Si$SSKJr SSKrSSKrSSKrSSKrSSKr"SS\R5r\ S:Xa\R"5 gg)) annotationsNct\rSrSrSSjrSSjrSrSrSrSr Sr S r SS jr S r S rS rSrSrSrg)TestModelInferencec[RRU5n[RR U5nUR R n[XRSS9HzupgURnURURS55 URn URU RS55 U Rn URX5 M| g)zCheck that the model inference infers the expected types for outputs. Restricted to the simple case of tensor types, so expected types specify only the element type (ints corresponding to onnx.TensorProto.DataType). Fstrict tensor_type elem_typeN)onnxparser parse_modelshape_inference infer_shapesgraphoutputziptype assertTrueHasFieldr r assertEqual) self model_textexpectedmodelinferredoutputsrexpected_elem_type inferred_typer r s ]/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/onnx/test/model_inference_test.py_checkTestModelInference._checks  '' 3''44U;..''*-g*N &F"KKM OOM22=A B'33K OOK00= >#--I   Y ; +Oc [RRU5nUR[RR 5 [RR USS5 SSS5 g!,(df  g=f)z8Check that the model inference raises an InferenceError.TN)r r r assertRaisesrInferenceErrorr)rrrs r _check_inference_error)TestModelInference._check_inference_errorsU '' 3   t33BB C  - -eT4 @D C Cs "A44 Bc*SnURU5 g)zTest that model inference handles unknown ops. This special treatment is to support custom ops. See comments in shape inference code for details. z agraph (float[N] x) => (y) { y = SomeUnknownOp (x) } N)r!rrs r test_unknown_op"TestModelInference.test_unknown_op%s   Er#c\SnURU[RR5 g)z3Test that model inference infers model output type.z < ir_version: 7, opset_import: [ "" : 17] > agraph (float[N] x) => (y) { y = Cast (x) } Nr!r TensorProtoINT32r*s r test_mi_basic TestModelInference.test_mi_basic5s%   E4++112r#c\SnURU[RR5 g)Test use of functions.a < ir_version: 7, opset_import: [ "" : 17, "local" : 1] > agraph (float[N] x) => (y) { y = local.cast(x) } < opset_import: [ "" : 17 ], domain: "local" > cast (x) => (y) { y = Cast (x) } Nr.r*s r test_mi_function#TestModelInference.test_mi_functionC% $ E4++112r#c\SnURU[RR5 g)z0Test use of functions with attribute parameters.a < ir_version: 7, opset_import: [ "" : 17, "local" : 1] > agraph (float[N] x) => (y) { y = local.cast(x) } < opset_import: [ "" : 17 ], domain: "local" > cast(x) => (y) { y = Cast (x) } Nr.r*s r test_mi_function_attr(TestModelInference.test_mi_function_attrYr7r#c\SnURU[RR5 g)z1Test use of function attributes within subgraphs.a < ir_version: 7, opset_import: [ "" : 17, "local" : 1] > agraph (float[N] x, bool flag) => (y) { y = local.cast(x, flag) } < opset_import: [ "" : 17 ], domain: "local" > cast(x, flag) => (y) { y = If (flag) < then_branch = g1 () => (z_then) { z_then = Cast (x) }, else_branch = g2 () => (z_else) { z_else = Cast (x) } > } Nr.r*s r test_mi_function_subgraph_attr1TestModelInference.test_mi_function_subgraph_attros% * E4++112r#cSnURU[RR[RR5 g)z-Test use of multiple invocation of functions.a < ir_version: 7, opset_import: [ "" : 17, "local" : 1] > agraph (float[N] x, bool flag) => (y, z) { y = local.cast(x, flag) z = local.cast(x, flag) } < opset_import: [ "" : 17 ], domain: "local" > cast(x, flag) => (y) { y = If (flag) < then_branch = g1 () => (z_then) { z_then = Cast (x) }, else_branch = g2 () => (z_else) { z_else = Cast (x) } > } Nr!r r/r0INT64r*s r test_mi_function_multiple_calls2TestModelInference.test_mi_function_multiple_callss3 , E4++1143C3C3I3IJr#c[RRU5n[RR USSS5nUR R n[XRSS9HupgURnURURS55 URn URU RS55 U Rn UR[U R5[U55 [U RUSS9HAupURU RS55 URU R U 5 MC M g)zCheck that the model inference infers the expected shapes for outputs. Restricted to the simple case of tensor type outputs with completely known shapes. Trr shape dim_valueN)r r rrrrrrrrrr rDrlendimrE) rrrrrrrexpected_shaperr inferred_shape inferred_dim expected_dims r _check_shapeTestModelInference._check_shapes  '' 3''44UD$M..''&)'D&I "F"KKM OOM22=A B'33K OOK009 :(..N   S!3!34c.6I J.1""N4/*  5 5k BC  !7!7F /'Jr#c0SnURU/SQ5 g)Na < ir_version: 7, opset_import: [ "" : 17] > mymodel (float[4, 8, 16] x) => (y) { shape = Constant() y = Reshape(x, shape) } rLr*s r test_mi_constant#TestModelInference.test_mi_constants  %,r#c0SnURU/SQ5 g)Nae < ir_version: 7, opset_import: [ "" : 17] > mymodel (float[4, 8, 16] x) => (y) { shape = Constant() two = Constant() shape2 = Mul(shape, two) y = Reshape(x, shape2) } rOrSr*s r test_mi_constant_2%TestModelInference.test_mi_constant_2s  %,r#c6SnURUSS//SQ5 g)Na: < ir_version: 7, opset_import: [ "" : 17, "local" : 1] > main (float x) => (y, z) { y, z = local.expand(x) } < opset_import: [ "" : 17 ], domain: "local" > expand (x) => (y, z) { shape1 = Constant() shape2 = Constant() z = Expand (x, shape2) y = Expand (x, shape1) } rQ)rPrPrPrSr*s r test_mi_constant_in_function/TestModelInference.test_mi_constant_in_functions!& %!Q3r#cSnURU[RR[RR5 g)z2Test use of default values of function attributes.a agraph (float[N] x) => (y, z) { y = local.cast (x) # casts to INT32 type (encoding value 6) z = local.cast (x) # uses default-attribute value of 1 (FLOAT type) } cast (x) => (y) { y = Cast (x) } N)r!r r/r0FLOATr*s r test_mi_function_default_attr0TestModelInference.test_mi_function_default_attrs3   E4++1143C3C3I3IJr#cSnURU[RR[RR5 g)r4a? agraph (float[N] x) => (y, z) { y = local.cast:to_int32 (x) z = local.cast:to_int64 (x) } cast (x) => (y) { y = Cast (x) } cast (x) => (y) { y = Cast (x) } Nr?r*s r test_mi_overloaded_function.TestModelInference.test_mi_overloaded_functions3 $ E4++1143C3C3I3IJr#N)rstrrint)rrd)rrdrztyping.Sequence[int])__name__ __module__ __qualname____firstlineno__r!r'r+r1r5r9r<rArLrTrWrZr^ra__static_attributes__rcr#r rrsM< A  33,3,32K4G* - -4,K$Kr#r__main__) __future__rtypingunittestr onnx.parseronnx.shape_inferenceTestCaserrfmainrcr#r rssH#  AK**AKH z MMOr#