use crate::utils::to_pascal_case;
pub fn classifier_model(name: &str) -> String {
format!(
r#"""
{} - Image Classifier Model
Auto-generated model template.
"""
import torch
import torch.nn as nn
import torchvision.transforms as transforms
class {}(nn.Module):
"""Simple CNN classifier"""
def __init__(self, num_classes=10):
super().__init__()
self.features = nn.Sequential(
nn.Conv2d(3, 32, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
nn.Conv2d(32, 64, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=2, stride=2),
)
self.classifier = nn.Sequential(
nn.Linear(64 * 56 * 56, 512),
nn.ReLU(inplace=True),
nn.Dropout(0.5),
nn.Linear(512, num_classes),
)
def forward(self, x):
x = self.features(x)
x = torch.flatten(x, 1)
x = self.classifier(x)
return x
def load_model(checkpoint_path=None):
"""Load model from checkpoint"""
model = {}()
if checkpoint_path:
model.load_state_dict(torch.load(checkpoint_path))
model.eval()
return model
def preprocess(image):
"""Preprocess image for inference"""
transform = transforms.Compose([
transforms.Resize((224, 224)),
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225]),
])
return transform(image).unsqueeze(0)
"#,
name,
to_pascal_case(name),
to_pascal_case(name)
)
}
pub fn detector_model(name: &str) -> String {
format!(
r#"""
{} - Object Detector Model
Auto-generated model template.
"""
import torch
import torch.nn as nn
import torchvision.transforms as transforms
class {}(nn.Module):
"""Simple object detector"""
def __init__(self, num_classes=80):
super().__init__()
# TODO: Implement detector architecture
# Consider using pretrained models like:
# - torchvision.models.detection.fasterrcnn_resnet50_fpn
# - torchvision.models.detection.retinanet_resnet50_fpn
pass
def forward(self, images):
# TODO: Implement forward pass
# Return: List[Dict[str, Tensor]] with keys:
# - boxes: [N, 4] (x1, y1, x2, y2)
# - labels: [N]
# - scores: [N]
pass
def load_model(checkpoint_path=None):
"""Load model from checkpoint"""
model = {}()
if checkpoint_path:
model.load_state_dict(torch.load(checkpoint_path))
model.eval()
return model
def preprocess(image):
"""Preprocess image for inference"""
transform = transforms.Compose([
transforms.ToTensor(),
])
return transform(image)
"#,
name,
to_pascal_case(name),
to_pascal_case(name)
)
}
pub fn rl_policy(name: &str) -> String {
format!(
r#"""
{} - RL Policy
Auto-generated policy template.
"""
from stable_baselines3 import PPO
from stable_baselines3.common.vec_env import DummyVecEnv
import gymnasium as gym
def create_env():
"""Create training environment"""
# TODO: Replace with your custom environment
env = gym.make("CartPole-v1")
return env
def train_policy(total_timesteps=100000):
"""Train RL policy"""
# Create environment
env = DummyVecEnv([create_env])
# Create model
model = PPO(
"MlpPolicy",
env,
learning_rate=0.0003,
n_steps=2048,
batch_size=64,
n_epochs=10,
gamma=0.99,
gae_lambda=0.95,
verbose=1,
)
# Train
model.learn(total_timesteps=total_timesteps)
# Save
model.save("{}_policy")
return model
def load_policy(path="{}_policy"):
"""Load trained policy"""
model = PPO.load(path)
return model
if __name__ == "__main__":
# Train policy
model = train_policy()
# Test policy
env = create_env()
obs, _ = env.reset()
for _ in range(1000):
action, _ = model.predict(obs, deterministic=True)
obs, reward, terminated, truncated, info = env.step(action)
if terminated or truncated:
obs, _ = env.reset()
env.close()
"#,
name, name, name
)
}
pub fn llm(name: &str) -> String {
format!(
r#"""
{} - LLM Integration
Auto-generated LLM template.
"""
from transformers import AutoTokenizer, AutoModelForCausalLM
import torch
class {}:
"""LLM wrapper for robot control"""
def __init__(self, model_name="gpt2"):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
# Load model and tokenizer
self.tokenizer = AutoTokenizer.from_pretrained(model_name)
self.model = AutoModelForCausalLM.from_pretrained(model_name)
self.model.to(self.device)
self.model.eval()
def generate_response(self, prompt, max_length=100):
"""Generate text response"""
inputs = self.tokenizer(prompt, return_tensors="pt").to(self.device)
with torch.no_grad():
outputs = self.model.generate(
**inputs,
max_length=max_length,
num_return_sequences=1,
temperature=0.7,
do_sample=True,
)
response = self.tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
def parse_command(self, text):
"""Parse natural language command"""
# TODO: Implement command parsing
# Extract robot actions from LLM response
pass
if __name__ == "__main__":
llm = {}()
# Test generation
prompt = "Robot, please pick up the red cup"
response = llm.generate_response(prompt)
print(f"Response: {{response}}")
"#,
name,
to_pascal_case(name),
to_pascal_case(name)
)
}