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        1,
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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"
    },
    {
      "expected_shape": [
        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",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.act.lab.scale"
    },
    {
      "expected_shape": [
        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",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.convolution.weight"
    },
    {
      "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_small_symmetric.normalization.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.bias"
    },
    {
      "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_small_symmetric.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.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_small_symmetric.normalization.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.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_small_symmetric.normalization.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_small_symmetric.normalization.weight"
    },
    {
      "expected_shape": [
        32,
        1,
        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.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
      "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.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.bias"
    },
    {
      "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.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.normalization.running_mean"
    },
    {
      "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.running_var",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.0.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_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
      ],
      "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"
    },
    {
      "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.bias",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.1.normalization.bias"
    },
    {
      "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.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,
        1,
        3,
        3
      ],
      "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
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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.normalization.running_mean",
      "target": "model.backbone.encoder.blocks.1.layers.0.depthwise_convolution.conv_symmetric.2.normalization.running_mean"
    },
    {
      "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.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
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      "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
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      "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
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      "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
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      "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"
    },
    {
      "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.0.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.0.convolution.weight"
    },
    {
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        48
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      "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"
    },
    {
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        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"
    },
    {
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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.1.convolution.weight",
      "target": "model.backbone.encoder.blocks.1.layers.0.pointwise_convolution.conv_symmetric.1.convolution.weight"
    },
    {
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        48
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      "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"
    },
    {
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        48
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      "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"
    },
    {
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        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"
    },
    {
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        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"
    },
    {
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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.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
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      "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"
    },
    {
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        48
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      "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"
    },
    {
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        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"
    },
    {
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        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"
    }
  ]
}