def test_output_layout():
pipe = CommonPipeline(1, 1, 0, 999, images_dir)
with pipe:
images, _ = pipe.load()
out1, out2 = fn.python_function(images,
function=lambda x: (x, x.mean(2)),
num_outputs=2,
output_layouts=['ABC', 'DE'])
out3, out4 = fn.python_function(images,
function=lambda x: (x, x / 2),
num_outputs=2,
output_layouts='FGH')
out5, out6 = fn.python_function(images,
function=lambda x: (x, x / 2),
num_outputs=2,
output_layouts=['IJK'])
pipe.set_outputs(out1, out2, out3, out4, out5, out6)
pipe.build()
out1, out2, out3, out4, out5, out6 = pipe.run()
assert (out1.layout() == 'ABC')
assert (out2.layout() == 'DE')
assert (out3.layout() == 'FGH')
assert (out4.layout() == 'FGH')
assert (out5.layout() == 'IJK')
assert (out6.layout() == '')
def test_invalid_layouts_arg():
pipe = Pipeline(1, 1, 0, 999, exec_async=False, exec_pipelined=False)
with pipe:
out = fn.python_function(function=lambda: numpy.zeros((1, 1)), output_layouts=['HW', 'HWC'])
pipe.set_outputs(out)
pipe.build()
pipe.run()
def pipe(max_batch_size, input_data, device):
pipe = Pipeline(batch_size=max_batch_size, num_threads=4, device_id=0, exec_async=False,
exec_pipelined=False)
with pipe:
data = fn.external_source(source=input_data, cycle=False, device=device)
processed = fn.python_function(data, function=resize, num_outputs=1)
pipe.set_outputs(processed)
return pipe
def bricon_ref_pipe(data_iterator, contrast_center, dtype, has_3_dims=False):
brightness, brightness_shift = brightness_params()
contrast = contrast_param()
inp = fn.external_source(source=data_iterator)
layout = "FHWC" if has_3_dims else "HWC"
return fn.python_function(inp, brightness, brightness_shift, contrast,\
function=ref_operator(contrast_center, dali_type_to_np(dtype)),
output_layouts=layout)
def test_python_function():
pipe = dali.pipeline.Pipeline(3,1,0, exec_async=False, exec_pipelined=False)
with pipe:
def func(inp):
ret = [x*x for x in inp]
return [ret]
out_cpu = fn.python_function(np.array([[1,2],[3,4]]), function=func, batch_processing=True)
pipe.set_outputs(out_cpu)
pipe.build()
o = pipe.run()
assert np.array_equal(o[0].at(0), np.array([[1,4],[9,16]]))
def create_ref_pipe(channel_first, seq_len, interp, dtype, w, h, batch_size=2):
pipe = dali.pipeline.Pipeline(batch_size, 1, 0, 0, exec_async=False, exec_pipelined=False)
with pipe:
layout = "FCHW" if channel_first else "FHWC"
ext = fn.external_source(GetSequences(channel_first, seq_len, batch_size), layout=layout)
pil_resized = fn.python_function(ext, function=resize_PIL(channel_first, interp, w, h),
batch_processing=False)
if dtype is not None: # unfortunately, PIL can't quite handle that
pil_resized = fn.cast(pil_resized, dtype=dtype)
pil_resized = fn.reshape(pil_resized, layout=layout)
pipe.set_outputs(pil_resized)
return pipe
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 test_python_function_cpu():
def resize(image):
return np.array(Image.fromarray(image).resize((50, 10)))
pipe = Pipeline(batch_size=batch_size,
num_threads=4,
device_id=None,
exec_async=False,
exec_pipelined=False)
with pipe:
data = fn.external_source(source=get_data, layout="HWC")
processed = fn.python_function(data, function=resize)
pipe.set_outputs(processed)
pipe.build()
for _ in range(3):
pipe.run()
def test_python_operator_not_allowed_in_tf_dataset_error():
pipeline = Pipeline(1, 1, 0, exec_pipelined=False, exec_async=False)
with pipeline:
output = fn.python_function(function=lambda: np.zeros((3, 3, 3)))
pipeline.set_outputs(output)
shapes = ((1, 3, 3, 3))
dtypes = (tf.float32)
with tf.device('/cpu:0'):
_ = dali_tf.DALIDataset(pipeline=pipeline,
batch_size=1,
output_shapes=shapes,
output_dtypes=dtypes,
num_threads=1,
device_id=0)
def bricon_ref_pipe(data_iterator, contrast_center, dtype, dev='cpu'):
brightness, brightness_shift = brightness_params()
contrast = contrast_param()
inp = fn.external_source(source=data_iterator)
return fn.python_function(inp, brightness, brightness_shift, contrast,\
function=ref_operator(contrast_center, dali_type_to_np(dtype)))
