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| """
NVIDIA Cosmos 3: Mixture-of-Transformers 架构
用于座舱合成数据生成
与传统MoE不同:
- MoE: 多个专家网络，路由到最佳专家
- MoT: 多个transformer，按模态/任务路由
座舱数据合成应用:
1. 真实座舱场景 → Cosmos 3 理解
2. 文本/参数变体 → Cosmos 3 生成
3. 输出: 合成座舱图像/视频 + 标注
依赖: torch, numpy (概念实现)
"""
import torch
import torch.nn as nn
from typing import List, Dict
class MixtureTransformerLayer(nn.Module):
"""
Mixture-of-Transformers (MoT) 层
与MoE的区别:
- MoE: shared attention + separate FFN experts
- MoT: separate transformer towers per modality
Cosmos 3的MoT:
- 视觉Transformer: 处理图像/视频token
- 文本Transformer: 处理文本prompt
- 物理Transformer: 处理物理约束(碰撞/重力)
- 跨模态注意力在顶层融合
"""
def __init__(self, dim=768, n_heads=12, n_towers=3):
super().__init__()
self.dim = dim
self.n_towers = n_towers
self.towers = nn.ModuleList([
nn.TransformerEncoderLayer(
d_model=dim,
nhead=n_heads,
dim_feedforward=dim*4,
dropout=0.1,
batch_first=True
)
for _ in range(n_towers)
])
self.cross_modal_attn = nn.MultiheadAttention(
dim, n_heads, batch_first=True
)
self.router = nn.Linear(dim, n_towers)
def forward(self, x, modality_ids):
"""
Args:
x: (B, N, D) 输入token序列
modality_ids: (B, N) 每个token的模态ID
Returns:
output: (B, N, D) 融合后的输出
"""
B, N, D = x.shape
route_logits = self.router(x)
route_weights = torch.softmax(route_logits, dim=-1)
tower_outputs = []
for tower in self.towers:
out = tower(x)
tower_outputs.append(out)
output = torch.zeros_like(x)
for i, tower_out in enumerate(tower_outputs):
weight = route_weights[:, :, i:i+1]
output += weight * tower_out
output, _ = self.cross_modal_attn(output, output, output)
return output
class CosmosCabinDataGenerator:
"""
使用Cosmos 3生成座舱合成数据
工作流:
1. 输入: 真实座舱场景描述 + 少量真实图像
2. Cosmos 3 理解场景物理(光照/遮挡/姿态)
3. 参数变体: 不同光照/姿态/遮挡/乘员
4. 输出: 合成图像 + 自动标注
优势:
- 无需3D建模(端到端)
- 物理一致(理解碰撞/遮挡)
- 自动标注(内置标注能力)
- 多样性(同一场景数千变体)
"""
def __init__(self):
self.cabin_scenarios = {
'normal_driving': {
'driver_pose': 'upright',
'lighting': 'daylight',
'passenger': True,
},
'night_fatigue': {
'driver_pose': 'slumped',
'lighting': 'night_ir',
'passenger': False,
},
'child_rear': {
'driver_pose': 'upright',
'passenger': False,
'rear_occupant': 'child',
'child_seat': True,
},
'oop_reclined': {
'driver_pose': 'reclined_30deg',
'belt_status': 'misuse',
},
'phone_distraction': {
'driver_pose': 'looking_down',
'phone_visible': True,
'gaze_direction': 'down_left',
},
}
self.variation_axes = [
'lighting',
'driver_pose',
'driver_gender',
'driver_age',
'glasses',
'facial_hair',
'passenger',
'rear_occupant',
'belt_status',
'clothing',
]
def generate_variations(self, base_scenario: str, n_variations: int = 100):
"""
从基础场景生成变体
模拟Cosmos 3的变体生成能力
"""
base = self.cabin_scenarios[base_scenario]
variations = []
for i in range(n_variations):
var = base.copy()
for axis in self.variation_axes:
if axis == 'lighting':
var['lighting'] = np.random.choice(
['daylight', 'night_ir', 'tunnel', 'backlit', 'dawn']
)
elif axis == 'driver_gender':
var['driver_gender'] = np.random.choice(['male', 'female'])
elif axis == 'driver_age':
var['driver_age'] = np.random.choice([
'young_20_30', 'middle_30_50', 'senior_50_70'
])
elif axis == 'glasses':
var['glasses'] = np.random.choice([
'none', 'regular', 'sunglasses'
])
elif axis == 'clothing':
var['clothing'] = np.random.choice([
'thick_dark', 'thick_light',
'thin_dark', 'thin_light'
])
var['variation_id'] = i
variations.append(var)
return variations
def estimate_data_savings(self, n_real=100, n_synthetic=10000):
"""
估算合成数据节省成本
传统: 10万真实标注图像 @ $2/张 = $200,000
Cosmos 3: 100真实 + 10K合成 @ $0.1/张 = $1,100
"""
real_cost_per = 2.0
synthetic_cost = 0.1
traditional_cost = n_synthetic * real_cost_per
traditional_time = n_synthetic * 0.01
cosmos_real_cost = n_real * real_cost_per
cosmos_synthetic_cost = n_synthetic * synthetic_cost
cosmos_total_cost = cosmos_real_cost + cosmos_synthetic_cost
cosmos_time = n_real * 0.01 + n_synthetic * 0.001
return {
'traditional_cost': traditional_cost,
'cosmos_cost': cosmos_total_cost,
'cost_savings': traditional_cost - cosmos_total_cost,
'cost_ratio': traditional_cost / cosmos_total_cost,
'traditional_time_hours': traditional_time / 60,
'cosmos_time_hours': cosmos_time / 60,
}
import numpy as np
if __name__ == "__main__":
gen = CosmosCabinDataGenerator()
print("=" * 60)
print("NVIDIA Cosmos 3: 座舱合成数据生成")
print("=" * 60)
print(f"\n座舱场景模板:")
for name, params in gen.cabin_scenarios.items():
print(f" {name}: {params}")
variations = gen.generate_variations('night_fatigue', n_variations=100)
print(f"\n从 'night_fatigue' 生成 100 个变体:")
print(f" 变体0: {variations[0]}")
print(f" 变体1: {variations[1]}")
savings = gen.estimate_data_savings(n_real=100, n_synthetic=10000)
print(f"\n成本节省分析 (100真实 + 10K合成 vs 10K全真实):")
print(f" 传统方案: ${savings['traditional_cost']:,.0f}")
print(f" Cosmos方案: ${savings['cosmos_cost']:,.0f}")
print(f" 节省: ${savings['cost_savings']:,.0f} ({savings['cost_ratio']:.1f}x)")
print(f" 传统时间: {savings['traditional_time_hours']:.0f}h")
print(f" Cosmos时间: {savings['cosmos_time_hours']:.0f}h")
|
