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| import torch
import torch.nn as nn
import torch.nn.functional as F
class TransGazeObject(nn.Module):
"""
TransGaze-Object: 驾驶员视线物体直接预测
输入: 驾驶员面部 + 交通场景
输出: 视线关注的物体类别
架构:
1. 人脸编码器: 提取头部姿态和眼睛方向特征
2. 场景编码器: 提取交通场景物体特征
3. Transformer跨模态融合
4. 分类头预测视线物体
"""
def __init__(self, num_objects: int = 15,
feat_dim: int = 256, num_heads: int = 8,
num_layers: int = 4):
super().__init__()
self.face_encoder = nn.Sequential(
nn.Conv2d(3, 32, 3, stride=2, padding=1),
nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d(32, 64, 3, stride=2, padding=1),
nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.BatchNorm2d(128), nn.ReLU(),
nn.AdaptiveAvgPool2d((4, 4)),
nn.Flatten(),
nn.Linear(128 * 16, feat_dim)
)
self.scene_encoder = nn.Sequential(
nn.Conv2d(3, 32, 3, stride=2, padding=1),
nn.BatchNorm2d(32), nn.ReLU(),
nn.Conv2d(32, 64, 3, stride=2, padding=1),
nn.BatchNorm2d(64), nn.ReLU(),
nn.Conv2d(64, 128, 3, stride=2, padding=1),
nn.BatchNorm2d(128), nn.ReLU(),
nn.Conv2d(128, 256, 3, stride=2, padding=1),
nn.BatchNorm2d(256), nn.ReLU(),
nn.AdaptiveAvgPool2d((8, 8)),
nn.Flatten(),
nn.Linear(256 * 64, feat_dim * 4)
)
self.object_tokenizer = nn.Linear(feat_dim * 4, 8 * feat_dim)
self.face_proj = nn.Linear(feat_dim, feat_dim)
encoder_layer = nn.TransformerEncoderLayer(
d_model=feat_dim, nhead=num_heads,
dim_feedforward=feat_dim * 4,
dropout=0.1, batch_first=True, activation='gelu'
)
self.transformer = nn.TransformerEncoder(encoder_layer, num_layers)
self.classifier = nn.Sequential(
nn.LayerNorm(feat_dim),
nn.Linear(feat_dim, 64),
nn.GELU(),
nn.Dropout(0.1),
nn.Linear(64, num_objects)
)
def forward(self, face_img: torch.Tensor,
scene_img: torch.Tensor) -> torch.Tensor:
"""
Args:
face_img: (B, 3, 224, 224) 驾驶员面部
scene_img: (B, 3, 480, 640) 前方交通场景
Returns:
logits: (B, num_objects) 视线物体类别
"""
B = face_img.shape[0]
face_feat = self.face_encoder(face_img)
scene_feat = self.scene_encoder(scene_img)
object_tokens = self.object_tokenizer(scene_feat)
object_tokens = object_tokens.view(B, 8, -1)
face_token = self.face_proj(face_feat).unsqueeze(1)
tokens = torch.cat([face_token, object_tokens], dim=1)
encoded = self.transformer(tokens)
face_output = encoded[:, 0, :]
logits = self.classifier(face_output)
return logits
if __name__ == "__main__":
print("=" * 60)
print("TransGaze-Object: 视线物体直接预测")
print("=" * 60)
gaze_objects = [
"road_ahead", "left_mirror", "right_mirror", "rear_mirror",
"center_console", "infotainment", "speedometer",
"steering_wheel", "phone", "passenger", "window_left",
"window_right", "door_panel", "gear_shift", "other"
]
model = TransGazeObject(
num_objects=len(gaze_objects),
feat_dim=256, num_heads=8, num_layers=4
)
params = sum(p.numel() for p in model.parameters())
print(f"\n模型参数: {params:,} ({params/1e6:.2f}M)")
B = 4
face = torch.randn(B, 3, 224, 224)
scene = torch.randn(B, 3, 480, 640)
model.eval()
with torch.no_grad():
logits = model(face, scene)
probs = F.softmax(logits, dim=1)
preds = torch.argmax(probs, dim=1)
print(f"\n预测结果:")
for i in range(B):
obj = gaze_objects[preds[i]]
conf = probs[i][preds[i]].item()
print(f" Sample {i}: {obj:<20} (置信度: {conf:.2%})")
print(f"\n{'='*60}")
print("方法对比")
print(f"{'='*60}")
comparison = [
("3-step pipeline", "视线→落点→关联", "高累积误差", "~120ms"),
("2-step pipeline", "视线落点→关联", "中等误差", "~80ms"),
("TransGaze-Object", "直接预测物体", "低误差", "~45ms"),
]
print(f"{'方法':<20} {'流程':<20} {'误差':<15} {'延迟':<10}")
print("-" * 65)
for m, f, e, l in comparison:
print(f"{m:<20} {f:<20} {e:<15} {l:<10}")
|