AutoAndroidController/py/venv/Lib/site-packages/modelscope/pipelines/cv/content_check_pipeline.py

# Copyright (c) Alibaba, Inc. and its affiliates.
import os.path as osp
from typing import Any, Dict

import cv2
import numpy as np
import PIL
import torch
import torch.nn.functional as F
from torch import nn
from torchvision import transforms

from modelscope.metainfo import Pipelines
from modelscope.outputs import OutputKeys
from modelscope.pipelines import pipeline
from modelscope.pipelines.base import Input, Pipeline
from modelscope.pipelines.builder import PIPELINES
from modelscope.preprocessors import LoadImage
from modelscope.utils.constant import ModelFile, Tasks
from modelscope.utils.logger import get_logger

logger = get_logger()


@PIPELINES.register_module(
    Tasks.image_classification, module_name=Pipelines.content_check)
class ContentCheckPipeline(Pipeline):

    def __init__(self, model: str, **kwargs):
        """
        use `model` to create a content check pipeline for prediction
        Args:
            model: model id on modelscope hub.
        Example:
        ContentCheckPipeline can judge whether the picture is pornographic

        ```python
        >>> from modelscope.pipelines import pipeline
        >>> cc_func = pipeline('image_classification', 'damo/cv_resnet50_image-classification_cc')
        >>> cc_func("https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/content_check.jpg")
        {'scores': [0.2789826989173889], 'labels': 'pornographic'}
        ```
        """

        # content check model
        super().__init__(model=model, **kwargs)
        self.test_transforms = transforms.Compose([
            transforms.Resize(224),
            transforms.CenterCrop(224),
            transforms.ToTensor(),
            transforms.Normalize(
                mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
        ])
        logger.info('content check model loaded!')

    def preprocess(self, input: Input) -> Dict[str, Any]:
        img = LoadImage.convert_to_img(input)
        img = self.test_transforms(img).float()
        result = {}
        result['img'] = img
        return result

    def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
        img = input['img'].unsqueeze(0)
        result = self.model(img)
        score = [1 - F.softmax(result[:, :5])[0][-1].tolist()]
        if score[0] < 0.5:
            label = 'pornographic'
        else:
            label = 'normal'
        return {OutputKeys.SCORES: score, OutputKeys.LABELS: label}

    def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
        return inputs