Adds runtime for operator QLinearConv

README.rst

@@ -7,8 +7,8 @@
 mlprodict
 =========
 
-.. image:: https://travis-ci.org/sdpython/mlprodict.svg?branch=master
-    :target: https://travis-ci.org/sdpython/mlprodict
+.. image:: https://travis-ci.com/sdpython/mlprodict.svg?branch=master
+    :target: https://travis-ci.com/sdpython/mlprodict
     :alt: Build status
 
 .. image:: https://ci.appveyor.com/api/projects/status/g8chk1ufyk1m8uep?svg=true

_doc/sphinxdoc/source/conf.py

@@ -2,7 +2,7 @@
 import sys
 import os
 import alabaster
-from pyquickhelper.helpgen.default_conf import set_sphinx_variables, get_default_stylesheet
+from pyquickhelper.helpgen.default_conf import set_sphinx_variables
 from sklearn.experimental import enable_hist_gradient_boosting
 try:
     from mlprodict.onnx_conv import register_converters, register_rewritten_operators
@@ -47,10 +47,7 @@
     'generate_visual_graphs',
 ])
 
-html_context = {
-    'css_files': get_default_stylesheet([
-        '_static/my-styles.css', '_static/gallery.css']),
-}
+html_css_files = ['my-styles.css']
 
 html_logo = "phdoc_static/project_ico.png"
 

_doc/sphinxdoc/source/index.rst

@@ -13,8 +13,8 @@ mlprodict
 :ref:`l-README`,
 :ref:`blog <ap-main-0>`
 
-.. image:: https://travis-ci.org/sdpython/mlprodict.svg?branch=master
-    :target: https://travis-ci.org/sdpython/mlprodict
+.. image:: https://travis-ci.com/sdpython/mlprodict.svg?branch=master
+    :target: https://travis-ci.com/sdpython/mlprodict
     :alt: Build status
 
 .. image:: https://ci.appveyor.com/api/projects/status/g8chk1ufyk1m8uep?svg=true

_unittests/ut_onnxrt/test_onnxrt_python_runtime_.py

@@ -47,7 +47,7 @@
     OnnxNeg, OnnxNot,
     OnnxOr,
     OnnxPad, OnnxPow,
-    OnnxQuantizeLinear,
+    OnnxQLinearConv, OnnxQuantizeLinear,
     OnnxReciprocal,
     OnnxReduceL1, OnnxReduceL2,
     OnnxReduceLogSumExp, OnnxReduceMax, OnnxReduceMean, OnnxReduceMin,
@@ -2252,6 +2252,53 @@ def test_onnxt_runtime_pad2(self):
     def test_onnxt_runtime_pow(self):
         self.common_test_onnxt_runtime_binary(OnnxPow, numpy.power)
 
+    @wraplog()
+    def test_onnxt_runtime_qlinear_conv(self):
+        x = numpy.array(
+            [[255, 174, 162, 25, 203, 168, 58],
+             [15, 59, 237, 95, 129, 0, 64],
+             [56, 242, 153, 221, 168, 12, 166],
+             [232, 178, 186, 195, 237, 162, 237],
+             [188, 39, 124, 77, 80, 102, 43],
+             [127, 230, 21, 83, 41, 40, 134],
+             [255, 154, 92, 141, 42, 148, 247], ],
+            dtype=numpy.uint8).reshape((1, 1, 7, 7))
+
+        x_scale = numpy.float32(0.00369204697)
+        x_zero_point = numpy.uint8(132)
+
+        w = numpy.array([0], dtype=numpy.uint8).reshape((1, 1, 1, 1))
+
+        w_scale = numpy.array([0.00172794575], dtype=numpy.float32)
+        w_zero_point = numpy.array([255], dtype=numpy.uint8)
+
+        y_scale = numpy.float32(0.00162681262)
+        y_zero_point = numpy.uint8(123)
+
+        output = numpy.array(
+            [[0, 81, 93, 230, 52, 87, 197],
+             [240, 196, 18, 160, 126, 255, 191],
+             [199, 13, 102, 34, 87, 243, 89],
+             [23, 77, 69, 60, 18, 93, 18],
+             [67, 216, 131, 178, 175, 153, 212],
+             [128, 25, 234, 172, 214, 215, 121],
+             [0, 101, 163, 114, 213, 107, 8], ],
+            dtype=numpy.uint8).reshape((1, 1, 7, 7))
+
+        node = OnnxQLinearConv('x', 'x_scale', 'x_zero_point', 'w',
+                               'w_scale', 'w_zero_point', 'y_scale',
+                               'y_zero_point', output_names=['y'],
+                               op_version=get_opset_number_from_onnx())
+        inputs = {'x': x, 'x_scale': x_scale, 'x_zero_point': x_zero_point,
+                  'w': w, 'w_scale': w_scale, 'w_zero_point': w_zero_point,
+                  'y_scale': y_scale, 'y_zero_point': y_zero_point}
+        model_def = node.to_onnx(inputs,
+                                 target_opset=get_opset_number_from_onnx())
+        oinf = OnnxInference(model_def)
+        got = oinf.run(inputs)['y']
+        self.assertEqualArray(output, got)
+        python_tested.append(OnnxQLinearConv)
+
     @wraplog()
     def test_onnxt_runtime_quantize_linear(self):
         X = numpy.array([[[[-162, 10], [-100, 232], [-20, -50]],
@@ -3507,5 +3554,5 @@ def test_make_constant(self):
 
 
 if __name__ == "__main__":
-    # TestOnnxrtPythonRuntime().test_onnxt_runtime_cast_in()
+    # TestOnnxrtPythonRuntime().test_onnxt_runtime_qlinear_conv()
     unittest.main()

bin/debug/debug.cpp

@@ -0,0 +1,59 @@
+// debug.cpp : Ce fichier contient la fonction 'main'. L'exécution du programme commence et se termine à cet endroit.
+//
+
+#include "op_qlinear_conv_.hpp"
+
+
+void test_qlinear_conv() {
+	QLinearConv<uint8_t> conv;
+
+	const std::string auto_pad = "NOTSET";
+	py_array_t<int64_t> dilations;
+	py_array_t<int64_t> kernel_shape;
+	py_array_t<int64_t> pads;
+	py_array_t<int64_t> strides;
+
+	conv.init(auto_pad, dilations, 1, kernel_shape, pads, strides);
+
+	py_array_t<uint8_t> x({
+		255, 174, 162, 25, 203, 168, 58,
+		15, 59, 237, 95, 129, 0, 64,
+		56, 242, 153, 221, 168, 12, 166,
+		232, 178, 186, 195, 237, 162, 237,
+		188, 39, 124, 77, 80, 102, 43,
+		127, 230, 21, 83, 41, 40, 134,
+		255, 154, 92, 141, 42, 148, 247 },
+		{ 1, 1, 7, 7 });
+
+	float x_scale = (float)0.00369204697;
+	uint8_t x_zero_point = 132;
+
+	py_array_t<uint8_t> w({ 0 }, { 1, 1, 1, 1 });
+
+	py_array_t<float> w_scale({ (float)0.00172794575 }, {});
+	uint8_t w_zero_point = 255;
+
+	float y_scale = (float)0.00162681262;
+	uint8_t y_zero_point = 123;
+
+	py_array_t<uint8_t> B;
+
+	py_array_t<uint8_t> output({
+			0, 81, 93, 230, 52, 87, 197,
+			240, 196, 18, 160, 126, 255, 191,
+			199, 13, 102, 34, 87, 243, 89,
+			23, 77, 69, 60, 18, 93, 18,
+			67, 216, 131, 178, 175, 153, 212,
+			128, 25, 234, 172, 214, 215, 121,
+			0, 101, 163, 114, 213, 107, 8 },
+		{ 1, 1, 7, 7 });
+	py_array_t < uint8_t> res = conv.compute(
+		x, x_scale, x_zero_point, w, w_scale, w_zero_point, y_scale, y_zero_point, B);
+	if (!res.equal(output))
+		throw std::runtime_error("failed");
+}
+
+
+int main() {
+	test_qlinear_conv();
+}

bin/debug/debug.sln

@@ -0,0 +1,31 @@
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+EndProject
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+		Debug|x64 = Debug|x64
+		Debug|x86 = Debug|x86
+		Release|x64 = Release|x64
+		Release|x86 = Release|x86
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+		{714F6D8B-15D6-4E4E-940C-CC1AFA599EF1}.Debug|x86.Build.0 = Debug|Win32
+		{714F6D8B-15D6-4E4E-940C-CC1AFA599EF1}.Release|x64.ActiveCfg = Release|x64
+		{714F6D8B-15D6-4E4E-940C-CC1AFA599EF1}.Release|x64.Build.0 = Release|x64
+		{714F6D8B-15D6-4E4E-940C-CC1AFA599EF1}.Release|x86.ActiveCfg = Release|Win32
+		{714F6D8B-15D6-4E4E-940C-CC1AFA599EF1}.Release|x86.Build.0 = Release|Win32
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+		HideSolutionNode = FALSE
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+		SolutionGuid = {CC1B1913-0A0E-45AE-827F-C6D97913A40D}
+	EndGlobalSection
+EndGlobal

bin/debug/debug.vcxproj

@@ -0,0 +1,154 @@
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+    <ClCompile Include="../../mlprodict/onnxrt/ops_cpu/op_qlinear_conv_.cpp" />
+    <ClCompile Include="../../mlprodict/onnxrt/ops_cpu/op_qlinear_conv_.hpp" />
+    <ClCompile Include="../../mlprodict/onnxrt/ops_cpu/op_conv_matrices_.cpp" />
+    <ClCompile Include="../../mlprodict/onnxrt/ops_cpu/op_conv_matrices_.hpp" />
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mlprodict/onnxrt/ops_cpu/_op_list.py

@@ -78,6 +78,7 @@
 from .op_pad import Pad
 from .op_pow import Pow
 from .op_quantize_linear import QuantizeLinear
+from .op_qlinear_conv import QLinearConv
 from .op_reciprocal import Reciprocal
 from .op_reduce_log_sum_exp import ReduceLogSumExp
 from .op_reduce_l1 import ReduceL1