Pluginize the SignX component

SignX/benchmark_results/efficiency_comparison_table.tex

@@ -1,14 +0,0 @@
-\begin{table}[t]
-\centering
-\caption{\textbf{Inference Efficiency on ASLLRP:} SignX achieves real-time performance by operating in latent space.}
-\label{tab:efficiency}
-\begin{tabular}{lcc}
-\toprule
-Method & FPS $\uparrow$ & Power (W) $\downarrow$ \\
-\midrule
-SignX (Full Pipeline) & 0.05 & 26.22 \\
-SignX (SMKD Feature Extraction) & 0.57 & 29.20 \\
-SignX (Latent-only) & 2.42 & 24.58 \\
-\bottomrule
-\end{tabular}
-\end{table}

SignX/detailed_prediction_20260101_150706/632051/analysis_report.txt

@@ -1,43 +0,0 @@
-================================================================================
-  Sign Language Recognition - Attention分析报告
-================================================================================
-
-生成时间: 2026-01-01 15:07:12
-
-翻译结果:
---------------------------------------------------------------------------------
-#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p
-
-视频信息:
---------------------------------------------------------------------------------
-总帧数: 28
-词数量: 8
-
-Attention权重信息:
---------------------------------------------------------------------------------
-形状: (26, 28)
-  - 解码步数: 26
-
-词-帧对应详情:
-================================================================================
-No.   Word                 Frames          Peak     Attn     Conf      
---------------------------------------------------------------------------------
-1     #IF                  2-2             2        0.472    medium    
-2     FRIEND               5-5             5        0.425    medium    
-3     GROUP/TOGETHER       8-8             8        0.375    medium    
-4     DEPART               27-27           27       0.348    medium    
-5     PARTY                27-27           27       0.383    medium    
-6     IX-1p                27-27           27       0.333    medium    
-7     JOIN                 11-11           11       0.520    high      
-8     IX-1p                14-14           14       0.368    medium    
-
-================================================================================
-
-统计摘要:
---------------------------------------------------------------------------------
-平均attention权重: 0.403
-高置信度词: 1 (12.5%)
-中置信度词: 7 (87.5%)
-低置信度词: 0 (0.0%)
-
-================================================================================

SignX/detailed_prediction_20260101_150706/632051/attention_keyframes/keyframes_index.txt

@@ -1,35 +0,0 @@
-关键帧索引
-============================================================
-
-样本目录: /common/users/sf895/output/huggingface_asllrp_repo/SignX/detailed_prediction_20260101_150706/632051
-视频路径: /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4
-总关键帧数: 26
-
-关键帧列表:
-------------------------------------------------------------
-Gloss   0: keyframe_000_feat2_frame9_att0.472.jpg
-Gloss   1: keyframe_001_feat5_frame20_att0.425.jpg
-Gloss   2: keyframe_002_feat8_frame32_att0.375.jpg
-Gloss   3: keyframe_003_feat27_frame104_att0.348.jpg
-Gloss   4: keyframe_004_feat27_frame104_att0.383.jpg
-Gloss   5: keyframe_005_feat27_frame104_att0.333.jpg
-Gloss   6: keyframe_006_feat11_frame43_att0.520.jpg
-Gloss   7: keyframe_007_feat14_frame54_att0.368.jpg
-Gloss   8: keyframe_008_feat17_frame66_att0.252.jpg
-Gloss   9: keyframe_009_feat19_frame73_att0.884.jpg
-Gloss  10: keyframe_010_feat0_frame1_att0.118.jpg
-Gloss  11: keyframe_011_feat27_frame104_att0.164.jpg
-Gloss  12: keyframe_012_feat25_frame96_att0.265.jpg
-Gloss  13: keyframe_013_feat25_frame96_att0.282.jpg
-Gloss  14: keyframe_014_feat25_frame96_att0.278.jpg
-Gloss  15: keyframe_015_feat25_frame96_att0.277.jpg
-Gloss  16: keyframe_016_feat27_frame104_att0.219.jpg
-Gloss  17: keyframe_017_feat27_frame104_att0.190.jpg
-Gloss  18: keyframe_018_feat27_frame104_att0.225.jpg
-Gloss  19: keyframe_019_feat23_frame88_att0.150.jpg
-Gloss  20: keyframe_020_feat27_frame104_att0.151.jpg
-Gloss  21: keyframe_021_feat25_frame96_att0.360.jpg
-Gloss  22: keyframe_022_feat25_frame96_att0.153.jpg
-Gloss  23: keyframe_023_feat27_frame104_att0.144.jpg
-Gloss  24: keyframe_024_feat25_frame96_att0.144.jpg
-Gloss  25: keyframe_025_feat27_frame104_att0.186.jpg

SignX/detailed_prediction_20260101_150706/632051/debug_video_path.txt

@@ -1,4 +0,0 @@
-video_path = '/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/videos/632051.mp4'
-video_path type = <class 'str'>
-video_path is None: False
-bool(video_path): True

SignX/detailed_prediction_20260101_150706/632051/feature_frame_mapping.json

@@ -1,176 +0,0 @@
-{
-  "original_frame_count": 106,
-  "feature_count": 28,
-  "downsampling_ratio": 3.7857142857142856,
-  "fps": 24.0,
-  "mapping": [
-    {
-      "feature_index": 0,
-      "frame_start": 0,
-      "frame_end": 3,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 1,
-      "frame_start": 3,
-      "frame_end": 7,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 2,
-      "frame_start": 7,
-      "frame_end": 11,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 3,
-      "frame_start": 11,
-      "frame_end": 15,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 4,
-      "frame_start": 15,
-      "frame_end": 18,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 5,
-      "frame_start": 18,
-      "frame_end": 22,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 6,
-      "frame_start": 22,
-      "frame_end": 26,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 7,
-      "frame_start": 26,
-      "frame_end": 30,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 8,
-      "frame_start": 30,
-      "frame_end": 34,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 9,
-      "frame_start": 34,
-      "frame_end": 37,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 10,
-      "frame_start": 37,
-      "frame_end": 41,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 11,
-      "frame_start": 41,
-      "frame_end": 45,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 12,
-      "frame_start": 45,
-      "frame_end": 49,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 13,
-      "frame_start": 49,
-      "frame_end": 53,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 14,
-      "frame_start": 53,
-      "frame_end": 56,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 15,
-      "frame_start": 56,
-      "frame_end": 60,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 16,
-      "frame_start": 60,
-      "frame_end": 64,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 17,
-      "frame_start": 64,
-      "frame_end": 68,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 18,
-      "frame_start": 68,
-      "frame_end": 71,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 19,
-      "frame_start": 71,
-      "frame_end": 75,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 20,
-      "frame_start": 75,
-      "frame_end": 79,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 21,
-      "frame_start": 79,
-      "frame_end": 83,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 22,
-      "frame_start": 83,
-      "frame_end": 87,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 23,
-      "frame_start": 87,
-      "frame_end": 90,
-      "frame_count": 3
-    },
-    {
-      "feature_index": 24,
-      "frame_start": 90,
-      "frame_end": 94,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 25,
-      "frame_start": 94,
-      "frame_end": 98,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 26,
-      "frame_start": 98,
-      "frame_end": 102,
-      "frame_count": 4
-    },
-    {
-      "feature_index": 27,
-      "frame_start": 102,
-      "frame_end": 106,
-      "frame_count": 4
-    }
-  ]
-}

SignX/detailed_prediction_20260101_150706/632051/frame_alignment.json

@@ -1,86 +0,0 @@
-{
-  "translation": "#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p",
-  "words": [
-    "#IF",
-    "FRIEND",
-    "GROUP/TOGETHER",
-    "DEPART",
-    "PARTY",
-    "IX-1p",
-    "JOIN",
-    "IX-1p"
-  ],
-  "total_video_frames": 28,
-  "frame_ranges": [
-    {
-      "word": "#IF",
-      "start_frame": 2,
-      "end_frame": 2,
-      "peak_frame": 2,
-      "avg_attention": 0.47214657068252563,
-      "confidence": "medium"
-    },
-    {
-      "word": "FRIEND",
-      "start_frame": 5,
-      "end_frame": 5,
-      "peak_frame": 5,
-      "avg_attention": 0.4252290427684784,
-      "confidence": "medium"
-    },
-    {
-      "word": "GROUP/TOGETHER",
-      "start_frame": 8,
-      "end_frame": 8,
-      "peak_frame": 8,
-      "avg_attention": 0.37518179416656494,
-      "confidence": "medium"
-    },
-    {
-      "word": "DEPART",
-      "start_frame": 27,
-      "end_frame": 27,
-      "peak_frame": 27,
-      "avg_attention": 0.3480324149131775,
-      "confidence": "medium"
-    },
-    {
-      "word": "PARTY",
-      "start_frame": 27,
-      "end_frame": 27,
-      "peak_frame": 27,
-      "avg_attention": 0.38299673795700073,
-      "confidence": "medium"
-    },
-    {
-      "word": "IX-1p",
-      "start_frame": 27,
-      "end_frame": 27,
-      "peak_frame": 27,
-      "avg_attention": 0.33272165060043335,
-      "confidence": "medium"
-    },
-    {
-      "word": "JOIN",
-      "start_frame": 11,
-      "end_frame": 11,
-      "peak_frame": 11,
-      "avg_attention": 0.5199229121208191,
-      "confidence": "high"
-    },
-    {
-      "word": "IX-1p",
-      "start_frame": 14,
-      "end_frame": 14,
-      "peak_frame": 14,
-      "avg_attention": 0.3677118122577667,
-      "confidence": "medium"
-    }
-  ],
-  "statistics": {
-    "avg_confidence": 0.4029928669333458,
-    "high_confidence_words": 1,
-    "medium_confidence_words": 7,
-    "low_confidence_words": 0
-  }
-}

SignX/detailed_prediction_20260101_150706/632051/interactive_alignment.html

@@ -1,579 +0,0 @@
-<!DOCTYPE html>
-<html lang="zh-CN">
-<head>
-    <meta charset="UTF-8">
-    <meta name="viewport" content="width=device-width, initial-scale=1.0">
-    <title>Interactive Word-Frame Alignment</title>
-    <style>
-        body {
-            font-family: 'Arial', sans-serif;
-            margin: 20px;
-            background-color: #f5f5f5;
-        }
-        .container {
-            max-width: 1800px;
-            margin: 0 auto;
-            background-color: white;
-            padding: 30px;
-            border-radius: 8px;
-            box-shadow: 0 2px 10px rgba(0,0,0,0.1);
-        }
-        h1 {
-            color: #333;
-            border-bottom: 3px solid #4CAF50;
-            padding-bottom: 10px;
-            margin-bottom: 20px;
-        }
-        .stats {
-            background-color: #E3F2FD;
-            padding: 15px;
-            border-radius: 5px;
-            margin-bottom: 20px;
-            border-left: 4px solid #2196F3;
-            font-size: 14px;
-        }
-        .controls {
-            background-color: #f9f9f9;
-            padding: 20px;
-            border-radius: 5px;
-            margin-bottom: 30px;
-            border: 1px solid #ddd;
-        }
-        .control-group {
-            margin-bottom: 15px;
-        }
-        label {
-            font-weight: bold;
-            display: inline-block;
-            width: 250px;
-            color: #555;
-        }
-        input[type="range"] {
-            width: 400px;
-            vertical-align: middle;
-        }
-        .value-display {
-            display: inline-block;
-            width: 80px;
-            font-family: monospace;
-            font-size: 14px;
-            color: #2196F3;
-            font-weight: bold;
-        }
-        .reset-btn {
-            margin-top: 15px;
-            padding: 10px 25px;
-            background-color: #2196F3;
-            color: white;
-            border: none;
-            border-radius: 5px;
-            cursor: pointer;
-            font-size: 14px;
-            font-weight: bold;
-        }
-        .reset-btn:hover {
-            background-color: #1976D2;
-        }
-        canvas {
-            border: 1px solid #999;
-            display: block;
-            margin: 20px auto;
-            background: white;
-        }
-        .legend {
-            margin-top: 20px;
-            padding: 15px;
-            background-color: #fff;
-            border: 1px solid #ddd;
-            border-radius: 5px;
-        }
-        .legend-item {
-            display: inline-block;
-            margin-right: 25px;
-            font-size: 13px;
-            margin-bottom: 10px;
-        }
-        .color-box {
-            display: inline-block;
-            width: 30px;
-            height: 15px;
-            margin-right: 8px;
-            vertical-align: middle;
-            border: 1px solid #666;
-        }
-        .info-panel {
-            margin-top: 20px;
-            padding: 15px;
-            background-color: #f9f9f9;
-            border-radius: 5px;
-            border: 1px solid #ddd;
-        }
-        .confidence {
-            display: inline-block;
-            padding: 3px 10px;
-            border-radius: 10px;
-            font-weight: bold;
-            font-size: 11px;
-            text-transform: uppercase;
-        }
-        .confidence.high {
-            background-color: #4CAF50;
-            color: white;
-        }
-        .confidence.medium {
-            background-color: #FF9800;
-            color: white;
-        }
-        .confidence.low {
-            background-color: #f44336;
-            color: white;
-        }
-    </style>
-</head>
-<body>
-    <div class="container">
-        <h1>🎯 Interactive Word-to-Frame Alignment Visualizer</h1>
-
-        <div class="stats">
-            <strong>Translation:</strong> #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p<br>
-            <strong>Total Words:</strong> 8 |
-            <strong>Total Features:</strong> 28
-        </div>
-
-        <div class="controls">
-            <h3>⚙️ Threshold Controls</h3>
-
-            <div class="control-group">
-                <label for="peak-threshold">Peak Threshold (% of max):</label>
-                <input type="range" id="peak-threshold" min="1" max="100" value="90" step="1">
-                <span class="value-display" id="peak-threshold-value">90%</span>
-                <br>
-                <small style="margin-left: 255px; color: #666;">
-                    帧的注意力权重 ≥ (峰值权重 × 阈值%) 时被认为是"显著帧"
-                </small>
-            </div>
-
-            <div class="control-group">
-                <label for="confidence-high">High Confidence (avg attn >):</label>
-                <input type="range" id="confidence-high" min="0" max="100" value="50" step="1">
-                <span class="value-display" id="confidence-high-value">0.50</span>
-            </div>
-
-            <div class="control-group">
-                <label for="confidence-medium">Medium Confidence (avg attn >):</label>
-                <input type="range" id="confidence-medium" min="0" max="100" value="20" step="1">
-                <span class="value-display" id="confidence-medium-value">0.20</span>
-            </div>
-
-            <button class="reset-btn" onclick="resetDefaults()">
-                Reset to Defaults
-            </button>
-        </div>
-
-        <div>
-            <h3>Word-to-Frame Alignment</h3>
-            <p style="color: #666; font-size: 13px;">
-                每个词显示为彩色矩形，宽度表示该词对应的特征帧范围。★ = 峰值帧。矩形内部显示注意力权重波形。
-            </p>
-            <canvas id="alignment-canvas" width="1600" height="600"></canvas>
-
-            <h3 style="margin-top: 30px;">Timeline Progress Bar</h3>
-            <canvas id="timeline-canvas" width="1600" height="100"></canvas>
-
-            <div class="legend">
-                <strong>Legend:</strong><br><br>
-                <div class="legend-item">
-                    <span class="confidence high">High</span>
-                    <span class="confidence medium">Medium</span>
-                    <span class="confidence low">Low</span>
-                    Confidence Levels (opacity reflects confidence)
-                </div>
-                <div class="legend-item">
-                    <span style="color: red; font-size: 20px;">★</span>
-                    Peak Frame (highest attention)
-                </div>
-                <div class="legend-item">
-                    <span style="color: blue;">━</span>
-                    Attention Waveform (within word region)
-                </div>
-            </div>
-        </div>
-
-        <div class="info-panel">
-            <h3>Alignment Details</h3>
-            <div id="alignment-details"></div>
-        </div>
-    </div>
-
-    <script>
-        // Attention data from Python
-        const attentionData = [{"word": "#IF", "word_idx": 0, "weights": [0.013499895110726357, 0.02982642501592636, 0.47214657068252563, 0.4107391834259033, 0.04950176924467087, 0.011385880410671234, 0.007043282967060804, 0.0014652750687673688, 0.0005238102748990059, 0.00040972864371724427, 0.0001160625834017992, 6.416538963094354e-05, 5.9505786339286715e-05, 5.076597517472692e-05, 6.82844765833579e-05, 0.00012157609307905659, 6.597878382308409e-05, 0.00010269331687595695, 0.00013462362403515726, 6.423696322599426e-05, 8.642762986710295e-05, 9.25226995605044e-05, 0.00011670421372400597, 0.0001578366500325501, 0.00020240909361746162, 0.0003825947642326355, 0.0007172566256485879, 0.0008544913143850863]}, {"word": "FRIEND", "word_idx": 1, "weights": [0.009660173207521439, 0.010518566705286503, 0.011222519911825657, 0.014483344741165638, 0.1795402616262436, 0.4252290427684784, 0.25737643241882324, 0.05393827706575394, 0.01512613520026207, 0.013365501537919044, 0.002376752672716975, 0.00014935070066712797, 8.692959818290547e-05, 0.0004998841905035079, 0.0008451194153167307, 0.0011626698542386293, 0.00042453958303667605, 0.00017692227265797555, 0.00016767902707215399, 4.8644251364748925e-05, 8.348096889676526e-05, 0.0001094180770451203, 0.00030694258748553693, 0.0002885134017560631, 0.00031121523352339864, 0.0006241592927835882, 0.0008697768207639456, 0.0010077793849632144]}, {"word": "GROUP/TOGETHER", "word_idx": 2, "weights": [0.010994982905685902, 0.004551935940980911, 0.002873026067391038, 0.003936904948204756, 0.008626177906990051, 0.014811795204877853, 0.02318989858031273, 0.12032425403594971, 0.37518179416656494, 0.2971201539039612, 0.08549409359693527, 0.014250868931412697, 0.008063109591603279, 0.00339426938444376, 0.0037573552690446377, 0.004879903048276901, 0.0018731161253526807, 0.0011690640822052956, 0.0013268929906189442, 0.0007135092164389789, 0.000632062554359436, 0.000777124660089612, 0.0009553946438245475, 0.0009487943025305867, 0.0007010120898485184, 0.001496487995609641, 0.0037835948169231415, 0.004172381013631821]}, {"word": "DEPART", "word_idx": 3, "weights": [0.22514434158802032, 0.12377114593982697, 0.00781786348670721, 0.0074639273807406425, 0.01298774778842926, 0.00438598683103919, 0.004350316245108843, 0.006786263547837734, 0.006216868292540312, 0.0061629218980669975, 0.004193580709397793, 0.0015793128404766321, 0.0011525226291269064, 0.0014239393640309572, 0.0007423617644235492, 0.0008507575839757919, 0.0008870838792063296, 0.00024679809575900435, 0.00034805957693606615, 0.005230794660747051, 0.0011639633448794484, 0.001367528340779245, 0.010013289749622345, 0.018452608957886696, 0.0018141826149076223, 0.001117207808420062, 0.19629621505737305, 0.3480324149131775]}, {"word": "PARTY", "word_idx": 4, "weights": [0.1664648950099945, 0.06123431771993637, 0.0020844682585448027, 0.0020428383722901344, 0.0058554718270897865, 0.004360921215265989, 0.004692059941589832, 0.009323552250862122, 0.015183845534920692, 0.016528787091374397, 0.015347503125667572, 0.007253072690218687, 0.005231750197708607, 0.009598116390407085, 0.00704572768881917, 0.007053114008158445, 0.006423295009881258, 0.0010452027199789882, 0.0009786873124539852, 0.004494669381529093, 0.005323153454810381, 0.006433582864701748, 0.022334398701786995, 0.03912580758333206, 0.004556183237582445, 0.0021732028108090162, 0.18481463193893433, 0.38299673795700073]}, {"word": "IX-1p", "word_idx": 5, "weights": [0.2268882542848587, 0.10439852625131607, 0.005018203519284725, 0.005008632782846689, 0.005379822570830584, 0.00215631234459579, 0.0024426421150565147, 0.007580526173114777, 0.011461855843663216, 0.010575865395367146, 0.010204891674220562, 0.004322281572967768, 0.0023845669347792864, 0.0016265056328848004, 0.0011272492120042443, 0.0014091862831264734, 0.0019118450582027435, 0.0019068039255216718, 0.002558623207733035, 0.005466249771416187, 0.002576562575995922, 0.0033958060666918755, 0.014094071462750435, 0.03357496112585068, 0.005502632353454828, 0.003941097296774387, 0.19036439061164856, 0.33272165060043335]}, {"word": "JOIN", "word_idx": 6, "weights": [0.006536237895488739, 0.002151536289602518, 0.0006580766057595611, 0.0008207014761865139, 0.0003112705599050969, 0.0003111894184257835, 0.0008894064230844378, 0.004121360369026661, 0.01069970428943634, 0.008291625417768955, 0.01931559480726719, 0.5199229121208191, 0.40212148427963257, 0.004480497911572456, 0.0010337198618799448, 0.0007998707587830722, 0.00024323497200384736, 7.284984894795343e-05, 0.00011325528612360358, 0.00540410028770566, 0.0011726750526577234, 0.0009422790608368814, 0.0003188242844771594, 0.00024731658049859107, 3.1396619306178764e-05, 4.355102646513842e-05, 0.0036189379170536995, 0.005326398182660341]}, {"word": "IX-1p", "word_idx": 7, "weights": [0.0013159031514078379, 0.0007256589597091079, 0.00017777174070943147, 0.0001744187029544264, 0.00025140171055682003, 0.00039260604535229504, 0.0003829205525107682, 0.000333531730575487, 0.0007308170897886157, 0.0010108469286933541, 0.0015992401167750359, 0.003526317421346903, 0.012568545527756214, 0.2852487564086914, 0.3677118122577667, 0.19535787403583527, 0.07697467505931854, 0.012815488502383232, 0.007124335505068302, 0.0009805350564420223, 0.007633780129253864, 0.007437399588525295, 0.005485337693244219, 0.003693929873406887, 0.0026681837625801563, 0.0011110405903309584, 0.0008843602845445275, 0.0016825739294290543]}];
-        const numGlosses = 8;
-        const numFeatures = 28;
-
-        // Colors for different words (matching matplotlib tab20)
-        const colors = [
-            '#1f77b4', '#ff7f0e', '#2ca02c', '#d62728', '#9467bd',
-            '#8c564b', '#e377c2', '#7f7f7f', '#bcbd22', '#17becf',
-            '#aec7e8', '#ffbb78', '#98df8a', '#ff9896', '#c5b0d5',
-            '#c49c94', '#f7b6d2', '#c7c7c7', '#dbdb8d', '#9edae5'
-        ];
-
-        // Get controls
-        const peakThresholdSlider = document.getElementById('peak-threshold');
-        const peakThresholdValue = document.getElementById('peak-threshold-value');
-        const confidenceHighSlider = document.getElementById('confidence-high');
-        const confidenceHighValue = document.getElementById('confidence-high-value');
-        const confidenceMediumSlider = document.getElementById('confidence-medium');
-        const confidenceMediumValue = document.getElementById('confidence-medium-value');
-        const alignmentCanvas = document.getElementById('alignment-canvas');
-        const timelineCanvas = document.getElementById('timeline-canvas');
-        const alignmentCtx = alignmentCanvas.getContext('2d');
-        const timelineCtx = timelineCanvas.getContext('2d');
-
-        // Update displays when sliders change
-        peakThresholdSlider.oninput = function() {
-            peakThresholdValue.textContent = this.value + '%';
-            updateVisualization();
-        };
-
-        confidenceHighSlider.oninput = function() {
-            confidenceHighValue.textContent = (this.value / 100).toFixed(2);
-            updateVisualization();
-        };
-
-        confidenceMediumSlider.oninput = function() {
-            confidenceMediumValue.textContent = (this.value / 100).toFixed(2);
-            updateVisualization();
-        };
-
-        function resetDefaults() {
-            peakThresholdSlider.value = 90;
-            confidenceHighSlider.value = 50;
-            confidenceMediumSlider.value = 20;
-            peakThresholdValue.textContent = '90%';
-            confidenceHighValue.textContent = '0.50';
-            confidenceMediumValue.textContent = '0.20';
-            updateVisualization();
-        }
-
-        function calculateAlignment(weights, peakThreshold) {
-            // Find peak
-            let peakIdx = 0;
-            let peakWeight = weights[0];
-            for (let i = 1; i < weights.length; i++) {
-                if (weights[i] > peakWeight) {
-                    peakWeight = weights[i];
-                    peakIdx = i;
-                }
-            }
-
-            // Find significant frames
-            const threshold = peakWeight * (peakThreshold / 100);
-            let startIdx = peakIdx;
-            let endIdx = peakIdx;
-            let sumWeight = 0;
-            let count = 0;
-
-            for (let i = 0; i < weights.length; i++) {
-                if (weights[i] >= threshold) {
-                    if (i < startIdx) startIdx = i;
-                    if (i > endIdx) endIdx = i;
-                    sumWeight += weights[i];
-                    count++;
-                }
-            }
-
-            const avgWeight = count > 0 ? sumWeight / count : peakWeight;
-
-            return {
-                startIdx: startIdx,
-                endIdx: endIdx,
-                peakIdx: peakIdx,
-                peakWeight: peakWeight,
-                avgWeight: avgWeight,
-                threshold: threshold
-            };
-        }
-
-        function getConfidenceLevel(avgWeight, highThreshold, mediumThreshold) {
-            if (avgWeight > highThreshold) return 'high';
-            if (avgWeight > mediumThreshold) return 'medium';
-            return 'low';
-        }
-
-        function drawAlignmentChart() {
-            const peakThreshold = parseInt(peakThresholdSlider.value);
-            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
-            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
-
-            // Canvas dimensions
-            const width = alignmentCanvas.width;
-            const height = alignmentCanvas.height;
-            const leftMargin = 180;
-            const rightMargin = 50;
-            const topMargin = 60;
-            const bottomMargin = 80;
-
-            const plotWidth = width - leftMargin - rightMargin;
-            const plotHeight = height - topMargin - bottomMargin;
-
-            const rowHeight = plotHeight / numGlosses;
-            const featureWidth = plotWidth / numFeatures;
-
-            // Clear canvas
-            alignmentCtx.clearRect(0, 0, width, height);
-
-            // Draw title
-            alignmentCtx.fillStyle = '#333';
-            alignmentCtx.font = 'bold 18px Arial';
-            alignmentCtx.textAlign = 'center';
-            alignmentCtx.fillText('Word-to-Frame Alignment', width / 2, 30);
-            alignmentCtx.font = '13px Arial';
-            alignmentCtx.fillText('(based on attention peaks, ★ = peak frame)', width / 2, 48);
-
-            // Calculate alignments
-            const alignments = [];
-            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
-                const data = attentionData[wordIdx];
-                const alignment = calculateAlignment(data.weights, peakThreshold);
-                alignment.word = data.word;
-                alignment.wordIdx = wordIdx;
-                alignment.weights = data.weights;
-                alignments.push(alignment);
-            }
-
-            // Draw grid
-            alignmentCtx.strokeStyle = '#e0e0e0';
-            alignmentCtx.lineWidth = 0.5;
-            for (let i = 0; i <= numFeatures; i++) {
-                const x = leftMargin + i * featureWidth;
-                alignmentCtx.beginPath();
-                alignmentCtx.moveTo(x, topMargin);
-                alignmentCtx.lineTo(x, topMargin + plotHeight);
-                alignmentCtx.stroke();
-            }
-
-            // Draw word regions
-            for (let wordIdx = 0; wordIdx < numGlosses; wordIdx++) {
-                const alignment = alignments[wordIdx];
-                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
-                const y = topMargin + wordIdx * rowHeight;
-
-                // Alpha based on confidence
-                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
-
-                // Draw rectangle for word region
-                const startX = leftMargin + alignment.startIdx * featureWidth;
-                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
-
-                alignmentCtx.fillStyle = colors[wordIdx % 20];
-                alignmentCtx.globalAlpha = alpha;
-                alignmentCtx.fillRect(startX, y, rectWidth, rowHeight * 0.8);
-                alignmentCtx.globalAlpha = 1.0;
-
-                // Draw border
-                alignmentCtx.strokeStyle = '#000';
-                alignmentCtx.lineWidth = 2;
-                alignmentCtx.strokeRect(startX, y, rectWidth, rowHeight * 0.8);
-
-                // Draw attention waveform inside rectangle
-                alignmentCtx.strokeStyle = 'rgba(0, 0, 255, 0.8)';
-                alignmentCtx.lineWidth = 1.5;
-                alignmentCtx.beginPath();
-                for (let i = alignment.startIdx; i <= alignment.endIdx; i++) {
-                    const x = leftMargin + i * featureWidth + featureWidth / 2;
-                    const weight = alignment.weights[i];
-                    const maxWeight = alignment.peakWeight;
-                    const normalizedWeight = weight / (maxWeight * 1.2); // Scale for visibility
-                    const waveY = y + rowHeight * 0.8 - (normalizedWeight * rowHeight * 0.6);
-
-                    if (i === alignment.startIdx) {
-                        alignmentCtx.moveTo(x, waveY);
-                    } else {
-                        alignmentCtx.lineTo(x, waveY);
-                    }
-                }
-                alignmentCtx.stroke();
-
-                // Draw word label
-                const labelX = startX + rectWidth / 2;
-                const labelY = y + rowHeight * 0.4;
-
-                alignmentCtx.fillStyle = 'rgba(0, 0, 0, 0.7)';
-                alignmentCtx.fillRect(labelX - 60, labelY - 12, 120, 24);
-                alignmentCtx.fillStyle = '#fff';
-                alignmentCtx.font = 'bold 13px Arial';
-                alignmentCtx.textAlign = 'center';
-                alignmentCtx.textBaseline = 'middle';
-                alignmentCtx.fillText(alignment.word, labelX, labelY);
-
-                // Mark peak frame with star
-                const peakX = leftMargin + alignment.peakIdx * featureWidth + featureWidth / 2;
-                const peakY = y + rowHeight * 0.4;
-
-                // Draw star
-                alignmentCtx.fillStyle = '#ff0000';
-                alignmentCtx.strokeStyle = '#ffff00';
-                alignmentCtx.lineWidth = 1.5;
-                alignmentCtx.font = '20px Arial';
-                alignmentCtx.textAlign = 'center';
-                alignmentCtx.strokeText('★', peakX, peakY);
-                alignmentCtx.fillText('★', peakX, peakY);
-
-                // Y-axis label (word names)
-                alignmentCtx.fillStyle = '#333';
-                alignmentCtx.font = '12px Arial';
-                alignmentCtx.textAlign = 'right';
-                alignmentCtx.textBaseline = 'middle';
-                alignmentCtx.fillText(alignment.word, leftMargin - 10, y + rowHeight * 0.4);
-            }
-
-            // Draw horizontal grid lines
-            alignmentCtx.strokeStyle = '#ccc';
-            alignmentCtx.lineWidth = 0.5;
-            for (let i = 0; i <= numGlosses; i++) {
-                const y = topMargin + i * rowHeight;
-                alignmentCtx.beginPath();
-                alignmentCtx.moveTo(leftMargin, y);
-                alignmentCtx.lineTo(leftMargin + plotWidth, y);
-                alignmentCtx.stroke();
-            }
-
-            // Draw axes
-            alignmentCtx.strokeStyle = '#000';
-            alignmentCtx.lineWidth = 2;
-            alignmentCtx.strokeRect(leftMargin, topMargin, plotWidth, plotHeight);
-
-            // X-axis labels (frame indices)
-            alignmentCtx.fillStyle = '#000';
-            alignmentCtx.font = '11px Arial';
-            alignmentCtx.textAlign = 'center';
-            alignmentCtx.textBaseline = 'top';
-            for (let i = 0; i < numFeatures; i++) {
-                const x = leftMargin + i * featureWidth + featureWidth / 2;
-                alignmentCtx.fillText(i.toString(), x, topMargin + plotHeight + 10);
-            }
-
-            // Axis titles
-            alignmentCtx.fillStyle = '#333';
-            alignmentCtx.font = 'bold 14px Arial';
-            alignmentCtx.textAlign = 'center';
-            alignmentCtx.fillText('Feature Frame Index', leftMargin + plotWidth / 2, height - 20);
-
-            alignmentCtx.save();
-            alignmentCtx.translate(30, topMargin + plotHeight / 2);
-            alignmentCtx.rotate(-Math.PI / 2);
-            alignmentCtx.fillText('Generated Word', 0, 0);
-            alignmentCtx.restore();
-
-            return alignments;
-        }
-
-        function drawTimeline(alignments) {
-            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
-            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
-
-            const width = timelineCanvas.width;
-            const height = timelineCanvas.height;
-            const leftMargin = 180;
-            const rightMargin = 50;
-            const plotWidth = width - leftMargin - rightMargin;
-            const featureWidth = plotWidth / numFeatures;
-
-            // Clear canvas
-            timelineCtx.clearRect(0, 0, width, height);
-
-            // Background bar
-            timelineCtx.fillStyle = '#ddd';
-            timelineCtx.fillRect(leftMargin, 30, plotWidth, 40);
-            timelineCtx.strokeStyle = '#000';
-            timelineCtx.lineWidth = 2;
-            timelineCtx.strokeRect(leftMargin, 30, plotWidth, 40);
-
-            // Draw word regions on timeline
-            for (let wordIdx = 0; wordIdx < alignments.length; wordIdx++) {
-                const alignment = alignments[wordIdx];
-                const confidence = getConfidenceLevel(alignment.avgWeight, highThreshold, mediumThreshold);
-                const alpha = confidence === 'high' ? 0.9 : confidence === 'medium' ? 0.7 : 0.5;
-
-                const startX = leftMargin + alignment.startIdx * featureWidth;
-                const rectWidth = (alignment.endIdx - alignment.startIdx + 1) * featureWidth;
-
-                timelineCtx.fillStyle = colors[wordIdx % 20];
-                timelineCtx.globalAlpha = alpha;
-                timelineCtx.fillRect(startX, 30, rectWidth, 40);
-                timelineCtx.globalAlpha = 1.0;
-                timelineCtx.strokeStyle = '#000';
-                timelineCtx.lineWidth = 0.5;
-                timelineCtx.strokeRect(startX, 30, rectWidth, 40);
-            }
-
-            // Title
-            timelineCtx.fillStyle = '#333';
-            timelineCtx.font = 'bold 13px Arial';
-            timelineCtx.textAlign = 'left';
-            timelineCtx.fillText('Timeline Progress Bar', leftMargin, 20);
-        }
-
-        function updateDetailsPanel(alignments, highThreshold, mediumThreshold) {
-            const panel = document.getElementById('alignment-details');
-            let html = '<table style="width: 100%; border-collapse: collapse;">';
-            html += '<tr style="background: #f0f0f0; font-weight: bold;">';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Word</th>';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Feature Range</th>';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Peak</th>';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Span</th>';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Avg Attention</th>';
-            html += '<th style="padding: 8px; border: 1px solid #ddd;">Confidence</th>';
-            html += '</tr>';
-
-            for (const align of alignments) {
-                const confidence = getConfidenceLevel(align.avgWeight, highThreshold, mediumThreshold);
-                const span = align.endIdx - align.startIdx + 1;
-
-                html += '<tr>';
-                html += `<td style="padding: 8px; border: 1px solid #ddd;"><strong>${align.word}</strong></td>`;
-                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.startIdx} → ${align.endIdx}</td>`;
-                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.peakIdx}</td>`;
-                html += `<td style="padding: 8px; border: 1px solid #ddd;">${span}</td>`;
-                html += `<td style="padding: 8px; border: 1px solid #ddd;">${align.avgWeight.toFixed(4)}</td>`;
-                html += `<td style="padding: 8px; border: 1px solid #ddd;"><span class="confidence ${confidence}">${confidence}</span></td>`;
-                html += '</tr>';
-            }
-
-            html += '</table>';
-            panel.innerHTML = html;
-        }
-
-        function updateVisualization() {
-            const alignments = drawAlignmentChart();
-            drawTimeline(alignments);
-            const highThreshold = parseInt(confidenceHighSlider.value) / 100;
-            const mediumThreshold = parseInt(confidenceMediumSlider.value) / 100;
-            updateDetailsPanel(alignments, highThreshold, mediumThreshold);
-        }
-
-        // Event listeners for sliders
-        peakSlider.addEventListener('input', function() {
-            peakValue.textContent = peakSlider.value + '%';
-            updateVisualization();
-        });
-
-        confidenceHighSlider.addEventListener('input', function() {
-            const val = parseInt(confidenceHighSlider.value) / 100;
-            confidenceHighValue.textContent = val.toFixed(2);
-            updateVisualization();
-        });
-
-        confidenceMediumSlider.addEventListener('input', function() {
-            const val = parseInt(confidenceMediumSlider.value) / 100;
-            confidenceMediumValue.textContent = val.toFixed(2);
-            updateVisualization();
-        });
-
-        // Initial visualization
-        updateVisualization();
-    </script>
-</body>
-</html>

SignX/detailed_prediction_20260101_150706/632051/translation.txt

@@ -1,3 +0,0 @@
-With BPE: #IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p JO@@ I@@ N IX-1p
-Clean: #IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p
-Ground Truth: #IF FRIEND GROUP/TOGETHER GO-OUT PARTY IX-1p JOIN IX-1p

SignX/eval/analyze_video2pose.py

@@ -0,0 +1,228 @@
+#!/usr/bin/env python3
+"""Analyze pose ViT features from video2text checkpoint outputs."""
+
+import argparse
+import json
+import os
+from pathlib import Path
+
+import cv2
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+from PIL import Image
+from torchvision import transforms
+
+from smkd.pretrained.video2text import (
+    MultimodalPose2TextDiffusion,
+    MultimodalVideo2TextDiffusion,
+)
+
+INPUT_DIMS = {
+    "dwpose": 384,
+    "mediapipe_pose": 258,
+    "primedepth_depth": 576,
+    "sapiens_segmentation": 576,
+    "smplerx": 165,
+}
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Pose-dimensional ablation via video2text checkpoint.")
+    parser.add_argument("--video", required=True, help="Path to input video (mp4).")
+    parser.add_argument(
+        "--checkpoint",
+        default="smkd/pretrained/video2text_checkpoint_epoch_14.pth",
+        help="Path to video2text checkpoint.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="pose_vit_feature_analysis",
+        help="Directory to store extracted features/plots.",
+    )
+    parser.add_argument("--num-frames", type=int, default=32, help="Number of frames sampled from video.")
+    parser.add_argument("--device", default="cuda", choices=["cuda", "cpu"], help="Torch device preference.")
+    parser.add_argument("--topk", type=int, default=32, help="Top dimensions to visualize.")
+    return parser.parse_args()
+
+
+def prepare_device(device_pref: str) -> torch.device:
+    if device_pref == "cuda" and torch.cuda.is_available():
+        return torch.device("cuda")
+    return torch.device("cpu")
+
+
+def ensure_dir(path: str) -> Path:
+    path_obj = Path(path)
+    path_obj.mkdir(parents=True, exist_ok=True)
+    return path_obj
+
+
+def load_video_frames(video_path: str, num_frames: int, transform) -> torch.Tensor:
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise RuntimeError(f"Failed to open video: {video_path}")
+
+    total_frames = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if total_frames <= 0:
+        raise RuntimeError(f"No frames found in video: {video_path}")
+
+    indices = np.linspace(0, max(total_frames - 1, 0), num=num_frames, dtype=np.int32)
+    frames = []
+
+    for idx in indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
+        ok, frame = cap.read()
+        if not ok:
+            continue
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frames.append(transform(Image.fromarray(frame)))
+
+    cap.release()
+
+    if not frames:
+        raise RuntimeError(f"No decodable frames in video: {video_path}")
+
+    while len(frames) < num_frames:
+        frames.append(frames[-1].clone())
+
+    return torch.stack(frames[:num_frames], dim=0)
+
+
+def compute_dimension_stats(pose_2048: torch.Tensor):
+    with torch.no_grad():
+        scores = pose_2048.abs().mean(dim=(0, 1))
+        normalized = scores / (scores.max() + 1e-8)
+    return scores.cpu().numpy(), normalized.cpu().numpy()
+
+
+def plot_top_dims(scores, top_indices, output_path):
+    plt.figure(figsize=(max(8, len(top_indices) * 0.35), 4))
+    plt.bar(range(len(top_indices)), scores[top_indices], color="#1f77b4")
+    plt.xticks(range(len(top_indices)), [str(i) for i in top_indices], rotation=60)
+    plt.ylabel("Mean |value|")
+    plt.xlabel("Dimension")
+    plt.title("Top pose dimensions")
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240)
+    plt.close()
+
+
+def plot_heatmap(norm_scores, output_path):
+    rows = 32
+    usable = (norm_scores.shape[0] // rows) * rows
+    reshaped = norm_scores[:usable].reshape(rows, -1)
+    plt.figure(figsize=(12, 4))
+    plt.imshow(reshaped, aspect="auto", cmap="magma")
+    plt.colorbar(label="Normalized importance")
+    plt.xlabel("Chunk index")
+    plt.ylabel("Row")
+    plt.title("Pose dimension importance heatmap")
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240)
+    plt.close()
+
+
+def plot_cumulative(scores, output_path):
+    sorted_scores = np.sort(scores)[::-1]
+    cumsum = np.cumsum(sorted_scores)
+    coverage = cumsum / cumsum[-1]
+    dims = np.arange(1, len(sorted_scores) + 1)
+    plt.figure(figsize=(8, 4))
+    plt.plot(dims, coverage, color="#ff7f0e")
+    plt.xlabel("Top-k dimensions")
+    plt.ylabel("Cumulative coverage")
+    plt.grid(alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240)
+    plt.close()
+    return coverage
+
+
+def save_csv(scores, norm_scores, output_path):
+    with open(output_path, "w", encoding="utf-8") as handle:
+        handle.write("dimension,score,normalized\n")
+        for idx, (score, norm) in enumerate(zip(scores, norm_scores)):
+            handle.write(f"{idx},{score:.8f},{norm:.6f}\n")
+
+
+def write_report(video_path, checkpoint, scores, norm_scores, coverage, top_indices, output_path):
+    levels = [0.25, 0.5, 0.9]
+    with open(output_path, "w", encoding="utf-8") as handle:
+        handle.write("Pose ViT dimensional analysis\n")
+        handle.write("=" * 60 + "\n\n")
+        handle.write(f"Video      : {video_path}\n")
+        handle.write(f"Checkpoint : {checkpoint}\n")
+        handle.write(f"Total dims : {scores.shape[0]}\n\n")
+        handle.write("Top dimensions:\n")
+        for rank, dim_idx in enumerate(top_indices, 1):
+            handle.write(
+                f"{rank:02d}. dim {dim_idx:04d} | score={scores[dim_idx]:.6f} "
+                f"| normalized={norm_scores[dim_idx]:.4f}\n"
+            )
+        handle.write("\nCoverage milestones:\n")
+        for level in levels:
+            required = int(np.argmax(coverage >= level) + 1)
+            handle.write(f"  - Top {required:4d} dims explain {level:.0%} of energy\n")
+        handle.write("\nScores computed as mean absolute activations on pose_2048.\n")
+
+
+def main():
+    args = parse_args()
+    video_path = os.path.abspath(args.video)
+    checkpoint_path = os.path.abspath(args.checkpoint)
+    output_dir = ensure_dir(args.output_dir)
+
+    if not os.path.exists(video_path):
+        raise FileNotFoundError(f"Video not found: {video_path}")
+    if not os.path.exists(checkpoint_path):
+        raise FileNotFoundError(f"Checkpoint not found: {checkpoint_path}")
+
+    device = prepare_device(args.device)
+    print(f"[INFO] Using device: {device}")
+
+    pose2text = MultimodalPose2TextDiffusion(
+        input_dims=INPUT_DIMS,
+        hidden_dim=2048,
+        device=device,
+        codebook=None,
+    )
+    video2text = MultimodalVideo2TextDiffusion(
+        pose2text_model=pose2text,
+        device=device,
+    )
+
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+    load_result = video2text.load_state_dict(checkpoint["model_state_dict"], strict=False)
+    if load_result.missing_keys:
+        print(f"[WARN] Missing keys ({len(load_result.missing_keys)}): {load_result.missing_keys[:5]}...")
+    if load_result.unexpected_keys:
+        print(f"[WARN] Unexpected keys ({len(load_result.unexpected_keys)}): {load_result.unexpected_keys[:5]}...")
+    video2text.eval()
+
+    frame_transform = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ]
+    )
+    frames = load_video_frames(video_path, args.num_frames, frame_transform)
+    video_tensor = frames.unsqueeze(0).to(device)
+
+    with torch.no_grad():
+        pose_features = video2text.video2pose(video_tensor)
+        concatenated = pose2text.pose_encoder(pose_features)
+        B, F, D = concatenated.shape
+        flattened = concatenated.reshape(B * F, D)
+        pose_2048 = pose2text.dim_match(flattened).view(B, F, -1)
+        pose_numpy = pose_2048.cpu().numpy()
+
+    np.save(output_dir / "pose_2048.npy", pose_numpy)
+    scores, norm_scores = compute_dimension_stats(pose_2048)
+    save_csv(scores, norm_scores, output_dir / "dimension_scores.csv")
+
+    topk = min(args.topk, scores.shape)

SignX/eval/pose_vit_dim_analysis.py

@@ -0,0 +1,442 @@
+#!/usr/bin/env python3
+"""
+Pose ViT dimensional importance analysis.
+
+This script mimics the pose-assist (video2pose → pad-to-2048) pipeline used in Sign-X:
+  1. Load the ViT-based video2pose encoder and the PadMatch+LayerNorm projection from
+     the video2text checkpoint (e.g., video2text_checkpoint_epoch_14.pth).
+  2. Sample frames from a given video, extract per-frame pose representations,
+     and project them to 2048 dimensions.
+  3. Compute simple importance scores (mean absolute activation per dimension),
+     then export CSV/plots/report summarising the dominant pose dimensions.
+"""
+
+import argparse
+import json
+import os
+import pickle
+import sys
+import types
+from pathlib import Path
+
+import cv2
+import matplotlib
+
+matplotlib.use("Agg")
+import matplotlib.pyplot as plt  # noqa: E402
+import numpy as np  # noqa: E402
+import torch  # noqa: E402
+import torch.nn as nn  # noqa: E402
+from PIL import Image  # noqa: E402
+from torchvision import transforms  # noqa: E402
+try:
+    import timm  # noqa: E402
+except ImportError as exc:
+    raise ImportError("timm is required for ViT backbone. Please install timm.") from exc
+
+
+INPUT_DIMS = {
+    "dwpose": 384,
+    "mediapipe_pose": 258,
+    "primedepth_depth": 576,
+    "sapiens_segmentation": 576,
+    "smplerx": 165,
+}
+POSE_TYPE_ORDER = ["dwpose", "mediapipe_pose", "primedepth_depth", "sapiens_segmentation", "smplerx"]
+
+
+class CodeBook:  # noqa: D401 - Dummy placeholder so torch.load can unpickle checkpoints.
+    """Placeholder CodeBook to satisfy torch.load when checkpoints store this object."""
+
+    def __init__(self, *args, **kwargs):
+        self.vocab_size = kwargs.get("vocab_size", 0)
+
+
+class Video2Pose(nn.Module):
+    """Minimal replica of the pose-assist encoder (ViT + temporal attention + per-type projection)."""
+
+    def __init__(self, input_dims):
+        super().__init__()
+        self.backbone = timm.create_model("vit_base_patch16_224", pretrained=True, num_classes=0)
+        self.temporal_attention = nn.MultiheadAttention(768, num_heads=8)
+        self.temporal_norm = nn.LayerNorm(768)
+        self.projections = nn.ModuleDict({pose: nn.Linear(768, dim) for pose, dim in input_dims.items()})
+
+    def forward(self, x):
+        # x: [B, F, 3, H, W]
+        B, F, C, H, W = x.shape
+        features = self.backbone(x.view(B * F, C, H, W))  # [B*F, 768]
+        features = features.view(B, F, -1).transpose(0, 1)  # [F, B, 768]
+        attended, _ = self.temporal_attention(features, features, features)
+        attended = self.temporal_norm(attended).transpose(0, 1)  # [B, F, 768]
+        return {pose: proj(attended) for pose, proj in self.projections.items()}
+
+
+class PadMatch(nn.Module):
+    """Pad features to hidden_dim and apply LayerNorm (weights loaded from checkpoint)."""
+
+    def __init__(self, input_dim, hidden_dim):
+        super().__init__()
+        self.input_dim = input_dim
+        self.hidden_dim = hidden_dim
+        self.pad = hidden_dim - input_dim
+        if self.pad < 0:
+            raise ValueError(f"hidden_dim {hidden_dim} must be >= input_dim {input_dim}")
+        self.layer_norm = nn.LayerNorm(hidden_dim)
+
+    def forward(self, x):
+        if self.pad > 0:
+            x = nn.functional.pad(x, (0, self.pad), "constant", 0.0)
+        return self.layer_norm(x)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Pose ViT dimensional analysis (video2pose → 2048D).")
+    parser.add_argument("--video", required=True, help="Path to input video (.mp4).")
+    parser.add_argument(
+        "--checkpoint",
+        default="smkd/pretrained/video2text_checkpoint_epoch_14.pth",
+        help="Path to video2text checkpoint containing video2pose weights.",
+    )
+    parser.add_argument(
+        "--output-dir",
+        default="pose_vit_feature_analysis",
+        help="Directory to store feature dumps, plots, and summary.",
+    )
+    parser.add_argument("--num-frames", type=int, default=32, help="Frames sampled uniformly from the video.")
+    parser.add_argument("--device", choices=["cuda", "cpu"], default="cuda", help="Torch device preference.")
+    parser.add_argument("--topk", type=int, default=32, help="Number of top dimensions to visualise.")
+    return parser.parse_args()
+
+
+def prepare_device(pref: str) -> torch.device:
+    if pref == "cuda" and torch.cuda.is_available():
+        return torch.device("cuda")
+    return torch.device("cpu")
+
+
+def ensure_dir(path: str) -> Path:
+    dst = Path(path)
+    dst.mkdir(parents=True, exist_ok=True)
+    return dst
+
+
+def load_checkpoint(checkpoint_path: str, device: torch.device):
+    """Load checkpoint with safe unpickling fallback."""
+    try:
+        ckpt = torch.load(checkpoint_path, map_location=device, weights_only=True)
+    except (TypeError, AttributeError, pickle.UnpicklingError):
+        torch.serialization.add_safe_globals([CodeBook])
+        ckpt = torch.load(checkpoint_path, map_location=device, weights_only=False)
+
+    if isinstance(ckpt, dict) and "model_state_dict" in ckpt:
+        return ckpt["model_state_dict"]
+    return ckpt
+
+
+def load_video2pose_weights(model: Video2Pose, state_dict):
+    sub_state = {k.replace("video2pose.", "", 1): v for k, v in state_dict.items() if k.startswith("video2pose.")}
+    missing, unexpected = model.load_state_dict(sub_state, strict=False)
+    if missing:
+        print(f"[WARN] Missing video2pose keys ({len(missing)}): {missing[:5]}...")
+    if unexpected:
+        print(f"[WARN] Unexpected video2pose keys ({len(unexpected)}): {unexpected[:5]}...")
+
+
+def load_padmatch_weights(projector: PadMatch, state_dict):
+    sub_state = {
+        k.replace("pose2text.dim_match.", "", 1): v
+        for k, v in state_dict.items()
+        if k.startswith("pose2text.dim_match.")
+    }
+    ln_state = {}
+    if "1.weight" in sub_state:
+        ln_state["weight"] = sub_state["1.weight"]
+    if "1.bias" in sub_state:
+        ln_state["bias"] = sub_state["1.bias"]
+    if ln_state:
+        projector.layer_norm.load_state_dict(ln_state, strict=False)
+    if "1.weight" not in sub_state or "1.bias" not in sub_state:
+        print("[WARN] LayerNorm weights not found in checkpoint; using default initialisation.")
+
+
+def load_and_process_video(video_path: str, num_frames: int):
+    cap = cv2.VideoCapture(video_path)
+    if not cap.isOpened():
+        raise RuntimeError(f"Unable to open video {video_path}")
+
+    total = int(cap.get(cv2.CAP_PROP_FRAME_COUNT))
+    if total <= 0:
+        raise RuntimeError(f"No frames found in video {video_path}")
+
+    indices = np.linspace(0, max(total - 1, 0), num=num_frames, dtype=np.int32)
+    transform = transforms.Compose(
+        [
+            transforms.Resize((224, 224)),
+            transforms.ToTensor(),
+            transforms.Normalize(mean=[0.5, 0.5, 0.5], std=[0.5, 0.5, 0.5]),
+        ]
+    )
+
+    frames = []
+    for idx in indices:
+        cap.set(cv2.CAP_PROP_POS_FRAMES, int(idx))
+        ok, frame = cap.read()
+        if not ok:
+            continue
+        frame = cv2.cvtColor(frame, cv2.COLOR_BGR2RGB)
+        frames.append(transform(Image.fromarray(frame)))
+
+    cap.release()
+
+    if not frames:
+        raise RuntimeError(f"Failed to decode frames from {video_path}")
+
+    while len(frames) < num_frames:
+        frames.append(frames[-1].clone())
+
+    return torch.stack(frames[:num_frames], dim=0)  # [F, 3, 224, 224]
+
+
+def compute_importance(pose_2048: torch.Tensor):
+    with torch.no_grad():
+        scores = pose_2048.abs().mean(dim=(0, 1)).cpu().numpy()
+    normalized = scores / (scores.max() + 1e-8)
+    return scores, normalized
+
+
+def plot_top_dimensions(scores, top_indices, output_path):
+    plt.figure(figsize=(max(8, len(top_indices) * 0.35), 4))
+    plt.bar(range(len(top_indices)), scores[top_indices], color="#1f77b4")
+    plt.xticks(range(len(top_indices)), [str(i) for i in top_indices], rotation=60)
+    plt.ylabel("Mean |activation|")
+    plt.xlabel("Dimension")
+    plt.title("Top pose dimensions")
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240)
+    plt.close()
+
+
+def plot_heatmap(normalized_scores, output_path):
+    # Only use the actual pose dimensions (excluding padding)
+    total_pose_dims = sum(INPUT_DIMS.values())  # 1959
+    rows = 32
+    cols = int(np.ceil(total_pose_dims / rows))  # 62 columns needed
+
+    # Only use real pose features, not padding
+    heat_data = normalized_scores[:total_pose_dims]
+    # Pad to fill the rectangle if needed
+    needed = rows * cols
+    if len(heat_data) < needed:
+        heat_data = np.pad(heat_data, (0, needed - len(heat_data)), constant_values=0)
+    heat = heat_data.reshape(rows, cols)
+
+    # Calculate pose type boundaries
+    boundaries = []
+    cumsum = 0
+    for pose_type in POSE_TYPE_ORDER:
+        cumsum += INPUT_DIMS[pose_type]
+        boundaries.append(cumsum)
+    # boundaries = [384, 642, 1218, 1794, 1959]
+
+    # Adjust figure size to reduce right-side whitespace
+    fig, ax = plt.subplots(figsize=(12, 6))
+    im = ax.imshow(heat, aspect="auto", cmap="magma", extent=[0, cols, rows, 0])
+
+    # Set limits to avoid extra space
+    ax.set_xlim(0, cols)
+    ax.set_ylim(rows, 0)  # Invert y-axis
+
+    # Draw red lines to separate pose types
+    # Convert dimension index to (row, col) in the heatmap
+    for boundary_dim in boundaries:
+        row = boundary_dim // cols
+        col = boundary_dim % cols
+
+        if col == 0:
+            # Boundary is at the start of a row, draw horizontal line
+            ax.axhline(y=row, color='red', linewidth=1.2, linestyle='-', alpha=0.9)
+        else:
+            # Boundary is in the middle of a row, draw an L-shaped line
+            # Vertical line from current position to end of row
+            ax.plot([col, col], [row, row + 1],
+                   color='red', linewidth=1.2, linestyle='-', alpha=0.9)
+            # Horizontal line at the bottom of current row
+            ax.plot([0, col], [row + 1, row + 1],
+                   color='red', linewidth=1.2, linestyle='-', alpha=0.9)
+            # Horizontal line at the top of next row (if boundary continues)
+            if row < rows - 1:
+                ax.plot([col, cols], [row + 1, row + 1],
+                       color='red', linewidth=1.2, linestyle='-', alpha=0.9)
+
+    # Add text labels for pose types at region centers (1.5x size)
+    pose_labels = POSE_TYPE_ORDER
+    pose_boundaries = [0] + boundaries
+    for i, pose_name in enumerate(pose_labels):
+        start_dim = pose_boundaries[i]
+        end_dim = pose_boundaries[i + 1] - 1  # Last dimension in region
+
+        # Calculate geometric center for regions spanning multiple rows
+        start_row = start_dim // cols
+        start_col = start_dim % cols
+        end_row = end_dim // cols
+        end_col = end_dim % cols
+
+        region_size = pose_boundaries[i + 1] - start_dim
+
+        # Calculate center row
+        center_row = (start_row + end_row) / 2.0
+
+        # Calculate center col based on region shape
+        if start_row == end_row:
+            # Single row: simple average
+            center_col = (start_col + end_col) / 2.0
+        else:
+            # Multi-row: calculate weighted average col
+            total_cells = 0
+            weighted_col = 0
+
+            # First partial row
+            first_row_cells = cols - start_col
+            weighted_col += (start_col + cols - 1) / 2.0 * first_row_cells
+            total_cells += first_row_cells
+
+            # Full middle rows
+            middle_rows = end_row - start_row - 1
+            if middle_rows > 0:
+                weighted_col += (cols / 2.0) * cols * middle_rows
+                total_cells += cols * middle_rows
+
+            # Last partial row
+            last_row_cells = end_col + 1
+            weighted_col += (end_col / 2.0) * last_row_cells
+            total_cells += last_row_cells
+
+            center_col = weighted_col / total_cells
+
+        # Add label if there's enough space
+        if region_size >= 50:
+            ax.text(center_col, center_row, pose_name,
+                   fontsize=14, ha='center', va='center',
+                   color='white', weight='bold',
+                   bbox=dict(boxstyle='round,pad=0.3', facecolor='black', alpha=0.5))
+
+    # Set labels and title with 2x font size
+    ax.set_xlabel("Chunk index", fontsize=20)
+    ax.set_ylabel("Row", fontsize=20)
+    ax.set_title("Pose dimension importance heatmap", fontsize=24)
+
+    # Set tick label size to 2x
+    ax.tick_params(axis='both', which='major', labelsize=20)
+
+    # Add colorbar with larger font, shrink to reduce width
+    cbar = plt.colorbar(im, ax=ax, fraction=0.046, pad=0.04)
+    cbar.ax.tick_params(labelsize=20)
+    cbar.set_label("Normalized importance", fontsize=20)
+
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240, bbox_inches='tight')
+    # Also save as PDF
+    pdf_path = output_path.parent / (output_path.stem + ".pdf")
+    plt.savefig(pdf_path, bbox_inches='tight')
+    plt.close()
+
+
+def plot_cumulative(scores, output_path):
+    sorted_scores = np.sort(scores)[::-1]
+    coverage = np.cumsum(sorted_scores) / sorted_scores.sum()
+    plt.figure(figsize=(8, 4))
+    plt.plot(np.arange(1, len(sorted_scores) + 1), coverage, color="#ff7f0e")
+    plt.xlabel("Top-k dimensions")
+    plt.ylabel("Cumulative coverage")
+    plt.grid(alpha=0.3)
+    plt.tight_layout()
+    plt.savefig(output_path, dpi=240)
+    plt.close()
+    return coverage
+
+
+def save_csv(scores, normalized, output_path):
+    with open(output_path, "w", encoding="utf-8") as handle:
+        handle.write("dimension,score,normalized\n")
+        for idx, (score, norm) in enumerate(zip(scores, normalized)):
+            handle.write(f"{idx},{score:.8f},{norm:.6f}\n")
+
+
+def write_report(video, checkpoint, scores, normalized, coverage, top_indices, output_path):
+    with open(output_path, "w", encoding="utf-8") as handle:
+        handle.write("Pose ViT dimensional analysis\n")
+        handle.write("=" * 60 + "\n\n")
+        handle.write(f"Video      : {video}\n")
+        handle.write(f"Checkpoint : {checkpoint}\n")
+        handle.write(f"Total dims : {scores.shape[0]}\n\n")
+        handle.write("Top dimensions:\n")
+        for rank, dim_idx in enumerate(top_indices, 1):
+            handle.write(
+                f"{rank:02d}. dim {dim_idx:04d} | score={scores[dim_idx]:.6f} "
+                f"| normalized={normalized[dim_idx]:.4f}\n"
+            )
+
+        handle.write("\nCoverage milestones:\n")
+        for pct in (0.25, 0.5, 0.9):
+            required = np.argmax(coverage >= pct) + 1
+            handle.write(f"  - Top {required:4d} dims explain {pct:.0%} of energy\n")
+
+        handle.write("\nScores = mean absolute activation over frames/batch.\n")
+
+
+def main():
+    args = parse_args()
+    video_abs = os.path.abspath(args.video)
+    ckpt_abs = os.path.abspath(args.checkpoint)
+    out_dir = ensure_dir(args.output_dir)
+
+    if not os.path.exists(video_abs):
+        raise FileNotFoundError(f"Video not found: {video_abs}")
+    if not os.path.exists(ckpt_abs):
+        raise FileNotFoundError(f"Checkpoint not found: {ckpt_abs}")
+
+    device = prepare_device(args.device)
+    print(f"[INFO] Using device: {device}")
+
+    state_dict = load_checkpoint(ckpt_abs, device)
+    video2pose = Video2Pose(INPUT_DIMS).to(device)
+    load_video2pose_weights(video2pose, state_dict)
+    projector = PadMatch(sum(INPUT_DIMS.values()), 2048).to(device)
+    load_padmatch_weights(projector, state_dict)
+
+    frames = load_and_process_video(video_abs, args.num_frames).unsqueeze(0).to(device)  # [1, F, 3, 224, 224]
+
+    with torch.no_grad():
+        pose_dict = video2pose(frames)
+        pose_concat = torch.cat([pose_dict[ptype] for ptype in POSE_TYPE_ORDER if ptype in pose_dict], dim=-1)
+        B, F, D = pose_concat.shape
+        pose_flat = pose_concat.reshape(B * F, D)
+        pose_2048 = projector(pose_flat).view(B, F, -1)
+        np.save(out_dir / "pose_2048.npy", pose_2048.cpu().numpy())
+
+    scores, normalized = compute_importance(pose_2048)
+    save_csv(scores, normalized, out_dir / "dimension_scores.csv")
+
+    topk = min(args.topk, scores.shape[0])
+    top_indices = np.argsort(scores)[::-1][:topk]
+    plot_top_dimensions(scores, top_indices, out_dir / "top_dimensions.png")
+    plot_heatmap(normalized, out_dir / "dimension_heatmap.png")
+    coverage = plot_cumulative(scores, out_dir / "cumulative_importance.png")
+    write_report(video_abs, ckpt_abs, scores, normalized, coverage, top_indices, out_dir / "analysis_report.txt")
+
+    meta = {
+        "video": video_abs,
+        "checkpoint": ckpt_abs,
+        "num_frames": args.num_frames,
+        "device": str(device),
+        "top_dimensions": top_indices.tolist(),
+    }
+    with open(out_dir / "metadata.json", "w", encoding="utf-8") as handle:
+        json.dump(meta, handle, indent=2)
+
+    print(f"[INFO] Analysis complete. Artifacts saved to: {out_dir}")
+
+
+if __name__ == "__main__":
+    main()

SignX/index.js

@@ -0,0 +1,757 @@
+import { ControlWavePlugin } from '../plugin-loader.js';
+
+export default class SignXPlugin extends ControlWavePlugin {
+    constructor() {
+        super({
+            id: 'signx-inference',
+            name: 'SignX',
+            version: '1.0.0',
+            author: 'ControlWave Team',
+            description: '使用SignX模型进行手语识别推理',
+            needsViser: false,
+            order: 100
+        });
+
+        this.currentVideoPath = null;
+        this.isInferring = false;
+        this.videoEl = null;
+        this.logsPre = null;
+    }
+
+    async render(container) {
+        container.innerHTML = '';
+        container.className = 'tab-content active';
+
+        // 创建样式
+        const style = document.createElement('style');
+        style.textContent = `
+            .signx-root {
+                display: flex;
+                gap: 20px;
+                width: 100%;
+                height: 100%;
+                padding: 20px;
+                box-sizing: border-box;
+            }
+
+            .signx-left, .signx-right {
+                background: #fff;
+                border-radius: 12px;
+                box-shadow: 0 4px 16px rgba(15, 23, 42, 0.08);
+                padding: 20px;
+                display: flex;
+                flex-direction: column;
+            }
+
+            .signx-left {
+                flex: 1.2;
+            }
+
+            .signx-right {
+                flex: 1;
+                overflow-y: auto;
+            }
+
+            .signx-video-frame {
+                position: relative;
+                width: 100%;
+                padding-top: 56.25%;
+                background: #0f172a;
+                border-radius: 10px;
+                overflow: hidden;
+            }
+
+            .signx-video-frame video {
+                position: absolute;
+                top: 0;
+                left: 0;
+                width: 100%;
+                height: 100%;
+                object-fit: contain;
+                background: #0f172a;
+            }
+
+            .signx-video-placeholder {
+                position: absolute;
+                inset: 0;
+                display: flex;
+                align-items: center;
+                justify-content: center;
+                color: #94a3b8;
+                font-size: 15px;
+                text-align: center;
+                padding: 20px;
+            }
+
+            .signx-video-controls {
+                margin-top: 15px;
+                display: flex;
+                gap: 10px;
+                flex-direction: column;
+            }
+
+            .signx-section-title {
+                font-size: 18px;
+                font-weight: 600;
+                color: #0f172a;
+                margin-bottom: 16px;
+                padding-bottom: 10px;
+                border-bottom: 2px solid #2196F3;
+            }
+
+            .signx-form-group {
+                margin-bottom: 15px;
+            }
+
+            .signx-form-group label {
+                display: block;
+                margin-bottom: 5px;
+                font-weight: 500;
+                color: #475569;
+                font-size: 13px;
+            }
+
+            .signx-form-group select {
+                width: 100%;
+                padding: 8px 12px;
+                border: 1px solid #cbd5e1;
+                border-radius: 6px;
+                font-size: 14px;
+                background: white;
+                cursor: pointer;
+                transition: all 0.2s;
+            }
+
+            .signx-form-group select:focus {
+                outline: none;
+                border-color: #2196F3;
+                box-shadow: 0 0 0 3px rgba(33, 150, 243, 0.1);
+            }
+
+            .signx-btn {
+                width: 100%;
+                padding: 10px 20px;
+                border: none;
+                border-radius: 6px;
+                font-size: 14px;
+                font-weight: 500;
+                cursor: pointer;
+                transition: all 0.2s;
+            }
+
+            .signx-btn-primary {
+                background: #2196F3;
+                color: white;
+            }
+
+            .signx-btn-primary:hover:not(:disabled) {
+                background: #1976D2;
+                transform: translateY(-1px);
+                box-shadow: 0 4px 8px rgba(33, 150, 243, 0.3);
+            }
+
+            .signx-btn:disabled {
+                background: #cbd5e1;
+                cursor: not-allowed;
+                opacity: 0.6;
+            }
+
+            .signx-status {
+                margin-top: 20px;
+                padding: 12px 15px;
+                background: #f1f5f9;
+                border-radius: 6px;
+                border-left: 3px solid #2196F3;
+                font-size: 13px;
+                line-height: 1.6;
+            }
+
+            .signx-status.info {
+                border-left-color: #2196F3;
+                background: #E3F2FD;
+            }
+
+            .signx-status.success {
+                border-left-color: #4CAF50;
+                background: #E8F5E9;
+            }
+
+            .signx-status.error {
+                border-left-color: #f44336;
+                background: #FFEBEE;
+            }
+
+            .signx-status.loading {
+                border-left-color: #FF9800;
+                background: #FFF3E0;
+            }
+
+            .signx-logs {
+                margin-top: 20px;
+            }
+
+            .signx-logs pre {
+                background: #0f172a;
+                color: #cbd5e1;
+                border-radius: 8px;
+                padding: 14px;
+                font-size: 12px;
+                line-height: 1.6;
+                max-height: 400px;
+                overflow-y: auto;
+                white-space: pre-wrap;
+                word-wrap: break-word;
+                font-family: 'Consolas', 'Monaco', 'Courier New', monospace;
+            }
+
+            .signx-result-section {
+                margin-top: 20px;
+                padding: 15px;
+                background: #f8fafc;
+                border-radius: 8px;
+                border: 1px solid #e2e8f0;
+            }
+
+            .signx-result-section h4 {
+                margin: 0 0 10px 0;
+                color: #0f172a;
+                font-size: 14px;
+                font-weight: 600;
+            }
+
+            .signx-result-content {
+                font-family: monospace;
+                font-size: 13px;
+                line-height: 1.6;
+                color: #1e293b;
+                background: white;
+                padding: 10px;
+                border-radius: 4px;
+                max-height: 150px;
+                overflow-y: auto;
+            }
+
+            .spinner {
+                display: inline-block;
+                width: 14px;
+                height: 14px;
+                border: 2px solid #f3f3f3;
+                border-top: 2px solid #2196F3;
+                border-radius: 50%;
+                animation: spin 1s linear infinite;
+                margin-right: 8px;
+                vertical-align: middle;
+            }
+
+            @keyframes spin {
+                0% { transform: rotate(0deg); }
+                100% { transform: rotate(360deg); }
+            }
+
+            .signx-analysis-item {
+                margin-top: 20px;
+                padding-top: 20px;
+                border-top: 1px solid #e2e8f0;
+            }
+
+            .signx-analysis-item:first-child {
+                margin-top: 0;
+                padding-top: 0;
+                border-top: none;
+            }
+
+            .signx-analysis-item h5 {
+                margin: 0 0 10px 0;
+                color: #475569;
+                font-size: 13px;
+                font-weight: 600;
+            }
+
+            .signx-analysis-item img {
+                display: block;
+                max-width: 100%;
+                height: auto;
+                border: 1px solid #e2e8f0;
+                border-radius: 4px;
+                transition: transform 0.2s, box-shadow 0.2s;
+            }
+
+            .signx-analysis-item img:hover {
+                transform: scale(1.02);
+                box-shadow: 0 4px 12px rgba(0,0,0,0.1);
+            }
+
+            .signx-link-button {
+                display: inline-block;
+                padding: 8px 16px;
+                background: #2196F3;
+                color: white;
+                text-decoration: none;
+                border-radius: 4px;
+                font-size: 13px;
+                font-weight: 500;
+                transition: background 0.2s;
+            }
+
+            .signx-link-button:hover {
+                background: #1976D2;
+            }
+
+            .signx-keyframes-grid {
+                display: grid;
+                grid-template-columns: repeat(auto-fill, minmax(150px, 1fr));
+                gap: 10px;
+                margin-top: 10px;
+            }
+
+            .signx-keyframe-preview {
+                text-align: center;
+            }
+
+            .signx-keyframe-preview img {
+                width: 100%;
+                height: auto;
+                border: 1px solid #e2e8f0;
+                border-radius: 4px;
+            }
+
+            .signx-keyframe-preview span {
+                display: block;
+                margin-top: 4px;
+                font-size: 11px;
+                color: #64748b;
+            }
+        `;
+
+        container.appendChild(style);
+
+        // 创建主布局
+        const root = document.createElement('div');
+        root.className = 'signx-root';
+        root.innerHTML = `
+            <!-- 左侧：视频预览 -->
+            <div class="signx-left">
+                <h3 class="signx-section-title">视频预览</h3>
+                <div class="signx-video-frame">
+                    <video id="signx-video" controls loop></video>
+                    <div class="signx-video-placeholder" id="signx-video-placeholder">
+                        选择视频后将在此处显示预览
+                    </div>
+                </div>
+                <div class="signx-video-controls">
+                    <div class="signx-form-group">
+                        <label>视频文件夹</label>
+                        <select id="signx-video-folder">
+                            <option value="videos">videos (测试视频)</option>
+                            <option value="good_videos" selected>good_videos (优质视频)</option>
+                        </select>
+                    </div>
+                    <div class="signx-form-group">
+                        <label>视频文件</label>
+                        <select id="signx-video-file">
+                            <option value="">加载中...</option>
+                        </select>
+                    </div>
+                    <button id="signx-run-inference" class="signx-btn signx-btn-primary" disabled>
+                        开始推理
+                    </button>
+                </div>
+            </div>
+
+            <!-- 右侧：状态和结果 -->
+            <div class="signx-right">
+                <h3 class="signx-section-title">推理状态与结果</h3>
+
+                <div id="signx-status" class="signx-status info">
+                    请选择视频文件
+                </div>
+
+                <div class="signx-logs">
+                    <label style="display: block; margin-bottom: 8px; font-weight: 600; color: #475569; font-size: 13px;">
+                        推理日志
+                    </label>
+                    <pre id="signx-logs">(等待推理任务...)</pre>
+                </div>
+
+                <div id="signx-results-container"></div>
+            </div>
+        `;
+
+        container.appendChild(root);
+
+        // 保存元素引用
+        this.videoEl = document.getElementById('signx-video');
+        this.videoPlaceholder = document.getElementById('signx-video-placeholder');
+        this.logsPre = document.getElementById('signx-logs');
+
+        // 隐藏视频元素（初始状态）
+        if (this.videoEl) {
+            this.videoEl.style.display = 'none';
+        }
+
+        // 设置事件监听器
+        this.setupEventListeners();
+
+        // 初始化：加载视频列表
+        this.loadVideoList();
+    }
+
+    setupEventListeners() {
+        const folderSelect = document.getElementById('signx-video-folder');
+        const fileSelect = document.getElementById('signx-video-file');
+        const runButton = document.getElementById('signx-run-inference');
+
+        if (folderSelect) {
+            folderSelect.addEventListener('change', () => {
+                this.loadVideoList();
+            });
+        }
+
+        if (fileSelect) {
+            fileSelect.addEventListener('change', (e) => {
+                this.currentVideoPath = e.target.value;
+                const runButton = document.getElementById('signx-run-inference');
+                if (runButton) {
+                    runButton.disabled = !this.currentVideoPath;
+                }
+
+                if (this.currentVideoPath) {
+                    this.updateStatus('已选择视频: ' + this.currentVideoPath.split('/').pop(), 'info');
+                    this.updateVideo(this.currentVideoPath);
+                }
+            });
+        }
+
+        if (runButton) {
+            runButton.addEventListener('click', () => {
+                this.runInference();
+            });
+        }
+    }
+
+    async loadVideoList() {
+        const folderSelect = document.getElementById('signx-video-folder');
+        const fileSelect = document.getElementById('signx-video-file');
+
+        if (!folderSelect || !fileSelect) {
+            console.error('SignX: 找不到文件夹或文件选择器元素');
+            return;
+        }
+
+        const folder = folderSelect.value;
+        console.log(`SignX: 正在加载视频列表，文件夹=${folder}`);
+        fileSelect.innerHTML = '<option value="">加载中...</option>';
+
+        try {
+            const data = await this.sendPluginMessage('list_videos', { folder: folder });
+
+            console.log('SignX: 收到响应数据:', data);
+
+            if (data && data.status === 'success') {
+                fileSelect.innerHTML = '<option value="">请选择视频文件</option>';
+
+                if (data.videos && data.videos.length > 0) {
+                    data.videos.forEach(video => {
+                        const option = document.createElement('option');
+                        option.value = video.path;
+                        option.textContent = video.name;
+                        fileSelect.appendChild(option);
+                    });
+                    console.log(`SignX: 成功加载 ${data.videos.length} 个视频`);
+                    this.updateStatus(`找到 ${data.videos.length} 个视频文件`, 'info');
+                } else {
+                    console.warn('SignX: 视频列表为空');
+                    this.updateStatus('未找到视频文件', 'info');
+                }
+            } else {
+                console.error('SignX: 响应状态不是success:', data);
+                fileSelect.innerHTML = '<option value="">加载失败</option>';
+                this.updateStatus('加载视频列表失败: ' + (data ? data.message : '未知错误'), 'error');
+            }
+        } catch (error) {
+            console.error('SignX: 加载视频列表异常:', error);
+            fileSelect.innerHTML = '<option value="">加载失败</option>';
+            this.updateStatus('加载视频列表失败: ' + error.message, 'error');
+        }
+    }
+
+    async updateVideo(videoPath) {
+        if (!this.videoEl || !this.videoPlaceholder) {
+            return;
+        }
+
+        try {
+            // 主后端已经为插件文件夹提供静态文件服务
+            // 视频路径：/plugins/SignX/eval/tiny_test_data/videos/xxx.mp4
+            const videoUrl = videoPath.replace(/.*\/plugins\/SignX/, '/plugins/SignX');
+
+            console.log('SignX: 加载视频 URL:', videoUrl);
+
+            this.videoEl.src = videoUrl;
+            this.videoEl.style.display = 'block';
+            this.videoPlaceholder.style.display = 'none';
+
+            this.videoEl.onloadeddata = () => {
+                console.log('SignX: 视频加载成功');
+            };
+
+            this.videoEl.onerror = () => {
+                this.videoEl.style.display = 'none';
+                this.videoPlaceholder.style.display = 'flex';
+                this.videoPlaceholder.textContent = '视频加载失败';
+                console.error('SignX: 视频加载失败，URL:', videoUrl);
+            };
+
+            this.videoEl.load();
+        } catch (error) {
+            this.videoEl.style.display = 'none';
+            this.videoPlaceholder.style.display = 'flex';
+            this.videoPlaceholder.textContent = '视频加载失败';
+            console.error('SignX: 视频加载异常:', error);
+        }
+    }
+
+    async runInference() {
+        if (!this.currentVideoPath || this.isInferring) {
+            return;
+        }
+
+        this.isInferring = true;
+        const runButton = document.getElementById('signx-run-inference');
+        const originalButtonText = runButton.textContent;
+
+        runButton.disabled = true;
+        runButton.innerHTML = '<span class="spinner"></span>推理中...';
+
+        this.updateStatus('正在运行 SignX 推理，这可能需要几分钟...', 'loading');
+        this.logsPre.textContent = '正在启动推理...\n';
+
+        try {
+            const data = await this.sendPluginMessage('run_inference', {
+                video_path: this.currentVideoPath
+            });
+
+            if (data.status === 'success') {
+                this.updateStatus('推理完成！', 'success');
+                this.displayResults(data);
+            } else {
+                this.updateStatus('推理失败: ' + data.message, 'error');
+                if (data.logs) {
+                    this.logsPre.textContent = data.logs.join('\n');
+                }
+            }
+        } catch (error) {
+            console.error('SignX: 推理失败:', error);
+            this.updateStatus('推理失败: ' + error.message, 'error');
+            this.logsPre.textContent = error.message;
+        } finally {
+            this.isInferring = false;
+            runButton.disabled = false;
+            runButton.textContent = originalButtonText;
+        }
+    }
+
+    displayResults(data) {
+        const resultsContainer = document.getElementById('signx-results-container');
+
+        if (!resultsContainer) return;
+
+        resultsContainer.innerHTML = '';
+
+        // 显示日志
+        if (data.logs && data.logs.length > 0) {
+            this.logsPre.textContent = data.logs.join('\n');
+        }
+
+        // 显示识别结果
+        if (data.output_clean) {
+            const resultItem = document.createElement('div');
+            resultItem.className = 'signx-result-section';
+            resultItem.innerHTML = `
+                <h4>识别结果 (Gloss序列)</h4>
+                <div class="signx-result-content">${this.escapeHtml(data.output_clean)}</div>
+            `;
+            resultsContainer.appendChild(resultItem);
+        }
+
+        // 显示执行时间
+        if (data.execution_time) {
+            const timeItem = document.createElement('div');
+            timeItem.className = 'signx-result-section';
+            timeItem.innerHTML = `
+                <h4>执行时间</h4>
+                <div class="signx-result-content">${data.execution_time.toFixed(2)} 秒</div>
+            `;
+            resultsContainer.appendChild(timeItem);
+        }
+
+        // 显示分析图片和文件
+        if (data.analysis_images && data.analysis_images.length > 0) {
+            const analysisSection = document.createElement('div');
+            analysisSection.className = 'signx-result-section';
+            analysisSection.innerHTML = `<h4>注意力分析可视化</h4>`;
+
+            data.analysis_images.forEach(file => {
+                if (file.type === 'image') {
+                    // 显示图片
+                    const imgContainer = document.createElement('div');
+                    imgContainer.className = 'signx-analysis-item';
+                    imgContainer.innerHTML = `
+                        <h5>${file.name}</h5>
+                        <img src="${file.url}" alt="${file.name}"
+                             onclick="window.open('${file.url}', '_blank')"
+                             style="cursor: pointer; max-width: 100%; border-radius: 4px; margin-top: 8px;">
+                    `;
+                    analysisSection.appendChild(imgContainer);
+                } else if (file.type === 'html') {
+                    // 显示HTML链接
+                    const linkContainer = document.createElement('div');
+                    linkContainer.className = 'signx-analysis-item';
+                    linkContainer.innerHTML = `
+                        <h5>${file.name}</h5>
+                        <a href="${file.url}" target="_blank" class="signx-link-button">
+                            打开交互式可视化 →
+                        </a>
+                    `;
+                    analysisSection.appendChild(linkContainer);
+                } else if (file.type === 'keyframes') {
+                    // 显示关键帧预览
+                    const keyframesContainer = document.createElement('div');
+                    keyframesContainer.className = 'signx-analysis-item';
+                    keyframesContainer.innerHTML = `
+                        <h5>${file.name} (共${file.count}个)</h5>
+                    `;
+
+                    if (file.previews && file.previews.length > 0) {
+                        const previewGrid = document.createElement('div');
+                        previewGrid.className = 'signx-keyframes-grid';
+
+                        file.previews.forEach(preview => {
+                            const imgDiv = document.createElement('div');
+                            imgDiv.className = 'signx-keyframe-preview';
+                            imgDiv.innerHTML = `
+                                <img src="${preview.url}" alt="${preview.name}"
+                                     onclick="window.open('${preview.url}', '_blank')"
+                                     style="cursor: pointer;">
+                                <span>${preview.name}</span>
+                            `;
+                            previewGrid.appendChild(imgDiv);
+                        });
+
+                        keyframesContainer.appendChild(previewGrid);
+                    }
+
+                    analysisSection.appendChild(keyframesContainer);
+                }
+            });
+
+            resultsContainer.appendChild(analysisSection);
+        }
+
+        // 显示详细分析目录
+        if (data.analysis_dir) {
+            const pathItem = document.createElement('div');
+            pathItem.className = 'signx-result-section';
+            pathItem.innerHTML = `
+                <h4>完整分析路径</h4>
+                <div class="signx-result-content">
+                    <code>${data.analysis_dir}</code>
+                </div>
+            `;
+            resultsContainer.appendChild(pathItem);
+        }
+    }
+
+    updateStatus(message, type = 'info') {
+        const statusBox = document.getElementById('signx-status');
+        if (!statusBox) return;
+
+        statusBox.className = 'signx-status ' + type;
+
+        if (type === 'loading') {
+            statusBox.innerHTML = `<span class="spinner"></span>${message}`;
+        } else {
+            statusBox.textContent = message;
+        }
+    }
+
+    escapeHtml(text) {
+        const div = document.createElement('div');
+        div.textContent = text;
+        return div.innerHTML;
+    }
+
+    /**
+     * 发送插件消息并等待响应
+     */
+    async sendPluginMessage(action, data) {
+        if (!window.controlWaveWebSocket || !window.controlWaveWebSocket.isConnected) {
+            throw new Error('WebSocket 未连接');
+        }
+
+        console.log(`SignX: 发送请求 action=${action}`, data);
+
+        // 对于推理操作，使用自定义超时（10分钟）
+        if (action === 'run_inference') {
+            return this.sendInferenceRequest(data);
+        }
+
+        const response = await window.controlWaveWebSocket.sendRequest('signx', {
+            action: action,
+            data: data
+        });
+
+        console.log('SignX: 收到响应', response);
+        return response;
+    }
+
+    /**
+     * 发送推理请求（10分钟超时）
+     */
+    async sendInferenceRequest(data) {
+        const REQUEST_TIMEOUT_MS = 600000; // 10分钟
+        const wsManager = window.controlWaveWebSocket.manager;
+
+        return new Promise((resolve, reject) => {
+            const requestId = `signx_${Date.now()}_${Math.random().toString(36).substr(2, 9)}`;
+
+            const message = {
+                type: 'plugin_message',
+                plugin: 'signx',
+                action: 'run_inference',
+                requestId: requestId,
+                data: data
+            };
+
+            const responseHandler = (event) => {
+                try {
+                    const response = JSON.parse(event.data);
+
+                    if (response.type === 'plugin_response' &&
+                        response.requestId === requestId) {
+                        wsManager.off('message', responseHandler);
+                        clearTimeout(timeoutId);
+
+                        console.log('SignX: 推理响应:', response);
+
+                        if (response.status === 'success') {
+                            resolve(response.response || response);
+                        } else {
+                            reject(new Error(response.message || 'Inference error'));
+                        }
+                    }
+                } catch (error) {
+                    // 忽略非JSON消息
+                }
+            };
+
+            wsManager.on('message', responseHandler);
+            wsManager.send(message);
+
+            const timeoutId = setTimeout(() => {
+                wsManager.off('message', responseHandler);
+                reject(new Error('推理超时（10分钟）'));
+            }, REQUEST_TIMEOUT_MS);
+        });
+    }
+}

SignX/inference_output.txt

@@ -1 +0,0 @@
-#IF FRIEND GROUP/TOGE@@ TH@@ E@@ R DEPART PARTY IX-1p JO@@ I@@ N IX-1p

SignX/inference_output.txt.clean

@@ -1 +0,0 @@
-#IF FRIEND GROUP/TOGETHER DEPART PARTY IX-1p JOIN IX-1p

SignX/pose_vit_feature_analysis_3381121/analysis_report.txt

@@ -0,0 +1,47 @@
+Pose ViT dimensional analysis
+============================================================
+
+Video      : /common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/3381121.mp4
+Checkpoint : /common/users/sf895/output/huggingface_asllrp_repo/SignX/smkd/pretrained/video2text_checkpoint_epoch_14.pth
+Total dims : 2048
+
+Top dimensions:
+01. dim 1192 | score=3.215288 | normalized=1.0000
+02. dim 1750 | score=3.139674 | normalized=0.9765
+03. dim 0580 | score=3.086235 | normalized=0.9599
+04. dim 1284 | score=3.076663 | normalized=0.9569
+05. dim 0887 | score=3.028741 | normalized=0.9420
+06. dim 0560 | score=2.881777 | normalized=0.8963
+07. dim 1863 | score=2.869644 | normalized=0.8925
+08. dim 1531 | score=2.851681 | normalized=0.8869
+09. dim 0747 | score=2.762800 | normalized=0.8593
+10. dim 1233 | score=2.757569 | normalized=0.8576
+11. dim 1312 | score=2.722223 | normalized=0.8466
+12. dim 0529 | score=2.714753 | normalized=0.8443
+13. dim 0090 | score=2.714646 | normalized=0.8443
+14. dim 0394 | score=2.684420 | normalized=0.8349
+15. dim 1249 | score=2.673448 | normalized=0.8315
+16. dim 1647 | score=2.625972 | normalized=0.8167
+17. dim 0294 | score=2.610163 | normalized=0.8118
+18. dim 0457 | score=2.591562 | normalized=0.8060
+19. dim 0263 | score=2.585209 | normalized=0.8040
+20. dim 0035 | score=2.580578 | normalized=0.8026
+21. dim 1074 | score=2.559113 | normalized=0.7959
+22. dim 1043 | score=2.549468 | normalized=0.7929
+23. dim 1714 | score=2.543901 | normalized=0.7912
+24. dim 0948 | score=2.543758 | normalized=0.7911
+25. dim 1903 | score=2.533813 | normalized=0.7881
+26. dim 1154 | score=2.530563 | normalized=0.7870
+27. dim 0695 | score=2.530224 | normalized=0.7869
+28. dim 0124 | score=2.526446 | normalized=0.7858
+29. dim 0150 | score=2.513446 | normalized=0.7817
+30. dim 1269 | score=2.508722 | normalized=0.7802
+31. dim 1897 | score=2.506876 | normalized=0.7797
+32. dim 1471 | score=2.490480 | normalized=0.7746
+
+Coverage milestones:
+  - Top  188 dims explain 25% of energy
+  - Top  466 dims explain 50% of energy
+  - Top 1266 dims explain 90% of energy
+
+Scores = mean absolute activation over frames/batch.

SignX/pose_vit_feature_analysis_3381121/dimension_scores.csv

@@ -0,0 +1,2049 @@
+dimension,score,normalized
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SignX/pose_vit_feature_analysis_3381121/metadata.json

@@ -0,0 +1,40 @@
+{
+  "video": "/common/users/sf895/output/huggingface_asllrp_repo/SignX/eval/tiny_test_data/good_videos/3381121.mp4",
+  "checkpoint": "/common/users/sf895/output/huggingface_asllrp_repo/SignX/smkd/pretrained/video2text_checkpoint_epoch_14.pth",
+  "num_frames": 32,
+  "device": "cuda",
+  "top_dimensions": [
+    1192,
+    1750,
+    580,
+    1284,
+    887,
+    560,
+    1863,
+    1531,
+    747,
+    1233,
+    1312,
+    529,
+    90,
+    394,
+    1249,
+    1647,
+    294,
+    457,
+    263,
+    35,
+    1074,
+    1043,
+    1714,
+    948,
+    1903,
+    1154,
+    695,
+    124,
+    150,
+    1269,
+    1897,
+    1471
+  ]
+}

SignX/{detailed_prediction_20260101_150706/632051/frame_alignment_short.png → pose_vit_feature_analysis_3381121/pose_2048.npy}

@@ -1,3 +1,3 @@
 version https://git-lfs.github.com/spec/v1
-oid sha256:0cc1e163cc2cdff156c73e48136bef57a7f8372b89c8242572b38c5c14cb17bb
-size 152046
+oid sha256:44137cba99895a0b4dc53b0e6c463b73d42be0906feb1c72dc5be16e85c3b83b
+size 262272

SignX/viser_backend.py

@@ -0,0 +1,278 @@
+#!/usr/bin/env python3
+"""
+SignX 插件的后端处理器
+通过 WebSocket plugin_message 机制处理视频列表和推理请求
+"""
+
+import os
+import subprocess
+import time
+from pathlib import Path
+from typing import Dict, List, Any
+
+# SignX 插件路径
+SIGNX_PLUGIN_DIR = Path(__file__).parent
+INFERENCE_SCRIPT = SIGNX_PLUGIN_DIR / "inference.sh"
+TEST_DATA_DIR = SIGNX_PLUGIN_DIR / "eval" / "tiny_test_data"
+
+
+# 使用 PluginBackend 作为类名，这样会被主程序自动识别
+class PluginBackend:
+    """SignX 后端处理类"""
+
+    def __init__(self, viser_server=None):
+        """
+        初始化 SignX 后端
+
+        Args:
+            viser_server: Viser 服务器实例（可选）
+        """
+        self.viser_server = viser_server
+        self.name = "signx"  # 插件标识符（用于 WebSocket 通信）
+        print("✅ SignX 后端已初始化")
+
+    def handle_message(self, message: Dict[str, Any]) -> Dict[str, Any]:
+        """
+        处理来自前端的 WebSocket 消息
+
+        Args:
+            message: 包含 plugin, action, data 的消息字典
+
+        Returns:
+            处理结果字典
+        """
+        action = message.get('action', '')
+        data = message.get('data', {})
+
+        print(f"📨 SignX 收到消息: action={action}")
+
+        # 根据 action 分发到不同的处理函数
+        if action == 'list_videos':
+            return self.list_videos(data)
+        elif action == 'run_inference':
+            return self.run_inference(data)
+        else:
+            return {
+                'status': 'error',
+                'message': f'未知的 action: {action}'
+            }
+
+    def list_videos(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """列出指定文件夹中的视频文件"""
+        folder = data.get('folder', 'good_videos')
+        folder_path = TEST_DATA_DIR / folder
+
+        if not folder_path.exists():
+            return {
+                "status": "error",
+                "message": f"文件夹不存在: {folder}"
+            }
+
+        videos = []
+        for video_file in sorted(folder_path.glob("*.mp4")):
+            videos.append({
+                "name": video_file.name,
+                "path": str(video_file)
+            })
+
+        print(f"📂 找到 {len(videos)} 个视频文件")
+
+        return {
+            "status": "success",
+            "videos": videos,
+            "count": len(videos)
+        }
+
+    def run_inference(self, data: Dict[str, Any]) -> Dict[str, Any]:
+        """运行 SignX 推理"""
+        video_path = data.get('video_path', '')
+
+        # 检查视频文件是否存在
+        if not os.path.exists(video_path):
+            return {
+                "status": "error",
+                "message": f"视频文件不存在: {video_path}"
+            }
+
+        # 检查推理脚本是否存在
+        if not INFERENCE_SCRIPT.exists():
+            return {
+                "status": "error",
+                "message": f"推理脚本不存在: {INFERENCE_SCRIPT}"
+            }
+
+        # 生成输出文件路径（使用时间戳避免冲突）
+        timestamp = int(time.time())
+        output_file = SIGNX_PLUGIN_DIR / f"inference_output_{timestamp}.txt"
+        output_file_clean = SIGNX_PLUGIN_DIR / f"inference_output_{timestamp}.txt.clean"
+
+        print(f"🎬 开始推理: {video_path}")
+
+        try:
+            # 运行推理脚本，实时收集输出
+            start_time = time.time()
+
+            # 切换到 SignX 目录并运行脚本
+            process = subprocess.Popen(
+                ["bash", str(INFERENCE_SCRIPT), video_path, str(output_file)],
+                cwd=str(SIGNX_PLUGIN_DIR),
+                stdout=subprocess.PIPE,
+                stderr=subprocess.STDOUT,  # 合并 stderr 到 stdout
+                text=True,
+                bufsize=1  # 行缓冲
+            )
+
+            # 收集所有输出
+            log_lines = []
+            try:
+                for line in process.stdout:
+                    line = line.rstrip()
+                    if line:
+                        log_lines.append(line)
+                        print(f"  {line}")  # 同时打印到控制台
+
+                # 等待进程结束
+                process.wait(timeout=600)  # 10分钟超时
+
+            except subprocess.TimeoutExpired:
+                process.kill()
+                print("⏱️ 推理超时")
+                return {
+                    "status": "error",
+                    "message": "推理超时（超过10分钟）",
+                    "logs": log_lines
+                }
+
+            execution_time = time.time() - start_time
+
+            # 检查是否成功
+            if process.returncode != 0:
+                print(f"❌ 推理失败: returncode={process.returncode}")
+                return {
+                    "status": "error",
+                    "message": "推理失败",
+                    "returncode": process.returncode,
+                    "logs": log_lines
+                }
+
+            # 读取输出文件
+            output_text = ""
+            output_clean = ""
+
+            if output_file.exists():
+                with open(output_file, 'r', encoding='utf-8') as f:
+                    output_text = f.read()
+
+            if output_file_clean.exists():
+                with open(output_file_clean, 'r', encoding='utf-8') as f:
+                    output_clean = f.read()
+
+            # 查找详细分析目录
+            analysis_dir = None
+            analysis_images = []
+
+            # 查找 detailed_prediction_* 目录
+            for item in SIGNX_PLUGIN_DIR.iterdir():
+                if item.is_dir() and item.name.startswith("detailed_prediction_"):
+                    # 使用最新的分析目录
+                    if analysis_dir is None or item.stat().st_mtime > analysis_dir.stat().st_mtime:
+                        analysis_dir = item
+
+            if analysis_dir:
+                # 查找分析图片
+                sample_dirs = [d for d in analysis_dir.iterdir() if d.is_dir()]
+                if sample_dirs:
+                    # 使用第一个样本目录
+                    sample_dir = sample_dirs[0]
+
+                    # 收集所有分析图片和HTML文件
+                    analysis_files = []
+
+                    # 主要分析图片
+                    image_files = [
+                        ("attention_heatmap.png", "注意力权重热图"),
+                        ("frame_alignment.png", "词-帧对齐图 (完整)"),
+                        ("frame_alignment_short.png", "词-帧对齐图 (简化)"),
+                        ("gloss_to_frames.png", "Gloss-视频帧对应图"),
+                    ]
+
+                    for img_file, display_name in image_files:
+                        img_path = sample_dir / img_file
+                        if img_path.exists():
+                            # 返回相对于插件目录的路径，用于Web访问
+                            rel_path = img_path.relative_to(SIGNX_PLUGIN_DIR)
+                            # 构造Web URL
+                            web_url = f"/plugins/SignX/{rel_path}"
+                            analysis_files.append({
+                                "name": display_name,
+                                "type": "image",
+                                "url": web_url,
+                                "filename": img_file
+                            })
+
+                    # 交互式HTML文件
+                    html_path = sample_dir / "interactive_alignment.html"
+                    if html_path.exists():
+                        rel_path = html_path.relative_to(SIGNX_PLUGIN_DIR)
+                        web_url = f"/plugins/SignX/{rel_path}"
+                        analysis_files.append({
+                            "name": "交互式对齐可视化",
+                            "type": "html",
+                            "url": web_url,
+                            "filename": "interactive_alignment.html"
+                        })
+
+                    # 关键帧文件夹
+                    keyframes_dir = sample_dir / "attention_keyframes"
+                    if keyframes_dir.exists():
+                        # 读取索引文件
+                        index_file = keyframes_dir / "keyframes_index.txt"
+                        keyframe_info = None
+                        if index_file.exists():
+                            with open(index_file, 'r', encoding='utf-8') as f:
+                                keyframe_info = f.read()
+
+                        rel_path = keyframes_dir.relative_to(SIGNX_PLUGIN_DIR)
+                        web_url = f"/plugins/SignX/{rel_path}"
+
+                        # 收集前几个关键帧作为预览
+                        keyframe_previews = []
+                        for kf in sorted(keyframes_dir.glob("keyframe_*.png"))[:6]:  # 只取前6个
+                            kf_rel = kf.relative_to(SIGNX_PLUGIN_DIR)
+                            keyframe_previews.append({
+                                "url": f"/plugins/SignX/{kf_rel}",
+                                "name": kf.stem
+                            })
+
+                        analysis_files.append({
+                            "name": "注意力关键帧",
+                            "type": "keyframes",
+                            "url": web_url,
+                            "info": keyframe_info,
+                            "previews": keyframe_previews,
+                            "count": len(list(keyframes_dir.glob("keyframe_*.png")))
+                        })
+
+                    analysis_images = analysis_files
+
+            print(f"✅ 推理完成，耗时 {execution_time:.2f} 秒")
+
+            return {
+                "status": "success",
+                "output": output_text,
+                "output_clean": output_clean,
+                "execution_time": execution_time,
+                "analysis_dir": str(analysis_dir) if analysis_dir else None,
+                "analysis_images": analysis_images,
+                "video_path": video_path,
+                "logs": log_lines  # 返回完整日志
+            }
+
+        except Exception as e:
+            print(f"❌ 推理出错: {e}")
+            import traceback
+            traceback.print_exc()
+            return {
+                "status": "error",
+                "message": f"推理过程出错: {str(e)}"
+            }