_layout=layout)
def test_dtype_arg():
batch_size = 2
src_data = [[np.ones((120, 120, 3), dtype=np.uint8)] * batch_size]
src_pipe = Pipeline(batch_size, 1, 0)
src_ext = fn.external_source(source=src_data, dtype=DALIDataType.UINT8)
src_pipe.set_outputs(src_ext)
src_pipe.build()
out, = src_pipe.run()
for i in range(batch_size):
t = out.at(i)
assert t.dtype == np.uint8
np.array_equal(t, np.ones((120, 120, 3), dtype=np.uint8))
def test_ndim_data_mismatch():
batch_size = 2
src_data = [[[np.ones((120, 120, 3), dtype=np.uint8)] * batch_size,
[np.ones((120, 120), dtype=np.uint8)] * batch_size]]
src_pipe = Pipeline(batch_size, 1, 0)
src_ext1, src_ext2 = fn.external_source(source=src_data,
num_outputs=2,
dtype=DALIDataType.UINT8,
ndim=3)
src_pipe.set_outputs(src_ext1, src_ext2)
src_pipe.build()
src_pipe.run()
def test_reduce():
reduce_fns = [fn.reductions.std_dev, fn.reductions.variance]
def pipe(max_batch_size, input_data, device, /, reduce_fn):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
data = fn.external_source(source=input_data,
cycle=False,
device=device)
mean = fn.reductions.mean(data)
reduced = reduce_fn(data, mean)
pipe.set_outputs(reduced)
return pipe
def test_external_source_generator():
pipe = Pipeline(1, 3, 0)
def gen():
for i in range(5):
yield [make_array([i + 1.5], dtype=datapy.float32)]
pipe.set_outputs(fn.external_source(gen()))
pipe.build()
for i in range(5):
check_output(pipe.run(), [np.array([i + 1.5], dtype=np.float32)])
def run_reduce_with_layout(
batch_size, get_batch, reduction, axes, axis_names, batch_fn):
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=0)
with pipe:
input = fn.external_source(source=get_batch, layout="ABC")
reduced = reduction(input, keep_dims=False, axes=axes)
reduced_by_name = reduction(input, keep_dims=False, axis_names=axis_names)
pipe.set_outputs(reduced, reduced_by_name)
pipe.build()
run_and_compare_with_layout(batch_fn, pipe)
def test_external_source_collection_cycling():
pipe = Pipeline(1, 3, 0)
batches = [[np.array([1.5, 2.5], dtype=np.float32)],
[np.array([-1, 3.5, 4.5], dtype=np.float32)]]
pipe.set_outputs(fn.external_source(batches, cycle=True))
pipe.build()
# epochs are cycles over the source iterable
for epoch in range(3):
for batch in batches:
check_output(pipe.run(), batch)
def get_pipeline(device, batch_size, tile, ratio, angle):
pipe = Pipeline(batch_size, 4, 0)
with pipe:
input, _ = fn.readers.file(file_root=img_dir)
decoded = fn.decoders.image(input, device='cpu', output_type=types.RGB)
decoded = decoded.gpu() if device == 'gpu' else decoded
grided = fn.grid_mask(decoded,
device=device,
tile=tile,
ratio=ratio,
angle=angle)
pipe.set_outputs(grided, decoded)
return pipe
def check_corrupted_videos():
corrupted_videos = [
corrupted_video_data_root + '/' + f
for f in os.listdir(corrupted_video_data_root)
]
for corrupted in corrupted_videos:
pipe = Pipeline(batch_size=BATCH_SIZE, num_threads=4, device_id=0)
with pipe:
vid = fn.video_reader(device="gpu",
filenames=corrupted,
sequence_length=1)
pipe.set_outputs(vid)
pipe.build()
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
with pipe:
data = fn.external_source(source=input_data,
cycle=False,
device=device)
spectrum = fn.spectrogram(data,
nfft=60,
window_length=50,
window_step=25)
processed = fn.mel_filter_bank(spectrum)
pipe.set_outputs(processed)
return pipe
def test_external_source_gen_function_cycle():
pipe = Pipeline(1, 3, 0)
def gen():
for i in range(5):
yield [make_array([i + 1.5], dtype=datapy.float32)]
pipe.set_outputs(fn.external_source(gen, cycle=True))
pipe.build()
for _ in range(3):
for i in range(5):
check_output(pipe.run(), [np.array([i + 1.5], dtype=np.float32)])
def test_fn_python_function():
pipe = Pipeline(1, 1, 0, exec_pipelined = False, exec_async = False)
batch1 = [np.array([1,2,3])]
batch2 = [np.array([2,3,4])]
# we need a context, because we use an operator with potential side-effects (python_function)
with pipe:
src = fn.external_source([batch1, batch2])
out = fn.python_function(src, function = lambda x: x+1)
pipe.set_outputs(out)
pipe.build()
assert(np.array_equal(pipe.run()[0].at(0), batch1[0] + 1))
assert(np.array_equal(pipe.run()[0].at(0), batch2[0] + 1))
def image_decoder_slice_pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
encoded = fn.external_source(source=input_data,
cycle=False,
device='cpu')
anch = fn.constant(fdata=.1)
sh = fn.constant(fdata=.4)
decoded = fn.image_decoder_slice(encoded,
anch,
sh,
axes=0,
device=device)
pipe.set_outputs(decoded)
return pipe
def test_move_to_device_end():
test_data_shape = [1, 3, 0, 4]
def get_data():
out = [
np.empty(test_data_shape, dtype=np.uint8)
for _ in range(batch_size)
]
return out
pipe = Pipeline(batch_size=batch_size, num_threads=3, device_id=None)
outs = fn.external_source(source=get_data)
pipe.set_outputs(outs.gpu())
assert_raises(RuntimeError, pipe.build)
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
data = fn.external_source(source=input_data,
cycle=False,
device=device)
paste_posx = fn.uniform(range=(0, 1))
paste_posy = fn.uniform(range=(0, 1))
paste_ratio = fn.uniform(range=(1, 2))
processed = fn.bbox_paste(data,
paste_x=paste_posx,
paste_y=paste_posy,
ratio=paste_ratio)
pipe.set_outputs(processed)
return pipe
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
depthwise = fn.coin_flip()
horizontal = fn.coin_flip()
vertical = fn.coin_flip()
data = fn.external_source(source=input_data,
cycle=False,
device=device)
processed = fn.flip(data,
depthwise=depthwise,
horizontal=horizontal,
vertical=vertical)
pipe.set_outputs(processed)
return pipe
def check_bad_device(device_id):
test_data_shape = [1, 3, 0, 4]
def get_data():
out = [
np.empty(test_data_shape, dtype=np.uint8)
for _ in range(batch_size)
]
return out
pipe = Pipeline(batch_size=batch_size, num_threads=3, device_id=device_id)
outs = fn.external_source(source=get_data, device="gpu")
pipe.set_outputs(outs)
assert_raises(RuntimeError, pipe.build)
