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/usr/local/lib64/python3.6/site-packages/caffe2/quantization/server/

/usr/local/lib64/python3.6/site-packages/caffe2/quantization/server

Name	Size	Mode	Actions
__pycache__/	-	0755	rm
batch_matmul_dnnlowp_op_test.py	10152	0644	edit dl rm
batch_permutation_dnnlowp_op_test.py	1449	0644	edit dl rm
channel_shuffle_dnnlowp_op_test.py	3890	0644	edit dl rm
compute_equalization_scale_test.py	3154	0644	edit dl rm
concat_dnnlowp_op_test.py	3265	0644	edit dl rm
conv_depthwise_dnnlowp_op_test.py	10931	0644	edit dl rm
conv_dnnlowp_acc16_op_test.py	14202	0644	edit dl rm
conv_dnnlowp_op_test.py	17919	0644	edit dl rm
conv_groupwise_dnnlowp_acc16_op_test.py	11664	0644	edit dl rm
conv_groupwise_dnnlowp_op_test.py	9537	0644	edit dl rm
dequantize_dnnlowp_op_test.py	1743	0644	edit dl rm
dnnlowp_test_utils.py	14544	0644	edit dl rm
elementwise_add_dnnlowp_op_test.py	6662	0644	edit dl rm
elementwise_linear_dnnlowp_op_test.py	3101	0644	edit dl rm
elementwise_mul_dnnlowp_op_test.py	6315	0644	edit dl rm
elementwise_sum_dnnlowp_op_test.py	9682	0644	edit dl rm
fully_connected_dnnlowp_acc16_op_test.py	8324	0644	edit dl rm
fully_connected_dnnlowp_op_test.py	10463	0644	edit dl rm
fully_connected_fp16_test.py	2519	0644	edit dl rm
fully_connected_rowwise_dnnlowp_op_test.py	5434	0644	edit dl rm
gather_dnnlowp_op_test.py	2790	0644	edit dl rm
group_norm_dnnlowp_op_test.py	4566	0644	edit dl rm
int8_gen_quant_params_min_max_test.py	3491	0644	edit dl rm
int8_gen_quant_params_test.py	3550	0644	edit dl rm
int8_quant_scheme_blob_fill_test.py	1838	0644	edit dl rm
lstm_unit_dnnlowp_op_test.py	3975	0644	edit dl rm
observer_test.py	1011	0644	edit dl rm
pool_dnnlowp_op_test.py	6125	0644	edit dl rm
quantize_dnnlowp_op_test.py	2668	0644	edit dl rm
relu_dnnlowp_op_test.py	2418	0644	edit dl rm
resize_nearest_3d_dnnlowp_op_test.py	2289	0644	edit dl rm
resize_nearest_dnnlowp_op_test.py	1994	0644	edit dl rm
sigmoid_dnnlowp_op_test.py	2203	0644	edit dl rm
spatial_batch_norm_dnnlowp_op_test.py	4094	0644	edit dl rm
tanh_dnnlowp_op_test.py	2187	0644	edit dl rm
utils.py	16059	0644	edit dl rm
__init__.py	0	0644	edit dl rm

Edit: /usr/local/lib64/python3.6/site-packages/caffe2/quantization/server/relu_dnnlowp_op_test.py (2418B)

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 DNNLowPReluOpTest(hu.HypothesisTestCase):
    @given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only)
    def test_dnnlowp_relu(self, size, is_empty, gc, dc):
        if is_empty:
            size = 0
        min_ = -10.0
        max_ = 10.0
        scale = (max_ - min_) / 255
        zero_point = int(np.round(-min_ / scale))
        X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32)

Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
        outputs = []

op_engine_list = [("Relu", ""), ("Relu", "DNNLOWP"), ("Int8Relu", "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,
                    Y_scale=scale,
                    Y_zero_point=zero_point,
                )
                net.Proto().op.extend([quantize])

relu = 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([relu])

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)
            )

# Y = max(0, X) so the only error is quantization of inputs
        check_quantized_results_close(outputs, ref=X)

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 DNNLowPReluOpTest(hu.HypothesisTestCase): @given(size=st.integers(1024, 2048), is_empty=st.booleans(), **hu.gcs_cpu_only) def test_dnnlowp_relu(self, size, is_empty, gc, dc): if is_empty: size = 0 min_ = -10.0 max_ = 10.0 scale = (max_ - min_) / 255 zero_point = int(np.round(-min_ / scale)) X = (np.random.rand(size) * (max_ - min_) + min_).astype(np.float32) Output = collections.namedtuple("Output", ["Y", "op_type", "engine"]) outputs = [] op_engine_list = [("Relu", ""), ("Relu", "DNNLOWP"), ("Int8Relu", "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, Y_scale=scale, Y_zero_point=zero_point, ) net.Proto().op.extend([quantize]) relu = 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([relu]) 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) ) # Y = max(0, X) so the only error is quantization of inputs check_quantized_results_close(outputs, ref=X)