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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/pool_dnnlowp_op_test.py (6125B)

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 assume, given

dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops")
workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"])

class DNNLowPOpPoolTest(hu.HypothesisTestCase):
    @given(
        stride=st.integers(1, 3),
        pad=st.integers(0, 3),
        kernel=st.integers(1, 5),
        size=st.integers(1, 20),
        input_channels=st.integers(1, 3),
        batch_size=st.integers(1, 3),
        order=st.sampled_from(["NCHW", "NHWC"]),
        in_quantized=st.booleans(),
        **hu.gcs_cpu_only
    )
    def test_dnnlowp_max_pool(
        self,
        stride,
        pad,
        kernel,
        size,
        input_channels,
        batch_size,
        order,
        in_quantized,
        gc,
        dc,
    ):
        assume(kernel <= size)
        assume(pad < kernel)

C = input_channels
        N = batch_size
        H = W = size

min_ = -10
        max_ = 20
        if order == "NCHW":
            X = np.round(np.random.rand(N, C, H, W) * (max_ - min_) + min_)
        elif order == "NHWC":
            X = np.round(np.random.rand(N, H, W, C) * (max_ - min_) + min_)
        X = X.astype(np.float32)
        Output = collections.namedtuple("Output", ["Y", "op_type", "engine"])
        outputs = []

op_engine_list = [
            ("MaxPool", ""),
            ("MaxPool", "DNNLOWP"),
            ("Int8MaxPool", "DNNLOWP"),
        ]

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

do_quantize = "DNNLOWP" in engine and in_quantized

if do_quantize:
                quantize = core.CreateOperator(
                    "Quantize", ["X"], ["X_q"], engine=engine, device_option=gc
                )
                net.Proto().op.extend([quantize])

max_pool = core.CreateOperator(
                op_type,
                ["X_q" if do_quantize else "X"],
                ["Y_q" if engine == "DNNLOWP" else "Y"],
                stride=stride,
                kernel=kernel,
                pad=pad,
                order=order,
                engine=engine,
                device_option=gc,
            )
            net.Proto().op.extend([max_pool])

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_i = max(X_j) so the only error is in quantization of inputs
        check_quantized_results_close(outputs, ref=X)

@given(
        ndim=st.integers(2, 3),
        stride=st.integers(1, 1),
        pad=st.integers(0, 0),
        kernel=st.integers(1, 5),
        size=st.integers(2, 2),
        input_channels=st.integers(1, 1),
        batch_size=st.integers(2, 2),
        order=st.sampled_from(["NCHW", "NHWC"]),
        in_quantized=st.booleans(),
        **hu.gcs_cpu_only
    )
    def test_dnnlowp_average_pool(
        self,
        ndim,
        stride,
        pad,
        kernel,
        size,
        input_channels,
        batch_size,
        order,
        in_quantized,
        gc,
        dc,
    ):
        kernel = 2  # Only kernel size 2 is supported
        assume(kernel <= size)
        assume(pad < kernel)

C = input_channels
        N = batch_size

strides = (stride,) * ndim
        pads = (pad,) * (ndim * 2)
        kernels = (kernel,) * ndim
        sizes = (size,) * ndim

# X has scale 1, so no input quantization error
        min_ = -100
        max_ = min_ + 255
        if order == "NCHW":
            X = np.round(np.random.rand(*((N, C) + sizes)) * (max_ - min_) + min_)
            X = X.astype(np.float32)
            X[(0,) * (ndim + 2)] = min_
            X[(0,) * (ndim + 1) + (1,)] = max_
        elif order == "NHWC":
            X = np.round(np.random.rand(*((N,) + sizes + (C,))) * (max_ - min_) + min_)
            X = X.astype(np.float32)
            X[(0,) * (ndim + 2)] = min_
            X[(0, 1) + (0,) * ndim] = max_

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

op_engine_list = [
            ("AveragePool", ""),
            ("AveragePool", "DNNLOWP"),
            ("Int8AveragePool", "DNNLOWP"),
        ]

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

do_quantize = "DNNLOWP" in engine and in_quantized

max_pool = core.CreateOperator(
                op_type,
                ["X_q" if do_quantize else "X"],
                ["Y_q" if engine == "DNNLOWP" else "Y"],
                strides=strides,
                kernels=kernels,
                pads=pads,
                order=order,
                engine=engine,
                device_option=gc,
            )
            net.Proto().op.extend([max_pool])

check_quantized_results_close(outputs)

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 assume, given dyndep.InitOpsLibrary("//caffe2/caffe2/quantization/server:dnnlowp_ops") workspace.GlobalInit(["caffe2", "--caffe2_omp_num_threads=11"]) class DNNLowPOpPoolTest(hu.HypothesisTestCase): @given( stride=st.integers(1, 3), pad=st.integers(0, 3), kernel=st.integers(1, 5), size=st.integers(1, 20), input_channels=st.integers(1, 3), batch_size=st.integers(1, 3), order=st.sampled_from(["NCHW", "NHWC"]), in_quantized=st.booleans(), **hu.gcs_cpu_only ) def test_dnnlowp_max_pool( self, stride, pad, kernel, size, input_channels, batch_size, order, in_quantized, gc, dc, ): assume(kernel <= size) assume(pad < kernel) C = input_channels N = batch_size H = W = size min_ = -10 max_ = 20 if order == "NCHW": X = np.round(np.random.rand(N, C, H, W) * (max_ - min_) + min_) elif order == "NHWC": X = np.round(np.random.rand(N, H, W, C) * (max_ - min_) + min_) X = X.astype(np.float32) Output = collections.namedtuple("Output", ["Y", "op_type", "engine"]) outputs = [] op_engine_list = [ ("MaxPool", ""), ("MaxPool", "DNNLOWP"), ("Int8MaxPool", "DNNLOWP"), ] for op_type, engine in op_engine_list: net = core.Net("test_net") do_quantize = "DNNLOWP" in engine and in_quantized if do_quantize: quantize = core.CreateOperator( "Quantize", ["X"], ["X_q"], engine=engine, device_option=gc ) net.Proto().op.extend([quantize]) max_pool = core.CreateOperator( op_type, ["X_q" if do_quantize else "X"], ["Y_q" if engine == "DNNLOWP" else "Y"], stride=stride, kernel=kernel, pad=pad, order=order, engine=engine, device_option=gc, ) net.Proto().op.extend([max_pool]) 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_i = max(X_j) so the only error is in quantization of inputs check_quantized_results_close(outputs, ref=X) @given( ndim=st.integers(2, 3), stride=st.integers(1, 1), pad=st.integers(0, 0), kernel=st.integers(1, 5), size=st.integers(2, 2), input_channels=st.integers(1, 1), batch_size=st.integers(2, 2), order=st.sampled_from(["NCHW", "NHWC"]), in_quantized=st.booleans(), **hu.gcs_cpu_only ) def test_dnnlowp_average_pool( self, ndim, stride, pad, kernel, size, input_channels, batch_size, order, in_quantized, gc, dc, ): kernel = 2 # Only kernel size 2 is supported assume(kernel <= size) assume(pad < kernel) C = input_channels N = batch_size strides = (stride,) * ndim pads = (pad,) * (ndim * 2) kernels = (kernel,) * ndim sizes = (size,) * ndim # X has scale 1, so no input quantization error min_ = -100 max_ = min_ + 255 if order == "NCHW": X = np.round(np.random.rand(*((N, C) + sizes)) * (max_ - min_) + min_) X = X.astype(np.float32) X[(0,) * (ndim + 2)] = min_ X[(0,) * (ndim + 1) + (1,)] = max_ elif order == "NHWC": X = np.round(np.random.rand(*((N,) + sizes + (C,))) * (max_ - min_) + min_) X = X.astype(np.float32) X[(0,) * (ndim + 2)] = min_ X[(0, 1) + (0,) * ndim] = max_ Output = collections.namedtuple("Output", ["Y", "op_type", "engine"]) outputs = [] op_engine_list = [ ("AveragePool", ""), ("AveragePool", "DNNLOWP"), ("Int8AveragePool", "DNNLOWP"), ] for op_type, engine in op_engine_list: net = core.Net("test_net") do_quantize = "DNNLOWP" in engine and in_quantized if do_quantize: quantize = core.CreateOperator( "Quantize", ["X"], ["X_q"], engine=engine, device_option=gc ) net.Proto().op.extend([quantize]) max_pool = core.CreateOperator( op_type, ["X_q" if do_quantize else "X"], ["Y_q" if engine == "DNNLOWP" else "Y"], strides=strides, kernels=kernels, pads=pads, order=order, engine=engine, device_option=gc, ) net.Proto().op.extend([max_pool]) 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) ) check_quantized_results_close(outputs)