mirror of
https://gitcode.com/gh_mirrors/eas/EasyFace.git
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1136 lines
39 KiB
Python
1136 lines
39 KiB
Python
# Copyright (c) Alibaba, Inc. and its affiliates.
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from collections import OrderedDict, namedtuple
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from dataclasses import dataclass, fields
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from modelscope.utils.constant import Tasks
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class OutputKeys(object):
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LOSS = 'loss'
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LOGITS = 'logits'
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SCORES = 'scores'
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SCORE = 'score'
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LABEL = 'label'
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LABELS = 'labels'
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INPUT_IDS = 'input_ids'
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LABEL_POS = 'label_pos'
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POSES = 'poses'
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CAPTION = 'caption'
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BOXES = 'boxes'
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KEYPOINTS = 'keypoints'
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MASKS = 'masks'
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DEPTHS = 'depths'
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DEPTHS_COLOR = 'depths_color'
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LAYOUT = 'layout'
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TEXT = 'text'
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POLYGONS = 'polygons'
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OUTPUT = 'output'
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OUTPUT_IMG = 'output_img'
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OUTPUT_IMGS = 'output_imgs'
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OUTPUT_VIDEO = 'output_video'
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OUTPUT_PCM = 'output_pcm'
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OUTPUT_PCM_LIST = 'output_pcm_list'
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OUTPUT_WAV = 'output_wav'
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IMG_EMBEDDING = 'img_embedding'
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SPK_EMBEDDING = 'spk_embedding'
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SPO_LIST = 'spo_list'
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TEXT_EMBEDDING = 'text_embedding'
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TRANSLATION = 'translation'
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RESPONSE = 'response'
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PREDICTION = 'prediction'
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PREDICTIONS = 'predictions'
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PROBABILITIES = 'probabilities'
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DIALOG_STATES = 'dialog_states'
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VIDEO_EMBEDDING = 'video_embedding'
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UUID = 'uuid'
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WORD = 'word'
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KWS_LIST = 'kws_list'
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SQL_STRING = 'sql_string'
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SQL_QUERY = 'sql_query'
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HISTORY = 'history'
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QUERT_RESULT = 'query_result'
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TIMESTAMPS = 'timestamps'
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SHOT_NUM = 'shot_num'
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SCENE_NUM = 'scene_num'
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SCENE_META_LIST = 'scene_meta_list'
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SHOT_META_LIST = 'shot_meta_list'
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MATCHES = 'matches'
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PCD12 = 'pcd12'
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PCD12_ALIGN = 'pcd12_align'
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TASK_OUTPUTS = {
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# ============ vision tasks ===================
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# ocr detection result for single sample
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# {
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# "polygons": np.array with shape [num_text, 8], each polygon is
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# [x1, y1, x2, y2, x3, y3, x4, y4]
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# }
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Tasks.ocr_detection: [OutputKeys.POLYGONS],
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Tasks.table_recognition: [OutputKeys.POLYGONS],
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Tasks.license_plate_detection: [OutputKeys.POLYGONS, OutputKeys.TEXT],
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# ocr recognition result for single sample
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# {
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# "text": "电子元器件提供BOM配单"
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# }
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Tasks.ocr_recognition: [OutputKeys.TEXT],
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Tasks.sudoku: [OutputKeys.TEXT],
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Tasks.text2sql: [OutputKeys.TEXT],
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# document vl embedding for single sample
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# {
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# "img_embedding": np.array with shape [M, D],
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# "text_embedding": np.array with shape [N, D]
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# }
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Tasks.document_vl_embedding:
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[OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING],
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# face 2d keypoint result for single sample
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# {
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# "keypoints": [
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# [[x, y]*106],
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# [[x, y]*106],
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# [[x, y]*106],
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# ],
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# "poses": [
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# [pitch, roll, yaw],
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# [pitch, roll, yaw],
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# [pitch, roll, yaw],
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# ],
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ]
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# }
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Tasks.face_2d_keypoints:
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[OutputKeys.KEYPOINTS, OutputKeys.POSES, OutputKeys.BOXES],
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# face detection result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05]
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# "keypoints": [
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# [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5],
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# [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5],
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# [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5],
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# [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5],
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# ],
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# }
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Tasks.face_detection:
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[OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
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# card detection result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05]
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# "keypoints": [
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# [x1, y1, x2, y2, x3, y3, x4, y4],
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# [x1, y1, x2, y2, x3, y3, x4, y4],
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# [x1, y1, x2, y2, x3, y3, x4, y4],
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# [x1, y1, x2, y2, x3, y3, x4, y4],
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# ],
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# }
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Tasks.card_detection:
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[OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
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# content check result for single sample
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# {
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# "scores": [0.9] # non sexy probability
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# }
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Tasks.content_check: [OutputKeys.SCORES],
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# image driving perception result for single sample
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# {
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# "masks": [
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# [np.array], # with fixed shape(h=720, w=1280, 3) containing only 0, 1
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# [np.array], # with fixed shape(h=720, w=1280, 3) containing only 0, 1
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# ]
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# }
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Tasks.image_driving_perception: [OutputKeys.BOXES, OutputKeys.MASKS],
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# facial expression recognition result for single sample
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# {
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# "scores": [0.9]
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# "boxes": [x1, y1, x2, y2]
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# }
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Tasks.face_liveness: [OutputKeys.SCORES, OutputKeys.BOXES],
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# face quality assessment for single sample
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# {
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# "scores": [0.9]
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# "boxes": [x1, y1, x2, y2]
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# }
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Tasks.face_quality_assessment: [OutputKeys.SCORES, OutputKeys.BOXES],
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# facial expression recognition result for single sample
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# {
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# "scores": [0.9, 0.1, 0.02, 0.02, 0.02, 0.02, 0.02],
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# "labels": ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
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# }
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Tasks.facial_expression_recognition:
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[OutputKeys.SCORES, OutputKeys.LABELS],
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# face processing base result for single img
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# {
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# "scores": [0.85]
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# "boxes": [x1, y1, x2, y2]
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# "keypoints": [x1, y1, x2, y2, x3, y3, x4, y4]
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# }
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Tasks.face_processing_base: [
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OutputKeys.OUTPUT_IMG, OutputKeys.SCORES, OutputKeys.BOXES,
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OutputKeys.KEYPOINTS
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],
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# face attribute recognition result for single sample
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# {
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# "scores": [[0.9, 0.1], [0.92, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01, 0.01]
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# "labels": [['Male', 'Female'], [0-2, 3-9, 10-19, 20-29, 30-39, 40-49, 50-59, 60-69, 70+]]
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# }
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Tasks.face_attribute_recognition: [OutputKeys.SCORES, OutputKeys.LABELS],
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# face recognition result for single sample
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# {
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# "img_embedding": np.array with shape [1, D],
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# }
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Tasks.face_recognition: [OutputKeys.IMG_EMBEDDING],
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# human detection result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05]
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# "labels": ["person", "person", "person", "person"],
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# }
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#
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Tasks.human_detection:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
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# face generation result for single sample
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# {
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# "output_img": np.array with shape(h, w, 3)
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# }
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Tasks.face_image_generation: [OutputKeys.OUTPUT_IMG],
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# image classification result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05]
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# "labels": ["dog", "horse", "cow", "cat"],
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# }
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Tasks.image_classification: [OutputKeys.SCORES, OutputKeys.LABELS],
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# object detection result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05]
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# "labels": ["dog", "horse", "cow", "cat"],
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# }
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Tasks.image_object_detection:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
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Tasks.domain_specific_object_detection:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
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Tasks.open_vocabulary_detection:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
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# video object detection result for single sample
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# {
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# "scores": [[0.8, 0.25, 0.05, 0.05], [0.9, 0.1, 0.05, 0.05]]
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# "labels": [["person", "traffic light", "car", "bus"],
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# ["person", "traffic light", "car", "bus"]]
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# "boxes":
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# [
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# [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ]
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# ],
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# }
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Tasks.video_object_detection:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
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# 3d object detection result for single sample
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# {
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# "output_img": np.array with shape(h, w, 3)
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# }
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Tasks.object_detection_3d: [OutputKeys.OUTPUT_IMG],
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# instance segmentation result for single sample
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# {
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# "scores": [0.9, 0.1, 0.05, 0.05],
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# "labels": ["dog", "horse", "cow", "cat"],
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# "masks": [
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# np.array # 2D array containing only 0, 1
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# ]
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# }
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Tasks.image_segmentation:
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[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.MASKS],
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# video panoptic segmentation result for single sample
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# "scores": [[0.8, 0.25, 0.05, 0.05], [0.9, 0.1, 0.05, 0.05]]
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# "labels": [["person", "traffic light", "car", "bus"],
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# ["person", "traffic light", "car", "bus"]]
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# "masks": [ #array containing only 0, 1
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# [np.array, np.array, np.array, np.array],
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# [np.array, np.array, np.array, np.array],
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# ]
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# "boxes":
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# [
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# [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ],
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# [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ]
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# ],
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# "uuid": [[0, 1, 2, 3],[0, 1, 2, 3]]
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# }
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Tasks.video_panoptic_segmentation: [
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OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.MASKS,
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OutputKeys.BOXES, OutputKeys.UUID
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],
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# semantic segmentation result for single sample
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# {
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# "masks": [np.array # 2D array with shape [height, width]]
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# }
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Tasks.semantic_segmentation: [OutputKeys.MASKS],
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# image matting result for single sample
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# {
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# "output_img": np.array with shape(h, w, 4)
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# for matting or (h, w, 3) for general purpose
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# , shape(h, w) for crowd counting
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# }
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Tasks.portrait_matting: [OutputKeys.OUTPUT_IMG],
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Tasks.universal_matting: [OutputKeys.OUTPUT_IMG],
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# image_quality_assessment_mos result for a single image is a score in range [0, 1]
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# {0.5}
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Tasks.image_quality_assessment_mos: [OutputKeys.SCORE],
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# image editing task result for a single image
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# {"output_img": np.array with shape (h, w, 3)}
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Tasks.skin_retouching: [OutputKeys.OUTPUT_IMG],
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Tasks.image_super_resolution: [OutputKeys.OUTPUT_IMG],
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Tasks.image_colorization: [OutputKeys.OUTPUT_IMG],
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Tasks.image_color_enhancement: [OutputKeys.OUTPUT_IMG],
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Tasks.image_denoising: [OutputKeys.OUTPUT_IMG],
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Tasks.image_portrait_enhancement: [OutputKeys.OUTPUT_IMG],
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Tasks.crowd_counting: [OutputKeys.SCORES, OutputKeys.OUTPUT_IMG],
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Tasks.image_inpainting: [OutputKeys.OUTPUT_IMG],
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Tasks.image_paintbyexample: [OutputKeys.OUTPUT_IMG],
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# image generation task result for a single image
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# {"output_img": np.array with shape (h, w, 3)}
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Tasks.image_to_image_generation: [OutputKeys.OUTPUT_IMG],
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Tasks.image_to_image_translation: [OutputKeys.OUTPUT_IMG],
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Tasks.image_style_transfer: [OutputKeys.OUTPUT_IMG],
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Tasks.image_portrait_stylization: [OutputKeys.OUTPUT_IMG],
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Tasks.image_body_reshaping: [OutputKeys.OUTPUT_IMG],
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# video editing task result for a single video
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# {"output_video": "path_to_rendered_video"}
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Tasks.video_frame_interpolation: [OutputKeys.OUTPUT_VIDEO],
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Tasks.video_super_resolution: [OutputKeys.OUTPUT_VIDEO],
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Tasks.video_deinterlace: [OutputKeys.OUTPUT_VIDEO],
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Tasks.nerf_recon_acc: [OutputKeys.OUTPUT_VIDEO],
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Tasks.video_colorization: [OutputKeys.OUTPUT_VIDEO],
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# image quality assessment degradation result for single image
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# {
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# "scores": [0.885272, 0.014790631, 0.014558001]
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# "labels": ['噪声强度', '模糊程度', '压缩强度'],
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# }
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Tasks.image_quality_assessment_degradation:
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[OutputKeys.SCORES, OutputKeys.LABELS],
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# live category recognition result for single video
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# {
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# "scores": [0.885272, 0.014790631, 0.014558001]
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# "labels": ['女装/女士精品>>棉衣/棉服', '女装/女士精品>>牛仔裤', '女装/女士精品>>裤子>>休闲裤'],
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# }
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Tasks.live_category: [OutputKeys.SCORES, OutputKeys.LABELS],
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# action recognition result for single video
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# {
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# "output_label": "abseiling"
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# }
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Tasks.action_recognition: [OutputKeys.LABELS],
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# human body keypoints detection result for single sample
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# {
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# "keypoints": [
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# [[x, y]*15],
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# [[x, y]*15],
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# [[x, y]*15]
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# ]
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# "scores": [
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# [[score]*15],
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# [[score]*15],
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# [[score]*15]
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# ]
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# "boxes": [
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# [x1, y1, x2, y2],
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# ]
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# }
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Tasks.body_2d_keypoints: [
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OutputKeys.KEYPOINTS, OutputKeys.SCORES, OutputKeys.BOXES
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],
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# 3D human body keypoints detection result for single sample
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# {
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# "keypoints": [ # 3d pose coordinate in camera coordinate
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# [[x, y, z]*17], # joints of per image
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# [[x, y, z]*17],
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# ...
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# ],
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# "timestamps": [ # timestamps of all frames
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# "00:00:0.230",
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# "00:00:0.560",
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# "00:00:0.690",
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# ],
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# "output_video": "path_to_rendered_video" , this is optional
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# and is only available when the "render" option is enabled.
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# }
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Tasks.body_3d_keypoints: [
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OutputKeys.KEYPOINTS, OutputKeys.TIMESTAMPS, OutputKeys.OUTPUT_VIDEO
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],
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# 3D face reconstruction result for single sample
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# {
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# "output": {
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# "vertices": np.array with shape(n, 3),
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# "faces": np.array with shape(n, 3),
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# "faces_uv": np.array with shape(n, 3),
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# "faces_normal": np.array with shape(n, 3),
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# "colors": np.array with shape(n, 3),
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# "UVs": np.array with shape(n, 2),
|
||
# "normals": np.array with shape(n, 3),
|
||
# "texture_map": np.array with shape(h, w, 3),
|
||
# }
|
||
# }
|
||
Tasks.face_reconstruction: [OutputKeys.OUTPUT],
|
||
|
||
# 2D hand keypoints result for single sample
|
||
# {
|
||
# "keypoints": [
|
||
# [[x, y, score] * 21],
|
||
# [[x, y, score] * 21],
|
||
# [[x, y, score] * 21],
|
||
# ],
|
||
# "boxes": [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ]
|
||
# }
|
||
Tasks.hand_2d_keypoints: [OutputKeys.KEYPOINTS, OutputKeys.BOXES],
|
||
|
||
# video single object tracking result for single video
|
||
# {
|
||
# "boxes": [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ],
|
||
# "timestamps": ["hh:mm:ss", "hh:mm:ss", "hh:mm:ss"]
|
||
# }
|
||
Tasks.video_single_object_tracking: [
|
||
OutputKeys.BOXES, OutputKeys.TIMESTAMPS
|
||
],
|
||
|
||
# video multi object tracking result for single video
|
||
# {
|
||
# "boxes": [
|
||
# [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ...
|
||
# ],
|
||
# [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ...
|
||
# ],
|
||
# [
|
||
# [x1, y1, x2, y2]
|
||
# ...
|
||
# ]
|
||
# ],
|
||
# "labels": [[obj_id0, obj_id1, ...], [obj_id1, obj_id2, ...], [obj_id3, ...]],
|
||
# "timestamps": ["hh:mm:ss", "hh:mm:ss", "hh:mm:ss"]
|
||
# }
|
||
Tasks.video_multi_object_tracking: [
|
||
OutputKeys.BOXES, OutputKeys.LABELS, OutputKeys.TIMESTAMPS
|
||
],
|
||
|
||
# live category recognition result for single video
|
||
# {
|
||
# "scores": [0.885272, 0.014790631, 0.014558001],
|
||
# 'labels': ['修身型棉衣', '高腰牛仔裤', '休闲连体裤']
|
||
# }
|
||
Tasks.live_category: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# video category recognition result for single video
|
||
# {
|
||
# "scores": [0.7716429233551025],
|
||
# "labels": ['生活>>好物推荐']
|
||
# }
|
||
Tasks.video_category: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# image embedding result for a single image
|
||
# {
|
||
# "image_bedding": np.array with shape [D]
|
||
# }
|
||
Tasks.product_retrieval_embedding: [OutputKeys.IMG_EMBEDDING],
|
||
|
||
# video embedding result for single video
|
||
# {
|
||
# "video_embedding": np.array with shape [D],
|
||
# }
|
||
Tasks.video_embedding: [OutputKeys.VIDEO_EMBEDDING],
|
||
|
||
# video stabilization task result for a single video
|
||
# {"output_video": "path_to_rendered_video"}
|
||
Tasks.video_stabilization: [OutputKeys.OUTPUT_VIDEO],
|
||
|
||
# virtual_try_on result for a single sample
|
||
# {
|
||
# "output_img": np.ndarray with shape [height, width, 3]
|
||
# }
|
||
Tasks.virtual_try_on: [OutputKeys.OUTPUT_IMG],
|
||
# text driven segmentation result for single sample
|
||
# {
|
||
# "masks": [
|
||
# np.array # 2D array containing only 0, 255
|
||
# ]
|
||
# }
|
||
Tasks.text_driven_segmentation: [OutputKeys.MASKS],
|
||
# shop segmentation result for single sample
|
||
# {
|
||
# "masks": [
|
||
# np.array # 2D array containing only 0, 255
|
||
# ]
|
||
# }
|
||
Tasks.shop_segmentation: [OutputKeys.MASKS],
|
||
# movide scene segmentation result for a single video
|
||
# {
|
||
# "shot_num":15,
|
||
# "shot_meta_list":
|
||
# [
|
||
# {
|
||
# "frame": [start_frame, end_frame],
|
||
# "timestamps": [start_timestamp, end_timestamp] # ['00:00:01.133', '00:00:02.245']
|
||
#
|
||
# }
|
||
# ]
|
||
# "scene_num":3,
|
||
# "scene_meta_list":
|
||
# [
|
||
# {
|
||
# "shot": [0,1,2],
|
||
# "frame": [start_frame, end_frame],
|
||
# "timestamps": [start_timestamp, end_timestamp] # ['00:00:01.133', '00:00:02.245']
|
||
# }
|
||
# ]
|
||
#
|
||
# }
|
||
Tasks.movie_scene_segmentation: [
|
||
OutputKeys.SHOT_NUM, OutputKeys.SHOT_META_LIST, OutputKeys.SCENE_NUM,
|
||
OutputKeys.SCENE_META_LIST
|
||
],
|
||
|
||
# human whole body keypoints detection result for single sample
|
||
# {
|
||
# "keypoints": [
|
||
# [[x, y]*133],
|
||
# [[x, y]*133],
|
||
# [[x, y]*133]
|
||
# ]
|
||
# "boxes": [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ]
|
||
# }
|
||
Tasks.human_wholebody_keypoint: [OutputKeys.KEYPOINTS, OutputKeys.BOXES],
|
||
|
||
# video summarization result for a single video
|
||
# {
|
||
# "output":
|
||
# [
|
||
# {
|
||
# "frame": [start_frame, end_frame]
|
||
# "timestamps": [start_time, end_time]
|
||
# },
|
||
# {
|
||
# "frame": [start_frame, end_frame]
|
||
# "timestamps": [start_time, end_time]
|
||
# }
|
||
# ]
|
||
# }
|
||
Tasks.video_summarization: [OutputKeys.OUTPUT],
|
||
|
||
# referring video object segmentation result for a single video
|
||
# {
|
||
# "masks": [np.array # 3D array with shape [frame_num, height, width]]
|
||
# "timestamps": ["hh:mm:ss", "hh:mm:ss", "hh:mm:ss"]
|
||
# "output_video": "path_to_rendered_video" , this is optional
|
||
# and is only available when the "render" option is enabled.
|
||
# }
|
||
Tasks.referring_video_object_segmentation: [
|
||
OutputKeys.MASKS, OutputKeys.TIMESTAMPS, OutputKeys.OUTPUT_VIDEO
|
||
],
|
||
|
||
# video human matting result for a single video
|
||
# {
|
||
# "masks": [np.array # 2D array with shape [height, width]]
|
||
# "output_video": "path_to_matting_video"
|
||
# }
|
||
Tasks.video_human_matting: [OutputKeys.MASKS, OutputKeys.OUTPUT_VIDEO],
|
||
|
||
# ============ nlp tasks ===================
|
||
|
||
# text classification result for single sample
|
||
# {
|
||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||
# "labels": ["happy", "sad", "calm", "angry"],
|
||
# }
|
||
Tasks.text_classification: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# sentence similarity result for single sample
|
||
# {
|
||
# "scores": 0.9
|
||
# "labels": "1",
|
||
# }
|
||
Tasks.sentence_similarity: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# nli result for single sample
|
||
# {
|
||
# "labels": ["happy", "sad", "calm", "angry"],
|
||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||
# }
|
||
Tasks.nli: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# sentiment classification result for single sample
|
||
# {
|
||
# 'scores': [0.07183828949928284, 0.9281617403030396],
|
||
# 'labels': ['1', '0']
|
||
# }
|
||
Tasks.sentiment_classification: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# zero-shot classification result for single sample
|
||
# {
|
||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||
# "labels": ["happy", "sad", "calm", "angry"],
|
||
# }
|
||
Tasks.zero_shot_classification: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# relation extraction result for a single sample
|
||
# {
|
||
# "uuid": "人生信息-1",
|
||
# "text": "《父老乡亲》是由是由由中国人民解放军海政文工团创作的军旅歌曲，石顺义作词，王锡仁作曲，范琳琳演唱",
|
||
# "spo_list": [{"subject": "石顺义", "predicate": "国籍", "object": "中国"}]
|
||
# }
|
||
Tasks.relation_extraction: [OutputKeys.SPO_LIST],
|
||
|
||
# translation result for a source sentence
|
||
# {
|
||
# "translation": “北京是中国的首都”
|
||
# }
|
||
Tasks.translation: [OutputKeys.TRANSLATION],
|
||
|
||
# word segmentation result for single sample
|
||
# {
|
||
# "output": ["今天", "天气", "不错", "，", "适合", "出去", "游玩"]
|
||
# }
|
||
# {
|
||
# 'output': ['รถ', 'คัน', 'เก่า', 'ก็', 'ยัง', 'เก็บ', 'เอา']
|
||
# }
|
||
Tasks.word_segmentation: [OutputKeys.OUTPUT],
|
||
|
||
# TODO @wenmeng.zwm support list of result check
|
||
# named entity recognition result for single sample
|
||
# {
|
||
# "output": [
|
||
# {"type": "LOC", "start": 2, "end": 5, "span": "温岭市"},
|
||
# {"type": "LOC", "start": 5, "end": 8, "span": "新河镇"}
|
||
# ]
|
||
# }
|
||
Tasks.named_entity_recognition: [OutputKeys.OUTPUT],
|
||
Tasks.part_of_speech: [OutputKeys.OUTPUT],
|
||
|
||
# text_error_correction result for a single sample
|
||
# {
|
||
# "output": "我想吃苹果"
|
||
# }
|
||
Tasks.text_error_correction: [OutputKeys.OUTPUT],
|
||
# word_alignment result for a single sample
|
||
# {
|
||
# "output": "0-0 1-3 2-4 3-1 4-2 5-5"
|
||
# }
|
||
Tasks.word_alignment: [OutputKeys.OUTPUT],
|
||
Tasks.sentence_embedding: [OutputKeys.TEXT_EMBEDDING, OutputKeys.SCORES],
|
||
Tasks.text_ranking: [OutputKeys.SCORES],
|
||
|
||
# text generation result for single sample
|
||
# {
|
||
# "text": "this is the text generated by a model."
|
||
# }
|
||
Tasks.text_generation: [OutputKeys.TEXT],
|
||
|
||
# fid dialogue result for single sample
|
||
# {
|
||
# "text": "My name is Mike"
|
||
# }
|
||
Tasks.fid_dialogue: [OutputKeys.TEXT],
|
||
|
||
# summarization result for single sample
|
||
# {
|
||
# "text": "this is the text generated by a model."
|
||
# }
|
||
Tasks.text_summarization: [OutputKeys.TEXT],
|
||
|
||
# text generation result for single sample
|
||
# {
|
||
# "text": "北京"
|
||
# }
|
||
Tasks.text2text_generation: [OutputKeys.TEXT],
|
||
|
||
# fill mask result for single sample
|
||
# {
|
||
# "text": "this is the text which masks filled by model."
|
||
# }
|
||
Tasks.fill_mask: [OutputKeys.TEXT],
|
||
|
||
# feature extraction result for single sample
|
||
# {
|
||
# "text_embedding": [[
|
||
# [1.08599677e-04, 1.72710388e-05, 2.95618793e-05, 1.93638436e-04],
|
||
# [6.45841064e-05, 1.15997791e-04, 5.11605394e-05, 9.87020373e-01],
|
||
# [2.66957268e-05, 4.72324500e-05, 9.74208378e-05, 4.18022355e-05]
|
||
# ],
|
||
# [
|
||
# [2.97343540e-05, 5.81317654e-05, 5.44203431e-05, 6.28319322e-05],
|
||
# [8.24327726e-05, 4.66077945e-05, 5.32869453e-05, 4.16190960e-05],
|
||
# [3.61441926e-05, 3.38475402e-05, 3.44323053e-05, 5.70138109e-05]
|
||
# ]
|
||
# ]
|
||
# }
|
||
Tasks.feature_extraction: [OutputKeys.TEXT_EMBEDDING],
|
||
|
||
# (Deprecated) dialog intent prediction result for single sample
|
||
# {'output': {'prediction': array([2.62349960e-03, 4.12110658e-03, 4.12748595e-05, 3.77560973e-05,
|
||
# 1.08599677e-04, 1.72710388e-05, 2.95618793e-05, 1.93638436e-04,
|
||
# 6.45841064e-05, 1.15997791e-04, 5.11605394e-05, 9.87020373e-01,
|
||
# 2.66957268e-05, 4.72324500e-05, 9.74208378e-05, 4.18022355e-05,
|
||
# 2.97343540e-05, 5.81317654e-05, 5.44203431e-05, 6.28319322e-05,
|
||
# 7.34537680e-05, 6.61411541e-05, 3.62534920e-05, 8.58885178e-05,
|
||
# 8.24327726e-05, 4.66077945e-05, 5.32869453e-05, 4.16190960e-05,
|
||
# 5.97518992e-05, 3.92273068e-05, 3.44069012e-05, 9.92335918e-05,
|
||
# 9.25978165e-05, 6.26462061e-05, 3.32317031e-05, 1.32061413e-03,
|
||
# 2.01607945e-05, 3.36636294e-05, 3.99156743e-05, 5.84108493e-05,
|
||
# 2.53432900e-05, 4.95731190e-04, 2.64443643e-05, 4.46992999e-05,
|
||
# 2.42672231e-05, 4.75615161e-05, 2.66230145e-05, 4.00083954e-05,
|
||
# 2.90536875e-04, 4.23891543e-05, 8.63691166e-05, 4.98188965e-05,
|
||
# 3.47019341e-05, 4.52718523e-05, 4.20905781e-05, 5.50173208e-05,
|
||
# 4.92360487e-05, 3.56021264e-05, 2.13957210e-05, 6.17428886e-05,
|
||
# 1.43893281e-04, 7.32152112e-05, 2.91354867e-04, 2.46623786e-05,
|
||
# 3.61441926e-05, 3.38475402e-05, 3.44323053e-05, 5.70138109e-05,
|
||
# 4.31488479e-05, 4.94503947e-05, 4.30105974e-05, 1.00963116e-04,
|
||
# 2.82062047e-05, 1.15582036e-04, 4.48261271e-05, 3.99339879e-05,
|
||
# 7.27692823e-05], dtype=float32), 'label_pos': array([11]), 'label': 'lost_or_stolen_card'}}
|
||
|
||
# (Deprecated) dialog modeling prediction result for single sample
|
||
# {'output' : ['you', 'are', 'welcome', '.', 'have', 'a', 'great', 'day', '!']}
|
||
|
||
# (Deprecated) dialog state tracking result for single sample
|
||
# {
|
||
# "output":{
|
||
# "dialog_states": {
|
||
# "taxi-leaveAt": "none",
|
||
# "taxi-destination": "none",
|
||
# "taxi-departure": "none",
|
||
# "taxi-arriveBy": "none",
|
||
# "restaurant-book_people": "none",
|
||
# "restaurant-book_day": "none",
|
||
# "restaurant-book_time": "none",
|
||
# "restaurant-food": "none",
|
||
# "restaurant-pricerange": "none",
|
||
# "restaurant-name": "none",
|
||
# "restaurant-area": "none",
|
||
# "hotel-book_people": "none",
|
||
# "hotel-book_day": "none",
|
||
# "hotel-book_stay": "none",
|
||
# "hotel-name": "none",
|
||
# "hotel-area": "none",
|
||
# "hotel-parking": "none",
|
||
# "hotel-pricerange": "cheap",
|
||
# "hotel-stars": "none",
|
||
# "hotel-internet": "none",
|
||
# "hotel-type": "true",
|
||
# "attraction-type": "none",
|
||
# "attraction-name": "none",
|
||
# "attraction-area": "none",
|
||
# "train-book_people": "none",
|
||
# "train-leaveAt": "none",
|
||
# "train-destination": "none",
|
||
# "train-day": "none",
|
||
# "train-arriveBy": "none",
|
||
# "train-departure": "none"
|
||
# }
|
||
# }
|
||
# }
|
||
Tasks.task_oriented_conversation: [OutputKeys.OUTPUT],
|
||
|
||
# table-question-answering result for single sample
|
||
# {
|
||
# "sql": "SELECT shop.Name FROM shop."
|
||
# "sql_history": {sel: 0, agg: 0, conds: [[0, 0, 'val']]}
|
||
# }
|
||
Tasks.table_question_answering: [OutputKeys.OUTPUT],
|
||
|
||
# ============ audio tasks ===================
|
||
# asr result for single sample
|
||
# { "text": "每一天都要快乐喔"}
|
||
Tasks.auto_speech_recognition: [OutputKeys.TEXT],
|
||
|
||
# itn result for single sample
|
||
# {"text": "123"}
|
||
Tasks.inverse_text_processing: [OutputKeys.TEXT],
|
||
|
||
# speaker verification for single compare task
|
||
# {'score': 84.2332}
|
||
Tasks.speaker_verification: [OutputKeys.SCORES],
|
||
|
||
# punctuation result for single sample
|
||
# { "text": "你好，明天！"}
|
||
Tasks.punctuation: [OutputKeys.TEXT],
|
||
# language model result for single sample
|
||
# { "text": " hel@@ lo 大 家 好 呀 </s>
|
||
# p( hel@@ | <s> ) = 0.00057767 [ -7.45650959 ]
|
||
# p( lo | hel@@ ) = 0.99832278 [ -0.00167861 ]
|
||
# p( 大 | lo ) = 0.49116334 [ -0.71097857 ]
|
||
# p( 家 | 大 ) = 0.99691027 [ -0.00309453 ]
|
||
# p( 好 | 家 ) = 0.97999156 [ -0.02021134 ]
|
||
# p( 呀 | 好 ) = 0.00461205 [ -5.37908363 ]
|
||
# p( </s> | 呀 ) = 0.01524554 [ -4.18346834 ]
|
||
# logprob= -17.755 ppl= 12.6345
|
||
# "}
|
||
Tasks.language_model: [OutputKeys.TEXT],
|
||
|
||
# audio processed for single file in PCM format
|
||
# {
|
||
# "output_pcm": pcm encoded audio bytes
|
||
# }
|
||
Tasks.speech_signal_process: [OutputKeys.OUTPUT_PCM],
|
||
Tasks.acoustic_echo_cancellation: [OutputKeys.OUTPUT_PCM],
|
||
Tasks.acoustic_noise_suppression: [OutputKeys.OUTPUT_PCM],
|
||
Tasks.speech_separation: [OutputKeys.OUTPUT_PCM_LIST],
|
||
|
||
# text_to_speech result for a single sample
|
||
# {
|
||
# "output_wav": {"input_label" : bytes}
|
||
# }
|
||
Tasks.text_to_speech: [OutputKeys.OUTPUT_WAV],
|
||
|
||
# {
|
||
# "kws_list": [
|
||
# {
|
||
# 'keyword': '', # the keyword spotted
|
||
# 'offset': 19.4, # the keyword start time in second
|
||
# 'length': 0.68, # the keyword length in second
|
||
# 'confidence': 0.85 # the possibility if it is the keyword
|
||
# },
|
||
# ...
|
||
# ]
|
||
# }
|
||
Tasks.keyword_spotting: [OutputKeys.KWS_LIST],
|
||
|
||
# ============ multi-modal tasks ===================
|
||
|
||
# image caption result for single sample
|
||
# {
|
||
# "caption": "this is an image caption text."
|
||
# }
|
||
Tasks.image_captioning: [OutputKeys.CAPTION],
|
||
|
||
# video caption result for single sample
|
||
# {
|
||
# "caption": "this is an video caption text."
|
||
# }
|
||
Tasks.video_captioning: [OutputKeys.CAPTION],
|
||
Tasks.ocr_recognition: [OutputKeys.TEXT],
|
||
|
||
# visual grounding result for single sample
|
||
# {
|
||
# "boxes": [
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# [x1, y1, x2, y2],
|
||
# ],
|
||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||
# }
|
||
Tasks.visual_grounding: [OutputKeys.BOXES, OutputKeys.SCORES],
|
||
|
||
# text_to_image result for samples
|
||
# {
|
||
# "output_imgs": np.ndarray list with shape [[height, width, 3], ...]
|
||
# }
|
||
Tasks.text_to_image_synthesis: [OutputKeys.OUTPUT_IMGS],
|
||
|
||
# text_to_speech result for a single sample
|
||
# {
|
||
# "output_wav": {"input_label" : bytes}
|
||
# }
|
||
Tasks.text_to_speech: [OutputKeys.OUTPUT_WAV],
|
||
|
||
# multi-modal embedding result for single sample
|
||
# {
|
||
# "img_embedding": np.array with shape [1, D],
|
||
# "text_embedding": np.array with shape [1, D]
|
||
# }
|
||
Tasks.multi_modal_embedding: [
|
||
OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING
|
||
],
|
||
|
||
# generative multi-modal embedding result for single sample
|
||
# {
|
||
# "img_embedding": np.array with shape [1, D],
|
||
# "text_embedding": np.array with shape [1, D],
|
||
# "caption": "this is an image caption text."
|
||
# }
|
||
Tasks.generative_multi_modal_embedding: [
|
||
OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING, OutputKeys.CAPTION
|
||
],
|
||
|
||
# multi-modal similarity result for single sample
|
||
# {
|
||
# "img_embedding": np.array with shape [1, D],
|
||
# "text_embedding": np.array with shape [1, D],
|
||
# "similarity": float
|
||
# }
|
||
Tasks.multi_modal_similarity: [
|
||
OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING, OutputKeys.SCORES
|
||
],
|
||
|
||
# VQA result for a sample
|
||
# {"text": "this is a text answser. "}
|
||
Tasks.visual_question_answering: [OutputKeys.TEXT],
|
||
|
||
# VideoQA result for a sample
|
||
# {"text": "this is a text answser. "}
|
||
Tasks.video_question_answering: [OutputKeys.TEXT],
|
||
|
||
# auto_speech_recognition result for a single sample
|
||
# {
|
||
# "text": "每天都要快乐喔"
|
||
# }
|
||
Tasks.auto_speech_recognition: [OutputKeys.TEXT],
|
||
|
||
# {
|
||
# "scores": [0.9, 0.1, 0.1],
|
||
# "labels": ["entailment", "contradiction", "neutral"]
|
||
# }
|
||
Tasks.visual_entailment: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# {
|
||
# 'labels': ['吸烟', '打电话', '吸烟'],
|
||
# 'scores': [0.7527753114700317, 0.753358006477356, 0.6880350708961487],
|
||
# 'boxes': [[547, 2, 1225, 719], [529, 8, 1255, 719], [584, 0, 1269, 719]],
|
||
# 'timestamps': [1, 3, 5]
|
||
# }
|
||
Tasks.action_detection: [
|
||
OutputKeys.TIMESTAMPS,
|
||
OutputKeys.LABELS,
|
||
OutputKeys.SCORES,
|
||
OutputKeys.BOXES,
|
||
],
|
||
|
||
# {
|
||
# 'output': [
|
||
# [{'label': '6527856', 'score': 0.9942756295204163}, {'label': '1000012000', 'score': 0.0379515215754509},
|
||
# {'label': '13421097', 'score': 2.2825044965202324e-08}],
|
||
# [{'label': '1000012000', 'score': 0.910681426525116}, {'label': '6527856', 'score': 0.0005046309670433402},
|
||
# {'label': '13421097', 'score': 2.75914817393641e-06}],
|
||
# [{'label': '1000012000', 'score': 0.910681426525116}, {'label': '6527856', 'score': 0.0005046309670433402},
|
||
# {'label': '13421097', 'score': 2.75914817393641e-06}]]
|
||
# }
|
||
Tasks.faq_question_answering: [OutputKeys.OUTPUT],
|
||
|
||
# image person reid result for single sample
|
||
# {
|
||
# "img_embedding": np.array with shape [1, D],
|
||
# }
|
||
Tasks.image_reid_person: [OutputKeys.IMG_EMBEDDING],
|
||
|
||
# {
|
||
# 'output': ['Done' / 'Decode_Error']
|
||
# }
|
||
Tasks.video_inpainting: [OutputKeys.OUTPUT],
|
||
|
||
# {
|
||
# 'output': ['bixin']
|
||
# }
|
||
Tasks.hand_static: [OutputKeys.OUTPUT],
|
||
|
||
# { 'labels': [2, 1, 0],
|
||
# 'boxes':[[[78, 282, 240, 504], [127, 87, 332, 370], [0, 0, 367, 639]]
|
||
# 'scores':[0.8202137351036072, 0.8987470269203186, 0.9679114818572998]
|
||
# }
|
||
Tasks.face_human_hand_detection: [
|
||
OutputKeys.LABELS, OutputKeys.BOXES, OutputKeys.SCORES
|
||
],
|
||
|
||
# {
|
||
# {'output': 'Happiness', 'boxes': (203, 104, 663, 564)}
|
||
# }
|
||
Tasks.face_emotion: [OutputKeys.OUTPUT, OutputKeys.BOXES],
|
||
|
||
# {
|
||
# "masks": [
|
||
# np.array # 2D array containing only 0, 255
|
||
# ]
|
||
# }
|
||
Tasks.product_segmentation: [OutputKeys.MASKS],
|
||
|
||
# image_skychange result for a single sample
|
||
# {
|
||
# "output_img": np.ndarray with shape [height, width, 3]
|
||
# }
|
||
Tasks.image_skychange: [OutputKeys.OUTPUT_IMG],
|
||
# {
|
||
# 'scores': [0.1, 0.2, 0.3, ...]
|
||
# }
|
||
Tasks.translation_evaluation: [OutputKeys.SCORES],
|
||
|
||
# video object segmentation result for a single video
|
||
# {
|
||
# "masks": [np.array # 3D array with shape [frame_num, height, width]]
|
||
# }
|
||
Tasks.video_object_segmentation: [OutputKeys.MASKS],
|
||
|
||
# motion generation result for a single input
|
||
# {
|
||
# "keypoints": [np.array # 3D array with shape [frame_num, joint_num, 3]]
|
||
# "output_video": "path_to_rendered_video"
|
||
# }
|
||
Tasks.motion_generation: [OutputKeys.KEYPOINTS, OutputKeys.OUTPUT_VIDEO],
|
||
|
||
# bad image detecting for a single input
|
||
# {
|
||
# "scores": [0.8, 0.1, 0.1]
|
||
# "labels": ["正常", "花屏", "绿屏"],
|
||
Tasks.bad_image_detecting: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
|
||
# vision efficient tuning result for single sample
|
||
# {
|
||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||
# "labels": ["dog", "horse", "cow", "cat"],
|
||
# }
|
||
Tasks.vision_efficient_tuning: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||
Tasks.document_grounded_dialog_generate: [OutputKeys.TEXT],
|
||
Tasks.document_grounded_dialog_rerank: [OutputKeys.OUTPUT],
|
||
Tasks.document_grounded_dialog_retrieval: [OutputKeys.OUTPUT],
|
||
}
|
||
|
||
|
||
class ModelOutputBase(list):
|
||
def __post_init__(self):
|
||
self.reconstruct()
|
||
self.post_init = True
|
||
|
||
def reconstruct(self):
|
||
# Low performance, but low frequency.
|
||
self.clear()
|
||
for idx, key in enumerate(self.keys()):
|
||
self.append(getattr(self, key))
|
||
|
||
def __getitem__(self, item):
|
||
if isinstance(item, str):
|
||
if hasattr(self, item):
|
||
return getattr(self, item)
|
||
elif isinstance(item, (int, slice)):
|
||
return super().__getitem__(item)
|
||
raise IndexError(f'No Index {item} found in the dataclass.')
|
||
|
||
def __setitem__(self, key, value):
|
||
if isinstance(key, str):
|
||
if key in [f.name for f in fields(self)]:
|
||
if key not in self.keys():
|
||
super().__setattr__(key, value)
|
||
self.reconstruct()
|
||
elif id(getattr(self, key)) != id(value):
|
||
super().__setattr__(key, value)
|
||
super().__setitem__(self.keys().index(key), value)
|
||
else:
|
||
super().__setattr__(key, value)
|
||
elif isinstance(key, int):
|
||
super().__setitem__(key, value)
|
||
key_name = self.keys()[key]
|
||
super().__setattr__(key_name, value)
|
||
|
||
def __setattr__(self, key, value):
|
||
if getattr(self, 'post_init', False):
|
||
return self.__setitem__(key, value)
|
||
else:
|
||
return super().__setattr__(key, value)
|
||
|
||
def keys(self):
|
||
return [
|
||
f.name for f in fields(self) if getattr(self, f.name) is not None
|
||
]
|
||
|
||
def items(self):
|
||
return self.to_dict().items()
|
||
|
||
def to_dict(self):
|
||
output = OrderedDict()
|
||
for key in self.keys():
|
||
output[key] = getattr(self, key)
|
||
return output
|