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import collections
import caffe2.python.hypothesis_test_util as hu
import hypothesis.strategies as st
import numpy as np
from caffe2.python import core, dyndep, workspace
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPSigmoidOpTest(hu.HypothesisTestCase):
@given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only)
def test_dnnlowp_sigmoid(self, size, is_empty, gc, dc):
if is_empty:
size = 0
X = (np.random.rand(size) * 20 - 10).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_engine_list = [
("Sigmoid", ""),
("Sigmoid", "DNNLOWP"),
("Int8Sigmoid", "DNNLOWP"),
]
for op_type, engine in op_engine_list:
net = core.Net("test_net")
if engine == "DNNLOWP":
quantize = core.CreateOperator(
"Quantize",
["X"],
["X_q"],
engine=engine,
device_option=gc,
followed_by="Sigmoid",
)
net.Proto().op.extend([quantize])
sigmoid = core.CreateOperator(
op_type,
["X_q" if engine == "DNNLOWP" else "X"],
["Y_q" if engine == "DNNLOWP" else "Y"],
engine=engine,
device_option=gc,
)
net.Proto().op.extend([sigmoid])
if engine == "DNNLOWP":
dequantize = core.CreateOperator(
"Dequantize", ["Y_q"], ["Y"], engine=engine, device_option=gc
)
net.Proto().op.extend([dequantize])
self.ws.create_blob("X").feed(X, device_option=gc)
self.ws.run(net)
outputs.append(
Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
)
for o in outputs:
np.testing.assert_allclose(o.Y, outputs[0].Y, atol=0.01, rtol=0)
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