def test_separated_exec_setup():
batch_size = 128
pipe = Pipeline(batch_size=batch_size,
num_threads=3,
device_id=None,
prefetch_queue_depth={
"cpu_size": 5,
"gpu_size": 3
})
inputs, labels = fn.caffe_reader(path=caffe_dir, shard_id=0, num_shards=1)
images = fn.image_decoder(inputs, output_type=types.RGB)
images = fn.resize(images, resize_x=224, resize_y=224)
images_cpu = fn.dump_image(images, suffix="cpu")
pipe.set_outputs(images, images_cpu)
pipe.build()
out = pipe.run()
assert (out[0].is_dense_tensor())
assert (out[1].is_dense_tensor())
assert (out[0].as_tensor().shape() == out[1].as_tensor().shape())
a_raw = out[0]
a_cpu = out[1]
for i in range(batch_size):
t_raw = a_raw.at(i)
t_cpu = a_cpu.at(i)
assert (np.sum(np.abs(t_cpu - t_raw)) == 0)
def test_caffe_reader_cpu():
pipe = Pipeline(batch_size=batch_size, num_threads=4, device_id=None)
out, _ = fn.caffe_reader(path=caffe_dir, shard_id=0, num_shards=1)
pipe.set_outputs(out)
pipe.build()
for _ in range(3):
pipe.run()
def test_compose_change_device():
batch_size = 3
pipe = Pipeline(batch_size, 1, 0)
size = fn.uniform(shape=2, range=(300,500))
c = ops.Compose([
ops.ImageDecoder(device="cpu"),
ops.Resize(size=size, device="gpu")
])
files, labels = fn.caffe_reader(path=caffe_db_folder, seed=1)
pipe.set_outputs(c(files), fn.resize(fn.image_decoder(files).gpu(), size=size))
pipe.build()
out = pipe.run()
assert isinstance(out[0], dali.backend.TensorListGPU)
test_utils.check_batch(out[0], out[1], batch_size=batch_size)