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
gemma4/image_processing_pil_gemma4.py:get_aspect_ratio_preserving_size
[ -0.00012554493150673807, 0.00836966186761856, 0.02946120873093605, -0.007253706920892, -0.0003888405335601419, 0.004491718485951424, 0.013893638737499714, -0.020087188109755516, 0.012833481654524803, 0.00903923436999321, 0, -0.04932520538568497, -0.0024829995818436146, 0.007476897910237312...
[ "Attempting", "Model_aspect_ratio_preserving_size", "Resized", "Resizing", "ValueError", "a", "and", "be", "but", "by", "def", "divisible", "elif", "exceeds", "f", "factor", "floor", "height", "ideal_height", "ideal_width", "if", "image", "int", "math", "max_patches",...
gemma4/image_processing_pil_gemma4.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" ]
gemma4/image_processing_pil_gemma4.py:pad_along_first_dim
[ -0.0001657665561651811, 0.0031460376922041178, 0.01794511266052723, 0.017832249402999878, -0.0006630662246607244, 0.04559638351202011, -0.0017846357077360153, -0.04446776211261749, 0.008464675396680832, 0.00578419491648674, -0.0034423014149069786, -0.005502039100974798, 0.0021726000122725964...
[ "Model", "Model_along_first_dim", "Modelding_length", "Modeldings", "constant", "constant_values", "current_length", "def", "if", "image", "mode", "ndim", "np", "pos_Modeldings", "positions", "return", "shape", "target_length" ]
gemma4/image_processing_pil_gemma4.py:Gemma4ImageProcessorKwargs
[ -0.0000922993422136642, 0.02182835526764393, -0.014514731243252754, -0.004106881096959114, -0.0004957793280482292, 0.006216580513864756, 0.04028119146823883, -0.017440181225538254, 0.012095609679818153, -0.006779167335480452, 0.012658195570111275, 0.0018284061225131154, -0.001659630099311471...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_soft_tokens", "patch_size", "pooling_kernel_size", "total" ]
gemma4/image_processing_pil_gemma4.py:Gemma4ImageProcessorPil
[ -0.00024230092822108418, 0.029656218364834785, -0.005885967053472996, 0.015394067391753197, -0.0011319167679175735, -0.011715337634086609, 0.004867241717875004, -0.023770250380039215, 0.003876814851537347, 0.017884284257888794, 0.014262150973081589, 0.007640437688678503, -0.00122388498857617...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "True", "_SUPPORTED_SOFT_TOKENS", "__init__", "_preprocess", "_validate_preprocess_kwargs", "and", "append", "arange", "aspect_ratio_preserving_resiz...
siglip/image_processing_pil_siglip.py:SiglipImageProcessorPil
[ -0.00031617010245099664, 0.017691072076559067, 0.004422768019139767, 0.04370735585689545, -0.0016766049666330218, 0.018500467762351036, 0.0125456303358078, -0.03861972689628601, 0.011158094741404057, 0.046482425183057785, 0.016534794121980667, 0.0017344189109280705, -0.001308040926232934, ...
[ "BICUBIC", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorPil", "PILImageResampling", "PilBackend", "True", "class", "default_to_square", "do_convert_rgb", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", ...
siglip/image_processing_siglip.py:SiglipImageProcessor
[ -0.00032496018684469163, 0.03420025482773781, 0.013460573740303516, 0.03235159069299698, -0.0016248009633272886, 0.03604891896247864, 0.012074076570570469, -0.03258267417550087, 0.012189618311822414, 0.04667872563004494, 0.024494776502251625, 0.006210350431501865, -0.0005235470016486943, 0...
[ "BICUBIC", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "PILImageResampling", "TorchvisionBackend", "True", "class", "default_to_square", "do_convert_rgb", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resampl...
pvt/image_processing_pvt.py:PvtImageProcessor
[ -0.00031087419483810663, 0.04002191126346588, 0.00741785392165184, 0.03703176602721214, -0.0015525741036981344, 0.029441405087709427, 0.013858160935342312, -0.03082147054374218, 0.009890471585094929, 0.04577218368649483, 0.016100767999887466, 0.0029613913502544165, -0.0005714335129596293, ...
[ "BICUBIC", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessor", "PILImageResampling", "TorchvisionBackend", "True", "class", "default_to_square", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
pvt/image_processing_pil_pvt.py:PvtImageProcessorPil
[ -0.00031166794360615313, 0.02398221753537655, -0.00011439834634074941, 0.0479644350707531, -0.0016069815028458834, 0.013086450286209583, 0.015795979648828506, -0.03597332537174225, 0.0099733741953969, 0.04335246980190277, 0.01078046765178442, -0.0014916823711246252, -0.001268290332518518, ...
[ "BICUBIC", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ModelImageProcessorPil", "PILImageResampling", "PilBackend", "True", "class", "default_to_square", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
pp_chart2table/image_processing_pp_chart2table.py:PPChart2TableImageProcessor
[ -0.0002838578075170517, 0.04211943596601486, 0.016431208699941635, 0.03563952445983887, -0.001518729724921286, 0.04096230864524841, 0.039110906422138214, -0.02198542095720768, 0.009893438778817654, 0.03170529007911682, 0.02129114419221878, -0.011050567030906677, -0.002777105662971735, 0.00...
[ "Model", "TorchvisionBackend", "True", "class", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
pp_chart2table/image_processing_pil_pp_chart2table.py:PPChart2TableImageProcessorPil
[ -0.0003611136053223163, 0.03355240449309349, 0.0049859341233968735, 0.0436415895819664, -0.0018623929936438799, 0.035664092749357224, 0.03707188740372658, -0.025692224502563477, 0.010265158489346504, 0.035664092749357224, 0.013491351157426834, -0.010793080553412437, -0.001532441470772028, ...
[ "Model", "PilBackend", "True", "class", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "image_std", "resample", "size", "width" ]
pi0/image_processing_pi0.py:PI0ImageProcessor
[ -0.00028759639826603234, 0.023467866703867912, 0.014092223718762398, 0.03129049018025398, -0.0014595517423003912, 0.039113111793994904, 0.03129049018025398, -0.0319807194173336, 0.011791452765464783, 0.028069408610463142, 0.020706940442323685, -0.00885796919465065, -0.0016752490773797035, ...
[ "BICUBIC", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "Model", "PILImageResampling", "TorchvisionBackend", "True", "class", "default_to_square", "do_convert_rgb", "do_normalize", "do_pad", "do_rescale", "do_resize", "height", "image_mean", "image_std", "max_heigh...
video_llama_3/image_processing_pil_video_llama_3.py:VideoLlama3ImageProcessorKwargs
[ -0.00011579359124880284, 0.0236466433852911, -0.028738025575876236, 0.004723671358078718, -0.0006894580437801778, 0.02794603258371353, 0.042767614126205444, -0.05385551229119301, 0.024664919823408127, 0.023759784176945686, 0.009447342716157436, -0.00364882405847311, 0.0021496948320418596, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_pixels", "merge_size", "min_pixels", "patch_size", "r", "temporal_patch_size", "total" ]
video_llama_3/image_processing_pil_video_llama_3.py:smart_resize
[ -0.00008191712549887598, 0.010708484798669815, 0.04662653058767319, -0.023313265293836594, -0.0002013069752138108, 0.03547185659408569, 0.025878839194774628, -0.031456176191568375, 0.017624381929636, 0.029671426862478256, -0.00513114919885993, -0.033240921795368195, 0.00253768777474761, 0....
[ "Model_resize", "ValueError", "absolute", "aspect", "be", "beta", "ceil", "def", "elif", "f", "factor", "floor", "got", "h_bar", "height", "if", "math", "max", "max_pixels", "min", "min_pixels", "must", "raise", "ratio", "return", "round", "smaller", "sqrt", "...
video_llama_3/image_processing_pil_video_llama_3.py:VideoLlama3ImageProcessorPil
[ -0.0001731857337290421, 0.02149602212011814, -0.017689434811472893, 0.032691869884729385, -0.0007417247397825122, -0.01220347173511982, 0.0034287276212126017, -0.03851370885968208, 0.004226431716233492, 0.029109198600053787, 0.004506327677518129, 0.0017143638106063008, 0.00003498701698845252...
[ "BICUBIC", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PilBackend", "SizeDict", "True", "__init__", "_preprocess", "_standardize_kwargs", "all_grids", "all_patches", ...
video_llama_3/image_processing_video_llama_3.py:VideoLlama3ImageProcessorKwargs
[ -0.00011579359124880284, 0.0236466433852911, -0.028738025575876236, 0.004723671358078718, -0.0006894580437801778, 0.02794603258371353, 0.042767614126205444, -0.05385551229119301, 0.024664919823408127, 0.023759784176945686, 0.009447342716157436, -0.00364882405847311, 0.0021496948320418596, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_pixels", "merge_size", "min_pixels", "patch_size", "r", "temporal_patch_size", "total" ]
video_llama_3/image_processing_video_llama_3.py:smart_resize
[ -0.00008191712549887598, 0.010708484798669815, 0.04662653058767319, -0.023313265293836594, -0.0002013069752138108, 0.03547185659408569, 0.025878839194774628, -0.031456176191568375, 0.017624381929636, 0.029671426862478256, -0.00513114919885993, -0.033240921795368195, 0.00253768777474761, 0....
[ "Model_resize", "ValueError", "absolute", "aspect", "be", "beta", "ceil", "def", "elif", "f", "factor", "floor", "got", "h_bar", "height", "if", "math", "max", "max_pixels", "min", "min_pixels", "must", "raise", "ratio", "return", "round", "smaller", "sqrt", "...
video_llama_3/image_processing_video_llama_3.py:VideoLlama3ImageProcessor
[ -0.000123638630611822, 0.03276597708463669, -0.005516720935702324, 0.019169211387634277, -0.00031693282653577626, 0.0021871847566217184, 0.0011423513060435653, -0.03410336375236511, 0.00596251618117094, 0.031428590416908264, 0.006324725225567818, 0.005656031891703606, 0.0008532806532457471, ...
[ "BICUBIC", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "_standardize_kwargs", "and", "auto_docstring"...
sam3/image_processing_sam3.py:Sam3ImageProcessorKwargs
[ -0.00011428739526309073, 0.01037623267620802, -0.023814303800463676, -0.014118480496108532, -0.0008788612321950495, 0.03334002569317818, 0.029257573187351227, -0.0598759651184082, 0.022226683795452118, 0.03175240382552147, -0.00479121133685112, 0.006322130560874939, 0.002112101996317506, 0...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "mask_size", "r", "total" ]
sam3/image_processing_sam3.py:_compute_stability_score
[ -0.00007422093767672777, 0.008713274262845516, 0.013997454196214676, -0.03552767634391785, -0.00018181934137828648, 0.03148021921515465, -0.0025718214455991983, -0.014615815132856369, 0.015290391631424427, 0.011242935433983803, 0.03440338000655174, 0.024734457954764366, -0.000948622648138552...
[ "_compute_stability_score", "def", "dtype", "int16", "int32", "intersections", "mask_threshold", "masks", "return", "stability_score_offset", "stability_scores", "sum", "torch", "unions" ]
sam3/image_processing_sam3.py:_batched_mask_to_box
[ -0.00005730432167183608, -0.02777203731238842, 0.009742609225213528, -0.028667908161878586, -0.0002922083076555282, 0.030235685408115387, -0.011422369629144669, -0.07480532675981522, 0.01119840145111084, 0.00879074539989233, -0.006859021261334419, 0.018701331689953804, -0.005571204703301191,...
[ "None", "_", "_batched_mask_to_box", "arange", "bottom_edges", "def", "device", "dim", "empty_filter", "height", "if", "in_height", "in_height_coords", "in_width", "in_width_coords", "left_edges", "masks", "max", "min", "numel", "out", "reshape", "return", "right_edges"...
sam3/image_processing_sam3.py:_is_box_near_crop_edge
[ -0.0002852178877219558, -0.01790953055024147, 0.020090818405151367, -0.04270734265446663, -0.001413532649166882, 0.03421179577708244, -0.011480468325316906, -0.0596984326839447, 0.003731152042746544, 0.03237491846084595, -0.00019911436538677663, 0.03122687339782715, 0.001499636098742485, -...
[ "None", "_", "_is_box_near_crop_edge", "any", "as_tensor", "atol", "boxes", "crop_box", "crop_box_torch", "def", "device", "dim", "dtype", "float", "if", "isclose", "left", "len", "logical_and", "near_crop_edge", "near_image_edge", "offset", "orig_box", "orig_box_torch"...
sam3/image_processing_sam3.py:_pad_masks
[ -0.000021583815396297723, -0.025450589135289192, 0.009990472346544266, -0.03884563595056534, -0.00018487955094315112, 0.04130139574408531, -0.02946910262107849, -0.07501226663589478, 0.015627555549144745, 0.022771580144762993, -0.02132044918835163, 0.015292678959667683, 0.0000610451388638466...
[ "_pad_masks", "and", "bottom", "crop_box", "def", "functional", "if", "left", "masks", "nn", "orig_height", "orig_width", "pad", "pad_x", "pad_y", "return", "right", "top", "torch", "value" ]
sam3/image_processing_sam3.py:_generate_crop_boxes
[ -0.00032523489790037274, 0.017114583402872086, 0.004394284915179014, -0.013645411469042301, -0.0015322178369387984, 0.046718187630176544, 0.003989548422396183, -0.04486796259880066, 0.0045966533944010735, 0.021046312525868416, -0.0029632514342665672, -0.02983488142490387, -0.0019947742111980...
[ "Only", "ValueError", "_build_point_grid", "_generate_crop_boxes", "_generate_crop_images", "_generate_per_layer_crops", "allowed", "append", "crop", "crop_boxes", "crop_n_layers", "crop_n_points_downscale_factor", "cropped_images", "def", "dtype", "float", "for", "generation", "...
sam3/image_processing_sam3.py:_generate_per_layer_crops
[ -0.00018473531235940754, 0.008202601224184036, 0.012501896359026432, 0, -0.000558625441044569, 0.043219223618507385, -0.008994576521217823, -0.04208783060312271, 0.005260978825390339, 0.025908907875418663, 0.011823059991002083, -0.03643086552619934, -0.0005055913934484124, -0.0400513224303...
[ "_generate_per_layer_crops", "append", "box", "ceil", "crop_box_x0", "crop_box_y0", "crop_boxes", "crop_height", "crop_n_layers", "crop_width", "def", "for", "i", "i_layer", "im_height", "im_width", "in", "int", "layer_idxs", "left", "math", "min", "n_crops_per_side", "...
sam3/image_processing_sam3.py:_build_point_grid
[ -0.000060851580201415345, -0.014910375699400902, -0.013853296637535095, 0.0181372482329607, -0.00017386165563948452, 0.015466732904314995, 0.043840955942869186, -0.05519064515829086, 0.012462403625249863, 0.05786116048693657, -0.0027817864902317524, -0.02614879421889782, -0.00293478486128151...
[ "None", "_build_point_grid", "def", "dim", "linspace", "n_per_side", "offset", "points", "points_one_side", "points_x", "points_y", "reshape", "return", "stack", "tile", "torch" ]
sam3/image_processing_sam3.py:_generate_crop_images
[ -0.00022432254627346992, 0.01952049508690834, -0.008625335060060024, -0.013448713347315788, -0.0011490988545119762, 0.009419773705303669, -0.00783089641481638, -0.04176478087902069, 0.005192224867641926, 0.02757837437093258, -0.00817137025296688, -0.0011136329267174006, 0.0006135620642453432...
[ "None", "_generate_crop_images", "_normalize_coordinates", "append", "bottom", "crop_box", "crop_boxes", "cropped_im", "cropped_im_size", "cropped_images", "def", "dims", "enumerate", "flip", "for", "i", "image", "in", "input_data_format", "layer_idxs", "left", "normalized_...
sam3/image_processing_sam3.py:_normalize_coordinates
[ -0.00018344115233048797, 0.03100769780576229, 0.029659537598490715, -0.009774165228009224, -0.0008706871303729713, -0.005167949479073286, 0.01224579382687807, -0.05842030048370361, 0.012807527557015419, 0.05931907519698143, 0, 0.005224123131483793, 0.0004704519233200699, 0.0012287923600524...
[ "False", "_normalize_coordinates", "coords", "deepcopy", "def", "float", "if", "int", "is_bounding_box", "max", "new_height", "new_width", "old_height", "old_width", "original_size", "reshape", "return", "scale", "target_size" ]
sam3/image_processing_sam3.py:_rle_to_mask
[ -0.00014648611249867827, -0.030454907566308975, 0.009431930258870125, 0.005909161176532507, -0.000479409092804417, 0.06363712251186371, -0.018750222399830818, -0.07909184694290161, 0.01465926505625248, -0.009886480867862701, 0.021363889798521996, -0.021136615425348282, 0.001001432305201888, ...
[ "Any", "False", "_rle_to_mask", "bool", "count", "counts", "def", "dtype", "empty", "for", "height", "idx", "in", "mask", "not", "parity", "reshape", "return", "rle", "size", "torch", "transpose", "width" ]
sam3/image_processing_sam3.py:_post_process_for_mask_generation
[ -0.0001770376111380756, 0.017625078558921814, -0.01361937914043665, 0.010929837822914124, -0.0009442006121389568, 0.06271781027317047, -0.03662354126572609, -0.054935310035943985, 0.01602279767394066, -0.008755315095186234, 0.007839726284146309, 0.021516328677535057, 0.0014019948430359364, ...
[ "_post_process_for_mask_generation", "_rle_to_mask", "amg_crops_nms_thresh", "batched_nms", "boxes", "def", "float", "for", "i", "idxs", "in", "iou_scores", "iou_threshold", "keep_by_nms", "mask_boxes", "masks", "return", "rle", "rle_masks", "scores", "shape", "torch", "z...
sam3/image_processing_sam3.py:_mask_to_rle
[ 0, -0.02436734549701214, 0.015342402271926403, 0.003976615145802498, 0, 0.05144217237830162, -0.023690475150942802, -0.06362584233283997, 0.01455271989107132, 0.0000850495052873157, 0.04151473566889763, -0.0021434237714856863, 0.0014665531925857067, -0.02481859177350998, 0.00345486076548...
[ "_mask_to_rle", "append", "batch_size", "btw_idxs", "change_indices", "continue", "counts", "cur_idxs", "def", "diff", "else", "flatten", "for", "height", "i", "if", "in", "input_mask", "item", "len", "nonzero", "out", "permute", "range", "return", "shape", "size"...
sam3/image_processing_sam3.py:_scale_boxes
[ -0.000020595505702658556, -0.008943840861320496, 0.005244845058768988, -0.023518988862633705, -0.0001759783481247723, 0.00999280996620655, -0.0007108144927769899, -0.04372544586658478, 0.005576098337769508, 0.037983719259500504, -0.04041291028261185, -0.020206455141305923, -0.000489978934638...
[ "Tensor", "_scale_boxes", "boxes", "def", "device", "dim", "elif", "else", "for", "i", "if", "image_height", "image_width", "in", "isinstance", "list", "or", "return", "scale_factor", "stack", "target_sizes", "tensor", "to", "torch", "tuple", "unbind", "unsqueeze"...
sam3/image_processing_sam3.py:Sam3ImageProcessor
[ -0.00025587156414985657, 0.04480595141649246, -0.020810885354876518, 0.010519163683056831, -0.0011514220386743546, 0.03661806136369705, -0.0012651426950469613, -0.03752782940864563, -0.003667492186650634, 0.027406686916947365, 0.006567369680851698, 0.028316451236605644, -0.001172744669020176...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "F", "FIRST", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "Input", "Make", "ModelImageProcessor", "ModelImageProcessorKwargs", "Modele", "NEAREST", "None", "PILImageReModelpling", "Semantic", "SemanticSegmentationPostP...
pp_ocrv5_server_rec/image_processing_pp_ocrv5_server_rec.py:PPOCRV5ServerRecImageProcessorKwargs
[ -0.00020953181956429034, -0.009999725967645645, 0.002225372474640608, 0.006502711679786444, -0.0012571909464895725, 0.0418485626578331, 0.04970961809158325, -0.03375630080699921, 0.013988055288791656, 0.022542733699083328, 0.023814376443624496, 0.004797556437551975, -0.0019363630563020706, ...
[ "False", "ImagesKwargs", "Model", "character_list", "class", "int", "max_image_width", "r", "str", "total" ]
pp_ocrv5_server_rec/image_processing_pp_ocrv5_server_rec.py:PPOCRV5ServerRecImageProcessor
[ -0.00035595311783254147, 0.01977436989545822, -0.004567763768136501, 0.03237908333539963, -0.001467177295126021, 0.026597104966640472, 0.015033146366477013, -0.0656832829117775, 0.0017201388254761696, 0.010870121419429779, 0.03376676142215729, 0.025787627324461937, -0.0010841211769729853, ...
[ "BILINEAR", "BatchFeature", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "Model", "ModelKwargs", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "_preprocess", "and", "append", "batch_size", "bool", "ceil", "center_crop", "character_list", "class", "crop_s...
oneformer/image_processing_pil_oneformer.py:make_pixel_mask
[ 0.00008329817501362413, -0.0015002393629401922, 0.03260054811835289, -0.00240038288757205, 0.00026690305094234645, 0.022552434355020523, 0.01138786319643259, -0.08931657671928406, 0.0192030631005764, 0.024338766932487488, 0.019314708188176155, -0.03728966787457466, 0.0013048592954874039, -...
[ "ChannelDimension", "FIRST", "Model_pixel_mask", "channel_dim", "def", "dtype", "get_image_size", "image", "input_height", "input_width", "int", "int64", "mask", "np", "output_size", "return", "zeros" ]
oneformer/image_processing_pil_oneformer.py:OneFormerImageProcessorKwargs
[ -0.0002538750704843551, 0.03111320175230503, -0.021203070878982544, 0.01048630103468895, -0.00128197914455086, 0.03295694664120674, 0.026849539950489998, -0.015902303159236908, 0.019474560394883156, 0.0063954913057386875, 0.022470645606517792, 0.01031345035880804, 0.0009434790117666125, 0....
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "NModel", "bool", "class", "class_info_file", "do_reduce_labels", "ignore_index", "int", "num_labels", "num_text", "r", "repo_path", "str", "total" ]
oneformer/image_processing_pil_oneformer.py:binary_mask_to_rle
[ 0.00014927414304111153, -0.03180701285600662, 0.009324717335402966, 0.005406048148870468, 0.0009153097053058445, 0.0672752633690834, -0.008581028319895267, -0.07322477549314499, 0.020136814564466476, 0.0078945467248559, 0.05102851614356041, 0.00972516555339098, 0.0013729645870625973, -0.02...
[ "Model_mask_to_rle", "concatenate", "def", "flatten", "if", "is_torch_tensor", "list", "mask", "np", "numpy", "pixels", "return", "runs", "where" ]
oneformer/image_processing_pil_oneformer.py:check_segment_validity
[ -0.00011050531611545011, 0.02222735434770584, -0.022564133629202843, -0.022788651287555695, -0.0003788753820117563, 0.029636474326252937, -0.0038168185856193304, -0.034126847982406616, -0.001550582586787641, 0.0018101823516190052, 0.042209524661302567, 0.014930495992302895, -0.00252583576366...
[ "False", "Model_segment_validity", "and", "area_ratio", "def", "if", "item", "k", "mask_exists", "mask_k", "mask_k_area", "mask_labels", "mask_probs", "mask_threshold", "not", "original_area", "overlap_mask_area_threshold", "return", "sum" ]
oneformer/image_processing_pil_oneformer.py:compute_segments
[ -0.0003289783198852092, 0.023136937990784645, -0.0478934608399868, -0.018972288817167282, -0.0013520647771656513, 0.03609362244606018, 0.032623082399368286, -0.05182673782110214, 0.0077508739195764065, -0.02232714369893074, 0.014171374030411243, 0.02267419919371605, -0.005697470623999834, ...
[ "BILINEAR", "InterpolationMode", "Model_segments", "None", "append", "argmax", "check_segment_validity", "current_segment_id", "def", "device", "dtype", "else", "for", "height", "id", "if", "in", "int", "int32", "interpolation", "is", "item", "k", "label_id", "label_i...
oneformer/image_processing_pil_oneformer.py:convert_segmentation_to_rle
[ 0.00008366425026906654, -0.015805833041667938, -0.01878374256193638, 0.009105534292757511, 0.0006299426313489676, 0.06276518851518631, -0.006127623375505209, -0.04925005882978439, 0.017294788733124733, -0.00521134352311492, 0.06047448888421059, 0.014145075343549252, 0.000044516542402561754, ...
[ "Model_segmentation_to_rle", "append", "binary_mask_to_rle", "def", "for", "idx", "in", "mask", "return", "rle", "run_length_encodings", "segment_ids", "segmentation", "torch", "unique", "where" ]
oneformer/image_processing_pil_oneformer.py:load_metadata
[ -0.0002837971842382103, 0.013395227491855621, -0.010103180073201656, -0.006300297565758228, -0.00067756581120193, 0.011976242065429688, 0.0329204760491848, 0.002979870652779937, 0.004313717596232891, 0.01816301979124546, 0.014530416578054428, 0.047223854809999466, -0.00022349029313772917, ...
[ "ImportError", "Install", "Model", "Model_metadata", "None", "RepositoryNotFoundError", "as", "class_info", "class_info_file", "dataset", "def", "downModel", "else", "except", "exists", "f", "files", "fname", "hf_hub_downModel", "huggingface_hub", "if", "install", "is", ...
oneformer/image_processing_pil_oneformer.py:prepare_metadata
[ -0.00013145622506272048, 0.0013287736801430583, -0.03274325653910637, -0.0023591062054038048, -0.0003730514436028898, 0.02933250181376934, 0.013188255950808525, 0.02000976726412773, 0.016485320404171944, 0.0012363990535959601, 0.016485320404171944, 0.04684104770421982, -0.0003623928059823811...
[ "Model_metadata", "append", "class_info", "class_names", "def", "for", "if", "in", "info", "int", "isthing", "items", "key", "metadata", "name", "return", "thing_ids" ]
oneformer/image_processing_pil_oneformer.py:remove_low_and_no_objects
[ -0.00004660735430661589, 0.0140138640999794, -0.0056843385100364685, -0.003630097256973386, -0.000397481577238068, 0.04457421600818634, 0.012381726875901222, -0.02577650360763073, 0.0228499136865139, -0.024088086560368538, -0.012212885543704033, 0.03556932508945465, 0.001378874178044498, -...
[ "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" ]
oneformer/image_processing_pil_oneformer.py:OneFormerImageProcessorPil
[ -0.0003164432127960026, 0.026581229642033577, -0.02082771621644497, 0.02991826832294464, -0.0013520756037905812, 0.02174827829003334, 0.0222085602581501, -0.031989533454179764, 0.006127491127699614, 0.01047139335423708, 0.016915328800678253, 0.023474333807826042, -0.0037973185535520315, -0...
[ "BILINEAR", "BatchFeature", "CONSTANT", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "Make", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "Model_demo", "NEAREST", "NModel", "PILImageResampling", "PaddingMode", "PilBackend", "Seman...
oneformer/image_processing_oneformer.py:OneFormerImageProcessorKwargs
[ -0.0002542976289987564, 0.030934136360883713, -0.020892083644866943, 0.01021519023925066, -0.0013778964057564735, 0.03324265405535698, 0.02700965665280819, -0.0173138827085495, 0.01939154788851738, 0.00735839968547225, 0.022277195006608963, 0.009407209232449532, 0.0009594776201993227, 0.01...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "NModel", "bool", "class", "class_info_file", "do_reduce_labels", "ignore_index", "int", "num_labels", "num_text", "r", "repo_path", "str", "total" ]
oneformer/image_processing_oneformer.py:prepare_metadata
[ -0.00014234079571906477, 0.001473227166570723, -0.032112084329128265, -0.0018646643729880452, -0.0005622461321763694, 0.029151394963264465, 0.012981479987502098, 0.01867511123418808, 0.01651153154671192, 0.0010889070108532906, 0.016169913113117218, 0.04623229056596756, -0.0004110090376343578...
[ "Model_metadata", "append", "class_info", "class_names", "def", "for", "if", "in", "info", "int", "isthing", "items", "key", "metadata", "name", "return", "thing_ids" ]
oneformer/image_processing_oneformer.py:load_metadata
[ -0.00025268003810197115, 0.012982770800590515, -0.020271344110369682, -0.004583829082548618, -0.0007829521782696247, 0.0230045598000288, 0.03325411677360535, 0.01201475691050291, 0.005779610946774483, 0.020840764045715332, 0.01269806083291769, 0.039859384298324585, -0.0006797448731958866, ...
[ "Model", "Model_metadata", "None", "RepositoryNotFoundError", "as", "class_info", "class_info_file", "dataset", "def", "else", "except", "exists", "f", "fname", "hf_api", "hf_hub_downModel", "if", "is", "isfile", "join", "json", "not", "open", "or", "os", "path", ...
oneformer/image_processing_oneformer.py:make_pixel_mask
[ 0.00004186432852293365, -0.003823608625680208, 0.030142316594719887, -0.011498735286295414, 0.00008983386942418292, 0.02645825408399105, 0.019201772287487984, -0.07814674824476242, 0.020764706656336784, 0.0272397231310606, 0.011442916467785835, -0.033044908195734024, 0.0002965389867313206, ...
[ "Model_pixel_mask", "def", "dtype", "image", "input_height", "input_width", "int", "int64", "mask", "output_size", "return", "shape", "torch", "zeros" ]
oneformer/image_processing_oneformer.py:binary_mask_to_rle
[ 0.0001262380537809804, -0.03299969434738159, 0.006258562672883272, 0.004437889903783798, 0.00071475631557405, 0.0641787201166153, -0.011834373697638512, -0.07510275393724442, 0.018775688484311104, 0.009843012318015099, 0.051206424832344055, 0.0035417776089161634, 0.0014650726225227118, -0....
[ "Model_mask_to_rle", "concat", "def", "device", "dtype", "flatten", "from_numpy", "if", "isinstance", "list", "mask", "ndarray", "np", "pixels", "return", "runs", "tensor", "torch", "where" ]
oneformer/image_processing_oneformer.py:convert_segmentation_to_rle
[ 0.00006262735405471176, -0.015230972319841385, -0.017979418858885765, 0.009333265013992786, 0.0006262735114432871, 0.06275618821382523, -0.005554151255637407, -0.04924299195408821, 0.01775038056075573, -0.004752521403133869, 0.06046581268310547, 0.01414304506033659, 0.00008857296779751778, ...
[ "Model_segmentation_to_rle", "append", "binary_mask_to_rle", "def", "for", "idx", "in", "mask", "return", "rle", "run_length_encodings", "segment_ids", "segmentation", "torch", "unique", "where" ]
oneformer/image_processing_oneformer.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" ]
oneformer/image_processing_oneformer.py:check_segment_validity
[ -0.00013257849786896259, 0.022476885467767715, -0.022589268162846565, -0.022814037278294563, -0.00047060978249646723, 0.02921994961798191, -0.004186319652944803, -0.03394009545445442, -0.001994823571294546, 0.0018121987814083695, 0.042256541550159454, 0.014722359366714954, -0.002374120987951...
[ "False", "Model_segment_validity", "and", "area_ratio", "def", "if", "item", "k", "mask_exists", "mask_k", "mask_k_area", "mask_labels", "mask_probs", "mask_threshold", "not", "original_area", "overlap_mask_area_threshold", "return", "sum" ]
oneformer/image_processing_oneformer.py:compute_segments
[ -0.00033893645741045475, 0.024011926725506783, -0.04779185354709625, -0.01890794187784195, -0.0014499955577775836, 0.03549589216709137, 0.03178390488028526, -0.05080784484744072, 0.007945976220071316, -0.022387931123375893, 0.014615955762565136, 0.022387931123375893, -0.005567983258515596, ...
[ "BILINEAR", "InterpolationMode", "Model_segments", "None", "append", "argmax", "check_segment_validity", "current_segment_id", "def", "device", "dtype", "else", "for", "height", "id", "if", "in", "int", "int32", "interpolation", "is", "item", "k", "label_id", "label_i...
oneformer/image_processing_oneformer.py:OneFormerImageProcessor
[ -0.00033061299473047256, 0.04024853929877281, -0.019664285704493523, 0.022999165579676628, -0.0012577668530866504, 0.03265881538391113, 0.01805434562265873, -0.02817397750914097, 0.005404803901910782, 0.009832142852246761, 0.018974311649799347, 0.030128907412290573, -0.0029036446940153837, ...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "InterpolationMode", "Make", "ModelImageProcessor", "ModelImageProcessorKwargs", "Model_demo", "NEAREST_EXACT", "NModel", "PILImageResampling", "SemanticSegmentationPostPr...
donut/image_processing_pil_donut.py:DonutImageProcessorKwargs
[ -0.00030220553162507713, 0.021425828337669373, -0.022583981975913048, -0.0011509144678711891, -0.001549029489979148, 0.03404969722032547, 0.030575236305594444, -0.045167963951826096, 0.01853044703602791, 0.027100777253508568, -0.0004415457951836288, 0.026984963566064835, 0.001093006809242069...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_align_long_axis", "do_thumbnail", "r", "total" ]
donut/image_processing_pil_donut.py:DonutImageProcessorPil
[ -0.00024414315703324974, 0.025362582877278328, -0.005689597222954035, 0.040534842759370804, -0.0010897985193878412, 0.0029297180008143187, 0.003609072882682085, -0.037364520132541656, 0.00163469766266644, 0.02943871170282364, -0.004444113001227379, -0.0009199597407132387, 0.00049182464135810...
[ "BICUBIC", "BILINEAR", "BatchFeature", "CONSTANT", "ChannelDimension", "FIRST", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "None", "PILImageResampling", "PaddingMode", "PilBackend", "SizeDict", "True", "__init_...
donut/image_processing_donut.py:DonutImageProcessorKwargs
[ -0.00030220553162507713, 0.021425828337669373, -0.022583981975913048, -0.0011509144678711891, -0.001549029489979148, 0.03404969722032547, 0.030575236305594444, -0.045167963951826096, 0.01853044703602791, 0.027100777253508568, -0.0004415457951836288, 0.026984963566064835, 0.001093006809242069...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_align_long_axis", "do_thumbnail", "r", "total" ]
donut/image_processing_donut.py:DonutImageProcessor
[ -0.0002546115138102323, 0.04096416383981705, -0.0017327727982774377, 0.026819080114364624, -0.0010467362590134144, 0.023537419736385345, 0.0007991972379386425, -0.03191130980849266, 0.003267514519393444, 0.03259027376770973, 0.004865908995270729, 0.010693684220314026, 0.0007178630330599844, ...
[ "BILINEAR", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "align_long_axis", "and", "auto_docstring", ...
owlvit/image_processing_pil_owlvit.py:_upcast
[ -0.0003486654895823449, 0.02070426009595394, 0.01690847799181938, -0.0250751581043005, -0.0012868272606283426, 0.012825138866901398, 0.012192508205771446, -0.0379578098654747, -0.005348600447177887, 0.048770032823085785, -0.024039944633841515, -0.04600946605205536, -0.004658458288758993, 0...
[ "_upcast", "def", "dtype", "else", "float", "float32", "float64", "if", "in", "int", "int64", "is_floating_point", "return", "t", "torch" ]
owlvit/image_processing_pil_owlvit.py:_scale_boxes
[ -0.000010294113053532783, -0.00899586919695139, 0.004829070996493101, -0.022738024592399597, -0.00008795807661954314, 0.010265224613249302, -0.00011813976743724197, -0.044593021273612976, 0.0053809648379683495, 0.03797029331326485, -0.0406193844974041, -0.019978556782007217, -0.0006381272105...
[ "Tensor", "_scale_boxes", "boxes", "def", "device", "dim", "elif", "else", "for", "i", "if", "image_height", "image_width", "in", "isinstance", "list", "or", "return", "scale_factor", "stack", "target_sizes", "tensor", "to", "torch", "tuple", "unbind", "unsqueeze"...
owlvit/image_processing_pil_owlvit.py:box_area
[ 0.0003176898753736168, -0.029680268839001656, 0.0073616416193544865, -0.0040605878457427025, 0.0016943460796028376, 0.04230022430419922, 0.029913971200585365, 0.005842572543770075, 0.026992686092853546, 0.02757694199681282, 0.0075953444465994835, 0.01045820489525795, -0.002804434858262539, ...
[ "Model_area", "Modeles", "_upcast", "def", "return" ]
owlvit/image_processing_pil_owlvit.py:box_iou
[ 0.00015035395335871726, 0.014147590845823288, 0.026691406965255737, -0.010309984907507896, 0.0010023596696555614, 0.04055260866880417, 0.025774963200092316, -0.027836959809064865, 0.02886795811355114, -0.008534376509487629, 0.055903032422065735, -0.014262146316468716, -0.0018472056835889816,...
[ "Model_area", "Model_iou", "Modeles1", "Modeles2", "None", "area1", "area2", "clamp", "def", "inter", "iou", "left_top", "max", "min", "return", "right_bottom", "torch", "union", "width_height" ]
owlvit/image_processing_pil_owlvit.py:OwlViTImageProcessorPil
[ -0.0003106187505181879, 0.026659347116947174, -0.03952937573194504, 0.03746097907423973, -0.0015943897888064384, 0.018730489537119865, 0.00999725516885519, -0.03240489587187767, -0.003490420989692211, 0.027693545445799828, -0.0020253059919923544, 0.03723115846514702, -0.0009839253034442663, ...
[ "BICUBIC", "Each", "False", "Make", "ModelImageProcessorPil", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "True", "ValueError", "__init__", "_scale_boxes", "alphas", "and", "append", "argsort", "as", "backends", "batch", "batch_boxes",...
owlvit/image_processing_owlvit.py:_upcast
[ -0.00035685126204043627, 0.022607790306210518, 0.017993954941630363, -0.024222632870078087, -0.0014706597430631518, 0.011822951026260853, 0.012745718471705914, -0.0380641371011734, -0.0056807841174304485, 0.04729180410504341, -0.02030087262392044, -0.04729180410504341, -0.004181287717074156,...
[ "_upcast", "def", "dtype", "else", "float", "float32", "float64", "if", "in", "int", "int64", "is_floating_point", "return", "t", "torch" ]
owlvit/image_processing_owlvit.py:_scale_boxes
[ -0.000010294113053532783, -0.00899586919695139, 0.004829070996493101, -0.022738024592399597, -0.00008795807661954314, 0.010265224613249302, -0.00011813976743724197, -0.044593021273612976, 0.0053809648379683495, 0.03797029331326485, -0.0406193844974041, -0.019978556782007217, -0.0006381272105...
[ "Tensor", "_scale_boxes", "boxes", "def", "device", "dim", "elif", "else", "for", "i", "if", "image_height", "image_width", "in", "isinstance", "list", "or", "return", "scale_factor", "stack", "target_sizes", "tensor", "to", "torch", "tuple", "unbind", "unsqueeze"...
owlvit/image_processing_owlvit.py:box_area
[ 0.0003176898753736168, -0.029680268839001656, 0.0073616416193544865, -0.0040605878457427025, 0.0016943460796028376, 0.04230022430419922, 0.029913971200585365, 0.005842572543770075, 0.026992686092853546, 0.02757694199681282, 0.0075953444465994835, 0.01045820489525795, -0.002804434858262539, ...
[ "Model_area", "Modeles", "_upcast", "def", "return" ]
owlvit/image_processing_owlvit.py:box_iou
[ 0.00011691644613165408, 0.012394928373396397, 0.026617681607604027, -0.010909821838140488, 0.0008532223291695118, 0.04089755192399025, 0.026846159249544144, -0.027988549321889877, 0.029016699641942978, -0.009253356605768204, 0.05574861913919449, -0.013937154784798622, -0.001913502812385559, ...
[ "Model_area", "Model_iou", "Modeles1", "Modeles2", "None", "area1", "area2", "clamp", "def", "inter", "iou", "left_top", "max", "min", "return", "right_bottom", "torch", "union", "width_height" ]
owlvit/image_processing_owlvit.py:OwlViTImageProcessor
[ -0.0002575580438133329, 0.040242999792099, -0.03137589991092682, 0.030011730268597603, -0.001321539282798767, 0.027738114818930626, 0.005485098343342543, -0.030466454103589058, -0.005456678103655577, 0.024782413616776466, 0.0012860139831900597, 0.05001955106854439, 0.0001087960772565566, -...
[ "BICUBIC", "Each", "False", "Make", "ModelImageProcessor", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "TorchvisionBackend", "True", "ValueError", "__init__", "_scale_boxes", "alphas", "and", "append", "argsort", "as", "batch", "batch_boxes", "batch...
phi4_multimodal/image_processing_phi4_multimodal.py:Phi4MultimodalImageProcessorKwargs
[ -0.00019641195831354707, 0.02022686041891575, -0.02491217851638794, -0.007770771160721779, -0.0009856309043243527, 0.022169552743434906, 0.05393829569220543, -0.03473991900682449, 0.018055615946650505, 0.027997631579637527, 0.003971092402935028, 0.005942354444414377, 0.001399881555698812, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "dynamic_hd", "int", "patch_size", "r", "total" ]
phi4_multimodal/image_processing_phi4_multimodal.py:Phi4MultimodalImageProcessor
[ -0.000276214093901217, 0.05099336802959442, 0.013881527818739414, 0.0002080458652926609, -0.0010694442316889763, 0.018697569146752357, 0.006487489677965641, -0.030369384214282036, 0.0005028512678109109, 0.03127593174576759, 0.01779101975262165, 0.015637965872883797, 0.0005488869501277804, ...
[ "B", "BICUBIC", "BatchFeature", "False", "H", "ModelImageProcessor", "ModelImageProcessorKwargs", "PILImageResampling", "TorchvisionBackend", "True", "ValueError", "W", "_", "__init__", "_preprocess", "abs", "and", "antialias", "append", "area", "aspect", "aspect_ratio", ...
llava_next/image_processing_llava_next.py:LlavaNextImageProcessorKwargs
[ -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_next/image_processing_llava_next.py:LlavaNextImageProcessor
[ -0.000146467617014423, 0.03660295531153679, -0.006974648218601942, 0.02901453711092472, -0.0004167352453805506, 0.0009206535760313272, -0.005049645435065031, -0.02879134751856327, 0.0014716507866978645, 0.017631910741329193, 0.01841307058930397, 0.02053336426615715, 0.0009276281925849617, ...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "InterpolationMode", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "TypeError", "__init__", "_get_im...
llava_next/image_processing_pil_llava_next.py:LlavaNextImageProcessorKwargs
[ -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_next/image_processing_pil_llava_next.py:LlavaNextImageProcessorPil
[ -0.00010989184374921024, 0.022215595468878746, -0.014289428479969501, 0.0404122918844223, -0.0005198058788664639, -0.006949351169168949, 0.001262884121388197, -0.03103485330939293, 0.0012210204731673002, 0.01797342300415039, 0.013954520225524902, 0.016633788123726845, -0.00041165834409184754...
[ "BICUBIC", "BatchFeature", "ChannelDimension", "FIRST", "False", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "SizeDict", "True", "TypeError", "__init__", "_get_padding_size", "_pad_for_batching", ...
chinese_clip/image_processing_pil_chinese_clip.py:ChineseCLIPImageProcessorPil
[ -0.0003433220845181495, 0.02798617072403431, 0.006678517907857895, 0.04741458594799042, -0.0016479459591209888, 0.016768572852015495, -0.0008854095940478146, -0.03099294938147068, 0.008442109450697899, 0.04371393471956253, 0.024516811594367027, 0.01607470214366913, -0.0002547811309341341, ...
[ "BICUBIC", "False", "ModelImageProcessorPil", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "PilBackend", "True", "class", "crop_size", "default_to_square", "do_center_crop", "do_convert_rgb", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", "imag...
chinese_clip/image_processing_chinese_clip.py:ChineseCLIPImageProcessor
[ -0.00032810631091706455, 0.0440744124352932, 0.01499914564192295, 0.038767024874687195, -0.0016297148540616035, 0.026883084326982498, 0.0005011733737774193, -0.027113839983940125, 0.009864822961390018, 0.04384365677833557, 0.026652328670024872, 0.021114181727170944, 0.0006021291483193636, ...
[ "BICUBIC", "False", "ModelImageProcessor", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "TorchvisionBackend", "True", "class", "crop_size", "default_to_square", "do_center_crop", "do_convert_rgb", "do_normalize", "do_rescale", "do_resize", "height", "image_mean", ...
segformer/image_processing_segformer.py:SegformerImageProcessorKwargs
[ -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" ]
segformer/image_processing_segformer.py:SegformerImageProcessor
[ -0.0002177757996832952, 0.05756363272666931, -0.021982962265610695, 0.02583564631640911, -0.0009277602657675743, 0.020849818363785744, 0.004447583574801683, -0.029235072433948517, 0.0019405062776058912, 0.016543878242373466, 0.029008444398641586, 0.026402216404676437, -0.0009560888283886015,...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "F", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "ImportError", "InterpolationMode", "Make", "ModelImageProcessor", "ModelImageProcessorKwargs", "NEAREST_EXACT", "None", "PILImageResampling", "PyTorch", "Semanti...
segformer/image_processing_pil_segformer.py:SegformerImageProcessorKwargs
[ -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" ]
segformer/image_processing_pil_segformer.py:SegformerImageProcessorPil
[ -0.00021320051746442914, 0.03827347233891487, -0.028761722147464752, 0.038047004491090775, -0.0009129582322202623, 0.005633447784930468, 0.00407646456733346, -0.030799955129623413, 0, 0.01494703721255064, 0.021627910435199738, 0.019702913239598274, -0.0017692989204078913, -0.02004261873662...
[ "BILINEAR", "BatchFeature", "ChannelDimension", "F", "FIRST", "False", "IMAGENET_DEFAULT_MEAN", "IMAGENET_DEFAULT_STD", "InterpolationMode", "Make", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "NEAREST_EXACT", "None", "PILImageResampling", "PilBackend", "SemanticSegmentati...
minimax_m3_vl/image_processing_minimax_m3_vl.py:MiniMaxM3VLImageProcessorKwargs
[ -0.00016544581740163267, 0.02800837531685829, -0.032562583684921265, 0.004269569646567106, -0.001003348850645125, 0.02174634113907814, 0.04212642088532448, -0.049413152039051056, 0.021974051371216774, 0.02049393393099308, 0.008140645921230316, 0.004895773250609636, 0.0013591463211923838, -...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "max_pixels", "merge_size", "patch_size", "r", "temporal_patch_size", "total" ]
minimax_m3_vl/image_processing_minimax_m3_vl.py:smart_resize
[ -0.00009243939712177962, 0.006641683168709278, 0.045542970299720764, -0.019869238138198853, -0.00019708776380866766, 0.03862222656607628, 0.021766861900687218, -0.025562109425663948, 0.01730186492204666, 0.025896983221173286, -0.006278902292251587, -0.03817572444677353, 0.0021069205831736326...
[ "MAX_RATIO", "Model_resize", "ValueError", "absolute", "aspect", "be", "beta", "ceil", "def", "elif", "f", "factor", "floor", "got", "h_bar", "height", "if", "math", "max", "max_pixels", "min", "min_pixels", "must", "raise", "ratio", "return", "round", "smaller"...
minimax_m3_vl/image_processing_minimax_m3_vl.py:MiniMaxM3VLImageProcessor
[ -0.0001680338173173368, 0.031366314738988876, -0.004985003266483545, 0.029125861823558807, -0.0007211451302282512, 0.0018203663639724255, -0.0022124452516436577, -0.029125861823558807, 0.007953600957989693, 0.019715968519449234, 0.014562930911779404, 0.018931809812784195, 0.00069664022885262...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "_preprocess", "auto_docstring", "batch_size", "cat", "channel", "class", "data",...
idefics/image_processing_idefics.py:IdeficsImageProcessorKwargs
[ -0.00016637935186736286, 0.03302674740552902, -0.011331590823829174, -0.0005978980334475636, -0.001046321471221745, 0.023574264720082283, 0.03143235296010971, -0.030521269887685776, 0.017652226611971855, 0.020043818280100822, 0.005238725338131189, 0.00837057176977396, -0.0008434632909484208,...
[ "Callable", "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "class", "image_num_channels", "image_size", "int", "r", "total", "transform" ]
idefics/image_processing_idefics.py:IdeficsImageProcessor
[ -0.0001787278742995113, 0.04418433457612991, -0.005438934545964003, 0.025007884949445724, -0.0006097774603404105, 0.013849657960236073, -0.00273348530754447, -0.022540738806128502, 0.004065182991325855, 0.04642719402909279, 0.02186788246035576, 0.01816716231405735, -0.00008366917609237134, ...
[ "BICUBIC", "ImportError", "ModelImageProcessor", "ModelImageProcessorKwargs", "Model_STANDARD_MEAN", "Model_STANDARD_STD", "None", "PILImageResampling", "To", "TorchvisionBackend", "True", "__init__", "auto_docstring", "be", "class", "def", "do_convert_rgb", "do_normalize", "do_r...
idefics/image_processing_pil_idefics.py:IdeficsImageProcessorKwargs
[ -0.00016637935186736286, 0.03302674740552902, -0.011331590823829174, -0.0005978980334475636, -0.001046321471221745, 0.023574264720082283, 0.03143235296010971, -0.030521269887685776, 0.017652226611971855, 0.020043818280100822, 0.005238725338131189, 0.00837057176977396, -0.0008434632909484208,...
[ "Callable", "False", "ImagesKwargs", "ModelImageProcessorKwargs", "None", "class", "image_num_channels", "image_size", "int", "r", "total", "transform" ]
idefics/image_processing_pil_idefics.py:IdeficsImageProcessorPil
[ -0.0002605057379696518, 0.027560442686080933, -0.012078959494829178, 0.029942210763692856, -0.0012121491599828005, 0.0018997425213456154, 0.0023108809255063534, -0.02483842521905899, 0.003331637941300869, 0.04196446016430855, 0.009924028068780899, 0.01105820294469595, -0.0014815155882388353,...
[ "BICUBIC", "ImportError", "ModelImageProcessorKwargs", "ModelImageProcessorPil", "Model_STANDARD_MEAN", "Model_STANDARD_STD", "None", "PILImageResampling", "PilBackend", "To", "True", "__init__", "auto_docstring", "be", "class", "def", "do_convert_rgb", "do_normalize", "do_rescal...
cohere2_vision/image_processing_cohere2_vision.py:Cohere2VisionImageProcessorKwargs
[ -0.00021735671907663345, 0.0301209706813097, -0.024832556024193764, -0.008220035582780838, -0.0013580303639173508, 0.02264821156859398, 0.028856350108981133, -0.03219034895300865, 0.017474761232733727, 0.012473761104047298, 0.008909828960895538, 0.007242829073220491, 0.0018969313241541386, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "crop_to_patches", "int", "max_patches", "min_patches", "r", "total" ]
cohere2_vision/image_processing_cohere2_vision.py:get_all_supported_aspect_ratios
[ -0.0002602352178655565, -0.0024241090286523104, 0.03331010788679123, 0.021218083798885345, -0.0009839030681177974, 0.03947019949555397, 0.006245645694434643, -0.025324810296297073, 0.014715767465531826, 0.014829843305051327, 0.013746123760938644, -0.03627607971429825, -0.0015043735038489103,...
[ "Model_all_supported_aspect_ratios", "aspect_ratios", "def", "for", "height", "if", "in", "max_image_tiles", "range", "return", "width" ]
cohere2_vision/image_processing_cohere2_vision.py:get_optimal_tiled_canvas
[ -0.0002873445919249207, 0.03609899431467056, 0.014189857058227062, 0.022249694913625717, -0.0011351885041221976, 0.021341543644666672, 0.018276534974575043, -0.05653239041566849, 0.0035474642645567656, 0.030196014791727066, -0.012487074360251427, -0.02928786352276802, -0.004200197756290436, ...
[ "Model_all_supported_aspect_ratios", "Model_optimal_tiled_canvas", "True", "all", "argmax", "argmin", "array", "axis", "best_grid", "candidate_resolutions", "def", "else", "if", "image_height", "image_width", "int", "keepdims", "key", "lambda", "max_image_tiles", "min", "mi...
cohere2_vision/image_processing_cohere2_vision.py:Cohere2VisionImageProcessor
[ 0.000025464298232691363, 0.02496584877371788, -0.016532938927412033, 0.018641166388988495, 0, -0.009764420799911022, -0.012538403272628784, -0.010818534530699253, -0.0015326256398111582, 0.00787811167538166, 0.013426078483462334, 0.003522958606481552, -0.00024965847842395306, -0.0081555107...
[ "BICUBIC", "BatchFeature", "False", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "OPENAI_CLIP_MEAN", "OPENAI_CLIP_STD", "PILImageResampling", "SizeDict", "TorchvisionBackend", "True", "__init__", "_preprocess", "and", "append", "box", "class", "column", "contiguo...
lfm2_vl/image_processing_lfm2_vl.py:round_by_factor
[ 0.00004237120447214693, 0.017491541802883148, 0.017718706279993057, -0.013970517553389072, 0, 0.050657324492931366, 0.0561092346906662, 0.009711213409900665, 0.01380014605820179, 0.033847272396087646, -0.0304398275911808, -0.05565490946173668, 0.0002236134751001373, 0.012948284856975079, ...
[ "Model", "Model_by_factor", "def", "factor", "number", "return" ]
lfm2_vl/image_processing_lfm2_vl.py:find_closest_aspect_ratio
[ -0.00006501483585452661, 0.026297200471162796, 0.0643560141324997, 0.005436974111944437, -0.00046117190504446626, 0.01775338500738144, 0.012926682829856873, -0.02407802641391754, 0.00909860897809267, 0.029736919328570366, 0.006879436317831278, -0.011761616915464401, -0.00006068051152396947, ...
[ "Model_closest_aspect_ratio", "abs", "area", "aspect_ratio", "best_ratio", "best_ratio_diff", "def", "elif", "float", "for", "height", "if", "image_size", "in", "inf", "int", "ratio", "ratio_diff", "return", "target_area", "target_aspect_ratio", "target_ratios", "width" ]
lfm2_vl/image_processing_lfm2_vl.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...
lfm2_vl/image_processing_lfm2_vl.py:convert_image_to_patches
[ 0.00014264574565459043, 0.0013736258260905743, 0.01403211522847414, -0.008678496815264225, 0.0008699629688635468, -0.023894043639302254, 0.010200051590800285, -0.010030990466475487, 0.019273025915026665, -0.0037193559110164642, -0.00929838977754116, -0.02141447365283966, -0.00233868602663278...
[ "Model_image_to_patches", "batch_size", "def", "image_height", "image_width", "images", "num_channels", "num_patches_height", "num_patches_width", "patch_size", "patched_image", "permute", "reshape", "return", "shape" ]
lfm2_vl/image_processing_lfm2_vl.py:pad_along_first_dim
[ -0.00012628636613953859, -0.002946681808680296, 0.02828814648091793, 0.00457437289878726, -0.0005893363850191236, 0.029635200276970863, 0.004770818166434765, -0.05971941724419594, 0.006258191075176001, 0.017511708661913872, 0.006707209162414074, -0.029186181724071503, 0.0021609000395983458, ...
[ "Model", "Model_along_first_dim", "Model_value", "Modelding_length", "Modeldings", "constant", "current_length", "def", "dtype", "functional", "if", "images", "int32", "mode", "nn", "ones", "pixel_mask", "return", "shape", "target_length", "torch", "value" ]
lfm2_vl/image_processing_lfm2_vl.py:Lfm2VlImageProcessorKwargs
[ -0.00015161943156272173, 0.03118627518415451, -0.01396651566028595, -0.007516116835176945, -0.0005538929253816605, 0.023782338947057724, 0.03365425392985344, -0.054295528680086136, 0.01256425492465496, 0.006366263143718243, 0.006871076766401529, 0.010152366943657398, -0.0002436427748762071, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "bool", "class", "do_image_splitting", "do_pad", "downsample_factor", "encoder_patch_size", "float", "int", "max_image_tokens", "max_pixels_tolerance", "max_tiles", "min_image_tokens", "min_tiles", "return_row_col_info", "tile_s...
lfm2_vl/image_processing_lfm2_vl.py:Lfm2VlImageProcessor
[ -0.00021268597629386932, 0.03734835982322693, -0.00877461489289999, 0.009505832567811012, -0.0010687030153349042, 0.01372439693659544, 0.004021698143333197, -0.04072321206331253, 0.0027701908256858587, 0.016986753791570663, 0.0010194864589720964, 0.0037123369984328747, 0.0019686634186655283,...
[ "BILINEAR", "BatchFeature", "False", "IMAGENET_STANDARD_MEAN", "IMAGENET_STANDARD_STD", "Image", "ModelImageProcessor", "ModelImageProcessorKwargs", "None", "PILImageResampling", "Set", "SizeDict", "TorchvisionBackend", "True", "_", "__init__", "_get_grid_layout", "_is_image_too_la...
bridgetower/image_processing_pil_bridgetower.py:BridgeTowerImageProcessorKwargs
[ -0.0002227751538157463, 0.01619664393365383, -0.017451314255595207, -0.006815137341618538, -0.0012404120061546564, 0.01882004551589489, 0.04220252484083176, -0.04973054304718971, 0.015056035481393337, 0.03330577537417412, -0.010949844494462013, -0.009980326518416405, 0.002067353343591094, ...
[ "False", "ImagesKwargs", "ModelImageProcessorKwargs", "class", "int", "r", "size_divisor", "total" ]