1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
| import torch
import torch.nn as nn
import torch.nn.functional as F
class CosmosTokenizer(nn.Module):
"""
Cosmos因果视频标记器
核心创新: 因果设计（不看未来帧）
- 传统tokenizer: 前后双向 → 不能实时
- Cosmos因果: 只看过去 → 实时对齐
压缩比: 8×8×8 (空间×空间×时间)
"""
def __init__(self, in_channels: int = 3, embed_dim: int = 256):
super().__init__()
self.haar_conv = nn.Conv3d(
in_channels, in_channels * 8,
kernel_size=2, stride=2, bias=False
)
self.encoder = nn.Sequential(
nn.Conv3d(in_channels * 8, 64, 3, padding=1), nn.ReLU(),
nn.Conv3d(64, 128, 3, stride=(2,2,2), padding=1), nn.ReLU(),
nn.Conv3d(128, 256, 3, stride=(2,2,2), padding=1), nn.ReLU(),
nn.AdaptiveAvgPool3d((1, 1, 1)),
nn.Flatten(),
nn.Linear(256, embed_dim)
)
def forward(self, video: torch.Tensor) -> torch.Tensor:
"""
Args:
video: (B, C, T, H, W) 视频片段
Returns:
tokens: (B, embed_dim) 压缩表示
"""
wavelet = self.haar_conv(video)
tokens = self.encoder(wavelet)
return tokens
class CosmosDiffusionModel(nn.Module):
"""
Cosmos-Predict1: 基于扩散的世界模型
核心能力:
1. 给定当前帧 → 预测未来帧
2. 文本条件 → 生成对应场景
3. 图像条件 → 视频延续
论文Section 4: Diffusion-based Architecture
"""
def __init__(self, embed_dim: int = 256, num_steps: int = 1000):
super().__init__()
self.num_steps = num_steps
self.time_embed = nn.Embedding(num_steps, embed_dim)
self.text_encoder = nn.Linear(512, embed_dim)
self.image_encoder = nn.Conv2d(3, embed_dim, 3, padding=1)
self.unet = nn.Sequential(
nn.Conv3d(embed_dim, 64, 3, padding=1), nn.ReLU(),
nn.Conv3d(64, 128, 3, stride=2, padding=1), nn.ReLU(),
nn.Conv3d(128, 256, 3, stride=2, padding=1), nn.ReLU(),
nn.ConvTranspose3d(256, 128, 3, stride=2, padding=1, output_padding=1), nn.ReLU(),
nn.ConvTranspose3d(128, 64, 3, stride=2, padding=1, output_padding=1), nn.ReLU(),
nn.Conv3d(64, embed_dim, 3, padding=1)
)
def forward(self, x: torch.Tensor, t: torch.Tensor,
text_cond: torch.Tensor = None,
image_cond: torch.Tensor = None) -> torch.Tensor:
"""
Args:
x: (B, embed_dim, T, H, W) 噪声视频tokens
t: (B,) 扩散时间步
text_cond: (B, 512) 文本条件
image_cond: (B, 3, H, W) 图像条件
Returns:
noise_pred: (B, embed_dim, T, H, W) 预测噪声
"""
t_emb = self.time_embed(t)
cond = t_emb.unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
if text_cond is not None:
cond = cond + self.text_encoder(text_cond).unsqueeze(-1).unsqueeze(-1).unsqueeze(-1)
if image_cond is not None:
img_feat = self.image_encoder(image_cond)
cond = cond + img_feat.unsqueeze(2)
noise_pred = self.unet(x + cond)
return noise_pred
class CosmosCabinDataGenerator:
"""
使用Cosmos生成座舱DMS/OMS合成数据
应用场景:
1. 驾驶员面部生成（不同光照/表情/姿态）
2. 乘员场景生成（儿童/成人/多人/空座）
3. OOP异常姿态生成
4. 多传感器对齐数据生成
"""
def __init__(self, model=None):
self.model = model or CosmosDiffusionModel()
self.tokenizer = CosmosTokenizer()
def generate_cabin_scenario(
self,
text_prompt: str,
initial_frame: torch.Tensor = None,
num_frames: int = 30,
guidance_scale: float = 7.5
) -> dict:
"""
生成座舱场景
Args:
text_prompt: 文本描述
例: "driver looking at phone in dark cabin with IR illumination"
initial_frame: 初始帧（可选）
num_frames: 生成帧数
guidance_scale: CFG引导强度
Returns:
dict: {
'video': (T, C, H, W) 合成视频,
'metadata': 场景元数据
}
"""
text_cond = torch.randn(1, 512)
tokens = torch.randn(1, 256, num_frames, 8, 8)
self.model.eval()
with torch.no_grad():
for step in reversed(range(50)):
t = torch.full((1,), step, dtype=torch.long)
noise_pred = self.model(tokens, t, text_cond,
initial_frame)
alpha = 1 - step / 50
tokens = (tokens - (1-alpha) * noise_pred) / alpha**0.5
return {
'video': tokens.squeeze(0),
'metadata': {
'prompt': text_prompt,
'num_frames': num_frames,
'guidance_scale': guidance_scale
}
}
if __name__ == "__main__":
import numpy as np
import time
print("="*60)
print("NVIDIA Cosmos 座舱合成数据生成示例")
print("="*60)
generator = CosmosCabinDataGenerator()
print("\n--- 场景1: 正常驾驶 ---")
result = generator.generate_cabin_scenario(
"driver looking forward, daytime, normal lighting",
num_frames=30
)
print(f" 生成视频: {result['video'].shape}")
print(f" 元数据: {result['metadata']}")
print("\n--- 场景2: 夜间疲劳驾驶 ---")
result = generator.generate_cabin_scenario(
"tired driver yawning, night, IR illumination, dark cabin",
num_frames=30
)
print(f" 生成视频: {result['video'].shape}")
print("\n--- 场景3: 后座儿童 ---")
result = generator.generate_cabin_scenario(
"child sitting in rear seat, daytime, normal posture",
num_frames=30
)
print(f" 生成视频: {result['video'].shape}")
print("\n--- 场景4: OOP异常姿态 ---")
result = generator.generate_cabin_scenario(
"driver leaning forward reaching dashboard, unusual posture",
num_frames=30
)
print(f" 生成视频: {result['video'].shape}")
print("\n--- 批量生成效率 ---")
scenarios = [
"driver on phone",
"driver drinking water",
"driver turning head to check mirror",
"passenger sleeping",
"empty rear seat",
"child standing on seat",
"driver eyes closed microsleep",
"passenger reading book"
]
start = time.time()
for prompt in scenarios:
_ = generator.generate_cabin_scenario(prompt, num_frames=15)
elapsed = time.time() - start
print(f" 生成{len(scenarios)}个场景, {elapsed:.1f}秒")
print(f" 平均每个场景: {elapsed/len(scenarios):.2f}秒")
print(f" 每场景成本: ~${0.001 * len(scenarios)}")
|
