def test_mask(self, benchmark, prefix, drop, shared_dist_ctx, io_backend):
io_backend = backends_by_name[io_backend]
hdr = os.path.join(prefix, K2IS_FILE)
flist = filelist(hdr)
ctx = shared_dist_ctx
ds = ctx.load(filetype="k2is", path=hdr, io_backend=io_backend)
def mask():
return np.ones(ds.shape.sig, dtype=bool)
udf = ApplyMasksUDF(mask_factories=[mask], backends=('numpy', ))
# warmup executor
ctx.run_udf(udf=udf, dataset=ds)
if drop == "cold_cache":
drop_cache(flist)
elif drop == "warm_cache":
warmup_cache(flist)
else:
raise ValueError("bad param")
benchmark.pedantic(
ctx.run_udf,
kwargs=dict(udf=udf, dataset=ds),
warmup_rounds=0,
rounds=1,
iterations=1,
)
def test_mask(self, benchmark, drop, shared_dist_ctx, lt_ctx, context,
chunked_emd):
if context == 'dist':
ctx = shared_dist_ctx
elif context == 'inline':
ctx = lt_ctx
else:
raise ValueError
ds = chunked_emd
def mask():
return np.ones(ds.shape.sig, dtype=bool)
udf = ApplyMasksUDF(mask_factories=[mask], backends=('numpy', ))
# warmup executor
ctx.run_udf(udf=udf, dataset=ds)
if drop == "cold_cache":
drop_cache([ds.path])
elif drop == "warm_cache":
warmup_cache([ds.path])
else:
raise ValueError("bad param")
benchmark.pedantic(ctx.run_udf,
kwargs=dict(udf=udf, dataset=ds),
warmup_rounds=0,
rounds=1,
iterations=1)
def test_sparse_roi(self, benchmark, prefix, drop, io_backend,
shared_dist_ctx, sparsity):
io_backend = backends_by_name[io_backend]
mib_hdr = os.path.join(prefix, MIB_FILE)
flist = filelist(mib_hdr)
ctx = shared_dist_ctx
ds = ctx.load(filetype="mib", path=mib_hdr, io_backend=io_backend)
sparse_roi = np.zeros(ds.shape.nav.size, dtype=bool)
sparse_roi[::sparsity] = True
def mask():
return np.ones(ds.shape.sig, dtype=bool)
udf = ApplyMasksUDF(mask_factories=[mask], backends=('numpy', ))
# warmup executor
ctx.run_udf(udf=udf, dataset=ds)
if drop == "cold_cache":
drop_cache(flist)
elif drop == "warm_cache":
warmup_cache(flist)
else:
raise ValueError("bad param")
benchmark.pedantic(
ctx.run_udf,
kwargs=dict(udf=udf, dataset=ds, roi=sparse_roi),
warmup_rounds=0,
rounds=1,
iterations=1,
)
def test_sequential(benchmark, prefix, drop):
hdr = os.path.join(prefix, K2IS_FILE)
flist = filelist(hdr)
if drop == "cold_cache":
drop_cache(flist)
elif drop == "warm_cache":
warmup_cache(flist)
else:
raise ValueError("bad param")
benchmark.pedantic(warmup_cache,
args=(flist, ),
warmup_rounds=0,
rounds=1,
iterations=1)