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imgutils.pose.format)": [[48, "imgutils.pose.format.OP18_RIGHT_HAND_MAX"]], "op18_right_hand_min (in module imgutils.pose.format)": [[48, "imgutils.pose.format.OP18_RIGHT_HAND_MIN"]], "right_ankle (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_ANKLE"]], "right_big_toe (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_BIG_TOE"]], "right_ear (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_EAR"]], "right_elbow (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_ELBOW"]], "right_eye (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_EYE"]], "right_heel (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HEEL"]], "right_hip (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HIP"]], "right_knee 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"left_shoulder (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.LEFT_SHOULDER"]], "left_small_toe (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.LEFT_SMALL_TOE"]], "left_wrist (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.LEFT_WRIST"]], "neck (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.NECK"]], "nose (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.NOSE"]], "op18keypointset (class in imgutils.pose.format)": [[48, "imgutils.pose.format.OP18KeyPointSet"]], "op18_body_max (in module imgutils.pose.format)": [[48, "imgutils.pose.format.OP18_BODY_MAX"]], "op18_body_min (in module imgutils.pose.format)": [[48, "imgutils.pose.format.OP18_BODY_MIN"]], "op18_face_max (in module imgutils.pose.format)": [[48, "imgutils.pose.format.OP18_FACE_MAX"]], "op18_face_min (in module imgutils.pose.format)": [[48, 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"imgutils.pose.format.OpenPose18.RIGHT_ANKLE"]], "right_big_toe (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_BIG_TOE"]], "right_ear (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_EAR"]], "right_elbow (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_ELBOW"]], "right_eye (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_EYE"]], "right_heel (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HEEL"]], "right_hip (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HIP"]], "right_knee (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_KNEE"]], "right_shoulder (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_SHOULDER"]], "right_small_toe 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"is_valid_size_dict() (in module imgutils.preprocess.transformers)": [[55, "imgutils.preprocess.transformers.is_valid_size_dict"]], "register_creators_for_transformers() (in module imgutils.preprocess.transformers)": [[55, "imgutils.preprocess.transformers.register_creators_for_transformers"]], "backgroundimageset (class in imgutils.resource.background)": [[56, "imgutils.resource.background.BackgroundImageSet"]], "__init__() (imgutils.resource.background.backgroundimageset method)": [[56, "imgutils.resource.background.BackgroundImageSet.__init__"]], "get_bg_image() (in module imgutils.resource.background)": [[56, "imgutils.resource.background.get_bg_image"]], "get_bg_image_file() (in module imgutils.resource.background)": [[56, "imgutils.resource.background.get_bg_image_file"]], "get_image() (imgutils.resource.background.backgroundimageset method)": [[56, "imgutils.resource.background.BackgroundImageSet.get_image"]], "get_image_file() (imgutils.resource.background.backgroundimageset 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"imgutils.segment.isnetis.get_isnetis_mask"]], "imgutils.segment.isnetis": [[67, "module-imgutils.segment.isnetis"]], "segment_rgba_with_isnetis() (in module imgutils.segment.isnetis)": [[67, "imgutils.segment.isnetis.segment_rgba_with_isnetis"]], "segment_with_isnetis() (in module imgutils.segment.isnetis)": [[67, "imgutils.segment.isnetis.segment_with_isnetis"]], "drop_blacklisted_tags() (in module imgutils.tagging.blacklist)": [[68, "imgutils.tagging.blacklist.drop_blacklisted_tags"]], "imgutils.tagging.blacklist": [[68, "module-imgutils.tagging.blacklist"]], "is_blacklisted() (in module imgutils.tagging.blacklist)": [[68, "imgutils.tagging.blacklist.is_blacklisted"]], "char_prefixes (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.CHAR_PREFIXES"]], "char_suffixes (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.CHAR_SUFFIXES"]], "char_whitelist_prefix (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.CHAR_WHITELIST_PREFIX"]], "char_whitelist_suffix (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.CHAR_WHITELIST_SUFFIX"]], "char_whitelist_word (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.CHAR_WHITELIST_WORD"]], "charactertagpool (class in imgutils.tagging.character)": [[69, "imgutils.tagging.character.CharacterTagPool"]], "__init__() (imgutils.tagging.character.charactertagpool method)": [[69, "imgutils.tagging.character.CharacterTagPool.__init__"]], "drop_basic_character_tags() (imgutils.tagging.character.charactertagpool method)": [[69, "imgutils.tagging.character.CharacterTagPool.drop_basic_character_tags"]], "drop_basic_character_tags() (in module imgutils.tagging.character)": [[69, "imgutils.tagging.character.drop_basic_character_tags"]], "imgutils.tagging.character": [[69, "module-imgutils.tagging.character"]], "is_basic_character_tag() (imgutils.tagging.character.charactertagpool method)": 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"module-imgutils.tagging.overlap"]], "convert_wd14_emb_to_prediction() (in module imgutils.tagging.wd14)": [[78, "imgutils.tagging.wd14.convert_wd14_emb_to_prediction"]], "denormalize_wd14_emb() (in module imgutils.tagging.wd14)": [[78, "imgutils.tagging.wd14.denormalize_wd14_emb"]], "get_wd14_tags() (in module imgutils.tagging.wd14)": [[78, "imgutils.tagging.wd14.get_wd14_tags"]], "imgutils.tagging.wd14": [[78, "module-imgutils.tagging.wd14"]], "imgutils.upscale.cdc": [[79, "module-imgutils.upscale.cdc"]], "upscale_with_cdc() (in module imgutils.upscale.cdc)": [[79, "imgutils.upscale.cdc.upscale_with_cdc"]], "imgutils.upscale": [[80, "module-imgutils.upscale"]], "imgutils.utils.cache": [[81, "module-imgutils.utils.cache"]], "ts_lru_cache() (in module imgutils.utils.cache)": [[81, "imgutils.utils.cache.ts_lru_cache"]], "imgutils.utils.func": [[82, "module-imgutils.utils.func"]], "sigmoid() (in module imgutils.utils.func)": [[82, "imgutils.utils.func.sigmoid"]], "imgutils.utils": [[83, 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a/dev/denormalize/.doctrees/nbsphinx/information/environment.ipynb +++ b/dev/denormalize/.doctrees/nbsphinx/information/environment.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T13:01:58.516913Z", - "iopub.status.busy": "2025-02-20T13:01:58.516718Z", - "iopub.status.idle": "2025-02-20T13:01:59.685040Z", - "shell.execute_reply": "2025-02-20T13:01:59.684280Z" + "iopub.execute_input": "2025-02-21T06:53:35.604218Z", + "iopub.status.busy": "2025-02-21T06:53:35.604007Z", + "iopub.status.idle": "2025-02-21T06:53:36.770852Z", + "shell.execute_reply": "2025-02-21T06:53:36.770166Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3037.50875 MHz\n", + "CPU Freq: 3243.47625 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/denormalize/.doctrees/nbsphinx/information/environment.result.ipynb b/dev/denormalize/.doctrees/nbsphinx/information/environment.result.ipynb index 05e21813835ca4329b56bbf1d94ce4132c348c6a..de078afbcd2b6ef92b3db4dc54f031f8019a0c05 100644 --- a/dev/denormalize/.doctrees/nbsphinx/information/environment.result.ipynb +++ b/dev/denormalize/.doctrees/nbsphinx/information/environment.result.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:58:18.267896Z", - "iopub.status.busy": "2025-02-20T12:58:18.267332Z", - "iopub.status.idle": "2025-02-20T12:58:19.434976Z", - "shell.execute_reply": "2025-02-20T12:58:19.434215Z" + "iopub.execute_input": "2025-02-21T06:50:08.809108Z", + "iopub.status.busy": "2025-02-21T06:50:08.808914Z", + "iopub.status.idle": "2025-02-21T06:50:09.971627Z", + "shell.execute_reply": "2025-02-21T06:50:09.970823Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3130.54975 MHz\n", + "CPU 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b/dev/denormalize/_sources/information/environment.result.ipynb.txt index 05e21813835ca4329b56bbf1d94ce4132c348c6a..de078afbcd2b6ef92b3db4dc54f031f8019a0c05 100644 --- a/dev/denormalize/_sources/information/environment.result.ipynb.txt +++ b/dev/denormalize/_sources/information/environment.result.ipynb.txt @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:58:18.267896Z", - "iopub.status.busy": "2025-02-20T12:58:18.267332Z", - "iopub.status.idle": "2025-02-20T12:58:19.434976Z", - "shell.execute_reply": "2025-02-20T12:58:19.434215Z" + "iopub.execute_input": "2025-02-21T06:50:08.809108Z", + "iopub.status.busy": "2025-02-21T06:50:08.808914Z", + "iopub.status.idle": "2025-02-21T06:50:09.971627Z", + "shell.execute_reply": "2025-02-21T06:50:09.970823Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3130.54975 MHz\n", + "CPU Freq: 3186.4217499999995 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/denormalize/information/environment.html b/dev/denormalize/information/environment.html index 2418265fe12f068b2f64f14fdc7531ab23f98088..23fa44c7c70a8340a3b6556c83b7bc47f912c341 100644 --- a/dev/denormalize/information/environment.html +++ b/dev/denormalize/information/environment.html @@ -138,7 +138,7 @@ OS: Linux-6.8.0-1021-azure-x86_64-with-glibc2.34 Python: CPython 3.8.18 CPU Brand: AMD EPYC 7763 64-Core Processor CPU Count: 4 -CPU Freq: 3037.50875 MHz +CPU Freq: 3243.47625 MHz Memory Size: 15.615 GiB Has CUDA: No diff --git a/dev/denormalize/information/environment.ipynb b/dev/denormalize/information/environment.ipynb index e09a32ced40a973af92275cfb0805b79f433d8a8..359b809181d5b234da69f41e1d74d755f89b7b07 100644 --- a/dev/denormalize/information/environment.ipynb +++ b/dev/denormalize/information/environment.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - 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b/dev/denormalize/information/environment.result.html @@ -140,7 +140,7 @@ OS: Linux-6.8.0-1021-azure-x86_64-with-glibc2.34 Python: CPython 3.8.18 CPU Brand: AMD EPYC 7763 64-Core Processor CPU Count: 4 -CPU Freq: 3130.54975 MHz +CPU Freq: 3186.4217499999995 MHz Memory Size: 15.615 GiB Has CUDA: No diff --git a/dev/denormalize/information/environment.result.ipynb b/dev/denormalize/information/environment.result.ipynb index 05e21813835ca4329b56bbf1d94ce4132c348c6a..de078afbcd2b6ef92b3db4dc54f031f8019a0c05 100644 --- a/dev/denormalize/information/environment.result.ipynb +++ b/dev/denormalize/information/environment.result.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:58:18.267896Z", - "iopub.status.busy": "2025-02-20T12:58:18.267332Z", - "iopub.status.idle": "2025-02-20T12:58:19.434976Z", - "shell.execute_reply": "2025-02-20T12:58:19.434215Z" + "iopub.execute_input": "2025-02-21T06:50:08.809108Z", + "iopub.status.busy": "2025-02-21T06:50:08.808914Z", + "iopub.status.idle": "2025-02-21T06:50:09.971627Z", + "shell.execute_reply": "2025-02-21T06:50:09.970823Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3130.54975 MHz\n", + "CPU Freq: 3186.4217499999995 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/denormalize/searchindex.js b/dev/denormalize/searchindex.js index 4841c304f429f927ce232be6905987d4028a2577..bc06c5b0254e6978e4928c7148e87d2b9771ddc5 100644 --- a/dev/denormalize/searchindex.js +++ b/dev/denormalize/searchindex.js @@ -1 +1 @@ -Search.setIndex({"docnames": ["api_doc/ascii/drawing", "api_doc/ascii/index", "api_doc/config/index", "api_doc/config/meta", "api_doc/data/background", "api_doc/data/decode", "api_doc/data/encode", "api_doc/data/image", "api_doc/data/index", "api_doc/data/layer", "api_doc/detect/booru_yolo", "api_doc/detect/censor", 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"api_doc/resource/background", "api_doc/resource/index", "api_doc/restore/adversarial", "api_doc/restore/index", "api_doc/restore/nafnet", "api_doc/restore/scunet", "api_doc/sd/index", "api_doc/sd/metadata", "api_doc/sd/model", "api_doc/sd/nai", "api_doc/segment/index", "api_doc/segment/isnetis", "api_doc/tagging/blacklist", "api_doc/tagging/character", "api_doc/tagging/deepdanbooru", "api_doc/tagging/format", "api_doc/tagging/index", "api_doc/tagging/match", "api_doc/tagging/mldanbooru", "api_doc/tagging/order", "api_doc/tagging/overlap", "api_doc/tagging/wd14", "api_doc/upscale/cdc", "api_doc/upscale/index", "api_doc/utils/cache", "api_doc/utils/func", "api_doc/utils/index", "api_doc/utils/onnxruntime", "api_doc/validate/aicheck", "api_doc/validate/bangumi_char", "api_doc/validate/classify", "api_doc/validate/color", "api_doc/validate/completeness", "api_doc/validate/dbrating", "api_doc/validate/furry", "api_doc/validate/index", "api_doc/validate/monochrome", "api_doc/validate/nsfw", 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attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_ELBOW"]], "right_eye (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_EYE"]], "right_heel (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HEEL"]], "right_hip (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_HIP"]], "right_knee (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_KNEE"]], "right_shoulder (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_SHOULDER"]], "right_small_toe (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_SMALL_TOE"]], "right_wrist (imgutils.pose.format.openpose18 attribute)": [[48, "imgutils.pose.format.OpenPose18.RIGHT_WRIST"]], "__init__() (imgutils.pose.format.op18keypointset method)": [[48, "imgutils.pose.format.OP18KeyPointSet.__init__"]], "__mul__() 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"save_with_metadata() (in module imgutils.sd.model)": [[59, "imgutils.sd.model.save_with_metadata"]], "naimetadata (class in imgutils.sd.nai)": [[60, "imgutils.sd.nai.NAIMetaData"], [60, "imgutils.sd.nai.NAIMetadata"]], "__init__() (imgutils.sd.nai.naimetadata method)": [[60, "imgutils.sd.nai.NAIMetaData.__init__"]], "add_naimeta_to_image() (in module imgutils.sd.nai)": [[60, "imgutils.sd.nai.add_naimeta_to_image"]], "get_naimeta_from_image() (in module imgutils.sd.nai)": [[60, "imgutils.sd.nai.get_naimeta_from_image"]], "imgutils.sd.nai": [[60, "module-imgutils.sd.nai"]], "pnginfo (imgutils.sd.nai.naimetadata property)": [[60, "imgutils.sd.nai.NAIMetaData.pnginfo"]], "save_image_with_naimeta() (in module imgutils.sd.nai)": [[60, "imgutils.sd.nai.save_image_with_naimeta"]], "imgutils.segment": [[61, "module-imgutils.segment"]], "get_isnetis_mask() (in module imgutils.segment.isnetis)": [[62, "imgutils.segment.isnetis.get_isnetis_mask"]], "imgutils.segment.isnetis": [[62, "module-imgutils.segment.isnetis"]], "segment_rgba_with_isnetis() (in module imgutils.segment.isnetis)": [[62, "imgutils.segment.isnetis.segment_rgba_with_isnetis"]], "segment_with_isnetis() (in module imgutils.segment.isnetis)": [[62, "imgutils.segment.isnetis.segment_with_isnetis"]], "drop_blacklisted_tags() (in module imgutils.tagging.blacklist)": [[63, "imgutils.tagging.blacklist.drop_blacklisted_tags"]], "imgutils.tagging.blacklist": [[63, "module-imgutils.tagging.blacklist"]], "is_blacklisted() (in module imgutils.tagging.blacklist)": [[63, "imgutils.tagging.blacklist.is_blacklisted"]], "char_prefixes (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.CHAR_PREFIXES"]], "char_suffixes (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.CHAR_SUFFIXES"]], "char_whitelist_prefix (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.CHAR_WHITELIST_PREFIX"]], "char_whitelist_suffix (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.CHAR_WHITELIST_SUFFIX"]], "char_whitelist_word (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.CHAR_WHITELIST_WORD"]], "charactertagpool (class in imgutils.tagging.character)": [[64, "imgutils.tagging.character.CharacterTagPool"]], "__init__() (imgutils.tagging.character.charactertagpool method)": [[64, "imgutils.tagging.character.CharacterTagPool.__init__"]], "drop_basic_character_tags() (imgutils.tagging.character.charactertagpool method)": [[64, "imgutils.tagging.character.CharacterTagPool.drop_basic_character_tags"]], "drop_basic_character_tags() (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.drop_basic_character_tags"]], "imgutils.tagging.character": [[64, "module-imgutils.tagging.character"]], "is_basic_character_tag() (imgutils.tagging.character.charactertagpool method)": [[64, "imgutils.tagging.character.CharacterTagPool.is_basic_character_tag"]], "is_basic_character_tag() (in module imgutils.tagging.character)": [[64, "imgutils.tagging.character.is_basic_character_tag"]], "get_deepdanbooru_tags() (in module imgutils.tagging.deepdanbooru)": [[65, "imgutils.tagging.deepdanbooru.get_deepdanbooru_tags"]], "imgutils.tagging.deepdanbooru": [[65, "module-imgutils.tagging.deepdanbooru"]], "add_underline() (in module imgutils.tagging.format)": [[66, "imgutils.tagging.format.add_underline"]], "imgutils.tagging.format": [[66, "module-imgutils.tagging.format"]], "remove_underline() (in module imgutils.tagging.format)": [[66, "imgutils.tagging.format.remove_underline"]], "tags_to_text() (in module imgutils.tagging.format)": [[66, "imgutils.tagging.format.tags_to_text"]], "imgutils.tagging": [[67, "module-imgutils.tagging"]], "imgutils.tagging.match": [[68, "module-imgutils.tagging.match"]], "tag_match_full() (in module imgutils.tagging.match)": [[68, "imgutils.tagging.match.tag_match_full"]], "tag_match_prefix() (in module 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"imgutils.utils.onnxruntime.open_onnx_model"]], "get_ai_created_score() (in module imgutils.validate.aicheck)": [[79, "imgutils.validate.aicheck.get_ai_created_score"]], "imgutils.validate.aicheck": [[79, "module-imgutils.validate.aicheck"]], "is_ai_created() (in module imgutils.validate.aicheck)": [[79, "imgutils.validate.aicheck.is_ai_created"]], "anime_bangumi_char() (in module imgutils.validate.bangumi_char)": [[80, "imgutils.validate.bangumi_char.anime_bangumi_char"]], "anime_bangumi_char_score() (in module imgutils.validate.bangumi_char)": [[80, "imgutils.validate.bangumi_char.anime_bangumi_char_score"]], "imgutils.validate.bangumi_char": [[80, "module-imgutils.validate.bangumi_char"]], "anime_classify() (in module imgutils.validate.classify)": [[81, "imgutils.validate.classify.anime_classify"]], "anime_classify_score() (in module imgutils.validate.classify)": [[81, "imgutils.validate.classify.anime_classify_score"]], "imgutils.validate.classify": [[81, "module-imgutils.validate.classify"]], "imgutils.validate.color": [[82, "module-imgutils.validate.color"]], "is_greyscale() (in module imgutils.validate.color)": [[82, "imgutils.validate.color.is_greyscale"]], "anime_completeness() (in module imgutils.validate.completeness)": [[83, "imgutils.validate.completeness.anime_completeness"]], "anime_completeness_score() (in module imgutils.validate.completeness)": [[83, "imgutils.validate.completeness.anime_completeness_score"]], "imgutils.validate.completeness": [[83, "module-imgutils.validate.completeness"]], "anime_dbrating() (in module imgutils.validate.dbrating)": [[84, "imgutils.validate.dbrating.anime_dbrating"]], "anime_dbrating_score() (in module imgutils.validate.dbrating)": [[84, "imgutils.validate.dbrating.anime_dbrating_score"]], "imgutils.validate.dbrating": [[84, "module-imgutils.validate.dbrating"]], "anime_furry() (in module imgutils.validate.furry)": [[85, "imgutils.validate.furry.anime_furry"]], "anime_furry_score() (in 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b/dev/siglip/.doctrees/information/environment.result.doctree index 856325936811f0d95500090ef298932bb6693f19..34a9f9dcaf8a67ee085447d764539b688bbd4b09 100644 Binary files a/dev/siglip/.doctrees/information/environment.result.doctree and b/dev/siglip/.doctrees/information/environment.result.doctree differ diff --git a/dev/siglip/.doctrees/nbsphinx/information/environment.ipynb b/dev/siglip/.doctrees/nbsphinx/information/environment.ipynb index 42b5ed060d51e9a5fa5a7b2540b6bf53518b5a0a..17f52e68935792e55711fe3a11adda5f7daf248d 100644 --- a/dev/siglip/.doctrees/nbsphinx/information/environment.ipynb +++ b/dev/siglip/.doctrees/nbsphinx/information/environment.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:58:47.739132Z", - "iopub.status.busy": "2025-02-20T12:58:47.738929Z", - "iopub.status.idle": "2025-02-20T12:58:48.909720Z", - "shell.execute_reply": "2025-02-20T12:58:48.909011Z" + "iopub.execute_input": "2025-02-21T06:50:35.817667Z", + "iopub.status.busy": "2025-02-21T06:50:35.817474Z", + "iopub.status.idle": "2025-02-21T06:50:36.978131Z", + "shell.execute_reply": "2025-02-21T06:50:36.977388Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3200.9852499999997 MHz\n", + "CPU Freq: 3110.6515 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/siglip/.doctrees/nbsphinx/information/environment.result.ipynb b/dev/siglip/.doctrees/nbsphinx/information/environment.result.ipynb index 9f4e766b7dda0d9d71983be29cda1960fc1fada2..970e8e9413e1df7fea6fcaac4da6141d68030ff7 100644 --- a/dev/siglip/.doctrees/nbsphinx/information/environment.result.ipynb +++ b/dev/siglip/.doctrees/nbsphinx/information/environment.result.ipynb @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:56:47.715115Z", - "iopub.status.busy": "2025-02-20T12:56:47.714719Z", - "iopub.status.idle": "2025-02-20T12:56:48.887275Z", - "shell.execute_reply": "2025-02-20T12:56:48.886491Z" + "iopub.execute_input": "2025-02-21T06:48:39.284744Z", + "iopub.status.busy": "2025-02-21T06:48:39.284172Z", + "iopub.status.idle": "2025-02-21T06:48:40.448017Z", + "shell.execute_reply": "2025-02-21T06:48:40.447279Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3239.456 MHz\n", + "CPU Freq: 3247.30175 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/siglip/.doctrees/tutorials/installation/index.doctree b/dev/siglip/.doctrees/tutorials/installation/index.doctree index c58484d0c216607a6f6beb260d44f50a8abc38a8..5545c39d33a87fafc8b8544ff3eb7c401f0cb4a2 100644 Binary files a/dev/siglip/.doctrees/tutorials/installation/index.doctree and b/dev/siglip/.doctrees/tutorials/installation/index.doctree differ diff --git a/dev/siglip/.doctrees/tutorials/quick_start/index.doctree b/dev/siglip/.doctrees/tutorials/quick_start/index.doctree index c4e07c29fe95a6184e474eee41eb07e7dd290627..3dd59686c5ddae5e60cc56a9556d2f19fda984aa 100644 Binary files a/dev/siglip/.doctrees/tutorials/quick_start/index.doctree and b/dev/siglip/.doctrees/tutorials/quick_start/index.doctree differ diff --git a/dev/siglip/_modules/imgutils/generic/siglip.html b/dev/siglip/_modules/imgutils/generic/siglip.html new file mode 100644 index 0000000000000000000000000000000000000000..7f812669a48b9dc6501ee26a646a510e90caf628 --- /dev/null +++ b/dev/siglip/_modules/imgutils/generic/siglip.html @@ -0,0 +1,767 @@ + + + + + + + + imgutils.generic.siglip — imgutils 0.10.0 documentation + + + + + + + + + + + + + + + + + + +
+ + +
+ +
+
+
+ +
+
+
+
+ + + +

Source code for imgutils.generic.siglip

+"""
+SigLIP (Sigmoid Loss Image-Paired) model implementation module.
+
+This module provides functionality for working with SigLIP models, which are designed for
+image-text matching and classification tasks. It includes components for:
+
+* Loading and managing SigLIP models from Hugging Face repositories
+* Image and text encoding using ONNX models
+* Prediction and classification of image-text pairs
+* Web interface creation using Gradio
+* Caching and thread-safe model operations
+
+The module supports multiple model variants and provides both high-level and low-level APIs
+for model interaction.
+"""
+
+import json
+import os
+from threading import Lock
+from typing import List, Union, Optional, Any, Dict
+
+import numpy as np
+from hfutils.operate import get_hf_client
+from hfutils.repository import hf_hub_repo_url
+from hfutils.utils import hf_normpath, hf_fs_path, parse_hf_fs_path
+from huggingface_hub import hf_hub_download, HfFileSystem
+from tokenizers import Tokenizer
+
+from ..data import MultiImagesTyping, load_images, ImageTyping
+from ..preprocess import create_pillow_transforms
+from ..utils import open_onnx_model, vreplace, sigmoid, ts_lru_cache
+
+try:
+    import gradio as gr
+except (ImportError, ModuleNotFoundError):
+    gr = None
+
+__all__ = [
+    'SigLIPModel',
+    'siglip_image_encode',
+    'siglip_text_encode',
+    'siglip_predict',
+]
+
+
+def _check_gradio_env():
+    """
+    Check if the Gradio library is installed and available.
+
+    This function verifies that Gradio is properly installed before attempting to use
+    web interface features.
+
+    :raises EnvironmentError: If Gradio is not installed, suggesting installation command.
+    """
+    if gr is None:
+        raise EnvironmentError(f'Gradio required for launching webui-based demo.\n'
+                               f'Please install it with `pip install dghs-imgutils[demo]`.')
+
+
+
[docs]class SigLIPModel: + """ + Main class for managing and using SigLIP models. + + This class handles model loading, caching, and inference operations for SigLIP models. + It provides thread-safe access to model components and supports multiple model variants. + + :param repo_id: Hugging Face repository ID containing the SigLIP models + :type repo_id: str + :param hf_token: Optional Hugging Face authentication token + :type hf_token: Optional[str] + """ + +
[docs] def __init__(self, repo_id: str, hf_token: Optional[str] = None): + self.repo_id = repo_id + self._model_names = None + + self._image_encoders = {} + self._image_preprocessors = {} + self._text_encoders = {} + self._text_tokenizers = {} + self._logit_scales = {} + + self._hf_token = hf_token + self._global_lock = Lock() + self._model_lock = Lock()
+ + def _get_hf_token(self) -> Optional[str]: + """ + Retrieve the Hugging Face authentication token. + + Checks both instance variable and environment for token presence. + + :return: Authentication token if available, None otherwise + :rtype: Optional[str] + """ + return self._hf_token or os.environ.get('HF_TOKEN') + + @property + def model_names(self) -> List[str]: + """ + Get available model names from the repository. + + This property implements lazy loading and caching of model names. + Thread-safe access to the model list is ensured via locks. + + :return: List of available model names in the repository + :rtype: List[str] + :raises RuntimeError: If repository access fails + """ + with self._global_lock: + if self._model_names is None: + hf_fs = HfFileSystem(token=self._get_hf_token()) + self._model_names = [ + hf_normpath(os.path.dirname(parse_hf_fs_path(fspath).filename)) + for fspath in hf_fs.glob(hf_fs_path( + repo_id=self.repo_id, + repo_type='model', + filename='**/image_encode.onnx', + )) + ] + + return self._model_names + + def _check_model_name(self, model_name: str): + """ + Validate model name availability in the repository. + + :param model_name: Name of the model to verify + :type model_name: str + :raises ValueError: If model name is not found in repository + """ + if model_name not in self.model_names: + raise ValueError(f'Unknown model {model_name!r} in model repository {self.repo_id!r}, ' + f'models {self.model_names!r} are available.') + + def _open_image_encoder(self, model_name: str): + """ + Open and cache the ONNX image encoder model. + + :param model_name: Name of the SigLIP model variant + :type model_name: str + :return: Loaded ONNX model for image encoding + :rtype: ONNXModel + :raises ValueError: If model name is invalid + """ + with self._model_lock: + if model_name not in self._image_encoders: + self._check_model_name(model_name) + self._image_encoders[model_name] = open_onnx_model(hf_hub_download( + repo_id=self.repo_id, + repo_type='model', + filename=f'{model_name}/image_encode.onnx', + )) + + return self._image_encoders[model_name] + + def _open_image_preprocessor(self, model_name: str): + """ + Load and cache the image preprocessing pipeline configuration. + + :param model_name: Name of the SigLIP model variant + :type model_name: str + :return: Configured image preprocessing transforms + :rtype: Callable + :raises ValueError: If model name is invalid or preprocessor config is missing + """ + with self._model_lock: + if model_name not in self._image_preprocessors: + self._check_model_name(model_name) + with open(hf_hub_download( + repo_id=self.repo_id, + repo_type='model', + filename=f'{model_name}/preprocessor.json', + ), 'r') as f: + self._image_preprocessors[model_name] = create_pillow_transforms(json.load(f)['stages']) + + return self._image_preprocessors[model_name] + + def _open_text_encoder(self, model_name: str): + """ + Open and cache the ONNX text encoder model. + + :param model_name: Name of the SigLIP model variant + :type model_name: str + :return: Loaded ONNX model for text encoding + :rtype: ONNXModel + :raises ValueError: If model name is invalid + """ + with self._model_lock: + if model_name not in self._text_encoders: + self._check_model_name(model_name) + self._text_encoders[model_name] = open_onnx_model(hf_hub_download( + repo_id=self.repo_id, + repo_type='model', + filename=f'{model_name}/text_encode.onnx', + )) + + return self._text_encoders[model_name] + + def _open_text_tokenizer(self, model_name: str): + """ + Load and cache the text tokenizer. + + :param model_name: Name of the SigLIP model variant + :type model_name: str + :return: Initialized tokenizer + :rtype: Tokenizer + :raises ValueError: If model name is invalid or tokenizer is missing + """ + with self._model_lock: + if model_name not in self._text_tokenizers: + self._check_model_name(model_name) + self._text_tokenizers[model_name] = Tokenizer.from_file(hf_hub_download( + repo_id=self.repo_id, + repo_type='model', + filename=f'{model_name}/tokenizer.json', + )) + + return self._text_tokenizers[model_name] + + def _get_logit_scale(self, model_name: str): + """ + Get the logit scale and bias parameters from model metadata. + + :param model_name: Name of the SigLIP model variant + :type model_name: str + :return: Tuple of logit scale and bias values + :rtype: tuple[float, float] + :raises ValueError: If model name is invalid or metadata is missing + """ + with self._model_lock: + if model_name not in self._logit_scales: + self._check_model_name(model_name) + with open(hf_hub_download( + repo_id=self.repo_id, + repo_type='model', + filename=f'{model_name}/meta.json', + ), 'r') as f: + meta_info = json.load(f) + self._logit_scales[model_name] = (meta_info['logit_scale'], meta_info['logit_bias']) + + return self._logit_scales[model_name] + + def _get_siglip_image_embedding(self, images: MultiImagesTyping, model_name: str, fmt: Any = 'embeddings'): + """ + Internal method to generate image embeddings. + + :param images: Input images to process + :type images: MultiImagesTyping + :param model_name: Name of the model variant to use + :type model_name: str + :param fmt: Output format specification + :type fmt: Any + :return: Image embeddings in specified format + :raises ValueError: If model name is invalid + """ + preprocessor = self._open_image_preprocessor(model_name) + model = self._open_image_encoder(model_name) + + images = load_images(images, mode='RGB', force_background='white') + input_ = np.stack([preprocessor(image) for image in images]) + encodings, embeddings = model.run(['encodings', 'embeddings'], {'pixel_values': input_}) + return vreplace(fmt, { + 'encodings': encodings, + 'embeddings': embeddings, + }) + +
[docs] def image_encode(self, images: MultiImagesTyping, model_name: str, fmt: Any = 'embeddings'): + """ + Generate embeddings for input images using the SigLIP model. + + :param images: Input images in various supported formats + :type images: MultiImagesTyping + :param model_name: Name of the SigLIP model variant to use + :type model_name: str + :param fmt: Output format, either 'encodings' or 'embeddings' + :type fmt: Any + :return: Image embeddings or encodings based on fmt parameter + :raises ValueError: If model name is invalid + """ + return self._get_siglip_image_embedding( + images=images, + model_name=model_name, + fmt=fmt, + )
+ + def _get_siglip_text_embedding(self, texts: Union[str, List[str]], model_name: str, fmt: Any = 'embeddings'): + """ + Internal method to generate text embeddings. + + :param texts: Input text or list of texts + :type texts: Union[str, List[str]] + :param model_name: Name of the SigLIP model variant to use + :type model_name: str + :param fmt: Output format, either 'encodings' or 'embeddings' + :type fmt: Any + :return: Text embeddings or encodings based on fmt parameter + :raises ValueError: If model name is invalid + """ + tokenizer = self._open_text_tokenizer(model_name) + model = self._open_text_encoder(model_name) + + if isinstance(texts, str): + texts = [texts] + encoded = tokenizer.encode_batch(texts) + input_ids = np.stack([np.array(item.ids, dtype=np.int64) for item in encoded]) + encodings, embeddings = model.run(['encodings', 'embeddings'], { + 'input_ids': input_ids, + }) + return vreplace(fmt, { + 'encodings': encodings, + 'embeddings': embeddings, + }) + +
[docs] def text_encode(self, texts: Union[str, List[str]], model_name: str, fmt: Any = 'embeddings'): + """ + Generate embeddings for input texts using the SigLIP model. + + :param texts: Input text or list of texts + :type texts: Union[str, List[str]] + :param model_name: Name of the SigLIP model variant to use + :type model_name: str + :param fmt: Output format, either 'encodings' or 'embeddings' + :type fmt: Any + :return: Text embeddings or encodings based on fmt parameter + :raises ValueError: If model name is invalid + """ + return self._get_siglip_text_embedding( + texts=texts, + model_name=model_name, + fmt=fmt, + )
+ +
[docs] def predict( + self, + images: Union[MultiImagesTyping, np.ndarray], + texts: Union[List[str], str, np.ndarray], + model_name: str, + fmt: Any = 'predictions', + ): + """ + Perform image-text classification using the SigLIP model. + + :param images: Input images or pre-computed image embeddings + :type images: Union[MultiImagesTyping, numpy.ndarray] + :param texts: Input texts or pre-computed text embeddings + :type texts: Union[List[str], str, numpy.ndarray] + :param model_name: Name of the SigLIP model variant to use + :type model_name: str + :param fmt: Output format, one of 'similarities', 'logits', or 'predictions' + :type fmt: Any + :return: Classification results in specified format + :raises ValueError: If model name is invalid + """ + extra_values = {} + if not isinstance(images, np.ndarray): + image_embeddings, image_encodings = \ + self._get_siglip_image_embedding(images, model_name=model_name, fmt=('embeddings', 'encodings')) + extra_values['image_embeddings'] = image_embeddings + extra_values['image_encodings'] = image_encodings + images = image_embeddings + images = images / np.linalg.norm(images, axis=-1, keepdims=True) + + if not isinstance(texts, np.ndarray): + text_embeddings, text_encodings = \ + self._get_siglip_text_embedding(texts, model_name=model_name, fmt=('embeddings', 'encodings')) + extra_values['text_embeddings'] = text_embeddings + extra_values['text_encodings'] = text_encodings + texts = text_embeddings + texts = texts / np.linalg.norm(texts, axis=-1, keepdims=True) + + similarities = images @ texts.T + logit_scale, logit_bias = self._get_logit_scale(model_name) + logits = similarities * np.exp(logit_scale) + logit_bias + predictions = sigmoid(logits) + + return vreplace(fmt, { + 'similarities': similarities, + 'logits': logits, + 'predictions': predictions, + **extra_values, + })
+ +
[docs] def clear(self): + """ + Clear all cached encoders, preprocessors, tokenizers, and scales. + + This method resets the internal state of the SigLIP model by clearing all cached + components, including image encoders, image preprocessors, text encoders, + text tokenizers, and logit scales. + """ + self._image_encoders.clear() + self._image_preprocessors.clear() + self._text_encoders.clear() + self._text_tokenizers.clear() + self._logit_scales.clear()
+ +
[docs] def make_ui(self, default_model_name: Optional[str] = None): + """ + Create an interactive Gradio UI for the SigLIP model. + + This method creates a user interface with image input, text labels input, + model selection, and prediction display. If no default model is specified, + it automatically selects the most recently updated model. + + :param default_model_name: Name of the model to select by default + :type default_model_name: Optional[str] + + :raises RuntimeError: If Gradio is not properly installed + """ + _check_gradio_env() + model_list = self.model_names + if not default_model_name: + hf_client = get_hf_client(hf_token=self._get_hf_token()) + selected_model_name, selected_time = None, None + for fileitem in hf_client.get_paths_info( + repo_id=self.repo_id, + repo_type='model', + paths=[f'{model_name}/image_encode.onnx' for model_name in model_list], + expand=True, + ): + if not selected_time or fileitem.last_commit.date > selected_time: + selected_model_name = os.path.dirname(fileitem.path) + selected_time = fileitem.last_commit.date + default_model_name = selected_model_name + + def _gr_detect(image: ImageTyping, raw_text: str, model_name: str) -> Dict[str, float]: + labels = [] + for line in raw_text.splitlines(keepends=False): + line = line.strip() + if line: + labels.append(line) + + prediction = self.predict(images=[image], texts=labels, model_name=model_name)[0] + return dict(zip(labels, prediction.tolist())) + + with gr.Row(): + with gr.Column(): + gr_input_image = gr.Image(type='pil', label='Original Image') + with gr.Row(): + gr_raw_text = gr.TextArea(value='', lines=5, autoscroll=True, label='Labels') + with gr.Row(): + gr_model = gr.Dropdown(model_list, value=default_model_name, label='Model') + + gr_submit = gr.Button(value='Submit', variant='primary') + + with gr.Column(): + gr_output_labels = gr.Label(label='Prediction') + + gr_submit.click( + _gr_detect, + inputs=[ + gr_input_image, + gr_raw_text, + gr_model, + ], + outputs=[gr_output_labels], + )
+ +
[docs] def launch_demo(self, default_model_name: Optional[str] = None, + server_name: Optional[str] = None, server_port: Optional[int] = None, **kwargs): + """ + Launch a web demo for the SigLIP model. + + Creates and launches a Gradio web interface for interacting with the model. + The demo includes the model UI and descriptive information about the model repository. + + :param default_model_name: Name of the model to select by default + :type default_model_name: Optional[str] + :param server_name: Server hostname to use for the demo + :type server_name: Optional[str] + :param server_port: Port number to use for the demo + :type server_port: Optional[int] + :param kwargs: Additional keyword arguments passed to gr.Blocks.launch() + + :raises RuntimeError: If Gradio is not properly installed + """ + _check_gradio_env() + with gr.Blocks() as demo: + with gr.Row(): + with gr.Column(): + repo_url = hf_hub_repo_url(repo_id=self.repo_id, repo_type='model') + gr.HTML(f'<h2 style="text-align: center;">SigLIP Demo For {self.repo_id}</h2>') + gr.Markdown(f'This is the quick demo for SigLIP model [{self.repo_id}]({repo_url}). ' + f'Powered by `dghs-imgutils`\'s quick demo module.') + + with gr.Row(): + self.make_ui( + default_model_name=default_model_name, + ) + + demo.launch( + server_name=server_name, + server_port=server_port, + **kwargs, + )
+ + +@ts_lru_cache() +def _open_models_for_repo_id(repo_id: str, hf_token: Optional[str] = None) -> SigLIPModel: + """ + Get or create a cached SigLIP model instance for the given repository ID. + + :param repo_id: Hugging Face repository ID for the model + :type repo_id: str + :param hf_token: Optional Hugging Face API token for private repositories + :type hf_token: Optional[str] + + :return: A cached SigLIP model instance + :rtype: SigLIPModel + """ + return SigLIPModel(repo_id, hf_token=hf_token) + + +
[docs]def siglip_image_encode(images: MultiImagesTyping, repo_id: str, model_name: str, + fmt: Any = 'embeddings', hf_token: Optional[str] = None): + """ + Encode images using a SigLIP model. + + :param images: One or more images to encode + :type images: MultiImagesTyping + :param repo_id: Hugging Face repository ID for the model + :type repo_id: str + :param model_name: Name of the specific model to use + :type model_name: str + :param fmt: Output format ('embeddings' or custom format) + :type fmt: Any + :param hf_token: Optional Hugging Face API token for private repositories + :type hf_token: Optional[str] + + :return: Encoded image features in the specified format + :rtype: Any + """ + model = _open_models_for_repo_id(repo_id, hf_token=hf_token) + return model.image_encode( + images=images, + model_name=model_name, + fmt=fmt, + )
+ + +
[docs]def siglip_text_encode(texts: Union[str, List[str]], repo_id: str, model_name: str, + fmt: Any = 'embeddings', hf_token: Optional[str] = None): + """ + Encode texts using a SigLIP model. + + :param texts: Single text or list of texts to encode + :type texts: Union[str, List[str]] + :param repo_id: Hugging Face repository ID for the model + :type repo_id: str + :param model_name: Name of the specific model to use + :type model_name: str + :param fmt: Output format ('embeddings' or custom format) + :type fmt: Any + :param hf_token: Optional Hugging Face API token for private repositories + :type hf_token: Optional[str] + + :return: Encoded text features in the specified format + :rtype: Any + """ + model = _open_models_for_repo_id(repo_id, hf_token=hf_token) + return model.text_encode( + texts=texts, + model_name=model_name, + fmt=fmt, + )
+ + +
[docs]def siglip_predict( + images: Union[MultiImagesTyping, np.ndarray], + texts: Union[List[str], str, np.ndarray], + repo_id: str, + model_name: str, + fmt: Any = 'predictions', + hf_token: Optional[str] = None, +): + """ + Predict similarity scores between images and texts using a SigLIP model. + + This function computes similarity scores between the given images and texts + using the specified SigLIP model. It can handle both raw inputs and + pre-computed embeddings. + + :param images: Images or image embeddings to compare + :type images: Union[MultiImagesTyping, np.ndarray] + :param texts: Texts or text embeddings to compare + :type texts: Union[List[str], str, np.ndarray] + :param repo_id: Hugging Face repository ID for the model + :type repo_id: str + :param model_name: Name of the specific model to use + :type model_name: str + :param fmt: Output format ('predictions' or custom format) + :type fmt: Any + :param hf_token: Optional Hugging Face API token for private repositories + :type hf_token: Optional[str] + + :return: Similarity scores in the specified format + :rtype: Any + """ + model = _open_models_for_repo_id(repo_id, hf_token=hf_token) + return model.predict( + images=images, + texts=texts, + model_name=model_name, + fmt=fmt + )
+
+ + +
+
+ +
+
+
+
+ +
+ + Other Versions + v: dev/siglip + + +
+
+
Tags
+
v0.10.0
+
v0.8.0
+
v0.9.0
+
v0.9.1
+
v0.9.2
+
v0.9.3
+
v0.9.4
+
+
+
Branches
+
dev/siglip
+
HEAD
+
dev/denormalize
+
main
+
+
+
+ + + \ No newline at end of file diff --git a/dev/siglip/_modules/index.html b/dev/siglip/_modules/index.html index 3245e73cd8c765a2e69229f5274b9429d2c3e10c..aeba076b183f08ebff96810df32aa50d697e8690 100644 --- a/dev/siglip/_modules/index.html +++ b/dev/siglip/_modules/index.html @@ -124,6 +124,7 @@
  • imgutils.edge.lineart_anime
  • imgutils.generic.classify
  • imgutils.generic.enhance
  • +
  • imgutils.generic.siglip
  • imgutils.generic.yolo
  • imgutils.metadata.geninfo
  • imgutils.metadata.lsb.read
  • diff --git a/dev/siglip/_sources/api_doc/generic/index.rst.txt b/dev/siglip/_sources/api_doc/generic/index.rst.txt index d4b9d162ef0c431e7e465a68698531022b20ef57..134f3c6f7f05dd4cef2d4a5f2dc9890ebb660952 100644 --- a/dev/siglip/_sources/api_doc/generic/index.rst.txt +++ b/dev/siglip/_sources/api_doc/generic/index.rst.txt @@ -11,4 +11,5 @@ imgutils.generic classify enhance + siglip yolo diff --git a/dev/siglip/_sources/api_doc/generic/siglip.rst.txt b/dev/siglip/_sources/api_doc/generic/siglip.rst.txt new file mode 100644 index 0000000000000000000000000000000000000000..948a926bec9b84cb2d390c5e0e4032ff846f49aa --- /dev/null +++ b/dev/siglip/_sources/api_doc/generic/siglip.rst.txt @@ -0,0 +1,35 @@ +imgutils.generic.siglip +======================================= + +.. currentmodule:: imgutils.generic.siglip + +.. automodule:: imgutils.generic.siglip + + + +SigLIPModel +----------------------------------------- + +.. autoclass:: SigLIPModel + :members: __init__, image_encode, text_encode, predict, clear, make_ui, launch_demo + + +siglip_image_encode +----------------------------------------- + +.. autofunction:: siglip_image_encode + + +siglip_text_encode +----------------------------------------- + +.. autofunction:: siglip_text_encode + + +siglip_predict +----------------------------------------- + +.. autofunction:: siglip_predict + + + diff --git a/dev/siglip/_sources/information/environment.result.ipynb.txt b/dev/siglip/_sources/information/environment.result.ipynb.txt index 9f4e766b7dda0d9d71983be29cda1960fc1fada2..970e8e9413e1df7fea6fcaac4da6141d68030ff7 100644 --- a/dev/siglip/_sources/information/environment.result.ipynb.txt +++ b/dev/siglip/_sources/information/environment.result.ipynb.txt @@ -29,10 +29,10 @@ "execution_count": 1, "metadata": { "execution": { - "iopub.execute_input": "2025-02-20T12:56:47.715115Z", - "iopub.status.busy": "2025-02-20T12:56:47.714719Z", - "iopub.status.idle": "2025-02-20T12:56:48.887275Z", - "shell.execute_reply": "2025-02-20T12:56:48.886491Z" + "iopub.execute_input": "2025-02-21T06:48:39.284744Z", + "iopub.status.busy": "2025-02-21T06:48:39.284172Z", + "iopub.status.idle": "2025-02-21T06:48:40.448017Z", + "shell.execute_reply": "2025-02-21T06:48:40.447279Z" }, "pycharm": { "name": "#%%\n" @@ -53,7 +53,7 @@ "text": [ "CPU Brand: AMD EPYC 7763 64-Core Processor\n", "CPU Count: 4\n", - "CPU Freq: 3239.456 MHz\n", + "CPU Freq: 3247.30175 MHz\n", "Memory Size: 15.615 GiB\n", "Has CUDA: No\n" ] diff --git a/dev/siglip/api_doc/generic/classify.html b/dev/siglip/api_doc/generic/classify.html index 9e0c94fc1e42b53767e4dd521f9a7e0d844652b8..fae6e34c9d624ca2fa04793e4f5109a5154eb8b5 100644 --- a/dev/siglip/api_doc/generic/classify.html +++ b/dev/siglip/api_doc/generic/classify.html @@ -82,6 +82,7 @@
  • imgutils.generic.enhance
  • +
  • imgutils.generic.siglip
  • imgutils.generic.yolo
  • diff --git a/dev/siglip/api_doc/generic/enhance.html b/dev/siglip/api_doc/generic/enhance.html index e0abf3f9f879933fffd5e2f374d9fa1ab93de546..33599aff135abde43a4c4bcfb25ac75a162a06e9 100644 --- a/dev/siglip/api_doc/generic/enhance.html +++ b/dev/siglip/api_doc/generic/enhance.html @@ -22,7 +22,7 @@ - + @@ -70,6 +70,7 @@ +
  • imgutils.generic.siglip
  • imgutils.generic.yolo
  • @@ -173,7 +174,7 @@ _process_rgba: Processes RGBA images.