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4fcc2fffc3
Summary: Pull Request resolved: https://github.com/pytorch/pytorch/pull/14958 Test with multiple threads Reviewed By: jianyuh Differential Revision: D13394791 fbshipit-source-id: 931a6c3bda15ebc816807e537dd0841c383e7a6f
52 lines
1.7 KiB
Python
52 lines
1.7 KiB
Python
from __future__ import absolute_import, division, print_function, unicode_literals
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import collections
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import caffe2.python.hypothesis_test_util as hu
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import hypothesis.strategies as st
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import numpy as np
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from caffe2.python import core, dyndep, workspace
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from dnnlowp_test_utils import check_quantized_results_close
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from hypothesis import given
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dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
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workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])
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class DNNLowPDequantizeOpTest(hu.HypothesisTestCase):
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@given(size=st.integers(1024, 2048), **hu.gcs_cpu_only)
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def test_dnnlowp_dequantize(self, size, gc, dc):
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min_ = -10.0
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max_ = 20.0
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X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32)
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Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
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outputs = []
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op_type_list = ["Dequantize", "Int8Dequantize"]
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engine = "DNNLOWP"
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outputs.append(Output(X, op_type="", engine=""))
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for op_type in op_type_list:
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net = core.Net("test_net")
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quantize = core.CreateOperator(
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"Quantize", ["X"], ["X_q"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([quantize])
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dequantize = core.CreateOperator(
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op_type, ["X_q"], ["Y"], engine=engine, device_option=gc
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)
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net.Proto().op.extend([dequantize])
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self.ws.create_blob("X").feed(X, device_option=gc)
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self.ws.run(net)
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outputs.append(
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Output(Y=self.ws.blobs["Y"].fetch(), op_type=op_type, engine=engine)
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)
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check_quantized_results_close(outputs)
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