Python PostProcessCocoPt示例

示例#1

文件： main_audit-test04b-bug-workaround.py 项目： wangxihuiok/inference_results_v0.5

        (imagenet.Imagenet, dataset.pre_process_vgg, dataset.PostProcessCommon(offset=-1),
         {"image_size": [224, 224, 3]}),
    "imagenet_mobilenet":
        (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
         {"image_size": [224, 224, 3]}),
    "imagenet_mobilenet_ncore":
        (imagenet.Imagenet, dataset.pre_process_mobilenet_uint8, dataset.PostProcessArgMax(offset=-1),
         {"image_size": [224, 224, 3]}),
    "coco-300":
        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
         {"image_size": [300, 300, 3]}),
    "coco-300-ncore":
        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
         {"image_size": [300, 300, 3]}),
    "coco-300-pt":
        (coco.Coco, dataset.pre_process_coco_pt_mobilenet, coco.PostProcessCocoPt(False,0.3),
         {"image_size": [300, 300, 3]}),         
    "coco-1200":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
         {"image_size": [1200, 1200, 3]}),
    "coco-1200-onnx":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
         {"image_size": [1200, 1200, 3]}),
    "coco-1200-pt":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(True,0.05),
         {"image_size": [1200, 1200, 3],"use_label_map": True}),
    "coco-1200-tf":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoTf(),
         {"image_size": [1200, 1200, 3],"use_label_map": False}),
}

示例#2

文件： main.py 项目： Intel-tensorflow/inference

        (imagenet.Imagenet, dataset.pre_process_mobilenet, dataset.PostProcessArgMax(offset=-1),
         {"image_size": [224, 224, 3]}),
    "coco":
        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
         {"image_size": [-1, -1, 3]}),
    "coco-300":
        (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(),
         {"image_size": [300, 300, 3]}),
    "coco-1200":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCoco(),
         {"image_size": [1200, 1200, 3]}),
    "coco-1200-onnx":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoOnnx(),
         {"image_size": [1200, 1200, 3]}),
    "coco-1200-pt":
        (coco.Coco, dataset.pre_process_coco_resnet34, coco.PostProcessCocoPt(),
         {"image_size": [1200, 1200, 3]}),
}

# pre-defined command line options so simplify things. They are used as defaults and can be
# overwritten from command line
DEFAULT_LATENCY_BUCKETS = "0.010,0.050,0.100"

SUPPORTED_PROFILES = {
    "defaults": {
        "dataset": "imagenet",
        "backend": "tensorflow",
        "cache": 0,
        "time": 60,
        "queries-single": 1024,
        "queries-multi": 24576,

示例#3

文件： main.py 项目： tjyhlt/inference

                  "image_size": [224, 224, 3]
              }),
 "imagenet_mobilenet": (imagenet.Imagenet, dataset.pre_process_mobilenet,
                        dataset.PostProcessArgMax(offset=-1), {
                            "image_size": [224, 224, 3]
                        }),
 "coco":
 (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(), {
     "image_size": [-1, -1, 3]
 }),
 "coco-300":
 (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(), {
     "image_size": [300, 300, 3]
 }),
 "coco-300-pt": (coco.Coco, dataset.pre_process_coco_pt_mobilenet,
                 coco.PostProcessCocoPt(False, 0.3), {
                     "image_size": [300, 300, 3]
                 }),
 "coco-1200": (coco.Coco, dataset.pre_process_coco_resnet34,
               coco.PostProcessCoco(), {
                   "image_size": [1200, 1200, 3]
               }),
 "coco-1200-onnx": (coco.Coco, dataset.pre_process_coco_resnet34,
                    coco.PostProcessCocoOnnx(), {
                        "image_size": [1200, 1200, 3]
                    }),
 "coco-1200-pt": (coco.Coco, dataset.pre_process_coco_resnet34,
                  coco.PostProcessCocoPt(True, 0.05), {
                      "image_size": [1200, 1200, 3]
                  }),
 "coco-1200-tf": (coco.Coco, dataset.pre_process_coco_resnet34_tf,

示例#4

文件： main.py 项目： shuaichengli0428/inference

        "image_size": [-1, -1, 3]
    }),
    "coco-300":
    (coco.Coco, dataset.pre_process_coco_mobilenet, coco.PostProcessCoco(), {
        "image_size": [300, 300, 3]
    }),
    "coco-1200": (coco.Coco, dataset.pre_process_coco_resnet34,
                  coco.PostProcessCoco(), {
                      "image_size": [1200, 1200, 3]
                  }),
    "coco-1200-onnx": (coco.Coco, dataset.pre_process_coco_resnet34,
                       coco.PostProcessCocoOnnx(), {
                           "image_size": [1200, 1200, 3]
                       }),
    "coco-1200-pt": (coco.Coco, dataset.pre_process_coco_resnet34,
                     coco.PostProcessCocoPt(), {
                         "image_size": [1200, 1200, 3]
                     }),
}

# pre-defined command line options so simplify things. They are used as defaults and can be
# overwritten from command line
DEFAULT_LATENCY_BUCKETS = "0.010,0.050,0.100,0.200,0.400"

SUPPORTED_PROFILES = {
    "defaults": {
        "dataset": "imagenet",
        "backend": "tensorflow",
        "cache": 0,
        "time": 128,
        "max-latency": DEFAULT_LATENCY_BUCKETS,