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8ea29a1df4
Link: https://code.alibaba-inc.com/Ali-MaaS/MaaS-lib/codereview/11199857 * init quadtree code * quadtree attention image matching can run in modelscope pipeline * jit build quadtree attention * update license info
462 lines
15 KiB
Python
462 lines
15 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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import enum
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class Fields(object):
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""" Names for different application fields
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"""
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cv = 'cv'
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nlp = 'nlp'
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audio = 'audio'
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multi_modal = 'multi-modal'
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science = 'science'
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class CVTasks(object):
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# ocr
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ocr_detection = 'ocr-detection'
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ocr_recognition = 'ocr-recognition'
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table_recognition = 'table-recognition'
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license_plate_detection = 'license-plate-detection'
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# human face body related
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animal_recognition = 'animal-recognition'
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face_detection = 'face-detection'
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face_liveness = 'face-liveness'
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card_detection = 'card-detection'
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face_recognition = 'face-recognition'
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facial_expression_recognition = 'facial-expression-recognition'
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facial_landmark_confidence = 'facial-landmark-confidence'
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face_processing_base = 'face-processing-base'
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face_attribute_recognition = 'face-attribute-recognition'
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face_2d_keypoints = 'face-2d-keypoints'
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human_detection = 'human-detection'
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human_object_interaction = 'human-object-interaction'
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face_image_generation = 'face-image-generation'
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body_2d_keypoints = 'body-2d-keypoints'
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body_3d_keypoints = 'body-3d-keypoints'
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hand_2d_keypoints = 'hand-2d-keypoints'
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general_recognition = 'general-recognition'
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human_wholebody_keypoint = 'human-wholebody-keypoint'
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image_classification = 'image-classification'
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image_multilabel_classification = 'image-multilabel-classification'
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image_classification_imagenet = 'image-classification-imagenet'
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image_classification_dailylife = 'image-classification-dailylife'
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image_object_detection = 'image-object-detection'
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video_object_detection = 'video-object-detection'
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image_segmentation = 'image-segmentation'
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semantic_segmentation = 'semantic-segmentation'
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image_depth_estimation = 'image-depth-estimation'
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indoor_layout_estimation = 'indoor-layout-estimation'
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video_depth_estimation = 'video-depth-estimation'
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panorama_depth_estimation = 'panorama-depth-estimation'
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portrait_matting = 'portrait-matting'
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text_driven_segmentation = 'text-driven-segmentation'
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shop_segmentation = 'shop-segmentation'
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hand_static = 'hand-static'
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face_human_hand_detection = 'face-human-hand-detection'
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face_emotion = 'face-emotion'
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product_segmentation = 'product-segmentation'
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image_matching = 'image-matching'
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crowd_counting = 'crowd-counting'
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# image editing
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skin_retouching = 'skin-retouching'
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image_super_resolution = 'image-super-resolution'
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image_colorization = 'image-colorization'
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image_color_enhancement = 'image-color-enhancement'
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image_denoising = 'image-denoising'
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image_deblurring = 'image-deblurring'
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image_portrait_enhancement = 'image-portrait-enhancement'
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image_inpainting = 'image-inpainting'
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image_skychange = 'image-skychange'
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# image generation
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image_to_image_translation = 'image-to-image-translation'
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image_to_image_generation = 'image-to-image-generation'
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image_style_transfer = 'image-style-transfer'
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image_portrait_stylization = 'image-portrait-stylization'
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image_body_reshaping = 'image-body-reshaping'
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image_embedding = 'image-embedding'
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image_face_fusion = 'image-face-fusion'
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product_retrieval_embedding = 'product-retrieval-embedding'
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# video recognition
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live_category = 'live-category'
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action_recognition = 'action-recognition'
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action_detection = 'action-detection'
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video_category = 'video-category'
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video_embedding = 'video-embedding'
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virtual_try_on = 'virtual-try-on'
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movie_scene_segmentation = 'movie-scene-segmentation'
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language_guided_video_summarization = 'language-guided-video-summarization'
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vop_retrieval = 'video-text-retrieval'
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# video segmentation
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video_object_segmentation = 'video-object-segmentation'
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referring_video_object_segmentation = 'referring-video-object-segmentation'
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video_human_matting = 'video-human-matting'
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# video editing
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video_inpainting = 'video-inpainting'
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video_frame_interpolation = 'video-frame-interpolation'
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video_stabilization = 'video-stabilization'
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video_super_resolution = 'video-super-resolution'
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# reid and tracking
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video_single_object_tracking = 'video-single-object-tracking'
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video_multi_object_tracking = 'video-multi-object-tracking'
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video_summarization = 'video-summarization'
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image_reid_person = 'image-reid-person'
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# pointcloud task
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pointcloud_sceneflow_estimation = 'pointcloud-sceneflow-estimation'
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# image multi-view depth estimation
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image_multi_view_depth_estimation = 'image-multi-view-depth-estimation'
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# domain specific object detection
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domain_specific_object_detection = 'domain-specific-object-detection'
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class NLPTasks(object):
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# nlp tasks
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word_segmentation = 'word-segmentation'
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part_of_speech = 'part-of-speech'
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named_entity_recognition = 'named-entity-recognition'
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nli = 'nli'
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sentiment_classification = 'sentiment-classification'
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sentiment_analysis = 'sentiment-analysis'
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sentence_similarity = 'sentence-similarity'
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text_classification = 'text-classification'
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sentence_embedding = 'sentence-embedding'
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text_ranking = 'text-ranking'
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relation_extraction = 'relation-extraction'
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zero_shot = 'zero-shot'
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translation = 'translation'
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token_classification = 'token-classification'
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conversational = 'conversational'
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text_generation = 'text-generation'
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text2text_generation = 'text2text-generation'
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task_oriented_conversation = 'task-oriented-conversation'
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dialog_intent_prediction = 'dialog-intent-prediction'
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dialog_state_tracking = 'dialog-state-tracking'
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table_question_answering = 'table-question-answering'
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fill_mask = 'fill-mask'
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text_summarization = 'text-summarization'
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question_answering = 'question-answering'
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code_translation = 'code-translation'
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code_generation = 'code-generation'
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zero_shot_classification = 'zero-shot-classification'
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backbone = 'backbone'
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text_error_correction = 'text-error-correction'
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faq_question_answering = 'faq-question-answering'
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information_extraction = 'information-extraction'
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document_segmentation = 'document-segmentation'
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extractive_summarization = 'extractive-summarization'
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feature_extraction = 'feature-extraction'
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translation_evaluation = 'translation-evaluation'
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sudoku = 'sudoku'
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text2sql = 'text2sql'
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class AudioTasks(object):
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# audio tasks
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auto_speech_recognition = 'auto-speech-recognition'
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text_to_speech = 'text-to-speech'
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speech_signal_process = 'speech-signal-process'
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speech_separation = 'speech-separation'
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acoustic_echo_cancellation = 'acoustic-echo-cancellation'
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acoustic_noise_suppression = 'acoustic-noise-suppression'
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keyword_spotting = 'keyword-spotting'
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inverse_text_processing = 'inverse-text-processing'
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punctuation = 'punctuation'
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speaker_verification = 'speaker-verification'
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class MultiModalTasks(object):
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# multi-modal tasks
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image_captioning = 'image-captioning'
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visual_grounding = 'visual-grounding'
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text_to_image_synthesis = 'text-to-image-synthesis'
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multi_modal_embedding = 'multi-modal-embedding'
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generative_multi_modal_embedding = 'generative-multi-modal-embedding'
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multi_modal_similarity = 'multi-modal-similarity'
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visual_question_answering = 'visual-question-answering'
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visual_entailment = 'visual-entailment'
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video_multi_modal_embedding = 'video-multi-modal-embedding'
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image_text_retrieval = 'image-text-retrieval'
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document_vl_embedding = 'document-vl-embedding'
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video_captioning = 'video-captioning'
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video_question_answering = 'video-question-answering'
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class ScienceTasks(object):
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protein_structure = 'protein-structure'
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class TasksIODescriptions(object):
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image_to_image = 'image_to_image',
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images_to_image = 'images_to_image',
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image_to_text = 'image_to_text',
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seed_to_image = 'seed_to_image',
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text_to_speech = 'text_to_speech',
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text_to_text = 'text_to_text',
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speech_to_text = 'speech_to_text',
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speech_to_speech = 'speech_to_speech'
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speeches_to_speech = 'speeches_to_speech',
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visual_grounding = 'visual_grounding',
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visual_question_answering = 'visual_question_answering',
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visual_entailment = 'visual_entailment',
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generative_multi_modal_embedding = 'generative_multi_modal_embedding'
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class Tasks(CVTasks, NLPTasks, AudioTasks, MultiModalTasks, ScienceTasks):
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""" Names for tasks supported by modelscope.
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Holds the standard task name to use for identifying different tasks.
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This should be used to register models, pipelines, trainers.
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"""
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reverse_field_index = {}
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@staticmethod
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def find_field_by_task(task_name):
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if len(Tasks.reverse_field_index) == 0:
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# Lazy init, not thread safe
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field_dict = {
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Fields.cv: [
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getattr(Tasks, attr) for attr in dir(CVTasks)
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if not attr.startswith('__')
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],
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Fields.nlp: [
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getattr(Tasks, attr) for attr in dir(NLPTasks)
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if not attr.startswith('__')
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],
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Fields.audio: [
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getattr(Tasks, attr) for attr in dir(AudioTasks)
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if not attr.startswith('__')
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],
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Fields.multi_modal: [
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getattr(Tasks, attr) for attr in dir(MultiModalTasks)
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if not attr.startswith('__')
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],
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Fields.science: [
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getattr(Tasks, attr) for attr in dir(ScienceTasks)
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if not attr.startswith('__')
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],
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}
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for field, tasks in field_dict.items():
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for task in tasks:
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if task in Tasks.reverse_field_index:
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raise ValueError(f'Duplicate task: {task}')
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Tasks.reverse_field_index[task] = field
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return Tasks.reverse_field_index.get(task_name)
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class InputFields(object):
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""" Names for input data fields in the input data for pipelines
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"""
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img = 'img'
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text = 'text'
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audio = 'audio'
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class Hubs(enum.Enum):
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""" Source from which an entity (such as a Dataset or Model) is stored
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"""
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modelscope = 'modelscope'
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huggingface = 'huggingface'
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class DownloadMode(enum.Enum):
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""" How to treat existing datasets
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"""
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REUSE_DATASET_IF_EXISTS = 'reuse_dataset_if_exists'
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FORCE_REDOWNLOAD = 'force_redownload'
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class DownloadChannel(enum.Enum):
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""" Channels of datasets downloading for uv/pv counting.
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"""
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LOCAL = 'local'
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DSW = 'dsw'
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EAIS = 'eais'
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class UploadMode(enum.Enum):
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""" How to upload object to remote.
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"""
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# Upload all objects from local, existing remote objects may be overwritten. (Default)
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OVERWRITE = 'overwrite'
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# Upload local objects in append mode, skipping all existing remote objects.
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APPEND = 'append'
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class DatasetFormations(enum.Enum):
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""" How a dataset is organized and interpreted
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"""
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# formation that is compatible with official huggingface dataset, which
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# organizes whole dataset into one single (zip) file.
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hf_compatible = 1
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# native modelscope formation that supports, among other things,
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# multiple files in a dataset
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native = 2
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# for local meta cache mark
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formation_mark_ext = '.formation_mark'
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DatasetMetaFormats = {
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DatasetFormations.native: ['.json'],
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DatasetFormations.hf_compatible: ['.py'],
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}
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class ModelFile(object):
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CONFIGURATION = 'configuration.json'
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README = 'README.md'
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TF_SAVED_MODEL_FILE = 'saved_model.pb'
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TF_GRAPH_FILE = 'tf_graph.pb'
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TF_CHECKPOINT_FOLDER = 'tf_ckpts'
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TF_CKPT_PREFIX = 'ckpt-'
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TORCH_MODEL_FILE = 'pytorch_model.pt'
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TORCH_MODEL_BIN_FILE = 'pytorch_model.bin'
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VOCAB_FILE = 'vocab.txt'
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ONNX_MODEL_FILE = 'model.onnx'
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LABEL_MAPPING = 'label_mapping.json'
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TRAIN_OUTPUT_DIR = 'output'
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TS_MODEL_FILE = 'model.ts'
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YAML_FILE = 'model.yaml'
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TOKENIZER_FOLDER = 'tokenizer'
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class Invoke(object):
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KEY = 'invoked_by'
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PRETRAINED = 'from_pretrained'
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PIPELINE = 'pipeline'
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TRAINER = 'trainer'
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LOCAL_TRAINER = 'local_trainer'
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PREPROCESSOR = 'preprocessor'
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class ConfigFields(object):
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""" First level keyword in configuration file
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"""
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framework = 'framework'
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task = 'task'
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pipeline = 'pipeline'
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model = 'model'
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dataset = 'dataset'
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preprocessor = 'preprocessor'
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train = 'train'
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evaluation = 'evaluation'
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postprocessor = 'postprocessor'
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class ConfigKeys(object):
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"""Fixed keywords in configuration file"""
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train = 'train'
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val = 'val'
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test = 'test'
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class Requirements(object):
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"""Requirement names for each module
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"""
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protobuf = 'protobuf'
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sentencepiece = 'sentencepiece'
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sklearn = 'sklearn'
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scipy = 'scipy'
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timm = 'timm'
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tokenizers = 'tokenizers'
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tf = 'tf'
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torch = 'torch'
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class Frameworks(object):
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tf = 'tensorflow'
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torch = 'pytorch'
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kaldi = 'kaldi'
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DEFAULT_MODEL_REVISION = None
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MASTER_MODEL_BRANCH = 'master'
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DEFAULT_REPOSITORY_REVISION = 'master'
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DEFAULT_DATASET_REVISION = 'master'
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DEFAULT_DATASET_NAMESPACE = 'modelscope'
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DEFAULT_DATA_ACCELERATION_ENDPOINT = 'https://oss-accelerate.aliyuncs.com'
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class ModeKeys:
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TRAIN = 'train'
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EVAL = 'eval'
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INFERENCE = 'inference'
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class LogKeys:
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ITER = 'iter'
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ITER_TIME = 'iter_time'
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EPOCH = 'epoch'
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LR = 'lr' # learning rate
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MODE = 'mode'
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DATA_LOAD_TIME = 'data_load_time'
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ETA = 'eta' # estimated time of arrival
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MEMORY = 'memory'
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LOSS = 'loss'
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class TrainerStages:
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before_run = 'before_run'
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before_train_epoch = 'before_train_epoch'
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before_train_iter = 'before_train_iter'
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after_train_iter = 'after_train_iter'
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after_train_epoch = 'after_train_epoch'
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before_val_epoch = 'before_val_epoch'
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before_val_iter = 'before_val_iter'
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after_val_iter = 'after_val_iter'
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after_val_epoch = 'after_val_epoch'
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after_run = 'after_run'
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class ColorCodes:
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MAGENTA = '\033[95m'
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YELLOW = '\033[93m'
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GREEN = '\033[92m'
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RED = '\033[91m'
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END = '\033[0m'
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class Devices:
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"""device used for training and inference"""
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cpu = 'cpu'
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gpu = 'gpu'
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# Supported extensions for text datasets.
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EXTENSIONS_TO_LOAD = {
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'csv': 'csv',
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'tsv': 'csv',
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'json': 'json',
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'jsonl': 'json',
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'parquet': 'parquet',
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'txt': 'text'
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}
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class DatasetPathName:
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META_NAME = 'meta'
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DATA_FILES_NAME = 'data_files'
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LOCK_FILE_NAME_ANY = 'any'
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LOCK_FILE_NAME_DELIMITER = '-'
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class MetaDataFields:
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ARGS_BIG_DATA = 'big_data'
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DatasetVisibilityMap = {1: 'private', 3: 'internal', 5: 'public'}
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