p2o 0.1.1

A PaddlePaddle New IR (PIR) to ONNX model converter.
Documentation
{
  "models": [
    {
      "name": "ppocrv5_det",
      "model_dir": "../inference_models/PP-OCRv5_server_det_infer",
      "default_opsets": [17],
      "input_kind": "det",
      "sample_inputs": {
        "x": {
          "shape": [1, 3, 640, 640],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "ppocrv5_rec",
      "model_dir": "../inference_models/PP-OCRv5_server_rec_infer",
      "default_opsets": [17],
      "input_kind": "rec",
      "sample_inputs": {
        "x": {
          "shape": [1, 3, 48, 320],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "slanet",
      "model_dir": "../inference_models/SLANet_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 3, 488, 488],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "ppdoclayout_plus_l",
      "model_dir": "../inference_models/PP-DocLayout_plus-L_infer",
      "default_opsets": [17],
      "input_kind": "layout",
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 800, 800],
          "dtype": "float32",
          "mode": "randn"
        },
        "im_shape": {
          "value": [[800.0, 800.0]],
          "dtype": "float32"
        },
        "scale_factor": {
          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      }
    },
    {
      "name": "ppdoclayoutv2",
      "model_dir": "../inference_models/PP-DocLayoutV2_infer",
      "semantic_baseline": "baselines/ppdoclayoutv2_opset17_seed20260412_semantic.npz",
      "default_opsets": [17],
      "input_kind": "layout",
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
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          "mode": "randn"
        },
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          "dtype": "float32"
        },
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          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      },
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        "mode": "instance_row_match",
        "key_output": 0,
        "reorder_outputs": [0],
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        "exact_columns": [0],
        "round_decimals": 6,
        "per_output": {
          "0": {
            "permutation_columns": [6, 7]
          }
        }
      }
    },
    {
      "name": "ppdoclayoutv3",
      "model_dir": "../inference_models/PP-DocLayoutV3_infer",
      "default_opsets": [17],
      "input_kind": "layout",
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 800, 800],
          "dtype": "float32",
          "mode": "randn"
        },
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          "value": [[800.0, 800.0]],
          "dtype": "float32"
        },
        "scale_factor": {
          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      },
      "compare_config": {
        "mode": "instance_row_match",
        "key_output": 0,
        "reorder_outputs": [0, 2],
        "sort_columns": [0, 1, 2, 3, 4, 5],
        "match_columns": [0, 1, 2, 3, 4, 5],
        "exact_columns": [0],
        "tie_group_columns": [0, 1, 2, 3, 4, 5],
        "tie_aware_outputs": [2],
        "round_decimals": 6,
        "per_output": {
          "0": {
            "permutation_columns": [6]
          },
          "2": {
            "int_tolerance": {
              "max_abs": 1,
              "mean_abs": 1e-5
            }
          }
        }
      }
    },
    {
      "name": "slanext_wired",
      "model_dir": "../inference_models/SLANeXt_wired_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 3, 512, 512],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "slanext_wireless",
      "model_dir": "../inference_models/SLANeXt_wireless_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 3, 512, 512],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "ppformulanet_l",
      "model_dir": "../inference_models/PP-FormulaNet-L_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 1, 768, 768],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "ppformulanet_plus_l",
      "model_dir": "../inference_models/PP-FormulaNet_plus-L_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 1, 768, 768],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "latex_ocr",
      "model_dir": "../inference_models/LaTeX_OCR_rec_infer",
      "semantic_baseline": "baselines/latex_ocr_opset17_seed20260412_semantic.npz",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 1, 192, 672],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "uvdoc",
      "model_dir": "../inference_models/UVDoc_infer",
      "default_opsets": [17],
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 256, 128],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "unimernet",
      "model_dir": "../inference_models/UniMERNet_infer",
      "default_opsets": [17],
      "sample_inputs": {
        "x": {
          "shape": [1, 1, 192, 672],
          "dtype": "float32",
          "mode": "randn"
        }
      }
    },
    {
      "name": "picodet_layout_1x",
      "model_dir": "../inference_models/PicoDet_layout_1x_infer",
      "default_opsets": [17],
      "input_kind": "layout",
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 800, 608],
          "dtype": "float32",
          "mode": "randn"
        },
        "scale_factor": {
          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      }
    },
    {
      "name": "rtdetr_h_layout_17cls",
      "model_dir": "../inference_models/RT-DETR-H_layout_17cls_infer",
      "default_opsets": [17],
      "input_kind": "layout",
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 640, 640],
          "dtype": "float32",
          "mode": "randn"
        },
        "im_shape": {
          "value": [[640.0, 640.0]],
          "dtype": "float32"
        },
        "scale_factor": {
          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      },
      "compare_config": {
        "mode": "instance_row_match",
        "key_output": 0,
        "reorder_outputs": [0],
        "sort_columns": [0, 2, 3, 4, 5, 1],
        "match_columns": [0, 2, 3, 4, 5],
        "exact_columns": [0],
        "round_decimals": 4
      }
    },
    {
      "name": "rtdetr_l_wired_table_cell_det",
      "model_dir": "../inference_models/RT-DETR-L_wired_table_cell_det_infer",
      "default_opsets": [17],
      "input_kind": "layout",
      "diff_tolerance": {
        "rtol": 0.001,
        "atol": 0.005
      },
      "sample_inputs": {
        "image": {
          "shape": [1, 3, 640, 640],
          "dtype": "float32",
          "mode": "randn"
        },
        "im_shape": {
          "value": [[640.0, 640.0]],
          "dtype": "float32"
        },
        "scale_factor": {
          "value": [[1.0, 1.0]],
          "dtype": "float32"
        }
      },
      "compare_config": {
        "mode": "instance_row_match",
        "key_output": 0,
        "reorder_outputs": [0],
        "sort_columns": [0, 2, 3, 4, 5, 1],
        "match_columns": [0, 2, 3, 4, 5],
        "exact_columns": [0],
        "round_decimals": 4
      }
    }
  ]
}