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.decoders.image_slice(encoded, anch, sh, axes=0, device=device)
pipe.set_outputs(decoded)
return pipe
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
anch = fn.constant(fdata=.1, device='cpu')
sh = fn.constant(fdata=.5, device='cpu')
data = fn.external_source(source=input_data, cycle=False, device=device)
processed = fn.slice(data, anch, sh, axes=0, device=device)
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)
# just to drive the variable batch size.
batch_size_setter = fn.external_source(source=input_data, cycle=False, device=device)
data = fn.constant(fdata=3.1415, shape=(10, 10), device=device)
pipe.set_outputs(data, batch_size_setter)
return pipe
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0)
shape = fn.external_source(source=input_data,
cycle=False,
device='cpu')
data = fn.constant(fdata=3.1415, shape=shape, device=device)
pipe.set_outputs(data)
return pipe
def make_pipe():
fake_data = fn.constant(idata=0, shape=[10, 10, 3], dtype=types.FLOAT, device=device)
axes = fn.random.uniform(range=wrong_axes_range, shape=(2,), dtype=types.INT32)
rel_start = fn.random.uniform(range=[0.0, 0.3], shape=(2,), dtype=types.FLOAT)
rel_shape = fn.random.uniform(range=[0.4, 0.6], shape=(2,), dtype=types.FLOAT)
if named_args:
sliced = fn.slice(fake_data, rel_start=rel_start, rel_shape=rel_shape, axes=axes)
else:
sliced = fn.slice(fake_data, rel_start, rel_shape, axes=axes)
return sliced
def make_pipe():
fake_data = fn.constant(idata=0,
shape=[10, 10, 3],
dtype=types.FLOAT,
device=device)
axes = fn.random.uniform(range=wrong_axes_range,
shape=(2, ),
dtype=types.INT32)
padded = fn.pad(fake_data, axes=axes)
return padded
def dali_const_dataset(batch_size, sample_size, device_id):
pipeline = Pipeline(batch_size, 4, device_id)
const = fn.constant(device='gpu', fdata=sample_size * [1.])
pipeline.set_outputs(const)
dali_dataset = dali_tf.DALIDataset(
pipeline=pipeline,
batch_size=batch_size,
output_shapes=((batch_size, sample_size)),
output_dtypes=(tf.float32),
device_id=device_id)
return dali_dataset
def make_pipe():
fake_data = fn.constant(idata=0,
shape=[10, 10, 3],
dtype=types.FLOAT,
device="cpu")
rel_start = fn.random.uniform(range=[0.0, 0.3],
shape=(2, ),
dtype=types.FLOAT,
device="gpu")
rel_shape = fn.random.uniform(range=[0.4, 0.6],
shape=(2, ),
dtype=types.FLOAT,
device="gpu")
sliced = fn.slice(fake_data, rel_start, rel_shape, device="cpu")
return sliced
def nan_check_pipeline(source):
return fn.constant(fdata=next(source))
def pipeline():
data = fn.constant(idata=255,
shape=(10, 10, 3),
dtype=type,
device=device)
return op(data)
