| // ResNet50 model with placeholder weights, for testing. |
| // Generated by resnet.ipynb with some manual and automated cleanup for testing. |
| |
| // RUN: iree-run-mlir --iree-input-type=mhlo --iree-hal-target-backends=llvm-cpu %s --function-input="1x224x224x3xf32" | FileCheck %s |
| // RUN: [[ $IREE_VULKAN_DISABLE == 1 ]] || (iree-run-mlir --iree-input-type=mhlo --iree-hal-target-backends=vulkan-spirv %s --function-input="1x224x224x3xf32" | FileCheck %s) |
| |
| module { |
| util.global private @"__iree_flow___sm_node188__m.layer-2.kernel" {noinline} = dense<1.000000e+00> : tensor<7x7x3x64xf32> |
| util.global private @"__iree_flow___sm_node189__m.layer-2.bias" {noinline} = dense<5.000000e-01> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node195__m.layer-3.gamma" {noinline} = dense<0.333333343> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node196__m.layer-3.beta" {noinline} = dense<2.500000e-01> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node197__m.layer-3.moving_mean" {noinline} = dense<2.000000e-01> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node198__m.layer-3.moving_variance" {noinline} = dense<0.166666672> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node215__m.layer-7.kernel" {noinline} = dense<0.142857149> : tensor<1x1x64x64xf32> |
| util.global private @"__iree_flow___sm_node216__m.layer-7.bias" {noinline} = dense<1.250000e-01> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node222__m.layer-8.gamma" {noinline} = dense<0.111111112> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node223__m.layer-8.beta" {noinline} = dense<1.000000e-01> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node224__m.layer-8.moving_mean" {noinline} = dense<0.0909090936> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node225__m.layer-8.moving_variance" {noinline} = dense<0.0833333358> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node234__m.layer-10.kernel" {noinline} = dense<0.0769230798> : tensor<3x3x64x64xf32> |
| util.global private @"__iree_flow___sm_node235__m.layer-10.bias" {noinline} = dense<0.0714285746> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node241__m.layer-11.gamma" {noinline} = dense<0.0666666701> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node242__m.layer-11.beta" {noinline} = dense<6.250000e-02> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node243__m.layer-11.moving_mean" {noinline} = dense<0.0588235296> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node244__m.layer-11.moving_variance" {noinline} = dense<0.055555556> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node253__m.layer-13.kernel" {noinline} = dense<0.0526315793> : tensor<1x1x64x256xf32> |
| util.global private @"__iree_flow___sm_node254__m.layer-13.bias" {noinline} = dense<5.000000e-02> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node259__m.layer-14.kernel" {noinline} = dense<0.0476190485> : tensor<1x1x64x256xf32> |
| util.global private @"__iree_flow___sm_node260__m.layer-14.bias" {noinline} = dense<0.0454545468> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node266__m.layer-15.gamma" {noinline} = dense<0.0434782617> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node267__m.layer-15.beta" {noinline} = dense<0.0416666679> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node268__m.layer-15.moving_mean" {noinline} = dense<4.000000e-02> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node269__m.layer-15.moving_variance" {noinline} = dense<0.0384615399> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node275__m.layer-16.gamma" {noinline} = dense<0.0370370373> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node276__m.layer-16.beta" {noinline} = dense<0.0357142873> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node277__m.layer-16.moving_mean" {noinline} = dense<0.0344827585> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node278__m.layer-16.moving_variance" {noinline} = dense<0.0333333351> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node291__m.layer-19.kernel" {noinline} = dense<0.0322580636> : tensor<1x1x256x64xf32> |
| util.global private @"__iree_flow___sm_node292__m.layer-19.bias" {noinline} = dense<3.125000e-02> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node298__m.layer-20.gamma" {noinline} = dense<0.0303030312> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node299__m.layer-20.beta" {noinline} = dense<0.0294117648> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node300__m.layer-20.moving_mean" {noinline} = dense<0.0285714287> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node301__m.layer-20.moving_variance" {noinline} = dense<0.027777778> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node310__m.layer-22.kernel" {noinline} = dense<0.0270270277> : tensor<3x3x64x64xf32> |
| util.global private @"__iree_flow___sm_node311__m.layer-22.bias" {noinline} = dense<0.0263157897> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node317__m.layer-23.gamma" {noinline} = dense<0.025641026> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node318__m.layer-23.beta" {noinline} = dense<2.500000e-02> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node319__m.layer-23.moving_mean" {noinline} = dense<0.024390243> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node320__m.layer-23.moving_variance" {noinline} = dense<0.0238095243> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node329__m.layer-25.kernel" {noinline} = dense<0.0232558139> : tensor<1x1x64x256xf32> |
| util.global private @"__iree_flow___sm_node330__m.layer-25.bias" {noinline} = dense<0.0227272734> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node336__m.layer-26.gamma" {noinline} = dense<0.0222222228> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node337__m.layer-26.beta" {noinline} = dense<0.0217391308> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node338__m.layer-26.moving_mean" {noinline} = dense<0.0212765951> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node339__m.layer-26.moving_variance" {noinline} = dense<0.020833334> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node352__m.layer-29.kernel" {noinline} = dense<0.0204081628> : tensor<1x1x256x64xf32> |
| util.global private @"__iree_flow___sm_node353__m.layer-29.bias" {noinline} = dense<2.000000e-02> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node359__m.layer-30.gamma" {noinline} = dense<0.0196078438> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node360__m.layer-30.beta" {noinline} = dense<0.0192307699> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node361__m.layer-30.moving_mean" {noinline} = dense<0.0188679248> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node362__m.layer-30.moving_variance" {noinline} = dense<0.0185185187> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node371__m.layer-32.kernel" {noinline} = dense<0.0181818176> : tensor<3x3x64x64xf32> |
| util.global private @"__iree_flow___sm_node372__m.layer-32.bias" {noinline} = dense<0.0178571437> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node378__m.layer-33.gamma" {noinline} = dense<0.0175438598> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node379__m.layer-33.beta" {noinline} = dense<0.0172413792> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node380__m.layer-33.moving_mean" {noinline} = dense<0.0169491526> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node381__m.layer-33.moving_variance" {noinline} = dense<0.0166666675> : tensor<64xf32> |
| util.global private @"__iree_flow___sm_node390__m.layer-35.kernel" {noinline} = dense<0.0163934417> : tensor<1x1x64x256xf32> |
| util.global private @"__iree_flow___sm_node391__m.layer-35.bias" {noinline} = dense<0.0161290318> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node397__m.layer-36.gamma" {noinline} = dense<0.0158730168> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node398__m.layer-36.beta" {noinline} = dense<1.562500e-02> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node399__m.layer-36.moving_mean" {noinline} = dense<0.0153846154> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node400__m.layer-36.moving_variance" {noinline} = dense<0.0151515156> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node413__m.layer-39.kernel" {noinline} = dense<0.0149253728> : tensor<1x1x256x128xf32> |
| util.global private @"__iree_flow___sm_node414__m.layer-39.bias" {noinline} = dense<0.0147058824> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node420__m.layer-40.gamma" {noinline} = dense<0.0144927539> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node421__m.layer-40.beta" {noinline} = dense<0.0142857144> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node422__m.layer-40.moving_mean" {noinline} = dense<0.0140845068> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node423__m.layer-40.moving_variance" {noinline} = dense<0.013888889> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node432__m.layer-42.kernel" {noinline} = dense<0.01369863> : tensor<3x3x128x128xf32> |
| util.global private @"__iree_flow___sm_node433__m.layer-42.bias" {noinline} = dense<0.0135135138> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node439__m.layer-43.gamma" {noinline} = dense<0.0133333337> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node440__m.layer-43.beta" {noinline} = dense<0.0131578948> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node441__m.layer-43.moving_mean" {noinline} = dense<0.012987013> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node442__m.layer-43.moving_variance" {noinline} = dense<0.012820513> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node451__m.layer-45.kernel" {noinline} = dense<0.0126582282> : tensor<1x1x256x512xf32> |
| util.global private @"__iree_flow___sm_node452__m.layer-45.bias" {noinline} = dense<1.250000e-02> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node457__m.layer-46.kernel" {noinline} = dense<0.0123456791> : tensor<1x1x128x512xf32> |
| util.global private @"__iree_flow___sm_node458__m.layer-46.bias" {noinline} = dense<0.0121951215> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node464__m.layer-47.gamma" {noinline} = dense<0.0120481923> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node465__m.layer-47.beta" {noinline} = dense<0.0119047621> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node466__m.layer-47.moving_mean" {noinline} = dense<0.0117647061> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node467__m.layer-47.moving_variance" {noinline} = dense<0.0116279069> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node473__m.layer-48.gamma" {noinline} = dense<0.0114942528> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node474__m.layer-48.beta" {noinline} = dense<0.0113636367> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node475__m.layer-48.moving_mean" {noinline} = dense<0.0112359552> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node476__m.layer-48.moving_variance" {noinline} = dense<0.0111111114> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node489__m.layer-51.kernel" {noinline} = dense<0.0109890113> : tensor<1x1x512x128xf32> |
| util.global private @"__iree_flow___sm_node490__m.layer-51.bias" {noinline} = dense<0.0108695654> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node496__m.layer-52.gamma" {noinline} = dense<0.0107526882> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node497__m.layer-52.beta" {noinline} = dense<0.0106382975> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node498__m.layer-52.moving_mean" {noinline} = dense<0.0105263162> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node499__m.layer-52.moving_variance" {noinline} = dense<0.010416667> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node508__m.layer-54.kernel" {noinline} = dense<0.010309278> : tensor<3x3x128x128xf32> |
| util.global private @"__iree_flow___sm_node509__m.layer-54.bias" {noinline} = dense<0.0102040814> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node515__m.layer-55.gamma" {noinline} = dense<0.0101010101> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node516__m.layer-55.beta" {noinline} = dense<0.00999999977> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node517__m.layer-55.moving_mean" {noinline} = dense<9.900990e-03> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node518__m.layer-55.moving_variance" {noinline} = dense<0.00980392192> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node527__m.layer-57.kernel" {noinline} = dense<0.00970873795> : tensor<1x1x128x512xf32> |
| util.global private @"__iree_flow___sm_node528__m.layer-57.bias" {noinline} = dense<0.00961538497> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node534__m.layer-58.gamma" {noinline} = dense<9.523810e-03> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node535__m.layer-58.beta" {noinline} = dense<0.0094339624> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node536__m.layer-58.moving_mean" {noinline} = dense<0.00934579409> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node537__m.layer-58.moving_variance" {noinline} = dense<0.00925925932> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node550__m.layer-61.kernel" {noinline} = dense<0.00917431153> : tensor<1x1x512x128xf32> |
| util.global private @"__iree_flow___sm_node551__m.layer-61.bias" {noinline} = dense<0.0090909088> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node557__m.layer-62.gamma" {noinline} = dense<0.00900900922> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node558__m.layer-62.beta" {noinline} = dense<0.00892857183> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node559__m.layer-62.moving_mean" {noinline} = dense<0.00884955748> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node560__m.layer-62.moving_variance" {noinline} = dense<0.00877192988> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node569__m.layer-64.kernel" {noinline} = dense<0.00869565178> : tensor<3x3x128x128xf32> |
| util.global private @"__iree_flow___sm_node570__m.layer-64.bias" {noinline} = dense<8.620690e-03> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node576__m.layer-65.gamma" {noinline} = dense<0.00854700897> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node577__m.layer-65.beta" {noinline} = dense<0.00847457629> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node578__m.layer-65.moving_mean" {noinline} = dense<0.00840336177> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node579__m.layer-65.moving_variance" {noinline} = dense<0.00833333377> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node588__m.layer-67.kernel" {noinline} = dense<0.00826446246> : tensor<1x1x128x512xf32> |
| util.global private @"__iree_flow___sm_node589__m.layer-67.bias" {noinline} = dense<0.00819672085> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node595__m.layer-68.gamma" {noinline} = dense<0.008130081> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node596__m.layer-68.beta" {noinline} = dense<0.00806451589> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node597__m.layer-68.moving_mean" {noinline} = dense<8.000000e-03> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node598__m.layer-68.moving_variance" {noinline} = dense<0.00793650839> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node611__m.layer-71.kernel" {noinline} = dense<0.00787401571> : tensor<1x1x512x128xf32> |
| util.global private @"__iree_flow___sm_node612__m.layer-71.bias" {noinline} = dense<7.812500e-03> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node618__m.layer-72.gamma" {noinline} = dense<0.00775193795> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node619__m.layer-72.beta" {noinline} = dense<0.0076923077> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node620__m.layer-72.moving_mean" {noinline} = dense<0.00763358781> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node621__m.layer-72.moving_variance" {noinline} = dense<0.0075757578> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node630__m.layer-74.kernel" {noinline} = dense<0.00751879718> : tensor<3x3x128x128xf32> |
| util.global private @"__iree_flow___sm_node631__m.layer-74.bias" {noinline} = dense<0.00746268639> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node637__m.layer-75.gamma" {noinline} = dense<0.00740740728> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node638__m.layer-75.beta" {noinline} = dense<0.0073529412> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node639__m.layer-75.moving_mean" {noinline} = dense<7.299270e-03> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node640__m.layer-75.moving_variance" {noinline} = dense<0.00724637694> : tensor<128xf32> |
| util.global private @"__iree_flow___sm_node649__m.layer-77.kernel" {noinline} = dense<0.00719424477> : tensor<1x1x128x512xf32> |
| util.global private @"__iree_flow___sm_node650__m.layer-77.bias" {noinline} = dense<0.00714285718> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node656__m.layer-78.gamma" {noinline} = dense<0.00709219835> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node657__m.layer-78.beta" {noinline} = dense<0.00704225338> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node658__m.layer-78.moving_mean" {noinline} = dense<0.00699300691> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node659__m.layer-78.moving_variance" {noinline} = dense<0.0069444445> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node672__m.layer-81.kernel" {noinline} = dense<0.0068965517> : tensor<1x1x512x256xf32> |
| util.global private @"__iree_flow___sm_node673__m.layer-81.bias" {noinline} = dense<0.00684931502> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node679__m.layer-82.gamma" {noinline} = dense<0.00680272094> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node680__m.layer-82.beta" {noinline} = dense<0.00675675692> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node681__m.layer-82.moving_mean" {noinline} = dense<0.00671140943> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node682__m.layer-82.moving_variance" {noinline} = dense<0.00666666683> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node691__m.layer-84.kernel" {noinline} = dense<0.00662251655> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node692__m.layer-84.bias" {noinline} = dense<0.00657894742> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node698__m.layer-85.gamma" {noinline} = dense<0.00653594779> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node699__m.layer-85.beta" {noinline} = dense<0.00649350649> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node700__m.layer-85.moving_mean" {noinline} = dense<0.0064516128> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node701__m.layer-85.moving_variance" {noinline} = dense<0.00641025649> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node710__m.layer-87.kernel" {noinline} = dense<0.00636942684> : tensor<1x1x512x1024xf32> |
| util.global private @"__iree_flow___sm_node711__m.layer-87.bias" {noinline} = dense<0.00632911408> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node716__m.layer-88.kernel" {noinline} = dense<0.00628930796> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node717__m.layer-88.bias" {noinline} = dense<6.250000e-03> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node723__m.layer-89.gamma" {noinline} = dense<0.00621118024> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node724__m.layer-89.beta" {noinline} = dense<0.00617283955> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node725__m.layer-89.moving_mean" {noinline} = dense<0.00613496918> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node726__m.layer-89.moving_variance" {noinline} = dense<0.00609756075> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node732__m.layer-90.gamma" {noinline} = dense<0.00606060587> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node733__m.layer-90.beta" {noinline} = dense<0.00602409616> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node734__m.layer-90.moving_mean" {noinline} = dense<0.00598802418> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node735__m.layer-90.moving_variance" {noinline} = dense<0.00595238106> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node748__m.layer-93.kernel" {noinline} = dense<5.917160e-03> : tensor<1x1x1024x256xf32> |
| util.global private @"__iree_flow___sm_node749__m.layer-93.bias" {noinline} = dense<0.00588235306> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node755__m.layer-94.gamma" {noinline} = dense<0.00584795326> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node756__m.layer-94.beta" {noinline} = dense<0.00581395347> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node757__m.layer-94.moving_mean" {noinline} = dense<0.00578034669> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node758__m.layer-94.moving_variance" {noinline} = dense<0.00574712642> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node767__m.layer-96.kernel" {noinline} = dense<0.00571428565> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node768__m.layer-96.bias" {noinline} = dense<0.00568181835> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node774__m.layer-97.gamma" {noinline} = dense<0.00564971752> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node775__m.layer-97.beta" {noinline} = dense<0.00561797759> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node776__m.layer-97.moving_mean" {noinline} = dense<0.00558659201> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node777__m.layer-97.moving_variance" {noinline} = dense<0.00555555569> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node786__m.layer-99.kernel" {noinline} = dense<0.00552486209> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node787__m.layer-99.bias" {noinline} = dense<0.00549450563> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node793__m.layer-100.gamma" {noinline} = dense<0.00546448072> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node794__m.layer-100.beta" {noinline} = dense<0.00543478271> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node795__m.layer-100.moving_mean" {noinline} = dense<0.00540540554> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node796__m.layer-100.moving_variance" {noinline} = dense<0.00537634408> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node809__m.layer-103.kernel" {noinline} = dense<0.00534759369> : tensor<1x1x1024x256xf32> |
| util.global private @"__iree_flow___sm_node810__m.layer-103.bias" {noinline} = dense<0.00531914877> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node816__m.layer-104.gamma" {noinline} = dense<0.00529100513> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node817__m.layer-104.beta" {noinline} = dense<0.00526315812> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node818__m.layer-104.moving_mean" {noinline} = dense<0.00523560215> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node819__m.layer-104.moving_variance" {noinline} = dense<0.00520833349> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node828__m.layer-106.kernel" {noinline} = dense<0.00518134702> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node829__m.layer-106.bias" {noinline} = dense<0.00515463902> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node835__m.layer-107.gamma" {noinline} = dense<0.00512820529> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node836__m.layer-107.beta" {noinline} = dense<0.00510204071> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node837__m.layer-107.moving_mean" {noinline} = dense<0.00507614203> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node838__m.layer-107.moving_variance" {noinline} = dense<0.00505050505> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node847__m.layer-109.kernel" {noinline} = dense<0.00502512557> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node848__m.layer-109.bias" {noinline} = dense<5.000000e-03> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node854__m.layer-110.gamma" {noinline} = dense<0.00497512426> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node855__m.layer-110.beta" {noinline} = dense<0.00495049497> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node856__m.layer-110.moving_mean" {noinline} = dense<0.00492610829> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node857__m.layer-110.moving_variance" {noinline} = dense<0.00490196096> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node870__m.layer-113.kernel" {noinline} = dense<0.00487804879> : tensor<1x1x1024x256xf32> |
| util.global private @"__iree_flow___sm_node871__m.layer-113.bias" {noinline} = dense<0.00485436898> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node877__m.layer-114.gamma" {noinline} = dense<0.00483091781> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node878__m.layer-114.beta" {noinline} = dense<0.00480769249> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node879__m.layer-114.moving_mean" {noinline} = dense<0.00478468882> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node880__m.layer-114.moving_variance" {noinline} = dense<0.00476190494> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node889__m.layer-116.kernel" {noinline} = dense<0.00473933667> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node890__m.layer-116.bias" {noinline} = dense<0.0047169812> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node896__m.layer-117.gamma" {noinline} = dense<0.00469483575> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node897__m.layer-117.beta" {noinline} = dense<0.00467289705> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node898__m.layer-117.moving_mean" {noinline} = dense<0.00465116277> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node899__m.layer-117.moving_variance" {noinline} = dense<0.00462962966> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node908__m.layer-119.kernel" {noinline} = dense<0.00460829493> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node909__m.layer-119.bias" {noinline} = dense<0.00458715577> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node915__m.layer-120.gamma" {noinline} = dense<4.566210e-03> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node916__m.layer-120.beta" {noinline} = dense<0.0045454544> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node917__m.layer-120.moving_mean" {noinline} = dense<0.00452488707> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node918__m.layer-120.moving_variance" {noinline} = dense<0.00450450461> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node931__m.layer-123.kernel" {noinline} = dense<0.00448430516> : tensor<1x1x1024x256xf32> |
| util.global private @"__iree_flow___sm_node932__m.layer-123.bias" {noinline} = dense<0.00446428591> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node938__m.layer-124.gamma" {noinline} = dense<0.00444444455> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node939__m.layer-124.beta" {noinline} = dense<0.00442477874> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node940__m.layer-124.moving_mean" {noinline} = dense<0.00440528616> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node941__m.layer-124.moving_variance" {noinline} = dense<0.00438596494> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node950__m.layer-126.kernel" {noinline} = dense<0.0043668123> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node951__m.layer-126.bias" {noinline} = dense<0.00434782589> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node957__m.layer-127.gamma" {noinline} = dense<0.00432900432> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node958__m.layer-127.beta" {noinline} = dense<0.00431034481> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node959__m.layer-127.moving_mean" {noinline} = dense<0.00429184549> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node960__m.layer-127.moving_variance" {noinline} = dense<0.00427350448> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node969__m.layer-129.kernel" {noinline} = dense<0.00425531901> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node970__m.layer-129.bias" {noinline} = dense<0.00423728814> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node976__m.layer-130.gamma" {noinline} = dense<0.00421940908> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node977__m.layer-130.beta" {noinline} = dense<0.00420168089> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node978__m.layer-130.moving_mean" {noinline} = dense<0.00418410031> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node979__m.layer-130.moving_variance" {noinline} = dense<0.00416666688> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node992__m.layer-133.kernel" {noinline} = dense<0.00414937781> : tensor<1x1x1024x256xf32> |
| util.global private @"__iree_flow___sm_node993__m.layer-133.bias" {noinline} = dense<0.00413223123> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node999__m.layer-134.gamma" {noinline} = dense<0.00411522621> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1000__m.layer-134.beta" {noinline} = dense<0.00409836043> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1001__m.layer-134.moving_mean" {noinline} = dense<0.00408163248> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1002__m.layer-134.moving_variance" {noinline} = dense<0.0040650405> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1011__m.layer-136.kernel" {noinline} = dense<0.0040485831> : tensor<3x3x256x256xf32> |
| util.global private @"__iree_flow___sm_node1012__m.layer-136.bias" {noinline} = dense<0.00403225794> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1018__m.layer-137.gamma" {noinline} = dense<0.00401606411> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1019__m.layer-137.beta" {noinline} = dense<4.000000e-03> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1020__m.layer-137.moving_mean" {noinline} = dense<0.00398406386> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1021__m.layer-137.moving_variance" {noinline} = dense<0.0039682542> : tensor<256xf32> |
| util.global private @"__iree_flow___sm_node1030__m.layer-139.kernel" {noinline} = dense<0.00395256933> : tensor<1x1x256x1024xf32> |
| util.global private @"__iree_flow___sm_node1031__m.layer-139.bias" {noinline} = dense<0.00393700786> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1037__m.layer-140.gamma" {noinline} = dense<0.00392156886> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1038__m.layer-140.beta" {noinline} = dense<3.906250e-03> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1039__m.layer-140.moving_mean" {noinline} = dense<0.00389105058> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1040__m.layer-140.moving_variance" {noinline} = dense<0.00387596898> : tensor<1024xf32> |
| util.global private @"__iree_flow___sm_node1053__m.layer-143.kernel" {noinline} = dense<0.00386100379> : tensor<1x1x1024x512xf32> |
| util.global private @"__iree_flow___sm_node1054__m.layer-143.bias" {noinline} = dense<0.00384615385> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1060__m.layer-144.gamma" {noinline} = dense<0.00383141753> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1061__m.layer-144.beta" {noinline} = dense<0.00381679391> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1062__m.layer-144.moving_mean" {noinline} = dense<0.00380228134> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1063__m.layer-144.moving_variance" {noinline} = dense<0.0037878789> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1072__m.layer-146.kernel" {noinline} = dense<0.00377358496> : tensor<3x3x512x512xf32> |
| util.global private @"__iree_flow___sm_node1073__m.layer-146.bias" {noinline} = dense<0.00375939859> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1079__m.layer-147.gamma" {noinline} = dense<0.00374531839> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1080__m.layer-147.beta" {noinline} = dense<0.0037313432> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1081__m.layer-147.moving_mean" {noinline} = dense<0.00371747208> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1082__m.layer-147.moving_variance" {noinline} = dense<0.00370370364> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1091__m.layer-149.kernel" {noinline} = dense<0.00369003695> : tensor<1x1x1024x2048xf32> |
| util.global private @"__iree_flow___sm_node1092__m.layer-149.bias" {noinline} = dense<0.0036764706> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1097__m.layer-150.kernel" {noinline} = dense<0.00366300368> : tensor<1x1x512x2048xf32> |
| util.global private @"__iree_flow___sm_node1098__m.layer-150.bias" {noinline} = dense<0.00364963501> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1104__m.layer-151.gamma" {noinline} = dense<0.00363636366> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1105__m.layer-151.beta" {noinline} = dense<0.00362318847> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1106__m.layer-151.moving_mean" {noinline} = dense<0.00361010828> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1107__m.layer-151.moving_variance" {noinline} = dense<0.00359712238> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1113__m.layer-152.gamma" {noinline} = dense<0.00358422939> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1114__m.layer-152.beta" {noinline} = dense<0.00357142859> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1115__m.layer-152.moving_mean" {noinline} = dense<0.00355871883> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1116__m.layer-152.moving_variance" {noinline} = dense<0.00354609918> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1129__m.layer-155.kernel" {noinline} = dense<0.00353356893> : tensor<1x1x2048x512xf32> |
| util.global private @"__iree_flow___sm_node1130__m.layer-155.bias" {noinline} = dense<0.00352112669> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1136__m.layer-156.gamma" {noinline} = dense<0.003508772> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1137__m.layer-156.beta" {noinline} = dense<0.00349650346> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1138__m.layer-156.moving_mean" {noinline} = dense<0.00348432059> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1139__m.layer-156.moving_variance" {noinline} = dense<0.00347222225> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1148__m.layer-158.kernel" {noinline} = dense<0.00346020772> : tensor<3x3x512x512xf32> |
| util.global private @"__iree_flow___sm_node1149__m.layer-158.bias" {noinline} = dense<0.00344827585> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1155__m.layer-159.gamma" {noinline} = dense<0.00343642617> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1156__m.layer-159.beta" {noinline} = dense<0.00342465751> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1157__m.layer-159.moving_mean" {noinline} = dense<0.00341296918> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1158__m.layer-159.moving_variance" {noinline} = dense<0.00340136047> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1167__m.layer-161.kernel" {noinline} = dense<0.00338983047> : tensor<1x1x512x2048xf32> |
| util.global private @"__iree_flow___sm_node1168__m.layer-161.bias" {noinline} = dense<0.00337837846> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1174__m.layer-162.gamma" {noinline} = dense<0.00336700329> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1175__m.layer-162.beta" {noinline} = dense<0.00335570471> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1176__m.layer-162.moving_mean" {noinline} = dense<0.00334448158> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1177__m.layer-162.moving_variance" {noinline} = dense<0.00333333341> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1190__m.layer-165.kernel" {noinline} = dense<0.00332225906> : tensor<1x1x2048x512xf32> |
| util.global private @"__iree_flow___sm_node1191__m.layer-165.bias" {noinline} = dense<0.00331125828> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1197__m.layer-166.gamma" {noinline} = dense<0.00330033014> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1198__m.layer-166.beta" {noinline} = dense<0.00328947371> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1199__m.layer-166.moving_mean" {noinline} = dense<0.00327868853> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1200__m.layer-166.moving_variance" {noinline} = dense<0.00326797389> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1209__m.layer-168.kernel" {noinline} = dense<0.00325732888> : tensor<3x3x512x512xf32> |
| util.global private @"__iree_flow___sm_node1210__m.layer-168.bias" {noinline} = dense<0.00324675324> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1216__m.layer-169.gamma" {noinline} = dense<0.00323624606> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1217__m.layer-169.beta" {noinline} = dense<0.0032258064> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1218__m.layer-169.moving_mean" {noinline} = dense<0.00321543403> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1219__m.layer-169.moving_variance" {noinline} = dense<0.00320512825> : tensor<512xf32> |
| util.global private @"__iree_flow___sm_node1228__m.layer-171.kernel" {noinline} = dense<0.00319488812> : tensor<1x1x512x2048xf32> |
| util.global private @"__iree_flow___sm_node1229__m.layer-171.bias" {noinline} = dense<0.00318471342> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1235__m.layer-172.gamma" {noinline} = dense<0.00317460322> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1236__m.layer-172.beta" {noinline} = dense<0.00316455704> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1237__m.layer-172.moving_mean" {noinline} = dense<0.00315457419> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1238__m.layer-172.moving_variance" {noinline} = dense<0.00314465398> : tensor<2048xf32> |
| util.global private @"__iree_flow___sm_node1255__m.layer-176.kernel" {noinline} = dense<0.00313479616> : tensor<2048x1000xf32> |
| util.global private @"__iree_flow___sm_node1256__m.layer-176.bias" {noinline} = dense<3.125000e-03> : tensor<1000xf32> |
| // CHECK-LABEL: EXEC @predict |
| func.func @predict(%arg0: tensor<1x224x224x3xf32>) -> tensor<1x1000xf32> attributes {iree.module.export, iree.reflection = {abi = "sip", abiv = 1 : i32, sip = "I8!S5!k0_0R3!_0"}} { |
| %0 = util.global.address @"__iree_flow___sm_node188__m.layer-2.kernel" : !util.ptr<tensor<7x7x3x64xf32>> |
| %1 = util.global.address @"__iree_flow___sm_node189__m.layer-2.bias" : !util.ptr<tensor<64xf32>> |
| %2 = util.global.address @"__iree_flow___sm_node195__m.layer-3.gamma" : !util.ptr<tensor<64xf32>> |
| %3 = util.global.address @"__iree_flow___sm_node196__m.layer-3.beta" : !util.ptr<tensor<64xf32>> |
| %4 = util.global.address @"__iree_flow___sm_node197__m.layer-3.moving_mean" : !util.ptr<tensor<64xf32>> |
| %5 = util.global.address @"__iree_flow___sm_node198__m.layer-3.moving_variance" : !util.ptr<tensor<64xf32>> |
| %6 = util.global.address @"__iree_flow___sm_node215__m.layer-7.kernel" : !util.ptr<tensor<1x1x64x64xf32>> |
| %7 = util.global.address @"__iree_flow___sm_node216__m.layer-7.bias" : !util.ptr<tensor<64xf32>> |
| %8 = util.global.address @"__iree_flow___sm_node222__m.layer-8.gamma" : !util.ptr<tensor<64xf32>> |
| %9 = util.global.address @"__iree_flow___sm_node223__m.layer-8.beta" : !util.ptr<tensor<64xf32>> |
| %10 = util.global.address @"__iree_flow___sm_node224__m.layer-8.moving_mean" : !util.ptr<tensor<64xf32>> |
| %11 = util.global.address @"__iree_flow___sm_node225__m.layer-8.moving_variance" : !util.ptr<tensor<64xf32>> |
| %12 = util.global.address @"__iree_flow___sm_node234__m.layer-10.kernel" : !util.ptr<tensor<3x3x64x64xf32>> |
| %13 = util.global.address @"__iree_flow___sm_node235__m.layer-10.bias" : !util.ptr<tensor<64xf32>> |
| %14 = util.global.address @"__iree_flow___sm_node241__m.layer-11.gamma" : !util.ptr<tensor<64xf32>> |
| %15 = util.global.address @"__iree_flow___sm_node242__m.layer-11.beta" : !util.ptr<tensor<64xf32>> |
| %16 = util.global.address @"__iree_flow___sm_node243__m.layer-11.moving_mean" : !util.ptr<tensor<64xf32>> |
| %17 = util.global.address @"__iree_flow___sm_node244__m.layer-11.moving_variance" : !util.ptr<tensor<64xf32>> |
| %18 = util.global.address @"__iree_flow___sm_node259__m.layer-14.kernel" : !util.ptr<tensor<1x1x64x256xf32>> |
| %19 = util.global.address @"__iree_flow___sm_node260__m.layer-14.bias" : !util.ptr<tensor<256xf32>> |
| %20 = util.global.address @"__iree_flow___sm_node253__m.layer-13.kernel" : !util.ptr<tensor<1x1x64x256xf32>> |
| %21 = util.global.address @"__iree_flow___sm_node254__m.layer-13.bias" : !util.ptr<tensor<256xf32>> |
| %22 = util.global.address @"__iree_flow___sm_node266__m.layer-15.gamma" : !util.ptr<tensor<256xf32>> |
| %23 = util.global.address @"__iree_flow___sm_node267__m.layer-15.beta" : !util.ptr<tensor<256xf32>> |
| %24 = util.global.address @"__iree_flow___sm_node268__m.layer-15.moving_mean" : !util.ptr<tensor<256xf32>> |
| %25 = util.global.address @"__iree_flow___sm_node269__m.layer-15.moving_variance" : !util.ptr<tensor<256xf32>> |
| %26 = util.global.address @"__iree_flow___sm_node275__m.layer-16.gamma" : !util.ptr<tensor<256xf32>> |
| %27 = util.global.address @"__iree_flow___sm_node276__m.layer-16.beta" : !util.ptr<tensor<256xf32>> |
| %28 = util.global.address @"__iree_flow___sm_node277__m.layer-16.moving_mean" : !util.ptr<tensor<256xf32>> |
| %29 = util.global.address @"__iree_flow___sm_node278__m.layer-16.moving_variance" : !util.ptr<tensor<256xf32>> |
| %30 = util.global.address @"__iree_flow___sm_node291__m.layer-19.kernel" : !util.ptr<tensor<1x1x256x64xf32>> |
| %31 = util.global.address @"__iree_flow___sm_node292__m.layer-19.bias" : !util.ptr<tensor<64xf32>> |
| %32 = util.global.address @"__iree_flow___sm_node298__m.layer-20.gamma" : !util.ptr<tensor<64xf32>> |
| %33 = util.global.address @"__iree_flow___sm_node299__m.layer-20.beta" : !util.ptr<tensor<64xf32>> |
| %34 = util.global.address @"__iree_flow___sm_node300__m.layer-20.moving_mean" : !util.ptr<tensor<64xf32>> |
| %35 = util.global.address @"__iree_flow___sm_node301__m.layer-20.moving_variance" : !util.ptr<tensor<64xf32>> |
| %36 = util.global.address @"__iree_flow___sm_node310__m.layer-22.kernel" : !util.ptr<tensor<3x3x64x64xf32>> |
| %37 = util.global.address @"__iree_flow___sm_node311__m.layer-22.bias" : !util.ptr<tensor<64xf32>> |
| %38 = util.global.address @"__iree_flow___sm_node317__m.layer-23.gamma" : !util.ptr<tensor<64xf32>> |
| %39 = util.global.address @"__iree_flow___sm_node318__m.layer-23.beta" : !util.ptr<tensor<64xf32>> |
| %40 = util.global.address @"__iree_flow___sm_node319__m.layer-23.moving_mean" : !util.ptr<tensor<64xf32>> |
| %41 = util.global.address @"__iree_flow___sm_node320__m.layer-23.moving_variance" : !util.ptr<tensor<64xf32>> |
| %42 = util.global.address @"__iree_flow___sm_node329__m.layer-25.kernel" : !util.ptr<tensor<1x1x64x256xf32>> |
| %43 = util.global.address @"__iree_flow___sm_node330__m.layer-25.bias" : !util.ptr<tensor<256xf32>> |
| %44 = util.global.address @"__iree_flow___sm_node336__m.layer-26.gamma" : !util.ptr<tensor<256xf32>> |
| %45 = util.global.address @"__iree_flow___sm_node337__m.layer-26.beta" : !util.ptr<tensor<256xf32>> |
| %46 = util.global.address @"__iree_flow___sm_node338__m.layer-26.moving_mean" : !util.ptr<tensor<256xf32>> |
| %47 = util.global.address @"__iree_flow___sm_node339__m.layer-26.moving_variance" : !util.ptr<tensor<256xf32>> |
| %48 = util.global.address @"__iree_flow___sm_node352__m.layer-29.kernel" : !util.ptr<tensor<1x1x256x64xf32>> |
| %49 = util.global.address @"__iree_flow___sm_node353__m.layer-29.bias" : !util.ptr<tensor<64xf32>> |
| %50 = util.global.address @"__iree_flow___sm_node359__m.layer-30.gamma" : !util.ptr<tensor<64xf32>> |
| %51 = util.global.address @"__iree_flow___sm_node360__m.layer-30.beta" : !util.ptr<tensor<64xf32>> |
| %52 = util.global.address @"__iree_flow___sm_node361__m.layer-30.moving_mean" : !util.ptr<tensor<64xf32>> |
| %53 = util.global.address @"__iree_flow___sm_node362__m.layer-30.moving_variance" : !util.ptr<tensor<64xf32>> |
| %54 = util.global.address @"__iree_flow___sm_node371__m.layer-32.kernel" : !util.ptr<tensor<3x3x64x64xf32>> |
| %55 = util.global.address @"__iree_flow___sm_node372__m.layer-32.bias" : !util.ptr<tensor<64xf32>> |
| %56 = util.global.address @"__iree_flow___sm_node378__m.layer-33.gamma" : !util.ptr<tensor<64xf32>> |
| %57 = util.global.address @"__iree_flow___sm_node379__m.layer-33.beta" : !util.ptr<tensor<64xf32>> |
| %58 = util.global.address @"__iree_flow___sm_node380__m.layer-33.moving_mean" : !util.ptr<tensor<64xf32>> |
| %59 = util.global.address @"__iree_flow___sm_node381__m.layer-33.moving_variance" : !util.ptr<tensor<64xf32>> |
| %60 = util.global.address @"__iree_flow___sm_node390__m.layer-35.kernel" : !util.ptr<tensor<1x1x64x256xf32>> |
| %61 = util.global.address @"__iree_flow___sm_node391__m.layer-35.bias" : !util.ptr<tensor<256xf32>> |
| %62 = util.global.address @"__iree_flow___sm_node397__m.layer-36.gamma" : !util.ptr<tensor<256xf32>> |
| %63 = util.global.address @"__iree_flow___sm_node398__m.layer-36.beta" : !util.ptr<tensor<256xf32>> |
| %64 = util.global.address @"__iree_flow___sm_node399__m.layer-36.moving_mean" : !util.ptr<tensor<256xf32>> |
| %65 = util.global.address @"__iree_flow___sm_node400__m.layer-36.moving_variance" : !util.ptr<tensor<256xf32>> |
| %66 = util.global.address @"__iree_flow___sm_node413__m.layer-39.kernel" : !util.ptr<tensor<1x1x256x128xf32>> |
| %67 = util.global.address @"__iree_flow___sm_node414__m.layer-39.bias" : !util.ptr<tensor<128xf32>> |
| %68 = util.global.address @"__iree_flow___sm_node420__m.layer-40.gamma" : !util.ptr<tensor<128xf32>> |
| %69 = util.global.address @"__iree_flow___sm_node421__m.layer-40.beta" : !util.ptr<tensor<128xf32>> |
| %70 = util.global.address @"__iree_flow___sm_node422__m.layer-40.moving_mean" : !util.ptr<tensor<128xf32>> |
| %71 = util.global.address @"__iree_flow___sm_node423__m.layer-40.moving_variance" : !util.ptr<tensor<128xf32>> |
| %72 = util.global.address @"__iree_flow___sm_node432__m.layer-42.kernel" : !util.ptr<tensor<3x3x128x128xf32>> |
| %73 = util.global.address @"__iree_flow___sm_node433__m.layer-42.bias" : !util.ptr<tensor<128xf32>> |
| %74 = util.global.address @"__iree_flow___sm_node439__m.layer-43.gamma" : !util.ptr<tensor<128xf32>> |
| %75 = util.global.address @"__iree_flow___sm_node440__m.layer-43.beta" : !util.ptr<tensor<128xf32>> |
| %76 = util.global.address @"__iree_flow___sm_node441__m.layer-43.moving_mean" : !util.ptr<tensor<128xf32>> |
| %77 = util.global.address @"__iree_flow___sm_node442__m.layer-43.moving_variance" : !util.ptr<tensor<128xf32>> |
| %78 = util.global.address @"__iree_flow___sm_node457__m.layer-46.kernel" : !util.ptr<tensor<1x1x128x512xf32>> |
| %79 = util.global.address @"__iree_flow___sm_node458__m.layer-46.bias" : !util.ptr<tensor<512xf32>> |
| %80 = util.global.address @"__iree_flow___sm_node451__m.layer-45.kernel" : !util.ptr<tensor<1x1x256x512xf32>> |
| %81 = util.global.address @"__iree_flow___sm_node452__m.layer-45.bias" : !util.ptr<tensor<512xf32>> |
| %82 = util.global.address @"__iree_flow___sm_node464__m.layer-47.gamma" : !util.ptr<tensor<512xf32>> |
| %83 = util.global.address @"__iree_flow___sm_node465__m.layer-47.beta" : !util.ptr<tensor<512xf32>> |
| %84 = util.global.address @"__iree_flow___sm_node466__m.layer-47.moving_mean" : !util.ptr<tensor<512xf32>> |
| %85 = util.global.address @"__iree_flow___sm_node467__m.layer-47.moving_variance" : !util.ptr<tensor<512xf32>> |
| %86 = util.global.address @"__iree_flow___sm_node473__m.layer-48.gamma" : !util.ptr<tensor<512xf32>> |
| %87 = util.global.address @"__iree_flow___sm_node474__m.layer-48.beta" : !util.ptr<tensor<512xf32>> |
| %88 = util.global.address @"__iree_flow___sm_node475__m.layer-48.moving_mean" : !util.ptr<tensor<512xf32>> |
| %89 = util.global.address @"__iree_flow___sm_node476__m.layer-48.moving_variance" : !util.ptr<tensor<512xf32>> |
| %90 = util.global.address @"__iree_flow___sm_node489__m.layer-51.kernel" : !util.ptr<tensor<1x1x512x128xf32>> |
| %91 = util.global.address @"__iree_flow___sm_node490__m.layer-51.bias" : !util.ptr<tensor<128xf32>> |
| %92 = util.global.address @"__iree_flow___sm_node496__m.layer-52.gamma" : !util.ptr<tensor<128xf32>> |
| %93 = util.global.address @"__iree_flow___sm_node497__m.layer-52.beta" : !util.ptr<tensor<128xf32>> |
| %94 = util.global.address @"__iree_flow___sm_node498__m.layer-52.moving_mean" : !util.ptr<tensor<128xf32>> |
| %95 = util.global.address @"__iree_flow___sm_node499__m.layer-52.moving_variance" : !util.ptr<tensor<128xf32>> |
| %96 = util.global.address @"__iree_flow___sm_node508__m.layer-54.kernel" : !util.ptr<tensor<3x3x128x128xf32>> |
| %97 = util.global.address @"__iree_flow___sm_node509__m.layer-54.bias" : !util.ptr<tensor<128xf32>> |
| %98 = util.global.address @"__iree_flow___sm_node515__m.layer-55.gamma" : !util.ptr<tensor<128xf32>> |
| %99 = util.global.address @"__iree_flow___sm_node516__m.layer-55.beta" : !util.ptr<tensor<128xf32>> |
| %100 = util.global.address @"__iree_flow___sm_node517__m.layer-55.moving_mean" : !util.ptr<tensor<128xf32>> |
| %101 = util.global.address @"__iree_flow___sm_node518__m.layer-55.moving_variance" : !util.ptr<tensor<128xf32>> |
| %102 = util.global.address @"__iree_flow___sm_node527__m.layer-57.kernel" : !util.ptr<tensor<1x1x128x512xf32>> |
| %103 = util.global.address @"__iree_flow___sm_node528__m.layer-57.bias" : !util.ptr<tensor<512xf32>> |
| %104 = util.global.address @"__iree_flow___sm_node534__m.layer-58.gamma" : !util.ptr<tensor<512xf32>> |
| %105 = util.global.address @"__iree_flow___sm_node535__m.layer-58.beta" : !util.ptr<tensor<512xf32>> |
| %106 = util.global.address @"__iree_flow___sm_node536__m.layer-58.moving_mean" : !util.ptr<tensor<512xf32>> |
| %107 = util.global.address @"__iree_flow___sm_node537__m.layer-58.moving_variance" : !util.ptr<tensor<512xf32>> |
| %108 = util.global.address @"__iree_flow___sm_node550__m.layer-61.kernel" : !util.ptr<tensor<1x1x512x128xf32>> |
| %109 = util.global.address @"__iree_flow___sm_node551__m.layer-61.bias" : !util.ptr<tensor<128xf32>> |
| %110 = util.global.address @"__iree_flow___sm_node557__m.layer-62.gamma" : !util.ptr<tensor<128xf32>> |
| %111 = util.global.address @"__iree_flow___sm_node558__m.layer-62.beta" : !util.ptr<tensor<128xf32>> |
| %112 = util.global.address @"__iree_flow___sm_node559__m.layer-62.moving_mean" : !util.ptr<tensor<128xf32>> |
| %113 = util.global.address @"__iree_flow___sm_node560__m.layer-62.moving_variance" : !util.ptr<tensor<128xf32>> |
| %114 = util.global.address @"__iree_flow___sm_node569__m.layer-64.kernel" : !util.ptr<tensor<3x3x128x128xf32>> |
| %115 = util.global.address @"__iree_flow___sm_node570__m.layer-64.bias" : !util.ptr<tensor<128xf32>> |
| %116 = util.global.address @"__iree_flow___sm_node576__m.layer-65.gamma" : !util.ptr<tensor<128xf32>> |
| %117 = util.global.address @"__iree_flow___sm_node577__m.layer-65.beta" : !util.ptr<tensor<128xf32>> |
| %118 = util.global.address @"__iree_flow___sm_node578__m.layer-65.moving_mean" : !util.ptr<tensor<128xf32>> |
| %119 = util.global.address @"__iree_flow___sm_node579__m.layer-65.moving_variance" : !util.ptr<tensor<128xf32>> |
| %120 = util.global.address @"__iree_flow___sm_node588__m.layer-67.kernel" : !util.ptr<tensor<1x1x128x512xf32>> |
| %121 = util.global.address @"__iree_flow___sm_node589__m.layer-67.bias" : !util.ptr<tensor<512xf32>> |
| %122 = util.global.address @"__iree_flow___sm_node595__m.layer-68.gamma" : !util.ptr<tensor<512xf32>> |
| %123 = util.global.address @"__iree_flow___sm_node596__m.layer-68.beta" : !util.ptr<tensor<512xf32>> |
| %124 = util.global.address @"__iree_flow___sm_node597__m.layer-68.moving_mean" : !util.ptr<tensor<512xf32>> |
| %125 = util.global.address @"__iree_flow___sm_node598__m.layer-68.moving_variance" : !util.ptr<tensor<512xf32>> |
| %126 = util.global.address @"__iree_flow___sm_node611__m.layer-71.kernel" : !util.ptr<tensor<1x1x512x128xf32>> |
| %127 = util.global.address @"__iree_flow___sm_node612__m.layer-71.bias" : !util.ptr<tensor<128xf32>> |
| %128 = util.global.address @"__iree_flow___sm_node618__m.layer-72.gamma" : !util.ptr<tensor<128xf32>> |
| %129 = util.global.address @"__iree_flow___sm_node619__m.layer-72.beta" : !util.ptr<tensor<128xf32>> |
| %130 = util.global.address @"__iree_flow___sm_node620__m.layer-72.moving_mean" : !util.ptr<tensor<128xf32>> |
| %131 = util.global.address @"__iree_flow___sm_node621__m.layer-72.moving_variance" : !util.ptr<tensor<128xf32>> |
| %132 = util.global.address @"__iree_flow___sm_node630__m.layer-74.kernel" : !util.ptr<tensor<3x3x128x128xf32>> |
| %133 = util.global.address @"__iree_flow___sm_node631__m.layer-74.bias" : !util.ptr<tensor<128xf32>> |
| %134 = util.global.address @"__iree_flow___sm_node637__m.layer-75.gamma" : !util.ptr<tensor<128xf32>> |
| %135 = util.global.address @"__iree_flow___sm_node638__m.layer-75.beta" : !util.ptr<tensor<128xf32>> |
| %136 = util.global.address @"__iree_flow___sm_node639__m.layer-75.moving_mean" : !util.ptr<tensor<128xf32>> |
| %137 = util.global.address @"__iree_flow___sm_node640__m.layer-75.moving_variance" : !util.ptr<tensor<128xf32>> |
| %138 = util.global.address @"__iree_flow___sm_node649__m.layer-77.kernel" : !util.ptr<tensor<1x1x128x512xf32>> |
| %139 = util.global.address @"__iree_flow___sm_node650__m.layer-77.bias" : !util.ptr<tensor<512xf32>> |
| %140 = util.global.address @"__iree_flow___sm_node656__m.layer-78.gamma" : !util.ptr<tensor<512xf32>> |
| %141 = util.global.address @"__iree_flow___sm_node657__m.layer-78.beta" : !util.ptr<tensor<512xf32>> |
| %142 = util.global.address @"__iree_flow___sm_node658__m.layer-78.moving_mean" : !util.ptr<tensor<512xf32>> |
| %143 = util.global.address @"__iree_flow___sm_node659__m.layer-78.moving_variance" : !util.ptr<tensor<512xf32>> |
| %144 = util.global.address @"__iree_flow___sm_node672__m.layer-81.kernel" : !util.ptr<tensor<1x1x512x256xf32>> |
| %145 = util.global.address @"__iree_flow___sm_node673__m.layer-81.bias" : !util.ptr<tensor<256xf32>> |
| %146 = util.global.address @"__iree_flow___sm_node679__m.layer-82.gamma" : !util.ptr<tensor<256xf32>> |
| %147 = util.global.address @"__iree_flow___sm_node680__m.layer-82.beta" : !util.ptr<tensor<256xf32>> |
| %148 = util.global.address @"__iree_flow___sm_node681__m.layer-82.moving_mean" : !util.ptr<tensor<256xf32>> |
| %149 = util.global.address @"__iree_flow___sm_node682__m.layer-82.moving_variance" : !util.ptr<tensor<256xf32>> |
| %150 = util.global.address @"__iree_flow___sm_node691__m.layer-84.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %151 = util.global.address @"__iree_flow___sm_node692__m.layer-84.bias" : !util.ptr<tensor<256xf32>> |
| %152 = util.global.address @"__iree_flow___sm_node698__m.layer-85.gamma" : !util.ptr<tensor<256xf32>> |
| %153 = util.global.address @"__iree_flow___sm_node699__m.layer-85.beta" : !util.ptr<tensor<256xf32>> |
| %154 = util.global.address @"__iree_flow___sm_node700__m.layer-85.moving_mean" : !util.ptr<tensor<256xf32>> |
| %155 = util.global.address @"__iree_flow___sm_node701__m.layer-85.moving_variance" : !util.ptr<tensor<256xf32>> |
| %156 = util.global.address @"__iree_flow___sm_node716__m.layer-88.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %157 = util.global.address @"__iree_flow___sm_node717__m.layer-88.bias" : !util.ptr<tensor<1024xf32>> |
| %158 = util.global.address @"__iree_flow___sm_node710__m.layer-87.kernel" : !util.ptr<tensor<1x1x512x1024xf32>> |
| %159 = util.global.address @"__iree_flow___sm_node711__m.layer-87.bias" : !util.ptr<tensor<1024xf32>> |
| %160 = util.global.address @"__iree_flow___sm_node723__m.layer-89.gamma" : !util.ptr<tensor<1024xf32>> |
| %161 = util.global.address @"__iree_flow___sm_node724__m.layer-89.beta" : !util.ptr<tensor<1024xf32>> |
| %162 = util.global.address @"__iree_flow___sm_node725__m.layer-89.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %163 = util.global.address @"__iree_flow___sm_node726__m.layer-89.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %164 = util.global.address @"__iree_flow___sm_node732__m.layer-90.gamma" : !util.ptr<tensor<1024xf32>> |
| %165 = util.global.address @"__iree_flow___sm_node733__m.layer-90.beta" : !util.ptr<tensor<1024xf32>> |
| %166 = util.global.address @"__iree_flow___sm_node734__m.layer-90.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %167 = util.global.address @"__iree_flow___sm_node735__m.layer-90.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %168 = util.global.address @"__iree_flow___sm_node748__m.layer-93.kernel" : !util.ptr<tensor<1x1x1024x256xf32>> |
| %169 = util.global.address @"__iree_flow___sm_node749__m.layer-93.bias" : !util.ptr<tensor<256xf32>> |
| %170 = util.global.address @"__iree_flow___sm_node755__m.layer-94.gamma" : !util.ptr<tensor<256xf32>> |
| %171 = util.global.address @"__iree_flow___sm_node756__m.layer-94.beta" : !util.ptr<tensor<256xf32>> |
| %172 = util.global.address @"__iree_flow___sm_node757__m.layer-94.moving_mean" : !util.ptr<tensor<256xf32>> |
| %173 = util.global.address @"__iree_flow___sm_node758__m.layer-94.moving_variance" : !util.ptr<tensor<256xf32>> |
| %174 = util.global.address @"__iree_flow___sm_node767__m.layer-96.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %175 = util.global.address @"__iree_flow___sm_node768__m.layer-96.bias" : !util.ptr<tensor<256xf32>> |
| %176 = util.global.address @"__iree_flow___sm_node774__m.layer-97.gamma" : !util.ptr<tensor<256xf32>> |
| %177 = util.global.address @"__iree_flow___sm_node775__m.layer-97.beta" : !util.ptr<tensor<256xf32>> |
| %178 = util.global.address @"__iree_flow___sm_node776__m.layer-97.moving_mean" : !util.ptr<tensor<256xf32>> |
| %179 = util.global.address @"__iree_flow___sm_node777__m.layer-97.moving_variance" : !util.ptr<tensor<256xf32>> |
| %180 = util.global.address @"__iree_flow___sm_node786__m.layer-99.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %181 = util.global.address @"__iree_flow___sm_node787__m.layer-99.bias" : !util.ptr<tensor<1024xf32>> |
| %182 = util.global.address @"__iree_flow___sm_node793__m.layer-100.gamma" : !util.ptr<tensor<1024xf32>> |
| %183 = util.global.address @"__iree_flow___sm_node794__m.layer-100.beta" : !util.ptr<tensor<1024xf32>> |
| %184 = util.global.address @"__iree_flow___sm_node795__m.layer-100.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %185 = util.global.address @"__iree_flow___sm_node796__m.layer-100.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %186 = util.global.address @"__iree_flow___sm_node809__m.layer-103.kernel" : !util.ptr<tensor<1x1x1024x256xf32>> |
| %187 = util.global.address @"__iree_flow___sm_node810__m.layer-103.bias" : !util.ptr<tensor<256xf32>> |
| %188 = util.global.address @"__iree_flow___sm_node816__m.layer-104.gamma" : !util.ptr<tensor<256xf32>> |
| %189 = util.global.address @"__iree_flow___sm_node817__m.layer-104.beta" : !util.ptr<tensor<256xf32>> |
| %190 = util.global.address @"__iree_flow___sm_node818__m.layer-104.moving_mean" : !util.ptr<tensor<256xf32>> |
| %191 = util.global.address @"__iree_flow___sm_node819__m.layer-104.moving_variance" : !util.ptr<tensor<256xf32>> |
| %192 = util.global.address @"__iree_flow___sm_node828__m.layer-106.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %193 = util.global.address @"__iree_flow___sm_node829__m.layer-106.bias" : !util.ptr<tensor<256xf32>> |
| %194 = util.global.address @"__iree_flow___sm_node835__m.layer-107.gamma" : !util.ptr<tensor<256xf32>> |
| %195 = util.global.address @"__iree_flow___sm_node836__m.layer-107.beta" : !util.ptr<tensor<256xf32>> |
| %196 = util.global.address @"__iree_flow___sm_node837__m.layer-107.moving_mean" : !util.ptr<tensor<256xf32>> |
| %197 = util.global.address @"__iree_flow___sm_node838__m.layer-107.moving_variance" : !util.ptr<tensor<256xf32>> |
| %198 = util.global.address @"__iree_flow___sm_node847__m.layer-109.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %199 = util.global.address @"__iree_flow___sm_node848__m.layer-109.bias" : !util.ptr<tensor<1024xf32>> |
| %200 = util.global.address @"__iree_flow___sm_node854__m.layer-110.gamma" : !util.ptr<tensor<1024xf32>> |
| %201 = util.global.address @"__iree_flow___sm_node855__m.layer-110.beta" : !util.ptr<tensor<1024xf32>> |
| %202 = util.global.address @"__iree_flow___sm_node856__m.layer-110.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %203 = util.global.address @"__iree_flow___sm_node857__m.layer-110.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %204 = util.global.address @"__iree_flow___sm_node870__m.layer-113.kernel" : !util.ptr<tensor<1x1x1024x256xf32>> |
| %205 = util.global.address @"__iree_flow___sm_node871__m.layer-113.bias" : !util.ptr<tensor<256xf32>> |
| %206 = util.global.address @"__iree_flow___sm_node877__m.layer-114.gamma" : !util.ptr<tensor<256xf32>> |
| %207 = util.global.address @"__iree_flow___sm_node878__m.layer-114.beta" : !util.ptr<tensor<256xf32>> |
| %208 = util.global.address @"__iree_flow___sm_node879__m.layer-114.moving_mean" : !util.ptr<tensor<256xf32>> |
| %209 = util.global.address @"__iree_flow___sm_node880__m.layer-114.moving_variance" : !util.ptr<tensor<256xf32>> |
| %210 = util.global.address @"__iree_flow___sm_node889__m.layer-116.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %211 = util.global.address @"__iree_flow___sm_node890__m.layer-116.bias" : !util.ptr<tensor<256xf32>> |
| %212 = util.global.address @"__iree_flow___sm_node896__m.layer-117.gamma" : !util.ptr<tensor<256xf32>> |
| %213 = util.global.address @"__iree_flow___sm_node897__m.layer-117.beta" : !util.ptr<tensor<256xf32>> |
| %214 = util.global.address @"__iree_flow___sm_node898__m.layer-117.moving_mean" : !util.ptr<tensor<256xf32>> |
| %215 = util.global.address @"__iree_flow___sm_node899__m.layer-117.moving_variance" : !util.ptr<tensor<256xf32>> |
| %216 = util.global.address @"__iree_flow___sm_node908__m.layer-119.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %217 = util.global.address @"__iree_flow___sm_node909__m.layer-119.bias" : !util.ptr<tensor<1024xf32>> |
| %218 = util.global.address @"__iree_flow___sm_node915__m.layer-120.gamma" : !util.ptr<tensor<1024xf32>> |
| %219 = util.global.address @"__iree_flow___sm_node916__m.layer-120.beta" : !util.ptr<tensor<1024xf32>> |
| %220 = util.global.address @"__iree_flow___sm_node917__m.layer-120.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %221 = util.global.address @"__iree_flow___sm_node918__m.layer-120.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %222 = util.global.address @"__iree_flow___sm_node931__m.layer-123.kernel" : !util.ptr<tensor<1x1x1024x256xf32>> |
| %223 = util.global.address @"__iree_flow___sm_node932__m.layer-123.bias" : !util.ptr<tensor<256xf32>> |
| %224 = util.global.address @"__iree_flow___sm_node938__m.layer-124.gamma" : !util.ptr<tensor<256xf32>> |
| %225 = util.global.address @"__iree_flow___sm_node939__m.layer-124.beta" : !util.ptr<tensor<256xf32>> |
| %226 = util.global.address @"__iree_flow___sm_node940__m.layer-124.moving_mean" : !util.ptr<tensor<256xf32>> |
| %227 = util.global.address @"__iree_flow___sm_node941__m.layer-124.moving_variance" : !util.ptr<tensor<256xf32>> |
| %228 = util.global.address @"__iree_flow___sm_node950__m.layer-126.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %229 = util.global.address @"__iree_flow___sm_node951__m.layer-126.bias" : !util.ptr<tensor<256xf32>> |
| %230 = util.global.address @"__iree_flow___sm_node957__m.layer-127.gamma" : !util.ptr<tensor<256xf32>> |
| %231 = util.global.address @"__iree_flow___sm_node958__m.layer-127.beta" : !util.ptr<tensor<256xf32>> |
| %232 = util.global.address @"__iree_flow___sm_node959__m.layer-127.moving_mean" : !util.ptr<tensor<256xf32>> |
| %233 = util.global.address @"__iree_flow___sm_node960__m.layer-127.moving_variance" : !util.ptr<tensor<256xf32>> |
| %234 = util.global.address @"__iree_flow___sm_node969__m.layer-129.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %235 = util.global.address @"__iree_flow___sm_node970__m.layer-129.bias" : !util.ptr<tensor<1024xf32>> |
| %236 = util.global.address @"__iree_flow___sm_node976__m.layer-130.gamma" : !util.ptr<tensor<1024xf32>> |
| %237 = util.global.address @"__iree_flow___sm_node977__m.layer-130.beta" : !util.ptr<tensor<1024xf32>> |
| %238 = util.global.address @"__iree_flow___sm_node978__m.layer-130.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %239 = util.global.address @"__iree_flow___sm_node979__m.layer-130.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %240 = util.global.address @"__iree_flow___sm_node992__m.layer-133.kernel" : !util.ptr<tensor<1x1x1024x256xf32>> |
| %241 = util.global.address @"__iree_flow___sm_node993__m.layer-133.bias" : !util.ptr<tensor<256xf32>> |
| %242 = util.global.address @"__iree_flow___sm_node999__m.layer-134.gamma" : !util.ptr<tensor<256xf32>> |
| %243 = util.global.address @"__iree_flow___sm_node1000__m.layer-134.beta" : !util.ptr<tensor<256xf32>> |
| %244 = util.global.address @"__iree_flow___sm_node1001__m.layer-134.moving_mean" : !util.ptr<tensor<256xf32>> |
| %245 = util.global.address @"__iree_flow___sm_node1002__m.layer-134.moving_variance" : !util.ptr<tensor<256xf32>> |
| %246 = util.global.address @"__iree_flow___sm_node1011__m.layer-136.kernel" : !util.ptr<tensor<3x3x256x256xf32>> |
| %247 = util.global.address @"__iree_flow___sm_node1012__m.layer-136.bias" : !util.ptr<tensor<256xf32>> |
| %248 = util.global.address @"__iree_flow___sm_node1018__m.layer-137.gamma" : !util.ptr<tensor<256xf32>> |
| %249 = util.global.address @"__iree_flow___sm_node1019__m.layer-137.beta" : !util.ptr<tensor<256xf32>> |
| %250 = util.global.address @"__iree_flow___sm_node1020__m.layer-137.moving_mean" : !util.ptr<tensor<256xf32>> |
| %251 = util.global.address @"__iree_flow___sm_node1021__m.layer-137.moving_variance" : !util.ptr<tensor<256xf32>> |
| %252 = util.global.address @"__iree_flow___sm_node1030__m.layer-139.kernel" : !util.ptr<tensor<1x1x256x1024xf32>> |
| %253 = util.global.address @"__iree_flow___sm_node1031__m.layer-139.bias" : !util.ptr<tensor<1024xf32>> |
| %254 = util.global.address @"__iree_flow___sm_node1037__m.layer-140.gamma" : !util.ptr<tensor<1024xf32>> |
| %255 = util.global.address @"__iree_flow___sm_node1038__m.layer-140.beta" : !util.ptr<tensor<1024xf32>> |
| %256 = util.global.address @"__iree_flow___sm_node1039__m.layer-140.moving_mean" : !util.ptr<tensor<1024xf32>> |
| %257 = util.global.address @"__iree_flow___sm_node1040__m.layer-140.moving_variance" : !util.ptr<tensor<1024xf32>> |
| %258 = util.global.address @"__iree_flow___sm_node1053__m.layer-143.kernel" : !util.ptr<tensor<1x1x1024x512xf32>> |
| %259 = util.global.address @"__iree_flow___sm_node1054__m.layer-143.bias" : !util.ptr<tensor<512xf32>> |
| %260 = util.global.address @"__iree_flow___sm_node1060__m.layer-144.gamma" : !util.ptr<tensor<512xf32>> |
| %261 = util.global.address @"__iree_flow___sm_node1061__m.layer-144.beta" : !util.ptr<tensor<512xf32>> |
| %262 = util.global.address @"__iree_flow___sm_node1062__m.layer-144.moving_mean" : !util.ptr<tensor<512xf32>> |
| %263 = util.global.address @"__iree_flow___sm_node1063__m.layer-144.moving_variance" : !util.ptr<tensor<512xf32>> |
| %264 = util.global.address @"__iree_flow___sm_node1072__m.layer-146.kernel" : !util.ptr<tensor<3x3x512x512xf32>> |
| %265 = util.global.address @"__iree_flow___sm_node1073__m.layer-146.bias" : !util.ptr<tensor<512xf32>> |
| %266 = util.global.address @"__iree_flow___sm_node1079__m.layer-147.gamma" : !util.ptr<tensor<512xf32>> |
| %267 = util.global.address @"__iree_flow___sm_node1080__m.layer-147.beta" : !util.ptr<tensor<512xf32>> |
| %268 = util.global.address @"__iree_flow___sm_node1081__m.layer-147.moving_mean" : !util.ptr<tensor<512xf32>> |
| %269 = util.global.address @"__iree_flow___sm_node1082__m.layer-147.moving_variance" : !util.ptr<tensor<512xf32>> |
| %270 = util.global.address @"__iree_flow___sm_node1097__m.layer-150.kernel" : !util.ptr<tensor<1x1x512x2048xf32>> |
| %271 = util.global.address @"__iree_flow___sm_node1098__m.layer-150.bias" : !util.ptr<tensor<2048xf32>> |
| %272 = util.global.address @"__iree_flow___sm_node1091__m.layer-149.kernel" : !util.ptr<tensor<1x1x1024x2048xf32>> |
| %273 = util.global.address @"__iree_flow___sm_node1092__m.layer-149.bias" : !util.ptr<tensor<2048xf32>> |
| %274 = util.global.address @"__iree_flow___sm_node1104__m.layer-151.gamma" : !util.ptr<tensor<2048xf32>> |
| %275 = util.global.address @"__iree_flow___sm_node1105__m.layer-151.beta" : !util.ptr<tensor<2048xf32>> |
| %276 = util.global.address @"__iree_flow___sm_node1106__m.layer-151.moving_mean" : !util.ptr<tensor<2048xf32>> |
| %277 = util.global.address @"__iree_flow___sm_node1107__m.layer-151.moving_variance" : !util.ptr<tensor<2048xf32>> |
| %278 = util.global.address @"__iree_flow___sm_node1113__m.layer-152.gamma" : !util.ptr<tensor<2048xf32>> |
| %279 = util.global.address @"__iree_flow___sm_node1114__m.layer-152.beta" : !util.ptr<tensor<2048xf32>> |
| %280 = util.global.address @"__iree_flow___sm_node1115__m.layer-152.moving_mean" : !util.ptr<tensor<2048xf32>> |
| %281 = util.global.address @"__iree_flow___sm_node1116__m.layer-152.moving_variance" : !util.ptr<tensor<2048xf32>> |
| %282 = util.global.address @"__iree_flow___sm_node1129__m.layer-155.kernel" : !util.ptr<tensor<1x1x2048x512xf32>> |
| %283 = util.global.address @"__iree_flow___sm_node1130__m.layer-155.bias" : !util.ptr<tensor<512xf32>> |
| %284 = util.global.address @"__iree_flow___sm_node1136__m.layer-156.gamma" : !util.ptr<tensor<512xf32>> |
| %285 = util.global.address @"__iree_flow___sm_node1137__m.layer-156.beta" : !util.ptr<tensor<512xf32>> |
| %286 = util.global.address @"__iree_flow___sm_node1138__m.layer-156.moving_mean" : !util.ptr<tensor<512xf32>> |
| %287 = util.global.address @"__iree_flow___sm_node1139__m.layer-156.moving_variance" : !util.ptr<tensor<512xf32>> |
| %288 = util.global.address @"__iree_flow___sm_node1148__m.layer-158.kernel" : !util.ptr<tensor<3x3x512x512xf32>> |
| %289 = util.global.address @"__iree_flow___sm_node1149__m.layer-158.bias" : !util.ptr<tensor<512xf32>> |
| %290 = util.global.address @"__iree_flow___sm_node1155__m.layer-159.gamma" : !util.ptr<tensor<512xf32>> |
| %291 = util.global.address @"__iree_flow___sm_node1156__m.layer-159.beta" : !util.ptr<tensor<512xf32>> |
| %292 = util.global.address @"__iree_flow___sm_node1157__m.layer-159.moving_mean" : !util.ptr<tensor<512xf32>> |
| %293 = util.global.address @"__iree_flow___sm_node1158__m.layer-159.moving_variance" : !util.ptr<tensor<512xf32>> |
| %294 = util.global.address @"__iree_flow___sm_node1167__m.layer-161.kernel" : !util.ptr<tensor<1x1x512x2048xf32>> |
| %295 = util.global.address @"__iree_flow___sm_node1168__m.layer-161.bias" : !util.ptr<tensor<2048xf32>> |
| %296 = util.global.address @"__iree_flow___sm_node1174__m.layer-162.gamma" : !util.ptr<tensor<2048xf32>> |
| %297 = util.global.address @"__iree_flow___sm_node1175__m.layer-162.beta" : !util.ptr<tensor<2048xf32>> |
| %298 = util.global.address @"__iree_flow___sm_node1176__m.layer-162.moving_mean" : !util.ptr<tensor<2048xf32>> |
| %299 = util.global.address @"__iree_flow___sm_node1177__m.layer-162.moving_variance" : !util.ptr<tensor<2048xf32>> |
| %300 = util.global.address @"__iree_flow___sm_node1190__m.layer-165.kernel" : !util.ptr<tensor<1x1x2048x512xf32>> |
| %301 = util.global.address @"__iree_flow___sm_node1191__m.layer-165.bias" : !util.ptr<tensor<512xf32>> |
| %302 = util.global.address @"__iree_flow___sm_node1197__m.layer-166.gamma" : !util.ptr<tensor<512xf32>> |
| %303 = util.global.address @"__iree_flow___sm_node1198__m.layer-166.beta" : !util.ptr<tensor<512xf32>> |
| %304 = util.global.address @"__iree_flow___sm_node1199__m.layer-166.moving_mean" : !util.ptr<tensor<512xf32>> |
| %305 = util.global.address @"__iree_flow___sm_node1200__m.layer-166.moving_variance" : !util.ptr<tensor<512xf32>> |
| %306 = util.global.address @"__iree_flow___sm_node1209__m.layer-168.kernel" : !util.ptr<tensor<3x3x512x512xf32>> |
| %307 = util.global.address @"__iree_flow___sm_node1210__m.layer-168.bias" : !util.ptr<tensor<512xf32>> |
| %308 = util.global.address @"__iree_flow___sm_node1216__m.layer-169.gamma" : !util.ptr<tensor<512xf32>> |
| %309 = util.global.address @"__iree_flow___sm_node1217__m.layer-169.beta" : !util.ptr<tensor<512xf32>> |
| %310 = util.global.address @"__iree_flow___sm_node1218__m.layer-169.moving_mean" : !util.ptr<tensor<512xf32>> |
| %311 = util.global.address @"__iree_flow___sm_node1219__m.layer-169.moving_variance" : !util.ptr<tensor<512xf32>> |
| %312 = util.global.address @"__iree_flow___sm_node1228__m.layer-171.kernel" : !util.ptr<tensor<1x1x512x2048xf32>> |
| %313 = util.global.address @"__iree_flow___sm_node1229__m.layer-171.bias" : !util.ptr<tensor<2048xf32>> |
| %314 = util.global.address @"__iree_flow___sm_node1235__m.layer-172.gamma" : !util.ptr<tensor<2048xf32>> |
| %315 = util.global.address @"__iree_flow___sm_node1236__m.layer-172.beta" : !util.ptr<tensor<2048xf32>> |
| %316 = util.global.address @"__iree_flow___sm_node1237__m.layer-172.moving_mean" : !util.ptr<tensor<2048xf32>> |
| %317 = util.global.address @"__iree_flow___sm_node1238__m.layer-172.moving_variance" : !util.ptr<tensor<2048xf32>> |
| %318 = util.global.address @"__iree_flow___sm_node1255__m.layer-176.kernel" : !util.ptr<tensor<2048x1000xf32>> |
| %319 = util.global.address @"__iree_flow___sm_node1256__m.layer-176.bias" : !util.ptr<tensor<1000xf32>> |
| %320 = mhlo.constant dense<0.000000e+00> : tensor<1x112x112x64xf32> |
| %321 = mhlo.constant dense<0.000000e+00> : tensor<1x56x56x64xf32> |
| %322 = mhlo.constant dense<0.000000e+00> : tensor<1x56x56x256xf32> |
| %323 = mhlo.constant dense<0.000000e+00> : tensor<1x28x28x128xf32> |
| %324 = mhlo.constant dense<0.000000e+00> : tensor<1x28x28x512xf32> |
| %325 = mhlo.constant dense<0.000000e+00> : tensor<1x14x14x256xf32> |
| %326 = mhlo.constant dense<0.000000e+00> : tensor<1x14x14x1024xf32> |
| %327 = mhlo.constant dense<0.000000e+00> : tensor<1x7x7x512xf32> |
| %328 = mhlo.constant dense<0.000000e+00> : tensor<1x7x7x2048xf32> |
| %329 = mhlo.constant dense<4.900000e+01> : tensor<1x2048xf32> |
| %330 = mhlo.constant dense<0xFF800000> : tensor<f32> |
| %331 = mhlo.constant dense<0.000000e+00> : tensor<f32> |
| %332 = util.global.load.indirect %5 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %333 = util.global.load.indirect %4 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %334 = util.global.load.indirect %3 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %335 = util.global.load.indirect %2 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %336 = util.global.load.indirect %1 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %337 = util.global.load.indirect %0 : !util.ptr<tensor<7x7x3x64xf32>> -> tensor<7x7x3x64xf32> |
| %338 = util.global.load.indirect %25 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %339 = util.global.load.indirect %24 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %340 = util.global.load.indirect %23 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %341 = util.global.load.indirect %22 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %342 = util.global.load.indirect %21 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %343 = util.global.load.indirect %20 : !util.ptr<tensor<1x1x64x256xf32>> -> tensor<1x1x64x256xf32> |
| %344 = util.global.load.indirect %11 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %345 = util.global.load.indirect %10 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %346 = util.global.load.indirect %9 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %347 = util.global.load.indirect %8 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %348 = util.global.load.indirect %7 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %349 = util.global.load.indirect %6 : !util.ptr<tensor<1x1x64x64xf32>> -> tensor<1x1x64x64xf32> |
| %350 = util.global.load.indirect %17 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %351 = util.global.load.indirect %16 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %352 = util.global.load.indirect %15 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %353 = util.global.load.indirect %14 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %354 = util.global.load.indirect %13 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %355 = util.global.load.indirect %12 : !util.ptr<tensor<3x3x64x64xf32>> -> tensor<3x3x64x64xf32> |
| %356 = util.global.load.indirect %29 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %357 = util.global.load.indirect %28 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %358 = util.global.load.indirect %27 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %359 = util.global.load.indirect %26 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %360 = util.global.load.indirect %19 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %361 = util.global.load.indirect %18 : !util.ptr<tensor<1x1x64x256xf32>> -> tensor<1x1x64x256xf32> |
| %362 = util.global.load.indirect %35 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %363 = util.global.load.indirect %34 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %364 = util.global.load.indirect %33 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %365 = util.global.load.indirect %32 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %366 = util.global.load.indirect %31 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %367 = util.global.load.indirect %30 : !util.ptr<tensor<1x1x256x64xf32>> -> tensor<1x1x256x64xf32> |
| %368 = util.global.load.indirect %41 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %369 = util.global.load.indirect %40 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %370 = util.global.load.indirect %39 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %371 = util.global.load.indirect %38 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %372 = util.global.load.indirect %37 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %373 = util.global.load.indirect %36 : !util.ptr<tensor<3x3x64x64xf32>> -> tensor<3x3x64x64xf32> |
| %374 = util.global.load.indirect %47 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %375 = util.global.load.indirect %46 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %376 = util.global.load.indirect %45 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %377 = util.global.load.indirect %44 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %378 = util.global.load.indirect %43 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %379 = util.global.load.indirect %42 : !util.ptr<tensor<1x1x64x256xf32>> -> tensor<1x1x64x256xf32> |
| %380 = util.global.load.indirect %53 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %381 = util.global.load.indirect %52 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %382 = util.global.load.indirect %51 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %383 = util.global.load.indirect %50 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %384 = util.global.load.indirect %49 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %385 = util.global.load.indirect %48 : !util.ptr<tensor<1x1x256x64xf32>> -> tensor<1x1x256x64xf32> |
| %386 = util.global.load.indirect %59 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %387 = util.global.load.indirect %58 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %388 = util.global.load.indirect %57 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %389 = util.global.load.indirect %56 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %390 = util.global.load.indirect %55 : !util.ptr<tensor<64xf32>> -> tensor<64xf32> |
| %391 = util.global.load.indirect %54 : !util.ptr<tensor<3x3x64x64xf32>> -> tensor<3x3x64x64xf32> |
| %392 = util.global.load.indirect %65 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %393 = util.global.load.indirect %64 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %394 = util.global.load.indirect %63 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %395 = util.global.load.indirect %62 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %396 = util.global.load.indirect %61 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %397 = util.global.load.indirect %60 : !util.ptr<tensor<1x1x64x256xf32>> -> tensor<1x1x64x256xf32> |
| %398 = util.global.load.indirect %85 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %399 = util.global.load.indirect %84 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %400 = util.global.load.indirect %83 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %401 = util.global.load.indirect %82 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %402 = util.global.load.indirect %81 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %403 = util.global.load.indirect %80 : !util.ptr<tensor<1x1x256x512xf32>> -> tensor<1x1x256x512xf32> |
| %404 = util.global.load.indirect %71 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %405 = util.global.load.indirect %70 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %406 = util.global.load.indirect %69 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %407 = util.global.load.indirect %68 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %408 = util.global.load.indirect %67 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %409 = util.global.load.indirect %66 : !util.ptr<tensor<1x1x256x128xf32>> -> tensor<1x1x256x128xf32> |
| %410 = util.global.load.indirect %77 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %411 = util.global.load.indirect %76 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %412 = util.global.load.indirect %75 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %413 = util.global.load.indirect %74 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %414 = util.global.load.indirect %73 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %415 = util.global.load.indirect %72 : !util.ptr<tensor<3x3x128x128xf32>> -> tensor<3x3x128x128xf32> |
| %416 = util.global.load.indirect %89 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %417 = util.global.load.indirect %88 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %418 = util.global.load.indirect %87 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %419 = util.global.load.indirect %86 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %420 = util.global.load.indirect %79 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %421 = util.global.load.indirect %78 : !util.ptr<tensor<1x1x128x512xf32>> -> tensor<1x1x128x512xf32> |
| %422 = util.global.load.indirect %95 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %423 = util.global.load.indirect %94 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %424 = util.global.load.indirect %93 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %425 = util.global.load.indirect %92 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %426 = util.global.load.indirect %91 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %427 = util.global.load.indirect %90 : !util.ptr<tensor<1x1x512x128xf32>> -> tensor<1x1x512x128xf32> |
| %428 = util.global.load.indirect %101 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %429 = util.global.load.indirect %100 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %430 = util.global.load.indirect %99 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %431 = util.global.load.indirect %98 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %432 = util.global.load.indirect %97 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %433 = util.global.load.indirect %96 : !util.ptr<tensor<3x3x128x128xf32>> -> tensor<3x3x128x128xf32> |
| %434 = util.global.load.indirect %107 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %435 = util.global.load.indirect %106 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %436 = util.global.load.indirect %105 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %437 = util.global.load.indirect %104 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %438 = util.global.load.indirect %103 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %439 = util.global.load.indirect %102 : !util.ptr<tensor<1x1x128x512xf32>> -> tensor<1x1x128x512xf32> |
| %440 = util.global.load.indirect %113 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %441 = util.global.load.indirect %112 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %442 = util.global.load.indirect %111 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %443 = util.global.load.indirect %110 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %444 = util.global.load.indirect %109 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %445 = util.global.load.indirect %108 : !util.ptr<tensor<1x1x512x128xf32>> -> tensor<1x1x512x128xf32> |
| %446 = util.global.load.indirect %119 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %447 = util.global.load.indirect %118 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %448 = util.global.load.indirect %117 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %449 = util.global.load.indirect %116 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %450 = util.global.load.indirect %115 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %451 = util.global.load.indirect %114 : !util.ptr<tensor<3x3x128x128xf32>> -> tensor<3x3x128x128xf32> |
| %452 = util.global.load.indirect %125 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %453 = util.global.load.indirect %124 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %454 = util.global.load.indirect %123 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %455 = util.global.load.indirect %122 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %456 = util.global.load.indirect %121 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %457 = util.global.load.indirect %120 : !util.ptr<tensor<1x1x128x512xf32>> -> tensor<1x1x128x512xf32> |
| %458 = util.global.load.indirect %131 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %459 = util.global.load.indirect %130 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %460 = util.global.load.indirect %129 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %461 = util.global.load.indirect %128 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %462 = util.global.load.indirect %127 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %463 = util.global.load.indirect %126 : !util.ptr<tensor<1x1x512x128xf32>> -> tensor<1x1x512x128xf32> |
| %464 = util.global.load.indirect %137 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %465 = util.global.load.indirect %136 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %466 = util.global.load.indirect %135 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %467 = util.global.load.indirect %134 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %468 = util.global.load.indirect %133 : !util.ptr<tensor<128xf32>> -> tensor<128xf32> |
| %469 = util.global.load.indirect %132 : !util.ptr<tensor<3x3x128x128xf32>> -> tensor<3x3x128x128xf32> |
| %470 = util.global.load.indirect %143 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %471 = util.global.load.indirect %142 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %472 = util.global.load.indirect %141 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %473 = util.global.load.indirect %140 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %474 = util.global.load.indirect %139 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %475 = util.global.load.indirect %138 : !util.ptr<tensor<1x1x128x512xf32>> -> tensor<1x1x128x512xf32> |
| %476 = util.global.load.indirect %163 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %477 = util.global.load.indirect %162 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %478 = util.global.load.indirect %161 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %479 = util.global.load.indirect %160 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %480 = util.global.load.indirect %159 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %481 = util.global.load.indirect %158 : !util.ptr<tensor<1x1x512x1024xf32>> -> tensor<1x1x512x1024xf32> |
| %482 = util.global.load.indirect %149 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %483 = util.global.load.indirect %148 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %484 = util.global.load.indirect %147 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %485 = util.global.load.indirect %146 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %486 = util.global.load.indirect %145 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %487 = util.global.load.indirect %144 : !util.ptr<tensor<1x1x512x256xf32>> -> tensor<1x1x512x256xf32> |
| %488 = util.global.load.indirect %155 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %489 = util.global.load.indirect %154 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %490 = util.global.load.indirect %153 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %491 = util.global.load.indirect %152 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %492 = util.global.load.indirect %151 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %493 = util.global.load.indirect %150 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %494 = util.global.load.indirect %167 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %495 = util.global.load.indirect %166 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %496 = util.global.load.indirect %165 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %497 = util.global.load.indirect %164 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %498 = util.global.load.indirect %157 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %499 = util.global.load.indirect %156 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %500 = util.global.load.indirect %173 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %501 = util.global.load.indirect %172 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %502 = util.global.load.indirect %171 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %503 = util.global.load.indirect %170 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %504 = util.global.load.indirect %169 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %505 = util.global.load.indirect %168 : !util.ptr<tensor<1x1x1024x256xf32>> -> tensor<1x1x1024x256xf32> |
| %506 = util.global.load.indirect %179 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %507 = util.global.load.indirect %178 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %508 = util.global.load.indirect %177 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %509 = util.global.load.indirect %176 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %510 = util.global.load.indirect %175 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %511 = util.global.load.indirect %174 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %512 = util.global.load.indirect %185 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %513 = util.global.load.indirect %184 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %514 = util.global.load.indirect %183 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %515 = util.global.load.indirect %182 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %516 = util.global.load.indirect %181 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %517 = util.global.load.indirect %180 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %518 = util.global.load.indirect %191 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %519 = util.global.load.indirect %190 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %520 = util.global.load.indirect %189 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %521 = util.global.load.indirect %188 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %522 = util.global.load.indirect %187 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %523 = util.global.load.indirect %186 : !util.ptr<tensor<1x1x1024x256xf32>> -> tensor<1x1x1024x256xf32> |
| %524 = util.global.load.indirect %197 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %525 = util.global.load.indirect %196 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %526 = util.global.load.indirect %195 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %527 = util.global.load.indirect %194 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %528 = util.global.load.indirect %193 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %529 = util.global.load.indirect %192 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %530 = util.global.load.indirect %203 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %531 = util.global.load.indirect %202 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %532 = util.global.load.indirect %201 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %533 = util.global.load.indirect %200 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %534 = util.global.load.indirect %199 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %535 = util.global.load.indirect %198 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %536 = util.global.load.indirect %209 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %537 = util.global.load.indirect %208 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %538 = util.global.load.indirect %207 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %539 = util.global.load.indirect %206 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %540 = util.global.load.indirect %205 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %541 = util.global.load.indirect %204 : !util.ptr<tensor<1x1x1024x256xf32>> -> tensor<1x1x1024x256xf32> |
| %542 = util.global.load.indirect %215 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %543 = util.global.load.indirect %214 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %544 = util.global.load.indirect %213 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %545 = util.global.load.indirect %212 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %546 = util.global.load.indirect %211 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %547 = util.global.load.indirect %210 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %548 = util.global.load.indirect %221 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %549 = util.global.load.indirect %220 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %550 = util.global.load.indirect %219 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %551 = util.global.load.indirect %218 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %552 = util.global.load.indirect %217 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %553 = util.global.load.indirect %216 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %554 = util.global.load.indirect %227 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %555 = util.global.load.indirect %226 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %556 = util.global.load.indirect %225 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %557 = util.global.load.indirect %224 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %558 = util.global.load.indirect %223 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %559 = util.global.load.indirect %222 : !util.ptr<tensor<1x1x1024x256xf32>> -> tensor<1x1x1024x256xf32> |
| %560 = util.global.load.indirect %233 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %561 = util.global.load.indirect %232 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %562 = util.global.load.indirect %231 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %563 = util.global.load.indirect %230 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %564 = util.global.load.indirect %229 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %565 = util.global.load.indirect %228 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %566 = util.global.load.indirect %239 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %567 = util.global.load.indirect %238 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %568 = util.global.load.indirect %237 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %569 = util.global.load.indirect %236 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %570 = util.global.load.indirect %235 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %571 = util.global.load.indirect %234 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %572 = util.global.load.indirect %245 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %573 = util.global.load.indirect %244 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %574 = util.global.load.indirect %243 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %575 = util.global.load.indirect %242 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %576 = util.global.load.indirect %241 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %577 = util.global.load.indirect %240 : !util.ptr<tensor<1x1x1024x256xf32>> -> tensor<1x1x1024x256xf32> |
| %578 = util.global.load.indirect %251 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %579 = util.global.load.indirect %250 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %580 = util.global.load.indirect %249 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %581 = util.global.load.indirect %248 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %582 = util.global.load.indirect %247 : !util.ptr<tensor<256xf32>> -> tensor<256xf32> |
| %583 = util.global.load.indirect %246 : !util.ptr<tensor<3x3x256x256xf32>> -> tensor<3x3x256x256xf32> |
| %584 = util.global.load.indirect %257 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %585 = util.global.load.indirect %256 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %586 = util.global.load.indirect %255 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %587 = util.global.load.indirect %254 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %588 = util.global.load.indirect %253 : !util.ptr<tensor<1024xf32>> -> tensor<1024xf32> |
| %589 = util.global.load.indirect %252 : !util.ptr<tensor<1x1x256x1024xf32>> -> tensor<1x1x256x1024xf32> |
| %590 = util.global.load.indirect %277 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %591 = util.global.load.indirect %276 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %592 = util.global.load.indirect %275 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %593 = util.global.load.indirect %274 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %594 = util.global.load.indirect %273 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %595 = util.global.load.indirect %272 : !util.ptr<tensor<1x1x1024x2048xf32>> -> tensor<1x1x1024x2048xf32> |
| %596 = util.global.load.indirect %263 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %597 = util.global.load.indirect %262 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %598 = util.global.load.indirect %261 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %599 = util.global.load.indirect %260 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %600 = util.global.load.indirect %259 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %601 = util.global.load.indirect %258 : !util.ptr<tensor<1x1x1024x512xf32>> -> tensor<1x1x1024x512xf32> |
| %602 = util.global.load.indirect %269 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %603 = util.global.load.indirect %268 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %604 = util.global.load.indirect %267 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %605 = util.global.load.indirect %266 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %606 = util.global.load.indirect %265 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %607 = util.global.load.indirect %264 : !util.ptr<tensor<3x3x512x512xf32>> -> tensor<3x3x512x512xf32> |
| %608 = util.global.load.indirect %281 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %609 = util.global.load.indirect %280 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %610 = util.global.load.indirect %279 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %611 = util.global.load.indirect %278 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %612 = util.global.load.indirect %271 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %613 = util.global.load.indirect %270 : !util.ptr<tensor<1x1x512x2048xf32>> -> tensor<1x1x512x2048xf32> |
| %614 = util.global.load.indirect %287 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %615 = util.global.load.indirect %286 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %616 = util.global.load.indirect %285 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %617 = util.global.load.indirect %284 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %618 = util.global.load.indirect %283 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %619 = util.global.load.indirect %282 : !util.ptr<tensor<1x1x2048x512xf32>> -> tensor<1x1x2048x512xf32> |
| %620 = util.global.load.indirect %293 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %621 = util.global.load.indirect %292 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %622 = util.global.load.indirect %291 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %623 = util.global.load.indirect %290 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %624 = util.global.load.indirect %289 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %625 = util.global.load.indirect %288 : !util.ptr<tensor<3x3x512x512xf32>> -> tensor<3x3x512x512xf32> |
| %626 = util.global.load.indirect %299 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %627 = util.global.load.indirect %298 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %628 = util.global.load.indirect %297 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %629 = util.global.load.indirect %296 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %630 = util.global.load.indirect %295 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %631 = util.global.load.indirect %294 : !util.ptr<tensor<1x1x512x2048xf32>> -> tensor<1x1x512x2048xf32> |
| %632 = util.global.load.indirect %305 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %633 = util.global.load.indirect %304 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %634 = util.global.load.indirect %303 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %635 = util.global.load.indirect %302 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %636 = util.global.load.indirect %301 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %637 = util.global.load.indirect %300 : !util.ptr<tensor<1x1x2048x512xf32>> -> tensor<1x1x2048x512xf32> |
| %638 = util.global.load.indirect %311 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %639 = util.global.load.indirect %310 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %640 = util.global.load.indirect %309 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %641 = util.global.load.indirect %308 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %642 = util.global.load.indirect %307 : !util.ptr<tensor<512xf32>> -> tensor<512xf32> |
| %643 = util.global.load.indirect %306 : !util.ptr<tensor<3x3x512x512xf32>> -> tensor<3x3x512x512xf32> |
| %644 = util.global.load.indirect %317 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %645 = util.global.load.indirect %316 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %646 = util.global.load.indirect %315 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %647 = util.global.load.indirect %314 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %648 = util.global.load.indirect %313 : !util.ptr<tensor<2048xf32>> -> tensor<2048xf32> |
| %649 = util.global.load.indirect %312 : !util.ptr<tensor<1x1x512x2048xf32>> -> tensor<1x1x512x2048xf32> |
| %650 = util.global.load.indirect %319 : !util.ptr<tensor<1000xf32>> -> tensor<1000xf32> |
| %651 = util.global.load.indirect %318 : !util.ptr<tensor<2048x1000xf32>> -> tensor<2048x1000xf32> |
| %652 = "mhlo.pad"(%arg0, %331) {edge_padding_high = dense<[0, 3, 3, 0]> : tensor<4xi64>, edge_padding_low = dense<[0, 3, 3, 0]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x224x224x3xf32>, tensor<f32>) -> tensor<1x230x230x3xf32> |
| %653 = "mhlo.convolution"(%652, %337) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x230x230x3xf32>, tensor<7x7x3x64xf32>) -> tensor<1x112x112x64xf32> |
| %654 = "mhlo.broadcast_in_dim"(%336) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x112x112x64xf32> |
| %655 = mhlo.add %653, %654 : tensor<1x112x112x64xf32> |
| %656 = "mhlo.batch_norm_inference"(%655, %335, %334, %333, %332) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x112x112x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x112x112x64xf32> |
| %657 = mhlo.maximum %656, %320 : tensor<1x112x112x64xf32> |
| %658 = "mhlo.pad"(%657, %331) {edge_padding_high = dense<[0, 1, 1, 0]> : tensor<4xi64>, edge_padding_low = dense<[0, 1, 1, 0]> : tensor<4xi64>, interior_padding = dense<0> : tensor<4xi64>} : (tensor<1x112x112x64xf32>, tensor<f32>) -> tensor<1x114x114x64xf32> |
| %659 = "mhlo.reduce_window"(%658, %330) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %944 = mhlo.maximum %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%944) : (tensor<f32>) -> () |
| }) {window_dimensions = dense<[1, 3, 3, 1]> : tensor<4xi64>, window_strides = dense<[1, 2, 2, 1]> : tensor<4xi64>} : (tensor<1x114x114x64xf32>, tensor<f32>) -> tensor<1x56x56x64xf32> |
| %660 = "mhlo.convolution"(%659, %343) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<1x1x64x256xf32>) -> tensor<1x56x56x256xf32> |
| %661 = "mhlo.broadcast_in_dim"(%342) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %662 = mhlo.add %660, %661 : tensor<1x56x56x256xf32> |
| %663 = "mhlo.batch_norm_inference"(%662, %341, %340, %339, %338) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %664 = "mhlo.convolution"(%659, %349) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<1x1x64x64xf32>) -> tensor<1x56x56x64xf32> |
| %665 = "mhlo.broadcast_in_dim"(%348) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %666 = mhlo.add %664, %665 : tensor<1x56x56x64xf32> |
| %667 = "mhlo.batch_norm_inference"(%666, %347, %346, %345, %344) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %668 = mhlo.maximum %667, %321 : tensor<1x56x56x64xf32> |
| %669 = "mhlo.convolution"(%668, %355) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<3x3x64x64xf32>) -> tensor<1x56x56x64xf32> |
| %670 = "mhlo.broadcast_in_dim"(%354) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %671 = mhlo.add %669, %670 : tensor<1x56x56x64xf32> |
| %672 = "mhlo.batch_norm_inference"(%671, %353, %352, %351, %350) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %673 = mhlo.maximum %672, %321 : tensor<1x56x56x64xf32> |
| %674 = "mhlo.convolution"(%673, %361) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<1x1x64x256xf32>) -> tensor<1x56x56x256xf32> |
| %675 = "mhlo.broadcast_in_dim"(%360) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %676 = mhlo.add %674, %675 : tensor<1x56x56x256xf32> |
| %677 = "mhlo.batch_norm_inference"(%676, %359, %358, %357, %356) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %678 = mhlo.add %663, %677 : tensor<1x56x56x256xf32> |
| %679 = mhlo.maximum %678, %322 : tensor<1x56x56x256xf32> |
| %680 = "mhlo.convolution"(%679, %367) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x256xf32>, tensor<1x1x256x64xf32>) -> tensor<1x56x56x64xf32> |
| %681 = "mhlo.broadcast_in_dim"(%366) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %682 = mhlo.add %680, %681 : tensor<1x56x56x64xf32> |
| %683 = "mhlo.batch_norm_inference"(%682, %365, %364, %363, %362) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %684 = mhlo.maximum %683, %321 : tensor<1x56x56x64xf32> |
| %685 = "mhlo.convolution"(%684, %373) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<3x3x64x64xf32>) -> tensor<1x56x56x64xf32> |
| %686 = "mhlo.broadcast_in_dim"(%372) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %687 = mhlo.add %685, %686 : tensor<1x56x56x64xf32> |
| %688 = "mhlo.batch_norm_inference"(%687, %371, %370, %369, %368) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %689 = mhlo.maximum %688, %321 : tensor<1x56x56x64xf32> |
| %690 = "mhlo.convolution"(%689, %379) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<1x1x64x256xf32>) -> tensor<1x56x56x256xf32> |
| %691 = "mhlo.broadcast_in_dim"(%378) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %692 = mhlo.add %690, %691 : tensor<1x56x56x256xf32> |
| %693 = "mhlo.batch_norm_inference"(%692, %377, %376, %375, %374) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %694 = mhlo.add %679, %693 : tensor<1x56x56x256xf32> |
| %695 = mhlo.maximum %694, %322 : tensor<1x56x56x256xf32> |
| %696 = "mhlo.convolution"(%695, %385) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x256xf32>, tensor<1x1x256x64xf32>) -> tensor<1x56x56x64xf32> |
| %697 = "mhlo.broadcast_in_dim"(%384) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %698 = mhlo.add %696, %697 : tensor<1x56x56x64xf32> |
| %699 = "mhlo.batch_norm_inference"(%698, %383, %382, %381, %380) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %700 = mhlo.maximum %699, %321 : tensor<1x56x56x64xf32> |
| %701 = "mhlo.convolution"(%700, %391) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<3x3x64x64xf32>) -> tensor<1x56x56x64xf32> |
| %702 = "mhlo.broadcast_in_dim"(%390) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %703 = mhlo.add %701, %702 : tensor<1x56x56x64xf32> |
| %704 = "mhlo.batch_norm_inference"(%703, %389, %388, %387, %386) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>, tensor<64xf32>) -> tensor<1x56x56x64xf32> |
| %705 = mhlo.maximum %704, %321 : tensor<1x56x56x64xf32> |
| %706 = "mhlo.convolution"(%705, %397) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x56x56x64xf32>, tensor<1x1x64x256xf32>) -> tensor<1x56x56x256xf32> |
| %707 = "mhlo.broadcast_in_dim"(%396) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %708 = mhlo.add %706, %707 : tensor<1x56x56x256xf32> |
| %709 = "mhlo.batch_norm_inference"(%708, %395, %394, %393, %392) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x56x56x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x56x56x256xf32> |
| %710 = mhlo.add %695, %709 : tensor<1x56x56x256xf32> |
| %711 = mhlo.maximum %710, %322 : tensor<1x56x56x256xf32> |
| %712 = "mhlo.convolution"(%711, %403) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x56x56x256xf32>, tensor<1x1x256x512xf32>) -> tensor<1x28x28x512xf32> |
| %713 = "mhlo.broadcast_in_dim"(%402) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %714 = mhlo.add %712, %713 : tensor<1x28x28x512xf32> |
| %715 = "mhlo.batch_norm_inference"(%714, %401, %400, %399, %398) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %716 = "mhlo.convolution"(%711, %409) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x56x56x256xf32>, tensor<1x1x256x128xf32>) -> tensor<1x28x28x128xf32> |
| %717 = "mhlo.broadcast_in_dim"(%408) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %718 = mhlo.add %716, %717 : tensor<1x28x28x128xf32> |
| %719 = "mhlo.batch_norm_inference"(%718, %407, %406, %405, %404) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %720 = mhlo.maximum %719, %323 : tensor<1x28x28x128xf32> |
| %721 = "mhlo.convolution"(%720, %415) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<3x3x128x128xf32>) -> tensor<1x28x28x128xf32> |
| %722 = "mhlo.broadcast_in_dim"(%414) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %723 = mhlo.add %721, %722 : tensor<1x28x28x128xf32> |
| %724 = "mhlo.batch_norm_inference"(%723, %413, %412, %411, %410) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %725 = mhlo.maximum %724, %323 : tensor<1x28x28x128xf32> |
| %726 = "mhlo.convolution"(%725, %421) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<1x1x128x512xf32>) -> tensor<1x28x28x512xf32> |
| %727 = "mhlo.broadcast_in_dim"(%420) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %728 = mhlo.add %726, %727 : tensor<1x28x28x512xf32> |
| %729 = "mhlo.batch_norm_inference"(%728, %419, %418, %417, %416) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %730 = mhlo.add %715, %729 : tensor<1x28x28x512xf32> |
| %731 = mhlo.maximum %730, %324 : tensor<1x28x28x512xf32> |
| %732 = "mhlo.convolution"(%731, %427) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x512xf32>, tensor<1x1x512x128xf32>) -> tensor<1x28x28x128xf32> |
| %733 = "mhlo.broadcast_in_dim"(%426) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %734 = mhlo.add %732, %733 : tensor<1x28x28x128xf32> |
| %735 = "mhlo.batch_norm_inference"(%734, %425, %424, %423, %422) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %736 = mhlo.maximum %735, %323 : tensor<1x28x28x128xf32> |
| %737 = "mhlo.convolution"(%736, %433) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<3x3x128x128xf32>) -> tensor<1x28x28x128xf32> |
| %738 = "mhlo.broadcast_in_dim"(%432) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %739 = mhlo.add %737, %738 : tensor<1x28x28x128xf32> |
| %740 = "mhlo.batch_norm_inference"(%739, %431, %430, %429, %428) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %741 = mhlo.maximum %740, %323 : tensor<1x28x28x128xf32> |
| %742 = "mhlo.convolution"(%741, %439) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<1x1x128x512xf32>) -> tensor<1x28x28x512xf32> |
| %743 = "mhlo.broadcast_in_dim"(%438) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %744 = mhlo.add %742, %743 : tensor<1x28x28x512xf32> |
| %745 = "mhlo.batch_norm_inference"(%744, %437, %436, %435, %434) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %746 = mhlo.add %731, %745 : tensor<1x28x28x512xf32> |
| %747 = mhlo.maximum %746, %324 : tensor<1x28x28x512xf32> |
| %748 = "mhlo.convolution"(%747, %445) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x512xf32>, tensor<1x1x512x128xf32>) -> tensor<1x28x28x128xf32> |
| %749 = "mhlo.broadcast_in_dim"(%444) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %750 = mhlo.add %748, %749 : tensor<1x28x28x128xf32> |
| %751 = "mhlo.batch_norm_inference"(%750, %443, %442, %441, %440) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %752 = mhlo.maximum %751, %323 : tensor<1x28x28x128xf32> |
| %753 = "mhlo.convolution"(%752, %451) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<3x3x128x128xf32>) -> tensor<1x28x28x128xf32> |
| %754 = "mhlo.broadcast_in_dim"(%450) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %755 = mhlo.add %753, %754 : tensor<1x28x28x128xf32> |
| %756 = "mhlo.batch_norm_inference"(%755, %449, %448, %447, %446) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %757 = mhlo.maximum %756, %323 : tensor<1x28x28x128xf32> |
| %758 = "mhlo.convolution"(%757, %457) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<1x1x128x512xf32>) -> tensor<1x28x28x512xf32> |
| %759 = "mhlo.broadcast_in_dim"(%456) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %760 = mhlo.add %758, %759 : tensor<1x28x28x512xf32> |
| %761 = "mhlo.batch_norm_inference"(%760, %455, %454, %453, %452) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %762 = mhlo.add %747, %761 : tensor<1x28x28x512xf32> |
| %763 = mhlo.maximum %762, %324 : tensor<1x28x28x512xf32> |
| %764 = "mhlo.convolution"(%763, %463) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x512xf32>, tensor<1x1x512x128xf32>) -> tensor<1x28x28x128xf32> |
| %765 = "mhlo.broadcast_in_dim"(%462) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %766 = mhlo.add %764, %765 : tensor<1x28x28x128xf32> |
| %767 = "mhlo.batch_norm_inference"(%766, %461, %460, %459, %458) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %768 = mhlo.maximum %767, %323 : tensor<1x28x28x128xf32> |
| %769 = "mhlo.convolution"(%768, %469) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<3x3x128x128xf32>) -> tensor<1x28x28x128xf32> |
| %770 = "mhlo.broadcast_in_dim"(%468) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %771 = mhlo.add %769, %770 : tensor<1x28x28x128xf32> |
| %772 = "mhlo.batch_norm_inference"(%771, %467, %466, %465, %464) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>, tensor<128xf32>) -> tensor<1x28x28x128xf32> |
| %773 = mhlo.maximum %772, %323 : tensor<1x28x28x128xf32> |
| %774 = "mhlo.convolution"(%773, %475) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x28x28x128xf32>, tensor<1x1x128x512xf32>) -> tensor<1x28x28x512xf32> |
| %775 = "mhlo.broadcast_in_dim"(%474) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %776 = mhlo.add %774, %775 : tensor<1x28x28x512xf32> |
| %777 = "mhlo.batch_norm_inference"(%776, %473, %472, %471, %470) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x28x28x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x28x28x512xf32> |
| %778 = mhlo.add %763, %777 : tensor<1x28x28x512xf32> |
| %779 = mhlo.maximum %778, %324 : tensor<1x28x28x512xf32> |
| %780 = "mhlo.convolution"(%779, %481) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x28x28x512xf32>, tensor<1x1x512x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %781 = "mhlo.broadcast_in_dim"(%480) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %782 = mhlo.add %780, %781 : tensor<1x14x14x1024xf32> |
| %783 = "mhlo.batch_norm_inference"(%782, %479, %478, %477, %476) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %784 = "mhlo.convolution"(%779, %487) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x28x28x512xf32>, tensor<1x1x512x256xf32>) -> tensor<1x14x14x256xf32> |
| %785 = "mhlo.broadcast_in_dim"(%486) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %786 = mhlo.add %784, %785 : tensor<1x14x14x256xf32> |
| %787 = "mhlo.batch_norm_inference"(%786, %485, %484, %483, %482) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %788 = mhlo.maximum %787, %325 : tensor<1x14x14x256xf32> |
| %789 = "mhlo.convolution"(%788, %493) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %790 = "mhlo.broadcast_in_dim"(%492) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %791 = mhlo.add %789, %790 : tensor<1x14x14x256xf32> |
| %792 = "mhlo.batch_norm_inference"(%791, %491, %490, %489, %488) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %793 = mhlo.maximum %792, %325 : tensor<1x14x14x256xf32> |
| %794 = "mhlo.convolution"(%793, %499) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %795 = "mhlo.broadcast_in_dim"(%498) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %796 = mhlo.add %794, %795 : tensor<1x14x14x1024xf32> |
| %797 = "mhlo.batch_norm_inference"(%796, %497, %496, %495, %494) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %798 = mhlo.add %783, %797 : tensor<1x14x14x1024xf32> |
| %799 = mhlo.maximum %798, %326 : tensor<1x14x14x1024xf32> |
| %800 = "mhlo.convolution"(%799, %505) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x256xf32>) -> tensor<1x14x14x256xf32> |
| %801 = "mhlo.broadcast_in_dim"(%504) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %802 = mhlo.add %800, %801 : tensor<1x14x14x256xf32> |
| %803 = "mhlo.batch_norm_inference"(%802, %503, %502, %501, %500) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %804 = mhlo.maximum %803, %325 : tensor<1x14x14x256xf32> |
| %805 = "mhlo.convolution"(%804, %511) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %806 = "mhlo.broadcast_in_dim"(%510) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %807 = mhlo.add %805, %806 : tensor<1x14x14x256xf32> |
| %808 = "mhlo.batch_norm_inference"(%807, %509, %508, %507, %506) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %809 = mhlo.maximum %808, %325 : tensor<1x14x14x256xf32> |
| %810 = "mhlo.convolution"(%809, %517) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %811 = "mhlo.broadcast_in_dim"(%516) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %812 = mhlo.add %810, %811 : tensor<1x14x14x1024xf32> |
| %813 = "mhlo.batch_norm_inference"(%812, %515, %514, %513, %512) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %814 = mhlo.add %799, %813 : tensor<1x14x14x1024xf32> |
| %815 = mhlo.maximum %814, %326 : tensor<1x14x14x1024xf32> |
| %816 = "mhlo.convolution"(%815, %523) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x256xf32>) -> tensor<1x14x14x256xf32> |
| %817 = "mhlo.broadcast_in_dim"(%522) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %818 = mhlo.add %816, %817 : tensor<1x14x14x256xf32> |
| %819 = "mhlo.batch_norm_inference"(%818, %521, %520, %519, %518) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %820 = mhlo.maximum %819, %325 : tensor<1x14x14x256xf32> |
| %821 = "mhlo.convolution"(%820, %529) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %822 = "mhlo.broadcast_in_dim"(%528) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %823 = mhlo.add %821, %822 : tensor<1x14x14x256xf32> |
| %824 = "mhlo.batch_norm_inference"(%823, %527, %526, %525, %524) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %825 = mhlo.maximum %824, %325 : tensor<1x14x14x256xf32> |
| %826 = "mhlo.convolution"(%825, %535) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %827 = "mhlo.broadcast_in_dim"(%534) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %828 = mhlo.add %826, %827 : tensor<1x14x14x1024xf32> |
| %829 = "mhlo.batch_norm_inference"(%828, %533, %532, %531, %530) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %830 = mhlo.add %815, %829 : tensor<1x14x14x1024xf32> |
| %831 = mhlo.maximum %830, %326 : tensor<1x14x14x1024xf32> |
| %832 = "mhlo.convolution"(%831, %541) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x256xf32>) -> tensor<1x14x14x256xf32> |
| %833 = "mhlo.broadcast_in_dim"(%540) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %834 = mhlo.add %832, %833 : tensor<1x14x14x256xf32> |
| %835 = "mhlo.batch_norm_inference"(%834, %539, %538, %537, %536) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %836 = mhlo.maximum %835, %325 : tensor<1x14x14x256xf32> |
| %837 = "mhlo.convolution"(%836, %547) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %838 = "mhlo.broadcast_in_dim"(%546) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %839 = mhlo.add %837, %838 : tensor<1x14x14x256xf32> |
| %840 = "mhlo.batch_norm_inference"(%839, %545, %544, %543, %542) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %841 = mhlo.maximum %840, %325 : tensor<1x14x14x256xf32> |
| %842 = "mhlo.convolution"(%841, %553) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %843 = "mhlo.broadcast_in_dim"(%552) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %844 = mhlo.add %842, %843 : tensor<1x14x14x1024xf32> |
| %845 = "mhlo.batch_norm_inference"(%844, %551, %550, %549, %548) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %846 = mhlo.add %831, %845 : tensor<1x14x14x1024xf32> |
| %847 = mhlo.maximum %846, %326 : tensor<1x14x14x1024xf32> |
| %848 = "mhlo.convolution"(%847, %559) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x256xf32>) -> tensor<1x14x14x256xf32> |
| %849 = "mhlo.broadcast_in_dim"(%558) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %850 = mhlo.add %848, %849 : tensor<1x14x14x256xf32> |
| %851 = "mhlo.batch_norm_inference"(%850, %557, %556, %555, %554) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %852 = mhlo.maximum %851, %325 : tensor<1x14x14x256xf32> |
| %853 = "mhlo.convolution"(%852, %565) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %854 = "mhlo.broadcast_in_dim"(%564) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %855 = mhlo.add %853, %854 : tensor<1x14x14x256xf32> |
| %856 = "mhlo.batch_norm_inference"(%855, %563, %562, %561, %560) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %857 = mhlo.maximum %856, %325 : tensor<1x14x14x256xf32> |
| %858 = "mhlo.convolution"(%857, %571) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %859 = "mhlo.broadcast_in_dim"(%570) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %860 = mhlo.add %858, %859 : tensor<1x14x14x1024xf32> |
| %861 = "mhlo.batch_norm_inference"(%860, %569, %568, %567, %566) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %862 = mhlo.add %847, %861 : tensor<1x14x14x1024xf32> |
| %863 = mhlo.maximum %862, %326 : tensor<1x14x14x1024xf32> |
| %864 = "mhlo.convolution"(%863, %577) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x256xf32>) -> tensor<1x14x14x256xf32> |
| %865 = "mhlo.broadcast_in_dim"(%576) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %866 = mhlo.add %864, %865 : tensor<1x14x14x256xf32> |
| %867 = "mhlo.batch_norm_inference"(%866, %575, %574, %573, %572) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %868 = mhlo.maximum %867, %325 : tensor<1x14x14x256xf32> |
| %869 = "mhlo.convolution"(%868, %583) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<3x3x256x256xf32>) -> tensor<1x14x14x256xf32> |
| %870 = "mhlo.broadcast_in_dim"(%582) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %871 = mhlo.add %869, %870 : tensor<1x14x14x256xf32> |
| %872 = "mhlo.batch_norm_inference"(%871, %581, %580, %579, %578) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>, tensor<256xf32>) -> tensor<1x14x14x256xf32> |
| %873 = mhlo.maximum %872, %325 : tensor<1x14x14x256xf32> |
| %874 = "mhlo.convolution"(%873, %589) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x14x14x256xf32>, tensor<1x1x256x1024xf32>) -> tensor<1x14x14x1024xf32> |
| %875 = "mhlo.broadcast_in_dim"(%588) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %876 = mhlo.add %874, %875 : tensor<1x14x14x1024xf32> |
| %877 = "mhlo.batch_norm_inference"(%876, %587, %586, %585, %584) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x14x14x1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>, tensor<1024xf32>) -> tensor<1x14x14x1024xf32> |
| %878 = mhlo.add %863, %877 : tensor<1x14x14x1024xf32> |
| %879 = mhlo.maximum %878, %326 : tensor<1x14x14x1024xf32> |
| %880 = "mhlo.convolution"(%879, %595) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x2048xf32>) -> tensor<1x7x7x2048xf32> |
| %881 = "mhlo.broadcast_in_dim"(%594) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %882 = mhlo.add %880, %881 : tensor<1x7x7x2048xf32> |
| %883 = "mhlo.batch_norm_inference"(%882, %593, %592, %591, %590) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %884 = "mhlo.convolution"(%879, %601) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<2> : tensor<2xi64>} : (tensor<1x14x14x1024xf32>, tensor<1x1x1024x512xf32>) -> tensor<1x7x7x512xf32> |
| %885 = "mhlo.broadcast_in_dim"(%600) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %886 = mhlo.add %884, %885 : tensor<1x7x7x512xf32> |
| %887 = "mhlo.batch_norm_inference"(%886, %599, %598, %597, %596) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %888 = mhlo.maximum %887, %327 : tensor<1x7x7x512xf32> |
| %889 = "mhlo.convolution"(%888, %607) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<3x3x512x512xf32>) -> tensor<1x7x7x512xf32> |
| %890 = "mhlo.broadcast_in_dim"(%606) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %891 = mhlo.add %889, %890 : tensor<1x7x7x512xf32> |
| %892 = "mhlo.batch_norm_inference"(%891, %605, %604, %603, %602) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %893 = mhlo.maximum %892, %327 : tensor<1x7x7x512xf32> |
| %894 = "mhlo.convolution"(%893, %613) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<1x1x512x2048xf32>) -> tensor<1x7x7x2048xf32> |
| %895 = "mhlo.broadcast_in_dim"(%612) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %896 = mhlo.add %894, %895 : tensor<1x7x7x2048xf32> |
| %897 = "mhlo.batch_norm_inference"(%896, %611, %610, %609, %608) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %898 = mhlo.add %883, %897 : tensor<1x7x7x2048xf32> |
| %899 = mhlo.maximum %898, %328 : tensor<1x7x7x2048xf32> |
| %900 = "mhlo.convolution"(%899, %619) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x2048xf32>, tensor<1x1x2048x512xf32>) -> tensor<1x7x7x512xf32> |
| %901 = "mhlo.broadcast_in_dim"(%618) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %902 = mhlo.add %900, %901 : tensor<1x7x7x512xf32> |
| %903 = "mhlo.batch_norm_inference"(%902, %617, %616, %615, %614) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %904 = mhlo.maximum %903, %327 : tensor<1x7x7x512xf32> |
| %905 = "mhlo.convolution"(%904, %625) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<3x3x512x512xf32>) -> tensor<1x7x7x512xf32> |
| %906 = "mhlo.broadcast_in_dim"(%624) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %907 = mhlo.add %905, %906 : tensor<1x7x7x512xf32> |
| %908 = "mhlo.batch_norm_inference"(%907, %623, %622, %621, %620) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %909 = mhlo.maximum %908, %327 : tensor<1x7x7x512xf32> |
| %910 = "mhlo.convolution"(%909, %631) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<1x1x512x2048xf32>) -> tensor<1x7x7x2048xf32> |
| %911 = "mhlo.broadcast_in_dim"(%630) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %912 = mhlo.add %910, %911 : tensor<1x7x7x2048xf32> |
| %913 = "mhlo.batch_norm_inference"(%912, %629, %628, %627, %626) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %914 = mhlo.add %899, %913 : tensor<1x7x7x2048xf32> |
| %915 = mhlo.maximum %914, %328 : tensor<1x7x7x2048xf32> |
| %916 = "mhlo.convolution"(%915, %637) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x2048xf32>, tensor<1x1x2048x512xf32>) -> tensor<1x7x7x512xf32> |
| %917 = "mhlo.broadcast_in_dim"(%636) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %918 = mhlo.add %916, %917 : tensor<1x7x7x512xf32> |
| %919 = "mhlo.batch_norm_inference"(%918, %635, %634, %633, %632) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %920 = mhlo.maximum %919, %327 : tensor<1x7x7x512xf32> |
| %921 = "mhlo.convolution"(%920, %643) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<1> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<3x3x512x512xf32>) -> tensor<1x7x7x512xf32> |
| %922 = "mhlo.broadcast_in_dim"(%642) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %923 = mhlo.add %921, %922 : tensor<1x7x7x512xf32> |
| %924 = "mhlo.batch_norm_inference"(%923, %641, %640, %639, %638) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>, tensor<512xf32>) -> tensor<1x7x7x512xf32> |
| %925 = mhlo.maximum %924, %327 : tensor<1x7x7x512xf32> |
| %926 = "mhlo.convolution"(%925, %649) {batch_group_count = 1 : i64, dimension_numbers = #mhlo.conv<raw input_batch_dimension = 0, input_feature_dimension = 3, input_spatial_dimensions = [1, 2], kernel_input_feature_dimension = 2, kernel_output_feature_dimension = 3, kernel_spatial_dimensions = [0, 1], output_batch_dimension = 0, output_feature_dimension = 3, output_spatial_dimensions = [1, 2]>, feature_group_count = 1 : i64, padding = dense<0> : tensor<2x2xi64>, rhs_dilation = dense<1> : tensor<2xi64>, window_strides = dense<1> : tensor<2xi64>} : (tensor<1x7x7x512xf32>, tensor<1x1x512x2048xf32>) -> tensor<1x7x7x2048xf32> |
| %927 = "mhlo.broadcast_in_dim"(%648) {broadcast_dimensions = dense<3> : tensor<1xi64>} : (tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %928 = mhlo.add %926, %927 : tensor<1x7x7x2048xf32> |
| %929 = "mhlo.batch_norm_inference"(%928, %647, %646, %645, %644) {epsilon = 1.001000e-05 : f32, feature_index = 3 : i64} : (tensor<1x7x7x2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>, tensor<2048xf32>) -> tensor<1x7x7x2048xf32> |
| %930 = mhlo.add %915, %929 : tensor<1x7x7x2048xf32> |
| %931 = mhlo.maximum %930, %328 : tensor<1x7x7x2048xf32> |
| %932 = "mhlo.reduce"(%931, %331) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %944 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%944) : (tensor<f32>) -> () |
| }) {dimensions = dense<[1, 2]> : tensor<2xi64>} : (tensor<1x7x7x2048xf32>, tensor<f32>) -> tensor<1x2048xf32> |
| %933 = mhlo.divide %932, %329 : tensor<1x2048xf32> |
| %934 = "mhlo.dot"(%933, %651) : (tensor<1x2048xf32>, tensor<2048x1000xf32>) -> tensor<1x1000xf32> |
| %935 = "mhlo.broadcast_in_dim"(%650) {broadcast_dimensions = dense<1> : tensor<1xi64>} : (tensor<1000xf32>) -> tensor<1x1000xf32> |
| %936 = mhlo.add %934, %935 : tensor<1x1000xf32> |
| %937 = "mhlo.reduce"(%936, %330) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %944 = mhlo.maximum %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%944) : (tensor<f32>) -> () |
| }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x1000xf32>, tensor<f32>) -> tensor<1xf32> |
| %938 = "mhlo.broadcast_in_dim"(%937) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x1000xf32> |
| %939 = mhlo.subtract %936, %938 : tensor<1x1000xf32> |
| %940 = "mhlo.exponential"(%939) : (tensor<1x1000xf32>) -> tensor<1x1000xf32> |
| %941 = "mhlo.reduce"(%940, %331) ( { |
| ^bb0(%arg1: tensor<f32>, %arg2: tensor<f32>): // no predecessors |
| %944 = mhlo.add %arg1, %arg2 : tensor<f32> |
| "mhlo.return"(%944) : (tensor<f32>) -> () |
| }) {dimensions = dense<1> : tensor<1xi64>} : (tensor<1x1000xf32>, tensor<f32>) -> tensor<1xf32> |
| %942 = "mhlo.broadcast_in_dim"(%941) {broadcast_dimensions = dense<0> : tensor<1xi64>} : (tensor<1xf32>) -> tensor<1x1000xf32> |
| %943 = mhlo.divide %940, %942 : tensor<1x1000xf32> |
| return %943 : tensor<1x1000xf32> |
| } |
| } |
| |
| // CHECK: 1x1000xf32=[0.001 0.001 0.001 0.001 0.001 |
