tensor_map.json

{
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  "architecture_summary": {
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      "model_type": "pp_lcnet_v3",
      "out_features": [
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        "stage3",
        "stage4",
        "stage5"
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      "out_indices": [
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        3,
        4,
        5
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      "scale": 0.75
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    "interpolate_mode": "nearest",
    "kernel_list": [
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      2,
      2
    ],
    "layer_list_out_channels": [
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      18,
      42,
      360
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    "neck_out_channels": 96,
    "postprocess": {
      "box_thresh": 0.6,
      "max_candidates": 1000,
      "name": "DBPostProcess",
      "thresh": 0.3,
      "unclip_ratio": 1.5
    }
  },
  "schema_version": 1,
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    {
      "equals": "pp_ocrv5_mobile_det",
      "key": "model_type",
      "path": "config.json"
    },
    {
      "equals": "pp_lcnet_v3",
      "key": "backbone_config.model_type",
      "path": "config.json"
    },
    {
      "equals": 4,
      "key": "reduction",
      "path": "config.json"
    },
    {
      "equals": 96,
      "key": "neck_out_channels",
      "path": "config.json"
    },
    {
      "equals": "BGR",
      "key": "image_mode",
      "path": "preprocessor_config.json"
    },
    {
      "equals": [
        "pixel_values",
        "original_image_size"
      ],
      "key": "model_input_names",
      "path": "preprocessor_config.json"
    },
    {
      "equals": "DBPostProcess",
      "key": "PostProcess.name",
      "path": "inference.yml"
    },
    {
      "equals": 0.3,
      "key": "PostProcess.thresh",
      "path": "inference.yml"
    },
    {
      "equals": 0.6,
      "key": "PostProcess.box_thresh",
      "path": "inference.yml"
    }
  ],
  "tensor_groups": {
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    "model.backbone.encoder": 831,
    "model.layer": 8,
    "model.neck": 40
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    {
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        3
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      "notes": "DB probability head down-projection tensor reused without transpose",
      "source": "head.conv_down.convolution.weight",
      "target": "head.conv_down.convolution.weight"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head down-projection tensor reused without transpose",
      "source": "head.conv_down.norm.bias",
      "target": "head.conv_down.norm.bias"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head down-projection tensor reused without transpose",
      "source": "head.conv_down.norm.running_mean",
      "target": "head.conv_down.norm.running_mean"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head down-projection tensor reused without transpose",
      "source": "head.conv_down.norm.running_var",
      "target": "head.conv_down.norm.running_var"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head down-projection tensor reused without transpose",
      "source": "head.conv_down.norm.weight",
      "target": "head.conv_down.norm.weight"
    },
    {
      "expected_shape": [
        1
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      "notes": "DB probability-map output tensor reused without transpose",
      "source": "head.conv_final.bias",
      "target": "head.conv_final.bias"
    },
    {
      "expected_shape": [
        24,
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        2,
        2
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      "notes": "DB probability-map output tensor reused without transpose",
      "source": "head.conv_final.weight",
      "target": "head.conv_final.weight"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.convolution.bias",
      "target": "head.conv_up.convolution.bias"
    },
    {
      "expected_shape": [
        24,
        24,
        2,
        2
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.convolution.weight",
      "target": "head.conv_up.convolution.weight"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.norm.bias",
      "target": "head.conv_up.norm.bias"
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    {
      "expected_shape": [
        24
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.norm.running_mean",
      "target": "head.conv_up.norm.running_mean"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.norm.running_var",
      "target": "head.conv_up.norm.running_var"
    },
    {
      "expected_shape": [
        24
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      "notes": "DB probability head upsampling tensor reused without transpose",
      "source": "head.conv_up.norm.weight",
      "target": "head.conv_up.norm.weight"
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    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.act.lab.scale"
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    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias"
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    {
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        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        16,
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        3
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
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    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var"
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    {
      "expected_shape": [
        16
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight"
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    {
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight",
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    {
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      "source": "model.backbone.encoder.blocks.0.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias",
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    {
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    {
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.lab.bias"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.0.layers.0.pointwise_convolution.lab.scale"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.act.lab.bias"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.act.lab.scale",
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    },
    {
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        32,
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        1,
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight",
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    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias",
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    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
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    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
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        32,
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        3
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
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        32,
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        3,
        3
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
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        32
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        32,
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        3,
        3
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        32,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        32
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.act.lab.scale"
    },
    {
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        48,
        32,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        32,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        32,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        32,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        48,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.1.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        48,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.1.layers.1.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        48,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        48,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.0.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        96,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        48,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.0.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        96,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.1.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        96,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.2.layers.1.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        96,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        96,
        1,
        3,
        3
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        96
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.0.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
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        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
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        192,
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        1,
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
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        192
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
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        192
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.0.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.1.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.1.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.2.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.2.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
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        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.3.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.3.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.4.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
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        192,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.3.layers.4.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        192,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        192,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        192
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.0.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.0.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        48
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.0.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.0.bias"
    },
    {
      "expected_shape": [
        48,
        192,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.0.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.0.weight"
    },
    {
      "expected_shape": [
        192
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.2.bias",
      "target": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.2.bias"
    },
    {
      "expected_shape": [
        192,
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        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.2.weight",
      "target": "model.backbone.encoder.blocks.4.layers.0.squeeze_excitation_module.convolutions.2.weight"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.act.lab.bias"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.act.lab.scale"
    },
    {
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        384,
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        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
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        384,
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        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
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        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
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        384
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
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        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.1.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.1.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.0.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.0.bias"
    },
    {
      "expected_shape": [
        96,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.0.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.0.weight"
    },
    {
      "expected_shape": [
        384
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.2.bias",
      "target": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.2.bias"
    },
    {
      "expected_shape": [
        384,
        96,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.2.weight",
      "target": "model.backbone.encoder.blocks.4.layers.1.squeeze_excitation_module.convolutions.2.weight"
    },
    {
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        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
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        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        384,
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        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
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        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
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      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.2.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.2.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
        1,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        1,
        5,
        5
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.3.depthwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.act.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.act.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.act.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.0.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.1.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.2.normalization.weight"
    },
    {
      "expected_shape": [
        384,
        384,
        1,
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.convolution.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.convolution.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.conv_symmetric.3.normalization.weight"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.bias"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.running_mean",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.running_mean"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.running_var",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.running_var"
    },
    {
      "expected_shape": [
        384
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.weight",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.identity.weight"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.lab.bias",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.lab.bias"
    },
    {
      "expected_shape": [
        1
      ],
      "notes": "PPLCNetV3 backbone block tensor reused without transpose",
      "source": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.lab.scale",
      "target": "model.backbone.encoder.blocks.4.layers.3.pointwise_convolution.lab.scale"
    },
    {
      "expected_shape": [
        16,
        3,
        3,
        3
      ],
      "notes": "PPLCNetV3 stem tensor reused without transpose",
      "source": "model.backbone.encoder.convolution.convolution.weight",
      "target": "model.backbone.encoder.convolution.convolution.weight"
    },
    {
      "expected_shape": [
        16
      ],
      "notes": "PPLCNetV3 stem tensor reused without transpose",
      "source": "model.backbone.encoder.convolution.normalization.bias",
      "target": "model.backbone.encoder.convolution.normalization.bias"
    },
    {
      "expected_shape": [
        16
      ],
      "notes": "PPLCNetV3 stem tensor reused without transpose",
      "source": "model.backbone.encoder.convolution.normalization.running_mean",
      "target": "model.backbone.encoder.convolution.normalization.running_mean"
    },
    {
      "expected_shape": [
        16
      ],
      "notes": "PPLCNetV3 stem tensor reused without transpose",
      "source": "model.backbone.encoder.convolution.normalization.running_var",
      "target": "model.backbone.encoder.convolution.normalization.running_var"
    },
    {
      "expected_shape": [
        16
      ],
      "notes": "PPLCNetV3 stem tensor reused without transpose",
      "source": "model.backbone.encoder.convolution.normalization.weight",
      "target": "model.backbone.encoder.convolution.normalization.weight"
    },
    {
      "expected_shape": [
        12
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.0.bias",
      "target": "model.layer.0.bias"
    },
    {
      "expected_shape": [
        12,
        48,
        1,
        1
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.0.weight",
      "target": "model.layer.0.weight"
    },
    {
      "expected_shape": [
        18
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.1.bias",
      "target": "model.layer.1.bias"
    },
    {
      "expected_shape": [
        18,
        96,
        1,
        1
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.1.weight",
      "target": "model.layer.1.weight"
    },
    {
      "expected_shape": [
        42
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.2.bias",
      "target": "model.layer.2.bias"
    },
    {
      "expected_shape": [
        42,
        192,
        1,
        1
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.2.weight",
      "target": "model.layer.2.weight"
    },
    {
      "expected_shape": [
        360
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.3.bias",
      "target": "model.layer.3.bias"
    },
    {
      "expected_shape": [
        360,
        384,
        1,
        1
      ],
      "notes": "PP-OCR detector feature projection tensor reused without transpose",
      "source": "model.layer.3.weight",
      "target": "model.layer.3.weight"
    },
    {
      "expected_shape": [
        24,
        96,
        3,
        3
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.0.in_conv.weight",
      "target": "model.neck.input_conv.0.in_conv.weight"
    },
    {
      "expected_shape": [
        6
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.0.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.input_conv.0.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        6,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.0.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.input_conv.0.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.0.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.input_conv.0.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        24,
        6,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.0.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.input_conv.0.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        24,
        96,
        3,
        3
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.1.in_conv.weight",
      "target": "model.neck.input_conv.1.in_conv.weight"
    },
    {
      "expected_shape": [
        6
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.1.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.input_conv.1.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        6,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.1.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.input_conv.1.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.1.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.input_conv.1.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        24,
        6,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.1.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.input_conv.1.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        24,
        96,
        3,
        3
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.2.in_conv.weight",
      "target": "model.neck.input_conv.2.in_conv.weight"
    },
    {
      "expected_shape": [
        6
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.2.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.input_conv.2.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        6,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.2.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.input_conv.2.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.2.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.input_conv.2.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        24,
        6,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.2.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.input_conv.2.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        24,
        96,
        3,
        3
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.3.in_conv.weight",
      "target": "model.neck.input_conv.3.in_conv.weight"
    },
    {
      "expected_shape": [
        6
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.3.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.input_conv.3.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        6,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.3.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.input_conv.3.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.3.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.input_conv.3.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        24,
        6,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck input-conv tensor reused without transpose",
      "source": "model.neck.input_conv.3.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.input_conv.3.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        96,
        12,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.0.in_conv.weight",
      "target": "model.neck.insert_conv.0.in_conv.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.0.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.insert_conv.0.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        24,
        96,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.0.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.insert_conv.0.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.0.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.insert_conv.0.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        96,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.0.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.insert_conv.0.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        96,
        18,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.1.in_conv.weight",
      "target": "model.neck.insert_conv.1.in_conv.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.1.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.insert_conv.1.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        24,
        96,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.1.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.insert_conv.1.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.1.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.insert_conv.1.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        96,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.1.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.insert_conv.1.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        96,
        42,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.2.in_conv.weight",
      "target": "model.neck.insert_conv.2.in_conv.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.2.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.insert_conv.2.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        24,
        96,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.2.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.insert_conv.2.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.2.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.insert_conv.2.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        96,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.2.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.insert_conv.2.squeeze_excitation_block.conv2.weight"
    },
    {
      "expected_shape": [
        96,
        360,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.3.in_conv.weight",
      "target": "model.neck.insert_conv.3.in_conv.weight"
    },
    {
      "expected_shape": [
        24
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.3.squeeze_excitation_block.conv1.bias",
      "target": "model.neck.insert_conv.3.squeeze_excitation_block.conv1.bias"
    },
    {
      "expected_shape": [
        24,
        96,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.3.squeeze_excitation_block.conv1.weight",
      "target": "model.neck.insert_conv.3.squeeze_excitation_block.conv1.weight"
    },
    {
      "expected_shape": [
        96
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.3.squeeze_excitation_block.conv2.bias",
      "target": "model.neck.insert_conv.3.squeeze_excitation_block.conv2.bias"
    },
    {
      "expected_shape": [
        96,
        24,
        1,
        1
      ],
      "notes": "PP-OCR DBNet neck insert-conv tensor reused without transpose",
      "source": "model.neck.insert_conv.3.squeeze_excitation_block.conv2.weight",
      "target": "model.neck.insert_conv.3.squeeze_excitation_block.conv2.weight"
    }
  ]
}