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47f00cb | 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 | #!/usr/bin/env python3
"""
Clean Multimodal Gemma3 Loader - No Unsloth bullshit
Pure transformers + PEFT implementation
"""
import os
import torch
import torchvision.transforms as transforms
from PIL import Image
from transformers import AutoTokenizer, AutoModelForCausalLM
from peft import PeftModel
import argparse
from multigemma3 import VisionEncoder, VisionProjector, MultimodalGemma3
class MultimodalGemma3Inference:
"""Clean inference class without Unsloth dependencies"""
def __init__(self, device='auto'):
"""
Initialize the inference model
Args:
model_dir: Directory containing saved model components
device: Device to run on ('auto', 'cuda', 'cpu')
"""
if device == 'auto':
device = "cuda" if torch.cuda.is_available() else 'cpu'
self.device = device
# Load metadata
#metadata_path = os.path.join(model_dir, 'metadata.pth')
#metadata = torch.load(metadata_path, map_location=device)
#print(f"Loading model from epoch {metadata['epoch']} with accuracy {metadata['accuracy']:.4f}")
# Load base language model
self.tokenizer = AutoTokenizer.from_pretrained("./saved_models_clean/best/")
self.language_model = AutoModelForCausalLM.from_pretrained(
"./saved_models_clean/best",
torch_dtype=torch.bfloat16,
device_map=device
)
# Load LoRA adapters
#print(f"Loading LoRA adapters from {model_dir}")
#self.language_model = PeftModel.from_pretrained(base_language_model, model_dir)
# Load vision encoder
print("Loading vision encoder...")
self.vision_encoder = VisionEncoder().to(device)
# Load projector
projector_path = os.path.join("./saved_models_clean/best/", "projector.pth")
print(f"Loading projector from {projector_path}")
self.projector = VisionProjector(
self.vision_encoder.output_dim,
self.language_model.config.hidden_size
).to(device=device, dtype=torch.bfloat16)
self.projector.load_state_dict(torch.load(projector_path, map_location=device))
# Create multimodal model
self.model = MultimodalGemma3(
self.language_model, self.projector, self.tokenizer
).to(device)
# Image preprocessing
self.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])
])
print("Model loaded successfully!")
def encode_image(self, image_path_or_pil):
"""
Encode an image to vision embeddings
Args:
image_path_or_pil: Path to image file or PIL Image
Returns:
Vision embeddings tensor
"""
# Load image
if isinstance(image_path_or_pil, str):
image = Image.open(image_path_or_pil).convert('RGB')
else:
image = image_path_or_pil.convert('RGB')
# Preprocess
image_tensor = self.transform(image).unsqueeze(0).to(self.device)
# Extract vision embeddings
with torch.no_grad():
vision_embeds = self.vision_encoder(image_tensor).squeeze(0)
return vision_embeds
def predict(self, image_path_or_pil, prompt="IMG", max_new_tokens=10):
"""
Predict text response for an image
Args:
image_path_or_pil: Path to image file or PIL Image
prompt: Text prompt (default: "IMG")
max_new_tokens: Max tokens to generate
Returns:
Generated text response
"""
# Encode image
vision_embeds = self.encode_image(image_path_or_pil)
# Generate response
response = self.model.generate_response(
vision_embeds, prompt=prompt, max_new_tokens=max_new_tokens
)
return response
def predict_batch(self, images, prompt="IMG", max_new_tokens=10):
"""
Predict for a batch of images
Args:
images: List of image paths or PIL Images
prompt: Text prompt
max_new_tokens: Max tokens to generate
Returns:
List of generated responses
"""
responses = []
for image in images:
response = self.predict(image, prompt, max_new_tokens)
responses.append(response)
return responses
def generate_text(self, prompt, max_new_tokens=50):
"""
Generate pure text response (no vision)
Args:
prompt: Text prompt
max_new_tokens: Max tokens to generate
Returns:
Generated text response
"""
response = self.model.generate_response(
vision_embeds=None, # No vision
prompt=prompt,
max_new_tokens=max_new_tokens
)
return response
def main():
parser = argparse.ArgumentParser(description='Load and test Clean Multimodal Gemma3')
#parser.add_argument('model_dir', type=str, help='Directory containing saved model')
parser.add_argument('image', type=str, help='Path to image file')
parser.add_argument('--prompt', type=str, default='IMG', help='Text prompt')
parser.add_argument('--max_tokens', type=int, default=10, help='Max tokens to generate')
parser.add_argument('--device', type=str, default='auto', help='Device (auto/cuda/cpu)')
args = parser.parse_args()
# Load model
inference_model = MultimodalGemma3Inference(device=args.device)
# Process image
print(f"Processing image: {args.image}")
response = inference_model.predict(
args.image,
prompt=args.prompt,
max_new_tokens=args.max_tokens
)
print(response)
if __name__ == "__main__":
main()
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