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usr
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local
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lib64
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python3.6
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site-packages
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caffe2
/
quantization
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server
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/usr/local/lib64/python3.6/site-packages/caffe2/quantization/server
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__pycache__/
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batch_matmul_dnnlowp_op_test.py
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batch_permutation_dnnlowp_op_test.py
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channel_shuffle_dnnlowp_op_test.py
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compute_equalization_scale_test.py
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concat_dnnlowp_op_test.py
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conv_depthwise_dnnlowp_op_test.py
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conv_dnnlowp_acc16_op_test.py
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conv_dnnlowp_op_test.py
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conv_groupwise_dnnlowp_acc16_op_test.py
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conv_groupwise_dnnlowp_op_test.py
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dequantize_dnnlowp_op_test.py
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dnnlowp_test_utils.py
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elementwise_add_dnnlowp_op_test.py
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elementwise_linear_dnnlowp_op_test.py
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elementwise_mul_dnnlowp_op_test.py
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elementwise_sum_dnnlowp_op_test.py
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fully_connected_dnnlowp_acc16_op_test.py
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fully_connected_dnnlowp_op_test.py
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fully_connected_fp16_test.py
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fully_connected_rowwise_dnnlowp_op_test.py
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gather_dnnlowp_op_test.py
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group_norm_dnnlowp_op_test.py
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int8_gen_quant_params_min_max_test.py
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int8_gen_quant_params_test.py
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int8_quant_scheme_blob_fill_test.py
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lstm_unit_dnnlowp_op_test.py
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observer_test.py
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pool_dnnlowp_op_test.py
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quantize_dnnlowp_op_test.py
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relu_dnnlowp_op_test.py
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resize_nearest_3d_dnnlowp_op_test.py
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resize_nearest_dnnlowp_op_test.py
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sigmoid_dnnlowp_op_test.py
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spatial_batch_norm_dnnlowp_op_test.py
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tanh_dnnlowp_op_test.py
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utils.py
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__init__.py
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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)
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