Auto-merge updates from auto-update branch

open/MLCommons/measurements/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config/README.md

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+| Model     | Scenario   |   Accuracy |   Throughput | Latency (in ms)   |
+|-----------|------------|------------|--------------|-------------------|
+| retinanet | offline    |     76.951 |         0.37 | -                 |

open/MLCommons/measurements/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config/retinanet/offline/README.md

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+*Check [CM MLPerf docs](https://docs.mlcommons.org/inference) for more details.*
+
+## Host platform
+
+* OS version: macOS-14.7.2-arm64-arm-64bit
+* CPU version: arm
+* Python version: 3.12.8 (v3.12.8:2dc476bcb91, Dec  3 2024, 14:43:19) [Clang 13.0.0 (clang-1300.0.29.30)]
+* MLC version: unknown
+
+## CM Run Command
+
+See [CM installation guide](https://docs.mlcommons.org/inference/install/).
+
+```bash
+pip install -U mlcflow
+
+mlc rm cache -f
+
+mlc pull repo anandhu-eng@mlperf-automations --checkout=ba4eb1ee4ca39562f3ee55c8e9972f185b2663bc
+
+
+```
+*Note that if you want to use the [latest automation recipes](https://docs.mlcommons.org/inference) for MLPerf,
+ you should simply reload anandhu-eng@mlperf-automations without checkout and clean MLC cache as follows:*
+
+```bash
+mlc rm repo anandhu-eng@mlperf-automations
+mlc pull repo anandhu-eng@mlperf-automations
+mlc rm cache -f
+
+```
+
+## Results
+
+Platform: gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config
+
+Model Precision: fp32
+
+### Accuracy Results 
+`mAP`: `76.951`, Required accuracy for closed division `>= 37.1745`
+
+### Performance Results 
+`Samples per second`: `0.369785`

open/MLCommons/measurements/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config/retinanet/offline/accuracy_console.out

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+python3 python/main.py --profile retinanet-pytorch --model "/Users/runner/MLC/repos/local/cache/download-file_c7b0b2ff/resnext50_32x4d_fpn.pth" --dataset-path /Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb --output "/Users/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_c47bb062/test_results/gh_macos-latest_x86-reference-cpu-pytorch-v2.6.0-default_config/retinanet/offline/accuracy" --scenario Offline --max-batchsize 1 --count 5 --threads 3 --user_conf /Users/runner/MLC/repos/anandhu-eng@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/770381c24ca44ef9afc5d38fb5fcb666.conf --accuracy --use_preprocessed_dataset --cache_dir /Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb --dataset-list /Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb/annotations/openimages-mlperf.json
+INFO:main:Namespace(dataset='openimages-800-retinanet', dataset_path='/Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb', dataset_list='/Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb/annotations/openimages-mlperf.json', data_format=None, profile='retinanet-pytorch', scenario='Offline', max_batchsize=1, model='/Users/runner/MLC/repos/local/cache/download-file_c7b0b2ff/resnext50_32x4d_fpn.pth', output='/Users/runner/MLC/repos/local/cache/get-mlperf-inference-results-dir_c47bb062/test_results/gh_macos-latest_x86-reference-cpu-pytorch-v2.6.0-default_config/retinanet/offline/accuracy', inputs=['image'], outputs=['boxes', 'labels', 'scores'], backend='pytorch-native', device=None, model_name='retinanet', threads=3, qps=None, cache=0, cache_dir='/Users/runner/MLC/repos/local/cache/get-preprocessed-dataset-openimages_6ebb1cbb', preprocessed_dir=None, use_preprocessed_dataset=True, accuracy=True, find_peak_performance=False, debug=False, user_conf='/Users/runner/MLC/repos/anandhu-eng@mlperf-automations/script/generate-mlperf-inference-user-conf/tmp/770381c24ca44ef9afc5d38fb5fcb666.conf', audit_conf='audit.config', time=None, count=5, performance_sample_count=None, max_latency=None, samples_per_query=8)
+INFO:coco:loaded 5 images, cache=0, already_preprocessed=True, took=0.0sec
+/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/serialization.py:1434: UserWarning: 'torch.load' received a zip file that looks like a TorchScript archive dispatching to 'torch.jit.load' (call 'torch.jit.load' directly to silence this warning)
+  warnings.warn(
+/Library/Frameworks/Python.framework/Versions/3.12/lib/python3.12/site-packages/torch/nn/modules/module.py:1750: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/TensorShape.cpp:3638.)
+  return forward_call(*args, **kwargs)
+code/__torch__/model/retinanet.py:159: UserWarning: RetinaNet always returns a (Losses, Detections) tuple in scripting
+INFO:main:starting TestScenario.Offline
+loading annotations into memory...
+Done (t=0.00s)
+creating index...
+index created!
+Loading and preparing results...
+Converting ndarray to lists...
+(595, 7)
+0/595
+DONE (t=0.00s)
+creating index...
+index created!
+Running per image evaluation...
+Evaluate annotation type *bbox*
+DONE (t=0.02s).
+Accumulating evaluation results...
+DONE (t=0.02s).
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.770
+ Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.982
+ Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.677
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.483
+ Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.850
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.597
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.824
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.831
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = -1.000
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.632
+ Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.875
+TestScenario.Offline qps=0.28, mean=11.9923, time=18.101, acc=30.924%, mAP=76.951%, queries=5, tiles=50.0:8.5803,80.0:17.0034,90.0:17.4265,95.0:17.6380,99.0:17.8073,99.9:17.8454

open/MLCommons/measurements/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config/retinanet/offline/cpu_info.json

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+{
+  "MLC_HOST_CPU_MEMSIZE": "7516192768",
+  "MLC_HOST_CPU_TOTAL_PHYSICAL_CORES": "3",
+  "MLC_HOST_CPU_TOTAL_CORES": "3",
+  "MLC_HOST_CPU_L1I_CACHE_SIZE": "196608",
+  "MLC_HOST_CPU_L2_CACHE_SIZE": "12582912",
+  "MLC_HOST_CPU_SOCKETS": "1",
+  "MLC_HOST_CPU_TOTAL_LOGICAL_CORES": "3",
+  "MLC_HOST_CPU_THREADS_PER_CORE": "1",
+  "MLC_HOST_CPU_PHYSICAL_CORES_PER_SOCKET": "3"
+}

open/MLCommons/measurements/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config/retinanet/offline/gh_macos-latest_x86-reference-cpu-pytorch_v2.6.0-default_config.json

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+{
+  "starting_weights_filename": "resnext50_32x4d_fpn.pth",
+  "retraining": "no",
+  "input_data_types": "fp32",
+  "weight_data_types": "fp32",
+  "weight_transformations": "no"
+}