Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
xet
 Community
1
identifier
stringlengths
24
117
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
444
eomt/image_processing_pil_eomt.py:EomtImageProcessorPil
[ -0.00029943851404823363, 0.03327891603112221, -0.032131366431713104, 0.03396744281053543, -0.0015348464949056506, 0.008434483781456947, 0.005250035785138607, -0.03626254200935364, -0.001936488552019, 0.017787005752325058, 0.0035000238567590714, 0.016868967562913895, -0.003241825383156538, ...
[ "BILINEAR", "BatchFeature", "CONSTANT", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "NEAREST", "None", "PILImageResampling", "PaddingMode", "PilBackend", "SemanticSegmentationPostProcessor...
eomt/image_processing_eomt.py:EomtImageProcessorKwargs
[ -0.000325166096445173, 0.01743907667696476, -0.019764285534620285, -0.003633140819147229, -0.001685777329839766, 0.016974033787846565, 0.02476348727941513, -0.060920506715774536, 0.017671598121523857, 0.007091891020536423, 0.009242710657417774, 0.012788656167685986, 0.002034558914601803, 0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "bool", "class", "do_split_image", "ignore_index", "int", "r", "total" ]
eomt/image_processing_eomt.py:convert_segmentation_map_to_binary_masks_fast
[ -0.00014377280604094267, -0.0022721397690474987, -0.0021169004030525684, -0.037483248859643936, -0.00028401747113093734, 0.007338587660342455, -0.012080444023013115, -0.05870870128273964, 0.010217572562396526, -0.006745855789631605, 0.02958015352487564, -0.0043749273754656315, -0.00242737890...
[ "Model_segmentation_map_to_binary_masks_fast", "None", "all_labels", "binary_masks", "class_id", "def", "device", "dim", "else", "enumerate", "float", "for", "i", "if", "ignore_index", "in", "instance_id_to_semantic_id", "int", "is", "item", "label", "labels", "long", "...
eomt/image_processing_eomt.py:get_target_size
[ -0.00015363605052698404, -0.012788203544914722, 0.034556567668914795, -0.01500482577830553, -0.0005648123333230615, 0.025576407089829445, 0.062292762100696564, -0.030919034034013748, 0.015232170931994915, 0.007729758508503437, -0.011992492713034153, -0.027849866077303886, -0.0021882036235183...
[ "Model_tarModel_size", "def", "int", "longest_edge", "or", "return", "shortest_edge", "size_dict", "tarModel_height", "tarModel_width" ]
eomt/image_processing_eomt.py:reorder_patches_and_offsets
[ 0.00008966984751168638, -0.0028694351203739643, -0.010915106162428856, 0.008214461617171764, -0.0000953841008595191, -0.00843951478600502, 0.022617900744080544, -0.03713386505842209, 0.024193275719881058, 0.0068922704085707664, -0.019917255267500877, 0.02531854435801506, -0.00174416648223996...
[ "Model_patches_and_offsets", "combined", "def", "key", "lambda", "list", "offsets", "patches", "return", "sort", "sorted_offsets", "sorted_patches", "x", "zip" ]
eomt/image_processing_eomt.py:remove_low_and_no_objects
[ -0.00004659569094656035, 0.01440422236919403, -0.006442513782531023, -0.0034603893291205168, -0.00029715741402469575, 0.04478812962770462, 0.012378628365695477, -0.025770055130124092, 0.02284419722855091, -0.023969527333974838, -0.012491161935031414, 0.035560425370931625, 0.00125896278768777...
[ "Model_low_and_no_objects", "ValueError", "and", "def", "have", "if", "labels", "mask", "masks", "must", "ne", "not", "num_labels", "object_mask_threshold", "raise", "return", "same", "scores", "shape", "the", "to_keep" ]
eomt/image_processing_eomt.py:check_segment_validity
[ -0.0001870640553534031, 0.021346479654312134, -0.017393426969647408, -0.02202414534986019, -0.0008435529889538884, 0.03185030445456505, -0.004913078621029854, -0.03885285183787346, 0.0011012073373422027, 0.0012423876905813813, 0.03998229652643204, 0.006720188073813915, -0.0014823944075033069...
[ "False", "Model_segment_validity", "and", "area_ratio", "def", "final_mask", "final_mask_area", "if", "item", "k", "mask_exists", "mask_k", "mask_k_area", "mask_labels", "mask_probs", "mask_threshold", "not", "original_area", "original_mask", "overlap_mask_area_threshold", "r...
eomt/image_processing_eomt.py:compute_segments
[ -0.0002652257971931249, 0.016974451020359993, -0.05000581219792366, -0.017318526282906532, -0.0009892204543575644, 0.038536589592695236, 0.02408536896109581, -0.04633565992116928, 0.007913763634860516, -0.02959059551358223, 0.022479677572846413, 0.028558366000652313, -0.006308072712272406, ...
[ "Model_segments", "None", "and", "append", "argmax", "check_segment_validity", "continue", "current_segment_id", "def", "device", "dtype", "else", "final_mask", "for", "height", "id", "if", "in", "int", "is", "item", "k", "label_id", "long", "mask_exists", "mask_lab...
eomt/image_processing_eomt.py:EomtImageProcessor
[ -0.00036517041735351086, 0.04859297350049019, -0.02186683937907219, 0.01781742461025715, -0.0016703834990039468, 0.019668584689497948, 0.0036444731522351503, -0.03586624190211296, -0.0012220555217936635, 0.0159662626683712, 0.003543237689882517, 0.023139512166380882, -0.002950287889689207, ...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "NEAREST", "None", "PILImageResampling", "SemanticSegmentationPostProcessorOutput", "TorchvisionBackend", "True", "_", ...
deepseek_ocr2/image_processing_deepseek_ocr2.py:DeepseekOcr2ImageProcessorKwargs
[ -0.00022816068667452782, 0.023842791095376015, -0.030801692977547646, -0.009183467365801334, -0.0011693235719576478, 0.02920456789433956, 0.030117210000753403, -0.04335053265094757, 0.01734021306037903, 0.0201922208070755, 0.001647035009227693, 0.00963978935033083, 0.0006060518207959831, -...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "background_color", "bool", "class", "crop_to_patches", "int", "list", "max_patches", "min_patches", "r", "tile_size", "total" ]
deepseek_ocr2/image_processing_deepseek_ocr2.py:get_all_supported_aspect_ratios
[ -0.00010235269292024896, -0.00019683210121002048, 0.03650404140353203, 0.015228680334985256, -0.00034992373548448086, 0.044566284865140915, -0.006802517455071211, -0.026762166991829872, 0.01315713208168745, 0.0200436320155859, 0.017804119735956192, -0.031353164464235306, -0.00074883678462356...
[ "Model_all_supported_aspect_ratios", "and", "aspect_ratios", "def", "for", "height", "if", "in", "key", "lambda", "max_image_tiles", "min_image_tiles", "range", "return", "sorted", "width", "x" ]
deepseek_ocr2/image_processing_deepseek_ocr2.py:get_optimal_tiled_canvas
[ -0.0002590991207398474, 0.008968002162873745, 0.01940247043967247, 0.016018318012356758, -0.000965893326792866, 0.01771039515733719, 0.012295751832425594, -0.04782934486865997, 0.00834757462143898, 0.020756131038069725, -0.01105489581823349, -0.03248785808682442, -0.003807170782238245, 0.0...
[ "Model_all_supported_aspect_ratios", "Model_optimal_tiled_canvas", "abs", "area", "aspect_ratio", "best_grid", "best_ratio_diff", "def", "elif", "float", "for", "grid", "grid_aspect_ratio", "if", "in", "inf", "int", "max_image_tiles", "min_image_tiles", "original_height", "or...
deepseek_ocr2/image_processing_deepseek_ocr2.py:DeepseekOcr2ImageProcessor
[ -0.00016041775234043598, 0.03213934600353241, -0.01774359866976738, 0.007114178501069546, -0.0007393165724352002, 0.01065731793642044, 0.00014210920198820531, -0.022542180493474007, 0.0002476013032719493, 0.03213934600353241, 0.01043412834405899, 0.00831382442265749, -0.0008334747981280088, ...
[ "BICUBIC", "BatchFeature", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "ValueError", "__init__", "_preprocess", "all_pixel_values_global", "and", "append...
deepseek_ocr2/image_processing_pil_deepseek_ocr2.py:DeepseekOcr2ImageProcessorKwargs
[ -0.00022816068667452782, 0.023842791095376015, -0.030801692977547646, -0.009183467365801334, -0.0011693235719576478, 0.02920456789433956, 0.030117210000753403, -0.04335053265094757, 0.01734021306037903, 0.0201922208070755, 0.001647035009227693, 0.00963978935033083, 0.0006060518207959831, -...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "background_color", "bool", "class", "crop_to_patches", "int", "list", "max_patches", "min_patches", "r", "tile_size", "total" ]
deepseek_ocr2/image_processing_pil_deepseek_ocr2.py:get_all_supported_aspect_ratios
[ -0.00010235269292024896, -0.00019683210121002048, 0.03650404140353203, 0.015228680334985256, -0.00034992373548448086, 0.044566284865140915, -0.006802517455071211, -0.026762166991829872, 0.01315713208168745, 0.0200436320155859, 0.017804119735956192, -0.031353164464235306, -0.00074883678462356...
[ "Model_all_supported_aspect_ratios", "and", "aspect_ratios", "def", "for", "height", "if", "in", "key", "lambda", "max_image_tiles", "min_image_tiles", "range", "return", "sorted", "width", "x" ]
deepseek_ocr2/image_processing_pil_deepseek_ocr2.py:get_optimal_tiled_canvas
[ -0.0002590991207398474, 0.008968002162873745, 0.01940247043967247, 0.016018318012356758, -0.000965893326792866, 0.01771039515733719, 0.012295751832425594, -0.04782934486865997, 0.00834757462143898, 0.020756131038069725, -0.01105489581823349, -0.03248785808682442, -0.003807170782238245, 0.0...
[ "Model_all_supported_aspect_ratios", "Model_optimal_tiled_canvas", "abs", "area", "aspect_ratio", "best_grid", "best_ratio_diff", "def", "elif", "float", "for", "grid", "grid_aspect_ratio", "if", "in", "inf", "int", "max_image_tiles", "min_image_tiles", "original_height", "or...
deepseek_ocr2/image_processing_pil_deepseek_ocr2.py:DeepseekOcr2ImageProcessorPil
[ -0.0001481303625041619, 0.01840301975607872, -0.025318093597888947, 0.02141442336142063, -0.0007423945353366435, -0.009536109864711761, -0.0019936603493988514, -0.02877563051879406, -0.0017427102429792285, 0.026768028736114502, 0.00925727654248476, 0.003443595254793763, -0.001533585018478334...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "SizeDict", "True", "ValueError", "__init__", "_preprocess", "all_pixel_values...
video_llava/image_processing_video_llava.py:VideoLlavaImageProcessor
[ -0.00006653009768342599, 0.03673505038022995, 0.0030612542759627104, 0.04296887665987015, -0.00018871936481446028, 0.011911061592400074, -0.01469402015209198, -0.037625595927238464, 0.0057885535061359406, 0.028942767530679703, 0.03250495344400406, 0.02037125453352928, 0.0004835390136577189, ...
[ "BICUBIC", "BaseImageProcessor", "BatchFeature", "ChannelDimension", "FIRST", "False", "If", "Image", "Invalid", "It", "Model", "ModelImageProcessor", "Must", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PIL", "PILImageResampling", "Tensor", "True", "ValueError", "__init...
vit/image_processing_pil_vit.py:ViTImageProcessorPil
[ -0.0002764312375802547, 0.026192758232355118, 0.0067779505625367165, 0.04802005738019943, -0.0015508870128542185, 0.019299926236271858, 0.017461838200688362, -0.03193678334355354, 0.0074672335758805275, 0.04135698452591896, 0.009879724122583866, -0.005686585325747728, -0.0010626447619870305,...
[ "BILINEAR", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorPil", "PILImageResampling", "PilBackend", "True", "class", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
vit/image_processing_vit.py:ViTImageProcessor
[ -0.00028664295678026974, 0.04035932943224907, 0.013816190883517265, 0.03669029846787453, -0.0014547130558639765, 0.034855782985687256, 0.016969263553619385, -0.028434982523322105, 0.009344561025500298, 0.041047271341085434, 0.01800117827951908, 0.004414301831275225, -0.00015675787290092558, ...
[ "BILINEAR", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "PILImageResampling", "TorchvisionBackend", "True", "class", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
perception_lm/image_processing_perception_lm.py:PerceptionLMImageProcessorKwargs
[ -0.00020383443916216493, 0.037661515176296234, -0.03788838908076286, 0.009131782688200474, -0.0008685119682922959, 0.03743463754653931, 0.044014059007167816, -0.0406109094619751, 0.015654483810067177, 0.027565505355596542, 0.016675429418683052, 0.01860387995839119, 0.00171575415879488, 0.0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "class", "int", "max_num_tiles", "r", "str", "tile_size", "total", "vision_input_type" ]
perception_lm/image_processing_perception_lm.py:PerceptionLMImageProcessor
[ -0.0001912984880618751, 0.03712064400315285, -0.011739962734282017, 0.022138215601444244, -0.0006533848354592919, 0.023703543469309807, -0.011125012300908566, -0.016547756269574165, -0.003312346525490284, 0.028064100071787834, 0.006820359267294407, 0.008553401567041874, 0.0003214513417333364...
[ "BICUBIC", "BatchFeature", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "_", "__add__", "__init__", "_asp_ratios", "_factors", "_find_closest_aspect_ratio...
pp_lcnet/image_processing_pp_lcnet.py:PPLCNetImageProcessorKwargs
[ -0.00025391316739842296, 0.011272314004600048, 0.0006472997483797371, 0.0052928379736840725, -0.0011229398660361767, 0.03913838788866997, 0.05172676220536232, -0.027236657217144966, 0.014247747138142586, 0.031585365533828735, -0.01602156274020672, -0.01853923685848713, 0.0005578937125392258,...
[ "False", "ImagesKwargs", "Model", "class", "int", "r", "resize_short", "size_divisor", "total" ]
pp_lcnet/image_processing_pp_lcnet.py:PPLCNetImageProcessor
[ -0.00020610533829312772, 0.036077242344617844, -0.00012154930300312117, 0.03134210407733917, -0.0006834946107119322, 0.022773759439587593, 0.008117379620671272, -0.029087277129292488, 0.0022830129601061344, 0.032920483499765396, 0.01860232837498188, 0.007018151227384806, -0.00107808946631848...
[ "BatchFeature", "False", "Model", "ModelKwargs", "None", "SizeDict", "TorchvisionBackend", "True", "_", "_preprocess", "ceil", "center_crop", "class", "crop_size", "data", "def", "disable_grouping", "do_center_crop", "do_normalize", "do_rescale", "do_resize", "for", "get_...
slanext/image_processing_slanext.py:SLANeXtImageProcessor
[ -0.0004732643428724259, 0.019318129867315292, -0.00983793567866087, 0.026234496384859085, -0.0020868349820375443, 0.02790396474301815, 0.0403057262301445, -0.05843137949705124, -0.002906662877649069, 0.019795119762420654, -0.005723889917135239, 0.01836414821445942, -0.002220988506451249, 0...
[ "BatchFeature", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Model", "None", "Resampling", "TorchvisionBackend", "True", "__init__", "_preprocess", "_resize", "and", "append", "arange", "argmax", "batch_index", "batch_size", "body", "bos_id", "break", "center_crop", "c...
llava_onevision/image_processing_llava_onevision.py:LlavaOnevisionImageProcessorKwargs
[ -0.00026232702657580376, -0.002299853600561619, -0.037487611174583435, 0.011384274810552597, -0.001322415773756802, 0.01897379197180271, 0.04967683553695679, -0.04139736294746399, 0.02035370282828808, 0.028058212250471115, 0.0023860980290919542, 0.00942939892411232, 0.001674580853432417, 0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "image_grid_pinpoints", "int", "list", "r", "total" ]
llava_onevision/image_processing_llava_onevision.py:LlavaOnevisionImageProcessor
[ -0.00012532855907920748, 0.03297533467411995, -0.0042890217155218124, 0.02562272548675537, -0.00042994655086658895, -0.005403053015470505, -0.004762485157698393, -0.027627982199192047, 0.0012602482456713915, 0.029187627136707306, 0.01715608686208725, 0.02406308241188526, 0.000459538016002625...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "InterpolationMode", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "TypeError", "ValueError", "_", ...
llava_onevision/image_processing_pil_llava_onevision.py:LlavaOnevisionImageProcessorKwargs
[ -0.00026232702657580376, -0.002299853600561619, -0.037487611174583435, 0.011384274810552597, -0.001322415773756802, 0.01897379197180271, 0.04967683553695679, -0.04139736294746399, 0.02035370282828808, 0.028058212250471115, 0.0023860980290919542, 0.00942939892411232, 0.001674580853432417, 0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "image_grid_pinpoints", "int", "list", "r", "total" ]
llava_onevision/image_processing_pil_llava_onevision.py:LlavaOnevisionImageProcessorPil
[ -0.0001831794361351058, 0.017388898879289627, -0.014808611012995243, 0.035002171993255615, -0.0008343867375515401, -0.012789255008101463, -0.0005609322688542306, -0.029392849653959274, 0.0004908157279714942, 0.02771005406975746, 0.012172229588031769, 0.018398577347397804, -0.0007397293811663...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "SizeDict", "True", "TypeError", "ValueError", "_", "__init__", "_get_padding...
textnet/image_processing_textnet.py:TextNetImageProcessorKwargs
[ -0.00016005588986445218, 0.014295981265604496, -0.018911220133304596, -0.011369245126843452, -0.0005839401856064796, 0.009005342610180378, 0.04277537763118744, -0.036471638828516006, 0.009399326518177986, 0.0276914294809103, -0.010975261218845844, -0.002926736371591687, -0.001378943095915019...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "size_divisor", "total" ]
textnet/image_processing_textnet.py:TextNetImageProcessor
[ -0.00019527578842826188, 0.0457313135266304, 0, 0.019727233797311783, -0.0006339895771816373, 0.018830541521310806, -0.00975153036415577, -0.03900612145662308, 0.0016112441662698984, 0.04236871749162674, 0.019278887659311295, 0.008182317949831486, 0.001015784335322678, 0.009022967889904976...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "auto_docstring", "center_...
textnet/image_processing_pil_textnet.py:TextNetImageProcessorKwargs
[ -0.00016005588986445218, 0.014295981265604496, -0.018911220133304596, -0.011369245126843452, -0.0005839401856064796, 0.009005342610180378, 0.04277537763118744, -0.036471638828516006, 0.009399326518177986, 0.0276914294809103, -0.010975261218845844, -0.002926736371591687, -0.001378943095915019...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "size_divisor", "total" ]
textnet/image_processing_pil_textnet.py:TextNetImageProcessorPil
[ -0.00023419347417075187, 0.029525987803936005, -0.00059164670528844, 0.0380907766520977, -0.0009508607327006757, 0.004028832074254751, 0.0003838660195469856, -0.040795449167490005, 0.002690583700314164, 0.04011927917599678, 0.007944969460368156, -0.0033104040194302797, 0.00015671593428123742...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "PILImageResampling", "PilBackend", "True", "__init__", "_preprocess", "append", "auto_docstring", "center_crop", ...
sapiens2/image_processing_sapiens2.py:Sapiens2ImageProcessorKwargs
[ -0.0002554524689912796, 0.01994340866804123, -0.019363658502697945, 0.009449928998947144, -0.0014566224999725819, 0.03455311432480812, 0.025624960660934448, -0.02701636217534542, 0.01878390833735466, 0.02446546033024788, 0.009276003576815128, 0.014957556501030922, 0.00286976364441216, 0.00...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_reduce_labels", "r", "total" ]
sapiens2/image_processing_sapiens2.py:box_xywh_to_xyxy
[ -0.00005162815432413481, -0.028795767575502396, 0.011979489587247372, -0.025646230205893517, -0.0001766342029441148, 0.03172033652663231, 0.03464490920305252, -0.018222322687506676, 0.022159243002533913, 0.020809441804885864, 0.017097488045692444, -0.008998677134513855, -0.001384952687658369...
[ "Model_xywh_to_xyxy", "b", "def", "dim", "h", "return", "stack", "torch", "unbind", "w", "x", "y" ]
sapiens2/image_processing_sapiens2.py:box_xywh_to_cxcywh
[ -0.00016995509213302284, -0.004214174579828978, 0.004128752276301384, -0.027335185557603836, -0.0005125347524881363, 0.013439799658954144, 0.03690250217914581, -0.014350973069667816, 0.01970411278307438, 0.025171151384711266, 0.01156050618737936, 0.00751717621460557, -0.0025626737624406815, ...
[ "Model_xywh_to_cxcywh", "b", "def", "dim", "h", "return", "stack", "torch", "unbind", "w", "x", "y" ]
sapiens2/image_processing_sapiens2.py:boxes_to_crop_params
[ -0.0002458442177157849, 0.011230449192225933, 0.007810007780790329, -0.02576732449233532, -0.001140147098340094, 0.03055594302713871, 0.03648470714688301, -0.029187766835093498, 0.008722125552594662, 0.037168797105550766, -0.006270809099078178, -0.014935927465558052, -0.0028218640945851803, ...
[ "Model", "Model_to_crop_params", "None", "aspect_ratio", "center", "center_x", "center_y", "def", "dim", "height", "int", "output_height", "output_size", "output_width", "padding", "return", "scale", "scaled_height", "scaled_width", "stack", "torch", "unbind", "where", ...
sapiens2/image_processing_sapiens2.py:crop_and_resize
[ -0.00008637137216283008, -0.005806948058307171, 0.012842289172112942, -0.00965963490307331, -0.0003699137596413493, 0.036181751638650894, 0.013903173618018627, -0.06298305094242096, 0.016304122284054756, 0.01245143637061119, -0.012395600788295269, -0.01451736968010664, -0.0020938513334840536...
[ "F", "Model_and_resize", "None", "True", "align_corners", "any", "arange", "bicubic", "bilinear", "boxes", "boxes_height", "boxes_to_Model_params", "boxes_width", "center", "center_x", "center_y", "def", "device", "dim", "dtype", "empty", "expand", "float32", "for", "...
sapiens2/image_processing_sapiens2.py:gaussian_blur_preserve_max
[ -0.00007217979145934805, 0.007027249317616224, 0.02866893634200096, -0.04053966701030731, -0.00030796710052527487, 0.00010105170076712966, -0.0009658968192525208, -0.00039370794547721744, 0.003863587277010083, 0.017694110050797462, 0.03762798011302948, -0.021501703187823296, 0.00226775766350...
[ "F", "Kernel", "Model_blur", "Model_blur_preserve_max", "None", "ValueError", "amax", "an", "be", "blurred", "border", "constant", "def", "dim", "greater", "heatmaps", "if", "integer", "kernel", "kernel_size", "mode", "must", "odd", "ones_like", "or", "origin_maxes"...
sapiens2/image_processing_sapiens2.py:get_keypoint_predictions
[ -0.00019477641035337, -0.004589640535414219, 0.004787958227097988, -0.0351305827498436, -0.0005241255857981741, -0.0015440457500517368, 0.07207435369491577, -0.00736608961597085, 0.016998669132590294, 0.0015015490353107452, 0.0351305827498436, 0.015752099454402924, -0.0003311198961455375, ...
[ "Model_keypoint_predictions", "_", "amax", "argmax", "def", "device", "dim", "flat_index", "float", "full", "heatmap_flat", "heatmap_width", "heatmaps", "locations", "locations_x", "locations_y", "num_keypoints", "num_persons", "reshape", "return", "scores", "shape", "sta...
sapiens2/image_processing_sapiens2.py:post_dark_unbiased_data_processing
[ -0.00023621783475391567, 0.02489345148205757, 0.018869008868932724, -0.04614950343966484, -0.0008098896942101419, -0.0029838040936738253, 0.03523730859160423, -0.017845990136265755, 0.016822971403598785, 0.02682582102715969, 0.03660133108496666, 0.038874704390764236, 0.0030832644551992416, ...
[ "F", "Model_dark_unbiased_data_processing", "None", "arange", "blur_kernel_size", "cat", "clamp", "def", "determinant", "device", "dim", "dtype", "dx", "dy", "eps", "finfo", "flatten", "for", "gaussian_blur_preserve_max", "gradient_x", "gradient_y", "heatmap_height", "hea...
sapiens2/image_processing_sapiens2.py:Sapiens2ImageProcessor
[ -0.0003650363360065967, 0.03672882914543152, -0.004881680011749268, 0.01638849638402462, -0.001656284206546843, 0.03370683640241623, 0.017667032778263092, -0.029406309127807617, 0.007787441834807396, 0.02150263823568821, 0.00819424819201231, 0.0276628527790308, -0.0009734302293509245, -0.0...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "F", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ImportError", "Make", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "PyTorch", "SemanticSegmentationPostProcessorOutput", "Size...
dinov3_vit/image_processing_dinov3_vit.py:DINOv3ViTImageProcessor
[ -0.00023836626496631652, 0.02406969480216503, -0.009266267530620098, 0.03977714851498604, -0.0006744882557541132, 0.024182699620723724, 0.007514717057347298, -0.027346789836883545, 0.005904420744627714, 0.022600652649998665, 0.025086725130677223, 0.01141333021223545, -0.0011794716119766235, ...
[ "BILINEAR", "BatchFeature", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Model", "PILImageResampling", "TorchvisionBackend", "True", "_preprocess", "antialias", "center_crop", "class", "crop_size", "data", "def", "disable_grouping", "do_center_crop", "do_normalize", "do_resc...
janus/image_processing_janus.py:JanusImageProcessorKwargs
[ -0.00004493823871598579, 0.01548856683075428, -0.008080990985035896, 0.007239221595227718, -0.0005752094439230859, 0.02514086291193962, 0.032772909849882126, -0.04264967516064644, 0.019641298800706863, 0.042200732976198196, -0.0028479883912950754, -0.006032684352248907, 0.003268873319029808,...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "min_size", "r", "total" ]
janus/image_processing_janus.py:JanusImageProcessor
[ -0.0002216832508565858, 0.03588990494608879, -0.002229099627584219, 0.03095504269003868, -0.0009463158785365522, -0.0017594464588910341, -0.012281076982617378, -0.02893623523414135, -0.003462814958766103, 0.04800274968147278, 0.01547752134501934, 0.027478208765387535, -0.00017349123663734645...
[ "BICUBIC", "BatchFeature", "False", "Got", "Image", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "Output", "PIL", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "ValueError", "__init__", "_preprocess", "and"...
janus/image_processing_pil_janus.py:JanusImageProcessorKwargs
[ -0.00004493823871598579, 0.01548856683075428, -0.008080990985035896, 0.007239221595227718, -0.0005752094439230859, 0.02514086291193962, 0.032772909849882126, -0.04264967516064644, 0.019641298800706863, 0.042200732976198196, -0.0028479883912950754, -0.006032684352248907, 0.003268873319029808,...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "min_size", "r", "total" ]
janus/image_processing_pil_janus.py:JanusImageProcessorPil
[ -0.00025419049779884517, 0.030276913195848465, -0.00426475191488862, 0.042478058487176895, -0.0012497699353843927, -0.012822498567402363, -0.00909437146037817, -0.0406704805791378, -0.002188862767070532, 0.04405968636274338, 0.008077609352767467, 0.01423466857522726, -0.00003420098073547706,...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "Got", "Image", "Iterable", "LAST", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "Output", "PIL", "PILImageResampling", "PilBackend", "True", "ValueError", "__init__...
layoutlmv3/image_processing_pil_layoutlmv3.py:LayoutLMv3ImageProcessorKwargs
[ -0.00035700088483281434, 0.03187011182308197, -0.02366824448108673, 0.012244215235114098, -0.001860066200606525, 0.03515085577964783, 0.03491651639342308, -0.06045947223901749, 0.018044106662273407, 0.008963468484580517, 0.026948990300297737, 0.03772858530282974, 0.0015012344811111689, 0.0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "apply_ocr", "bool", "class", "ocr_lang", "r", "str", "tesseract_config", "total" ]
layoutlmv3/image_processing_pil_layoutlmv3.py:normalize_box
[ 0.00017906483844853938, -0.05989224091172218, 0.01359093189239502, -0.028679169714450836, 0.0009574120631441474, 0.031558603048324585, 0.049986984580755234, -0.04169421270489693, 0.02798810601234436, 0.03478357195854187, -0.017737317830324173, -0.008695892989635468, -0.0005362947122193873, ...
[ "Model_box", "box", "def", "height", "int", "return", "width" ]
layoutlmv3/image_processing_pil_layoutlmv3.py:apply_tesseract
[ -0.00018682963855098933, 0.013818343169987202, 0.02492941915988922, 0.0016426907386630774, -0.0007966697448864579, 0.02131972834467888, 0.03519447147846222, -0.08257164806127548, 0.004906921647489071, -0.01556678581982851, 0.03023114986717701, 0.04895641282200813, -0.002523962641134858, 0....
[ "Model_tesseract", "None", "Not", "actual_box", "actual_boxes", "append", "are", "array", "as", "assert", "bounding", "box", "boxes", "config", "coord", "cpu", "data", "def", "dict", "elif", "else", "enumerate", "for", "h", "hasattr", "height", "idx", "if", "i...
layoutlmv3/image_processing_pil_layoutlmv3.py:LayoutLMv3ImageProcessorPil
[ -0.0003054196131415665, 0.02377629280090332, -0.014745873399078846, 0.0413798950612545, -0.0014074307400733232, 0.012802619487047195, 0.01434579212218523, -0.05212495103478432, 0.0038865094538778067, 0.022175965830683708, 0.020804256200790405, 0.03749338537454605, -0.0009430500795133412, -...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "True", "__init__", "_preprocess", "append", "apply_ocr", "apply_tesseract", ...
layoutlmv3/image_processing_layoutlmv3.py:normalize_box
[ 0.00017906483844853938, -0.05989224091172218, 0.01359093189239502, -0.028679169714450836, 0.0009574120631441474, 0.031558603048324585, 0.049986984580755234, -0.04169421270489693, 0.02798810601234436, 0.03478357195854187, -0.017737317830324173, -0.008695892989635468, -0.0005362947122193873, ...
[ "Model_box", "box", "def", "height", "int", "return", "width" ]
layoutlmv3/image_processing_layoutlmv3.py:apply_tesseract
[ -0.00018682963855098933, 0.013818343169987202, 0.02492941915988922, 0.0016426907386630774, -0.0007966697448864579, 0.02131972834467888, 0.03519447147846222, -0.08257164806127548, 0.004906921647489071, -0.01556678581982851, 0.03023114986717701, 0.04895641282200813, -0.002523962641134858, 0....
[ "Model_tesseract", "None", "Not", "actual_box", "actual_boxes", "append", "are", "array", "as", "assert", "bounding", "box", "boxes", "config", "coord", "cpu", "data", "def", "dict", "elif", "else", "enumerate", "for", "h", "hasattr", "height", "idx", "if", "i...
layoutlmv3/image_processing_layoutlmv3.py:LayoutLMv3ImageProcessorKwargs
[ -0.00035700088483281434, 0.03187011182308197, -0.02366824448108673, 0.012244215235114098, -0.001860066200606525, 0.03515085577964783, 0.03491651639342308, -0.06045947223901749, 0.018044106662273407, 0.008963468484580517, 0.026948990300297737, 0.03772858530282974, 0.0015012344811111689, 0.0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "apply_ocr", "bool", "class", "ocr_lang", "r", "str", "tesseract_config", "total" ]
layoutlmv3/image_processing_layoutlmv3.py:LayoutLMv3ImageProcessor
[ -0.00027476108516566455, 0.03221972659230232, -0.010721000842750072, 0.0294969342648983, -0.0011770410928875208, 0.026206890121102333, 0.002481713192537427, -0.05581727251410484, 0.00626809848472476, 0.021668901666998863, 0.027681738138198853, 0.04878339171409607, 0.0005069785402156413, 0....
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "Tensors", "TorchvisionBackend", "True", "__init__", "_preprocess", "append", "apply_ocr", "...
smolvlm/image_processing_pil_smolvlm.py:SmolVLMImageProcessorKwargs
[ -0.00023440206132363528, 0.009848465211689472, -0.02736957184970379, 0.008187968283891678, -0.0012954740086570382, 0.01168073806911707, 0.023819543421268463, -0.0480971559882164, 0.01282590813934803, 0.019009828567504883, 0.009104104712605476, -0.0025480040349066257, 0.00003287891377112828, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_image_splitting", "int", "max_image_size", "return_row_col_info", "total" ]
smolvlm/image_processing_pil_smolvlm.py:_make_pixel_mask
[ 0.00006175704038469121, -0.022044653072953224, 0.03295564278960228, -0.008405916392803192, 0, 0.008795593865215778, 0.01725717820227146, -0.09218673408031464, 0.019372574985027313, 0.02839084155857563, 0.012692376039922237, -0.03718643635511398, -0.0011203249450773, -0.028724851086735725, ...
[ "_make_pixel_mask", "def", "dtype", "h", "image", "int", "int64", "mask", "np", "output_size", "return", "shape", "w", "zeros" ]
smolvlm/image_processing_pil_smolvlm.py:_resize_output_size_rescale_to_max_len
[ -0.0001808300439734012, 0.0064607239328324795, 0.05033746361732483, 0.005702291149646044, -0.0006741624674759805, 0.030337311327457428, 0.0005196669371798635, -0.03910142555832863, 0.010449518449604511, 0.04786553606390953, 0.024382209405303, -0.03011259064078331, -0.0002387658751104027, 0...
[ "None", "_resize_output_size_rescale_to_max_len", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "min_len", "return", "width" ]
smolvlm/image_processing_pil_smolvlm.py:_resize_output_size_scale_below_upper_bound
[ -0.00024735828628763556, -0.00746315298601985, 0.054503630846738815, -0.010855494998395443, -0.0011025111889466643, 0.010798956267535686, 0.0151524618268013, -0.024198709055781364, 0.007632770109921694, 0.040255796164274216, 0.01238204911351204, -0.04206504300236702, 0.00004726310362457298, ...
[ "None", "_resize_output_size_scale_below_upper_bound", "and", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "return", "width" ]
smolvlm/image_processing_pil_smolvlm.py:get_max_height_width
[ 0.00006222455704119056, -0.029185494408011436, 0.0441124252974987, 0.029853863641619682, 0.00015229787095449865, -0.0034393209498375654, 0.016263671219348907, -0.015149721875786781, 0.02161063253879547, 0.01336740143597126, 0.02539806254208088, -0.02807154320180416, -0.0002993741072714329, ...
[ "Model_max_height_width", "append", "def", "for", "image", "image_sizes", "images", "images_list", "in", "max", "max_height", "max_width", "return", "shape", "size" ]
smolvlm/image_processing_pil_smolvlm.py:get_num_channels
[ 0, -0.009798076003789902, 0.007827199064195156, 0.04347192496061325, 0.000510316458530724, 0.027479661628603935, 0.011881574988365173, -0.03198452666401863, 0.018807802349328995, 0.0007038848125375807, 0.02714179828763008, -0.00748933432623744, -0.0014851968735456467, 0.004251464270055294,...
[ "Model_num_channels", "No", "ValueError", "batch", "def", "for", "found", "if", "images", "images_list", "in", "raise", "return", "shape", "the" ]
smolvlm/image_processing_pil_smolvlm.py:get_resize_output_image_size
[ -0.00004134767732466571, 0.042117178440093994, 0.04791107773780823, -0.0011072473134845495, -0.00044046156108379364, 0.0076323458924889565, 0.010585005395114422, -0.03565475344657898, 0.021392853930592537, 0.01883016712963581, 0.005431779194623232, -0.025069748982787132, 0.002520902547985315...
[ "MAX_IMAGE_SIZE", "Model_resize_output_image_size", "_resize_output_size_rescale_to_max_len", "_resize_output_size_scale_below_upper_bound", "def", "height", "image", "max_len", "resolution_max_side", "return", "shape", "width" ]
smolvlm/image_processing_pil_smolvlm.py:SmolVLMImageProcessorPil
[ -0.00022048047685530037, 0.02607666701078415, -0.005587857216596603, 0.04425131157040596, -0.0010441954946145415, -0.004430775530636311, -0.003809902584180236, -0.034994661808013916, 0.0034571338910609484, 0.03228539600968361, 0.0053620850667357445, -0.001460462692193687, 0.00019931435235776...
[ "BatchFeature", "CONSTANT", "False", "Got", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "LANCZOS", "MAX_IMAGE_SIZE", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "Padding", "PaddingMode", "PilBackend", "Please", "SizeDict", "True", "...
smolvlm/image_processing_smolvlm.py:SmolVLMImageProcessorKwargs
[ -0.00023440206132363528, 0.009848465211689472, -0.02736957184970379, 0.008187968283891678, -0.0012954740086570382, 0.01168073806911707, 0.023819543421268463, -0.0480971559882164, 0.01282590813934803, 0.019009828567504883, 0.009104104712605476, -0.0025480040349066257, 0.00003287891377112828, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_image_splitting", "int", "max_image_size", "return_row_col_info", "total" ]
smolvlm/image_processing_smolvlm.py:_resize_output_size_rescale_to_max_len
[ -0.0001808300439734012, 0.0064607239328324795, 0.05033746361732483, 0.005702291149646044, -0.0006741624674759805, 0.030337311327457428, 0.0005196669371798635, -0.03910142555832863, 0.010449518449604511, 0.04786553606390953, 0.024382209405303, -0.03011259064078331, -0.0002387658751104027, 0...
[ "None", "_resize_output_size_rescale_to_max_len", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "min_len", "return", "width" ]
smolvlm/image_processing_smolvlm.py:_resize_output_size_scale_below_upper_bound
[ -0.00024735828628763556, -0.00746315298601985, 0.054503630846738815, -0.010855494998395443, -0.0011025111889466643, 0.010798956267535686, 0.0151524618268013, -0.024198709055781364, 0.007632770109921694, 0.040255796164274216, 0.01238204911351204, -0.04206504300236702, 0.00004726310362457298, ...
[ "None", "_resize_output_size_scale_below_upper_bound", "and", "aspect_ratio", "def", "elif", "else", "height", "if", "int", "is", "max", "max_len", "return", "width" ]
smolvlm/image_processing_smolvlm.py:get_resize_output_image_size
[ -0.00004134767732466571, 0.042117178440093994, 0.04791107773780823, -0.0011072473134845495, -0.00044046156108379364, 0.0076323458924889565, 0.010585005395114422, -0.03565475344657898, 0.021392853930592537, 0.01883016712963581, 0.005431779194623232, -0.025069748982787132, 0.002520902547985315...
[ "MAX_IMAGE_SIZE", "Model_resize_output_image_size", "_resize_output_size_rescale_to_max_len", "_resize_output_size_scale_below_upper_bound", "def", "height", "image", "max_len", "resolution_max_side", "return", "shape", "width" ]
smolvlm/image_processing_smolvlm.py:get_max_height_width
[ 0.00006222455704119056, -0.029185494408011436, 0.0441124252974987, 0.029853863641619682, 0.00015229787095449865, -0.0034393209498375654, 0.016263671219348907, -0.015149721875786781, 0.02161063253879547, 0.01336740143597126, 0.02539806254208088, -0.02807154320180416, -0.0002993741072714329, ...
[ "Model_max_height_width", "append", "def", "for", "image", "image_sizes", "images", "images_list", "in", "max", "max_height", "max_width", "return", "shape", "size" ]
smolvlm/image_processing_smolvlm.py:get_num_channels
[ 0, -0.009798076003789902, 0.007827199064195156, 0.04347192496061325, 0.000510316458530724, 0.027479661628603935, 0.011881574988365173, -0.03198452666401863, 0.018807802349328995, 0.0007038848125375807, 0.02714179828763008, -0.00748933432623744, -0.0014851968735456467, 0.004251464270055294,...
[ "Model_num_channels", "No", "ValueError", "batch", "def", "for", "found", "if", "images", "images_list", "in", "raise", "return", "shape", "the" ]
smolvlm/image_processing_smolvlm.py:get_device_from_images
[ 0.000020999696062062867, 0.022404367104172707, -0.019927501678466797, 0.008781610988080502, -0.00017943195416592062, 0.04368288442492485, 0.02600707858800888, -0.06349780410528183, 0.01531152706593275, 0.0036590045783668756, 0.0021109641529619694, -0.003335323417559266, -0.000774020212702453...
[ "Model_device_from_images", "def", "device", "for", "if", "images", "images_list", "in", "return" ]
smolvlm/image_processing_smolvlm.py:SmolVLMImageProcessor
[ -0.00022266505402512848, 0.036048855632543564, -0.000658314093016088, 0.027261948212981224, -0.0009505069465376437, 0.005829776171594858, -0.005266512744128704, -0.026022769510746002, 0.0029852960724383593, 0.036724772304296494, 0.007998339831829071, 0.00811099261045456, 0.001401117653585970...
[ "BatchFeature", "False", "Got", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "LANCZOS", "MAX_IMAGE_SIZE", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "Padding", "Please", "SizeDict", "TorchvisionBackend", "True", "ValueError", "__init__",...
ovis2/image_processing_pil_ovis2.py:Ovis2ImageProcessorKwargs
[ -0.00008920527034206316, 0.024419279769062996, -0.028150003403425217, -0.011418274603784084, -0.0005723268841393292, 0.013227109797298908, 0.026341168209910393, -0.023967070505023003, 0.013001005165278912, 0.0014414158649742603, 0.008705020882189274, 0.009044177830219269, -0.0015191392740234...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "crop_to_patches", "int", "max_patches", "min_patches", "total", "use_covering_area_grid" ]
ovis2/image_processing_pil_ovis2.py:get_all_supported_aspect_ratios
[ -0.00010360487794969231, -0.004270628094673157, 0.037087034434080124, 0.013430002145469189, -0.0004442717763595283, 0.04427967220544815, 0.0011940901167690754, -0.02652284875512123, 0.01663297228515148, 0.01764443702995777, 0.01281188428401947, -0.030343936756253242, -0.0006286533316597342, ...
[ "Model_all_supported_aspect_ratios", "and", "aspect_ratios", "def", "for", "height", "if", "in", "key", "lambda", "max_image_tiles", "min_image_tiles", "range", "return", "sorted", "width", "x" ]
ovis2/image_processing_pil_ovis2.py:compute_patch_covering_area
[ 0.00008487388549838215, -0.027159642428159714, 0.006082628387957811, -0.012561334297060966, 0.00036425041616894305, 0.018445923924446106, 0.05047166720032692, -0.0006966731161810458, 0.02003023587167263, 0.03394955396652222, -0.012391586787998676, -0.005035850219428539, -0.003366664052009582...
[ "Model_patch_covering_area", "def", "h", "if", "left", "lower", "max", "min", "return", "right", "side", "upper", "w" ]
ovis2/image_processing_pil_ovis2.py:split_image_into_grid
[ -0.000022466545487986878, -0.061049386858940125, -0.006340607535094023, 0.009202297776937485, -0.0001920067734317854, -0.0015079984441399574, 0.03209581598639488, -0.03007580153644085, 0.023005742579698563, 0.0189657099545002, 0.004909762181341648, -0.01919015683233738, -0.000540073844604194...
[ "Model_image_into_grid", "col", "col_width", "def", "else", "for", "grid", "h", "if", "in", "int", "range", "return", "row", "row_height", "w" ]
ovis2/image_processing_pil_ovis2.py:get_min_tile_covering_grid
[ -0.00005613606481347233, 0.010248965583741665, 0.0049852305091917515, -0.0023115873336791992, -0.0002854671038221568, 0.01559625193476677, 0.012031394056975842, -0.02773904800415039, 0.010360367596149445, -0.0023812134750187397, -0.003453455865383148, -0.02350577898323536, -0.003397754859179...
[ "Model_all_supported_aspect_ratios", "Model_min_tile_covering_grid", "append", "candidate_tile_grids", "compute_patch_covering_area", "covering_threshold", "def", "evaluated_grids", "for", "if", "image_area", "image_height", "image_size", "image_width", "in", "int", "key", "lambda"...
ovis2/image_processing_pil_ovis2.py:get_optimal_tiled_canvas
[ -0.0002890990290325135, 0.01067006029188633, 0.01634562388062477, 0.016799669712781906, -0.0011351128341630101, 0.02134012058377266, 0.01277001854032278, -0.04858282580971718, 0.009591703303158283, 0.018502337858080864, -0.013280819170176983, -0.032464224845170975, -0.003944517113268375, 0...
[ "Model_all_supported_aspect_ratios", "Model_optimal_tiled_canvas", "abs", "area", "aspect_ratio", "best_grid", "best_ratio_diff", "def", "elif", "float", "for", "grid", "grid_aspect_ratio", "if", "in", "inf", "int", "max_image_tiles", "min_image_tiles", "original_height", "or...
ovis2/image_processing_pil_ovis2.py:Ovis2ImageProcessorPil
[ -0.00011004837870132178, 0.022582625970244408, -0.025824686512351036, 0.03130264952778816, -0.0006253542960621417, -0.015315939672291279, -0.007546174339950085, -0.01710466295480728, 0.0022079546470195055, 0.015986710786819458, 0.007126942742615938, 0.013527216389775276, -0.00255731469951570...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "SizeDict", "True", "_", "__init__", "_preprocess", "and", "append", "auto_...
ovis2/image_processing_ovis2.py:get_all_supported_aspect_ratios
[ -0.00010360487794969231, -0.004270628094673157, 0.037087034434080124, 0.013430002145469189, -0.0004442717763595283, 0.04427967220544815, 0.0011940901167690754, -0.02652284875512123, 0.01663297228515148, 0.01764443702995777, 0.01281188428401947, -0.030343936756253242, -0.0006286533316597342, ...
[ "Model_all_supported_aspect_ratios", "and", "aspect_ratios", "def", "for", "height", "if", "in", "key", "lambda", "max_image_tiles", "min_image_tiles", "range", "return", "sorted", "width", "x" ]
ovis2/image_processing_ovis2.py:get_optimal_tiled_canvas
[ -0.0002890990290325135, 0.01067006029188633, 0.01634562388062477, 0.016799669712781906, -0.0011351128341630101, 0.02134012058377266, 0.01277001854032278, -0.04858282580971718, 0.009591703303158283, 0.018502337858080864, -0.013280819170176983, -0.032464224845170975, -0.003944517113268375, 0...
[ "Model_all_supported_aspect_ratios", "Model_optimal_tiled_canvas", "abs", "area", "aspect_ratio", "best_grid", "best_ratio_diff", "def", "elif", "float", "for", "grid", "grid_aspect_ratio", "if", "in", "inf", "int", "max_image_tiles", "min_image_tiles", "original_height", "or...
ovis2/image_processing_ovis2.py:compute_patch_covering_area
[ 0.00008487388549838215, -0.027159642428159714, 0.006082628387957811, -0.012561334297060966, 0.00036425041616894305, 0.018445923924446106, 0.05047166720032692, -0.0006966731161810458, 0.02003023587167263, 0.03394955396652222, -0.012391586787998676, -0.005035850219428539, -0.003366664052009582...
[ "Model_patch_covering_area", "def", "h", "if", "left", "lower", "max", "min", "return", "right", "side", "upper", "w" ]
ovis2/image_processing_ovis2.py:split_image_into_grid
[ -0.000022466545487986878, -0.061049386858940125, -0.006340607535094023, 0.009202297776937485, -0.0001920067734317854, -0.0015079984441399574, 0.03209581598639488, -0.03007580153644085, 0.023005742579698563, 0.0189657099545002, 0.004909762181341648, -0.01919015683233738, -0.000540073844604194...
[ "Model_image_into_grid", "col", "col_width", "def", "else", "for", "grid", "h", "if", "in", "int", "range", "return", "row", "row_height", "w" ]
ovis2/image_processing_ovis2.py:get_min_tile_covering_grid
[ -0.00005613606481347233, 0.010248965583741665, 0.0049852305091917515, -0.0023115873336791992, -0.0002854671038221568, 0.01559625193476677, 0.012031394056975842, -0.02773904800415039, 0.010360367596149445, -0.0023812134750187397, -0.003453455865383148, -0.02350577898323536, -0.003397754859179...
[ "Model_all_supported_aspect_ratios", "Model_min_tile_covering_grid", "append", "candidate_tile_grids", "compute_patch_covering_area", "covering_threshold", "def", "evaluated_grids", "for", "if", "image_area", "image_height", "image_size", "image_width", "in", "int", "key", "lambda"...
ovis2/image_processing_ovis2.py:Ovis2ImageProcessorKwargs
[ -0.00008920527034206316, 0.024419279769062996, -0.028150003403425217, -0.011418274603784084, -0.0005723268841393292, 0.013227109797298908, 0.026341168209910393, -0.023967070505023003, 0.013001005165278912, 0.0014414158649742603, 0.008705020882189274, 0.009044177830219269, -0.0015191392740234...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "crop_to_patches", "int", "max_patches", "min_patches", "total", "use_covering_area_grid" ]
ovis2/image_processing_ovis2.py:Ovis2ImageProcessor
[ -0.00011261113832006231, 0.031733643263578415, -0.021118516102433205, 0.02368849515914917, -0.0005342481890693307, -0.004413658287376165, -0.013687927275896072, -0.01145316381007433, 0.0005028218147344887, 0.01631377451121807, 0.012402938678860664, 0.01687246561050415, -0.0010196109069511294...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "_", "__init__", "_preprocess", "and", "append", "auto_docstring", "box", "cent...
glm46v/image_processing_pil_glm46v.py:Glm46VImageProcessorKwargs
[ -0.0000708146981196478, 0.018578961491584778, -0.029726339504122734, -0.0018015963723883033, -0.00030261187930591404, 0.011485177092254162, 0.03940992057323456, -0.037157922983169556, 0.014187571592628956, 0.0071500856429338455, 0.0019282711436972022, 0.012104475870728493, -0.002561644883826...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "merge_size", "patch_size", "temporal_patch_size", "total" ]
glm46v/image_processing_pil_glm46v.py:smart_resize
[ -0.00013493926962837577, 0.01885269209742546, 0.011684153228998184, -0.00925701018422842, -0.0005750353448092937, 0.04312412068247795, 0.03251242637634277, -0.05531628057360649, 0.023029636591672897, 0.03273820877075195, -0.008749003522098064, -0.033641330897808075, 0.0016157435020431876, ...
[ "Model_resize", "ValueError", "absolute", "aspect", "be", "beta", "ceil", "def", "elif", "f", "factor", "floor", "got", "h_bar", "height", "if", "int", "larger", "math", "max", "max_pixels", "min", "min_pixels", "must", "num_frames", "or", "raise", "ratio", "r...
glm46v/image_processing_pil_glm46v.py:Glm46VImageProcessorPil
[ -0.00014745636144652963, 0.024011829867959023, -0.014853829517960548, 0.04087595269083977, -0.0005270038382150233, -0.008487902581691742, 0, -0.04579000174999237, 0.004439396318048239, 0.025687074288725853, 0.012285121716558933, 0.011950073763728142, -0.0002600118168629706, -0.010554037056...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "SizeDict", "True", "__init__", "_preprocess", "_standardize_kwargs", "append", "array", "asarray", "auto_...
glm46v/image_processing_glm46v.py:Glm46VImageProcessorKwargs
[ -0.0000708146981196478, 0.018578961491584778, -0.029726339504122734, -0.0018015963723883033, -0.00030261187930591404, 0.011485177092254162, 0.03940992057323456, -0.037157922983169556, 0.014187571592628956, 0.0071500856429338455, 0.0019282711436972022, 0.012104475870728493, -0.002561644883826...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "merge_size", "patch_size", "temporal_patch_size", "total" ]
glm46v/image_processing_glm46v.py:smart_resize
[ -0.00013493926962837577, 0.01885269209742546, 0.011684153228998184, -0.00925701018422842, -0.0005750353448092937, 0.04312412068247795, 0.03251242637634277, -0.05531628057360649, 0.023029636591672897, 0.03273820877075195, -0.008749003522098064, -0.033641330897808075, 0.0016157435020431876, ...
[ "Model_resize", "ValueError", "absolute", "aspect", "be", "beta", "ceil", "def", "elif", "f", "factor", "floor", "got", "h_bar", "height", "if", "int", "larger", "math", "max", "max_pixels", "min", "min_pixels", "must", "num_frames", "or", "raise", "ratio", "r...
glm46v/image_processing_glm46v.py:Glm46VImageProcessor
[ -0.00015985372010618448, 0.034884799271821976, -0.0051153190433979034, 0.031754110008478165, -0.0004210354818496853, 0.002585612004622817, -0.009895207360386848, -0.0411461740732193, 0.0064290896989405155, 0.027952563017606735, 0.01878412254154682, 0.019007742404937744, 0.0017190825892612338...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "_standardize_kwargs", "auto_docstring", "batch_size", "...
siglip2/image_processing_siglip2.py:Siglip2ImageProcessorKwargs
[ -0.00016264122677966952, 0.02184191718697548, -0.028439994901418686, 0.0011091597843915224, -0.0009953997796401381, 0.017177756875753403, 0.040498554706573486, -0.023662075400352478, 0.018315356224775314, 0.01040903851389885, 0.0038109594024717808, 0.004379759076982737, 0.0003946049255318939...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_num_patches", "patch_size", "r", "total" ]
siglip2/image_processing_siglip2.py:get_image_size_for_max_num_patches
[ -0.00006659436621703207, 0.011811141856014729, 0.031199241057038307, 0.002924928907305002, -0.00037780331331305206, -0.0035377710592001677, 0.02852502092719078, -0.014485361985862255, 0.008245513774454594, 0.0022006607614457607, 0.009638337418437004, -0.03654768317937851, -0.0040391874499619...
[ "Model_image_size_for_max_num_patches", "Model_scaled_image_size", "ceil", "def", "else", "eps", "if", "image_height", "image_width", "int", "math", "max", "max_num_patches", "num_patches", "patch_size", "return", "scale", "scale_max", "scale_min", "scaled_size", "size", "t...
siglip2/image_processing_siglip2.py:convert_image_to_patches
[ 0.0001692940859356895, 0.007487939670681953, 0.014975879341363907, -0.0030632480047643185, 0.0009927192004397511, -0.028817221522331238, 0.014295157045125961, -0.015202785842120647, 0.017471859231591225, 0.002708705374971032, -0.009416650980710983, -0.018946755677461624, -0.00347451725974679...
[ "Model_image_to_patches", "def", "image", "image_height", "image_width", "num_channels", "num_patches_height", "num_patches_width", "patch_size", "patched_image", "permute", "reshape", "return", "shape" ]
siglip2/image_processing_siglip2.py:pad_along_first_dim
[ -0.00008803996024653316, -0.004176860675215721, 0.03857288882136345, -0.008185526356101036, -0.00018746816203929484, 0.04597350209951401, 0.0011423294199630618, -0.05920489877462387, 0.004429154563695192, 0.01861366257071495, -0.0029994905926287174, -0.032742105424404144, 0.00180810422170907...
[ "Model", "Model_along_first_dim", "Model_value", "Modelding", "Modelding_length", "constant", "current_length", "def", "dtype", "functional", "if", "int32", "mask", "mode", "ndim", "nn", "ones", "return", "shape", "target_length", "tensor", "torch", "value" ]
siglip2/image_processing_siglip2.py:Siglip2ImageProcessor
[ -0.0001537166244816035, 0.04605909064412117, -0.010564524680376053, 0.021352635696530342, -0.0006043858011253178, 0.005449953023344278, 0.0002724976511672139, -0.02325313352048397, 0.004499704577028751, 0.0290664155036211, 0.012912196107208729, 0.01621011644601822, -0.000016703581422916614, ...
[ "BILINEAR", "BatchFeature", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "_validate_preprocess_kwargs", "append", "auto_docstring", "batch_feature", "class", "convert_image_to_pa...
siglip2/image_processing_pil_siglip2.py:convert_image_to_patches
[ 0.00020087423035874963, 0.0035268536303192377, 0.02127489075064659, 0.0014861137606203556, 0.0010808099759742618, -0.02832859940826893, 0.013822990469634533, -0.01592772640287876, 0.01717919111251831, -0.002915342804044485, -0.005005856975913048, -0.012799065560102463, -0.003398862900212407,...
[ "Model_image_to_patches", "def", "image", "image_height", "image_width", "num_channels", "num_patches_height", "num_patches_width", "patch_size", "patched_image", "reshape", "return", "shape", "transpose" ]
siglip2/image_processing_pil_siglip2.py:pad_along_first_dim
[ -0.00006705903797410429, -0.0068724555894732475, 0.035231851041316986, 0.019411180168390274, 0, 0.03590507432818413, -0.012061860412359238, -0.04173964634537697, 0.006479743402451277, 0.0018934315303340554, 0, -0.036353886127471924, 0.002033685799688101, 0.02165524661540985, -0.001332414...
[ "Model", "Model_along_first_dim", "Model_value", "Modelding_length", "Modeldings", "array", "constant", "constant_values", "current_length", "def", "dtype", "if", "int32", "mask", "mode", "ndim", "np", "ones", "return", "shape", "target_length" ]
siglip2/image_processing_pil_siglip2.py:Siglip2ImageProcessorKwargs
[ -0.00016264122677966952, 0.02184191718697548, -0.028439994901418686, 0.0011091597843915224, -0.0009953997796401381, 0.017177756875753403, 0.040498554706573486, -0.023662075400352478, 0.018315356224775314, 0.01040903851389885, 0.0038109594024717808, 0.004379759076982737, 0.0003946049255318939...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_num_patches", "patch_size", "r", "total" ]
siglip2/image_processing_pil_siglip2.py:get_image_size_for_max_num_patches
[ -0.00006659436621703207, 0.011811141856014729, 0.031199241057038307, 0.002924928907305002, -0.00037780331331305206, -0.0035377710592001677, 0.02852502092719078, -0.014485361985862255, 0.008245513774454594, 0.0022006607614457607, 0.009638337418437004, -0.03654768317937851, -0.0040391874499619...
[ "Model_image_size_for_max_num_patches", "Model_scaled_image_size", "ceil", "def", "else", "eps", "if", "image_height", "image_width", "int", "math", "max", "max_num_patches", "num_patches", "patch_size", "return", "scale", "scale_max", "scale_min", "scaled_size", "size", "t...