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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 caffe2.quantization.server.dnnlowp_test_utils import check_quantized_results_close
from hypothesis import given
dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
class DNNLowPDequantizeOpTest(hu.HypothesisTestCase):
@given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only)
def test_dnnlowp_dequantize(self, size, is_empty, gc, dc):
if is_empty:
size = 0
min_ = -10.0
max_ = 20.0
X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32)
Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
outputs = []
op_type_list = ["Dequantize", "Int8Dequantize"]
engine = "DNNLOWP"
outputs.append(Output(X, op_type="", engine=""))
for op_type in op_type_list:
net = core.Net("test_net")
quantize = core.CreateOperator(
"Quantize", ["X"], ["X_q"], engine=engine, device_option=gc
)
net.Proto().op.extend([quantize])
dequantize = core.CreateOperator(
op_type, ["X_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)
)
check_quantized_results_close(outputs)
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