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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/gather_dnnlowp_op_test.py (2790B)

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 DNNLowPGatherOpTest(hu.HypothesisTestCase):
    @given(
        dim1=st.integers(256, 512),
        dim2=st.integers(32, 256),
        is_empty=st.booleans(),
        in_quantized=st.booleans(),
        out_quantized=st.booleans(),
        **hu.gcs_cpu_only
    )
    def test_dnnlowp_gather(self, dim1, dim2, is_empty, in_quantized, out_quantized, gc, dc):
        if is_empty:
            dim2 = 0
        # FIXME : DNNLOWP Gather doesn't support quantized input and
        # dequantized output
        if in_quantized:
            out_quantized = True

data = (np.random.rand(dim1) * 2 - 1).astype(np.float32)
        index = np.floor(np.random.rand(dim2) * dim1).astype(np.int32)

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

op_engine_list = [
            ("Gather", ""),
            ("Gather", "DNNLOWP"),
            ("Int8Gather", "DNNLOWP"),
        ]

for op_type, engine in op_engine_list:
            net = core.Net("test_net")

do_quantize = "DNNLOWP" in engine and in_quantized
            do_dequantize = "DNNLOWP" in engine and out_quantized

if do_quantize:
                quantize_data = core.CreateOperator(
                    "Quantize", ["data"], ["data_q"], engine=engine, device_option=gc
                )
                net.Proto().op.extend([quantize_data])

gather = core.CreateOperator(
                op_type,
                ["data_q" if do_quantize else "data", "index"],
                ["out_q" if do_dequantize else "out"],
                dequantize_output=not do_dequantize,
                engine=engine,
                device_option=gc,
            )
            net.Proto().op.extend([gather])

if do_dequantize:
                dequantize = core.CreateOperator(
                    "Dequantize", ["out_q"], ["out"], engine=engine, device_option=gc
                )
                net.Proto().op.extend([dequantize])

self.ws.create_blob("data").feed(data, device_option=gc)
            self.ws.create_blob("index").feed(index, device_option=gc)
            self.ws.run(net)
            outputs.append(
                Output(out=self.ws.blobs["out"].fetch(), op_type=op_type, engine=engine)
            )

check_quantized_results_close(outputs, ref=data)

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 DNNLowPGatherOpTest(hu.HypothesisTestCase): @given( dim1=st.integers(256, 512), dim2=st.integers(32, 256), is_empty=st.booleans(), in_quantized=st.booleans(), out_quantized=st.booleans(), **hu.gcs_cpu_only ) def test_dnnlowp_gather(self, dim1, dim2, is_empty, in_quantized, out_quantized, gc, dc): if is_empty: dim2 = 0 # FIXME : DNNLOWP Gather doesn't support quantized input and # dequantized output if in_quantized: out_quantized = True data = (np.random.rand(dim1) * 2 - 1).astype(np.float32) index = np.floor(np.random.rand(dim2) * dim1).astype(np.int32) Output = collections.namedtuple("Output", ["out", "op_type", "engine"]) outputs = [] op_engine_list = [ ("Gather", ""), ("Gather", "DNNLOWP"), ("Int8Gather", "DNNLOWP"), ] for op_type, engine in op_engine_list: net = core.Net("test_net") do_quantize = "DNNLOWP" in engine and in_quantized do_dequantize = "DNNLOWP" in engine and out_quantized if do_quantize: quantize_data = core.CreateOperator( "Quantize", ["data"], ["data_q"], engine=engine, device_option=gc ) net.Proto().op.extend([quantize_data]) gather = core.CreateOperator( op_type, ["data_q" if do_quantize else "data", "index"], ["out_q" if do_dequantize else "out"], dequantize_output=not do_dequantize, engine=engine, device_option=gc, ) net.Proto().op.extend([gather]) if do_dequantize: dequantize = core.CreateOperator( "Dequantize", ["out_q"], ["out"], engine=engine, device_option=gc ) net.Proto().op.extend([dequantize]) self.ws.create_blob("data").feed(data, device_option=gc) self.ws.create_blob("index").feed(index, device_option=gc) self.ws.run(net) outputs.append( Output(out=self.ws.blobs["out"].fetch(), op_type=op_type, engine=engine) ) check_quantized_results_close(outputs, ref=data)