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[EP Perf] Display percentage of cuda/trt ops in cuda/trt ep on EP Perf Dashboard (#18868)
### Description Display percentage of cuda/trt ops in cuda/trt ep on EP Perf Dashboard:  Check [here](https://msit.powerbi.com/groups/d1ae6355-afd0-4c40-b78e-676a86cab1e2/reports/82101bbb-dad2-4f24-9ddf-a37f0d41509a/ReportSectionda402bdf6824e505a614?experience=power-bi) to preview on ep perf dashboard ### Motivation and Context <!-- - Why is this change required? What problem does it solve? - If it fixes an open issue, please link to the issue here. --> - brief overview of op metrics towards various models - easy to identify models which haven't reached 100% ops on cuda/trt ep.
This commit is contained in:
Yifan Li
GitHub
parent
ce70a30b94
commit
54e471a054
6 changed files with 280 additions and 236 deletions
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@ -1590,6 +1590,34 @@ def output_metrics(model_to_metrics, csv_filename):
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logger.info(f"Tensorrt ratio metrics are saved to csv file: {csv_filename}")
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def output_op_metrics(model_to_metrics, csv_filename):
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with open(csv_filename, mode="w", newline="") as csv_file:
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csv_writer = csv.writer(csv_file)
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csv_writer.writerow([model_title, "Ep", "op percentage in each ep"])
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for model, ep_info in model_to_metrics.items():
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if cuda in ep_info:
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cuda_data = ep_info[cuda]["ratio_of_ops_in_cuda_not_fallback_cpu"]
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csv_writer.writerow([model, cuda, cuda_data])
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if cuda_fp16 in ep_info:
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cuda_fp16_data = ep_info[cuda_fp16]["ratio_of_ops_in_cuda_not_fallback_cpu"]
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csv_writer.writerow([model, cuda_fp16, cuda_fp16_data])
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if cuda in ep_info and trt in ep_info:
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total_ops_in_cuda = ep_info[cuda]["total_ops"]
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cuda_cpu_ops_in_trt = ep_info[trt]["total_ops"]
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trt_data = (total_ops_in_cuda - cuda_cpu_ops_in_trt) / total_ops_in_cuda
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csv_writer.writerow([model, trt, trt_data])
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if cuda_fp16 in ep_info and trt_fp16 in ep_info:
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total_ops_in_cuda = ep_info[cuda_fp16]["total_ops"]
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cuda_cpu_ops_in_trt = ep_info[trt_fp16]["total_ops"]
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trt_fp16_data = (total_ops_in_cuda - cuda_cpu_ops_in_trt) / total_ops_in_cuda
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csv_writer.writerow([model, trt_fp16, trt_fp16_data])
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logger.info(
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f"op metrics for cuda/trt ep are saved to csv file: {csv_filename} and will be displayed at Perf Dashboard"
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)
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def output_system_info(result, csv_filename):
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with open(csv_filename, mode="a", newline="") as csv_file:
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column_names = ["cpu_info", "cuda", "gpu_info", "linux_distro", "memory", "trt"]
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@ -1,3 +1,7 @@
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# -------------------------------------------------------------------------
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# Copyright (c) Microsoft Corporation. All rights reserved.
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# Licensed under the MIT License.
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# --------------------------------------------------------------------------
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import argparse # noqa: F401
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import copy # noqa: F401
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import csv # noqa: F401
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@ -65,6 +69,7 @@ def main():
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benchmark_fail_csv = fail_name + csv_ending # noqa: F405
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benchmark_metrics_csv = metrics_name + csv_ending # noqa: F405
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benchmark_op_metrics_csv = op_metrics_name + csv_ending # noqa: F405
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benchmark_success_csv = success_name + csv_ending # noqa: F405
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benchmark_latency_csv = latency_name + csv_ending # noqa: F405
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benchmark_status_csv = status_name + csv_ending # noqa: F405
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@ -166,6 +171,9 @@ def main():
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model_to_metrics = read_map_from_file(METRICS_FILE) # noqa: F405
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output_metrics(model_to_metrics, os.path.join(path, benchmark_metrics_csv)) # noqa: F405
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logger.info(f"\nSaved model metrics results to {benchmark_metrics_csv}") # noqa: F405
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# Output op metrics for dashboard display
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output_op_metrics(model_to_metrics, os.path.join(path, benchmark_op_metrics_csv)) # noqa: F405
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logger.info(f"\nSaved model op metrics results to {benchmark_op_metrics_csv}") # noqa: F405
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if benchmark:
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logger.info("\n=========================================") # noqa: F405
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@ -1,236 +1,236 @@
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[
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{
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"model_name": "FasterRCNN-10",
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"working_directory": "./onnx-zoo-models/FasterRCNN-10",
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"model_path": "./faster_rcnn_R_50_FPN_1x.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "MaskRCNN-10",
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"working_directory": "./onnx-zoo-models/MaskRCNN-10/",
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"model_path": "./mask_rcnn_R_50_FPN_1x.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "mobilenetv2-7",
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"working_directory": "./onnx-zoo-models/mobilenetv2-7/",
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"model_path": "./mobilenetv2-7/mobilenetv2-7.onnx",
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"test_data_path": "./mobilenetv2-7/"
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},
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{
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"model_name": "resnet50-v1-7",
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"working_directory": "./onnx-zoo-models/resnet50-v1-7/",
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"model_path": "./resnet50v1/resnet50-v1-7.onnx",
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"test_data_path": "./resnet50v1/"
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},
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{
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"model_name": "resnet101-v1-7",
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"working_directory": "./onnx-zoo-models/resnet101-v1-7/",
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"model_path": "./resnet101v1/resnet101-v1-7.onnx",
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"test_data_path": "./resnet101v1/"
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},
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{
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"model_name": "resnet152-v1-7",
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"working_directory": "./onnx-zoo-models/resnet152-v1-7/",
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"model_path": "./resnet152v1/resnet152-v1-7.onnx",
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"test_data_path": "./resnet152v1/"
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},
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{
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"model_name": "resnet18-v2-7",
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"working_directory": "./onnx-zoo-models/resnet18-v2-7/",
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"model_path": "./resnet18v2/resnet18-v2-7.onnx",
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"test_data_path": "./resnet18v2/"
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},
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{
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"model_name": "resnet34-v2-7",
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"working_directory": "./onnx-zoo-models/resnet34-v2-7/",
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"model_path": "./resnet34v2/resnet34-v2-7.onnx",
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"test_data_path": "./resnet34v2/"
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},
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{
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"model_name": "resnet50-v2-7",
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"working_directory": "./onnx-zoo-models/resnet50-v2-7/",
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"model_path": "./resnet50v2/resnet50-v2-7.onnx",
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"test_data_path": "./resnet50v2/"
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},
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{
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"model_name": "resnet101-v2-7",
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"working_directory": "./onnx-zoo-models/resnet101-v2-7/",
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"model_path": "./resnet101v2/resnet101-v2-7.onnx",
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"test_data_path": "./resnet101v2/"
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},
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{
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"model_name": "resnet152-v2-7",
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"working_directory": "./onnx-zoo-models/resnet152-v2-7/",
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"model_path": "./resnet152v2/resnet152-v2-7.onnx",
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"test_data_path": "./resnet152v2/"
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},
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{
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"model_name": "squeezenet1.1-7",
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"working_directory": "./onnx-zoo-models/squeezenet1.1-7/",
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"model_path": "./squeezenet1.1/squeezenet1.1.onnx",
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"test_data_path": "./squeezenet1.1/"
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},
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{
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"model_name": "vgg16-7",
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"working_directory": "./onnx-zoo-models/vgg16-7/",
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"model_path": "./vgg16/vgg16.onnx",
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"test_data_path": "./vgg16/"
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},
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{
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"model_name": "vgg19-bn-7",
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"working_directory": "./onnx-zoo-models/vgg19-bn-7/",
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"model_path": "./vgg19-bn/vgg19-bn.onnx",
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"test_data_path": "./vgg19-bn/"
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},
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{
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"model_name": "bvlcalexnet-9",
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"working_directory": "./onnx-zoo-models/bvlcalexnet-9/",
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"model_path": "./bvlc_alexnet/model.onnx",
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"test_data_path": "./bvlc_alexnet/"
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},
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{
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"model_name": "googlenet-9",
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"working_directory": "./onnx-zoo-models/googlenet-9/",
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"model_path": "./bvlc_googlenet/model.onnx",
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"test_data_path": "./bvlc_googlenet/"
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},
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{
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"model_name": "caffenet-9",
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"working_directory": "./onnx-zoo-models/caffenet-9/",
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"model_path": "./bvlc_reference_caffenet/model.onnx",
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"test_data_path": "./bvlc_reference_caffenet/"
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},
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{
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"model_name": "rcnn-ilsvrc13-9",
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"working_directory": "./onnx-zoo-models/rcnn-ilsvrc13-9/",
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"model_path": "./bvlc_reference_rcnn_ilsvrc13/model.onnx",
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"test_data_path": "./bvlc_reference_rcnn_ilsvrc13/"
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},
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{
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"model_name": "densenet-9",
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"working_directory": "./onnx-zoo-models/densenet-9/",
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"model_path": "./densenet121/model.onnx",
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"test_data_path": "./densenet121/"
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},
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{
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"model_name": "inception-v1-9",
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"working_directory": "./onnx-zoo-models/inception-v1-9/",
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"model_path": "./inception_v1/model.onnx",
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"test_data_path": "./inception_v1/"
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},
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{
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"model_name": "inception-v2-9",
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"working_directory": "./onnx-zoo-models/inception-v2-9/",
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"model_path": "./inception_v2/model.onnx",
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"test_data_path": "./inception_v2/"
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},
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{
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"model_name": "shufflenet-9",
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"working_directory": "./onnx-zoo-models/shufflenet-9/",
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"model_path": "./shufflenet/model.onnx",
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"test_data_path": "./shufflenet/"
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},
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{
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"model_name": "shufflenet-v2-10",
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"working_directory": "./onnx-zoo-models/shufflenet-v2-10/",
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"model_path": "./model/test_shufflenetv2/model.onnx",
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"test_data_path": "./model/test_shufflenetv2/"
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},
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{
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"model_name": "mnist-8",
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"working_directory": "./onnx-zoo-models/mnist-8/",
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"model_path": "./mnist/model.onnx",
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"test_data_path": "./mnist/"
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},
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{
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"model_name": "tinyyolov2-8",
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"working_directory": "./onnx-zoo-models/tinyyolov2-8/",
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"model_path": "./tiny_yolov2/Model.onnx",
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"test_data_path": "./tiny_yolov2/"
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},
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{
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"model_name": "ssd-10",
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"working_directory": "./onnx-zoo-models/ssd-10/",
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"model_path": "./model.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "ssd_mobilenet_v1_10",
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"working_directory": "./onnx-zoo-models/ssd_mobilenet_v1_10/",
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"model_path": "./ssd_mobilenet_v1/ssd_mobilenet_v1.onnx",
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"test_data_path": "./ssd_mobilenet_v1/"
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},
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{
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"model_name": "retinanet-9",
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"working_directory": "./onnx-zoo-models/retinanet-9/",
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"model_path": "./test_retinanet_resnet101/retinanet-9.onnx",
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"test_data_path": "./test_retinanet_resnet101/"
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},
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{
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"model_name": "yolov3-10",
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"working_directory": "./onnx-zoo-models/yolov3-10/",
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"model_path": "./yolov3/yolov3.onnx",
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"test_data_path": "./yolov3/"
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},
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{
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"model_name": "tiny-yolov3-11",
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"working_directory": "./onnx-zoo-models/tiny-yolov3-11/",
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"model_path": "./yolov3-tiny.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "yolov4",
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"working_directory": "./onnx-zoo-models/yolov4/",
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"model_path": "./yolov4/yolov4_shape.onnx",
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"test_data_path": "./yolov4/"
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},
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{
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"model_name": "ResNet101-DUC-7",
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"working_directory": "./onnx-zoo-models/ResNet101-DUC-7/",
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"model_path": "./ResNet101_DUC_HDC/ResNet101_DUC_HDC.onnx",
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"test_data_path": "./ResNet101_DUC_HDC/"
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},
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{
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"model_name": "emotion-ferplus-8",
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"working_directory": "./onnx-zoo-models/emotion-ferplus-8/",
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"model_path": "./emotion_ferplus/model.onnx",
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"test_data_path": "./emotion_ferplus/"
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},
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{
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"model_name": "bertsquad-10",
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"working_directory": "./onnx-zoo-models/bertsquad-10/",
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"model_path": "./bertsquad-10/bertsquad10.onnx",
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"test_data_path": "./bertsquad-10/"
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},
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{
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"model_name": "gpt2-lm-head-10",
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"working_directory": "./onnx-zoo-models/gpt2-lm-head-10/",
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"model_path": "./GPT-2-LM-HEAD/model.onnx",
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"test_data_path": "./GPT-2-LM-HEAD/"
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},
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{
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"model_name": "gpt2-10",
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"working_directory": "./onnx-zoo-models/gpt2-10/",
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"model_path": "./GPT2/model.onnx",
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"test_data_path": "./GPT2/"
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},
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{
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"model_name": "zfnet512-9",
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"working_directory": "./onnx-zoo-models/zfnet512-9/",
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"model_path": "./zfnet512/model.onnx",
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"test_data_path": "./zfnet512/"
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},
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{
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"model_name": "arcfaceresnet100-8",
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"working_directory": "./onnx-zoo-models/arcfaceresnet100-8/",
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"model_path": "./resnet100/resnet100.onnx",
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"test_data_path": "./resnet100/"
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},
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{
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"model_name": "mosaic-9",
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"working_directory": "./onnx-zoo-models/mosaic-9/",
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"model_path": "./mosaic/mosaic.onnx",
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"test_data_path": "./mosaic/"
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}
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]
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{
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"model_name": "FasterRCNN-10",
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"working_directory": "./onnx-zoo-models/FasterRCNN-10",
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"model_path": "./faster_rcnn_R_50_FPN_1x.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "MaskRCNN-10",
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"working_directory": "./onnx-zoo-models/MaskRCNN-10/",
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"model_path": "./mask_rcnn_R_50_FPN_1x.onnx",
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"test_data_path": "./"
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},
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{
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"model_name": "mobilenetv2-7",
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"working_directory": "./onnx-zoo-models/mobilenetv2-7/",
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"model_path": "./mobilenetv2-7/mobilenetv2-7.onnx",
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"test_data_path": "./mobilenetv2-7/"
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},
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{
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"model_name": "resnet50-v1-7",
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"working_directory": "./onnx-zoo-models/resnet50-v1-7/",
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"model_path": "./resnet50v1/resnet50-v1-7.onnx",
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"test_data_path": "./resnet50v1/"
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},
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{
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"model_name": "resnet101-v1-7",
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"working_directory": "./onnx-zoo-models/resnet101-v1-7/",
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"model_path": "./resnet101v1/resnet101-v1-7.onnx",
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"test_data_path": "./resnet101v1/"
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},
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{
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"model_name": "resnet152-v1-7",
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"working_directory": "./onnx-zoo-models/resnet152-v1-7/",
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"model_path": "./resnet152v1/resnet152-v1-7.onnx",
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"test_data_path": "./resnet152v1/"
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},
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{
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"model_name": "resnet18-v2-7",
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"working_directory": "./onnx-zoo-models/resnet18-v2-7/",
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"model_path": "./resnet18v2/resnet18-v2-7.onnx",
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"test_data_path": "./resnet18v2/"
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},
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{
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"model_name": "resnet34-v2-7",
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"working_directory": "./onnx-zoo-models/resnet34-v2-7/",
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"model_path": "./resnet34v2/resnet34-v2-7.onnx",
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"test_data_path": "./resnet34v2/"
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},
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{
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"model_name": "resnet50-v2-7",
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"working_directory": "./onnx-zoo-models/resnet50-v2-7/",
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"model_path": "./resnet50v2/resnet50-v2-7.onnx",
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"test_data_path": "./resnet50v2/"
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},
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{
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"model_name": "resnet101-v2-7",
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"working_directory": "./onnx-zoo-models/resnet101-v2-7/",
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"model_path": "./resnet101v2/resnet101-v2-7.onnx",
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"test_data_path": "./resnet101v2/"
|
||||
},
|
||||
{
|
||||
"model_name": "resnet152-v2-7",
|
||||
"working_directory": "./onnx-zoo-models/resnet152-v2-7/",
|
||||
"model_path": "./resnet152v2/resnet152-v2-7.onnx",
|
||||
"test_data_path": "./resnet152v2/"
|
||||
},
|
||||
{
|
||||
"model_name": "squeezenet1.1-7",
|
||||
"working_directory": "./onnx-zoo-models/squeezenet1.1-7/",
|
||||
"model_path": "./squeezenet1.1/squeezenet1.1.onnx",
|
||||
"test_data_path": "./squeezenet1.1/"
|
||||
},
|
||||
{
|
||||
"model_name": "vgg16-7",
|
||||
"working_directory": "./onnx-zoo-models/vgg16-7/",
|
||||
"model_path": "./vgg16/vgg16.onnx",
|
||||
"test_data_path": "./vgg16/"
|
||||
},
|
||||
{
|
||||
"model_name": "vgg19-bn-7",
|
||||
"working_directory": "./onnx-zoo-models/vgg19-bn-7/",
|
||||
"model_path": "./vgg19-bn/vgg19-bn.onnx",
|
||||
"test_data_path": "./vgg19-bn/"
|
||||
},
|
||||
{
|
||||
"model_name": "bvlcalexnet-9",
|
||||
"working_directory": "./onnx-zoo-models/bvlcalexnet-9/",
|
||||
"model_path": "./bvlc_alexnet/model.onnx",
|
||||
"test_data_path": "./bvlc_alexnet/"
|
||||
},
|
||||
{
|
||||
"model_name": "googlenet-9",
|
||||
"working_directory": "./onnx-zoo-models/googlenet-9/",
|
||||
"model_path": "./bvlc_googlenet/model.onnx",
|
||||
"test_data_path": "./bvlc_googlenet/"
|
||||
},
|
||||
{
|
||||
"model_name": "caffenet-9",
|
||||
"working_directory": "./onnx-zoo-models/caffenet-9/",
|
||||
"model_path": "./bvlc_reference_caffenet/model.onnx",
|
||||
"test_data_path": "./bvlc_reference_caffenet/"
|
||||
},
|
||||
{
|
||||
"model_name": "rcnn-ilsvrc13-9",
|
||||
"working_directory": "./onnx-zoo-models/rcnn-ilsvrc13-9/",
|
||||
"model_path": "./bvlc_reference_rcnn_ilsvrc13/model.onnx",
|
||||
"test_data_path": "./bvlc_reference_rcnn_ilsvrc13/"
|
||||
},
|
||||
{
|
||||
"model_name": "densenet-9",
|
||||
"working_directory": "./onnx-zoo-models/densenet-9/",
|
||||
"model_path": "./densenet121/model.onnx",
|
||||
"test_data_path": "./densenet121/"
|
||||
},
|
||||
{
|
||||
"model_name": "inception-v1-9",
|
||||
"working_directory": "./onnx-zoo-models/inception-v1-9/",
|
||||
"model_path": "./inception_v1/model.onnx",
|
||||
"test_data_path": "./inception_v1/"
|
||||
},
|
||||
{
|
||||
"model_name": "inception-v2-9",
|
||||
"working_directory": "./onnx-zoo-models/inception-v2-9/",
|
||||
"model_path": "./inception_v2/model.onnx",
|
||||
"test_data_path": "./inception_v2/"
|
||||
},
|
||||
{
|
||||
"model_name": "shufflenet-9",
|
||||
"working_directory": "./onnx-zoo-models/shufflenet-9/",
|
||||
"model_path": "./shufflenet/model.onnx",
|
||||
"test_data_path": "./shufflenet/"
|
||||
},
|
||||
{
|
||||
"model_name": "shufflenet-v2-10",
|
||||
"working_directory": "./onnx-zoo-models/shufflenet-v2-10/",
|
||||
"model_path": "./model/test_shufflenetv2/model.onnx",
|
||||
"test_data_path": "./model/test_shufflenetv2/"
|
||||
},
|
||||
{
|
||||
"model_name": "mnist-8",
|
||||
"working_directory": "./onnx-zoo-models/mnist-8/",
|
||||
"model_path": "./mnist/model.onnx",
|
||||
"test_data_path": "./mnist/"
|
||||
},
|
||||
{
|
||||
"model_name": "tinyyolov2-8",
|
||||
"working_directory": "./onnx-zoo-models/tinyyolov2-8/",
|
||||
"model_path": "./tiny_yolov2/Model.onnx",
|
||||
"test_data_path": "./tiny_yolov2/"
|
||||
},
|
||||
{
|
||||
"model_name": "ssd-10",
|
||||
"working_directory": "./onnx-zoo-models/ssd-10/",
|
||||
"model_path": "./model.onnx",
|
||||
"test_data_path": "./"
|
||||
},
|
||||
{
|
||||
"model_name": "ssd_mobilenet_v1_10",
|
||||
"working_directory": "./onnx-zoo-models/ssd_mobilenet_v1_10/",
|
||||
"model_path": "./ssd_mobilenet_v1/ssd_mobilenet_v1.onnx",
|
||||
"test_data_path": "./ssd_mobilenet_v1/"
|
||||
},
|
||||
{
|
||||
"model_name": "retinanet-9",
|
||||
"working_directory": "./onnx-zoo-models/retinanet-9/",
|
||||
"model_path": "./test_retinanet_resnet101/retinanet-9.onnx",
|
||||
"test_data_path": "./test_retinanet_resnet101/"
|
||||
},
|
||||
{
|
||||
"model_name": "yolov3-10",
|
||||
"working_directory": "./onnx-zoo-models/yolov3-10/",
|
||||
"model_path": "./yolov3/yolov3.onnx",
|
||||
"test_data_path": "./yolov3/"
|
||||
},
|
||||
{
|
||||
"model_name": "tiny-yolov3-11",
|
||||
"working_directory": "./onnx-zoo-models/tiny-yolov3-11/",
|
||||
"model_path": "./yolov3-tiny.onnx",
|
||||
"test_data_path": "./"
|
||||
},
|
||||
{
|
||||
"model_name": "yolov4",
|
||||
"working_directory": "./onnx-zoo-models/yolov4/",
|
||||
"model_path": "./yolov4/yolov4_shape.onnx",
|
||||
"test_data_path": "./yolov4/"
|
||||
},
|
||||
{
|
||||
"model_name": "ResNet101-DUC-7",
|
||||
"working_directory": "./onnx-zoo-models/ResNet101-DUC-7/",
|
||||
"model_path": "./ResNet101_DUC_HDC/ResNet101_DUC_HDC.onnx",
|
||||
"test_data_path": "./ResNet101_DUC_HDC/"
|
||||
},
|
||||
{
|
||||
"model_name": "emotion-ferplus-8",
|
||||
"working_directory": "./onnx-zoo-models/emotion-ferplus-8/",
|
||||
"model_path": "./emotion_ferplus/model.onnx",
|
||||
"test_data_path": "./emotion_ferplus/"
|
||||
},
|
||||
{
|
||||
"model_name": "bertsquad-10",
|
||||
"working_directory": "./onnx-zoo-models/bertsquad-10/",
|
||||
"model_path": "./bertsquad-10/bertsquad10.onnx",
|
||||
"test_data_path": "./bertsquad-10/"
|
||||
},
|
||||
{
|
||||
"model_name": "gpt2-lm-head-10",
|
||||
"working_directory": "./onnx-zoo-models/gpt2-lm-head-10/",
|
||||
"model_path": "./GPT-2-LM-HEAD/model.onnx",
|
||||
"test_data_path": "./GPT-2-LM-HEAD/"
|
||||
},
|
||||
{
|
||||
"model_name": "gpt2-10",
|
||||
"working_directory": "./onnx-zoo-models/gpt2-10/",
|
||||
"model_path": "./GPT2/model.onnx",
|
||||
"test_data_path": "./GPT2/"
|
||||
},
|
||||
{
|
||||
"model_name": "zfnet512-9",
|
||||
"working_directory": "./onnx-zoo-models/zfnet512-9/",
|
||||
"model_path": "./zfnet512/model.onnx",
|
||||
"test_data_path": "./zfnet512/"
|
||||
},
|
||||
{
|
||||
"model_name": "arcfaceresnet100-8",
|
||||
"working_directory": "./onnx-zoo-models/arcfaceresnet100-8/",
|
||||
"model_path": "./resnet100/resnet100.onnx",
|
||||
"test_data_path": "./resnet100/"
|
||||
},
|
||||
{
|
||||
"model_name": "mosaic-9",
|
||||
"working_directory": "./onnx-zoo-models/mosaic-9/",
|
||||
"model_path": "./mosaic/mosaic.onnx",
|
||||
"test_data_path": "./mosaic/"
|
||||
}
|
||||
]
|
||||
|
|
@ -39,6 +39,7 @@ latency_over_time_name = "latency_over_time"
|
|||
specs_name = "specs"
|
||||
session_name = "session"
|
||||
session_over_time_name = "session_over_time"
|
||||
op_metrics_name = "op_metrics"
|
||||
|
||||
# column names
|
||||
model_title = "Model"
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ from perf_utils import (
|
|||
memory_name,
|
||||
memory_over_time_name,
|
||||
model_title,
|
||||
op_metrics_name,
|
||||
ort_provider_list,
|
||||
provider_list,
|
||||
second,
|
||||
|
|
@ -415,6 +416,7 @@ def main():
|
|||
specs_name,
|
||||
session_name,
|
||||
session_over_time_name,
|
||||
op_metrics_name,
|
||||
]
|
||||
|
||||
table_results = {}
|
||||
|
|
@ -457,6 +459,11 @@ def main():
|
|||
table_results[status_name] = pd.concat(
|
||||
[table_results[status_name], get_status(table, model_group)], ignore_index=True
|
||||
)
|
||||
elif op_metrics_name in csv:
|
||||
table = table.assign(Group=model_group)
|
||||
table_results[op_metrics_name] = pd.concat(
|
||||
[table_results[op_metrics_name], table], ignore_index=True
|
||||
)
|
||||
os.chdir(result_file)
|
||||
|
||||
if not table_results[memory_name].empty:
|
||||
|
|
|
|||
|
|
@ -66,7 +66,7 @@ ARG ONNXRUNTIME_BRANCH=main
|
|||
ARG ONNXRUNTIME_COMMIT_ID
|
||||
|
||||
# The supported CUDA architecture
|
||||
ARG CMAKE_CUDA_ARCHITECTURES=37;50;52;60;61;70;75;80
|
||||
ARG CMAKE_CUDA_ARCHITECTURES=75
|
||||
|
||||
WORKDIR ${ONNXRUNTIME_LOCAL_CODE_DIR}
|
||||
|
||||
|
|
|
|||
Loading…
Reference in a new issue