為了讓ModelScope的使用者能夠快速、方便的使用平台提供的各類模型，提供了一套功能完備的Python library，其中包含了ModelScope官方模型的實現，以及使用這些模型進行推理，finetune等任務所需的資料預處理，後處理，效果評估等功能相關的程式碼，同時也提供了簡單易用的API，以及豐富的使用範例。透過呼叫library，使用者可以只寫短短的幾行程式碼，就可以完成模型的推理、訓練和評估等任務，也可以在此基礎上快速進行二次開發，實現自己的創新想法。
目前library提供的演算法模型，涵蓋了圖像，自然語言處理，語音，多模態，科學5個主要的AI領域，數十個應用場景任務，具體任務可參考文檔：任務的介紹。
ModelScope Library目前已支援Pytorch和Tensorflow等深度學習框架，未來將不斷更新並擴展更多框架，敬請期待！所有官方模型均可透過ModelScope Library進行模型推理，有些模型也能夠使用該函式庫進行訓練和評估。如需完整的使用信息，請查看相應模型的模型卡片。
在深度學習中，推理是指模型對資料進行預測的過程。 ModelScope執行推理時會利用pipeline來依序執行必要的操作。一個典型的pipeline通常包括資料預處理、模型前向推理和資料後處理三個步驟。
pipeline()方法是ModelScope框架中最基礎的使用者方法之一，可用來快速進行各種領域的模型推理。借助pipeline()方法，使用者只需一行程式碼即可輕鬆完成對特定任務的模型推理。
pipeline()方法是ModelScope框架中最基礎的使用者方法之一，可用來快速進行各種領域的模型推理。借助pipeline()方法，使用者只需一行程式碼即可輕鬆完成對特定任務的模型推理。
本文將簡單介紹如何使用pipeline方法載入模型進行推理。透過pipeline方法，使用者可以輕鬆地從模型倉庫中根據任務類型和模型名稱拉取所需模型進行推理。此方法的主要優點在於簡單易用，能夠快速且有效率地進行模型推論。 pipeline方法的便利之處在於它提供了一種直接的方式來獲取和應用模型，無需用戶深入了解模型的具體細節，從而降低了使用模型的門檻。透過pipeline方法，使用者可以更專注於解決問題和
pipeline函數支援指定特定任務名稱，載入任務預設模型，建立對應pipeline物件。
from modelscope.pipelines import pipelineword_segmentation = pipeline('word-segmentation')input_str = '开源技术小栈作者是Tinywan，你知道不？'print(word_segmentation(input_str))
pipeline;$word_segmentation = $pipeline("word-segmentation");$input_str = "开源技术小栈作者是Tinywan，你知道不？";PyCore::print($word_segmentation($input_str));
線上轉換工具：https://www.swoole.com/ py2php/
/usr/local/php-8.2.14/bin/php demo.php 2024-03-25 21:41:42,434 - modelscope - INFO - PyTorch version 2.2.1 Found.2024-03-25 21:41:42,434 - modelscope - INFO - Loading ast index from /home/www/.cache/modelscope/ast_indexer2024-03-25 21:41:42,577 - modelscope - INFO - Loading done! Current index file version is 1.13.0, with md5 f54e9d2dceb89a6c989540d66db83a65 and a total number of 972 components indexed2024-03-25 21:41:44,661 - modelscope - WARNING - Model revision not specified, use revision: v1.0.32024-03-25 21:41:44,879 - modelscope - INFO - initiate model from /home/www/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base2024-03-25 21:41:44,879 - modelscope - INFO - initiate model from location /home/www/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base.2024-03-25 21:41:44,880 - modelscope - INFO - initialize model from /home/www/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-baseYou are using a model of type bert to instantiate a model of type structbert. This is not supported for all configurations of models and can yield errors.2024-03-25 21:41:48,633 - modelscope - WARNING - No preprocessor field found in cfg.2024-03-25 21:41:48,633 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.2024-03-25 21:41:48,633 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/home/www/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base'}. trying to build by task and model information.2024-03-25 21:41:48,639 - modelscope - INFO - cuda is not available, using cpu instead.2024-03-25 21:41:48,640 - modelscope - WARNING - No preprocessor field found in cfg.2024-03-25 21:41:48,640 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.2024-03-25 21:41:48,640 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/home/www/.cache/modelscope/hub/damo/nlp_structbert_word-segmentation_chinese-base', 'sequence_length': 512}. trying to build by task and model information./home/www/anaconda3/envs/tinywan-modelscope/lib/python3.10/site-packages/transformers/modeling_utils.py:962: FutureWarning: The `device` argument is deprecated and will be removed in v5 of Transformers.warnings.warn({'output': ['开源', '技术', '小', '栈', '作者', '是', 'Tinywan', '，', '你', '知道', '不', '？']}
pipeline物件也支援傳入多個樣本列表輸入，傳回對應輸出列表，每個元素對應輸入樣本的回傳結果。多條文字的推理方式是輸入data在pipeline內部用迭代器單條處理後append到同一個返回List。
from modelscope.pipelines import pipelineword_segmentation = pipeline('word-segmentation')inputs =['开源技术小栈作者是Tinywan，你知道不？','webman这个框架不错，建议你看看']print(word_segmentation(inputs))
pipeline;$word_segmentation = $pipeline("word-segmentation");$inputs = new PyList(["开源技术小栈作者是Tinywan，你知道不？", "webman这个框架不错，建议你看看"]);PyCore::print($word_segmentation($inputs));
[{'output': ['开源', '技术', '小', '栈', '作者', '是', 'Tinywan', '，', '你', '知道', '不', '？']},{'output': ['webman', '这个', '框架', '不错', '，', '建议', '你', '看看']}]
pipeline對於批次推理的支援類似於上面的“輸入多條文字”，差異在於會在使用者指定的batch_size尺度上，在模型forward過程實現批次前向推理。
inputs =['今天天气不错，适合出去游玩','这本书很好，建议你看看']# 指定batch_size参数来支持批量推理print(word_segmentation(inputs, batch_size=2))# 输出[{'output': ['今天', '天气', '不错', '，', '适合', '出去', '游玩']}, {'output': ['这', '本', '书', '很', '好', '，', '建议', '你', '看看']}]
from modelscope.msdatasets import MsDatasetfrom modelscope.pipelines import pipelineinputs = ['今天天气不错，适合出去游玩', '这本书很好，建议你看看']dataset = MsDataset.load(inputs, target='sentence')word_segmentation = pipeline('word-segmentation')outputs = word_segmentation(dataset)for o in outputs:print(o)# 输出{'output': ['今天', '天气', '不错', '，', '适合', '出去', '游玩']}{'output': ['这', '本', '书', '很', '好', '，', '建议', '你', '看看']}
pipeline函數支援傳入實例化的預處理對象、模型對象，從而支援使用者在推理過程中客製化預處理、模型。
from modelscope.models import Modelfrom modelscope.pipelines import pipelinemodel = Model.from_pretrained('damo/nlp_structbert_word-segmentation_chinese-base')word_segmentation = pipeline('word-segmentation', model=model)inputs =['开源技术小栈作者是Tinywan，你知道不？','webman这个框架不错，建议你看看']print(word_segmentation(inputs))
Model;$pipeline = PyCore::import('modelscope.pipelines')->pipeline;$model = $Model->from_pretrained("damo/nlp_structbert_word-segmentation_chinese-base");$word_segmentation = $pipeline("word-segmentation", model: $model);$inputs = new PyList(["开源技术小栈作者是Tinywan，你知道不？", "webman这个框架不错，建议你看看"]);PyCore::print($word_segmentation($inputs));
[{'output': ['开源', '技术', '小', '栈', '作者', '是', 'Tinywan', '，', '你', '知道', '不', '？']},{'output': ['webman', '这个', '框架', '不错', '，', '建议', '你', '看看']}]
建立預處理器和模型物件進行推理
from modelscope.models import Modelfrom modelscope.pipelines import pipelinefrom modelscope.preprocessors import Preprocessor, TokenClassificationTransformersPreprocessormodel = Model.from_pretrained('damo/nlp_structbert_word-segmentation_chinese-base')tokenizer = Preprocessor.from_pretrained(model.model_dir)# Or call the constructor directly: # tokenizer = TokenClassificationTransformersPreprocessor(model.model_dir)word_segmentation = pipeline('word-segmentation', model=model, preprocessor=tokenizer)inputs =['开源技术小栈作者是Tinywan，你知道不？','webman这个框架不错，建议你看看']print(word_segmentation(inputs))[{'output': ['开源', '技术', '小', '栈', '作者', '是', 'Tinywan', '，', '你', '知道', '不', '？']},{'output': ['webman', '这个', '框架', '不错', '，', '建议', '你', '看看']}]
#注意：
pip install opencv-python
没有安装会提示：PHP Fatal error: Uncaught PyError: No module named 'cv2' in /home/www/build/ai/demo3.php:4
否则提示modelscope.pipelines.cv.image_matting_pipeline requires the TensorFlow library but it was not found in your environment. Checkout the instructions on the installation page: https://www.tensorflow.org/install and follow the ones that match your environment.。
报错信息表明，你正在尝试使用一个名为 modelscope.pipelines.cv.image_matting_pipeline 的模块，该模块依赖于 TensorFlow 库。然而，该模块无法正常工作，因为缺少必要的 TensorFlow 依赖。
可以使用以下命令安装最新版本的 TensorFlow
pip install tensorflow
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import cv2from modelscope.pipelines import pipelineportrait_matting = pipeline('portrait-matting')result = portrait_matting('https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/image_matting.png')cv2.imwrite('result.png', result['output_img'])
pipeline;$portrait_matting = $pipeline("portrait-matting");$result = $portrait_matting("https://modelscope.oss-cn-beijing.aliyuncs.com/test/images/image_matting.png");$cv2->imwrite("tinywan_result.png", $result->__getitem__("output_img"));
加载本地文件图片$result = $portrait_matting("./tinywan.png");
/usr/local/php-8.2.14/bin/php tinywan-images.php 2024-03-25 22:17:25,630 - modelscope - INFO - PyTorch version 2.2.1 Found.2024-03-25 22:17:25,631 - modelscope - INFO - TensorFlow version 2.16.1 Found.2024-03-25 22:17:25,631 - modelscope - INFO - Loading ast index from /home/www/.cache/modelscope/ast_indexer2024-03-25 22:17:25,668 - modelscope - INFO - Loading done! Current index file version is 1.13.0, with md5 f54e9d2dceb89a6c989540d66db83a65 and a total number of 972 components indexed2024-03-25 22:17:26,990 - modelscope - WARNING - Model revision not specified, use revision: v1.0.02024-03-25 22:17:27.623085: I tensorflow/core/util/port.cc:113] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.2024-03-25 22:17:27.678592: I tensorflow/core/platform/cpu_feature_guard.cc:210] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.To enable the following instructions: AVX2 AVX512F AVX512_VNNI FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.2024-03-25 22:17:28.551510: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Could not find TensorRT2024-03-25 22:17:29,206 - modelscope - INFO - initiate model from /home/www/.cache/modelscope/hub/damo/cv_unet_image-matting2024-03-25 22:17:29,206 - modelscope - INFO - initiate model from location /home/www/.cache/modelscope/hub/damo/cv_unet_image-matting.2024-03-25 22:17:29,209 - modelscope - WARNING - No preprocessor field found in cfg.2024-03-25 22:17:29,210 - modelscope - WARNING - No val key and type key found in preprocessor domain of configuration.json file.2024-03-25 22:17:29,210 - modelscope - WARNING - Cannot find available config to build preprocessor at mode inference, current config: {'model_dir': '/home/www/.cache/modelscope/hub/damo/cv_unet_image-matting'}. trying to build by task and model information.2024-03-25 22:17:29,210 - modelscope - WARNING - Find task: portrait-matting, model type: None. Insufficient information to build preprocessor, skip building preprocessorWARNING:tensorflow:From /home/www/anaconda3/envs/tinywan-modelscope/lib/python3.10/site-packages/modelscope/utils/device.py:60: is_gpu_available (from tensorflow.python.framework.test_util) is deprecated and will be removed in a future version.Instructions for updating:Use `tf.config.list_physical_devices('GPU')` instead.2024-03-25 22:17:29,213 - modelscope - INFO - loading model from /home/www/.cache/modelscope/hub/damo/cv_unet_image-matting/tf_graph.pbWARNING:tensorflow:From /home/www/anaconda3/envs/tinywan-modelscope/lib/python3.10/site-packages/modelscope/pipelines/cv/image_matting_pipeline.py:45: FastGFile.__init__ (from tensorflow.python.platform.gfile) is deprecated and will be removed in a future version.Instructions for updating:Use tf.gfile.GFile.2024-03-25 22:17:29,745 - modelscope - INFO - load model done
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以上是TensorFlow深度學習架構模型推理Pipeline進行人像摳圖推理的詳細內容。更多資訊請關注PHP中文網其他相關文章！
