Deepfake-QualityAssess-88M

- Python >= 3.9
- PyTorch >= 2.0.0
- torch_npu >= 2.0.0
- transformers >= 4.30.0
- Pillow >= 9.0.0
- NumPy >= 1.20.0
- 昇腾 CANN 工具包（可选，用于 ATC 模型转换）
- 昇腾驱动和固件（适配 Ascend910）

# 安装 PyTorch 和 torch_npu
pip install torch torch_npu transformers Pillow numpy -i https://mirrors.aliyun.com/pypi/simple/

# 设置 transformers 离线模式（避免访问 huggingface.co）
export TRANSFORMERS_OFFLINE=1
export HF_HUB_OFFLINE=1

python3 inference.py [image_path]

# 使用默认测试图片
python3 inference.py

# 指定测试图片
python3 inference.py /path/to/your/image.jpg

# 安装依赖
pip install -r requirements.txt

# 运行推理
python3 inference.py <image_path>

# 运行精度对比
python3 compare_cpu_npu.py <image_path>

python3 compare_cpu_npu.py

from transformers import ViTForImageClassification, ViTImageProcessor
from PIL import Image
import torch

# 加载模型和处理器
model = ViTForImageClassification.from_pretrained("MODEL_PATH")
processor = ViTImageProcessor.from_pretrained("MODEL_PATH")

# 迁移到 NPU
device = torch.device("npu:0")
model = model.to(device)
model.eval()

# 预处理图像
image = Image.open("image.jpg").convert("RGB")
inputs = processor(images=image, return_tensors="pt")
inputs = {k: v.to(device) for k, v in inputs.items()}

# 推理
with torch.no_grad():
    outputs = model(**inputs)
    logits = outputs.logits
    probs = torch.nn.functional.softmax(logits, dim=-1)
    pred = torch.argmax(logits, dim=-1).item()

print(f"Predicted: {pred} -> {model.config.id2label[pred]}")

Deepfake-QualityAssess-88M/
├── inference.py          # NPU 推理脚本
├── compare_cpu_npu.py    # CPU/NPU 精度对比脚本
├── requirements.txt      # Python 依赖
├── model.om              # ATC 转换后的 OM 模型（可选）
└── readme.md             # 本文档

# NPU 推理
python3 inference.py --device npu

# CPU 推理
python3 inference.py --device cpu