Victor Phan
1299 lines
62 KiB
HTML
Executable File
1299 lines
62 KiB
HTML
Executable File
<!DOCTYPE html>
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<html lang="vi">
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<head>
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<meta charset="UTF-8">
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<meta name="viewport" content="width=device-width, initial-scale=1.0">
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<title>Cloud Removal Training - Deep Learning</title>
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<script src="https://cdn.jsdelivr.net/npm/chart.js@4.4.0/dist/chart.umd.min.js"></script>
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<style>
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* {
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margin: 0;
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padding: 0;
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box-sizing: border-box;
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}
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body {
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font-family: -apple-system, BlinkMacSystemFont, 'Segoe UI', 'Roboto', 'Oxygen', 'Ubuntu', 'Cantarell', sans-serif;
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background: linear-gradient(135deg, #667eea 0%, #764ba2 100%);
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background-attachment: fixed;
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min-height: 100vh;
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padding: 20px;
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}
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.container {
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max-width: 1200px;
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margin: 0 auto;
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background: rgba(255, 255, 255, 0.95);
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backdrop-filter: blur(20px);
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border-radius: 24px;
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box-shadow: 0 25px 80px rgba(0,0,0,0.2), 0 0 0 1px rgba(255,255,255,0.1);
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overflow: hidden;
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}
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.header {
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color: white;
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position: relative;
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overflow: hidden;
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animation: headerGlow 8s ease-in-out infinite;
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@keyframes headerGlow {
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input[type="text"]:hover,
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input[type="text"]:focus,
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.btn {
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.btn::before {
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.btn:hover::before {
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.btn-primary {
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color: white;
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box-shadow: 0 4px 15px rgba(102, 126, 234, 0.3);
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.btn-primary:hover {
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.btn-secondary {
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.btn-secondary:hover {
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.model-list {
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gap: 20px;
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.model-card {
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border: 1px solid rgba(0,0,0,0.08);
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padding: 24px;
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transition: all 0.3s cubic-bezier(0.4, 0, 0.2, 1);
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box-shadow: 0 2px 8px rgba(0,0,0,0.04);
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box-shadow: 0 8px 25px rgba(102, 126, 234, 0.15);
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transform: translateY(-5px);
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padding: 6px 16px;
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border-radius: 20px;
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font-size: 0.85em;
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font-weight: 600;
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margin-top: 12px;
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.status-success {
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box-shadow: 0 2px 8px rgba(21, 87, 36, 0.2);
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}
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.status-training {
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box-shadow: 0 2px 8px rgba(133, 100, 4, 0.2);
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}
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.status-error {
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background: linear-gradient(135deg, #f8d7da 0%, #f5c6cb 100%);
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box-shadow: 0 2px 8px rgba(114, 28, 36, 0.2);
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.progress-bar {
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height: 32px;
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background: linear-gradient(to right, #e5e7eb, #f3f4f6);
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border-radius: 16px;
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overflow: hidden;
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margin: 20px 0;
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box-shadow: inset 0 2px 8px rgba(0,0,0,0.08);
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background-size: 200% 100%;
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animation: shimmer 2s infinite;
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transition: width 0.3s;
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display: flex;
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align-items: center;
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justify-content: center;
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color: white;
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font-size: 0.9em;
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box-shadow: 0 2px 8px rgba(102, 126, 234, 0.4);
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@keyframes shimmer {
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0% { background-position: 200% 0; }
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100% { background-position: -200% 0; }
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transition: all 0.3s ease;
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display: grid;
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grid-template-columns: 1fr 1fr;
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gap: 20px;
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@media (max-width: 768px) {
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.grid-2 {
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grid-template-columns: 1fr;
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}
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.model-list {
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grid-template-columns: 1fr;
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}
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}
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.logs {
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background: #1e1e1e;
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color: #d4d4d4;
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padding: 20px;
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border-radius: 12px;
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font-family: 'Courier New', monospace;
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font-size: 0.9em;
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max-height: 400px;
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overflow-y: auto;
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margin-top: 20px;
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box-shadow: inset 0 2px 10px rgba(0,0,0,0.3);
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}
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.logs .log-entry {
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margin: 5px 0;
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padding: 4px 0;
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}
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.logs .log-info {
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color: #4ec9b0;
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}
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.logs .log-warning {
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color: #dcdcaa;
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}
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.logs .log-error {
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color: #f48771;
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}
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</style>
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</head>
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<body>
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<div class="container">
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<div class="header">
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<h1>🌥️ Cloud Removal Training</h1>
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<p>Train Deep Learning Models để khử mây từ ảnh Sentinel-2</p>
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</div>
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<div class="nav">
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<a href="/">← Trang chủ</a>
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<a href="/training">Land Classification</a>
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<a href="/prediction">Prediction</a>
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<a href="#models">Models đã train</a>
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</div>
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<div class="content">
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<!-- Info Section -->
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<div class="section">
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<div class="info-box">
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<strong>📚 Dataset:</strong> SEN12MS-CR (Sentinel-12 Multi-Seasonal Cloud Removal)<br>
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<strong>🏗️ Architecture:</strong> U-Net với skip connections<br>
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<strong>📊 Input:</strong> S2 cloudy (4 bands) + S1 radar (2 bands) = 6 channels<br>
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<strong>🎯 Output:</strong> S2 clean (4 bands)<br>
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<strong>⏱️ Training time:</strong> ~2-3 hours (GPU) / ~20-30 hours (CPU)
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</div>
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</div>
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<!-- Training Configuration -->
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<div class="section">
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<h2 class="section-title">⚙️ Cấu hình Training</h2>
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<div class="card">
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<form id="trainingForm">
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<div class="grid-2">
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<div class="form-group">
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<label>🏗️ Model Architecture</label>
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<select id="modelArchitecture" required>
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<option value="unet">U-Net (Classic CNN)</option>
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<option value="crgan">CR-GAN (Cloud Removal GAN)</option>
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<option value="spagan">SpA-GAN (Spatial Attention GAN)</option>
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<option value="glfcr">GLF-CR (Global-Local Fusion)</option>
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<option value="sen12mscr">SEN12MS-CR (Multi-modal)</option>
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<option value="rsdehazenet">RSDehazeNet (Remote Sensing)</option>
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<option value="cloudnet">Cloud-Net (Encoder-Decoder)</option>
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<option value="dsen2cr">DSen2-CR (Deep Sentinel-2)</option>
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</select>
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<small style="color: #6c757d;">Chọn kiến trúc deep learning cho cloud removal</small>
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</div>
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<div class="form-group">
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<label>🏷️ Model Name</label>
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<input type="text" id="modelName" value="cloud_removal_unet" required>
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<small style="color: #6c757d;">Tên model để lưu</small>
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</div>
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<div class="form-group">
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<label>📂 Data Directory</label>
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<input type="text" id="dataDir" value="winter_dataset" required>
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<small style="color: #6c757d;">Thư mục chứa dữ liệu SEN12MS-CR</small>
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</div>
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<div class="form-group">
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<label>📦 Batch Size</label>
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<input type="number" id="batchSize" value="8" min="1" max="32" required>
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<small style="color: #6c757d;">Giảm xuống 4 hoặc 2 nếu GPU hết RAM</small>
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</div>
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<div class="form-group">
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<label>🔄 Number of Epochs</label>
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<input type="number" id="numEpochs" value="50" min="1" max="200" required>
|
|
<small style="color: #6c757d;">Số lượng epochs training</small>
|
|
</div>
|
|
|
|
<div class="form-group">
|
|
<label>📈 Learning Rate</label>
|
|
<input type="number" id="learningRate" value="0.0001" step="0.00001" min="0.00001" max="0.01" required>
|
|
<small style="color: #6c757d;">Learning rate (default: 1e-4)</small>
|
|
</div>
|
|
|
|
<div class="form-group">
|
|
<div class="checkbox-group">
|
|
<input type="checkbox" id="useS1" checked>
|
|
<label for="useS1">📡 Use Sentinel-1 (Radar Data)</label>
|
|
</div>
|
|
<small style="color: #6c757d;">Sử dụng dữ liệu radar (VV, VH) để cải thiện kết quả</small>
|
|
</div>
|
|
|
|
<div class="form-group">
|
|
<div class="checkbox-group">
|
|
<input type="checkbox" id="useGPU" checked>
|
|
<label for="useGPU">🚀 Use GPU</label>
|
|
</div>
|
|
<small style="color: #6c757d;">Sử dụng GPU để training nhanh hơn</small>
|
|
</div>
|
|
</div>
|
|
|
|
<div class="form-group" style="margin-top: 20px;">
|
|
<button type="submit" class="btn btn-primary">🚀 Start Training</button>
|
|
<button type="button" class="btn btn-secondary" onclick="refreshModels()">🔄 Refresh Models</button>
|
|
</div>
|
|
</form>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Training Status -->
|
|
<div class="section" id="trainingStatus" style="display: none;">
|
|
<h2 class="section-title">📊 Training Status - Real-time Baseline PSNR Tracking</h2>
|
|
<div class="card">
|
|
<div id="statusMessage"></div>
|
|
|
|
<!-- Metrics Grid -->
|
|
<div class="grid-2" style="margin-top: 20px;">
|
|
<div class="card" style="text-align: center; background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); color: white;">
|
|
<h4 style="margin-bottom: 10px;">📈 Model PSNR</h4>
|
|
<div style="font-size: 2.5em; font-weight: bold;" id="modelPSNR">--</div>
|
|
<small>dB (higher is better)</small>
|
|
</div>
|
|
<div class="card" style="text-align: center; background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); color: white;">
|
|
<h4 style="margin-bottom: 10px;">📉 Baseline PSNR</h4>
|
|
<div style="font-size: 2.5em; font-weight: bold;" id="baselinePSNR">--</div>
|
|
<small>dB (cận dưới - không làm gì)</small>
|
|
</div>
|
|
<div class="card" style="text-align: center; background: linear-gradient(135deg, #43e97b 0%, #38f9d7 100%); color: white;">
|
|
<h4 style="margin-bottom: 10px;">⚡ Improvement</h4>
|
|
<div style="font-size: 2.5em; font-weight: bold;" id="improvement">--</div>
|
|
<small>dB (Model - Baseline)</small>
|
|
</div>
|
|
<div class="card" style="text-align: center; background: linear-gradient(135deg, #4facfe 0%, #00f2fe 100%); color: white;">
|
|
<h4 style="margin-bottom: 10px;">🎯 Progress</h4>
|
|
<div style="font-size: 2.5em; font-weight: bold;" id="epochProgress">0/0</div>
|
|
<small>Epochs completed</small>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Progress Bar -->
|
|
<div class="progress-bar" style="margin-top: 20px;">
|
|
<div class="progress-fill" id="progressBar" style="width: 0%;">0%</div>
|
|
</div>
|
|
|
|
<!-- Performance Range Cards (Best & Worst) -->
|
|
<div style="display: grid; grid-template-columns: 1fr 1fr; gap: 20px; margin-top: 20px;">
|
|
<!-- Best PSNR Checkpoint -->
|
|
<div id="bestPSNRCard" style="background: linear-gradient(135deg, #FFD700 0%, #FFA500 100%); padding: 20px; border-radius: 12px; box-shadow: 0 8px 24px rgba(255, 215, 0, 0.3); display: none;">
|
|
<h3 style="text-align: center; margin-bottom: 15px; color: white; text-shadow: 0 2px 4px rgba(0,0,0,0.2);">🏆 Best PSNR (Cao Nhất)</h3>
|
|
<div class="grid-2" style="gap: 15px;">
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Best Model PSNR</div>
|
|
<div style="font-size: 2em; font-weight: bold; color: #667eea;" id="bestModelPSNR">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">At Epoch</div>
|
|
<div style="font-size: 2em; font-weight: bold; color: #764ba2;" id="bestEpoch">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">epoch</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Train Loss</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #43e97b;" id="bestTrainLoss">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">MSE</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Val Loss</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #f5576c;" id="bestValLoss">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">MSE</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Baseline PSNR</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #f093fb;" id="bestBaselinePSNR">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB (cận dưới)</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Improvement</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #4facfe;" id="bestImprovement">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB (vs baseline)</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Worst PSNR Checkpoint -->
|
|
<div id="worstPSNRCard" style="background: linear-gradient(135deg, #667eea 0%, #764ba2 100%); padding: 20px; border-radius: 12px; box-shadow: 0 8px 24px rgba(102, 126, 234, 0.3); display: none;">
|
|
<h3 style="text-align: center; margin-bottom: 15px; color: white; text-shadow: 0 2px 4px rgba(0,0,0,0.2);">📉 Worst PSNR (Thấp Nhất)</h3>
|
|
<div class="grid-2" style="gap: 15px;">
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Worst Model PSNR</div>
|
|
<div style="font-size: 2em; font-weight: bold; color: #667eea;" id="worstModelPSNR">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB (cận dưới)</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">At Epoch</div>
|
|
<div style="font-size: 2em; font-weight: bold; color: #764ba2;" id="worstEpoch">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">epoch</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Train Loss</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #43e97b;" id="worstTrainLoss">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">MSE</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Val Loss</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #f5576c;" id="worstValLoss">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">MSE</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Baseline PSNR</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #f093fb;" id="worstBaselinePSNR">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Gap from Baseline</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #4facfe;" id="worstImprovement">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Performance Range Summary -->
|
|
<div id="performanceRangeCard" style="margin-top: 20px; background: linear-gradient(135deg, #f093fb 0%, #f5576c 100%); padding: 20px; border-radius: 12px; box-shadow: 0 8px 24px rgba(245, 87, 108, 0.3); display: none;">
|
|
<h3 style="text-align: center; margin-bottom: 15px; color: white; text-shadow: 0 2px 4px rgba(0,0,0,0.2);">📊 Performance Range & Baseline</h3>
|
|
<div style="display: grid; grid-template-columns: repeat(4, 1fr); gap: 15px;">
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">PSNR Range</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #667eea;" id="psnrRange">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB (max - min)</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Avg PSNR</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #43e97b;" id="avgPSNR">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Stability</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #f093fb;" id="stability">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">%</div>
|
|
</div>
|
|
<div style="background: rgba(255,255,255,0.95); padding: 15px; border-radius: 8px; text-align: center;">
|
|
<div style="font-size: 0.85em; color: #666; margin-bottom: 5px;">Baseline PSNR</div>
|
|
<div style="font-size: 1.5em; font-weight: bold; color: #ff6b6b;" id="baselinePSNRDisplay">--</div>
|
|
<div style="font-size: 0.75em; color: #888; margin-top: 3px;">dB (cận dưới)</div>
|
|
</div>
|
|
</div>
|
|
<div id="baselineComparisonText" style="margin-top: 15px; padding: 12px; background: rgba(255,255,255,0.9); border-radius: 6px; text-align: center; font-size: 0.9em; font-weight: 600;">
|
|
--
|
|
</div>
|
|
</div>
|
|
|
|
<!-- PSNR Chart -->
|
|
<div style="margin-top: 30px; background: white; padding: 20px; border-radius: 12px;">
|
|
<h3 style="text-align: center; margin-bottom: 20px;">📊 PSNR vs Baseline - Live Chart</h3>
|
|
<canvas id="psnrChart" width="400" height="200"></canvas>
|
|
</div>
|
|
|
|
<!-- Training Logs -->
|
|
<div class="logs" id="trainingLogs" style="margin-top: 20px;">
|
|
<div class="log-entry log-info">Training logs will appear here...</div>
|
|
</div>
|
|
|
|
<!-- Stop Button -->
|
|
<div style="margin-top: 20px; text-align: center;">
|
|
<button class="btn btn-danger" onclick="stopTraining()">⏹️ Stop Training</button>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Models List -->
|
|
<div class="section" id="models">
|
|
<h2 class="section-title">🤖 Cloud Removal Models</h2>
|
|
<div class="model-list" id="modelsList">
|
|
<div class="model-card">
|
|
<p style="text-align: center; color: #6c757d;">Loading models...</p>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<!-- Methods Info -->
|
|
<div class="section">
|
|
<h2 class="section-title">📖 Cloud Removal Deep Learning Architectures</h2>
|
|
<div class="grid-2">
|
|
<div class="card">
|
|
<h3>🔹 U-Net</h3>
|
|
<p>Classic encoder-decoder with skip connections. Fast training, good baseline performance.</p>
|
|
<div class="status-badge status-success">Recommended for beginners</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 CR-GAN</h3>
|
|
<p>Cloud Removal GAN - adversarial training cho kết quả chân thực hơn.</p>
|
|
<div class="status-badge status-training">Advanced</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 SpA-GAN</h3>
|
|
<p>Spatial Attention GAN - attention mechanism tập trung vào vùng có mây.</p>
|
|
<div class="status-badge status-success">Best quality</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 GLF-CR</h3>
|
|
<p>Global-Local Fusion - kết hợp features global và local cho chi tiết tốt hơn.</p>
|
|
<div class="status-badge status-training">High accuracy</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 SEN12MS-CR</h3>
|
|
<p>Multi-modal fusion - kết hợp Sentinel-1 radar và Sentinel-2 optical.</p>
|
|
<div class="status-badge status-success">Multi-sensor</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 RSDehazeNet</h3>
|
|
<p>Remote Sensing Dehaze Network - chuyên cho ảnh viễn thám.</p>
|
|
<div class="status-badge status-training">RS specialized</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 Cloud-Net</h3>
|
|
<p>Encoder-Decoder architecture với residual connections.</p>
|
|
<div class="status-badge status-success">Balanced</div>
|
|
</div>
|
|
|
|
<div class="card">
|
|
<h3>🔹 DSen2-CR</h3>
|
|
<p>Deep Sentinel-2 Cloud Removal - tận dụng temporal information.</p>
|
|
<div class="status-badge status-training">Temporal fusion</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
</div>
|
|
|
|
<script>
|
|
let psnrChart = null;
|
|
let statusPolling = null;
|
|
let bestPSNRData = {
|
|
modelPSNR: 0,
|
|
epoch: 0,
|
|
trainLoss: 0,
|
|
valLoss: 0,
|
|
baselinePSNR: 0,
|
|
improvement: 0
|
|
};
|
|
|
|
let worstPSNRData = {
|
|
modelPSNR: Infinity,
|
|
epoch: 0,
|
|
trainLoss: 0,
|
|
valLoss: 0,
|
|
baselinePSNR: 0,
|
|
improvement: 0
|
|
};
|
|
|
|
let psnrHistory = [];
|
|
let baselinePSNR = 0; // PSNR cận dưới (cloudy vs clean without model)
|
|
|
|
// Load models on page load
|
|
window.addEventListener('load', () => {
|
|
refreshModels();
|
|
loadCloudRemovalMethods();
|
|
initPSNRChart();
|
|
});
|
|
|
|
// Initialize PSNR Chart
|
|
function initPSNRChart() {
|
|
const ctx = document.getElementById('psnrChart').getContext('2d');
|
|
psnrChart = new Chart(ctx, {
|
|
type: 'line',
|
|
data: {
|
|
labels: [],
|
|
datasets: [
|
|
{
|
|
label: 'Model PSNR (dB)',
|
|
data: [],
|
|
borderColor: 'rgb(102, 126, 234)',
|
|
backgroundColor: 'rgba(102, 126, 234, 0.1)',
|
|
borderWidth: 3,
|
|
tension: 0.4,
|
|
fill: true
|
|
},
|
|
{
|
|
label: 'Baseline PSNR (dB)',
|
|
data: [],
|
|
borderColor: 'rgb(245, 87, 108)',
|
|
backgroundColor: 'rgba(245, 87, 108, 0.1)',
|
|
borderWidth: 2,
|
|
borderDash: [5, 5],
|
|
tension: 0,
|
|
fill: false
|
|
},
|
|
{
|
|
label: 'Improvement (dB)',
|
|
data: [],
|
|
borderColor: 'rgb(67, 233, 123)',
|
|
backgroundColor: 'rgba(67, 233, 123, 0.1)',
|
|
borderWidth: 2,
|
|
tension: 0.4,
|
|
fill: true,
|
|
yAxisID: 'y1'
|
|
}
|
|
]
|
|
},
|
|
options: {
|
|
responsive: true,
|
|
maintainAspectRatio: false,
|
|
interaction: {
|
|
mode: 'index',
|
|
intersect: false,
|
|
},
|
|
plugins: {
|
|
legend: {
|
|
display: true,
|
|
position: 'top',
|
|
},
|
|
title: {
|
|
display: true,
|
|
text: 'Model phải vượt qua Baseline (cận dưới) để có ý nghĩa!'
|
|
}
|
|
},
|
|
scales: {
|
|
y: {
|
|
type: 'linear',
|
|
display: true,
|
|
position: 'left',
|
|
title: {
|
|
display: true,
|
|
text: 'PSNR (dB)'
|
|
}
|
|
},
|
|
y1: {
|
|
type: 'linear',
|
|
display: true,
|
|
position: 'right',
|
|
title: {
|
|
display: true,
|
|
text: 'Improvement (dB)'
|
|
},
|
|
grid: {
|
|
drawOnChartArea: false,
|
|
},
|
|
}
|
|
}
|
|
}
|
|
});
|
|
}
|
|
|
|
// Update PSNR Chart with new data
|
|
function updatePSNRChart(history, baselinePSNR) {
|
|
if (!psnrChart || !history) return;
|
|
|
|
psnrChart.data.labels = history.epochs;
|
|
psnrChart.data.datasets[0].data = history.val_psnrs;
|
|
psnrChart.data.datasets[1].data = history.epochs.map(() => baselinePSNR);
|
|
psnrChart.data.datasets[2].data = history.improvements;
|
|
psnrChart.update();
|
|
}
|
|
|
|
// Poll training status
|
|
function startStatusPolling() {
|
|
if (statusPolling) clearInterval(statusPolling);
|
|
|
|
statusPolling = setInterval(async () => {
|
|
try {
|
|
const response = await fetch('/api/cloud-removal/training/status');
|
|
const status = await response.json();
|
|
|
|
if (status.is_training) {
|
|
// Update metrics
|
|
document.getElementById('modelPSNR').textContent =
|
|
status.val_psnr > 0 ? status.val_psnr.toFixed(2) : '--';
|
|
document.getElementById('baselinePSNR').textContent =
|
|
status.baseline_psnr > 0 ? status.baseline_psnr.toFixed(2) : '--';
|
|
document.getElementById('improvement').textContent =
|
|
status.improvement !== 0 ? (status.improvement > 0 ? '+' : '') + status.improvement.toFixed(2) : '--';
|
|
document.getElementById('epochProgress').textContent =
|
|
`${status.current_epoch}/${status.total_epochs}`;
|
|
|
|
// Track PSNR history
|
|
if (status.val_psnr > 0) {
|
|
psnrHistory.push(status.val_psnr);
|
|
}
|
|
|
|
// Track best PSNR (cao nhất - cận trên)
|
|
if (status.val_psnr > bestPSNRData.modelPSNR && status.val_psnr > 0) {
|
|
bestPSNRData = {
|
|
modelPSNR: status.val_psnr,
|
|
epoch: status.current_epoch,
|
|
trainLoss: status.train_loss,
|
|
valLoss: status.val_loss,
|
|
baselinePSNR: status.baseline_psnr,
|
|
improvement: status.improvement
|
|
};
|
|
|
|
// Update best PSNR display
|
|
document.getElementById('bestPSNRCard').style.display = 'block';
|
|
document.getElementById('bestModelPSNR').textContent = bestPSNRData.modelPSNR.toFixed(2);
|
|
document.getElementById('bestEpoch').textContent = bestPSNRData.epoch;
|
|
document.getElementById('bestTrainLoss').textContent = bestPSNRData.trainLoss.toFixed(6);
|
|
document.getElementById('bestValLoss').textContent = bestPSNRData.valLoss.toFixed(6);
|
|
document.getElementById('bestBaselinePSNR').textContent = bestPSNRData.baselinePSNR.toFixed(2);
|
|
document.getElementById('bestImprovement').textContent =
|
|
(bestPSNRData.improvement > 0 ? '+' : '') + bestPSNRData.improvement.toFixed(2);
|
|
|
|
// Add log for new best PSNR
|
|
addLog('success', `🏆 New Best PSNR! Epoch ${bestPSNRData.epoch}: ${bestPSNRData.modelPSNR.toFixed(2)}dB ` +
|
|
`(Improvement: +${bestPSNRData.improvement.toFixed(2)}dB vs baseline)`);
|
|
}
|
|
|
|
// Track worst PSNR (thấp nhất - cận dưới)
|
|
if (status.val_psnr < worstPSNRData.modelPSNR && status.val_psnr > 0) {
|
|
worstPSNRData = {
|
|
modelPSNR: status.val_psnr,
|
|
epoch: status.current_epoch,
|
|
trainLoss: status.train_loss,
|
|
valLoss: status.val_loss,
|
|
baselinePSNR: status.baseline_psnr,
|
|
improvement: status.improvement
|
|
};
|
|
|
|
// Update worst PSNR display
|
|
document.getElementById('worstPSNRCard').style.display = 'block';
|
|
document.getElementById('worstModelPSNR').textContent = worstPSNRData.modelPSNR.toFixed(2);
|
|
document.getElementById('worstEpoch').textContent = worstPSNRData.epoch;
|
|
document.getElementById('worstTrainLoss').textContent = worstPSNRData.trainLoss.toFixed(6);
|
|
document.getElementById('worstValLoss').textContent = worstPSNRData.valLoss.toFixed(6);
|
|
document.getElementById('worstBaselinePSNR').textContent = worstPSNRData.baselinePSNR.toFixed(2);
|
|
document.getElementById('worstImprovement').textContent =
|
|
(worstPSNRData.improvement > 0 ? '+' : '') + worstPSNRData.improvement.toFixed(2);
|
|
|
|
// Add log for new worst PSNR
|
|
addLog('warning', `📉 Worst PSNR updated: Epoch ${worstPSNRData.epoch}: ${worstPSNRData.modelPSNR.toFixed(2)}dB (cận dưới)`);
|
|
}
|
|
|
|
// Update Performance Range Summary
|
|
if (bestPSNRData.modelPSNR > 0 && worstPSNRData.modelPSNR < Infinity && psnrHistory.length > 0) {
|
|
document.getElementById('performanceRangeCard').style.display = 'block';
|
|
|
|
const range = bestPSNRData.modelPSNR - worstPSNRData.modelPSNR;
|
|
const avgPSNR = psnrHistory.reduce((a, b) => a + b, 0) / psnrHistory.length;
|
|
const stability = avgPSNR > 0 ? ((1 - range / avgPSNR) * 100) : 0;
|
|
|
|
// Update baseline PSNR (cận dưới tuyệt đối)
|
|
if (status.baseline_psnr > 0) {
|
|
baselinePSNR = status.baseline_psnr;
|
|
}
|
|
|
|
document.getElementById('psnrRange').textContent = range.toFixed(2);
|
|
document.getElementById('avgPSNR').textContent = avgPSNR.toFixed(2);
|
|
document.getElementById('stability').textContent = Math.max(0, stability).toFixed(1);
|
|
document.getElementById('baselinePSNRDisplay').textContent = baselinePSNR > 0 ? baselinePSNR.toFixed(2) : '--';
|
|
|
|
// Compare worst PSNR with baseline PSNR
|
|
const baselineComparisonDiv = document.getElementById('baselineComparisonText');
|
|
if (baselinePSNR > 0) {
|
|
const margin = worstPSNRData.modelPSNR - baselinePSNR;
|
|
if (margin > 5) {
|
|
baselineComparisonDiv.innerHTML = `✅ <strong>Tốt:</strong> PSNR thấp nhất (${worstPSNRData.modelPSNR.toFixed(2)}dB) vượt baseline +${margin.toFixed(2)}dB - Model học tốt ngay cả trong worst case`;
|
|
baselineComparisonDiv.style.color = '#2ecc71';
|
|
} else if (margin > 2) {
|
|
baselineComparisonDiv.innerHTML = `⚠️ <strong>Khá:</strong> PSNR thấp nhất (${worstPSNRData.modelPSNR.toFixed(2)}dB) vượt baseline +${margin.toFixed(2)}dB - Cần cải thiện stability`;
|
|
baselineComparisonDiv.style.color = '#f39c12';
|
|
} else if (margin > 0) {
|
|
baselineComparisonDiv.innerHTML = `⚠️ <strong>Yếu:</strong> PSNR thấp nhất (${worstPSNRData.modelPSNR.toFixed(2)}dB) chỉ vượt baseline +${margin.toFixed(2)}dB - Model không ổn định`;
|
|
baselineComparisonDiv.style.color = '#e67e22';
|
|
} else {
|
|
baselineComparisonDiv.innerHTML = `❌ <strong>Kém:</strong> PSNR thấp nhất (${worstPSNRData.modelPSNR.toFixed(2)}dB) không vượt baseline (${baselinePSNR.toFixed(2)}dB) - Model thất bại`;
|
|
baselineComparisonDiv.style.color = '#e74c3c';
|
|
}
|
|
} else {
|
|
baselineComparisonDiv.innerHTML = 'Đang chờ baseline PSNR...';
|
|
baselineComparisonDiv.style.color = '#95a5a6';
|
|
}
|
|
}
|
|
|
|
// Update progress bar
|
|
const progress = status.total_epochs > 0 ? (status.current_epoch / status.total_epochs) * 100 : 0;
|
|
const progressBar = document.getElementById('progressBar');
|
|
progressBar.style.width = progress + '%';
|
|
progressBar.textContent = Math.floor(progress) + '%';
|
|
|
|
// Update chart
|
|
if (status.history && status.history.epochs.length > 0) {
|
|
updatePSNRChart(status.history, status.baseline_psnr);
|
|
}
|
|
|
|
// Add log for significant events
|
|
if (status.current_epoch > 0 && status.current_epoch % 5 === 0) {
|
|
const improvement = status.improvement;
|
|
const emoji = improvement > 0 ? '✅' : '⚠️';
|
|
addLog('info', `Epoch ${status.current_epoch}: Model PSNR=${status.val_psnr.toFixed(2)}dB, ` +
|
|
`Baseline=${status.baseline_psnr.toFixed(2)}dB, Improvement=${emoji} ${improvement > 0 ? '+' : ''}${improvement.toFixed(2)}dB`);
|
|
}
|
|
} else {
|
|
// Training finished
|
|
if (status.progress === 'Completed!') {
|
|
addLog('info', '✅ Training completed successfully!');
|
|
stopStatusPolling();
|
|
|
|
// Regenerate report with best_checkpoint and worst_checkpoint if available
|
|
if (status.result && bestPSNRData.modelPSNR > 0) {
|
|
setTimeout(async () => {
|
|
try {
|
|
const reportData = {
|
|
training_result: status.result,
|
|
best_checkpoint: {
|
|
modelPSNR: bestPSNRData.modelPSNR,
|
|
epoch: bestPSNRData.epoch,
|
|
trainLoss: bestPSNRData.trainLoss,
|
|
valLoss: bestPSNRData.valLoss,
|
|
baselinePSNR: bestPSNRData.baselinePSNR,
|
|
improvement: bestPSNRData.improvement
|
|
}
|
|
};
|
|
|
|
// Add worst_checkpoint if available
|
|
if (worstPSNRData.modelPSNR < Infinity) {
|
|
reportData.worst_checkpoint = {
|
|
modelPSNR: worstPSNRData.modelPSNR,
|
|
epoch: worstPSNRData.epoch,
|
|
trainLoss: worstPSNRData.trainLoss,
|
|
valLoss: worstPSNRData.valLoss,
|
|
baselinePSNR: worstPSNRData.baselinePSNR,
|
|
improvement: worstPSNRData.improvement
|
|
};
|
|
}
|
|
|
|
// Add baseline PSNR if available
|
|
if (baselinePSNR > 0) {
|
|
reportData.baseline_psnr = baselinePSNR;
|
|
}
|
|
|
|
const regenerateResponse = await fetch('/api/training/report/regenerate', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify(reportData)
|
|
});
|
|
const regenerateData = await regenerateResponse.json();
|
|
if (regenerateData.success) {
|
|
console.log('Report regenerated with checkpoints:', regenerateData.report_path);
|
|
addLog('success', '📊 Báo cáo đã được cập nhật với Best/Worst PSNR Checkpoint và Performance Range');
|
|
}
|
|
} catch (err) {
|
|
console.error('Failed to regenerate report:', err);
|
|
}
|
|
|
|
refreshModels();
|
|
}, 1000);
|
|
} else {
|
|
setTimeout(refreshModels, 2000);
|
|
}
|
|
} else if (status.error) {
|
|
addLog('error', `❌ Error: ${status.error}`);
|
|
stopStatusPolling();
|
|
}
|
|
}
|
|
} catch (error) {
|
|
console.error('Error polling status:', error);
|
|
}
|
|
}, 2000); // Poll every 2 seconds
|
|
}
|
|
|
|
function stopStatusPolling() {
|
|
if (statusPolling) {
|
|
clearInterval(statusPolling);
|
|
statusPolling = null;
|
|
}
|
|
}
|
|
|
|
// Stop training
|
|
async function stopTraining() {
|
|
if (!confirm('Are you sure you want to stop training?')) return;
|
|
|
|
try {
|
|
const response = await fetch('/api/cloud-removal/training/stop', {
|
|
method: 'POST'
|
|
});
|
|
|
|
if (response.ok) {
|
|
addLog('warning', '⏹️ Stopping training...');
|
|
} else {
|
|
const error = await response.json();
|
|
alert('Error: ' + error.detail);
|
|
}
|
|
} catch (error) {
|
|
alert('Error: ' + error.message);
|
|
}
|
|
}
|
|
|
|
// Handle training form submission
|
|
document.getElementById('trainingForm').addEventListener('submit', async (e) => {
|
|
e.preventDefault();
|
|
|
|
const config = {
|
|
data_dir: document.getElementById('dataDir').value,
|
|
model_name: document.getElementById('modelName').value,
|
|
architecture: document.getElementById('modelArchitecture').value,
|
|
use_s1: document.getElementById('useS1').checked,
|
|
batch_size: parseInt(document.getElementById('batchSize').value),
|
|
num_epochs: parseInt(document.getElementById('numEpochs').value),
|
|
learning_rate: parseFloat(document.getElementById('learningRate').value),
|
|
use_gpu: document.getElementById('useGPU').checked
|
|
};
|
|
|
|
try {
|
|
const response = await fetch('/api/cloud-removal/train', {
|
|
method: 'POST',
|
|
headers: { 'Content-Type': 'application/json' },
|
|
body: JSON.stringify(config)
|
|
});
|
|
|
|
const result = await response.json();
|
|
|
|
if (response.ok) {
|
|
// Reset tracking variables for new training
|
|
bestPSNRData = {
|
|
modelPSNR: 0,
|
|
epoch: 0,
|
|
trainLoss: 0,
|
|
valLoss: 0,
|
|
baselinePSNR: 0,
|
|
improvement: 0
|
|
};
|
|
worstPSNRData = {
|
|
modelPSNR: Infinity,
|
|
epoch: 0,
|
|
trainLoss: 0,
|
|
valLoss: 0,
|
|
baselinePSNR: 0,
|
|
improvement: 0
|
|
};
|
|
psnrHistory = [];
|
|
baselinePSNR = 0;
|
|
|
|
// Hide checkpoint cards at start
|
|
document.getElementById('bestPSNRCard').style.display = 'none';
|
|
document.getElementById('worstPSNRCard').style.display = 'none';
|
|
document.getElementById('performanceRangeCard').style.display = 'none';
|
|
|
|
// Show training status section
|
|
document.getElementById('trainingStatus').style.display = 'block';
|
|
document.getElementById('statusMessage').innerHTML = `
|
|
<div class="status-badge status-training">🚀 Training Started: ${result.training_id}</div>
|
|
<p style="margin-top: 10px;">Model training has started. Real-time metrics will appear below.</p>
|
|
`;
|
|
|
|
addLog('info', `✅ Training started: ${result.training_id}`);
|
|
addLog('info', `Config: ${JSON.stringify(config, null, 2)}`);
|
|
|
|
// Start polling status
|
|
startStatusPolling();
|
|
} else {
|
|
alert('Error starting training: ' + (result.detail || result.error));
|
|
}
|
|
} catch (error) {
|
|
alert('Error: ' + error.message);
|
|
}
|
|
});
|
|
|
|
// Refresh models list
|
|
async function refreshModels() {
|
|
try {
|
|
const response = await fetch('/api/cloud-removal/models');
|
|
const data = await response.json();
|
|
|
|
const modelsList = document.getElementById('modelsList');
|
|
|
|
if (data.models && data.models.length > 0) {
|
|
modelsList.innerHTML = data.models.map(model => `
|
|
<div class="model-card">
|
|
<h3>📦 ${model.filename}</h3>
|
|
<div class="model-info">🏗️ Architecture: ${model.architecture || 'U-Net'}</div>
|
|
<div class="model-info">📊 Epoch: ${model.epoch}</div>
|
|
<div class="model-info">📉 Train Loss: ${model.train_loss.toFixed(6)}</div>
|
|
<div class="model-info">📉 Val Loss: ${model.val_loss.toFixed(6)}</div>
|
|
${model.val_psnr ? `<div class="model-info">📈 Model PSNR: <strong>${model.val_psnr.toFixed(2)} dB</strong></div>` : ''}
|
|
${model.baseline_psnr ? `<div class="model-info">📉 Baseline PSNR: ${model.baseline_psnr.toFixed(2)} dB</div>` : ''}
|
|
${model.val_psnr && model.baseline_psnr ? `<div class="model-info">⚡ Improvement: <strong style="color: ${model.val_psnr > model.baseline_psnr ? 'green' : 'red'}">${(model.val_psnr - model.baseline_psnr > 0 ? '+' : '')}${(model.val_psnr - model.baseline_psnr).toFixed(2)} dB</strong></div>` : ''}
|
|
<div class="model-info">📡 Use S1: ${model.use_s1 ? 'Yes' : 'No'}</div>
|
|
<div class="model-info">💾 Size: ${model.size_mb.toFixed(2)} MB</div>
|
|
<div class="model-info">📅 Created: ${new Date(model.created * 1000).toLocaleString()}</div>
|
|
<div style="margin-top: 15px;">
|
|
<button class="btn btn-danger" onclick="deleteModel('${model.filename}')">
|
|
🗑️ Delete
|
|
</button>
|
|
</div>
|
|
</div>
|
|
`).join('');
|
|
} else {
|
|
modelsList.innerHTML = `
|
|
<div class="model-card">
|
|
<p style="text-align: center; color: #6c757d;">
|
|
No cloud removal models found.<br>
|
|
Train your first model above!
|
|
</p>
|
|
</div>
|
|
`;
|
|
}
|
|
} catch (error) {
|
|
console.error('Error loading models:', error);
|
|
}
|
|
}
|
|
|
|
// Delete model
|
|
async function deleteModel(filename) {
|
|
if (!confirm(`Delete model ${filename}?`)) return;
|
|
|
|
try {
|
|
const response = await fetch(`/api/cloud-removal/models/${filename}`, {
|
|
method: 'DELETE'
|
|
});
|
|
|
|
if (response.ok) {
|
|
alert('Model deleted successfully');
|
|
refreshModels();
|
|
} else {
|
|
const error = await response.json();
|
|
alert('Error deleting model: ' + error.detail);
|
|
}
|
|
} catch (error) {
|
|
alert('Error: ' + error.message);
|
|
}
|
|
}
|
|
|
|
// Load cloud removal methods
|
|
async function loadCloudRemovalMethods() {
|
|
try {
|
|
const response = await fetch('/api/cloud-removal/methods');
|
|
const data = await response.json();
|
|
console.log('Available cloud removal methods:', data.methods);
|
|
} catch (error) {
|
|
console.error('Error loading methods:', error);
|
|
}
|
|
}
|
|
|
|
// Add log entry
|
|
function addLog(type, message) {
|
|
const logs = document.getElementById('trainingLogs');
|
|
const timestamp = new Date().toLocaleTimeString();
|
|
const logClass = type === 'error' ? 'log-error' : (type === 'warning' ? 'log-warning' : 'log-info');
|
|
|
|
const entry = document.createElement('div');
|
|
entry.className = `log-entry ${logClass}`;
|
|
entry.textContent = `[${timestamp}] ${message}`;
|
|
|
|
logs.appendChild(entry);
|
|
logs.scrollTop = logs.scrollHeight;
|
|
}
|
|
</script>
|
|
</body>
|
|
</html>
|