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      "processed_png": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/073_PartNetMobility_electronics_electronics_3811/073_PartNetMobility_electronics_electronics_3811_synthetic_processed.png",
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      "texture_render": {
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        "texture_like_meshes": 70,
        "status": "textured_visual_render"
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      "mask_exr": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/073_PartNetMobility_electronics_electronics_3811/073_PartNetMobility_electronics_electronics_3811_synthetic_mask.exr",
      "mask_preview": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/073_PartNetMobility_electronics_electronics_3811/073_PartNetMobility_electronics_electronics_3811_synthetic_mask_segments_3.png",
      "reference_object_json": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/073_PartNetMobility_electronics_electronics_3811/reference_object.json"
    },
    {
      "sample_name": "097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179",
      "benchmark_index": 97,
      "sample_id": "PartNetMobility/major_appliances/PartNetMobility_7179",
      "source_dataset": "PartNetMobility",
      "category": "major_appliances",
      "object_id": "major_appliances/PartNetMobility_7179",
      "gt_sdf": "/data/share/ud4scenesmith/scenesmith_unified_articulated_v0.4_sourcefaithful_scenesmith_format_20260507/artvip_sdf/major_appliances/PartNetMobility_7179/PartNetMobility_7179.sdf",
      "num_parts": 6,
      "movable_parts": 5,
      "part_names": [
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        "link_1",
        "link_2",
        "link_3",
        "link_4",
        "link_5"
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      "view": {
        "phi_deg": 55.0,
        "theta_deg": -45.0,
        "radius": 3.2956192016601564,
        "score": 2024127,
        "visible_movable": 2,
        "total_pixels": 4647
      },
      "visible_pixels": {
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        "4": 22853,
        "5": 9060,
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      "mask_cardinality": {
        "enabled": true,
        "num_parts": 6,
        "fullres_positive_labels_before": 3,
        "downsample_positive_labels_before": 3,
        "downsample_positive_labels_after": 6,
        "injected_labels": [
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        ],
        "cells": [
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      "processed_png": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179_synthetic_processed.png",
      "partcolor_debug_png": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179_synthetic_partcolor_debug.png",
      "texture_render": {
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        "status": "textured_visual_render"
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      "mask_exr": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179_synthetic_mask.exr",
      "mask_preview": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179_synthetic_mask_segments_3.png",
      "reference_object_json": "/data/250010098/PAct-Transporter/reports/20260513_pact_transporter_exploration/runs/pact_cardinality_fix_hard5_eval_20260522/pact_inputs/097_PartNetMobility_major_appliances_major_appliances_PartNetMobility_7179/reference_object.json"
    }
  ],
  "run_status": [
    {
      "variant": "official_pact",
      "ok": true,
      "runtime_seconds": 378.03710865974426,
      "tail": "_desp.dtype_a) / 8 < int_max assert faild. your data exceed int32 range. this will be fixed in cumm + nvrtc (spconv 2.2/2.3).\nis_postprocessing_arti_info: True\nis_postprocessing_arti_info: True\nProcessing batch  4\n\n\nSampling:   0%|          | 0/25 [00:00<?, ?it/s]\u001b[A\n\nSampling:   4%|▍         | 1/25 [00:00<00:09,  2.43it/s]\u001b[A\n\nSampling:   8%|▊         | 2/25 [00:00<00:09,  2.37it/s]\u001b[A\n\nSampling:  12%|█▏        | 3/25 [00:01<00:09,  2.35it/s]\u001b[A\n\nSampling:  16%|█▌        | 4/25 [00:01<00:08,  2.35it/s]\u001b[A\n\nSampling:  20%|██        | 5/25 [00:02<00:08,  2.33it/s]\u001b[A\n\nSampling:  24%|██▍       | 6/25 [00:02<00:08,  2.33it/s]\u001b[A\n\nSampling:  28%|██▊       | 7/25 [00:02<00:07,  2.32it/s]\u001b[A\n\nSampling:  32%|███▏      | 8/25 [00:03<00:07,  2.32it/s]\u001b[A\n\nSampling:  36%|███▌      | 9/25 [00:03<00:06,  2.32it/s]\u001b[A\n\nSampling:  40%|████      | 10/25 [00:04<00:06,  2.32it/s]\u001b[A\n\nSampling:  44%|████▍     | 11/25 [00:04<00:06,  2.32it/s]\u001b[A\n\nSampling:  48%|████▊     | 12/25 [00:05<00:05,  2.31it/s]\u001b[A\n\nSampling:  52%|█████▏    | 13/25 [00:05<00:05,  2.32it/s]\u001b[A\n\nSampling:  56%|█████▌    | 14/25 [00:06<00:04,  2.32it/s]\u001b[A\n\nSampling:  60%|██████    | 15/25 [00:06<00:04,  2.32it/s]\u001b[A\n\nSampling:  64%|██████▍   | 16/25 [00:06<00:03,  2.31it/s]\u001b[A\n\nSampling:  68%|██████▊   | 17/25 [00:07<00:03,  2.32it/s]\u001b[A\n\nSampling:  72%|███████▏  | 18/25 [00:07<00:03,  2.32it/s]\u001b[A\n\nSampling:  76%|███████▌  | 19/25 [00:08<00:02,  2.32it/s]\u001b[A\n\nSampling:  80%|████████  | 20/25 [00:08<00:01,  2.73it/s]\u001b[A\n\nSampling:  84%|████████▍ | 21/25 [00:08<00:01,  3.11it/s]\u001b[A\n\nSampling:  88%|████████▊ | 22/25 [00:08<00:00,  3.45it/s]\u001b[A\n\nSampling:  92%|█████████▏| 23/25 [00:09<00:00,  3.73it/s]\u001b[A\n\nSampling:  96%|█████████▌| 24/25 [00:09<00:00,  3.96it/s]\u001b[A\n\nSampling: 100%|██████████| 25/25 [00:09<00:00,  4.15it/s]\u001b[A\nSampling: 100%|██████████| 25/25 [00:09<00:00,  2.64it/s]\n\u001b[91m## Skipping.\u001b[0m\nEmpty part detected, adding default: 2 of obj_idx: 0\n\n\nSampling:   0%|          | 0/25 [00:00<?, ?it/s]\u001b[A\n\nSampling:   4%|▍         | 1/25 [00:00<00:05,  4.04it/s]\u001b[A\n\nSampling:   8%|▊         | 2/25 [00:00<00:05,  4.06it/s]\u001b[A\n\nSampling:  12%|█▏        | 3/25 [00:00<00:05,  4.07it/s]\u001b[A\n\nSampling:  16%|█▌        | 4/25 [00:00<00:05,  4.08it/s]\u001b[A\n\nSampling:  20%|██        | 5/25 [00:01<00:04,  4.02it/s]\u001b[A\n\nSampling:  24%|██▍       | 6/25 [00:01<00:04,  4.04it/s]\u001b[A\n\nSampling:  28%|██▊       | 7/25 [00:01<00:04,  4.06it/s]\u001b[A\n\nSampling:  32%|███▏      | 8/25 [00:01<00:04,  4.07it/s]\u001b[A\n\nSampling:  36%|███▌      | 9/25 [00:02<00:03,  4.07it/s]\u001b[A\n\nSampling:  40%|████      | 10/25 [00:02<00:03,  4.07it/s]\u001b[A\n\nSampling:  44%|████▍     | 11/25 [00:02<00:03,  4.08it/s]\u001b[A\n\nSampling:  48%|████▊     | 12/25 [00:02<00:03,  4.08it/s]\u001b[A\n\nSampling:  52%|█████▏    | 13/25 [00:03<00:02,  4.08it/s]\u001b[A\n\nSampling:  56%|█████▌    | 14/25 [00:03<00:02,  4.08it/s]\u001b[A\n\nSampling:  60%|██████    | 15/25 [00:03<00:02,  4.08it/s]\u001b[A\n\nSampling:  64%|██████▍   | 16/25 [00:03<00:02,  4.08it/s]\u001b[A\n\nSampling:  68%|██████▊   | 17/25 [00:04<00:01,  4.09it/s]\u001b[A\n\nSampling:  72%|███████▏  | 18/25 [00:04<00:01,  4.08it/s]\u001b[A\n\nSampling:  76%|███████▌  | 19/25 [00:04<00:01,  4.09it/s]\u001b[A\n\nSampling:  80%|████████  | 20/25 [00:04<00:01,  4.81it/s]\u001b[A\n\nSampling:  84%|████████▍ | 21/25 [00:04<00:00,  5.48it/s]\u001b[A\n\nSampling:  88%|████████▊ | 22/25 [00:05<00:00,  6.08it/s]\u001b[A\n\nSampling:  92%|█████████▏| 23/25 [00:05<00:00,  6.58it/s]\u001b[A\n\nSampling:  96%|█████████▌| 24/25 [00:05<00:00,  6.98it/s]\u001b[A\n\nSampling: 100%|██████████| 25/25 [00:05<00:00,  7.30it/s]\u001b[A\nSampling: 100%|██████████| 25/25 [00:05<00:00,  4.63it/s]\n\n100%|██████████| 5/5 [05:15<00:00, 67.21s/it]\n100%|██████████| 5/5 [05:15<00:00, 63.05s/it]\n\u001b[31mMean feature regression averaging over 20 steps\u001b[0m\nWarning: Feature dimensions mismatch. Using 8 dimensions for normalization\nis_postprocessing_arti_info: True\nis_postprocessing_arti_info: True\n\u001b[91m[Warning] Some empty parts are found and removed during exporting to Singapo style. 1 [0, 1, 2, 3, 4] \u001b[0m\n"
    },
    {
      "variant": "phase2_rgb_edge_dropout",
      "ok": false,
      "runtime_seconds": 367.1562063694,
      "tail": "data/250010098/PAct/modules/pact/pipelines/pact_i23d_gen_pipe.py\", line 1104, in decode_slat\n    ret[\"mesh\"].extend(self.models[\"slat_decoder_mesh\"](slat[idx]))\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1739, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1750, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/data/250010098/PAct/modules/pact/models/structured_latent_vae/decoder_mesh.py\", line 209, in forward\n    h = block(h)  # Progressively increase resolution\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1739, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1750, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/data/250010098/PAct/modules/pact/models/structured_latent_vae/decoder_mesh.py\", line 88, in forward\n    h = self.out_layers(h)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1739, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1750, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/container.py\", line 250, in forward\n    input = module(input)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1739, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1750, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/data/250010098/PAct/modules/pact/modules/sparse/conv/conv_spconv.py\", line 26, in forward\n    new_data = self.conv(x.data)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1739, in _wrapped_call_impl\n    return self._call_impl(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/nn/modules/module.py\", line 1750, in _call_impl\n    return forward_call(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/spconv/pytorch/conv.py\", line 755, in forward\n    return self._conv_forward(self.training,\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/spconv/pytorch/conv.py\", line 327, in _conv_forward\n    out_features = Fsp.indice_subm_conv(\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/autograd/function.py\", line 575, in apply\n    return super().apply(*args, **kwargs)  # type: ignore[misc]\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/torch/amp/autocast_mode.py\", line 514, in decorate_fwd\n    return fwd(*args, **kwargs)\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/spconv/pytorch/functional.py\", line 331, in forward\n    raise e\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/spconv/pytorch/functional.py\", line 312, in forward\n    return ops.indice_conv(features,\n  File \"/data/250010098/conda_envs/trellis2/lib/python3.10/site-packages/spconv/pytorch/ops.py\", line 860, in indice_conv\n    ConvGemmOps.indice_conv(alloc, ext_mm, GEMM_CPP, ALL_WEIGHT_IS_KRSC,\nRuntimeError: /io/build/temp.linux-x86_64-cpython-310/spconv/build/core_cc/src/cumm/gemm/main/GemmMainUnitTest/GemmMainUnitTest_matmul_split_Turing_f16f16f16_1.cu(126)\nint64_t(a.dim(0)) * int64_t(a.dim(1)) * tv::bit_size(algo_desp.dtype_a) / 8 < int_max assert faild. your data exceed int32 range. this will be fixed in cumm + nvrtc (spconv 2.2/2.3).\n"
    }
  ],
  "variant_results": [
    {
      "variant": {
        "name": "official_pact",
        "label": "Official PAct",
        "kind": "official",
        "note": "Released PAct cache, no OT wrapper."
      },
      "records": [
        {
          "sample_name": "000_ArtVIP_major_appliances_major_appliances_model_refrigerator_6",
          "benchmark_index": 0,
          "sample_id": "ArtVIP/major_appliances/model_refrigerator_6",
          "source_dataset": "ArtVIP",
          "category": "major_appliances",
          "object_id": "major_appliances/model_refrigerator_6",
          "num_parts": 7,
          "movable_parts": 6,
          "variant": "official_pact",
          "variant_label": "Official PAct",
          "variant_note": "Released PAct cache, no OT wrapper.",
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                "note": "Requires dense surface sampling."
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              "normal_consistency": {
                "value": null,
                "status": "not_provided",
                "note": "Requires dense surface sampling."
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                "value": 0.7028193704504472,
                "status": "measured",
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                "value": null,
                "status": "not_provided",
                "note": "Requires mesh repair/manifold pass."
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                "value": 0.0,
                "status": "not_provided",
                "note": "PAct smoke export is untextured PLY."
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                "value": 1.0,
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                "value": 2,
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                "value": 0.0,
                "status": "measured",
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                "value": 6,
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                "value": 0.14285714285714285,
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                "value": 0.1,
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                "value": 0.11764705882352941,
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                "value": 0.3,
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                "value": 0.2971806295495528,
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        {
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        {
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        {
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        },
        {
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                "status": "not_provided",
                "note": "This smoke run exports articulated object JSON/PLY, not textured GLB."
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                "status": "measured",
                "note": ""
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                "note": "Not sampled in this run."
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              "reproducible_seed_match": {
                "value": 1,
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                "value": null,
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                "note": "PLY-level Chamfer hook present; omitted in smoke run for speed."
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                "note": "Requires dense surface sampling."
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                "note": "2D mask GT is conditioning input; not a predicted mask output."
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                "status": "not_applicable",
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                "note": "Aggregate-only metric; sample count is 5."
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                "note": "Requires larger batch clustering."
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                "status": "not_provided",
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                "note": "Not sampled in this run."
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            "geometry": {
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                "value": null,
                "status": "not_provided",
                "note": "PLY-level Chamfer hook present; omitted in smoke run for speed."
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                "status": "not_provided",
                "note": "Requires dense surface sampling."
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                "value": null,
                "status": "not_provided",
                "note": "Requires dense surface sampling."
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                "status": "measured",
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                "note": "Requires mesh repair/manifold pass."
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                "status": "not_provided",
                "note": "PAct smoke export is untextured PLY."
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