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@@ -2,140 +2,141 @@
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class Models(object):
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""" Names for different models.
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"""Names for different models.
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Holds the standard model name to use for identifying different model.
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This should be used to register models.
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Model name should only contain model info but not task info.
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"""
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# tinynas models
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tinynas_detection = 'tinynas-detection'
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tinynas_damoyolo = 'tinynas-damoyolo'
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tinynas_detection = "tinynas-detection"
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tinynas_damoyolo = "tinynas-damoyolo"
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# vision models
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detection = 'detection'
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realtime_object_detection = 'realtime-object-detection'
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realtime_video_object_detection = 'realtime-video-object-detection'
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scrfd = 'scrfd'
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classification_model = 'ClassificationModel'
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nafnet = 'nafnet'
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csrnet = 'csrnet'
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cascade_mask_rcnn_swin = 'cascade_mask_rcnn_swin'
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gpen = 'gpen'
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product_retrieval_embedding = 'product-retrieval-embedding'
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body_2d_keypoints = 'body-2d-keypoints'
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body_3d_keypoints = 'body-3d-keypoints'
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crowd_counting = 'HRNetCrowdCounting'
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face_2d_keypoints = 'face-2d-keypoints'
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panoptic_segmentation = 'swinL-panoptic-segmentation'
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image_reid_person = 'passvitb'
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image_inpainting = 'FFTInpainting'
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video_summarization = 'pgl-video-summarization'
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swinL_semantic_segmentation = 'swinL-semantic-segmentation'
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vitadapter_semantic_segmentation = 'vitadapter-semantic-segmentation'
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text_driven_segmentation = 'text-driven-segmentation'
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resnet50_bert = 'resnet50-bert'
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referring_video_object_segmentation = 'swinT-referring-video-object-segmentation'
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fer = 'fer'
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retinaface = 'retinaface'
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shop_segmentation = 'shop-segmentation'
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mogface = 'mogface'
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mtcnn = 'mtcnn'
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ulfd = 'ulfd'
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video_inpainting = 'video-inpainting'
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human_wholebody_keypoint = 'human-wholebody-keypoint'
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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_body_reshaping = 'image-body-reshaping'
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detection = "detection"
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realtime_object_detection = "realtime-object-detection"
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realtime_video_object_detection = "realtime-video-object-detection"
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scrfd = "scrfd"
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classification_model = "ClassificationModel"
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nafnet = "nafnet"
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csrnet = "csrnet"
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cascade_mask_rcnn_swin = "cascade_mask_rcnn_swin"
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gpen = "gpen"
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product_retrieval_embedding = "product-retrieval-embedding"
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body_2d_keypoints = "body-2d-keypoints"
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body_3d_keypoints = "body-3d-keypoints"
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crowd_counting = "HRNetCrowdCounting"
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face_2d_keypoints = "face-2d-keypoints"
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panoptic_segmentation = "swinL-panoptic-segmentation"
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image_reid_person = "passvitb"
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image_inpainting = "FFTInpainting"
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video_summarization = "pgl-video-summarization"
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swinL_semantic_segmentation = "swinL-semantic-segmentation"
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vitadapter_semantic_segmentation = "vitadapter-semantic-segmentation"
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text_driven_segmentation = "text-driven-segmentation"
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resnet50_bert = "resnet50-bert"
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referring_video_object_segmentation = "swinT-referring-video-object-segmentation"
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fer = "fer"
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retinaface = "retinaface"
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shop_segmentation = "shop-segmentation"
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mogface = "mogface"
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mtcnn = "mtcnn"
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ulfd = "ulfd"
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video_inpainting = "video-inpainting"
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human_wholebody_keypoint = "human-wholebody-keypoint"
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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_body_reshaping = "image-body-reshaping"
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# EasyCV models
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yolox = 'YOLOX'
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segformer = 'Segformer'
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hand_2d_keypoints = 'HRNet-Hand2D-Keypoints'
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image_object_detection_auto = 'image-object-detection-auto'
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yolox = "YOLOX"
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segformer = "Segformer"
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hand_2d_keypoints = "HRNet-Hand2D-Keypoints"
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image_object_detection_auto = "image-object-detection-auto"
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# nlp models
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bert = 'bert'
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palm = 'palm-v2'
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structbert = 'structbert'
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deberta_v2 = 'deberta_v2'
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veco = 'veco'
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translation = 'csanmt-translation'
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space_dst = 'space-dst'
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space_intent = 'space-intent'
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space_modeling = 'space-modeling'
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space_T_en = 'space-T-en'
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space_T_cn = 'space-T-cn'
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tcrf = 'transformer-crf'
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tcrf_wseg = 'transformer-crf-for-word-segmentation'
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transformer_softmax = 'transformer-softmax'
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lcrf = 'lstm-crf'
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lcrf_wseg = 'lstm-crf-for-word-segmentation'
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gcnncrf = 'gcnn-crf'
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bart = 'bart'
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gpt3 = 'gpt3'
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gpt_neo = 'gpt-neo'
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plug = 'plug'
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bert_for_ds = 'bert-for-document-segmentation'
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ponet = 'ponet'
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T5 = 'T5'
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mglm = 'mglm'
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bloom = 'bloom'
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bert = "bert"
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palm = "palm-v2"
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structbert = "structbert"
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deberta_v2 = "deberta_v2"
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veco = "veco"
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translation = "csanmt-translation"
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space_dst = "space-dst"
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space_intent = "space-intent"
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space_modeling = "space-modeling"
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space_T_en = "space-T-en"
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space_T_cn = "space-T-cn"
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tcrf = "transformer-crf"
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tcrf_wseg = "transformer-crf-for-word-segmentation"
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transformer_softmax = "transformer-softmax"
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lcrf = "lstm-crf"
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lcrf_wseg = "lstm-crf-for-word-segmentation"
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gcnncrf = "gcnn-crf"
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bart = "bart"
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gpt3 = "gpt3"
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gpt_neo = "gpt-neo"
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plug = "plug"
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bert_for_ds = "bert-for-document-segmentation"
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ponet = "ponet"
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T5 = "T5"
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mglm = "mglm"
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bloom = "bloom"
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# audio models
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sambert_hifigan = 'sambert-hifigan'
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speech_frcrn_ans_cirm_16k = 'speech_frcrn_ans_cirm_16k'
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speech_dfsmn_kws_char_farfield = 'speech_dfsmn_kws_char_farfield'
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kws_kwsbp = 'kws-kwsbp'
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generic_asr = 'generic-asr'
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sambert_hifigan = "sambert-hifigan"
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speech_frcrn_ans_cirm_16k = "speech_frcrn_ans_cirm_16k"
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speech_dfsmn_kws_char_farfield = "speech_dfsmn_kws_char_farfield"
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kws_kwsbp = "kws-kwsbp"
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generic_asr = "generic-asr"
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# multi-modal models
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ofa = 'ofa'
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clip = 'clip-multi-modal-embedding'
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gemm = 'gemm-generative-multi-modal'
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mplug = 'mplug'
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diffusion = 'diffusion-text-to-image-synthesis'
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multi_stage_diffusion = 'multi-stage-diffusion-text-to-image-synthesis'
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team = 'team-multi-modal-similarity'
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video_clip = 'video-clip-multi-modal-embedding'
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ofa = "ofa"
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clip = "clip-multi-modal-embedding"
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gemm = "gemm-generative-multi-modal"
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mplug = "mplug"
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diffusion = "diffusion-text-to-image-synthesis"
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multi_stage_diffusion = "multi-stage-diffusion-text-to-image-synthesis"
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team = "team-multi-modal-similarity"
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video_clip = "video-clip-multi-modal-embedding"
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# science models
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unifold = 'unifold'
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unifold_symmetry = 'unifold-symmetry'
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unifold = "unifold"
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unifold_symmetry = "unifold-symmetry"
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class TaskModels(object):
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# nlp task
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text_classification = 'text-classification'
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token_classification = 'token-classification'
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information_extraction = 'information-extraction'
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fill_mask = 'fill-mask'
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feature_extraction = 'feature-extraction'
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text_generation = 'text-generation'
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text_classification = "text-classification"
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token_classification = "token-classification"
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information_extraction = "information-extraction"
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fill_mask = "fill-mask"
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feature_extraction = "feature-extraction"
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text_generation = "text-generation"
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class Heads(object):
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# nlp heads
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# text cls
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text_classification = 'text-classification'
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text_classification = "text-classification"
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# fill mask
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fill_mask = 'fill-mask'
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bert_mlm = 'bert-mlm'
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roberta_mlm = 'roberta-mlm'
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fill_mask = "fill-mask"
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bert_mlm = "bert-mlm"
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roberta_mlm = "roberta-mlm"
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# token cls
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token_classification = 'token-classification'
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token_classification = "token-classification"
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# extraction
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information_extraction = 'information-extraction'
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information_extraction = "information-extraction"
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# text gen
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text_generation = 'text-generation'
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text_generation = "text-generation"
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class Pipelines(object):
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""" Names for different pipelines.
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"""Names for different pipelines.
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Holds the standard pipline name to use for identifying different pipeline.
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This should be used to register pipelines.
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@@ -144,148 +145,151 @@ class Pipelines(object):
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should use task name for this pipeline.
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For pipeline which suuport only one model, we should use ${Model}-${Task} as its name.
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"""
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# vision tasks
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portrait_matting = 'unet-image-matting'
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image_denoise = 'nafnet-image-denoise'
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person_image_cartoon = 'unet-person-image-cartoon'
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ocr_detection = 'resnet18-ocr-detection'
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table_recognition = 'dla34-table-recognition'
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action_recognition = 'TAdaConv_action-recognition'
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animal_recognition = 'resnet101-animal-recognition'
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general_recognition = 'resnet101-general-recognition'
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cmdssl_video_embedding = 'cmdssl-r2p1d_video_embedding'
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hicossl_video_embedding = 'hicossl-s3dg-video_embedding'
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body_2d_keypoints = 'hrnetv2w32_body-2d-keypoints_image'
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body_3d_keypoints = 'canonical_body-3d-keypoints_video'
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hand_2d_keypoints = 'hrnetv2w18_hand-2d-keypoints_image'
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human_detection = 'resnet18-human-detection'
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object_detection = 'vit-object-detection'
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easycv_detection = 'easycv-detection'
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easycv_segmentation = 'easycv-segmentation'
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face_2d_keypoints = 'mobilenet_face-2d-keypoints_alignment'
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salient_detection = 'u2net-salient-detection'
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image_classification = 'image-classification'
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face_detection = 'resnet-face-detection-scrfd10gkps'
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card_detection = 'resnet-card-detection-scrfd34gkps'
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ulfd_face_detection = 'manual-face-detection-ulfd'
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facial_expression_recognition = 'vgg19-facial-expression-recognition-fer'
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retina_face_detection = 'resnet50-face-detection-retinaface'
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mog_face_detection = 'resnet101-face-detection-cvpr22papermogface'
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mtcnn_face_detection = 'manual-face-detection-mtcnn'
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live_category = 'live-category'
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general_image_classification = 'vit-base_image-classification_ImageNet-labels'
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daily_image_classification = 'vit-base_image-classification_Dailylife-labels'
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image_color_enhance = 'csrnet-image-color-enhance'
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virtual_try_on = 'virtual-try-on'
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image_colorization = 'unet-image-colorization'
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image_style_transfer = 'AAMS-style-transfer'
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image_super_resolution = 'rrdb-image-super-resolution'
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face_image_generation = 'gan-face-image-generation'
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product_retrieval_embedding = 'resnet50-product-retrieval-embedding'
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realtime_object_detection = 'cspnet_realtime-object-detection_yolox'
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realtime_video_object_detection = 'cspnet_realtime-video-object-detection_streamyolo'
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face_recognition = 'ir101-face-recognition-cfglint'
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image_instance_segmentation = 'cascade-mask-rcnn-swin-image-instance-segmentation'
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image2image_translation = 'image-to-image-translation'
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live_category = 'live-category'
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video_category = 'video-category'
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ocr_recognition = 'convnextTiny-ocr-recognition'
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image_portrait_enhancement = 'gpen-image-portrait-enhancement'
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image_to_image_generation = 'image-to-image-generation'
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image_object_detection_auto = 'yolox_image-object-detection-auto'
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skin_retouching = 'unet-skin-retouching'
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tinynas_classification = 'tinynas-classification'
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tinynas_detection = 'tinynas-detection'
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crowd_counting = 'hrnet-crowd-counting'
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action_detection = 'ResNetC3D-action-detection'
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video_single_object_tracking = 'ostrack-vitb-video-single-object-tracking'
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image_panoptic_segmentation = 'image-panoptic-segmentation'
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video_summarization = 'googlenet_pgl_video_summarization'
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image_semantic_segmentation = 'image-semantic-segmentation'
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image_reid_person = 'passvitb-image-reid-person'
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image_inpainting = 'fft-inpainting'
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text_driven_segmentation = 'text-driven-segmentation'
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movie_scene_segmentation = 'resnet50-bert-movie-scene-segmentation'
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shop_segmentation = 'shop-segmentation'
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video_inpainting = 'video-inpainting'
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human_wholebody_keypoint = 'hrnetw48_human-wholebody-keypoint_image'
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pst_action_recognition = 'patchshift-action-recognition'
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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_body_reshaping = 'flow-based-body-reshaping'
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referring_video_object_segmentation = 'referring-video-object-segmentation'
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portrait_matting = "unet-image-matting"
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image_denoise = "nafnet-image-denoise"
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person_image_cartoon = "unet-person-image-cartoon"
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ocr_detection = "resnet18-ocr-detection"
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table_recognition = "dla34-table-recognition"
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action_recognition = "TAdaConv_action-recognition"
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animal_recognition = "resnet101-animal-recognition"
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general_recognition = "resnet101-general-recognition"
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cmdssl_video_embedding = "cmdssl-r2p1d_video_embedding"
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hicossl_video_embedding = "hicossl-s3dg-video_embedding"
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body_2d_keypoints = "hrnetv2w32_body-2d-keypoints_image"
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body_3d_keypoints = "canonical_body-3d-keypoints_video"
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hand_2d_keypoints = "hrnetv2w18_hand-2d-keypoints_image"
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human_detection = "resnet18-human-detection"
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object_detection = "vit-object-detection"
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easycv_detection = "easycv-detection"
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easycv_segmentation = "easycv-segmentation"
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face_2d_keypoints = "mobilenet_face-2d-keypoints_alignment"
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salient_detection = "u2net-salient-detection"
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image_classification = "image-classification"
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face_detection = "resnet-face-detection-scrfd10gkps"
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card_detection = "resnet-card-detection-scrfd34gkps"
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ulfd_face_detection = "manual-face-detection-ulfd"
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facial_expression_recognition = "vgg19-facial-expression-recognition-fer"
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retina_face_detection = "resnet50-face-detection-retinaface"
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mog_face_detection = "resnet101-face-detection-cvpr22papermogface"
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mtcnn_face_detection = "manual-face-detection-mtcnn"
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live_category = "live-category"
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general_image_classification = "vit-base_image-classification_ImageNet-labels"
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daily_image_classification = "vit-base_image-classification_Dailylife-labels"
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image_color_enhance = "csrnet-image-color-enhance"
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virtual_try_on = "virtual-try-on"
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image_colorization = "unet-image-colorization"
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image_style_transfer = "AAMS-style-transfer"
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image_super_resolution = "rrdb-image-super-resolution"
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face_image_generation = "gan-face-image-generation"
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product_retrieval_embedding = "resnet50-product-retrieval-embedding"
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realtime_object_detection = "cspnet_realtime-object-detection_yolox"
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realtime_video_object_detection = (
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"cspnet_realtime-video-object-detection_streamyolo"
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)
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face_recognition = "ir101-face-recognition-cfglint"
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image_instance_segmentation = "cascade-mask-rcnn-swin-image-instance-segmentation"
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image2image_translation = "image-to-image-translation"
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live_category = "live-category"
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video_category = "video-category"
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ocr_recognition = "convnextTiny-ocr-recognition"
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image_portrait_enhancement = "gpen-image-portrait-enhancement"
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image_to_image_generation = "image-to-image-generation"
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image_object_detection_auto = "yolox_image-object-detection-auto"
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skin_retouching = "unet-skin-retouching"
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tinynas_classification = "tinynas-classification"
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tinynas_detection = "tinynas-detection"
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crowd_counting = "hrnet-crowd-counting"
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action_detection = "ResNetC3D-action-detection"
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video_single_object_tracking = "ostrack-vitb-video-single-object-tracking"
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image_panoptic_segmentation = "image-panoptic-segmentation"
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video_summarization = "googlenet_pgl_video_summarization"
|
||||
image_semantic_segmentation = "image-semantic-segmentation"
|
||||
image_reid_person = "passvitb-image-reid-person"
|
||||
image_inpainting = "fft-inpainting"
|
||||
text_driven_segmentation = "text-driven-segmentation"
|
||||
movie_scene_segmentation = "resnet50-bert-movie-scene-segmentation"
|
||||
shop_segmentation = "shop-segmentation"
|
||||
video_inpainting = "video-inpainting"
|
||||
human_wholebody_keypoint = "hrnetw48_human-wholebody-keypoint_image"
|
||||
pst_action_recognition = "patchshift-action-recognition"
|
||||
hand_static = "hand-static"
|
||||
face_human_hand_detection = "face-human-hand-detection"
|
||||
face_emotion = "face-emotion"
|
||||
product_segmentation = "product-segmentation"
|
||||
image_body_reshaping = "flow-based-body-reshaping"
|
||||
referring_video_object_segmentation = "referring-video-object-segmentation"
|
||||
|
||||
# nlp tasks
|
||||
automatic_post_editing = 'automatic-post-editing'
|
||||
translation_quality_estimation = 'translation-quality-estimation'
|
||||
domain_classification = 'domain-classification'
|
||||
sentence_similarity = 'sentence-similarity'
|
||||
word_segmentation = 'word-segmentation'
|
||||
multilingual_word_segmentation = 'multilingual-word-segmentation'
|
||||
word_segmentation_thai = 'word-segmentation-thai'
|
||||
part_of_speech = 'part-of-speech'
|
||||
named_entity_recognition = 'named-entity-recognition'
|
||||
named_entity_recognition_thai = 'named-entity-recognition-thai'
|
||||
named_entity_recognition_viet = 'named-entity-recognition-viet'
|
||||
text_generation = 'text-generation'
|
||||
text2text_generation = 'text2text-generation'
|
||||
sentiment_analysis = 'sentiment-analysis'
|
||||
sentiment_classification = 'sentiment-classification'
|
||||
text_classification = 'text-classification'
|
||||
fill_mask = 'fill-mask'
|
||||
fill_mask_ponet = 'fill-mask-ponet'
|
||||
csanmt_translation = 'csanmt-translation'
|
||||
nli = 'nli'
|
||||
dialog_intent_prediction = 'dialog-intent-prediction'
|
||||
dialog_modeling = 'dialog-modeling'
|
||||
dialog_state_tracking = 'dialog-state-tracking'
|
||||
zero_shot_classification = 'zero-shot-classification'
|
||||
text_error_correction = 'text-error-correction'
|
||||
plug_generation = 'plug-generation'
|
||||
gpt3_generation = 'gpt3-generation'
|
||||
faq_question_answering = 'faq-question-answering'
|
||||
conversational_text_to_sql = 'conversational-text-to-sql'
|
||||
table_question_answering_pipeline = 'table-question-answering-pipeline'
|
||||
sentence_embedding = 'sentence-embedding'
|
||||
text_ranking = 'text-ranking'
|
||||
relation_extraction = 'relation-extraction'
|
||||
document_segmentation = 'document-segmentation'
|
||||
feature_extraction = 'feature-extraction'
|
||||
mglm_text_summarization = 'mglm-text-summarization'
|
||||
translation_en_to_de = 'translation_en_to_de' # keep it underscore
|
||||
translation_en_to_ro = 'translation_en_to_ro' # keep it underscore
|
||||
translation_en_to_fr = 'translation_en_to_fr' # keep it underscore
|
||||
token_classification = 'token-classification'
|
||||
automatic_post_editing = "automatic-post-editing"
|
||||
translation_quality_estimation = "translation-quality-estimation"
|
||||
domain_classification = "domain-classification"
|
||||
sentence_similarity = "sentence-similarity"
|
||||
word_segmentation = "word-segmentation"
|
||||
multilingual_word_segmentation = "multilingual-word-segmentation"
|
||||
word_segmentation_thai = "word-segmentation-thai"
|
||||
part_of_speech = "part-of-speech"
|
||||
named_entity_recognition = "named-entity-recognition"
|
||||
named_entity_recognition_thai = "named-entity-recognition-thai"
|
||||
named_entity_recognition_viet = "named-entity-recognition-viet"
|
||||
text_generation = "text-generation"
|
||||
text2text_generation = "text2text-generation"
|
||||
sentiment_analysis = "sentiment-analysis"
|
||||
sentiment_classification = "sentiment-classification"
|
||||
text_classification = "text-classification"
|
||||
fill_mask = "fill-mask"
|
||||
fill_mask_ponet = "fill-mask-ponet"
|
||||
csanmt_translation = "csanmt-translation"
|
||||
nli = "nli"
|
||||
dialog_intent_prediction = "dialog-intent-prediction"
|
||||
dialog_modeling = "dialog-modeling"
|
||||
dialog_state_tracking = "dialog-state-tracking"
|
||||
zero_shot_classification = "zero-shot-classification"
|
||||
text_error_correction = "text-error-correction"
|
||||
plug_generation = "plug-generation"
|
||||
gpt3_generation = "gpt3-generation"
|
||||
faq_question_answering = "faq-question-answering"
|
||||
conversational_text_to_sql = "conversational-text-to-sql"
|
||||
table_question_answering_pipeline = "table-question-answering-pipeline"
|
||||
sentence_embedding = "sentence-embedding"
|
||||
text_ranking = "text-ranking"
|
||||
relation_extraction = "relation-extraction"
|
||||
document_segmentation = "document-segmentation"
|
||||
feature_extraction = "feature-extraction"
|
||||
mglm_text_summarization = "mglm-text-summarization"
|
||||
translation_en_to_de = "translation_en_to_de" # keep it underscore
|
||||
translation_en_to_ro = "translation_en_to_ro" # keep it underscore
|
||||
translation_en_to_fr = "translation_en_to_fr" # keep it underscore
|
||||
token_classification = "token-classification"
|
||||
|
||||
# audio tasks
|
||||
sambert_hifigan_tts = 'sambert-hifigan-tts'
|
||||
speech_dfsmn_aec_psm_16k = 'speech-dfsmn-aec-psm-16k'
|
||||
speech_frcrn_ans_cirm_16k = 'speech_frcrn_ans_cirm_16k'
|
||||
speech_dfsmn_kws_char_farfield = 'speech_dfsmn_kws_char_farfield'
|
||||
kws_kwsbp = 'kws-kwsbp'
|
||||
asr_inference = 'asr-inference'
|
||||
sambert_hifigan_tts = "sambert-hifigan-tts"
|
||||
speech_dfsmn_aec_psm_16k = "speech-dfsmn-aec-psm-16k"
|
||||
speech_frcrn_ans_cirm_16k = "speech_frcrn_ans_cirm_16k"
|
||||
speech_dfsmn_kws_char_farfield = "speech_dfsmn_kws_char_farfield"
|
||||
kws_kwsbp = "kws-kwsbp"
|
||||
asr_inference = "asr-inference"
|
||||
|
||||
# multi-modal tasks
|
||||
image_captioning = 'image-captioning'
|
||||
multi_modal_embedding = 'multi-modal-embedding'
|
||||
generative_multi_modal_embedding = 'generative-multi-modal-embedding'
|
||||
visual_question_answering = 'visual-question-answering'
|
||||
visual_grounding = 'visual-grounding'
|
||||
visual_entailment = 'visual-entailment'
|
||||
multi_modal_similarity = 'multi-modal-similarity'
|
||||
text_to_image_synthesis = 'text-to-image-synthesis'
|
||||
video_multi_modal_embedding = 'video-multi-modal-embedding'
|
||||
image_text_retrieval = 'image-text-retrieval'
|
||||
ofa_ocr_recognition = 'ofa-ocr-recognition'
|
||||
image_captioning = "image-captioning"
|
||||
multi_modal_embedding = "multi-modal-embedding"
|
||||
generative_multi_modal_embedding = "generative-multi-modal-embedding"
|
||||
visual_question_answering = "visual-question-answering"
|
||||
visual_grounding = "visual-grounding"
|
||||
visual_entailment = "visual-entailment"
|
||||
multi_modal_similarity = "multi-modal-similarity"
|
||||
text_to_image_synthesis = "text-to-image-synthesis"
|
||||
video_multi_modal_embedding = "video-multi-modal-embedding"
|
||||
image_text_retrieval = "image-text-retrieval"
|
||||
ofa_ocr_recognition = "ofa-ocr-recognition"
|
||||
|
||||
# science tasks
|
||||
protein_structure = 'unifold-protein-structure'
|
||||
protein_structure = "unifold-protein-structure"
|
||||
|
||||
|
||||
class Trainers(object):
|
||||
""" Names for different trainer.
|
||||
"""Names for different trainer.
|
||||
|
||||
Holds the standard trainer name to use for identifying different trainer.
|
||||
This should be used to register trainers.
|
||||
@@ -294,41 +298,41 @@ class Trainers(object):
|
||||
For a model specific Trainer, you can use ${ModelName}-${Task}-trainer.
|
||||
"""
|
||||
|
||||
default = 'trainer'
|
||||
easycv = 'easycv'
|
||||
default = "trainer"
|
||||
easycv = "easycv"
|
||||
|
||||
# multi-modal trainers
|
||||
clip_multi_modal_embedding = 'clip-multi-modal-embedding'
|
||||
ofa = 'ofa'
|
||||
mplug = 'mplug'
|
||||
clip_multi_modal_embedding = "clip-multi-modal-embedding"
|
||||
ofa = "ofa"
|
||||
mplug = "mplug"
|
||||
|
||||
# cv trainers
|
||||
image_instance_segmentation = 'image-instance-segmentation'
|
||||
image_portrait_enhancement = 'image-portrait-enhancement'
|
||||
video_summarization = 'video-summarization'
|
||||
movie_scene_segmentation = 'movie-scene-segmentation'
|
||||
face_detection_scrfd = 'face-detection-scrfd'
|
||||
card_detection_scrfd = 'card-detection-scrfd'
|
||||
image_inpainting = 'image-inpainting'
|
||||
referring_video_object_segmentation = 'referring-video-object-segmentation'
|
||||
image_classification_team = 'image-classification-team'
|
||||
image_instance_segmentation = "image-instance-segmentation"
|
||||
image_portrait_enhancement = "image-portrait-enhancement"
|
||||
video_summarization = "video-summarization"
|
||||
movie_scene_segmentation = "movie-scene-segmentation"
|
||||
face_detection_scrfd = "face-detection-scrfd"
|
||||
card_detection_scrfd = "card-detection-scrfd"
|
||||
image_inpainting = "image-inpainting"
|
||||
referring_video_object_segmentation = "referring-video-object-segmentation"
|
||||
image_classification_team = "image-classification-team"
|
||||
|
||||
# nlp trainers
|
||||
bert_sentiment_analysis = 'bert-sentiment-analysis'
|
||||
dialog_modeling_trainer = 'dialog-modeling-trainer'
|
||||
dialog_intent_trainer = 'dialog-intent-trainer'
|
||||
nlp_base_trainer = 'nlp-base-trainer'
|
||||
nlp_veco_trainer = 'nlp-veco-trainer'
|
||||
nlp_text_ranking_trainer = 'nlp-text-ranking-trainer'
|
||||
text_generation_trainer = 'text-generation-trainer'
|
||||
bert_sentiment_analysis = "bert-sentiment-analysis"
|
||||
dialog_modeling_trainer = "dialog-modeling-trainer"
|
||||
dialog_intent_trainer = "dialog-intent-trainer"
|
||||
nlp_base_trainer = "nlp-base-trainer"
|
||||
nlp_veco_trainer = "nlp-veco-trainer"
|
||||
nlp_text_ranking_trainer = "nlp-text-ranking-trainer"
|
||||
text_generation_trainer = "text-generation-trainer"
|
||||
|
||||
# audio trainers
|
||||
speech_frcrn_ans_cirm_16k = 'speech_frcrn_ans_cirm_16k'
|
||||
speech_dfsmn_kws_char_farfield = 'speech_dfsmn_kws_char_farfield'
|
||||
speech_frcrn_ans_cirm_16k = "speech_frcrn_ans_cirm_16k"
|
||||
speech_dfsmn_kws_char_farfield = "speech_dfsmn_kws_char_farfield"
|
||||
|
||||
|
||||
class Preprocessors(object):
|
||||
""" Names for different preprocessor.
|
||||
"""Names for different preprocessor.
|
||||
|
||||
Holds the standard preprocessor name to use for identifying different preprocessor.
|
||||
This should be used to register preprocessors.
|
||||
@@ -339,168 +343,171 @@ class Preprocessors(object):
|
||||
"""
|
||||
|
||||
# cv preprocessor
|
||||
load_image = 'load-image'
|
||||
image_denoie_preprocessor = 'image-denoise-preprocessor'
|
||||
image_color_enhance_preprocessor = 'image-color-enhance-preprocessor'
|
||||
image_instance_segmentation_preprocessor = 'image-instance-segmentation-preprocessor'
|
||||
image_portrait_enhancement_preprocessor = 'image-portrait-enhancement-preprocessor'
|
||||
video_summarization_preprocessor = 'video-summarization-preprocessor'
|
||||
movie_scene_segmentation_preprocessor = 'movie-scene-segmentation-preprocessor'
|
||||
load_image = "load-image"
|
||||
image_denoie_preprocessor = "image-denoise-preprocessor"
|
||||
image_color_enhance_preprocessor = "image-color-enhance-preprocessor"
|
||||
image_instance_segmentation_preprocessor = (
|
||||
"image-instance-segmentation-preprocessor"
|
||||
)
|
||||
image_portrait_enhancement_preprocessor = "image-portrait-enhancement-preprocessor"
|
||||
video_summarization_preprocessor = "video-summarization-preprocessor"
|
||||
movie_scene_segmentation_preprocessor = "movie-scene-segmentation-preprocessor"
|
||||
|
||||
# nlp preprocessor
|
||||
sen_sim_tokenizer = 'sen-sim-tokenizer'
|
||||
cross_encoder_tokenizer = 'cross-encoder-tokenizer'
|
||||
bert_seq_cls_tokenizer = 'bert-seq-cls-tokenizer'
|
||||
text_gen_tokenizer = 'text-gen-tokenizer'
|
||||
text2text_gen_preprocessor = 'text2text-gen-preprocessor'
|
||||
text_gen_jieba_tokenizer = 'text-gen-jieba-tokenizer'
|
||||
text2text_translate_preprocessor = 'text2text-translate-preprocessor'
|
||||
token_cls_tokenizer = 'token-cls-tokenizer'
|
||||
ner_tokenizer = 'ner-tokenizer'
|
||||
thai_ner_tokenizer = 'thai-ner-tokenizer'
|
||||
viet_ner_tokenizer = 'viet-ner-tokenizer'
|
||||
nli_tokenizer = 'nli-tokenizer'
|
||||
sen_cls_tokenizer = 'sen-cls-tokenizer'
|
||||
dialog_intent_preprocessor = 'dialog-intent-preprocessor'
|
||||
dialog_modeling_preprocessor = 'dialog-modeling-preprocessor'
|
||||
dialog_state_tracking_preprocessor = 'dialog-state-tracking-preprocessor'
|
||||
sbert_token_cls_tokenizer = 'sbert-token-cls-tokenizer'
|
||||
zero_shot_cls_tokenizer = 'zero-shot-cls-tokenizer'
|
||||
text_error_correction = 'text-error-correction'
|
||||
sentence_embedding = 'sentence-embedding'
|
||||
text_ranking = 'text-ranking'
|
||||
sequence_labeling_tokenizer = 'sequence-labeling-tokenizer'
|
||||
word_segment_text_to_label_preprocessor = 'word-segment-text-to-label-preprocessor'
|
||||
thai_wseg_tokenizer = 'thai-wseg-tokenizer'
|
||||
fill_mask = 'fill-mask'
|
||||
fill_mask_ponet = 'fill-mask-ponet'
|
||||
faq_question_answering_preprocessor = 'faq-question-answering-preprocessor'
|
||||
conversational_text_to_sql = 'conversational-text-to-sql'
|
||||
table_question_answering_preprocessor = 'table-question-answering-preprocessor'
|
||||
re_tokenizer = 're-tokenizer'
|
||||
document_segmentation = 'document-segmentation'
|
||||
feature_extraction = 'feature-extraction'
|
||||
mglm_summarization = 'mglm-summarization'
|
||||
sentence_piece = 'sentence-piece'
|
||||
sen_sim_tokenizer = "sen-sim-tokenizer"
|
||||
cross_encoder_tokenizer = "cross-encoder-tokenizer"
|
||||
bert_seq_cls_tokenizer = "bert-seq-cls-tokenizer"
|
||||
text_gen_tokenizer = "text-gen-tokenizer"
|
||||
text2text_gen_preprocessor = "text2text-gen-preprocessor"
|
||||
text_gen_jieba_tokenizer = "text-gen-jieba-tokenizer"
|
||||
text2text_translate_preprocessor = "text2text-translate-preprocessor"
|
||||
token_cls_tokenizer = "token-cls-tokenizer"
|
||||
ner_tokenizer = "ner-tokenizer"
|
||||
thai_ner_tokenizer = "thai-ner-tokenizer"
|
||||
viet_ner_tokenizer = "viet-ner-tokenizer"
|
||||
nli_tokenizer = "nli-tokenizer"
|
||||
sen_cls_tokenizer = "sen-cls-tokenizer"
|
||||
dialog_intent_preprocessor = "dialog-intent-preprocessor"
|
||||
dialog_modeling_preprocessor = "dialog-modeling-preprocessor"
|
||||
dialog_state_tracking_preprocessor = "dialog-state-tracking-preprocessor"
|
||||
sbert_token_cls_tokenizer = "sbert-token-cls-tokenizer"
|
||||
zero_shot_cls_tokenizer = "zero-shot-cls-tokenizer"
|
||||
text_error_correction = "text-error-correction"
|
||||
sentence_embedding = "sentence-embedding"
|
||||
text_ranking = "text-ranking"
|
||||
sequence_labeling_tokenizer = "sequence-labeling-tokenizer"
|
||||
word_segment_text_to_label_preprocessor = "word-segment-text-to-label-preprocessor"
|
||||
thai_wseg_tokenizer = "thai-wseg-tokenizer"
|
||||
fill_mask = "fill-mask"
|
||||
fill_mask_ponet = "fill-mask-ponet"
|
||||
faq_question_answering_preprocessor = "faq-question-answering-preprocessor"
|
||||
conversational_text_to_sql = "conversational-text-to-sql"
|
||||
table_question_answering_preprocessor = "table-question-answering-preprocessor"
|
||||
re_tokenizer = "re-tokenizer"
|
||||
document_segmentation = "document-segmentation"
|
||||
feature_extraction = "feature-extraction"
|
||||
mglm_summarization = "mglm-summarization"
|
||||
sentence_piece = "sentence-piece"
|
||||
|
||||
# audio preprocessor
|
||||
linear_aec_fbank = 'linear-aec-fbank'
|
||||
text_to_tacotron_symbols = 'text-to-tacotron-symbols'
|
||||
wav_to_lists = 'wav-to-lists'
|
||||
wav_to_scp = 'wav-to-scp'
|
||||
linear_aec_fbank = "linear-aec-fbank"
|
||||
text_to_tacotron_symbols = "text-to-tacotron-symbols"
|
||||
wav_to_lists = "wav-to-lists"
|
||||
wav_to_scp = "wav-to-scp"
|
||||
|
||||
# multi-modal preprocessor
|
||||
ofa_tasks_preprocessor = 'ofa-tasks-preprocessor'
|
||||
clip_preprocessor = 'clip-preprocessor'
|
||||
mplug_tasks_preprocessor = 'mplug-tasks-preprocessor'
|
||||
ofa_tasks_preprocessor = "ofa-tasks-preprocessor"
|
||||
clip_preprocessor = "clip-preprocessor"
|
||||
mplug_tasks_preprocessor = "mplug-tasks-preprocessor"
|
||||
|
||||
# science preprocessor
|
||||
unifold_preprocessor = 'unifold-preprocessor'
|
||||
unifold_preprocessor = "unifold-preprocessor"
|
||||
|
||||
|
||||
class Metrics(object):
|
||||
""" Names for different metrics.
|
||||
"""
|
||||
"""Names for different metrics."""
|
||||
|
||||
# accuracy
|
||||
accuracy = 'accuracy'
|
||||
multi_average_precision = 'mAP'
|
||||
audio_noise_metric = 'audio-noise-metric'
|
||||
accuracy = "accuracy"
|
||||
multi_average_precision = "mAP"
|
||||
audio_noise_metric = "audio-noise-metric"
|
||||
|
||||
# text gen
|
||||
BLEU = 'bleu'
|
||||
BLEU = "bleu"
|
||||
|
||||
# metrics for image denoise task
|
||||
image_denoise_metric = 'image-denoise-metric'
|
||||
image_denoise_metric = "image-denoise-metric"
|
||||
|
||||
# metric for image instance segmentation task
|
||||
image_ins_seg_coco_metric = 'image-ins-seg-coco-metric'
|
||||
image_ins_seg_coco_metric = "image-ins-seg-coco-metric"
|
||||
# metrics for sequence classification task
|
||||
seq_cls_metric = 'seq-cls-metric'
|
||||
seq_cls_metric = "seq-cls-metric"
|
||||
# metrics for token-classification task
|
||||
token_cls_metric = 'token-cls-metric'
|
||||
token_cls_metric = "token-cls-metric"
|
||||
# metrics for text-generation task
|
||||
text_gen_metric = 'text-gen-metric'
|
||||
text_gen_metric = "text-gen-metric"
|
||||
# metrics for image-color-enhance task
|
||||
image_color_enhance_metric = 'image-color-enhance-metric'
|
||||
image_color_enhance_metric = "image-color-enhance-metric"
|
||||
# metrics for image-portrait-enhancement task
|
||||
image_portrait_enhancement_metric = 'image-portrait-enhancement-metric'
|
||||
video_summarization_metric = 'video-summarization-metric'
|
||||
image_portrait_enhancement_metric = "image-portrait-enhancement-metric"
|
||||
video_summarization_metric = "video-summarization-metric"
|
||||
# metric for movie-scene-segmentation task
|
||||
movie_scene_segmentation_metric = 'movie-scene-segmentation-metric'
|
||||
movie_scene_segmentation_metric = "movie-scene-segmentation-metric"
|
||||
# metric for inpainting task
|
||||
image_inpainting_metric = 'image-inpainting-metric'
|
||||
image_inpainting_metric = "image-inpainting-metric"
|
||||
# metric for ocr
|
||||
NED = 'ned'
|
||||
NED = "ned"
|
||||
# metric for cross-modal retrieval
|
||||
inbatch_recall = 'inbatch_recall'
|
||||
inbatch_recall = "inbatch_recall"
|
||||
# metric for referring-video-object-segmentation task
|
||||
referring_video_object_segmentation_metric = 'referring-video-object-segmentation-metric'
|
||||
referring_video_object_segmentation_metric = (
|
||||
"referring-video-object-segmentation-metric"
|
||||
)
|
||||
|
||||
|
||||
class Optimizers(object):
|
||||
""" Names for different OPTIMIZER.
|
||||
"""Names for different OPTIMIZER.
|
||||
|
||||
Holds the standard optimizer name to use for identifying different optimizer.
|
||||
This should be used to register optimizer.
|
||||
Holds the standard optimizer name to use for identifying different optimizer.
|
||||
This should be used to register optimizer.
|
||||
"""
|
||||
|
||||
default = 'optimizer'
|
||||
default = "optimizer"
|
||||
|
||||
SGD = 'SGD'
|
||||
SGD = "SGD"
|
||||
|
||||
|
||||
class Hooks(object):
|
||||
""" Names for different hooks.
|
||||
"""Names for different hooks.
|
||||
|
||||
All kinds of hooks are defined here
|
||||
All kinds of hooks are defined here
|
||||
"""
|
||||
|
||||
# lr
|
||||
LrSchedulerHook = 'LrSchedulerHook'
|
||||
PlateauLrSchedulerHook = 'PlateauLrSchedulerHook'
|
||||
NoneLrSchedulerHook = 'NoneLrSchedulerHook'
|
||||
LrSchedulerHook = "LrSchedulerHook"
|
||||
PlateauLrSchedulerHook = "PlateauLrSchedulerHook"
|
||||
NoneLrSchedulerHook = "NoneLrSchedulerHook"
|
||||
|
||||
# optimizer
|
||||
OptimizerHook = 'OptimizerHook'
|
||||
TorchAMPOptimizerHook = 'TorchAMPOptimizerHook'
|
||||
ApexAMPOptimizerHook = 'ApexAMPOptimizerHook'
|
||||
NoneOptimizerHook = 'NoneOptimizerHook'
|
||||
OptimizerHook = "OptimizerHook"
|
||||
TorchAMPOptimizerHook = "TorchAMPOptimizerHook"
|
||||
ApexAMPOptimizerHook = "ApexAMPOptimizerHook"
|
||||
NoneOptimizerHook = "NoneOptimizerHook"
|
||||
|
||||
# checkpoint
|
||||
CheckpointHook = 'CheckpointHook'
|
||||
BestCkptSaverHook = 'BestCkptSaverHook'
|
||||
CheckpointHook = "CheckpointHook"
|
||||
BestCkptSaverHook = "BestCkptSaverHook"
|
||||
|
||||
# logger
|
||||
TextLoggerHook = 'TextLoggerHook'
|
||||
TensorboardHook = 'TensorboardHook'
|
||||
TextLoggerHook = "TextLoggerHook"
|
||||
TensorboardHook = "TensorboardHook"
|
||||
|
||||
IterTimerHook = 'IterTimerHook'
|
||||
EvaluationHook = 'EvaluationHook'
|
||||
IterTimerHook = "IterTimerHook"
|
||||
EvaluationHook = "EvaluationHook"
|
||||
|
||||
# Compression
|
||||
SparsityHook = 'SparsityHook'
|
||||
SparsityHook = "SparsityHook"
|
||||
|
||||
# CLIP logit_scale clamp
|
||||
ClipClampLogitScaleHook = 'ClipClampLogitScaleHook'
|
||||
ClipClampLogitScaleHook = "ClipClampLogitScaleHook"
|
||||
|
||||
|
||||
class LR_Schedulers(object):
|
||||
"""learning rate scheduler is defined here
|
||||
"""learning rate scheduler is defined here"""
|
||||
|
||||
"""
|
||||
LinearWarmup = 'LinearWarmup'
|
||||
ConstantWarmup = 'ConstantWarmup'
|
||||
ExponentialWarmup = 'ExponentialWarmup'
|
||||
LinearWarmup = "LinearWarmup"
|
||||
ConstantWarmup = "ConstantWarmup"
|
||||
ExponentialWarmup = "ExponentialWarmup"
|
||||
|
||||
|
||||
class Datasets(object):
|
||||
""" Names for different datasets.
|
||||
"""
|
||||
ClsDataset = 'ClsDataset'
|
||||
Face2dKeypointsDataset = 'FaceKeypointDataset'
|
||||
HandCocoWholeBodyDataset = 'HandCocoWholeBodyDataset'
|
||||
HumanWholeBodyKeypointDataset = 'WholeBodyCocoTopDownDataset'
|
||||
SegDataset = 'SegDataset'
|
||||
DetDataset = 'DetDataset'
|
||||
DetImagesMixDataset = 'DetImagesMixDataset'
|
||||
PairedDataset = 'PairedDataset'
|
||||
"""Names for different datasets."""
|
||||
|
||||
ClsDataset = "ClsDataset"
|
||||
Face2dKeypointsDataset = "FaceKeypointDataset"
|
||||
HandCocoWholeBodyDataset = "HandCocoWholeBodyDataset"
|
||||
HumanWholeBodyKeypointDataset = "WholeBodyCocoTopDownDataset"
|
||||
SegDataset = "SegDataset"
|
||||
DetDataset = "DetDataset"
|
||||
DetImagesMixDataset = "DetImagesMixDataset"
|
||||
PairedDataset = "PairedDataset"
|
||||
|
||||
@@ -6,53 +6,51 @@ from modelscope.utils.constant import Tasks
|
||||
|
||||
|
||||
class OutputKeys(object):
|
||||
LOSS = 'loss'
|
||||
LOGITS = 'logits'
|
||||
SCORES = 'scores'
|
||||
SCORE = 'score'
|
||||
LABEL = 'label'
|
||||
LABELS = 'labels'
|
||||
INPUT_IDS = 'input_ids'
|
||||
LABEL_POS = 'label_pos'
|
||||
POSES = 'poses'
|
||||
CAPTION = 'caption'
|
||||
BOXES = 'boxes'
|
||||
KEYPOINTS = 'keypoints'
|
||||
MASKS = 'masks'
|
||||
TEXT = 'text'
|
||||
POLYGONS = 'polygons'
|
||||
OUTPUT = 'output'
|
||||
OUTPUT_IMG = 'output_img'
|
||||
OUTPUT_VIDEO = 'output_video'
|
||||
OUTPUT_PCM = 'output_pcm'
|
||||
IMG_EMBEDDING = 'img_embedding'
|
||||
SPO_LIST = 'spo_list'
|
||||
TEXT_EMBEDDING = 'text_embedding'
|
||||
TRANSLATION = 'translation'
|
||||
RESPONSE = 'response'
|
||||
PREDICTION = 'prediction'
|
||||
PREDICTIONS = 'predictions'
|
||||
PROBABILITIES = 'probabilities'
|
||||
DIALOG_STATES = 'dialog_states'
|
||||
VIDEO_EMBEDDING = 'video_embedding'
|
||||
UUID = 'uuid'
|
||||
WORD = 'word'
|
||||
KWS_LIST = 'kws_list'
|
||||
SQL_STRING = 'sql_string'
|
||||
SQL_QUERY = 'sql_query'
|
||||
HISTORY = 'history'
|
||||
QUERT_RESULT = 'query_result'
|
||||
TIMESTAMPS = 'timestamps'
|
||||
SHOT_NUM = 'shot_num'
|
||||
SCENE_NUM = 'scene_num'
|
||||
SCENE_META_LIST = 'scene_meta_list'
|
||||
SHOT_META_LIST = 'shot_meta_list'
|
||||
LOSS = "loss"
|
||||
LOGITS = "logits"
|
||||
SCORES = "scores"
|
||||
SCORE = "score"
|
||||
LABEL = "label"
|
||||
LABELS = "labels"
|
||||
INPUT_IDS = "input_ids"
|
||||
LABEL_POS = "label_pos"
|
||||
POSES = "poses"
|
||||
CAPTION = "caption"
|
||||
BOXES = "boxes"
|
||||
KEYPOINTS = "keypoints"
|
||||
MASKS = "masks"
|
||||
TEXT = "text"
|
||||
POLYGONS = "polygons"
|
||||
OUTPUT = "output"
|
||||
OUTPUT_IMG = "output_img"
|
||||
OUTPUT_VIDEO = "output_video"
|
||||
OUTPUT_PCM = "output_pcm"
|
||||
IMG_EMBEDDING = "img_embedding"
|
||||
SPO_LIST = "spo_list"
|
||||
TEXT_EMBEDDING = "text_embedding"
|
||||
TRANSLATION = "translation"
|
||||
RESPONSE = "response"
|
||||
PREDICTION = "prediction"
|
||||
PREDICTIONS = "predictions"
|
||||
PROBABILITIES = "probabilities"
|
||||
DIALOG_STATES = "dialog_states"
|
||||
VIDEO_EMBEDDING = "video_embedding"
|
||||
UUID = "uuid"
|
||||
WORD = "word"
|
||||
KWS_LIST = "kws_list"
|
||||
SQL_STRING = "sql_string"
|
||||
SQL_QUERY = "sql_query"
|
||||
HISTORY = "history"
|
||||
QUERT_RESULT = "query_result"
|
||||
TIMESTAMPS = "timestamps"
|
||||
SHOT_NUM = "shot_num"
|
||||
SCENE_NUM = "scene_num"
|
||||
SCENE_META_LIST = "scene_meta_list"
|
||||
SHOT_META_LIST = "shot_meta_list"
|
||||
|
||||
|
||||
TASK_OUTPUTS = {
|
||||
|
||||
# ============ vision tasks ===================
|
||||
|
||||
# ocr detection result for single sample
|
||||
# {
|
||||
# "polygons": np.array with shape [num_text, 8], each polygon is
|
||||
@@ -60,13 +58,11 @@ TASK_OUTPUTS = {
|
||||
# }
|
||||
Tasks.ocr_detection: [OutputKeys.POLYGONS],
|
||||
Tasks.table_recognition: [OutputKeys.POLYGONS],
|
||||
|
||||
# ocr recognition result for single sample
|
||||
# {
|
||||
# "text": "电子元器件提供BOM配单"
|
||||
# }
|
||||
Tasks.ocr_recognition: [OutputKeys.TEXT],
|
||||
|
||||
# face 2d keypoint result for single sample
|
||||
# {
|
||||
# "keypoints": [
|
||||
@@ -85,9 +81,7 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2],
|
||||
# ]
|
||||
# }
|
||||
Tasks.face_2d_keypoints:
|
||||
[OutputKeys.KEYPOINTS, OutputKeys.POSES, OutputKeys.BOXES],
|
||||
|
||||
Tasks.face_2d_keypoints: [OutputKeys.KEYPOINTS, OutputKeys.POSES, OutputKeys.BOXES],
|
||||
# face detection result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
@@ -104,9 +98,7 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2, x3, y3, x4, y4, x5, y5],
|
||||
# ],
|
||||
# }
|
||||
Tasks.face_detection:
|
||||
[OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
|
||||
|
||||
Tasks.face_detection: [OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
|
||||
# card detection result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
@@ -123,23 +115,18 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2, x3, y3, x4, y4],
|
||||
# ],
|
||||
# }
|
||||
Tasks.card_detection:
|
||||
[OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
|
||||
|
||||
Tasks.card_detection: [OutputKeys.SCORES, OutputKeys.BOXES, OutputKeys.KEYPOINTS],
|
||||
# facial expression recognition result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.02, 0.02, 0.02, 0.02, 0.02],
|
||||
# "labels": ['Angry', 'Disgust', 'Fear', 'Happy', 'Sad', 'Surprise', 'Neutral']
|
||||
# }
|
||||
Tasks.facial_expression_recognition:
|
||||
[OutputKeys.SCORES, OutputKeys.LABELS],
|
||||
|
||||
Tasks.facial_expression_recognition: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||||
# face recognition result for single sample
|
||||
# {
|
||||
# "img_embedding": np.array with shape [1, D],
|
||||
# }
|
||||
Tasks.face_recognition: [OutputKeys.IMG_EMBEDDING],
|
||||
|
||||
# human detection result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
@@ -151,22 +138,18 @@ TASK_OUTPUTS = {
|
||||
# ],
|
||||
# }
|
||||
#
|
||||
Tasks.human_detection:
|
||||
[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
|
||||
|
||||
Tasks.human_detection: [OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
|
||||
# face generation result for single sample
|
||||
# {
|
||||
# "output_img": np.array with shape(h, w, 3)
|
||||
# }
|
||||
Tasks.face_image_generation: [OutputKeys.OUTPUT_IMG],
|
||||
|
||||
# image classification result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
# "labels": ["dog", "horse", "cow", "cat"],
|
||||
# }
|
||||
Tasks.image_classification: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||||
|
||||
# object detection result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
@@ -177,12 +160,13 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2],
|
||||
# ],
|
||||
# }
|
||||
Tasks.image_object_detection:
|
||||
[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
|
||||
|
||||
Tasks.image_object_detection: [
|
||||
OutputKeys.SCORES,
|
||||
OutputKeys.LABELS,
|
||||
OutputKeys.BOXES,
|
||||
],
|
||||
# video object detection result for single sample
|
||||
# {
|
||||
|
||||
# "scores": [[0.8, 0.25, 0.05, 0.05], [0.9, 0.1, 0.05, 0.05]]
|
||||
# "labels": [["person", "traffic light", "car", "bus"],
|
||||
# ["person", "traffic light", "car", "bus"]]
|
||||
@@ -201,11 +185,12 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2],
|
||||
# ]
|
||||
# ],
|
||||
|
||||
# }
|
||||
Tasks.video_object_detection:
|
||||
[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.BOXES],
|
||||
|
||||
Tasks.video_object_detection: [
|
||||
OutputKeys.SCORES,
|
||||
OutputKeys.LABELS,
|
||||
OutputKeys.BOXES,
|
||||
],
|
||||
# instance segmentation result for single sample
|
||||
# {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05],
|
||||
@@ -214,15 +199,12 @@ TASK_OUTPUTS = {
|
||||
# np.array # 2D array containing only 0, 1
|
||||
# ]
|
||||
# }
|
||||
Tasks.image_segmentation:
|
||||
[OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.MASKS],
|
||||
|
||||
Tasks.image_segmentation: [OutputKeys.SCORES, OutputKeys.LABELS, OutputKeys.MASKS],
|
||||
# semantic segmentation result for single sample
|
||||
# {
|
||||
# "masks": [np.array # 2D array with shape [height, width]]
|
||||
# }
|
||||
Tasks.semantic_segmentation: [OutputKeys.MASKS],
|
||||
|
||||
# image matting result for single sample
|
||||
# {
|
||||
# "output_img": np.array with shape(h, w, 4)
|
||||
@@ -230,7 +212,6 @@ TASK_OUTPUTS = {
|
||||
# , shape(h, w) for crowd counting
|
||||
# }
|
||||
Tasks.portrait_matting: [OutputKeys.OUTPUT_IMG],
|
||||
|
||||
# image editing task result for a single image
|
||||
# {"output_img": np.array with shape (h, w, 3)}
|
||||
Tasks.skin_retouching: [OutputKeys.OUTPUT_IMG],
|
||||
@@ -241,7 +222,6 @@ TASK_OUTPUTS = {
|
||||
Tasks.image_portrait_enhancement: [OutputKeys.OUTPUT_IMG],
|
||||
Tasks.crowd_counting: [OutputKeys.SCORES, OutputKeys.OUTPUT_IMG],
|
||||
Tasks.image_inpainting: [OutputKeys.OUTPUT_IMG],
|
||||
|
||||
# image generation task result for a single image
|
||||
# {"output_img": np.array with shape (h, w, 3)}
|
||||
Tasks.image_to_image_generation: [OutputKeys.OUTPUT_IMG],
|
||||
@@ -249,20 +229,17 @@ TASK_OUTPUTS = {
|
||||
Tasks.image_style_transfer: [OutputKeys.OUTPUT_IMG],
|
||||
Tasks.image_portrait_stylization: [OutputKeys.OUTPUT_IMG],
|
||||
Tasks.image_body_reshaping: [OutputKeys.OUTPUT_IMG],
|
||||
|
||||
# live category recognition result for single video
|
||||
# {
|
||||
# "scores": [0.885272, 0.014790631, 0.014558001]
|
||||
# "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'],
|
||||
# }
|
||||
Tasks.live_category: [OutputKeys.SCORES, OutputKeys.LABELS],
|
||||
|
||||
# action recognition result for single video
|
||||
# {
|
||||
# "output_label": "abseiling"
|
||||
# }
|
||||
Tasks.action_recognition: [OutputKeys.LABELS],
|
||||
|
||||
# human body keypoints detection result for single sample
|
||||
# {
|
||||
# "keypoints": [
|
||||
@@ -281,9 +258,11 @@ TASK_OUTPUTS = {
|
||||
# [x1, y1, x2, y2],
|
||||
# ]
|
||||
# }
|
||||
Tasks.body_2d_keypoints:
|
||||
[OutputKeys.KEYPOINTS, OutputKeys.SCORES, OutputKeys.BOXES],
|
||||
|
||||
Tasks.body_2d_keypoints: [
|
||||
OutputKeys.KEYPOINTS,
|
||||
OutputKeys.SCORES,
|
||||
OutputKeys.BOXES,
|
||||
],
|
||||
# 3D human body keypoints detection result for single sample
|
||||
# {
|
||||
# "keypoints": [ # 3d pose coordinate in camera coordinate
|
||||
@@ -299,9 +278,11 @@ TASK_OUTPUTS = {
|
||||
# "output_video": "path_to_rendered_video" , this is optional
|
||||
# and is only avaialbe when the "render" option is enabled.
|
||||
# }
|
||||
Tasks.body_3d_keypoints:
|
||||
[OutputKeys.KEYPOINTS, OutputKeys.TIMESTAMPS, OutputKeys.OUTPUT_VIDEO],
|
||||
|
||||
Tasks.body_3d_keypoints: [
|
||||
OutputKeys.KEYPOINTS,
|
||||
OutputKeys.TIMESTAMPS,
|
||||
OutputKeys.OUTPUT_VIDEO,
|
||||
],
|
||||
# 2D hand keypoints result for single sample
|
||||
# {
|
||||
# "keypoints": [
|
||||
@@ -316,7 +297,6 @@ TASK_OUTPUTS = {
|
||||
# ]
|
||||
# }
|
||||
Tasks.hand_2d_keypoints: [OutputKeys.KEYPOINTS, OutputKeys.BOXES],
|
||||
|
||||
# video single object tracking result for single video
|
||||
# {
|
||||
# "boxes": [
|
||||
@@ -326,35 +306,29 @@ TASK_OUTPUTS = {
|
||||
# ],
|
||||
# "timestamps": ["hh:mm:ss", "hh:mm:ss", "hh:mm:ss"]
|
||||
# }
|
||||
Tasks.video_single_object_tracking:
|
||||
[OutputKeys.BOXES, OutputKeys.TIMESTAMPS],
|
||||
|
||||
Tasks.video_single_object_tracking: [OutputKeys.BOXES, 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],
|
||||
|
||||
# virtual_try_on result for a single sample
|
||||
# {
|
||||
# "output_img": np.ndarray with shape [height, width, 3]
|
||||
@@ -397,10 +371,11 @@ TASK_OUTPUTS = {
|
||||
#
|
||||
# }
|
||||
Tasks.movie_scene_segmentation: [
|
||||
OutputKeys.SHOT_NUM, OutputKeys.SHOT_META_LIST, OutputKeys.SCENE_NUM,
|
||||
OutputKeys.SCENE_META_LIST
|
||||
OutputKeys.SHOT_NUM,
|
||||
OutputKeys.SHOT_META_LIST,
|
||||
OutputKeys.SCENE_NUM,
|
||||
OutputKeys.SCENE_META_LIST,
|
||||
],
|
||||
|
||||
# human whole body keypoints detection result for single sample
|
||||
# {
|
||||
# "keypoints": [
|
||||
@@ -415,7 +390,6 @@ TASK_OUTPUTS = {
|
||||
# ]
|
||||
# }
|
||||
Tasks.human_wholebody_keypoint: [OutputKeys.KEYPOINTS, OutputKeys.BOXES],
|
||||
|
||||
# video summarization result for a single video
|
||||
# {
|
||||
# "output":
|
||||
@@ -431,50 +405,42 @@ TASK_OUTPUTS = {
|
||||
# ]
|
||||
# }
|
||||
Tasks.video_summarization: [OutputKeys.OUTPUT],
|
||||
|
||||
# referring video object segmentation result for a single video
|
||||
# {
|
||||
# "masks": [np.array # 2D array with shape [height, width]]
|
||||
# }
|
||||
Tasks.referring_video_object_segmentation: [OutputKeys.MASKS],
|
||||
|
||||
# ============ 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",
|
||||
@@ -482,19 +448,16 @@ TASK_OUTPUTS = {
|
||||
# "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"
|
||||
# }
|
||||
Tasks.word_segmentation: [OutputKeys.OUTPUT],
|
||||
|
||||
# TODO @wenmeng.zwm support list of result check
|
||||
# named entity recognition result for single sample
|
||||
# {
|
||||
@@ -505,7 +468,6 @@ TASK_OUTPUTS = {
|
||||
# }
|
||||
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"
|
||||
@@ -513,31 +475,26 @@ TASK_OUTPUTS = {
|
||||
Tasks.text_error_correction: [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],
|
||||
|
||||
# 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": [[
|
||||
@@ -553,7 +510,6 @@ TASK_OUTPUTS = {
|
||||
# ]
|
||||
# }
|
||||
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,
|
||||
@@ -575,10 +531,8 @@ TASK_OUTPUTS = {
|
||||
# 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":{
|
||||
@@ -617,19 +571,16 @@ TASK_OUTPUTS = {
|
||||
# }
|
||||
# }
|
||||
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],
|
||||
|
||||
# audio processed for single file in PCM format
|
||||
# {
|
||||
# "output_pcm": pcm encoded audio bytes
|
||||
@@ -637,13 +588,11 @@ TASK_OUTPUTS = {
|
||||
Tasks.speech_signal_process: [OutputKeys.OUTPUT_PCM],
|
||||
Tasks.acoustic_echo_cancellation: [OutputKeys.OUTPUT_PCM],
|
||||
Tasks.acoustic_noise_suppression: [OutputKeys.OUTPUT_PCM],
|
||||
|
||||
# text_to_speech result for a single sample
|
||||
# {
|
||||
# "output_pcm": {"input_label" : np.ndarray with shape [D]}
|
||||
# }
|
||||
Tasks.text_to_speech: [OutputKeys.OUTPUT_PCM],
|
||||
|
||||
# {
|
||||
# "kws_list": [
|
||||
# {
|
||||
@@ -656,16 +605,13 @@ TASK_OUTPUTS = {
|
||||
# ]
|
||||
# }
|
||||
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],
|
||||
Tasks.ocr_recognition: [OutputKeys.TEXT],
|
||||
|
||||
# visual grounding result for single sample
|
||||
# {
|
||||
# "boxes": [
|
||||
@@ -676,27 +622,22 @@ TASK_OUTPUTS = {
|
||||
# "scores": [0.9, 0.1, 0.05, 0.05]
|
||||
# }
|
||||
Tasks.visual_grounding: [OutputKeys.BOXES, OutputKeys.SCORES],
|
||||
|
||||
# text_to_image result for a single sample
|
||||
# {
|
||||
# "output_img": np.ndarray with shape [height, width, 3]
|
||||
# }
|
||||
Tasks.text_to_image_synthesis: [OutputKeys.OUTPUT_IMG],
|
||||
|
||||
# text_to_speech result for a single sample
|
||||
# {
|
||||
# "output_pcm": {"input_label" : np.ndarray with shape [D]}
|
||||
# }
|
||||
Tasks.text_to_speech: [OutputKeys.OUTPUT_PCM],
|
||||
|
||||
# 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],
|
||||
|
||||
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],
|
||||
@@ -704,9 +645,10 @@ TASK_OUTPUTS = {
|
||||
# "caption": "this is an image caption text."
|
||||
# }
|
||||
Tasks.generative_multi_modal_embedding: [
|
||||
OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING, OutputKeys.CAPTION
|
||||
OutputKeys.IMG_EMBEDDING,
|
||||
OutputKeys.TEXT_EMBEDDING,
|
||||
OutputKeys.CAPTION,
|
||||
],
|
||||
|
||||
# multi-modal similarity result for single sample
|
||||
# {
|
||||
# "img_embedding": np.array with shape [1, D],
|
||||
@@ -714,25 +656,23 @@ TASK_OUTPUTS = {
|
||||
# "similarity": float
|
||||
# }
|
||||
Tasks.multi_modal_similarity: [
|
||||
OutputKeys.IMG_EMBEDDING, OutputKeys.TEXT_EMBEDDING, OutputKeys.SCORES
|
||||
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],
|
||||
|
||||
# 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],
|
||||
@@ -745,7 +685,6 @@ TASK_OUTPUTS = {
|
||||
OutputKeys.SCORES,
|
||||
OutputKeys.BOXES,
|
||||
],
|
||||
|
||||
# {
|
||||
# 'output': [
|
||||
# [{'label': '6527856', 'score': 0.9942756295204163}, {'label': '1000012000', 'score': 0.0379515215754509},
|
||||
@@ -756,36 +695,32 @@ TASK_OUTPUTS = {
|
||||
# {'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
|
||||
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
|
||||
@@ -796,7 +731,6 @@ TASK_OUTPUTS = {
|
||||
|
||||
|
||||
class ModelOutputBase(list):
|
||||
|
||||
def __post_init__(self):
|
||||
self.reconstruct()
|
||||
self.post_init = True
|
||||
@@ -813,7 +747,7 @@ class ModelOutputBase(list):
|
||||
return getattr(self, item)
|
||||
elif isinstance(item, (int, slice)):
|
||||
return super().__getitem__(item)
|
||||
raise IndexError(f'No Index {item} found in the dataclass.')
|
||||
raise IndexError(f"No Index {item} found in the dataclass.")
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
if isinstance(key, str):
|
||||
@@ -832,15 +766,13 @@ class ModelOutputBase(list):
|
||||
super().__setattr__(key_name, value)
|
||||
|
||||
def __setattr__(self, key, value):
|
||||
if getattr(self, 'post_init', False):
|
||||
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
|
||||
]
|
||||
return [f.name for f in fields(self) if getattr(self, f.name) is not None]
|
||||
|
||||
def items(self):
|
||||
return self.to_dict().items()
|
||||
|
||||
@@ -13,206 +13,297 @@ from modelscope.utils.registry import Registry, build_from_cfg
|
||||
from .base import Pipeline
|
||||
from .util import is_official_hub_path
|
||||
|
||||
PIPELINES = Registry('pipelines')
|
||||
PIPELINES = Registry("pipelines")
|
||||
|
||||
DEFAULT_MODEL_FOR_PIPELINE = {
|
||||
# TaskName: (pipeline_module_name, model_repo)
|
||||
Tasks.sentence_embedding:
|
||||
(Pipelines.sentence_embedding,
|
||||
'damo/nlp_corom_sentence-embedding_english-base'),
|
||||
Tasks.text_ranking: (Pipelines.text_ranking,
|
||||
'damo/nlp_corom_passage-ranking_english-base'),
|
||||
Tasks.word_segmentation:
|
||||
(Pipelines.word_segmentation,
|
||||
'damo/nlp_structbert_word-segmentation_chinese-base'),
|
||||
Tasks.part_of_speech: (Pipelines.part_of_speech,
|
||||
'damo/nlp_structbert_part-of-speech_chinese-base'),
|
||||
Tasks.token_classification:
|
||||
(Pipelines.part_of_speech,
|
||||
'damo/nlp_structbert_part-of-speech_chinese-base'),
|
||||
Tasks.named_entity_recognition:
|
||||
(Pipelines.named_entity_recognition,
|
||||
'damo/nlp_raner_named-entity-recognition_chinese-base-news'),
|
||||
Tasks.relation_extraction:
|
||||
(Pipelines.relation_extraction,
|
||||
'damo/nlp_bert_relation-extraction_chinese-base'),
|
||||
Tasks.information_extraction:
|
||||
(Pipelines.relation_extraction,
|
||||
'damo/nlp_bert_relation-extraction_chinese-base'),
|
||||
Tasks.sentence_similarity:
|
||||
(Pipelines.sentence_similarity,
|
||||
'damo/nlp_structbert_sentence-similarity_chinese-base'),
|
||||
Tasks.translation: (Pipelines.csanmt_translation,
|
||||
'damo/nlp_csanmt_translation_zh2en'),
|
||||
Tasks.nli: (Pipelines.nli, 'damo/nlp_structbert_nli_chinese-base'),
|
||||
Tasks.sentiment_classification:
|
||||
(Pipelines.sentiment_classification,
|
||||
'damo/nlp_structbert_sentiment-classification_chinese-base'
|
||||
), # TODO: revise back after passing the pr
|
||||
Tasks.portrait_matting: (Pipelines.portrait_matting,
|
||||
'damo/cv_unet_image-matting'),
|
||||
Tasks.human_detection: (Pipelines.human_detection,
|
||||
'damo/cv_resnet18_human-detection'),
|
||||
Tasks.image_object_detection: (Pipelines.object_detection,
|
||||
'damo/cv_vit_object-detection_coco'),
|
||||
Tasks.image_denoising: (Pipelines.image_denoise,
|
||||
'damo/cv_nafnet_image-denoise_sidd'),
|
||||
Tasks.text_classification:
|
||||
(Pipelines.sentiment_classification,
|
||||
'damo/nlp_structbert_sentiment-classification_chinese-base'),
|
||||
Tasks.text_generation: (Pipelines.text_generation,
|
||||
'damo/nlp_palm2.0_text-generation_chinese-base'),
|
||||
Tasks.zero_shot_classification:
|
||||
(Pipelines.zero_shot_classification,
|
||||
'damo/nlp_structbert_zero-shot-classification_chinese-base'),
|
||||
Tasks.task_oriented_conversation: (Pipelines.dialog_modeling,
|
||||
'damo/nlp_space_dialog-modeling'),
|
||||
Tasks.dialog_state_tracking: (Pipelines.dialog_state_tracking,
|
||||
'damo/nlp_space_dialog-state-tracking'),
|
||||
Tasks.table_question_answering:
|
||||
(Pipelines.table_question_answering_pipeline,
|
||||
'damo/nlp-convai-text2sql-pretrain-cn'),
|
||||
Tasks.text_error_correction:
|
||||
(Pipelines.text_error_correction,
|
||||
'damo/nlp_bart_text-error-correction_chinese'),
|
||||
Tasks.image_captioning: (Pipelines.image_captioning,
|
||||
'damo/ofa_image-caption_coco_large_en'),
|
||||
Tasks.image_portrait_stylization:
|
||||
(Pipelines.person_image_cartoon,
|
||||
'damo/cv_unet_person-image-cartoon_compound-models'),
|
||||
Tasks.ocr_detection: (Pipelines.ocr_detection,
|
||||
'damo/cv_resnet18_ocr-detection-line-level_damo'),
|
||||
Tasks.table_recognition: (Pipelines.table_recognition,
|
||||
'damo/cv_dla34_table-structure-recognition_cycle-centernet'),
|
||||
Tasks.fill_mask: (Pipelines.fill_mask, 'damo/nlp_veco_fill-mask-large'),
|
||||
Tasks.feature_extraction: (Pipelines.feature_extraction,
|
||||
'damo/pert_feature-extraction_base-test'),
|
||||
Tasks.action_recognition: (Pipelines.action_recognition,
|
||||
'damo/cv_TAdaConv_action-recognition'),
|
||||
Tasks.action_detection: (Pipelines.action_detection,
|
||||
'damo/cv_ResNetC3D_action-detection_detection2d'),
|
||||
Tasks.live_category: (Pipelines.live_category,
|
||||
'damo/cv_resnet50_live-category'),
|
||||
Tasks.video_category: (Pipelines.video_category,
|
||||
'damo/cv_resnet50_video-category'),
|
||||
Tasks.multi_modal_embedding: (Pipelines.multi_modal_embedding,
|
||||
'damo/multi-modal_clip-vit-base-patch16_zh'),
|
||||
Tasks.generative_multi_modal_embedding:
|
||||
(Pipelines.generative_multi_modal_embedding,
|
||||
'damo/multi-modal_gemm-vit-large-patch14_generative-multi-modal-embedding'
|
||||
),
|
||||
Tasks.multi_modal_similarity:
|
||||
(Pipelines.multi_modal_similarity,
|
||||
'damo/multi-modal_team-vit-large-patch14_multi-modal-similarity'),
|
||||
Tasks.visual_question_answering:
|
||||
(Pipelines.visual_question_answering,
|
||||
'damo/mplug_visual-question-answering_coco_large_en'),
|
||||
Tasks.video_embedding: (Pipelines.cmdssl_video_embedding,
|
||||
'damo/cv_r2p1d_video_embedding'),
|
||||
Tasks.text_to_image_synthesis:
|
||||
(Pipelines.text_to_image_synthesis,
|
||||
'damo/cv_diffusion_text-to-image-synthesis_tiny'),
|
||||
Tasks.body_2d_keypoints: (Pipelines.body_2d_keypoints,
|
||||
'damo/cv_hrnetv2w32_body-2d-keypoints_image'),
|
||||
Tasks.body_3d_keypoints: (Pipelines.body_3d_keypoints,
|
||||
'damo/cv_canonical_body-3d-keypoints_video'),
|
||||
Tasks.hand_2d_keypoints:
|
||||
(Pipelines.hand_2d_keypoints,
|
||||
'damo/cv_hrnetw18_hand-pose-keypoints_coco-wholebody'),
|
||||
Tasks.face_detection: (Pipelines.face_detection,
|
||||
'damo/cv_resnet_facedetection_scrfd10gkps'),
|
||||
Tasks.card_detection: (Pipelines.card_detection,
|
||||
'damo/cv_resnet_carddetection_scrfd34gkps'),
|
||||
Tasks.face_detection:
|
||||
(Pipelines.face_detection,
|
||||
'damo/cv_resnet101_face-detection_cvpr22papermogface'),
|
||||
Tasks.face_recognition: (Pipelines.face_recognition,
|
||||
'damo/cv_ir101_facerecognition_cfglint'),
|
||||
Tasks.facial_expression_recognition:
|
||||
(Pipelines.facial_expression_recognition,
|
||||
'damo/cv_vgg19_facial-expression-recognition_fer'),
|
||||
Tasks.face_2d_keypoints: (Pipelines.face_2d_keypoints,
|
||||
'damo/cv_mobilenet_face-2d-keypoints_alignment'),
|
||||
Tasks.video_multi_modal_embedding:
|
||||
(Pipelines.video_multi_modal_embedding,
|
||||
'damo/multi_modal_clip_vtretrival_msrvtt_53'),
|
||||
Tasks.image_color_enhancement:
|
||||
(Pipelines.image_color_enhance,
|
||||
'damo/cv_csrnet_image-color-enhance-models'),
|
||||
Tasks.virtual_try_on: (Pipelines.virtual_try_on,
|
||||
'damo/cv_daflow_virtual-try-on_base'),
|
||||
Tasks.image_colorization: (Pipelines.image_colorization,
|
||||
'damo/cv_unet_image-colorization'),
|
||||
Tasks.image_segmentation:
|
||||
(Pipelines.image_instance_segmentation,
|
||||
'damo/cv_swin-b_image-instance-segmentation_coco'),
|
||||
Tasks.image_style_transfer: (Pipelines.image_style_transfer,
|
||||
'damo/cv_aams_style-transfer_damo'),
|
||||
Tasks.face_image_generation: (Pipelines.face_image_generation,
|
||||
'damo/cv_gan_face-image-generation'),
|
||||
Tasks.image_super_resolution: (Pipelines.image_super_resolution,
|
||||
'damo/cv_rrdb_image-super-resolution'),
|
||||
Tasks.image_portrait_enhancement:
|
||||
(Pipelines.image_portrait_enhancement,
|
||||
'damo/cv_gpen_image-portrait-enhancement'),
|
||||
Tasks.product_retrieval_embedding:
|
||||
(Pipelines.product_retrieval_embedding,
|
||||
'damo/cv_resnet50_product-bag-embedding-models'),
|
||||
Tasks.image_to_image_generation:
|
||||
(Pipelines.image_to_image_generation,
|
||||
'damo/cv_latent_diffusion_image2image_generate'),
|
||||
Tasks.image_classification:
|
||||
(Pipelines.daily_image_classification,
|
||||
'damo/cv_vit-base_image-classification_Dailylife-labels'),
|
||||
Tasks.image_object_detection:
|
||||
(Pipelines.image_object_detection_auto,
|
||||
'damo/cv_yolox_image-object-detection-auto'),
|
||||
Tasks.ocr_recognition:
|
||||
(Pipelines.ocr_recognition,
|
||||
'damo/cv_convnextTiny_ocr-recognition-general_damo'),
|
||||
Tasks.skin_retouching: (Pipelines.skin_retouching,
|
||||
'damo/cv_unet_skin-retouching'),
|
||||
Tasks.faq_question_answering:
|
||||
(Pipelines.faq_question_answering,
|
||||
'damo/nlp_structbert_faq-question-answering_chinese-base'),
|
||||
Tasks.crowd_counting: (Pipelines.crowd_counting,
|
||||
'damo/cv_hrnet_crowd-counting_dcanet'),
|
||||
Tasks.video_single_object_tracking:
|
||||
(Pipelines.video_single_object_tracking,
|
||||
'damo/cv_vitb_video-single-object-tracking_ostrack'),
|
||||
Tasks.image_reid_person: (Pipelines.image_reid_person,
|
||||
'damo/cv_passvitb_image-reid-person_market'),
|
||||
Tasks.text_driven_segmentation:
|
||||
(Pipelines.text_driven_segmentation,
|
||||
'damo/cv_vitl16_segmentation_text-driven-seg'),
|
||||
Tasks.movie_scene_segmentation:
|
||||
(Pipelines.movie_scene_segmentation,
|
||||
'damo/cv_resnet50-bert_video-scene-segmentation_movienet'),
|
||||
Tasks.shop_segmentation: (Pipelines.shop_segmentation,
|
||||
'damo/cv_vitb16_segmentation_shop-seg'),
|
||||
Tasks.image_inpainting: (Pipelines.image_inpainting,
|
||||
'damo/cv_fft_inpainting_lama'),
|
||||
Tasks.video_inpainting: (Pipelines.video_inpainting,
|
||||
'damo/cv_video-inpainting'),
|
||||
Tasks.human_wholebody_keypoint:
|
||||
(Pipelines.human_wholebody_keypoint,
|
||||
'damo/cv_hrnetw48_human-wholebody-keypoint_image'),
|
||||
Tasks.hand_static: (Pipelines.hand_static,
|
||||
'damo/cv_mobileface_hand-static'),
|
||||
Tasks.face_human_hand_detection:
|
||||
(Pipelines.face_human_hand_detection,
|
||||
'damo/cv_nanodet_face-human-hand-detection'),
|
||||
Tasks.face_emotion: (Pipelines.face_emotion, 'damo/cv_face-emotion'),
|
||||
Tasks.product_segmentation: (Pipelines.product_segmentation,
|
||||
'damo/cv_F3Net_product-segmentation'),
|
||||
Tasks.referring_video_object_segmentation:
|
||||
(Pipelines.referring_video_object_segmentation,
|
||||
'damo/cv_swin-t_referring_video-object-segmentation'),
|
||||
Tasks.sentence_embedding: (
|
||||
Pipelines.sentence_embedding,
|
||||
"damo/nlp_corom_sentence-embedding_english-base",
|
||||
),
|
||||
Tasks.text_ranking: (
|
||||
Pipelines.text_ranking,
|
||||
"damo/nlp_corom_passage-ranking_english-base",
|
||||
),
|
||||
Tasks.word_segmentation: (
|
||||
Pipelines.word_segmentation,
|
||||
"damo/nlp_structbert_word-segmentation_chinese-base",
|
||||
),
|
||||
Tasks.part_of_speech: (
|
||||
Pipelines.part_of_speech,
|
||||
"damo/nlp_structbert_part-of-speech_chinese-base",
|
||||
),
|
||||
Tasks.token_classification: (
|
||||
Pipelines.part_of_speech,
|
||||
"damo/nlp_structbert_part-of-speech_chinese-base",
|
||||
),
|
||||
Tasks.named_entity_recognition: (
|
||||
Pipelines.named_entity_recognition,
|
||||
"damo/nlp_raner_named-entity-recognition_chinese-base-news",
|
||||
),
|
||||
Tasks.relation_extraction: (
|
||||
Pipelines.relation_extraction,
|
||||
"damo/nlp_bert_relation-extraction_chinese-base",
|
||||
),
|
||||
Tasks.information_extraction: (
|
||||
Pipelines.relation_extraction,
|
||||
"damo/nlp_bert_relation-extraction_chinese-base",
|
||||
),
|
||||
Tasks.sentence_similarity: (
|
||||
Pipelines.sentence_similarity,
|
||||
"damo/nlp_structbert_sentence-similarity_chinese-base",
|
||||
),
|
||||
Tasks.translation: (
|
||||
Pipelines.csanmt_translation,
|
||||
"damo/nlp_csanmt_translation_zh2en",
|
||||
),
|
||||
Tasks.nli: (Pipelines.nli, "damo/nlp_structbert_nli_chinese-base"),
|
||||
Tasks.sentiment_classification: (
|
||||
Pipelines.sentiment_classification,
|
||||
"damo/nlp_structbert_sentiment-classification_chinese-base",
|
||||
), # TODO: revise back after passing the pr
|
||||
Tasks.portrait_matting: (Pipelines.portrait_matting, "damo/cv_unet_image-matting"),
|
||||
Tasks.human_detection: (
|
||||
Pipelines.human_detection,
|
||||
"damo/cv_resnet18_human-detection",
|
||||
),
|
||||
Tasks.image_object_detection: (
|
||||
Pipelines.object_detection,
|
||||
"damo/cv_vit_object-detection_coco",
|
||||
),
|
||||
Tasks.image_denoising: (
|
||||
Pipelines.image_denoise,
|
||||
"damo/cv_nafnet_image-denoise_sidd",
|
||||
),
|
||||
Tasks.text_classification: (
|
||||
Pipelines.sentiment_classification,
|
||||
"damo/nlp_structbert_sentiment-classification_chinese-base",
|
||||
),
|
||||
Tasks.text_generation: (
|
||||
Pipelines.text_generation,
|
||||
"damo/nlp_palm2.0_text-generation_chinese-base",
|
||||
),
|
||||
Tasks.zero_shot_classification: (
|
||||
Pipelines.zero_shot_classification,
|
||||
"damo/nlp_structbert_zero-shot-classification_chinese-base",
|
||||
),
|
||||
Tasks.task_oriented_conversation: (
|
||||
Pipelines.dialog_modeling,
|
||||
"damo/nlp_space_dialog-modeling",
|
||||
),
|
||||
Tasks.dialog_state_tracking: (
|
||||
Pipelines.dialog_state_tracking,
|
||||
"damo/nlp_space_dialog-state-tracking",
|
||||
),
|
||||
Tasks.table_question_answering: (
|
||||
Pipelines.table_question_answering_pipeline,
|
||||
"damo/nlp-convai-text2sql-pretrain-cn",
|
||||
),
|
||||
Tasks.text_error_correction: (
|
||||
Pipelines.text_error_correction,
|
||||
"damo/nlp_bart_text-error-correction_chinese",
|
||||
),
|
||||
Tasks.image_captioning: (
|
||||
Pipelines.image_captioning,
|
||||
"damo/ofa_image-caption_coco_large_en",
|
||||
),
|
||||
Tasks.image_portrait_stylization: (
|
||||
Pipelines.person_image_cartoon,
|
||||
"damo/cv_unet_person-image-cartoon_compound-models",
|
||||
),
|
||||
Tasks.ocr_detection: (
|
||||
Pipelines.ocr_detection,
|
||||
"damo/cv_resnet18_ocr-detection-line-level_damo",
|
||||
),
|
||||
Tasks.table_recognition: (
|
||||
Pipelines.table_recognition,
|
||||
"damo/cv_dla34_table-structure-recognition_cycle-centernet",
|
||||
),
|
||||
Tasks.fill_mask: (Pipelines.fill_mask, "damo/nlp_veco_fill-mask-large"),
|
||||
Tasks.feature_extraction: (
|
||||
Pipelines.feature_extraction,
|
||||
"damo/pert_feature-extraction_base-test",
|
||||
),
|
||||
Tasks.action_recognition: (
|
||||
Pipelines.action_recognition,
|
||||
"damo/cv_TAdaConv_action-recognition",
|
||||
),
|
||||
Tasks.action_detection: (
|
||||
Pipelines.action_detection,
|
||||
"damo/cv_ResNetC3D_action-detection_detection2d",
|
||||
),
|
||||
Tasks.live_category: (Pipelines.live_category, "damo/cv_resnet50_live-category"),
|
||||
Tasks.video_category: (Pipelines.video_category, "damo/cv_resnet50_video-category"),
|
||||
Tasks.multi_modal_embedding: (
|
||||
Pipelines.multi_modal_embedding,
|
||||
"damo/multi-modal_clip-vit-base-patch16_zh",
|
||||
),
|
||||
Tasks.generative_multi_modal_embedding: (
|
||||
Pipelines.generative_multi_modal_embedding,
|
||||
"damo/multi-modal_gemm-vit-large-patch14_generative-multi-modal-embedding",
|
||||
),
|
||||
Tasks.multi_modal_similarity: (
|
||||
Pipelines.multi_modal_similarity,
|
||||
"damo/multi-modal_team-vit-large-patch14_multi-modal-similarity",
|
||||
),
|
||||
Tasks.visual_question_answering: (
|
||||
Pipelines.visual_question_answering,
|
||||
"damo/mplug_visual-question-answering_coco_large_en",
|
||||
),
|
||||
Tasks.video_embedding: (
|
||||
Pipelines.cmdssl_video_embedding,
|
||||
"damo/cv_r2p1d_video_embedding",
|
||||
),
|
||||
Tasks.text_to_image_synthesis: (
|
||||
Pipelines.text_to_image_synthesis,
|
||||
"damo/cv_diffusion_text-to-image-synthesis_tiny",
|
||||
),
|
||||
Tasks.body_2d_keypoints: (
|
||||
Pipelines.body_2d_keypoints,
|
||||
"damo/cv_hrnetv2w32_body-2d-keypoints_image",
|
||||
),
|
||||
Tasks.body_3d_keypoints: (
|
||||
Pipelines.body_3d_keypoints,
|
||||
"damo/cv_canonical_body-3d-keypoints_video",
|
||||
),
|
||||
Tasks.hand_2d_keypoints: (
|
||||
Pipelines.hand_2d_keypoints,
|
||||
"damo/cv_hrnetw18_hand-pose-keypoints_coco-wholebody",
|
||||
),
|
||||
Tasks.face_detection: (
|
||||
Pipelines.face_detection,
|
||||
"damo/cv_resnet_facedetection_scrfd10gkps",
|
||||
),
|
||||
Tasks.card_detection: (
|
||||
Pipelines.card_detection,
|
||||
"damo/cv_resnet_carddetection_scrfd34gkps",
|
||||
),
|
||||
Tasks.face_detection: (
|
||||
Pipelines.face_detection,
|
||||
"damo/cv_resnet101_face-detection_cvpr22papermogface",
|
||||
),
|
||||
Tasks.face_recognition: (
|
||||
Pipelines.face_recognition,
|
||||
"damo/cv_ir101_facerecognition_cfglint",
|
||||
),
|
||||
Tasks.facial_expression_recognition: (
|
||||
Pipelines.facial_expression_recognition,
|
||||
"damo/cv_vgg19_facial-expression-recognition_fer",
|
||||
),
|
||||
Tasks.face_2d_keypoints: (
|
||||
Pipelines.face_2d_keypoints,
|
||||
"damo/cv_mobilenet_face-2d-keypoints_alignment",
|
||||
),
|
||||
Tasks.video_multi_modal_embedding: (
|
||||
Pipelines.video_multi_modal_embedding,
|
||||
"damo/multi_modal_clip_vtretrival_msrvtt_53",
|
||||
),
|
||||
Tasks.image_color_enhancement: (
|
||||
Pipelines.image_color_enhance,
|
||||
"damo/cv_csrnet_image-color-enhance-models",
|
||||
),
|
||||
Tasks.virtual_try_on: (
|
||||
Pipelines.virtual_try_on,
|
||||
"damo/cv_daflow_virtual-try-on_base",
|
||||
),
|
||||
Tasks.image_colorization: (
|
||||
Pipelines.image_colorization,
|
||||
"damo/cv_unet_image-colorization",
|
||||
),
|
||||
Tasks.image_segmentation: (
|
||||
Pipelines.image_instance_segmentation,
|
||||
"damo/cv_swin-b_image-instance-segmentation_coco",
|
||||
),
|
||||
Tasks.image_style_transfer: (
|
||||
Pipelines.image_style_transfer,
|
||||
"damo/cv_aams_style-transfer_damo",
|
||||
),
|
||||
Tasks.face_image_generation: (
|
||||
Pipelines.face_image_generation,
|
||||
"damo/cv_gan_face-image-generation",
|
||||
),
|
||||
Tasks.image_super_resolution: (
|
||||
Pipelines.image_super_resolution,
|
||||
"damo/cv_rrdb_image-super-resolution",
|
||||
),
|
||||
Tasks.image_portrait_enhancement: (
|
||||
Pipelines.image_portrait_enhancement,
|
||||
"damo/cv_gpen_image-portrait-enhancement",
|
||||
),
|
||||
Tasks.product_retrieval_embedding: (
|
||||
Pipelines.product_retrieval_embedding,
|
||||
"damo/cv_resnet50_product-bag-embedding-models",
|
||||
),
|
||||
Tasks.image_to_image_generation: (
|
||||
Pipelines.image_to_image_generation,
|
||||
"damo/cv_latent_diffusion_image2image_generate",
|
||||
),
|
||||
Tasks.image_classification: (
|
||||
Pipelines.daily_image_classification,
|
||||
"damo/cv_vit-base_image-classification_Dailylife-labels",
|
||||
),
|
||||
Tasks.image_object_detection: (
|
||||
Pipelines.image_object_detection_auto,
|
||||
"damo/cv_yolox_image-object-detection-auto",
|
||||
),
|
||||
Tasks.ocr_recognition: (
|
||||
Pipelines.ocr_recognition,
|
||||
"damo/cv_convnextTiny_ocr-recognition-general_damo",
|
||||
),
|
||||
Tasks.skin_retouching: (Pipelines.skin_retouching, "damo/cv_unet_skin-retouching"),
|
||||
Tasks.faq_question_answering: (
|
||||
Pipelines.faq_question_answering,
|
||||
"damo/nlp_structbert_faq-question-answering_chinese-base",
|
||||
),
|
||||
Tasks.crowd_counting: (
|
||||
Pipelines.crowd_counting,
|
||||
"damo/cv_hrnet_crowd-counting_dcanet",
|
||||
),
|
||||
Tasks.video_single_object_tracking: (
|
||||
Pipelines.video_single_object_tracking,
|
||||
"damo/cv_vitb_video-single-object-tracking_ostrack",
|
||||
),
|
||||
Tasks.image_reid_person: (
|
||||
Pipelines.image_reid_person,
|
||||
"damo/cv_passvitb_image-reid-person_market",
|
||||
),
|
||||
Tasks.text_driven_segmentation: (
|
||||
Pipelines.text_driven_segmentation,
|
||||
"damo/cv_vitl16_segmentation_text-driven-seg",
|
||||
),
|
||||
Tasks.movie_scene_segmentation: (
|
||||
Pipelines.movie_scene_segmentation,
|
||||
"damo/cv_resnet50-bert_video-scene-segmentation_movienet",
|
||||
),
|
||||
Tasks.shop_segmentation: (
|
||||
Pipelines.shop_segmentation,
|
||||
"damo/cv_vitb16_segmentation_shop-seg",
|
||||
),
|
||||
Tasks.image_inpainting: (Pipelines.image_inpainting, "damo/cv_fft_inpainting_lama"),
|
||||
Tasks.video_inpainting: (Pipelines.video_inpainting, "damo/cv_video-inpainting"),
|
||||
Tasks.human_wholebody_keypoint: (
|
||||
Pipelines.human_wholebody_keypoint,
|
||||
"damo/cv_hrnetw48_human-wholebody-keypoint_image",
|
||||
),
|
||||
Tasks.hand_static: (Pipelines.hand_static, "damo/cv_mobileface_hand-static"),
|
||||
Tasks.face_human_hand_detection: (
|
||||
Pipelines.face_human_hand_detection,
|
||||
"damo/cv_nanodet_face-human-hand-detection",
|
||||
),
|
||||
Tasks.face_emotion: (Pipelines.face_emotion, "damo/cv_face-emotion"),
|
||||
Tasks.product_segmentation: (
|
||||
Pipelines.product_segmentation,
|
||||
"damo/cv_F3Net_product-segmentation",
|
||||
),
|
||||
Tasks.referring_video_object_segmentation: (
|
||||
Pipelines.referring_video_object_segmentation,
|
||||
"damo/cv_swin-t_referring_video-object-segmentation",
|
||||
),
|
||||
}
|
||||
|
||||
|
||||
def normalize_model_input(model, model_revision):
|
||||
""" normalize the input model, to ensure that a model str is a valid local path: in other words,
|
||||
"""normalize the input model, to ensure that a model str is a valid local path: in other words,
|
||||
for model represented by a model id, the model shall be downloaded locally
|
||||
"""
|
||||
if isinstance(model, str) and is_official_hub_path(model, model_revision):
|
||||
@@ -222,18 +313,15 @@ def normalize_model_input(model, model_revision):
|
||||
model = snapshot_download(model, revision=model_revision)
|
||||
elif isinstance(model, list) and isinstance(model[0], str):
|
||||
for idx in range(len(model)):
|
||||
if is_official_hub_path(
|
||||
model[idx],
|
||||
model_revision) and not os.path.exists(model[idx]):
|
||||
model[idx] = snapshot_download(
|
||||
model[idx], revision=model_revision)
|
||||
if is_official_hub_path(model[idx], model_revision) and not os.path.exists(
|
||||
model[idx]
|
||||
):
|
||||
model[idx] = snapshot_download(model[idx], revision=model_revision)
|
||||
return model
|
||||
|
||||
|
||||
def build_pipeline(cfg: ConfigDict,
|
||||
task_name: str = None,
|
||||
default_args: dict = None):
|
||||
""" build pipeline given model config dict.
|
||||
def build_pipeline(cfg: ConfigDict, task_name: str = None, default_args: dict = None):
|
||||
"""build pipeline given model config dict.
|
||||
|
||||
Args:
|
||||
cfg (:obj:`ConfigDict`): config dict for model object.
|
||||
@@ -242,19 +330,22 @@ def build_pipeline(cfg: ConfigDict,
|
||||
default_args (dict, optional): Default initialization arguments.
|
||||
"""
|
||||
return build_from_cfg(
|
||||
cfg, PIPELINES, group_key=task_name, default_args=default_args)
|
||||
cfg, PIPELINES, group_key=task_name, default_args=default_args
|
||||
)
|
||||
|
||||
|
||||
def pipeline(task: str = None,
|
||||
model: Union[str, List[str], Model, List[Model]] = None,
|
||||
preprocessor=None,
|
||||
config_file: str = None,
|
||||
pipeline_name: str = None,
|
||||
framework: str = None,
|
||||
device: str = 'gpu',
|
||||
model_revision: Optional[str] = DEFAULT_MODEL_REVISION,
|
||||
**kwargs) -> Pipeline:
|
||||
""" Factory method to build an obj:`Pipeline`.
|
||||
def pipeline(
|
||||
task: str = None,
|
||||
model: Union[str, List[str], Model, List[Model]] = None,
|
||||
preprocessor=None,
|
||||
config_file: str = None,
|
||||
pipeline_name: str = None,
|
||||
framework: str = None,
|
||||
device: str = "gpu",
|
||||
model_revision: Optional[str] = DEFAULT_MODEL_REVISION,
|
||||
**kwargs,
|
||||
) -> Pipeline:
|
||||
"""Factory method to build an obj:`Pipeline`.
|
||||
|
||||
|
||||
Args:
|
||||
@@ -284,19 +375,21 @@ def pipeline(task: str = None,
|
||||
>>> p = pipeline('audio-kws', model=['damo/audio-tts', 'damo/auto-tts2'])
|
||||
"""
|
||||
if task is None and pipeline_name is None:
|
||||
raise ValueError('task or pipeline_name is required')
|
||||
raise ValueError("task or pipeline_name is required")
|
||||
|
||||
model = normalize_model_input(model, model_revision)
|
||||
if pipeline_name is None:
|
||||
# get default pipeline for this task
|
||||
if isinstance(model, str) \
|
||||
or (isinstance(model, list) and isinstance(model[0], str)):
|
||||
if isinstance(model, str) or (
|
||||
isinstance(model, list) and isinstance(model[0], str)
|
||||
):
|
||||
if is_official_hub_path(model, revision=model_revision):
|
||||
# read config file from hub and parse
|
||||
cfg = read_config(
|
||||
model, revision=model_revision) if isinstance(
|
||||
model, str) else read_config(
|
||||
model[0], revision=model_revision)
|
||||
cfg = (
|
||||
read_config(model, revision=model_revision)
|
||||
if isinstance(model, str)
|
||||
else read_config(model[0], revision=model_revision)
|
||||
)
|
||||
check_config(cfg)
|
||||
pipeline_name = cfg.pipeline.type
|
||||
else:
|
||||
@@ -305,7 +398,7 @@ def pipeline(task: str = None,
|
||||
elif model is not None:
|
||||
# get pipeline info from Model object
|
||||
first_model = model[0] if isinstance(model, list) else model
|
||||
if not hasattr(first_model, 'pipeline'):
|
||||
if not hasattr(first_model, "pipeline"):
|
||||
# model is instantiated by user, we should parse config again
|
||||
cfg = read_config(first_model.model_dir)
|
||||
check_config(cfg)
|
||||
@@ -327,11 +420,10 @@ def pipeline(task: str = None,
|
||||
return build_pipeline(cfg, task_name=task)
|
||||
|
||||
|
||||
def add_default_pipeline_info(task: str,
|
||||
model_name: str,
|
||||
modelhub_name: str = None,
|
||||
overwrite: bool = False):
|
||||
""" Add default model for a task.
|
||||
def add_default_pipeline_info(
|
||||
task: str, model_name: str, modelhub_name: str = None, overwrite: bool = False
|
||||
):
|
||||
"""Add default model for a task.
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
@@ -340,14 +432,15 @@ def add_default_pipeline_info(task: str,
|
||||
overwrite (bool): overwrite default info.
|
||||
"""
|
||||
if not overwrite:
|
||||
assert task not in DEFAULT_MODEL_FOR_PIPELINE, \
|
||||
f'task {task} already has default model.'
|
||||
assert (
|
||||
task not in DEFAULT_MODEL_FOR_PIPELINE
|
||||
), f"task {task} already has default model."
|
||||
|
||||
DEFAULT_MODEL_FOR_PIPELINE[task] = (model_name, modelhub_name)
|
||||
|
||||
|
||||
def get_default_pipeline_info(task):
|
||||
""" Get default info for certain task.
|
||||
"""Get default info for certain task.
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
@@ -367,7 +460,7 @@ def get_default_pipeline_info(task):
|
||||
|
||||
|
||||
def get_pipeline_by_model_name(task: str, model: Union[str, List[str]]):
|
||||
""" Get pipeline name by task name and model name
|
||||
"""Get pipeline name by task name and model name
|
||||
|
||||
Args:
|
||||
task (str): task name.
|
||||
@@ -376,7 +469,8 @@ def get_pipeline_by_model_name(task: str, model: Union[str, List[str]]):
|
||||
if isinstance(model, str):
|
||||
model_key = model
|
||||
else:
|
||||
model_key = '_'.join(model)
|
||||
assert model_key in PIPELINES.modules[task], \
|
||||
f'pipeline for task {task} model {model_key} not found.'
|
||||
model_key = "_".join(model)
|
||||
assert (
|
||||
model_key in PIPELINES.modules[task]
|
||||
), f"pipeline for task {task} model {model_key} not found."
|
||||
return model_key
|
||||
|
||||
@@ -47,92 +47,91 @@ if TYPE_CHECKING:
|
||||
from .video_category_pipeline import VideoCategoryPipeline
|
||||
from .virtual_try_on_pipeline import VirtualTryonPipeline
|
||||
from .shop_segmentation_pipleline import ShopSegmentationPipeline
|
||||
from .easycv_pipelines import (EasyCVDetectionPipeline,
|
||||
EasyCVSegmentationPipeline,
|
||||
Face2DKeypointsPipeline,
|
||||
HumanWholebodyKeypointsPipeline)
|
||||
from .easycv_pipelines import (
|
||||
EasyCVDetectionPipeline,
|
||||
EasyCVSegmentationPipeline,
|
||||
Face2DKeypointsPipeline,
|
||||
HumanWholebodyKeypointsPipeline,
|
||||
)
|
||||
from .text_driven_segmentation_pipleline import TextDrivenSegmentationPipeline
|
||||
from .movie_scene_segmentation_pipeline import MovieSceneSegmentationPipeline
|
||||
from .mog_face_detection_pipeline import MogFaceDetectionPipeline
|
||||
from .ulfd_face_detection_pipeline import UlfdFaceDetectionPipeline
|
||||
from .retina_face_detection_pipeline import RetinaFaceDetectionPipeline
|
||||
from .facial_expression_recognition_pipeline import FacialExpressionRecognitionPipeline
|
||||
from .facial_expression_recognition_pipeline import (
|
||||
FacialExpressionRecognitionPipeline,
|
||||
)
|
||||
from .mtcnn_face_detection_pipeline import MtcnnFaceDetectionPipelin
|
||||
from .hand_static_pipeline import HandStaticPipeline
|
||||
from .referring_video_object_segmentation_pipeline import ReferringVideoObjectSegmentationPipeline
|
||||
from .referring_video_object_segmentation_pipeline import (
|
||||
ReferringVideoObjectSegmentationPipeline,
|
||||
)
|
||||
|
||||
else:
|
||||
_import_structure = {
|
||||
'action_recognition_pipeline': ['ActionRecognitionPipeline'],
|
||||
'action_detection_pipeline': ['ActionDetectionPipeline'],
|
||||
'animal_recognition_pipeline': ['AnimalRecognitionPipeline'],
|
||||
'body_2d_keypoints_pipeline': ['Body2DKeypointsPipeline'],
|
||||
'body_3d_keypoints_pipeline': ['Body3DKeypointsPipeline'],
|
||||
'hand_2d_keypoints_pipeline': ['Hand2DKeypointsPipeline'],
|
||||
'cmdssl_video_embedding_pipeline': ['CMDSSLVideoEmbeddingPipeline'],
|
||||
'hicossl_video_embedding_pipeline': ['HICOSSLVideoEmbeddingPipeline'],
|
||||
'crowd_counting_pipeline': ['CrowdCountingPipeline'],
|
||||
'image_detection_pipeline': ['ImageDetectionPipeline'],
|
||||
'image_salient_detection_pipeline': ['ImageSalientDetectionPipeline'],
|
||||
'face_detection_pipeline': ['FaceDetectionPipeline'],
|
||||
'face_image_generation_pipeline': ['FaceImageGenerationPipeline'],
|
||||
'face_recognition_pipeline': ['FaceRecognitionPipeline'],
|
||||
'general_recognition_pipeline': ['GeneralRecognitionPipeline'],
|
||||
'image_classification_pipeline':
|
||||
['GeneralImageClassificationPipeline', 'ImageClassificationPipeline'],
|
||||
'image_cartoon_pipeline': ['ImageCartoonPipeline'],
|
||||
'image_denoise_pipeline': ['ImageDenoisePipeline'],
|
||||
'image_color_enhance_pipeline': ['ImageColorEnhancePipeline'],
|
||||
'image_colorization_pipeline': ['ImageColorizationPipeline'],
|
||||
'image_instance_segmentation_pipeline':
|
||||
['ImageInstanceSegmentationPipeline'],
|
||||
'image_matting_pipeline': ['ImageMattingPipeline'],
|
||||
'image_panoptic_segmentation_pipeline':
|
||||
['ImagePanopticSegmentationPipeline'],
|
||||
'image_portrait_enhancement_pipeline':
|
||||
['ImagePortraitEnhancementPipeline'],
|
||||
'image_reid_person_pipeline': ['ImageReidPersonPipeline'],
|
||||
'image_semantic_segmentation_pipeline':
|
||||
['ImageSemanticSegmentationPipeline'],
|
||||
'image_style_transfer_pipeline': ['ImageStyleTransferPipeline'],
|
||||
'image_super_resolution_pipeline': ['ImageSuperResolutionPipeline'],
|
||||
'image_to_image_translation_pipeline':
|
||||
['Image2ImageTranslationPipeline'],
|
||||
'product_retrieval_embedding_pipeline':
|
||||
['ProductRetrievalEmbeddingPipeline'],
|
||||
'realtime_object_detection_pipeline':
|
||||
['RealtimeObjectDetectionPipeline'],
|
||||
'live_category_pipeline': ['LiveCategoryPipeline'],
|
||||
'image_to_image_generation_pipeline':
|
||||
['Image2ImageGenerationPipeline'],
|
||||
'image_inpainting_pipeline': ['ImageInpaintingPipeline'],
|
||||
'ocr_detection_pipeline': ['OCRDetectionPipeline'],
|
||||
'ocr_recognition_pipeline': ['OCRRecognitionPipeline'],
|
||||
'table_recognition_pipeline': ['TableRecognitionPipeline'],
|
||||
'skin_retouching_pipeline': ['SkinRetouchingPipeline'],
|
||||
'tinynas_classification_pipeline': ['TinynasClassificationPipeline'],
|
||||
'video_category_pipeline': ['VideoCategoryPipeline'],
|
||||
'virtual_try_on_pipeline': ['VirtualTryonPipeline'],
|
||||
'shop_segmentation_pipleline': ['ShopSegmentationPipeline'],
|
||||
'easycv_pipeline': [
|
||||
'EasyCVDetectionPipeline',
|
||||
'EasyCVSegmentationPipeline',
|
||||
'Face2DKeypointsPipeline',
|
||||
'HumanWholebodyKeypointsPipeline',
|
||||
"action_recognition_pipeline": ["ActionRecognitionPipeline"],
|
||||
"action_detection_pipeline": ["ActionDetectionPipeline"],
|
||||
"animal_recognition_pipeline": ["AnimalRecognitionPipeline"],
|
||||
"body_2d_keypoints_pipeline": ["Body2DKeypointsPipeline"],
|
||||
"body_3d_keypoints_pipeline": ["Body3DKeypointsPipeline"],
|
||||
"hand_2d_keypoints_pipeline": ["Hand2DKeypointsPipeline"],
|
||||
"cmdssl_video_embedding_pipeline": ["CMDSSLVideoEmbeddingPipeline"],
|
||||
"hicossl_video_embedding_pipeline": ["HICOSSLVideoEmbeddingPipeline"],
|
||||
"crowd_counting_pipeline": ["CrowdCountingPipeline"],
|
||||
"image_detection_pipeline": ["ImageDetectionPipeline"],
|
||||
"image_salient_detection_pipeline": ["ImageSalientDetectionPipeline"],
|
||||
"face_detection_pipeline": ["FaceDetectionPipeline"],
|
||||
"face_image_generation_pipeline": ["FaceImageGenerationPipeline"],
|
||||
"face_recognition_pipeline": ["FaceRecognitionPipeline"],
|
||||
"general_recognition_pipeline": ["GeneralRecognitionPipeline"],
|
||||
"image_classification_pipeline": [
|
||||
"GeneralImageClassificationPipeline",
|
||||
"ImageClassificationPipeline",
|
||||
],
|
||||
'text_driven_segmentation_pipeline':
|
||||
['TextDrivenSegmentationPipeline'],
|
||||
'movie_scene_segmentation_pipeline':
|
||||
['MovieSceneSegmentationPipeline'],
|
||||
'mog_face_detection_pipeline': ['MogFaceDetectionPipeline'],
|
||||
'ulfd_face_detection_pipeline': ['UlfdFaceDetectionPipeline'],
|
||||
'retina_face_detection_pipeline': ['RetinaFaceDetectionPipeline'],
|
||||
'facial_expression_recognition_pipelin':
|
||||
['FacialExpressionRecognitionPipeline'],
|
||||
'mtcnn_face_detection_pipeline': ['MtcnnFaceDetectionPipeline'],
|
||||
'hand_static_pipeline': ['HandStaticPipeline'],
|
||||
'referring_video_object_segmentation_pipeline': [
|
||||
'ReferringVideoObjectSegmentationPipeline'
|
||||
"image_cartoon_pipeline": ["ImageCartoonPipeline"],
|
||||
"image_denoise_pipeline": ["ImageDenoisePipeline"],
|
||||
"image_color_enhance_pipeline": ["ImageColorEnhancePipeline"],
|
||||
"image_colorization_pipeline": ["ImageColorizationPipeline"],
|
||||
"image_instance_segmentation_pipeline": ["ImageInstanceSegmentationPipeline"],
|
||||
"image_matting_pipeline": ["ImageMattingPipeline"],
|
||||
"image_panoptic_segmentation_pipeline": ["ImagePanopticSegmentationPipeline"],
|
||||
"image_portrait_enhancement_pipeline": ["ImagePortraitEnhancementPipeline"],
|
||||
"image_reid_person_pipeline": ["ImageReidPersonPipeline"],
|
||||
"image_semantic_segmentation_pipeline": ["ImageSemanticSegmentationPipeline"],
|
||||
"image_style_transfer_pipeline": ["ImageStyleTransferPipeline"],
|
||||
"image_super_resolution_pipeline": ["ImageSuperResolutionPipeline"],
|
||||
"image_to_image_translation_pipeline": ["Image2ImageTranslationPipeline"],
|
||||
"product_retrieval_embedding_pipeline": ["ProductRetrievalEmbeddingPipeline"],
|
||||
"realtime_object_detection_pipeline": ["RealtimeObjectDetectionPipeline"],
|
||||
"live_category_pipeline": ["LiveCategoryPipeline"],
|
||||
"image_to_image_generation_pipeline": ["Image2ImageGenerationPipeline"],
|
||||
"image_inpainting_pipeline": ["ImageInpaintingPipeline"],
|
||||
"ocr_detection_pipeline": ["OCRDetectionPipeline"],
|
||||
"ocr_recognition_pipeline": ["OCRRecognitionPipeline"],
|
||||
"table_recognition_pipeline": ["TableRecognitionPipeline"],
|
||||
"skin_retouching_pipeline": ["SkinRetouchingPipeline"],
|
||||
"tinynas_classification_pipeline": ["TinynasClassificationPipeline"],
|
||||
"video_category_pipeline": ["VideoCategoryPipeline"],
|
||||
"virtual_try_on_pipeline": ["VirtualTryonPipeline"],
|
||||
"shop_segmentation_pipleline": ["ShopSegmentationPipeline"],
|
||||
"easycv_pipeline": [
|
||||
"EasyCVDetectionPipeline",
|
||||
"EasyCVSegmentationPipeline",
|
||||
"Face2DKeypointsPipeline",
|
||||
"HumanWholebodyKeypointsPipeline",
|
||||
],
|
||||
"text_driven_segmentation_pipeline": ["TextDrivenSegmentationPipeline"],
|
||||
"movie_scene_segmentation_pipeline": ["MovieSceneSegmentationPipeline"],
|
||||
"mog_face_detection_pipeline": ["MogFaceDetectionPipeline"],
|
||||
"ulfd_face_detection_pipeline": ["UlfdFaceDetectionPipeline"],
|
||||
"retina_face_detection_pipeline": ["RetinaFaceDetectionPipeline"],
|
||||
"facial_expression_recognition_pipelin": [
|
||||
"FacialExpressionRecognitionPipeline"
|
||||
],
|
||||
"mtcnn_face_detection_pipeline": ["MtcnnFaceDetectionPipeline"],
|
||||
"hand_static_pipeline": ["HandStaticPipeline"],
|
||||
"referring_video_object_segmentation_pipeline": [
|
||||
"ReferringVideoObjectSegmentationPipeline"
|
||||
],
|
||||
}
|
||||
|
||||
@@ -140,7 +139,7 @@ else:
|
||||
|
||||
sys.modules[__name__] = LazyImportModule(
|
||||
__name__,
|
||||
globals()['__file__'],
|
||||
globals()["__file__"],
|
||||
_import_structure,
|
||||
module_spec=__spec__,
|
||||
extra_objects={},
|
||||
|
||||
@@ -15,20 +15,34 @@ import numpy as np
|
||||
|
||||
BatchNorm = nn.BatchNorm2d
|
||||
|
||||
def get_model_url(data='imagenet', name='dla34', hash='ba72cf86'):
|
||||
return join('http://dl.yf.io/dla/models', data, '{}-{}.pth'.format(name, hash))
|
||||
|
||||
def get_model_url(data="imagenet", name="dla34", hash="ba72cf86"):
|
||||
return join("http://dl.yf.io/dla/models", data, "{}-{}.pth".format(name, hash))
|
||||
|
||||
|
||||
class BasicBlock(nn.Module):
|
||||
def __init__(self, inplanes, planes, stride=1, dilation=1):
|
||||
super(BasicBlock, self).__init__()
|
||||
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=3,
|
||||
stride=stride, padding=dilation,
|
||||
bias=False, dilation=dilation)
|
||||
self.conv1 = nn.Conv2d(
|
||||
inplanes,
|
||||
planes,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=dilation,
|
||||
bias=False,
|
||||
dilation=dilation,
|
||||
)
|
||||
self.bn1 = BatchNorm(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3,
|
||||
stride=1, padding=dilation,
|
||||
bias=False, dilation=dilation)
|
||||
self.conv2 = nn.Conv2d(
|
||||
planes,
|
||||
planes,
|
||||
kernel_size=3,
|
||||
stride=1,
|
||||
padding=dilation,
|
||||
bias=False,
|
||||
dilation=dilation,
|
||||
)
|
||||
self.bn2 = BatchNorm(planes)
|
||||
self.stride = stride
|
||||
|
||||
@@ -56,15 +70,19 @@ class Bottleneck(nn.Module):
|
||||
super(Bottleneck, self).__init__()
|
||||
expansion = Bottleneck.expansion
|
||||
bottle_planes = planes // expansion
|
||||
self.conv1 = nn.Conv2d(inplanes, bottle_planes,
|
||||
kernel_size=1, bias=False)
|
||||
self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False)
|
||||
self.bn1 = BatchNorm(bottle_planes)
|
||||
self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3,
|
||||
stride=stride, padding=dilation,
|
||||
bias=False, dilation=dilation)
|
||||
self.conv2 = nn.Conv2d(
|
||||
bottle_planes,
|
||||
bottle_planes,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=dilation,
|
||||
bias=False,
|
||||
dilation=dilation,
|
||||
)
|
||||
self.bn2 = BatchNorm(bottle_planes)
|
||||
self.conv3 = nn.Conv2d(bottle_planes, planes,
|
||||
kernel_size=1, bias=False)
|
||||
self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False)
|
||||
self.bn3 = BatchNorm(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.stride = stride
|
||||
@@ -100,15 +118,20 @@ class BottleneckX(nn.Module):
|
||||
# dim = int(math.floor(planes * (BottleneckV5.expansion / 64.0)))
|
||||
# bottle_planes = dim * cardinality
|
||||
bottle_planes = planes * cardinality // 32
|
||||
self.conv1 = nn.Conv2d(inplanes, bottle_planes,
|
||||
kernel_size=1, bias=False)
|
||||
self.conv1 = nn.Conv2d(inplanes, bottle_planes, kernel_size=1, bias=False)
|
||||
self.bn1 = BatchNorm(bottle_planes)
|
||||
self.conv2 = nn.Conv2d(bottle_planes, bottle_planes, kernel_size=3,
|
||||
stride=stride, padding=dilation, bias=False,
|
||||
dilation=dilation, groups=cardinality)
|
||||
self.conv2 = nn.Conv2d(
|
||||
bottle_planes,
|
||||
bottle_planes,
|
||||
kernel_size=3,
|
||||
stride=stride,
|
||||
padding=dilation,
|
||||
bias=False,
|
||||
dilation=dilation,
|
||||
groups=cardinality,
|
||||
)
|
||||
self.bn2 = BatchNorm(bottle_planes)
|
||||
self.conv3 = nn.Conv2d(bottle_planes, planes,
|
||||
kernel_size=1, bias=False)
|
||||
self.conv3 = nn.Conv2d(bottle_planes, planes, kernel_size=1, bias=False)
|
||||
self.bn3 = BatchNorm(planes)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.stride = stride
|
||||
@@ -138,8 +161,13 @@ class Root(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, kernel_size, residual):
|
||||
super(Root, self).__init__()
|
||||
self.conv = nn.Conv2d(
|
||||
in_channels, out_channels, 1,
|
||||
stride=1, bias=False, padding=(kernel_size - 1) // 2)
|
||||
in_channels,
|
||||
out_channels,
|
||||
1,
|
||||
stride=1,
|
||||
bias=False,
|
||||
padding=(kernel_size - 1) // 2,
|
||||
)
|
||||
self.bn = BatchNorm(out_channels)
|
||||
self.relu = nn.ReLU(inplace=True)
|
||||
self.residual = residual
|
||||
@@ -156,31 +184,51 @@ class Root(nn.Module):
|
||||
|
||||
|
||||
class Tree(nn.Module):
|
||||
def __init__(self, levels, block, in_channels, out_channels, stride=1,
|
||||
level_root=False, root_dim=0, root_kernel_size=1,
|
||||
dilation=1, root_residual=False):
|
||||
def __init__(
|
||||
self,
|
||||
levels,
|
||||
block,
|
||||
in_channels,
|
||||
out_channels,
|
||||
stride=1,
|
||||
level_root=False,
|
||||
root_dim=0,
|
||||
root_kernel_size=1,
|
||||
dilation=1,
|
||||
root_residual=False,
|
||||
):
|
||||
super(Tree, self).__init__()
|
||||
if root_dim == 0:
|
||||
root_dim = 2 * out_channels
|
||||
if level_root:
|
||||
root_dim += in_channels
|
||||
if levels == 1:
|
||||
self.tree1 = block(in_channels, out_channels, stride,
|
||||
dilation=dilation)
|
||||
self.tree2 = block(out_channels, out_channels, 1,
|
||||
dilation=dilation)
|
||||
self.tree1 = block(in_channels, out_channels, stride, dilation=dilation)
|
||||
self.tree2 = block(out_channels, out_channels, 1, dilation=dilation)
|
||||
else:
|
||||
self.tree1 = Tree(levels - 1, block, in_channels, out_channels,
|
||||
stride, root_dim=0,
|
||||
root_kernel_size=root_kernel_size,
|
||||
dilation=dilation, root_residual=root_residual)
|
||||
self.tree2 = Tree(levels - 1, block, out_channels, out_channels,
|
||||
root_dim=root_dim + out_channels,
|
||||
root_kernel_size=root_kernel_size,
|
||||
dilation=dilation, root_residual=root_residual)
|
||||
self.tree1 = Tree(
|
||||
levels - 1,
|
||||
block,
|
||||
in_channels,
|
||||
out_channels,
|
||||
stride,
|
||||
root_dim=0,
|
||||
root_kernel_size=root_kernel_size,
|
||||
dilation=dilation,
|
||||
root_residual=root_residual,
|
||||
)
|
||||
self.tree2 = Tree(
|
||||
levels - 1,
|
||||
block,
|
||||
out_channels,
|
||||
out_channels,
|
||||
root_dim=root_dim + out_channels,
|
||||
root_kernel_size=root_kernel_size,
|
||||
dilation=dilation,
|
||||
root_residual=root_residual,
|
||||
)
|
||||
if levels == 1:
|
||||
self.root = Root(root_dim, out_channels, root_kernel_size,
|
||||
root_residual)
|
||||
self.root = Root(root_dim, out_channels, root_kernel_size, root_residual)
|
||||
self.level_root = level_root
|
||||
self.root_dim = root_dim
|
||||
self.downsample = None
|
||||
@@ -190,9 +238,10 @@ class Tree(nn.Module):
|
||||
self.downsample = nn.MaxPool2d(stride, stride=stride)
|
||||
if in_channels != out_channels:
|
||||
self.project = nn.Sequential(
|
||||
nn.Conv2d(in_channels, out_channels,
|
||||
kernel_size=1, stride=1, bias=False),
|
||||
BatchNorm(out_channels)
|
||||
nn.Conv2d(
|
||||
in_channels, out_channels, kernel_size=1, stride=1, bias=False
|
||||
),
|
||||
BatchNorm(out_channels),
|
||||
)
|
||||
|
||||
def forward(self, x, residual=None, children=None):
|
||||
@@ -212,40 +261,76 @@ class Tree(nn.Module):
|
||||
|
||||
|
||||
class DLA(nn.Module):
|
||||
def __init__(self, levels, channels, num_classes=1000,
|
||||
block=BasicBlock, residual_root=False, return_levels=False,
|
||||
pool_size=7, linear_root=False):
|
||||
def __init__(
|
||||
self,
|
||||
levels,
|
||||
channels,
|
||||
num_classes=1000,
|
||||
block=BasicBlock,
|
||||
residual_root=False,
|
||||
return_levels=False,
|
||||
pool_size=7,
|
||||
linear_root=False,
|
||||
):
|
||||
super(DLA, self).__init__()
|
||||
self.channels = channels
|
||||
self.return_levels = return_levels
|
||||
self.num_classes = num_classes
|
||||
self.base_layer = nn.Sequential(
|
||||
nn.Conv2d(3, channels[0], kernel_size=7, stride=1,
|
||||
padding=3, bias=False),
|
||||
nn.Conv2d(3, channels[0], kernel_size=7, stride=1, padding=3, bias=False),
|
||||
BatchNorm(channels[0]),
|
||||
nn.ReLU(inplace=True))
|
||||
self.level0 = self._make_conv_level(
|
||||
channels[0], channels[0], levels[0])
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
self.level0 = self._make_conv_level(channels[0], channels[0], levels[0])
|
||||
self.level1 = self._make_conv_level(
|
||||
channels[0], channels[1], levels[1], stride=2)
|
||||
self.level2 = Tree(levels[2], block, channels[1], channels[2], 2,
|
||||
level_root=False,
|
||||
root_residual=residual_root)
|
||||
self.level3 = Tree(levels[3], block, channels[2], channels[3], 2,
|
||||
level_root=True, root_residual=residual_root)
|
||||
self.level4 = Tree(levels[4], block, channels[3], channels[4], 2,
|
||||
level_root=True, root_residual=residual_root)
|
||||
self.level5 = Tree(levels[5], block, channels[4], channels[5], 2,
|
||||
level_root=True, root_residual=residual_root)
|
||||
channels[0], channels[1], levels[1], stride=2
|
||||
)
|
||||
self.level2 = Tree(
|
||||
levels[2],
|
||||
block,
|
||||
channels[1],
|
||||
channels[2],
|
||||
2,
|
||||
level_root=False,
|
||||
root_residual=residual_root,
|
||||
)
|
||||
self.level3 = Tree(
|
||||
levels[3],
|
||||
block,
|
||||
channels[2],
|
||||
channels[3],
|
||||
2,
|
||||
level_root=True,
|
||||
root_residual=residual_root,
|
||||
)
|
||||
self.level4 = Tree(
|
||||
levels[4],
|
||||
block,
|
||||
channels[3],
|
||||
channels[4],
|
||||
2,
|
||||
level_root=True,
|
||||
root_residual=residual_root,
|
||||
)
|
||||
self.level5 = Tree(
|
||||
levels[5],
|
||||
block,
|
||||
channels[4],
|
||||
channels[5],
|
||||
2,
|
||||
level_root=True,
|
||||
root_residual=residual_root,
|
||||
)
|
||||
|
||||
self.avgpool = nn.AvgPool2d(pool_size)
|
||||
self.fc = nn.Conv2d(channels[-1], num_classes, kernel_size=1,
|
||||
stride=1, padding=0, bias=True)
|
||||
self.fc = nn.Conv2d(
|
||||
channels[-1], num_classes, kernel_size=1, stride=1, padding=0, bias=True
|
||||
)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.data.normal_(0, math.sqrt(2. / n))
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / n))
|
||||
elif isinstance(m, BatchNorm):
|
||||
m.weight.data.fill_(1)
|
||||
m.bias.data.zero_()
|
||||
@@ -255,8 +340,7 @@ class DLA(nn.Module):
|
||||
if stride != 1 or inplanes != planes:
|
||||
downsample = nn.Sequential(
|
||||
nn.MaxPool2d(stride, stride=stride),
|
||||
nn.Conv2d(inplanes, planes,
|
||||
kernel_size=1, stride=1, bias=False),
|
||||
nn.Conv2d(inplanes, planes, kernel_size=1, stride=1, bias=False),
|
||||
BatchNorm(planes),
|
||||
)
|
||||
|
||||
@@ -270,12 +354,21 @@ class DLA(nn.Module):
|
||||
def _make_conv_level(self, inplanes, planes, convs, stride=1, dilation=1):
|
||||
modules = []
|
||||
for i in range(convs):
|
||||
modules.extend([
|
||||
nn.Conv2d(inplanes, planes, kernel_size=3,
|
||||
stride=stride if i == 0 else 1,
|
||||
padding=dilation, bias=False, dilation=dilation),
|
||||
BatchNorm(planes),
|
||||
nn.ReLU(inplace=True)])
|
||||
modules.extend(
|
||||
[
|
||||
nn.Conv2d(
|
||||
inplanes,
|
||||
planes,
|
||||
kernel_size=3,
|
||||
stride=stride if i == 0 else 1,
|
||||
padding=dilation,
|
||||
bias=False,
|
||||
dilation=dilation,
|
||||
),
|
||||
BatchNorm(planes),
|
||||
nn.ReLU(inplace=True),
|
||||
]
|
||||
)
|
||||
inplanes = planes
|
||||
return nn.Sequential(*modules)
|
||||
|
||||
@@ -283,7 +376,7 @@ class DLA(nn.Module):
|
||||
y = []
|
||||
x = self.base_layer(x)
|
||||
for i in range(6):
|
||||
x = getattr(self, 'level{}'.format(i))(x)
|
||||
x = getattr(self, "level{}".format(i))(x)
|
||||
y.append(x)
|
||||
if self.return_levels:
|
||||
return y
|
||||
@@ -294,113 +387,138 @@ class DLA(nn.Module):
|
||||
|
||||
return x
|
||||
|
||||
def load_pretrained_model(self, data='imagenet', name='dla34', hash='ba72cf86'):
|
||||
def load_pretrained_model(self, data="imagenet", name="dla34", hash="ba72cf86"):
|
||||
fc = self.fc
|
||||
if name.endswith('.pth'):
|
||||
if name.endswith(".pth"):
|
||||
model_weights = torch.load(data + name)
|
||||
else:
|
||||
model_url = get_model_url(data, name, hash)
|
||||
model_weights = model_zoo.load_url(model_url)
|
||||
num_classes = len(model_weights[list(model_weights.keys())[-1]])
|
||||
self.fc = nn.Conv2d(
|
||||
self.channels[-1], num_classes,
|
||||
kernel_size=1, stride=1, padding=0, bias=True)
|
||||
self.channels[-1],
|
||||
num_classes,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=True,
|
||||
)
|
||||
self.load_state_dict(model_weights)
|
||||
self.fc = fc
|
||||
|
||||
|
||||
def dla34(pretrained, **kwargs): # DLA-34
|
||||
model = DLA([1, 1, 1, 2, 2, 1],
|
||||
[16, 32, 64, 128, 256, 512],
|
||||
block=BasicBlock, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 2, 1], [16, 32, 64, 128, 256, 512], block=BasicBlock, **kwargs
|
||||
)
|
||||
if pretrained:
|
||||
model.load_pretrained_model(data='imagenet', name='dla34', hash='ba72cf86')
|
||||
model.load_pretrained_model(data="imagenet", name="dla34", hash="ba72cf86")
|
||||
return model
|
||||
|
||||
|
||||
def dla46_c(pretrained=None, **kwargs): # DLA-46-C
|
||||
Bottleneck.expansion = 2
|
||||
model = DLA([1, 1, 1, 2, 2, 1],
|
||||
[16, 32, 64, 64, 128, 256],
|
||||
block=Bottleneck, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 2, 1], [16, 32, 64, 64, 128, 256], block=Bottleneck, **kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla46_c')
|
||||
model.load_pretrained_model(pretrained, "dla46_c")
|
||||
return model
|
||||
|
||||
|
||||
def dla46x_c(pretrained=None, **kwargs): # DLA-X-46-C
|
||||
BottleneckX.expansion = 2
|
||||
model = DLA([1, 1, 1, 2, 2, 1],
|
||||
[16, 32, 64, 64, 128, 256],
|
||||
block=BottleneckX, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 2, 1], [16, 32, 64, 64, 128, 256], block=BottleneckX, **kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla46x_c')
|
||||
model.load_pretrained_model(pretrained, "dla46x_c")
|
||||
return model
|
||||
|
||||
|
||||
def dla60x_c(pretrained, **kwargs): # DLA-X-60-C
|
||||
BottleneckX.expansion = 2
|
||||
model = DLA([1, 1, 1, 2, 3, 1],
|
||||
[16, 32, 64, 64, 128, 256],
|
||||
block=BottleneckX, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 3, 1], [16, 32, 64, 64, 128, 256], block=BottleneckX, **kwargs
|
||||
)
|
||||
if pretrained:
|
||||
model.load_pretrained_model(data='imagenet', name='dla60x_c', hash='b870c45c')
|
||||
model.load_pretrained_model(data="imagenet", name="dla60x_c", hash="b870c45c")
|
||||
return model
|
||||
|
||||
|
||||
def dla60(pretrained=None, **kwargs): # DLA-60
|
||||
Bottleneck.expansion = 2
|
||||
model = DLA([1, 1, 1, 2, 3, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=Bottleneck, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], block=Bottleneck, **kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla60')
|
||||
model.load_pretrained_model(pretrained, "dla60")
|
||||
return model
|
||||
|
||||
|
||||
def dla60x(pretrained=None, **kwargs): # DLA-X-60
|
||||
BottleneckX.expansion = 2
|
||||
model = DLA([1, 1, 1, 2, 3, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=BottleneckX, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 2, 3, 1], [16, 32, 128, 256, 512, 1024], block=BottleneckX, **kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla60x')
|
||||
model.load_pretrained_model(pretrained, "dla60x")
|
||||
return model
|
||||
|
||||
|
||||
def dla102(pretrained=None, **kwargs): # DLA-102
|
||||
Bottleneck.expansion = 2
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=Bottleneck, residual_root=True, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 3, 4, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=Bottleneck,
|
||||
residual_root=True,
|
||||
**kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla102')
|
||||
model.load_pretrained_model(pretrained, "dla102")
|
||||
return model
|
||||
|
||||
|
||||
def dla102x(pretrained=None, **kwargs): # DLA-X-102
|
||||
BottleneckX.expansion = 2
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=BottleneckX, residual_root=True, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 3, 4, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=BottleneckX,
|
||||
residual_root=True,
|
||||
**kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla102x')
|
||||
model.load_pretrained_model(pretrained, "dla102x")
|
||||
return model
|
||||
|
||||
|
||||
def dla102x2(pretrained=None, **kwargs): # DLA-X-102 64
|
||||
BottleneckX.cardinality = 64
|
||||
model = DLA([1, 1, 1, 3, 4, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=BottleneckX, residual_root=True, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 1, 3, 4, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=BottleneckX,
|
||||
residual_root=True,
|
||||
**kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla102x2')
|
||||
model.load_pretrained_model(pretrained, "dla102x2")
|
||||
return model
|
||||
|
||||
|
||||
def dla169(pretrained=None, **kwargs): # DLA-169
|
||||
Bottleneck.expansion = 2
|
||||
model = DLA([1, 1, 2, 3, 5, 1], [16, 32, 128, 256, 512, 1024],
|
||||
block=Bottleneck, residual_root=True, **kwargs)
|
||||
model = DLA(
|
||||
[1, 1, 2, 3, 5, 1],
|
||||
[16, 32, 128, 256, 512, 1024],
|
||||
block=Bottleneck,
|
||||
residual_root=True,
|
||||
**kwargs
|
||||
)
|
||||
if pretrained is not None:
|
||||
model.load_pretrained_model(pretrained, 'dla169')
|
||||
model.load_pretrained_model(pretrained, "dla169")
|
||||
return model
|
||||
|
||||
|
||||
@@ -421,11 +539,10 @@ class Identity(nn.Module):
|
||||
def fill_up_weights(up):
|
||||
w = up.weight.data
|
||||
f = math.ceil(w.size(2) / 2)
|
||||
c = (2 * f - 1 - f % 2) / (2. * f)
|
||||
c = (2 * f - 1 - f % 2) / (2.0 * f)
|
||||
for i in range(w.size(2)):
|
||||
for j in range(w.size(3)):
|
||||
w[0, 0, i, j] = \
|
||||
(1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
|
||||
w[0, 0, i, j] = (1 - math.fabs(i / f - c)) * (1 - math.fabs(j / f - c))
|
||||
for c in range(1, w.size(0)):
|
||||
w[c, 0, :, :] = w[0, 0, :, :]
|
||||
|
||||
@@ -440,50 +557,64 @@ class IDAUp(nn.Module):
|
||||
proj = Identity()
|
||||
else:
|
||||
proj = nn.Sequential(
|
||||
nn.Conv2d(c, out_dim,
|
||||
kernel_size=1, stride=1, bias=False),
|
||||
nn.Conv2d(c, out_dim, kernel_size=1, stride=1, bias=False),
|
||||
BatchNorm(out_dim),
|
||||
nn.ReLU(inplace=True))
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
f = int(up_factors[i])
|
||||
if f == 1:
|
||||
up = Identity()
|
||||
else:
|
||||
up = nn.ConvTranspose2d(
|
||||
out_dim, out_dim, f * 2, stride=f, padding=f // 2,
|
||||
output_padding=0, groups=out_dim, bias=False)
|
||||
out_dim,
|
||||
out_dim,
|
||||
f * 2,
|
||||
stride=f,
|
||||
padding=f // 2,
|
||||
output_padding=0,
|
||||
groups=out_dim,
|
||||
bias=False,
|
||||
)
|
||||
fill_up_weights(up)
|
||||
setattr(self, 'proj_' + str(i), proj)
|
||||
setattr(self, 'up_' + str(i), up)
|
||||
setattr(self, "proj_" + str(i), proj)
|
||||
setattr(self, "up_" + str(i), up)
|
||||
|
||||
for i in range(1, len(channels)):
|
||||
node = nn.Sequential(
|
||||
nn.Conv2d(out_dim * 2, out_dim,
|
||||
kernel_size=node_kernel, stride=1,
|
||||
padding=node_kernel // 2, bias=False),
|
||||
nn.Conv2d(
|
||||
out_dim * 2,
|
||||
out_dim,
|
||||
kernel_size=node_kernel,
|
||||
stride=1,
|
||||
padding=node_kernel // 2,
|
||||
bias=False,
|
||||
),
|
||||
BatchNorm(out_dim),
|
||||
nn.ReLU(inplace=True))
|
||||
setattr(self, 'node_' + str(i), node)
|
||||
nn.ReLU(inplace=True),
|
||||
)
|
||||
setattr(self, "node_" + str(i), node)
|
||||
|
||||
for m in self.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
|
||||
m.weight.data.normal_(0, math.sqrt(2. / n))
|
||||
m.weight.data.normal_(0, math.sqrt(2.0 / n))
|
||||
elif isinstance(m, BatchNorm):
|
||||
m.weight.data.fill_(1)
|
||||
m.bias.data.zero_()
|
||||
|
||||
def forward(self, layers):
|
||||
assert len(self.channels) == len(layers), \
|
||||
'{} vs {} layers'.format(len(self.channels), len(layers))
|
||||
assert len(self.channels) == len(layers), "{} vs {} layers".format(
|
||||
len(self.channels), len(layers)
|
||||
)
|
||||
layers = list(layers)
|
||||
for i, l in enumerate(layers):
|
||||
upsample = getattr(self, 'up_' + str(i))
|
||||
project = getattr(self, 'proj_' + str(i))
|
||||
upsample = getattr(self, "up_" + str(i))
|
||||
project = getattr(self, "proj_" + str(i))
|
||||
layers[i] = upsample(project(l))
|
||||
x = layers[0]
|
||||
y = []
|
||||
for i in range(1, len(layers)):
|
||||
node = getattr(self, 'node_' + str(i))
|
||||
node = getattr(self, "node_" + str(i))
|
||||
x = node(torch.cat([x, layers[i]], 1))
|
||||
y.append(x)
|
||||
return x, y
|
||||
@@ -499,21 +630,24 @@ class DLAUp(nn.Module):
|
||||
scales = np.array(scales, dtype=int)
|
||||
for i in range(len(channels) - 1):
|
||||
j = -i - 2
|
||||
setattr(self, 'ida_{}'.format(i),
|
||||
IDAUp(3, channels[j], in_channels[j:],
|
||||
scales[j:] // scales[j]))
|
||||
scales[j + 1:] = scales[j]
|
||||
in_channels[j + 1:] = [channels[j] for _ in channels[j + 1:]]
|
||||
setattr(
|
||||
self,
|
||||
"ida_{}".format(i),
|
||||
IDAUp(3, channels[j], in_channels[j:], scales[j:] // scales[j]),
|
||||
)
|
||||
scales[j + 1 :] = scales[j]
|
||||
in_channels[j + 1 :] = [channels[j] for _ in channels[j + 1 :]]
|
||||
|
||||
def forward(self, layers):
|
||||
layers = list(layers)
|
||||
assert len(layers) > 1
|
||||
for i in range(len(layers) - 1):
|
||||
ida = getattr(self, 'ida_{}'.format(i))
|
||||
x, y = ida(layers[-i - 2:])
|
||||
layers[-i - 1:] = y
|
||||
ida = getattr(self, "ida_{}".format(i))
|
||||
x, y = ida(layers[-i - 2 :])
|
||||
layers[-i - 1 :] = y
|
||||
return x
|
||||
|
||||
|
||||
def fill_fc_weights(layers):
|
||||
for m in layers.modules():
|
||||
if isinstance(m, nn.Conv2d):
|
||||
@@ -523,38 +657,55 @@ def fill_fc_weights(layers):
|
||||
if m.bias is not None:
|
||||
nn.init.constant_(m.bias, 0)
|
||||
|
||||
|
||||
class DLASeg(nn.Module):
|
||||
def __init__(self, base_name='dla34',
|
||||
pretrained=False, down_ratio=4, head_conv=256):
|
||||
def __init__(
|
||||
self, base_name="dla34", pretrained=False, down_ratio=4, head_conv=256
|
||||
):
|
||||
super(DLASeg, self).__init__()
|
||||
assert down_ratio in [2, 4, 8, 16]
|
||||
self.heads = {'hm': 2,'v2c':8, 'c2v': 8, 'reg': 2}
|
||||
self.heads = {"hm": 2, "v2c": 8, "c2v": 8, "reg": 2}
|
||||
self.first_level = int(np.log2(down_ratio))
|
||||
self.base = globals()[base_name](
|
||||
pretrained=pretrained, return_levels=True)
|
||||
self.base = globals()[base_name](pretrained=pretrained, return_levels=True)
|
||||
channels = self.base.channels
|
||||
scales = [2 ** i for i in range(len(channels[self.first_level:]))]
|
||||
self.dla_up = DLAUp(channels[self.first_level:], scales=scales)
|
||||
scales = [2**i for i in range(len(channels[self.first_level :]))]
|
||||
self.dla_up = DLAUp(channels[self.first_level :], scales=scales)
|
||||
|
||||
for head in self.heads:
|
||||
classes = self.heads[head]
|
||||
if head_conv > 0:
|
||||
fc = nn.Sequential(
|
||||
nn.Conv2d(channels[self.first_level], head_conv,
|
||||
kernel_size=3, padding=1, bias=True),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(head_conv, classes,
|
||||
kernel_size=1, stride=1,
|
||||
padding=0, bias=True))
|
||||
if 'hm' in head:
|
||||
nn.Conv2d(
|
||||
channels[self.first_level],
|
||||
head_conv,
|
||||
kernel_size=3,
|
||||
padding=1,
|
||||
bias=True,
|
||||
),
|
||||
nn.ReLU(inplace=True),
|
||||
nn.Conv2d(
|
||||
head_conv,
|
||||
classes,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=True,
|
||||
),
|
||||
)
|
||||
if "hm" in head:
|
||||
fc[-1].bias.data.fill_(-2.19)
|
||||
else:
|
||||
fill_fc_weights(fc)
|
||||
else:
|
||||
fc = nn.Conv2d(channels[self.first_level], classes,
|
||||
kernel_size=1, stride=1,
|
||||
padding=0, bias=True)
|
||||
if 'hm' in head:
|
||||
fc = nn.Conv2d(
|
||||
channels[self.first_level],
|
||||
classes,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
padding=0,
|
||||
bias=True,
|
||||
)
|
||||
if "hm" in head:
|
||||
fc.bias.data.fill_(-2.19)
|
||||
else:
|
||||
fill_fc_weights(fc)
|
||||
@@ -562,7 +713,7 @@ class DLASeg(nn.Module):
|
||||
|
||||
def forward(self, x):
|
||||
x = self.base(x)
|
||||
x = self.dla_up(x[self.first_level:])
|
||||
x = self.dla_up(x[self.first_level :])
|
||||
ret = {}
|
||||
for head in self.heads:
|
||||
ret[head] = self.__getattr__(head)(x)
|
||||
@@ -570,5 +721,5 @@ class DLASeg(nn.Module):
|
||||
|
||||
|
||||
def TableRecModel():
|
||||
model = DLASeg()
|
||||
return model
|
||||
model = DLASeg()
|
||||
return model
|
||||
|
||||
@@ -1,11 +1,12 @@
|
||||
import numpy as np
|
||||
import cv2
|
||||
import cv2
|
||||
import copy
|
||||
import math
|
||||
import random
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
|
||||
|
||||
def transform_preds(coords, center, scale, output_size, rot=0):
|
||||
target_coords = np.zeros(coords.shape)
|
||||
trans = get_affine_transform(center, scale, rot, output_size, inv=1)
|
||||
@@ -13,12 +14,10 @@ def transform_preds(coords, center, scale, output_size, rot=0):
|
||||
target_coords[p, 0:2] = affine_transform(coords[p, 0:2], trans)
|
||||
return target_coords
|
||||
|
||||
def get_affine_transform(center,
|
||||
scale,
|
||||
rot,
|
||||
output_size,
|
||||
shift=np.array([0, 0], dtype=np.float32),
|
||||
inv=0):
|
||||
|
||||
def get_affine_transform(
|
||||
center, scale, rot, output_size, shift=np.array([0, 0], dtype=np.float32), inv=0
|
||||
):
|
||||
if not isinstance(scale, np.ndarray) and not isinstance(scale, list):
|
||||
scale = np.array([scale, scale], dtype=np.float32)
|
||||
|
||||
@@ -27,7 +26,7 @@ def get_affine_transform(center,
|
||||
dst_w = output_size[0]
|
||||
dst_h = output_size[1]
|
||||
|
||||
rot_rad = np.pi * rot / 180
|
||||
rot_rad = np.pi * rot / 180
|
||||
src_dir = get_dir([0, src_w * -0.5], rot_rad)
|
||||
dst_dir = np.array([0, dst_w * -0.5], np.float32)
|
||||
|
||||
@@ -38,8 +37,8 @@ def get_affine_transform(center,
|
||||
dst[0, :] = [dst_w * 0.5, dst_h * 0.5]
|
||||
dst[1, :] = np.array([dst_w * 0.5, dst_h * 0.5], np.float32) + dst_dir
|
||||
|
||||
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
|
||||
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
|
||||
src[2:, :] = get_3rd_point(src[0, :], src[1, :])
|
||||
dst[2:, :] = get_3rd_point(dst[0, :], dst[1, :])
|
||||
|
||||
if inv:
|
||||
trans = cv2.getAffineTransform(np.float32(dst), np.float32(src))
|
||||
@@ -48,11 +47,13 @@ def get_affine_transform(center,
|
||||
|
||||
return trans
|
||||
|
||||
def affine_transform(pt, t):
|
||||
new_pt = np.array([pt[0], pt[1], 1.], dtype=np.float32).T
|
||||
|
||||
def affine_transform(pt, t):
|
||||
new_pt = np.array([pt[0], pt[1], 1.0], dtype=np.float32).T
|
||||
new_pt = np.dot(t, new_pt)
|
||||
return new_pt[:2]
|
||||
|
||||
|
||||
def get_dir(src_point, rot_rad):
|
||||
sn, cs = np.sin(rot_rad), np.cos(rot_rad)
|
||||
|
||||
@@ -62,17 +63,20 @@ def get_dir(src_point, rot_rad):
|
||||
|
||||
return src_result
|
||||
|
||||
|
||||
def get_3rd_point(a, b):
|
||||
direct = a - b
|
||||
return b + np.array([-direct[1], direct[0]], dtype=np.float32)
|
||||
|
||||
|
||||
def _sigmoid(x):
|
||||
y = torch.clamp(x.sigmoid_(), min=1e-4, max=1-1e-4)
|
||||
return y
|
||||
y = torch.clamp(x.sigmoid_(), min=1e-4, max=1 - 1e-4)
|
||||
return y
|
||||
|
||||
|
||||
def _gather_feat(feat, ind, mask=None):
|
||||
dim = feat.size(2)
|
||||
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
|
||||
dim = feat.size(2)
|
||||
ind = ind.unsqueeze(2).expand(ind.size(0), ind.size(1), dim)
|
||||
feat = feat.gather(1, ind)
|
||||
if mask is not None:
|
||||
mask = mask.unsqueeze(2).expand_as(feat)
|
||||
@@ -80,19 +84,21 @@ def _gather_feat(feat, ind, mask=None):
|
||||
feat = feat.view(-1, dim)
|
||||
return feat
|
||||
|
||||
|
||||
def _tranpose_and_gather_feat(feat, ind):
|
||||
feat = feat.permute(0, 2, 3, 1).contiguous()
|
||||
feat = feat.view(feat.size(0), -1, feat.size(3))
|
||||
feat = _gather_feat(feat, ind)
|
||||
return feat
|
||||
|
||||
|
||||
def _nms(heat, kernel=3):
|
||||
pad = (kernel - 1) // 2
|
||||
|
||||
hmax = nn.functional.max_pool2d(
|
||||
heat, (kernel, kernel), stride=1, padding=pad)
|
||||
hmax = nn.functional.max_pool2d(heat, (kernel, kernel), stride=1, padding=pad)
|
||||
keep = (hmax == heat).float()
|
||||
return heat * keep,keep
|
||||
return heat * keep, keep
|
||||
|
||||
|
||||
def _topk(scores, K=40):
|
||||
batch, cat, height, width = scores.size()
|
||||
@@ -100,13 +106,12 @@ def _topk(scores, K=40):
|
||||
topk_scores, topk_inds = torch.topk(scores.view(batch, cat, -1), K)
|
||||
|
||||
topk_inds = topk_inds % (height * width)
|
||||
topk_ys = (topk_inds / width).int().float()
|
||||
topk_xs = (topk_inds % width).int().float()
|
||||
topk_ys = (topk_inds / width).int().float()
|
||||
topk_xs = (topk_inds % width).int().float()
|
||||
|
||||
topk_score, topk_ind = torch.topk(topk_scores.view(batch, -1), K)
|
||||
topk_clses = (topk_ind / K).int()
|
||||
topk_inds = _gather_feat(
|
||||
topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
|
||||
topk_inds = _gather_feat(topk_inds.view(batch, -1, 1), topk_ind).view(batch, K)
|
||||
topk_ys = _gather_feat(topk_ys.view(batch, -1, 1), topk_ind).view(batch, K)
|
||||
topk_xs = _gather_feat(topk_xs.view(batch, -1, 1), topk_ind).view(batch, K)
|
||||
|
||||
@@ -118,37 +123,43 @@ def bbox_decode(heat, wh, reg=None, K=100):
|
||||
|
||||
# heat = torch.sigmoid(heat)
|
||||
# perform nms on heatmaps
|
||||
heat,keep = _nms(heat)
|
||||
|
||||
heat, keep = _nms(heat)
|
||||
|
||||
scores, inds, clses, ys, xs = _topk(heat, K=K)
|
||||
if reg is not None:
|
||||
reg = _tranpose_and_gather_feat(reg, inds)
|
||||
reg = reg.view(batch, K, 2)
|
||||
xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
|
||||
ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
|
||||
reg = _tranpose_and_gather_feat(reg, inds)
|
||||
reg = reg.view(batch, K, 2)
|
||||
xs = xs.view(batch, K, 1) + reg[:, :, 0:1]
|
||||
ys = ys.view(batch, K, 1) + reg[:, :, 1:2]
|
||||
else:
|
||||
xs = xs.view(batch, K, 1) + 0.5
|
||||
ys = ys.view(batch, K, 1) + 0.5
|
||||
xs = xs.view(batch, K, 1) + 0.5
|
||||
ys = ys.view(batch, K, 1) + 0.5
|
||||
wh = _tranpose_and_gather_feat(wh, inds)
|
||||
wh = wh.view(batch, K, 8)
|
||||
clses = clses.view(batch, K, 1).float()
|
||||
clses = clses.view(batch, K, 1).float()
|
||||
scores = scores.view(batch, K, 1)
|
||||
|
||||
bboxes = torch.cat([xs - wh[..., 0:1],
|
||||
ys - wh[..., 1:2],
|
||||
xs - wh[..., 2:3],
|
||||
ys - wh[..., 3:4],
|
||||
xs - wh[..., 4:5],
|
||||
ys - wh[..., 5:6],
|
||||
xs - wh[..., 6:7],
|
||||
ys - wh[..., 7:8]], dim=2)
|
||||
bboxes = torch.cat(
|
||||
[
|
||||
xs - wh[..., 0:1],
|
||||
ys - wh[..., 1:2],
|
||||
xs - wh[..., 2:3],
|
||||
ys - wh[..., 3:4],
|
||||
xs - wh[..., 4:5],
|
||||
ys - wh[..., 5:6],
|
||||
xs - wh[..., 6:7],
|
||||
ys - wh[..., 7:8],
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
detections = torch.cat([bboxes, scores, clses], dim=2)
|
||||
|
||||
return detections,keep
|
||||
return detections, keep
|
||||
|
||||
|
||||
def gbox_decode(mk, st_reg, reg=None, K=400):
|
||||
batch, cat, height, width = mk.size()
|
||||
mk,keep = _nms(mk)
|
||||
mk, keep = _nms(mk)
|
||||
scores, inds, clses, ys, xs = _topk(mk, K=K)
|
||||
if reg is not None:
|
||||
reg = _tranpose_and_gather_feat(reg, inds)
|
||||
@@ -159,17 +170,23 @@ def gbox_decode(mk, st_reg, reg=None, K=400):
|
||||
xs = xs.view(batch, K, 1) + 0.5
|
||||
ys = ys.view(batch, K, 1) + 0.5
|
||||
scores = scores.view(batch, K, 1)
|
||||
clses = clses.view(batch, K, 1).float()
|
||||
clses = clses.view(batch, K, 1).float()
|
||||
st_Reg = _tranpose_and_gather_feat(st_reg, inds)
|
||||
bboxes = torch.cat([xs - st_Reg[..., 0:1],
|
||||
ys - st_Reg[..., 1:2],
|
||||
xs - st_Reg[..., 2:3],
|
||||
ys - st_Reg[..., 3:4],
|
||||
xs - st_Reg[..., 4:5],
|
||||
ys - st_Reg[..., 5:6],
|
||||
xs - st_Reg[..., 6:7],
|
||||
ys - st_Reg[..., 7:8]], dim=2)
|
||||
return torch.cat([xs,ys,bboxes,scores,clses], dim=2), keep
|
||||
bboxes = torch.cat(
|
||||
[
|
||||
xs - st_Reg[..., 0:1],
|
||||
ys - st_Reg[..., 1:2],
|
||||
xs - st_Reg[..., 2:3],
|
||||
ys - st_Reg[..., 3:4],
|
||||
xs - st_Reg[..., 4:5],
|
||||
ys - st_Reg[..., 5:6],
|
||||
xs - st_Reg[..., 6:7],
|
||||
ys - st_Reg[..., 7:8],
|
||||
],
|
||||
dim=2,
|
||||
)
|
||||
return torch.cat([xs, ys, bboxes, scores, clses], dim=2), keep
|
||||
|
||||
|
||||
def bbox_post_process(bbox, c, s, h, w):
|
||||
# dets: batch x max_dets x dim
|
||||
@@ -179,102 +196,116 @@ def bbox_post_process(bbox, c, s, h, w):
|
||||
bbox[i, :, 2:4] = transform_preds(bbox[i, :, 2:4], c[i], s[i], (w, h))
|
||||
bbox[i, :, 4:6] = transform_preds(bbox[i, :, 4:6], c[i], s[i], (w, h))
|
||||
bbox[i, :, 6:8] = transform_preds(bbox[i, :, 6:8], c[i], s[i], (w, h))
|
||||
return bbox
|
||||
return bbox
|
||||
|
||||
|
||||
def gbox_post_process(gbox, c, s, h, w):
|
||||
for i in range(gbox.shape[0]):
|
||||
gbox[i, :, 0:2] = transform_preds(gbox[i, :, 0:2], c[i], s[i], (w, h))
|
||||
gbox[i, :, 2:4] = transform_preds(gbox[i, :, 2:4], c[i], s[i], (w, h))
|
||||
gbox[i, :, 4:6] = transform_preds(gbox[i, :, 4:6], c[i], s[i], (w, h))
|
||||
gbox[i, :, 6:8] = transform_preds(gbox[i, :, 6:8], c[i], s[i], (w, h))
|
||||
gbox[i, :, 8:10] = transform_preds(gbox[i, :, 8:10], c[i], s[i], (w, h))
|
||||
gbox[i, :, 0:2] = transform_preds(gbox[i, :, 0:2], c[i], s[i], (w, h))
|
||||
gbox[i, :, 2:4] = transform_preds(gbox[i, :, 2:4], c[i], s[i], (w, h))
|
||||
gbox[i, :, 4:6] = transform_preds(gbox[i, :, 4:6], c[i], s[i], (w, h))
|
||||
gbox[i, :, 6:8] = transform_preds(gbox[i, :, 6:8], c[i], s[i], (w, h))
|
||||
gbox[i, :, 8:10] = transform_preds(gbox[i, :, 8:10], c[i], s[i], (w, h))
|
||||
return gbox
|
||||
|
||||
def nms(dets,thresh):
|
||||
if len(dets)<2:
|
||||
|
||||
def nms(dets, thresh):
|
||||
if len(dets) < 2:
|
||||
return dets
|
||||
scores = dets[:,8]
|
||||
scores = dets[:, 8]
|
||||
index_keep = []
|
||||
keep = []
|
||||
for i in range(len(dets)):
|
||||
box = dets[i]
|
||||
if box[-1]<thresh:
|
||||
if box[-1] < thresh:
|
||||
break
|
||||
max_score_index = -1
|
||||
ctx = (dets[i][0] + dets[i][2] + dets[i][4] + dets[i][6])/4
|
||||
cty = (dets[i][1] + dets[i][3] + dets[i][5] + dets[i][7])/4
|
||||
ctx = (dets[i][0] + dets[i][2] + dets[i][4] + dets[i][6]) / 4
|
||||
cty = (dets[i][1] + dets[i][3] + dets[i][5] + dets[i][7]) / 4
|
||||
for j in range(len(dets)):
|
||||
if i==j or dets[j][-1]<thresh:
|
||||
if i == j or dets[j][-1] < thresh:
|
||||
break
|
||||
x1,y1 = dets[j][0],dets[j][1]
|
||||
x2,y2 = dets[j][2],dets[j][3]
|
||||
x3,y3 = dets[j][4],dets[j][5]
|
||||
x4,y4 = dets[j][6],dets[j][7]
|
||||
a = (x2 - x1)*(cty - y1) - (y2 - y1)*(ctx - x1)
|
||||
b = (x3 - x2)*(cty - y2) - (y3 - y2)*(ctx - x2)
|
||||
c = (x4 - x3)*(cty - y3) - (y4 - y3)*(ctx - x3)
|
||||
d = (x1 - x4)*(cty - y4) - (y1 - y4)*(ctx - x4)
|
||||
if ((a > 0 and b > 0 and c > 0 and d > 0) or (a < 0 and b < 0 and c < 0 and d < 0)):
|
||||
x1, y1 = dets[j][0], dets[j][1]
|
||||
x2, y2 = dets[j][2], dets[j][3]
|
||||
x3, y3 = dets[j][4], dets[j][5]
|
||||
x4, y4 = dets[j][6], dets[j][7]
|
||||
a = (x2 - x1) * (cty - y1) - (y2 - y1) * (ctx - x1)
|
||||
b = (x3 - x2) * (cty - y2) - (y3 - y2) * (ctx - x2)
|
||||
c = (x4 - x3) * (cty - y3) - (y4 - y3) * (ctx - x3)
|
||||
d = (x1 - x4) * (cty - y4) - (y1 - y4) * (ctx - x4)
|
||||
if (a > 0 and b > 0 and c > 0 and d > 0) or (
|
||||
a < 0 and b < 0 and c < 0 and d < 0
|
||||
):
|
||||
if dets[i][8] > dets[j][8] and max_score_index < 0:
|
||||
max_score_index = i
|
||||
elif dets[i][8] < dets[j][8]:
|
||||
max_score_index = i
|
||||
elif dets[i][8] < dets[j][8]:
|
||||
max_score_index = -2
|
||||
break
|
||||
if max_score_index > -1:
|
||||
if max_score_index > -1:
|
||||
index_keep.append(max_score_index)
|
||||
elif max_score_index==-1:
|
||||
elif max_score_index == -1:
|
||||
index_keep.append(i)
|
||||
for i in range(0,len(index_keep)):
|
||||
for i in range(0, len(index_keep)):
|
||||
keep.append(dets[index_keep[i]])
|
||||
return np.array(keep)
|
||||
|
||||
|
||||
def group_bbox_by_gbox(bboxes,gboxes,score_thred=0.3, v2c_dist_thred=2, c2v_dist_thred=0.5):
|
||||
|
||||
def point_in_box(box,point):
|
||||
x1,y1,x2,y2 = box[0],box[1],box[2],box[3]
|
||||
x3,y3,x4,y4 = box[4],box[5],box[6],box[7]
|
||||
ctx,cty = point[0],point[1]
|
||||
a = (x2 - x1)*(cty - y1) - (y2 - y1)*(ctx - x1)
|
||||
b = (x3 - x2)*(cty - y2) - (y3 - y2)*(ctx - x2)
|
||||
c = (x4 - x3)*(cty - y3) - (y4 - y3)*(ctx - x3)
|
||||
d = (x1 - x4)*(cty - y4) - (y1 - y4)*(ctx - x4)
|
||||
if ((a > 0 and b > 0 and c > 0 and d > 0) or (a < 0 and b < 0 and c < 0 and d < 0)):
|
||||
def group_bbox_by_gbox(
|
||||
bboxes, gboxes, score_thred=0.3, v2c_dist_thred=2, c2v_dist_thred=0.5
|
||||
):
|
||||
def point_in_box(box, point):
|
||||
x1, y1, x2, y2 = box[0], box[1], box[2], box[3]
|
||||
x3, y3, x4, y4 = box[4], box[5], box[6], box[7]
|
||||
ctx, cty = point[0], point[1]
|
||||
a = (x2 - x1) * (cty - y1) - (y2 - y1) * (ctx - x1)
|
||||
b = (x3 - x2) * (cty - y2) - (y3 - y2) * (ctx - x2)
|
||||
c = (x4 - x3) * (cty - y3) - (y4 - y3) * (ctx - x3)
|
||||
d = (x1 - x4) * (cty - y4) - (y1 - y4) * (ctx - x4)
|
||||
if (a > 0 and b > 0 and c > 0 and d > 0) or (
|
||||
a < 0 and b < 0 and c < 0 and d < 0
|
||||
):
|
||||
return True
|
||||
else :
|
||||
else:
|
||||
return False
|
||||
|
||||
def get_distance(pt1,pt2):
|
||||
return math.sqrt((pt1[0]-pt2[0])*(pt1[0]-pt2[0]) + (pt1[1]-pt2[1])*(pt1[1]-pt2[1]))
|
||||
def get_distance(pt1, pt2):
|
||||
return math.sqrt(
|
||||
(pt1[0] - pt2[0]) * (pt1[0] - pt2[0])
|
||||
+ (pt1[1] - pt2[1]) * (pt1[1] - pt2[1])
|
||||
)
|
||||
|
||||
dets = copy.deepcopy(bboxes)
|
||||
sign = np.zeros((len(dets),4))
|
||||
sign = np.zeros((len(dets), 4))
|
||||
|
||||
for idx,gbox in enumerate(gboxes): #vertex x,y, gbox, score
|
||||
for idx, gbox in enumerate(gboxes): # vertex x,y, gbox, score
|
||||
if gbox[10] < score_thred:
|
||||
break
|
||||
vertex = [gbox[0],gbox[1]]
|
||||
for i in range(0,4):
|
||||
center = [gbox[2*i+2],gbox[2*i+3]]
|
||||
if get_distance(vertex,center) < v2c_dist_thred:
|
||||
vertex = [gbox[0], gbox[1]]
|
||||
for i in range(0, 4):
|
||||
center = [gbox[2 * i + 2], gbox[2 * i + 3]]
|
||||
if get_distance(vertex, center) < v2c_dist_thred:
|
||||
continue
|
||||
for k,bbox in enumerate(dets):
|
||||
for k, bbox in enumerate(dets):
|
||||
if bbox[8] < score_thred:
|
||||
break
|
||||
if sum(sign[k])==4:
|
||||
if sum(sign[k]) == 4:
|
||||
continue
|
||||
w = (abs(bbox[6] - bbox[0]) + abs(bbox[4] - bbox[2])) / 2
|
||||
h = (abs(bbox[3] - bbox[1]) + abs(bbox[5] - bbox[7])) / 2
|
||||
m = max(w,h)
|
||||
if point_in_box(bbox,center):
|
||||
min_dist,min_id = 1e4,-1
|
||||
for j in range(0,4):
|
||||
dist = get_distance(vertex,[bbox[2*j],bbox[2*j+1]])
|
||||
w = (abs(bbox[6] - bbox[0]) + abs(bbox[4] - bbox[2])) / 2
|
||||
h = (abs(bbox[3] - bbox[1]) + abs(bbox[5] - bbox[7])) / 2
|
||||
m = max(w, h)
|
||||
if point_in_box(bbox, center):
|
||||
min_dist, min_id = 1e4, -1
|
||||
for j in range(0, 4):
|
||||
dist = get_distance(vertex, [bbox[2 * j], bbox[2 * j + 1]])
|
||||
if dist < min_dist:
|
||||
min_dist = dist
|
||||
min_id = j
|
||||
if min_id>-1 and min_dist<c2v_dist_thred*m and sign[k][min_id]==0:
|
||||
bboxes[k][2*min_id] = vertex[0]
|
||||
bboxes[k][2*min_id+1] = vertex[1]
|
||||
if (
|
||||
min_id > -1
|
||||
and min_dist < c2v_dist_thred * m
|
||||
and sign[k][min_id] == 0
|
||||
):
|
||||
bboxes[k][2 * min_id] = vertex[0]
|
||||
bboxes[k][2 * min_id + 1] = vertex[1]
|
||||
sign[k][min_id] = 1
|
||||
return bboxes
|
||||
|
||||
@@ -16,15 +16,25 @@ from modelscope.pipelines.cv.ocr_utils.model_dla34 import TableRecModel
|
||||
from modelscope.preprocessors import load_image
|
||||
from modelscope.utils.constant import ModelFile, Tasks
|
||||
from modelscope.utils.logger import get_logger
|
||||
from modelscope.pipelines.cv.ocr_utils.table_process import get_affine_transform,bbox_decode,gbox_decode,nms
|
||||
from modelscope.pipelines.cv.ocr_utils.table_process import bbox_post_process,gbox_post_process,group_bbox_by_gbox
|
||||
from modelscope.pipelines.cv.ocr_utils.table_process import (
|
||||
get_affine_transform,
|
||||
bbox_decode,
|
||||
gbox_decode,
|
||||
nms,
|
||||
)
|
||||
from modelscope.pipelines.cv.ocr_utils.table_process import (
|
||||
bbox_post_process,
|
||||
gbox_post_process,
|
||||
group_bbox_by_gbox,
|
||||
)
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
@PIPELINES.register_module(
|
||||
Tasks.table_recognition, module_name=Pipelines.table_recognition)
|
||||
class TableRecognitionPipeline(Pipeline):
|
||||
|
||||
@PIPELINES.register_module(
|
||||
Tasks.table_recognition, module_name=Pipelines.table_recognition
|
||||
)
|
||||
class TableRecognitionPipeline(Pipeline):
|
||||
def __init__(self, model: str, **kwargs):
|
||||
"""
|
||||
Args:
|
||||
@@ -32,17 +42,16 @@ class TableRecognitionPipeline(Pipeline):
|
||||
"""
|
||||
super().__init__(model=model, **kwargs)
|
||||
model_path = osp.join(self.model, ModelFile.TORCH_MODEL_FILE)
|
||||
logger.info(f'loading model from {model_path}')
|
||||
logger.info(f"loading model from {model_path}")
|
||||
|
||||
self.K = 1000
|
||||
self.MK = 4000
|
||||
self.device = torch.device(
|
||||
'cuda' if torch.cuda.is_available() else 'cpu')
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
self.infer_model = TableRecModel().to(self.device)
|
||||
self.infer_model.eval()
|
||||
checkpoint = torch.load(model_path, map_location=self.device)
|
||||
if 'state_dict' in checkpoint:
|
||||
self.infer_model.load_state_dict(checkpoint['state_dict'])
|
||||
if "state_dict" in checkpoint:
|
||||
self.infer_model.load_state_dict(checkpoint["state_dict"])
|
||||
else:
|
||||
self.infer_model.load_state_dict(checkpoint)
|
||||
|
||||
@@ -55,60 +64,77 @@ class TableRecognitionPipeline(Pipeline):
|
||||
if len(input.shape) == 3:
|
||||
img = input
|
||||
else:
|
||||
raise TypeError(f'input should be either str, PIL.Image,'
|
||||
f' np.array, but got {type(input)}')
|
||||
|
||||
raise TypeError(
|
||||
f"input should be either str, PIL.Image,"
|
||||
f" np.array, but got {type(input)}"
|
||||
)
|
||||
|
||||
mean = np.array([0.408, 0.447, 0.470], dtype=np.float32).reshape(1, 1, 3)
|
||||
std = np.array([0.289, 0.274, 0.278], dtype=np.float32).reshape(1, 1, 3)
|
||||
height, width = img.shape[0:2]
|
||||
inp_height, inp_width = 1024, 1024
|
||||
c = np.array([width / 2., height / 2.], dtype=np.float32)
|
||||
s = max(height, width) * 1.0
|
||||
c = np.array([width / 2.0, height / 2.0], dtype=np.float32)
|
||||
s = max(height, width) * 1.0
|
||||
|
||||
trans_input = get_affine_transform(c, s, 0, [inp_width, inp_height])
|
||||
resized_image = cv2.resize(img, (width, height))
|
||||
inp_image = cv2.warpAffine(
|
||||
resized_image, trans_input, (inp_width, inp_height),
|
||||
flags=cv2.INTER_LINEAR)
|
||||
inp_image = ((inp_image / 255. - mean) / std).astype(np.float32)
|
||||
|
||||
resized_image, trans_input, (inp_width, inp_height), flags=cv2.INTER_LINEAR
|
||||
)
|
||||
inp_image = ((inp_image / 255.0 - mean) / std).astype(np.float32)
|
||||
|
||||
images = inp_image.transpose(2, 0, 1).reshape(1, 3, inp_height, inp_width)
|
||||
images = torch.from_numpy(images).to(self.device)
|
||||
meta = {'c': c, 's': s,
|
||||
'input_height':inp_height,
|
||||
'input_width':inp_width,
|
||||
'out_height': inp_height // 4,
|
||||
'out_width': inp_width // 4}
|
||||
meta = {
|
||||
"c": c,
|
||||
"s": s,
|
||||
"input_height": inp_height,
|
||||
"input_width": inp_width,
|
||||
"out_height": inp_height // 4,
|
||||
"out_width": inp_width // 4,
|
||||
}
|
||||
|
||||
result = {'img': images, 'meta': meta}
|
||||
result = {"img": images, "meta": meta}
|
||||
|
||||
return result
|
||||
|
||||
def forward(self, input: Dict[str, Any]) -> Dict[str, Any]:
|
||||
pred = self.infer_model(input['img'])
|
||||
return {'results': pred, 'meta': input['meta']}
|
||||
pred = self.infer_model(input["img"])
|
||||
return {"results": pred, "meta": input["meta"]}
|
||||
|
||||
def postprocess(self, inputs: Dict[str, Any]) -> Dict[str, Any]:
|
||||
output = inputs['results'][0]
|
||||
meta = inputs['meta']
|
||||
hm = output['hm'].sigmoid_()
|
||||
v2c = output['v2c']
|
||||
c2v = output['c2v']
|
||||
reg = output['reg']
|
||||
bbox, _ = bbox_decode(hm[:,0:1,:,:], c2v, reg=reg, K=self.K)
|
||||
gbox, _ = gbox_decode(hm[:,1:2,:,:], v2c, reg=reg, K=self.MK)
|
||||
output = inputs["results"][0]
|
||||
meta = inputs["meta"]
|
||||
hm = output["hm"].sigmoid_()
|
||||
v2c = output["v2c"]
|
||||
c2v = output["c2v"]
|
||||
reg = output["reg"]
|
||||
bbox, _ = bbox_decode(hm[:, 0:1, :, :], c2v, reg=reg, K=self.K)
|
||||
gbox, _ = gbox_decode(hm[:, 1:2, :, :], v2c, reg=reg, K=self.MK)
|
||||
|
||||
bbox = bbox.detach().cpu().numpy()
|
||||
gbox = gbox.detach().cpu().numpy()
|
||||
bbox = nms(bbox,0.3)
|
||||
bbox = bbox_post_process(bbox.copy(),[meta['c'].cpu().numpy()],[meta['s']],meta['out_height'],meta['out_width'])
|
||||
gbox = gbox_post_process(gbox.copy(),[meta['c'].cpu().numpy()],[meta['s']],meta['out_height'],meta['out_width'])
|
||||
bbox = group_bbox_by_gbox(bbox[0],gbox[0])
|
||||
|
||||
bbox = nms(bbox, 0.3)
|
||||
bbox = bbox_post_process(
|
||||
bbox.copy(),
|
||||
[meta["c"].cpu().numpy()],
|
||||
[meta["s"]],
|
||||
meta["out_height"],
|
||||
meta["out_width"],
|
||||
)
|
||||
gbox = gbox_post_process(
|
||||
gbox.copy(),
|
||||
[meta["c"].cpu().numpy()],
|
||||
[meta["s"]],
|
||||
meta["out_height"],
|
||||
meta["out_width"],
|
||||
)
|
||||
bbox = group_bbox_by_gbox(bbox[0], gbox[0])
|
||||
|
||||
res = []
|
||||
for box in bbox:
|
||||
if box[8] > 0.3:
|
||||
res.append(box[0:8])
|
||||
|
||||
|
||||
result = {OutputKeys.POLYGONS: np.array(res)}
|
||||
return result
|
||||
return result
|
||||
|
||||
@@ -3,182 +3,183 @@ import enum
|
||||
|
||||
|
||||
class Fields(object):
|
||||
""" Names for different application fields
|
||||
"""
|
||||
cv = 'cv'
|
||||
nlp = 'nlp'
|
||||
audio = 'audio'
|
||||
multi_modal = 'multi-modal'
|
||||
science = 'science'
|
||||
"""Names for different application fields"""
|
||||
|
||||
cv = "cv"
|
||||
nlp = "nlp"
|
||||
audio = "audio"
|
||||
multi_modal = "multi-modal"
|
||||
science = "science"
|
||||
|
||||
|
||||
class CVTasks(object):
|
||||
# ocr
|
||||
ocr_detection = 'ocr-detection'
|
||||
ocr_recognition = 'ocr-recognition'
|
||||
table_recognition = 'table-recognition'
|
||||
ocr_detection = "ocr-detection"
|
||||
ocr_recognition = "ocr-recognition"
|
||||
table_recognition = "table-recognition"
|
||||
|
||||
# human face body related
|
||||
animal_recognition = 'animal-recognition'
|
||||
face_detection = 'face-detection'
|
||||
card_detection = 'card-detection'
|
||||
face_recognition = 'face-recognition'
|
||||
facial_expression_recognition = 'facial-expression-recognition'
|
||||
face_2d_keypoints = 'face-2d-keypoints'
|
||||
human_detection = 'human-detection'
|
||||
human_object_interaction = 'human-object-interaction'
|
||||
face_image_generation = 'face-image-generation'
|
||||
body_2d_keypoints = 'body-2d-keypoints'
|
||||
body_3d_keypoints = 'body-3d-keypoints'
|
||||
hand_2d_keypoints = 'hand-2d-keypoints'
|
||||
general_recognition = 'general-recognition'
|
||||
human_wholebody_keypoint = 'human-wholebody-keypoint'
|
||||
animal_recognition = "animal-recognition"
|
||||
face_detection = "face-detection"
|
||||
card_detection = "card-detection"
|
||||
face_recognition = "face-recognition"
|
||||
facial_expression_recognition = "facial-expression-recognition"
|
||||
face_2d_keypoints = "face-2d-keypoints"
|
||||
human_detection = "human-detection"
|
||||
human_object_interaction = "human-object-interaction"
|
||||
face_image_generation = "face-image-generation"
|
||||
body_2d_keypoints = "body-2d-keypoints"
|
||||
body_3d_keypoints = "body-3d-keypoints"
|
||||
hand_2d_keypoints = "hand-2d-keypoints"
|
||||
general_recognition = "general-recognition"
|
||||
human_wholebody_keypoint = "human-wholebody-keypoint"
|
||||
|
||||
image_classification = 'image-classification'
|
||||
image_multilabel_classification = 'image-multilabel-classification'
|
||||
image_classification_imagenet = 'image-classification-imagenet'
|
||||
image_classification_dailylife = 'image-classification-dailylife'
|
||||
image_classification = "image-classification"
|
||||
image_multilabel_classification = "image-multilabel-classification"
|
||||
image_classification_imagenet = "image-classification-imagenet"
|
||||
image_classification_dailylife = "image-classification-dailylife"
|
||||
|
||||
image_object_detection = 'image-object-detection'
|
||||
video_object_detection = 'video-object-detection'
|
||||
image_object_detection = "image-object-detection"
|
||||
video_object_detection = "video-object-detection"
|
||||
|
||||
image_segmentation = 'image-segmentation'
|
||||
semantic_segmentation = 'semantic-segmentation'
|
||||
portrait_matting = 'portrait-matting'
|
||||
text_driven_segmentation = 'text-driven-segmentation'
|
||||
shop_segmentation = 'shop-segmentation'
|
||||
hand_static = 'hand-static'
|
||||
face_human_hand_detection = 'face-human-hand-detection'
|
||||
face_emotion = 'face-emotion'
|
||||
product_segmentation = 'product-segmentation'
|
||||
image_segmentation = "image-segmentation"
|
||||
semantic_segmentation = "semantic-segmentation"
|
||||
portrait_matting = "portrait-matting"
|
||||
text_driven_segmentation = "text-driven-segmentation"
|
||||
shop_segmentation = "shop-segmentation"
|
||||
hand_static = "hand-static"
|
||||
face_human_hand_detection = "face-human-hand-detection"
|
||||
face_emotion = "face-emotion"
|
||||
product_segmentation = "product-segmentation"
|
||||
|
||||
crowd_counting = 'crowd-counting'
|
||||
crowd_counting = "crowd-counting"
|
||||
|
||||
# image editing
|
||||
skin_retouching = 'skin-retouching'
|
||||
image_super_resolution = 'image-super-resolution'
|
||||
image_colorization = 'image-colorization'
|
||||
image_color_enhancement = 'image-color-enhancement'
|
||||
image_denoising = 'image-denoising'
|
||||
image_portrait_enhancement = 'image-portrait-enhancement'
|
||||
image_inpainting = 'image-inpainting'
|
||||
skin_retouching = "skin-retouching"
|
||||
image_super_resolution = "image-super-resolution"
|
||||
image_colorization = "image-colorization"
|
||||
image_color_enhancement = "image-color-enhancement"
|
||||
image_denoising = "image-denoising"
|
||||
image_portrait_enhancement = "image-portrait-enhancement"
|
||||
image_inpainting = "image-inpainting"
|
||||
|
||||
# image generation
|
||||
image_to_image_translation = 'image-to-image-translation'
|
||||
image_to_image_generation = 'image-to-image-generation'
|
||||
image_style_transfer = 'image-style-transfer'
|
||||
image_portrait_stylization = 'image-portrait-stylization'
|
||||
image_body_reshaping = 'image-body-reshaping'
|
||||
image_embedding = 'image-embedding'
|
||||
image_to_image_translation = "image-to-image-translation"
|
||||
image_to_image_generation = "image-to-image-generation"
|
||||
image_style_transfer = "image-style-transfer"
|
||||
image_portrait_stylization = "image-portrait-stylization"
|
||||
image_body_reshaping = "image-body-reshaping"
|
||||
image_embedding = "image-embedding"
|
||||
|
||||
product_retrieval_embedding = 'product-retrieval-embedding'
|
||||
product_retrieval_embedding = "product-retrieval-embedding"
|
||||
|
||||
# video recognition
|
||||
live_category = 'live-category'
|
||||
action_recognition = 'action-recognition'
|
||||
action_detection = 'action-detection'
|
||||
video_category = 'video-category'
|
||||
video_embedding = 'video-embedding'
|
||||
virtual_try_on = 'virtual-try-on'
|
||||
movie_scene_segmentation = 'movie-scene-segmentation'
|
||||
live_category = "live-category"
|
||||
action_recognition = "action-recognition"
|
||||
action_detection = "action-detection"
|
||||
video_category = "video-category"
|
||||
video_embedding = "video-embedding"
|
||||
virtual_try_on = "virtual-try-on"
|
||||
movie_scene_segmentation = "movie-scene-segmentation"
|
||||
|
||||
# video segmentation
|
||||
referring_video_object_segmentation = 'referring-video-object-segmentation'
|
||||
referring_video_object_segmentation = "referring-video-object-segmentation"
|
||||
|
||||
# video editing
|
||||
video_inpainting = 'video-inpainting'
|
||||
video_inpainting = "video-inpainting"
|
||||
|
||||
# reid and tracking
|
||||
video_single_object_tracking = 'video-single-object-tracking'
|
||||
video_summarization = 'video-summarization'
|
||||
image_reid_person = 'image-reid-person'
|
||||
video_single_object_tracking = "video-single-object-tracking"
|
||||
video_summarization = "video-summarization"
|
||||
image_reid_person = "image-reid-person"
|
||||
|
||||
|
||||
class NLPTasks(object):
|
||||
# nlp tasks
|
||||
word_segmentation = 'word-segmentation'
|
||||
part_of_speech = 'part-of-speech'
|
||||
named_entity_recognition = 'named-entity-recognition'
|
||||
nli = 'nli'
|
||||
sentiment_classification = 'sentiment-classification'
|
||||
sentiment_analysis = 'sentiment-analysis'
|
||||
sentence_similarity = 'sentence-similarity'
|
||||
text_classification = 'text-classification'
|
||||
sentence_embedding = 'sentence-embedding'
|
||||
text_ranking = 'text-ranking'
|
||||
relation_extraction = 'relation-extraction'
|
||||
zero_shot = 'zero-shot'
|
||||
translation = 'translation'
|
||||
token_classification = 'token-classification'
|
||||
conversational = 'conversational'
|
||||
text_generation = 'text-generation'
|
||||
text2text_generation = 'text2text-generation'
|
||||
task_oriented_conversation = 'task-oriented-conversation'
|
||||
dialog_intent_prediction = 'dialog-intent-prediction'
|
||||
dialog_state_tracking = 'dialog-state-tracking'
|
||||
table_question_answering = 'table-question-answering'
|
||||
fill_mask = 'fill-mask'
|
||||
text_summarization = 'text-summarization'
|
||||
question_answering = 'question-answering'
|
||||
zero_shot_classification = 'zero-shot-classification'
|
||||
backbone = 'backbone'
|
||||
text_error_correction = 'text-error-correction'
|
||||
faq_question_answering = 'faq-question-answering'
|
||||
information_extraction = 'information-extraction'
|
||||
document_segmentation = 'document-segmentation'
|
||||
feature_extraction = 'feature-extraction'
|
||||
word_segmentation = "word-segmentation"
|
||||
part_of_speech = "part-of-speech"
|
||||
named_entity_recognition = "named-entity-recognition"
|
||||
nli = "nli"
|
||||
sentiment_classification = "sentiment-classification"
|
||||
sentiment_analysis = "sentiment-analysis"
|
||||
sentence_similarity = "sentence-similarity"
|
||||
text_classification = "text-classification"
|
||||
sentence_embedding = "sentence-embedding"
|
||||
text_ranking = "text-ranking"
|
||||
relation_extraction = "relation-extraction"
|
||||
zero_shot = "zero-shot"
|
||||
translation = "translation"
|
||||
token_classification = "token-classification"
|
||||
conversational = "conversational"
|
||||
text_generation = "text-generation"
|
||||
text2text_generation = "text2text-generation"
|
||||
task_oriented_conversation = "task-oriented-conversation"
|
||||
dialog_intent_prediction = "dialog-intent-prediction"
|
||||
dialog_state_tracking = "dialog-state-tracking"
|
||||
table_question_answering = "table-question-answering"
|
||||
fill_mask = "fill-mask"
|
||||
text_summarization = "text-summarization"
|
||||
question_answering = "question-answering"
|
||||
zero_shot_classification = "zero-shot-classification"
|
||||
backbone = "backbone"
|
||||
text_error_correction = "text-error-correction"
|
||||
faq_question_answering = "faq-question-answering"
|
||||
information_extraction = "information-extraction"
|
||||
document_segmentation = "document-segmentation"
|
||||
feature_extraction = "feature-extraction"
|
||||
|
||||
|
||||
class AudioTasks(object):
|
||||
# audio tasks
|
||||
auto_speech_recognition = 'auto-speech-recognition'
|
||||
text_to_speech = 'text-to-speech'
|
||||
speech_signal_process = 'speech-signal-process'
|
||||
acoustic_echo_cancellation = 'acoustic-echo-cancellation'
|
||||
acoustic_noise_suppression = 'acoustic-noise-suppression'
|
||||
keyword_spotting = 'keyword-spotting'
|
||||
auto_speech_recognition = "auto-speech-recognition"
|
||||
text_to_speech = "text-to-speech"
|
||||
speech_signal_process = "speech-signal-process"
|
||||
acoustic_echo_cancellation = "acoustic-echo-cancellation"
|
||||
acoustic_noise_suppression = "acoustic-noise-suppression"
|
||||
keyword_spotting = "keyword-spotting"
|
||||
|
||||
|
||||
class MultiModalTasks(object):
|
||||
# multi-modal tasks
|
||||
image_captioning = 'image-captioning'
|
||||
visual_grounding = 'visual-grounding'
|
||||
text_to_image_synthesis = 'text-to-image-synthesis'
|
||||
multi_modal_embedding = 'multi-modal-embedding'
|
||||
generative_multi_modal_embedding = 'generative-multi-modal-embedding'
|
||||
multi_modal_similarity = 'multi-modal-similarity'
|
||||
visual_question_answering = 'visual-question-answering'
|
||||
visual_entailment = 'visual-entailment'
|
||||
video_multi_modal_embedding = 'video-multi-modal-embedding'
|
||||
image_text_retrieval = 'image-text-retrieval'
|
||||
image_captioning = "image-captioning"
|
||||
visual_grounding = "visual-grounding"
|
||||
text_to_image_synthesis = "text-to-image-synthesis"
|
||||
multi_modal_embedding = "multi-modal-embedding"
|
||||
generative_multi_modal_embedding = "generative-multi-modal-embedding"
|
||||
multi_modal_similarity = "multi-modal-similarity"
|
||||
visual_question_answering = "visual-question-answering"
|
||||
visual_entailment = "visual-entailment"
|
||||
video_multi_modal_embedding = "video-multi-modal-embedding"
|
||||
image_text_retrieval = "image-text-retrieval"
|
||||
|
||||
|
||||
class ScienceTasks(object):
|
||||
protein_structure = 'protein-structure'
|
||||
protein_structure = "protein-structure"
|
||||
|
||||
|
||||
class TasksIODescriptions(object):
|
||||
image_to_image = 'image_to_image',
|
||||
images_to_image = 'images_to_image',
|
||||
image_to_text = 'image_to_text',
|
||||
seed_to_image = 'seed_to_image',
|
||||
text_to_speech = 'text_to_speech',
|
||||
text_to_text = 'text_to_text',
|
||||
speech_to_text = 'speech_to_text',
|
||||
speech_to_speech = 'speech_to_speech'
|
||||
speeches_to_speech = 'speeches_to_speech',
|
||||
visual_grounding = 'visual_grounding',
|
||||
visual_question_answering = 'visual_question_answering',
|
||||
visual_entailment = 'visual_entailment',
|
||||
generative_multi_modal_embedding = 'generative_multi_modal_embedding'
|
||||
image_to_image = ("image_to_image",)
|
||||
images_to_image = ("images_to_image",)
|
||||
image_to_text = ("image_to_text",)
|
||||
seed_to_image = ("seed_to_image",)
|
||||
text_to_speech = ("text_to_speech",)
|
||||
text_to_text = ("text_to_text",)
|
||||
speech_to_text = ("speech_to_text",)
|
||||
speech_to_speech = "speech_to_speech"
|
||||
speeches_to_speech = ("speeches_to_speech",)
|
||||
visual_grounding = ("visual_grounding",)
|
||||
visual_question_answering = ("visual_question_answering",)
|
||||
visual_entailment = ("visual_entailment",)
|
||||
generative_multi_modal_embedding = "generative_multi_modal_embedding"
|
||||
|
||||
|
||||
class Tasks(CVTasks, NLPTasks, AudioTasks, MultiModalTasks, ScienceTasks):
|
||||
""" Names for tasks supported by modelscope.
|
||||
"""Names for tasks supported by modelscope.
|
||||
|
||||
Holds the standard task name to use for identifying different tasks.
|
||||
This should be used to register models, pipelines, trainers.
|
||||
"""
|
||||
|
||||
reverse_field_index = {}
|
||||
|
||||
@staticmethod
|
||||
@@ -187,78 +188,83 @@ class Tasks(CVTasks, NLPTasks, AudioTasks, MultiModalTasks, ScienceTasks):
|
||||
# Lazy init, not thread safe
|
||||
field_dict = {
|
||||
Fields.cv: [
|
||||
getattr(Tasks, attr) for attr in dir(CVTasks)
|
||||
if not attr.startswith('__')
|
||||
getattr(Tasks, attr)
|
||||
for attr in dir(CVTasks)
|
||||
if not attr.startswith("__")
|
||||
],
|
||||
Fields.nlp: [
|
||||
getattr(Tasks, attr) for attr in dir(NLPTasks)
|
||||
if not attr.startswith('__')
|
||||
getattr(Tasks, attr)
|
||||
for attr in dir(NLPTasks)
|
||||
if not attr.startswith("__")
|
||||
],
|
||||
Fields.audio: [
|
||||
getattr(Tasks, attr) for attr in dir(AudioTasks)
|
||||
if not attr.startswith('__')
|
||||
getattr(Tasks, attr)
|
||||
for attr in dir(AudioTasks)
|
||||
if not attr.startswith("__")
|
||||
],
|
||||
Fields.multi_modal: [
|
||||
getattr(Tasks, attr) for attr in dir(MultiModalTasks)
|
||||
if not attr.startswith('__')
|
||||
getattr(Tasks, attr)
|
||||
for attr in dir(MultiModalTasks)
|
||||
if not attr.startswith("__")
|
||||
],
|
||||
Fields.science: [
|
||||
getattr(Tasks, attr) for attr in dir(ScienceTasks)
|
||||
if not attr.startswith('__')
|
||||
getattr(Tasks, attr)
|
||||
for attr in dir(ScienceTasks)
|
||||
if not attr.startswith("__")
|
||||
],
|
||||
}
|
||||
|
||||
for field, tasks in field_dict.items():
|
||||
for task in tasks:
|
||||
if task in Tasks.reverse_field_index:
|
||||
raise ValueError(f'Duplicate task: {task}')
|
||||
raise ValueError(f"Duplicate task: {task}")
|
||||
Tasks.reverse_field_index[task] = field
|
||||
|
||||
return Tasks.reverse_field_index.get(task_name)
|
||||
|
||||
|
||||
class InputFields(object):
|
||||
""" Names for input data fields in the input data for pipelines
|
||||
"""
|
||||
img = 'img'
|
||||
text = 'text'
|
||||
audio = 'audio'
|
||||
"""Names for input data fields in the input data for pipelines"""
|
||||
|
||||
img = "img"
|
||||
text = "text"
|
||||
audio = "audio"
|
||||
|
||||
|
||||
class Hubs(enum.Enum):
|
||||
""" Source from which an entity (such as a Dataset or Model) is stored
|
||||
"""
|
||||
modelscope = 'modelscope'
|
||||
huggingface = 'huggingface'
|
||||
"""Source from which an entity (such as a Dataset or Model) is stored"""
|
||||
|
||||
modelscope = "modelscope"
|
||||
huggingface = "huggingface"
|
||||
|
||||
|
||||
class DownloadMode(enum.Enum):
|
||||
""" How to treat existing datasets
|
||||
"""
|
||||
REUSE_DATASET_IF_EXISTS = 'reuse_dataset_if_exists'
|
||||
FORCE_REDOWNLOAD = 'force_redownload'
|
||||
"""How to treat existing datasets"""
|
||||
|
||||
REUSE_DATASET_IF_EXISTS = "reuse_dataset_if_exists"
|
||||
FORCE_REDOWNLOAD = "force_redownload"
|
||||
|
||||
|
||||
class DownloadChannel(enum.Enum):
|
||||
""" Channels of datasets downloading for uv/pv counting.
|
||||
"""
|
||||
LOCAL = 'local'
|
||||
DSW = 'dsw'
|
||||
EAIS = 'eais'
|
||||
"""Channels of datasets downloading for uv/pv counting."""
|
||||
|
||||
LOCAL = "local"
|
||||
DSW = "dsw"
|
||||
EAIS = "eais"
|
||||
|
||||
|
||||
class UploadMode(enum.Enum):
|
||||
""" How to upload object to remote.
|
||||
"""
|
||||
"""How to upload object to remote."""
|
||||
|
||||
# Upload all objects from local, existing remote objects may be overwritten. (Default)
|
||||
OVERWRITE = 'overwrite'
|
||||
OVERWRITE = "overwrite"
|
||||
# Upload local objects in append mode, skipping all existing remote objects.
|
||||
APPEND = 'append'
|
||||
APPEND = "append"
|
||||
|
||||
|
||||
class DatasetFormations(enum.Enum):
|
||||
""" How a dataset is organized and interpreted
|
||||
"""
|
||||
"""How a dataset is organized and interpreted"""
|
||||
|
||||
# formation that is compatible with official huggingface dataset, which
|
||||
# organizes whole dataset into one single (zip) file.
|
||||
hf_compatible = 1
|
||||
@@ -268,114 +274,116 @@ class DatasetFormations(enum.Enum):
|
||||
|
||||
|
||||
DatasetMetaFormats = {
|
||||
DatasetFormations.native: ['.json'],
|
||||
DatasetFormations.hf_compatible: ['.py'],
|
||||
DatasetFormations.native: [".json"],
|
||||
DatasetFormations.hf_compatible: [".py"],
|
||||
}
|
||||
|
||||
|
||||
class ModelFile(object):
|
||||
CONFIGURATION = 'configuration.json'
|
||||
README = 'README.md'
|
||||
TF_SAVED_MODEL_FILE = 'saved_model.pb'
|
||||
TF_GRAPH_FILE = 'tf_graph.pb'
|
||||
TF_CHECKPOINT_FOLDER = 'tf_ckpts'
|
||||
TF_CKPT_PREFIX = 'ckpt-'
|
||||
TORCH_MODEL_FILE = 'pytorch_model.pt'
|
||||
TORCH_MODEL_BIN_FILE = 'pytorch_model.bin'
|
||||
VOCAB_FILE = 'vocab.txt'
|
||||
ONNX_MODEL_FILE = 'model.onnx'
|
||||
LABEL_MAPPING = 'label_mapping.json'
|
||||
TRAIN_OUTPUT_DIR = 'output'
|
||||
TS_MODEL_FILE = 'model.ts'
|
||||
CONFIGURATION = "configuration.json"
|
||||
README = "README.md"
|
||||
TF_SAVED_MODEL_FILE = "saved_model.pb"
|
||||
TF_GRAPH_FILE = "tf_graph.pb"
|
||||
TF_CHECKPOINT_FOLDER = "tf_ckpts"
|
||||
TF_CKPT_PREFIX = "ckpt-"
|
||||
TORCH_MODEL_FILE = "pytorch_model.pt"
|
||||
TORCH_MODEL_BIN_FILE = "pytorch_model.bin"
|
||||
VOCAB_FILE = "vocab.txt"
|
||||
ONNX_MODEL_FILE = "model.onnx"
|
||||
LABEL_MAPPING = "label_mapping.json"
|
||||
TRAIN_OUTPUT_DIR = "output"
|
||||
TS_MODEL_FILE = "model.ts"
|
||||
|
||||
|
||||
class ConfigFields(object):
|
||||
""" First level keyword in configuration file
|
||||
"""
|
||||
framework = 'framework'
|
||||
task = 'task'
|
||||
pipeline = 'pipeline'
|
||||
model = 'model'
|
||||
dataset = 'dataset'
|
||||
preprocessor = 'preprocessor'
|
||||
train = 'train'
|
||||
evaluation = 'evaluation'
|
||||
postprocessor = 'postprocessor'
|
||||
"""First level keyword in configuration file"""
|
||||
|
||||
framework = "framework"
|
||||
task = "task"
|
||||
pipeline = "pipeline"
|
||||
model = "model"
|
||||
dataset = "dataset"
|
||||
preprocessor = "preprocessor"
|
||||
train = "train"
|
||||
evaluation = "evaluation"
|
||||
postprocessor = "postprocessor"
|
||||
|
||||
|
||||
class ConfigKeys(object):
|
||||
"""Fixed keywords in configuration file"""
|
||||
train = 'train'
|
||||
val = 'val'
|
||||
test = 'test'
|
||||
|
||||
train = "train"
|
||||
val = "val"
|
||||
test = "test"
|
||||
|
||||
|
||||
class Requirements(object):
|
||||
"""Requirement names for each module
|
||||
"""
|
||||
protobuf = 'protobuf'
|
||||
sentencepiece = 'sentencepiece'
|
||||
sklearn = 'sklearn'
|
||||
scipy = 'scipy'
|
||||
timm = 'timm'
|
||||
tokenizers = 'tokenizers'
|
||||
tf = 'tf'
|
||||
torch = 'torch'
|
||||
"""Requirement names for each module"""
|
||||
|
||||
protobuf = "protobuf"
|
||||
sentencepiece = "sentencepiece"
|
||||
sklearn = "sklearn"
|
||||
scipy = "scipy"
|
||||
timm = "timm"
|
||||
tokenizers = "tokenizers"
|
||||
tf = "tf"
|
||||
torch = "torch"
|
||||
|
||||
|
||||
class Frameworks(object):
|
||||
tf = 'tensorflow'
|
||||
torch = 'pytorch'
|
||||
kaldi = 'kaldi'
|
||||
tf = "tensorflow"
|
||||
torch = "pytorch"
|
||||
kaldi = "kaldi"
|
||||
|
||||
|
||||
DEFAULT_MODEL_REVISION = None
|
||||
MASTER_MODEL_BRANCH = 'master'
|
||||
DEFAULT_REPOSITORY_REVISION = 'master'
|
||||
DEFAULT_DATASET_REVISION = 'master'
|
||||
DEFAULT_DATASET_NAMESPACE = 'modelscope'
|
||||
MASTER_MODEL_BRANCH = "master"
|
||||
DEFAULT_REPOSITORY_REVISION = "master"
|
||||
DEFAULT_DATASET_REVISION = "master"
|
||||
DEFAULT_DATASET_NAMESPACE = "modelscope"
|
||||
|
||||
|
||||
class ModeKeys:
|
||||
TRAIN = 'train'
|
||||
EVAL = 'eval'
|
||||
INFERENCE = 'inference'
|
||||
TRAIN = "train"
|
||||
EVAL = "eval"
|
||||
INFERENCE = "inference"
|
||||
|
||||
|
||||
class LogKeys:
|
||||
ITER = 'iter'
|
||||
ITER_TIME = 'iter_time'
|
||||
EPOCH = 'epoch'
|
||||
LR = 'lr' # learning rate
|
||||
MODE = 'mode'
|
||||
DATA_LOAD_TIME = 'data_load_time'
|
||||
ETA = 'eta' # estimated time of arrival
|
||||
MEMORY = 'memory'
|
||||
LOSS = 'loss'
|
||||
ITER = "iter"
|
||||
ITER_TIME = "iter_time"
|
||||
EPOCH = "epoch"
|
||||
LR = "lr" # learning rate
|
||||
MODE = "mode"
|
||||
DATA_LOAD_TIME = "data_load_time"
|
||||
ETA = "eta" # estimated time of arrival
|
||||
MEMORY = "memory"
|
||||
LOSS = "loss"
|
||||
|
||||
|
||||
class TrainerStages:
|
||||
before_run = 'before_run'
|
||||
before_train_epoch = 'before_train_epoch'
|
||||
before_train_iter = 'before_train_iter'
|
||||
after_train_iter = 'after_train_iter'
|
||||
after_train_epoch = 'after_train_epoch'
|
||||
before_val_epoch = 'before_val_epoch'
|
||||
before_val_iter = 'before_val_iter'
|
||||
after_val_iter = 'after_val_iter'
|
||||
after_val_epoch = 'after_val_epoch'
|
||||
after_run = 'after_run'
|
||||
before_run = "before_run"
|
||||
before_train_epoch = "before_train_epoch"
|
||||
before_train_iter = "before_train_iter"
|
||||
after_train_iter = "after_train_iter"
|
||||
after_train_epoch = "after_train_epoch"
|
||||
before_val_epoch = "before_val_epoch"
|
||||
before_val_iter = "before_val_iter"
|
||||
after_val_iter = "after_val_iter"
|
||||
after_val_epoch = "after_val_epoch"
|
||||
after_run = "after_run"
|
||||
|
||||
|
||||
class ColorCodes:
|
||||
MAGENTA = '\033[95m'
|
||||
YELLOW = '\033[93m'
|
||||
GREEN = '\033[92m'
|
||||
RED = '\033[91m'
|
||||
END = '\033[0m'
|
||||
MAGENTA = "\033[95m"
|
||||
YELLOW = "\033[93m"
|
||||
GREEN = "\033[92m"
|
||||
RED = "\033[91m"
|
||||
END = "\033[0m"
|
||||
|
||||
|
||||
class Devices:
|
||||
"""device used for training and inference"""
|
||||
cpu = 'cpu'
|
||||
gpu = 'gpu'
|
||||
|
||||
cpu = "cpu"
|
||||
gpu = "gpu"
|
||||
|
||||
@@ -9,31 +9,30 @@ from modelscope.utils.test_utils import test_level
|
||||
|
||||
|
||||
class TableRecognitionTest(unittest.TestCase, DemoCompatibilityCheck):
|
||||
|
||||
def setUp(self) -> None:
|
||||
self.model_id = 'damo/cv_dla34_table-structure-recognition_cycle-centernet'
|
||||
self.test_image = 'data/test/images/table_recognition.jpg'
|
||||
self.model_id = "damo/cv_dla34_table-structure-recognition_cycle-centernet"
|
||||
self.test_image = "data/test/images/table_recognition.jpg"
|
||||
self.task = Tasks.table_recognition
|
||||
|
||||
def pipeline_inference(self, pipeline: Pipeline, input_location: str):
|
||||
result = pipeline(input_location)
|
||||
print('table recognition results: ')
|
||||
print("table recognition results: ")
|
||||
print(result)
|
||||
|
||||
@unittest.skipUnless(test_level() >= 0, 'skip test in current test level')
|
||||
@unittest.skipUnless(test_level() >= 0, "skip test in current test level")
|
||||
def test_run_with_model_from_modelhub(self):
|
||||
table_recognition = pipeline(Tasks.table_recognition, model=self.model_id)
|
||||
self.pipeline_inference(table_recognition, self.test_image)
|
||||
|
||||
@unittest.skipUnless(test_level() >= 2, 'skip test in current test level')
|
||||
@unittest.skipUnless(test_level() >= 2, "skip test in current test level")
|
||||
def test_run_modelhub_default_model(self):
|
||||
table_recognition = pipeline(Tasks.table_recognition)
|
||||
self.pipeline_inference(table_recognition, self.test_image)
|
||||
|
||||
@unittest.skip('demo compatibility test is only enabled on a needed-basis')
|
||||
@unittest.skip("demo compatibility test is only enabled on a needed-basis")
|
||||
def test_demo_compatibility(self):
|
||||
self.compatibility_check()
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
unittest.main()
|
||||
|
||||
Reference in New Issue
Block a user