def _test_extremely_large_data(device):
in_size = 30000
out_size = 10
channels = 3
def get_data():
out = np.full([in_size, in_size, channels], 42, dtype=np.uint8)
for c in range(channels):
out[in_size - 1, in_size - 1, c] = c
return [out]
pipe = Pipeline(1, 3, 0, prefetch_queue_depth=1)
input = fn.external_source(source=get_data, device=device)
rotated = fn.warp_affine(input,
matrix=[-1, 0, in_size, 0, -1, in_size],
fill_value=255.0,
size=[out_size, out_size],
interp_type=types.INTERP_NN)
pipe.set_outputs(rotated)
pipe.build()
out = None
try:
out, = pipe.run()
except RuntimeError as e:
if "bad_alloc" in str(e):
print("Skipping test due to out-of-memory error:", e)
return
raise
except MemoryError as e:
print("Skipping test due to out-of-memory error:", e)
return
if device == "cpu":
out = out.at(0)
else:
out = out.as_cpu().at(0)
assert out.shape == (out_size, out_size, channels)
for c in range(channels):
assert out[0, 0, c] == c
def dali_frame_splicing_graph(x, nfeatures, x_len, stacking=1, subsampling=1):
if stacking > 1:
seq = [x]
for n in range(1, stacking):
f = fn.slice(x,
n,
x_len,
axes=(1, ),
out_of_bounds_policy='pad',
fill_values=0)
seq.append(f)
x = fn.cat(*seq, axis=0)
nfeatures = nfeatures * stacking
if subsampling > 1:
out_len = (x_len + subsampling - 1) // subsampling
m = fn.transforms.scale(scale=[subsampling, 1], center=[0.5, 0])
x = fn.reshape(x, rel_shape=[1, 1, -1],
layout="HWC") # Layout required by WarpAffine
size = fn.cat(nfeatures, out_len)
x = fn.warp_affine(x, matrix=m, size=size, interp_type=types.INTERP_NN)
x = fn.reshape(x, rel_shape=[1, 1], layout="ft")
return x