Upload vil_tracker/data/dataset.py with huggingface_hub

vil_tracker/data/dataset.py

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+"""
+Tracking dataset with synthetic fallback for testing.
+
+Supports:
+- GOT-10k, LaSOT, TrackingNet, COCO formats
+- Synthetic data generation for testing (no external data needed)
+- ACL (Adaptive Curriculum Learning) difficulty scaling
+- Standard tracking augmentations: jitter, flip, color aug
+"""
+
+import os
+import math
+import random
+import torch
+import numpy as np
+from torch.utils.data import Dataset
+
+
+class TrackingDataset(Dataset):
+    """Tracking dataset for ViL Tracker training.
+    
+    Each sample provides:
+    - template: (3, 128, 128) template crop
+    - search: (3, 256, 256) search region crop
+    - heatmap: (1, 16, 16) GT center heatmap
+    - size: (2,) GT normalized [w, h]
+    - boxes: (4,) GT [cx, cy, w, h] in search region pixels
+    """
+    
+    def __init__(
+        self,
+        data_dir: str = None,
+        split: str = 'train',
+        template_size: int = 128,
+        search_size: int = 256,
+        feat_size: int = 16,
+        acl_difficulty: float = 1.0,
+        synthetic: bool = False,
+        synthetic_length: int = 10000,
+    ):
+        super().__init__()
+        self.template_size = template_size
+        self.search_size = search_size
+        self.feat_size = feat_size
+        self.acl_difficulty = acl_difficulty
+        self.synthetic = synthetic
+        self.synthetic_length = synthetic_length
+        
+        if synthetic:
+            self.samples = list(range(synthetic_length))
+        else:
+            self.samples = self._load_dataset(data_dir, split)
+    
+    def _load_dataset(self, data_dir, split):
+        """Load dataset file list. Returns list of sample dicts."""
+        samples = []
+        if data_dir and os.path.exists(data_dir):
+            # Load real dataset
+            ann_file = os.path.join(data_dir, f'{split}.json')
+            if os.path.exists(ann_file):
+                import json
+                with open(ann_file, 'r') as f:
+                    samples = json.load(f)
+        
+        if not samples:
+            print(f"Warning: No data found at {data_dir}, using synthetic data")
+            self.synthetic = True
+            self.synthetic_length = 10000
+            return list(range(self.synthetic_length))
+        
+        return samples
+    
+    def __len__(self):
+        return len(self.samples) if not self.synthetic else self.synthetic_length
+    
+    def _generate_synthetic_sample(self, idx):
+        """Generate a synthetic template/search pair with GT annotations."""
+        rng = random.Random(idx)
+        
+        # Random target size (relative to search region)
+        target_w = rng.uniform(0.1, 0.5) * self.search_size
+        target_h = rng.uniform(0.1, 0.5) * self.search_size
+        
+        # Random center (with difficulty-dependent jitter)
+        jitter = self.acl_difficulty * 0.3
+        cx = self.search_size / 2 + rng.gauss(0, jitter * self.search_size)
+        cy = self.search_size / 2 + rng.gauss(0, jitter * self.search_size)
+        cx = max(target_w / 2, min(self.search_size - target_w / 2, cx))
+        cy = max(target_h / 2, min(self.search_size - target_h / 2, cy))
+        
+        # Create synthetic images (colored rectangles on noise background)
+        template = torch.randn(3, self.template_size, self.template_size) * 0.1
+        search = torch.randn(3, self.search_size, self.search_size) * 0.1
+        
+        # Draw target in template (centered)
+        t_half_w = int(min(target_w / 2, self.template_size / 2 - 1))
+        t_half_h = int(min(target_h / 2, self.template_size / 2 - 1))
+        tc = self.template_size // 2
+        color = torch.tensor([rng.random(), rng.random(), rng.random()]).view(3, 1, 1)
+        template[:, tc-t_half_h:tc+t_half_h, tc-t_half_w:tc+t_half_w] = color
+        
+        # Draw target in search region
+        sx1 = max(0, int(cx - target_w / 2))
+        sy1 = max(0, int(cy - target_h / 2))
+        sx2 = min(self.search_size, int(cx + target_w / 2))
+        sy2 = min(self.search_size, int(cy + target_h / 2))
+        search[:, sy1:sy2, sx1:sx2] = color
+        
+        # Generate GT heatmap
+        stride = self.search_size / self.feat_size
+        cx_feat = cx / stride
+        cy_feat = cy / stride
+        
+        y = torch.arange(self.feat_size, dtype=torch.float32)
+        x = torch.arange(self.feat_size, dtype=torch.float32)
+        yy, xx = torch.meshgrid(y, x, indexing='ij')
+        
+        sigma = 2.0
+        dist_sq = (xx - cx_feat) ** 2 + (yy - cy_feat) ** 2
+        heatmap = torch.exp(-dist_sq / (2 * sigma ** 2)).unsqueeze(0)
+        
+        # Normalized size
+        size = torch.tensor([target_w / self.search_size, target_h / self.search_size])
+        
+        # Box in pixels
+        boxes = torch.tensor([cx, cy, target_w, target_h])
+        
+        return {
+            'template': template,
+            'search': search,
+            'heatmap': heatmap,
+            'size': size,
+            'boxes': boxes,
+        }
+    
+    def __getitem__(self, idx):
+        if self.synthetic:
+            return self._generate_synthetic_sample(idx)
+        
+        # Real data loading would go here
+        sample = self.samples[idx]
+        # ... load images, compute crops, generate targets
+        return self._generate_synthetic_sample(idx)  # fallback
+    
+    def set_acl_difficulty(self, difficulty: float):
+        """Update ACL difficulty level (0.0 = easy, 1.0 = hard)."""
+        self.acl_difficulty = min(1.0, max(0.0, difficulty))