Datasets:
itazap
/
transformers_code_embeddings_v3

Dataset card Data Studio Files
xet
 Community
1
identifier
stringlengths
24
102
embedding
listlengths
2.56k
2.56k
tokens
listlengths
4
448
qwen3_omni_moe/modeling_qwen3_omni_moe.py:Qwen3OmniMoeCode2WavDecoderBlock
[ -0.00026497076032683253, 0.009166556410491467, 0.004153595771640539, -0.028874652460217476, -0.0009811079362407327, 0.025666357949376106, 0.046978600323200226, -0.06141592562198639, 0.004611923359334469, -0.029562143608927727, 0.009395720437169075, -0.008880101144313812, -0.00386714097112417...
[ "ModelCausalTransConvNet", "ModelCode2WavDecoderBlock", "ModelCode2WavDecoderResidualUnit", "ModelPreTrainedModel", "ModuleList", "SnakeBeta", "__init__", "append", "block", "class", "config", "decoder_dim", "def", "dilation", "for", "forward", "hidden", "in", "in_dim", "kwargs...
qwen3_omni_moe/modeling_qwen3_omni_moe.py:Qwen3OmniMoeCode2Wav
[ -0.0002290591219207272, 0.03750177472829819, -0.0016620104433968663, -0.0008949286420829594, -0.0006179269403219223, 0.004147923551499844, 0.03932004049420357, -0.03636535629630089, 0.001690420787781477, -0.02954685129225254, 0.03318338841199875, 0.016023484990000725, -0.005255930125713348, ...
[ "Embedding", "False", "ModelCausalConvNet", "ModelCausalTransConvNet", "ModelCode2Wav", "ModelCode2WavDecoderBlock", "ModelCode2WavTransformerModel", "ModelConvNeXtBlock", "ModelPreTrainedModel", "ModuleList", "SnakeBeta", "__init__", "_from_config", "append", "arange", "audio", "blo...
qwen3_omni_moe/modeling_qwen3_omni_moe.py:Qwen3OmniMoeForConditionalGeneration
[ -0.0003596796013880521, 0.05254955217242241, -0.019531691446900368, 0.015927629545331, -0.0014387184055522084, 0.04045850411057472, 0.06603571772575378, -0.02685607597231865, -0.0079638147726655, -0.005318898241966963, 0.04162110760807991, -0.008138204924762249, -0.002819306682795286, 0.00...
[ "Cannot", "Ethan", "False", "GenerationMixin", "Model", "ModelCode2Wav", "ModelConfig", "ModelForConditionalGeneration", "ModelPreTrainedModel", "ModelTalkerForConditionalGeneration", "ModelThinkerForConditionalGeneration", "None", "NotImplementedError", "Omni", "True", "Use", "Value...
detr/modeling_detr.py:DetrDecoderOutput
[ -0.00020858926291111857, 0.049063049256801605, 0.023162322118878365, 0.012665112502872944, -0.0012479699216783047, 0.03171982988715172, 0.06572166085243225, -0.044270843267440796, 0.015631714835762978, -0.014947114512324333, 0.012094611302018166, 0.01357791293412447, -0.004364329390227795, ...
[ "BaseModelOutputWithCrossAttentions", "ModelDecoderOutput", "None", "class", "intermediate_hidden_states", "r" ]
detr/modeling_detr.py:DetrModelOutput
[ -0.00018109165830537677, 0.024884123355150223, 0.034315094351768494, 0.018748313188552856, -0.000944517261814326, 0.046586714684963226, 0.04613221064209938, -0.04272342845797539, 0.016930295154452324, -0.010339978151023388, 0.02499775029718876, -0.009942286647856236, -0.0032951580360531807, ...
[ "ModelModelOutput", "None", "Seq2SeqModelOutput", "class", "intermediate_hidden_states", "r" ]
detr/modeling_detr.py:DetrObjectDetectionOutput
[ -0.00018221529899165034, 0.011206685565412045, -0.0008568563498556614, -0.0022612474858760834, -0.0009528526570647955, 0.05256333574652672, 0.052108243107795715, -0.021730730310082436, 0.014051022008061409, -0.014904322102665901, -0.004209617618471384, 0.03526977077126503, -0.004693155176937...
[ "ModelObjectDetectionOutput", "ModelOutput", "None", "auxiliary_outputs", "class", "cross_attentions", "decoder_attentions", "decoder_hidden_states", "encoder_attentions", "encoder_hidden_states", "encoder_last_hidden_state", "last_hidden_state", "logits", "loss", "loss_dict", "pred_bo...
detr/modeling_detr.py:DetrSegmentationOutput
[ -0.0002218172448920086, 0.021436987444758415, -0.011687719263136387, -0.007183671463280916, -0.0011687719961628318, 0.05039972811937332, 0.03968123346567154, -0.03375185281038284, 0.01573566161096096, -0.017218006774783134, 0.007468738127499819, 0.027594421058893204, -0.003876902163028717, ...
[ "ModelOutput", "ModelSegmentationOutput", "None", "auxiliary_outputs", "class", "cross_attentions", "decoder_attentions", "decoder_hidden_states", "encoder_attentions", "encoder_hidden_states", "encoder_last_hidden_state", "last_hidden_state", "logits", "loss", "loss_dict", "pred_boxes...
detr/modeling_detr.py:DetrFrozenBatchNorm2d
[ -0.00022441272449214011, 0.023253431543707848, 0.018465960398316383, 0.0027356978971511126, -0.0008050361066125333, 0.029408752918243408, -0.01937786117196083, -0.014704376459121704, 0.01687013730406761, 0.020061785355210304, 0.01607222482562065, -0.003034914843738079, 0.00237948726862669, ...
[ "ModelFrozenBatchNorm2d", "Module", "__init__", "_load_from_state_dict", "bias", "class", "def", "del", "epsilon", "error_msgs", "forward", "if", "in", "local_metadata", "missing_keys", "n", "nn", "num_batches_tracked", "num_batches_tracked_key", "ones", "prefix", "register...
detr/modeling_detr.py:replace_batch_norm
[ 0.00006147739622974768, 0.005075003486126661, 0.02018596976995468, -0.013628380373120308, 0.0004401425248943269, 0.03170451521873474, -0.014312650077044964, -0.013457313179969788, 0.028169121593236923, 0.00610140897333622, 0.011119389906525612, -0.007698039058595896, 0.0019815322011709213, ...
[ "BatchNorm2d", "ModelFrozenBatchNorm2d", "Model_batch_norm", "_modules", "bias", "children", "copy_", "def", "device", "for", "if", "in", "isinstance", "len", "list", "meta", "model", "module", "name", "named_children", "new_module", "nn", "num_features", "r", "runnin...
detr/modeling_detr.py:DetrConvEncoder
[ -0.00017933815252035856, 0.014853418804705143, 0.02509327605366707, 0.007539235521107912, -0.00040614817407913506, 0.0337577722966671, -0.0050636655651032925, -0.04118448123335838, 0.00167382275685668, 0.01097127515822649, 0.01344684511423111, 0.014178263954818249, -0.00011780055501731113, ...
[ "ModelConvEncoder", "Module", "None", "True", "__init__", "and", "append", "backbone", "backbone_config", "backbone_kwargs", "backbone_model_type", "bool", "channels", "class", "config", "copy", "create_model", "def", "dilation", "elif", "else", "feature_info", "feature_m...
detr/modeling_detr.py:DetrConvModel
[ -0.00013853727432433516, 0.01874930039048195, 0.0037837442941963673, 0.010165282525122166, -0.0004941456718370318, 0.040209341794252396, 0.01999172382056713, -0.024735521525144577, 0.010391178540885448, 0.006183880381286144, 0.012367760762572289, 0.02789805456995964, 0.0018353982595726848, ...
[ "ModelConvModel", "Module", "__init__", "class", "conv_encoder", "def", "dtype", "feature_map", "for", "forward", "in", "mask", "nn", "out", "pixel_mask", "pixel_values", "pos", "position_embedding", "return", "self", "super", "to" ]
detr/modeling_detr.py:DetrSinePositionEmbedding
[ -0.00012078697909601033, 0.04175526276230812, 0.02144189178943634, 0.025504566729068756, -0.0006171597051434219, -0.0010156686184927821, 0.0507834292948246, -0.013429395854473114, 0.012300875037908554, 0.023473229259252548, 0.02934153750538826, 0.029115833342075348, 0.0010438816389068961, ...
[ "False", "ModelSinePositionEmbedding", "Module", "None", "__init__", "and", "arange", "cat", "class", "cos", "cumsum", "def", "device", "dim", "dim_t", "div", "dtype", "embedding_dim", "flatten", "float", "float32", "floor", "forward", "if", "int64", "is", "math",...
detr/modeling_detr.py:DetrLearnedPositionEmbedding
[ -0.0001925936376210302, -0.006191397551447153, 0.016586128622293472, 0.028628112748265266, -0.0010721342405304313, 0.029764149338006973, 0.04089730605483055, -0.012610002420842648, 0.008917884901165962, 0.015222885645925999, 0.0004135881899856031, 0.025788022205233574, 0.0009301296668127179,...
[ "Embedding", "ModelLearnedPositionEmbedding", "Module", "None", "__init__", "arange", "cat", "class", "column_embeddings", "def", "device", "dim", "embedding_dim", "forward", "height", "height_values", "nn", "permute", "pixel_mask", "pixel_values", "pos", "repeat", "retur...
detr/modeling_detr.py:build_position_encoding
[ -0.00012929800141137093, 0.046703144907951355, -0.00034538505133241415, 0.027432430535554886, -0.0005526160821318626, 0.024938572198152542, 0.03786128759384155, 0, 0.00935196503996849, 0.03174000233411789, 0.01609671488404274, 0.00021431586355902255, 0.004931035917252302, 0.001544491155073...
[ "ModelLearnedPositionEmbedding", "ModelSinePositionEmbedding", "Model_position_encoding", "True", "config", "d_model", "def", "elif", "else", "if", "learned", "n_steps", "normalize", "position_embedding", "position_embedding_type", "return", "sine" ]
detr/modeling_detr.py:DetrAttention
[ -0.000033623258786974475, 0.04005606845021248, 0.02992953546345234, -0.014514699578285217, -0.0002865668502636254, 0.02452871762216091, 0.03127973899245262, -0.003656804095953703, 0.006497859489172697, 0.01316449511796236, 0.014627216383814812, 0.031729806214571, -0.0034598992206156254, -0...
[ "Attention", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_shape", "and", "attention_mask", "attn_output", "attn_probs", "attn_weights", "attn_weights_reshaped", "batch_size", "be", "bmm", "bool", "but", "by", "class", "contiguous", ...
detr/modeling_detr.py:DetrEncoderLayer
[ -0.0001351773098576814, 0.048948414623737335, 0.02049003355205059, 0.016961194574832916, -0.0007719335262663662, 0.03938639909029007, 0.02208370342850685, -0.0036995892878621817, 0.007456095423549414, 0.022766703739762306, 0.008708263747394085, 0.03323938697576523, 0.00023833892191760242, ...
[ "ACT2FN", "False", "LayerNorm", "Linear", "ModelAttention", "ModelEncoderLayer", "Module", "None", "__init__", "activation_dropout", "activation_fn", "activation_function", "any", "attention_dropout", "attention_mask", "attn_weights", "clamp", "clamp_value", "class", "config", ...
detr/modeling_detr.py:DetrDecoderLayer
[ -0.00008042600529734045, 0.055129166692495346, 0.019126446917653084, -0.003712780773639679, -0.00039202181505970657, 0.023401768878102303, 0.034202586859464645, -0.009000680409371853, 0.0023064243141561747, 0.00016260995471384376, -0.0011461804388090968, 0.03802787512540817, -0.0018704539397...
[ "ACT2FN", "False", "GradientCheckpointingLayer", "LayerNorm", "Linear", "ModelAttention", "ModelDecoderLayer", "None", "__init__", "activation_dropout", "activation_fn", "activation_function", "attention_dropout", "attention_mask", "class", "config", "cross_attn_weights", "d_model"...
detr/modeling_detr.py:DetrPreTrainedModel
[ -0.00016010223771445453, 0.05089491605758667, -0.00605221651494503, 0.015426114201545715, -0.0007811581599526107, 0.029275838285684586, 0.021731680259108543, -0.011147337965667248, 0.003659479785710573, 0.010471741668879986, 0.007938255555927753, -0.004503975156694651, -0.001731215394102037,...
[ "Conv2d", "Embedding", "False", "GroupNorm", "LayerNorm", "Linear", "ModelConfig", "ModelConvEncoder", "ModelDecoderLayer", "ModelEncoderLayer", "ModelLearnedPositionEmbedding", "ModelMHAttentionMap", "ModelPreTrainedModel", "None", "PreTrainedModel", "_init_weights", "_is_hf_initial...
detr/modeling_detr.py:DetrEncoder
[ 0, 0.03743509203195572, 0.01668793335556984, 0.021536454558372498, 0, 0.04374944791197777, 0.014376428909599781, -0.013079731725156307, 0.0062297857366502285, -0.010260824114084244, 0.017364470288157463, 0.025595681741833687, -0.0023678825236856937, -0.005384113639593124, -0.021085429936...
[ "BaseModelOutput", "False", "ModelEncoder", "ModelEncoderLayer", "ModelPreTrainedModel", "ModuleList", "None", "True", "_", "__init__", "_prepare_4d_attention_mask", "all_attentions", "attention_mask", "attentions", "class", "config", "def", "dropout", "dropout_probability", "d...
detr/modeling_detr.py:DetrDecoder
[ -0.00011362737859599292, 0.06358905136585236, 0.0016982448287308216, 0.002818663604557514, -0.0006835259264335036, 0.02829938381910324, 0.037431854754686356, -0.022323817014694214, 0.0036642628256231546, -0.014882544055581093, -0.012233000248670578, 0.03089255467057228, -0.005045407917350531...
[ "False", "LayerNorm", "ModelDecoder", "ModelDecoderLayer", "ModelDecoderOutput", "ModelPreTrainedModel", "ModuleList", "None", "_", "__init__", "_prepare_4d_attention_mask", "all_cross_attentions", "all_hidden_states", "all_self_attns", "and", "attention_mask", "attentions", "auxil...
detr/modeling_detr.py:DetrModel
[ -0.00003537136217346415, 0.02805953286588192, 0.007434099446982145, 0.013247229158878326, -0.00010087388363899663, 0.038008928298950195, 0.013806184753775597, -0.015427153557538986, 0.010396559722721577, -0.0011668181978166103, 0.005757234990596771, 0.013303125277161598, -0.00068122619995847...
[ "BaseModelOutput", "Conv2d", "Embedding", "False", "ModelConvEncoder", "ModelConvModel", "ModelDecoder", "ModelEncoder", "ModelModel", "ModelModelOutput", "ModelPreTrainedModel", "None", "True", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "backbone", "b...
detr/modeling_detr.py:DetrMLPPredictionHead
[ -0.00024052357184700668, -0.0006734660128131509, 0.02770831435918808, 0.04241766780614853, -0.0006734660128131509, 0.040137145668268204, 0.04583844542503357, -0.009635196067392826, -0.0012685391120612621, -0.013569092378020287, 0.022007014602422714, -0.0588374100625515, -0.002750876825302839...
[ "Linear", "ModelMLPPredictionHead", "Module", "ModuleList", "__init__", "class", "def", "else", "enumerate", "for", "forward", "functional", "h", "hidden_dim", "i", "if", "in", "input_dim", "k", "layer", "layers", "n", "nn", "num_layers", "output_dim", "relu", "re...
detr/modeling_detr.py:DetrForObjectDetection
[ -0.00019276134844403714, 0.011210084892809391, -0.004084076266735792, 0.005238883662968874, -0.0005774038727395236, 0.03424990549683571, 0.03492589294910431, -0.019941557198762894, 0.005914868786931038, 0.017462946474552155, 0.009013133123517036, 0.03988311439752579, -0.0023941134568303823, ...
[ "Linear", "ModelForObjectDetection", "ModelMLPPredictionHead", "ModelModel", "ModelObjectDetectionOutput", "ModelPreTrainedModel", "None", "__init__", "auto_docstring", "auxiliary_loss", "auxiliary_outputs", "bbox_predictor", "class", "class_labels_classifier", "config", "cross_attenti...
detr/modeling_detr.py:DetrForSegmentation
[ -0.00020690822566393763, 0.025695359334349632, -0.019947713240981102, -0.005409549456089735, -0.000493057887069881, 0.03786684572696686, 0.023554081097245216, -0.0302033182233572, 0.004846054594963789, -0.0017961395205929875, 0.014087368734180927, 0.03313349187374115, -0.0033527936320751905,...
[ "BaseModelOutput", "Model", "ModelForObjectDetection", "ModelForSegmentation", "ModelMHAttentionMap", "ModelMaskHeadSmallConv", "ModelPreTrainedModel", "ModelSegmentationOutput", "None", "__init__", "and", "attention_mask", "attentions", "auto_docstring", "auxiliary_loss", "auxiliary_o...
detr/modeling_detr.py:_expand
[ -0.00022749749768991023, -0.030248349532485008, 0.04176078364253044, 0.006292334292083979, -0.000828866905067116, 0.046049728989601135, 0.026072273030877113, -0.06410844624042511, 0.003244925756007433, 0.013600470498204231, 0.0013755663530901074, -0.02934541366994381, -0.0020033889450132847,...
[ "_expand", "def", "flatten", "int", "length", "repeat", "return", "tensor", "unsqueeze" ]
detr/modeling_detr.py:DetrMaskHeadSmallConv
[ -0.00021306339476723224, -0.00419351551681757, 0.013065510429441929, -0.013521947897970676, -0.0007559738587588072, 0.03354812413454056, 0.027043895795941353, -0.05773928761482239, 0.008843468502163887, 0.012209691107273102, 0.00456437049433589, 0.007360047660768032, 0.0015048158820718527, ...
[ "Conv2d", "GroupNorm", "ModelMaskHeadSmallConv", "Module", "The", "ValueError", "__init__", "_expand", "a", "adapter1", "adapter2", "adapter3", "as", "attention", "bbox_mask", "be", "bias", "by", "cat", "class", "constant_", "context_dim", "cur_fpn", "def", "dim", "...
detr/modeling_detr.py:DetrMHAttentionMap
[ -0.00014680984895676374, 0.02060290426015854, 0.051394056528806686, -0.03667769581079483, -0.00048464935389347374, 0.019244471564888954, 0.04641313478350639, -0.022866958752274513, 0.008886417374014854, 0.022187743335962296, 0.01550877932459116, 0.030338343232870102, 0.000502337294165045, ...
[ "Dropout", "Linear", "ModelMHAttentionMap", "Module", "None", "__init__", "bias", "bnchw", "bqnc", "bqnhw", "class", "conv2d", "def", "dim", "dropout", "dtype", "einsum", "finfo", "flatten", "float", "forward", "functional", "hidden_dim", "if", "is", "k", "k_linea...
sam/modeling_sam.py:SamVisionEncoderOutput
[ -0.0000384392224077601, 0.033963579684495926, 0.03193926066160202, 0.03801221773028374, -0.00032860043575055897, 0.03756237030029297, 0.04386025294661522, -0.03598789870738983, 0.02125535160303116, -0.0006818019901402295, 0.02339213341474533, 0.04318547993898392, -0.0009488996583968401, 0....
[ "ModelOutput", "ModelVisionEncoderOutput", "None", "attentions", "class", "hidden_states", "image_embeds", "last_hidden_state", "r" ]
sam/modeling_sam.py:SamImageSegmentationOutput
[ -0.00006864943861728534, 0.04029765725135803, -0.0008325384114868939, -0.013152708299458027, -0.00041801825864240527, 0.02720092050731182, 0.03537238761782646, -0.029327740892767906, 0.01869363524019718, -0.018581697717308998, 0.028656112030148506, 0.03313362970948219, -0.005037207156419754,...
[ "ModelImageSegmentationOutput", "ModelOutput", "None", "class", "iou_scores", "mask_decoder_attentions", "pred_masks", "r", "vision_attentions", "vision_hidden_states" ]
sam/modeling_sam.py:SamPatchEmbeddings
[ -0.0001002231365418993, 0.009902749210596085, 0.018567655235528946, 0.013447484001517296, -0.00009538781159790233, 0, 0.017779937013983727, -0.0124347023665905, 0.008045983500778675, 0.0012659764615818858, 0.009115030989050865, 0.0006646376568824053, -0.003024277277290821, -0.0128848273307...
[ "Conv2d", "Input", "Iterable", "Make", "ModelPatchEmbeddings", "Module", "ValueError", "__init__", "abc", "batch_size", "channel", "class", "collections", "config", "configuration", "def", "dimension", "doesn", "else", "embeddings", "f", "forward", "height", "hidden_siz...
sam/modeling_sam.py:SamMLPBlock
[ -0.00022680613619741052, 0.005571930203586817, 0.04206093028187752, 0.016230033710598946, -0.0008357895421795547, 0.05897673964500427, 0.023087793961167336, -0.04046078771352768, 0.002843113150447607, -0.008515052497386932, 0.022287720814347267, -0.03360302746295929, 0.001857310184277594, ...
[ "ACT2FN", "Linear", "ModelMLPBlock", "Module", "__init__", "act", "class", "config", "def", "forward", "hidden_act", "hidden_size", "hidden_states", "lin1", "lin2", "mlp_dim", "nn", "return", "self", "super" ]
sam/modeling_sam.py:SamLayerNorm
[ -0.0001214670337503776, 0.036052823066711426, 0.017125090584158897, 0.0576845183968544, -0.000281662680208683, 0.018477071076631546, -0.0048164320178329945, -0.03875678405165672, 0.010590516962110996, 0.047995321452617645, 0.009407533332705498, 0.00543608982115984, 0.003577115945518017, 0....
[ "LayerNorm", "ModelLayerNorm", "NotImplementedError", "Unsupported", "__init__", "channels_first", "channels_last", "class", "data", "data_format", "def", "else", "eps", "f", "features", "format", "forward", "if", "in", "kwargs", "nn", "normalized_shape", "not", "permut...
sam/modeling_sam.py:eager_attention_forward
[ 0.0000415438917116262, 0.02851148322224617, 0.024890977889299393, -0.012558629736304283, 0.00017766642849892378, 0.03733646497130394, 0.06019090861082077, -0.023080725222826004, 0.022628162056207657, 0.012388918548822403, 0.024438414722681046, 0.02919032797217369, 0.00250324048101902, -0.0...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "kwargs", "matmul", "module", "nn", "not", "p", "query", "return", "scaling", "softmax", "...
sam/modeling_sam.py:SamAttention
[ -0.00009485405462328345, 0.038222670555114746, 0.03237685188651085, -0.009218408726155758, 0, 0.022933602333068848, 0.03485008329153061, -0.019448595121502876, 0.001433350145816803, 0.020460370928049088, 0.0028104905504733324, 0.0063798134215176105, -0.0034850083757191896, -0.0042438409291...
[ "ALL_ATTENTION_FUNCTIONS", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_attn_implementation", "_recombine_heads", "_separate_heads", "attention_downModelple_rate", "attention_interface", "attention_mask", "attention_similarity", "attn_output", "a...
sam/modeling_sam.py:SamTwoWayAttentionBlock
[ -0.00009303367551183328, 0.028310323134064674, 0.03527555987238884, -0.027636267244815826, -0.0001983548136195168, 0.04358891025185585, 0.021906796842813492, -0.022131482139229774, 0.008875061757862568, 0.016514355316758156, 0.020671028643846512, 0.036623671650886536, -0.00039319891948252916...
[ "False", "LayerNorm", "ModelAttention", "ModelMLPBlock", "ModelTwoWayAttentionBlock", "Module", "_", "__init__", "attention_downModelple_rate", "attention_similarity", "attn_out", "class", "config", "cross_attn_image_to_token", "cross_attn_token_to_image", "def", "downModelple_rate",...
sam/modeling_sam.py:SamTwoWayTransformer
[ -0.0002629486261866987, 0.04207178205251694, 0.04229919612407684, -0.018420618027448654, -0.0008670198149047792, 0.04298144206404686, 0.02353745698928833, -0.024901947006583214, 0.009551431983709335, 0.006452901754528284, 0.012280412018299103, 0.020467353984713554, -0.0004797035944648087, ...
[ "LayerNorm", "ModelAttention", "ModelTwoWayAttentionBlock", "ModelTwoWayTransformer", "Module", "ModuleList", "None", "_", "__init__", "append", "attention_similarity", "attn_out", "class", "config", "def", "final_attn_token_to_image", "flatten", "for", "forward", "hidden_size"...
sam/modeling_sam.py:SamFeedForward
[ -0.00017931373440660536, 0.038859594613313675, 0.014771191403269768, 0.03386011719703674, -0.00036217825254425406, 0.038405098021030426, 0.047267813235521317, -0.026360895484685898, 0.0034087365493178368, -0.007669657003134489, 0.038859594613313675, -0.03590535745024681, 0.003366127144545316...
[ "F", "False", "Linear", "ModelFeedForward", "Module", "ModuleList", "ReLU", "_", "__init__", "activation", "class", "def", "for", "forward", "hidden_dim", "hidden_states", "if", "in", "input_dim", "layer", "layers", "nn", "num_layers", "output_dim", "proj_in", "proj...
sam/modeling_sam.py:SamMaskDecoder
[ -0.00033930785139091313, 0.04714746028184891, 0.01904478669166565, -0.013702956959605217, -0.0016112587181851268, 0.04250239208340645, 0.02078668773174286, -0.06874703615903854, 0.0032805807422846556, -0.027057532221078873, 0.013702956959605217, 0.014980350621044636, -0.005109576974064112, ...
[ "ConvTranspose2d", "Embedding", "GELU", "ModelFeedForward", "ModelLayerNorm", "ModelMaskDecoder", "ModelTwoWayTransformer", "Module", "ModuleList", "None", "_", "__init__", "activation", "attention_similarity", "batch_size", "cat", "channels_first", "class", "config", "current_...
sam/modeling_sam.py:SamPositionalEmbedding
[ -0.00032549293246120214, 0.03610801696777344, 0.009374196641147137, 0.01573939248919487, -0.0015912988455966115, 0.010531505569815636, 0.03379340097308159, 0, 0.010415773838758469, 0.02603943645954132, 0.004166309721767902, 0.004282040521502495, 0.002618409926071763, -0.003790184622630477,...
[ "ModelPositionalEmbedding", "Module", "None", "Parameter", "__init__", "cat", "class", "clone", "config", "coordinates", "cos", "def", "dim", "dtype", "forward", "if", "input_coords", "input_shape", "is", "nn", "not", "np", "num_pos_feats", "pi", "positional_embedding...
sam/modeling_sam.py:SamMaskEmbedding
[ -0.000023781873096595518, 0.005298953503370285, 0.03021530993282795, 0.0033259389456361532, 0, 0.05344051122665405, -0.009244983084499836, -0.04284260421991348, 0.013360127806663513, 0.006088159512728453, 0.030666284263134003, 0.012965524569153786, 0.0017898061778396368, -0.001359970774501...
[ "ACT2FN", "Conv2d", "ModelLayerNorm", "ModelMaskEmbedding", "Module", "__init__", "activation", "channels_first", "class", "config", "conv1", "conv2", "conv3", "data_format", "def", "dense_embeddings", "eps", "forward", "hidden_act", "hidden_size", "hidden_states", "kernel_...
sam/modeling_sam.py:SamPromptEncoder
[ -0.00015911966329440475, 0.01103229634463787, -0.005233268719166517, 0.01855688914656639, -0.0007319504511542618, 0.03688747435808182, 0.022177744656801224, -0.025798602029681206, 0.004186614882200956, 0.0009547179797664285, -0.0028995138127356768, 0.018217433243989944, -0.003238969016820192...
[ "Embedding", "ModelMaskEmbedding", "ModelPositionalEmbedding", "ModelPromptEncoder", "Module", "ModuleList", "None", "Tensor", "__init__", "_embed_boxes", "_embed_points", "also", "batch_size", "be", "box_embeddings", "boxes", "cat", "class", "config", "coords", "corner_embed...
sam/modeling_sam.py:SamVisionAttention
[ -0.00003578346149879508, 0.03932340443134308, 0.037312548607587814, -0.0017804453382268548, -0.00010211377957602963, 0.0022063557989895344, 0.03708912059664726, -0.02602941356599331, 0.002639248501509428, 0.03641883656382561, 0.013293991796672344, 0.03150340914726257, 0.0015151241095736623, ...
[ "F", "Linear", "ModelVisionAttention", "Module", "None", "Parameter", "_", "__init__", "arange", "attention_dropout", "attn_output", "attn_probs", "attn_weights", "batch_size", "bhwc", "bhwk", "class", "config", "decomposed_rel_pos", "def", "dim", "dropout", "dtype", "e...
sam/modeling_sam.py:SamVisionSdpaAttention
[ -0.00012180607882328331, 0.052045371383428574, 0.031631022691726685, 0.005748546216636896, -0.0003785627777688205, 0.009478090330958366, 0.048680368810892105, -0.025349685922265053, 0.005103587172925472, 0.03320135921239853, 0.008244256488978863, 0.03544469177722931, 0.0006940317689441144, ...
[ "False", "If", "ModelVisionAttention", "ModelVisionSdpaAttention", "None", "The", "True", "_", "__class__", "__init__", "__name__", "attention", "attn_bias", "attn_implementation", "attn_mask", "attn_output", "batch_size", "be", "class", "config", "decomposed_rel_pos", "def...
sam/modeling_sam.py:SamVisionLayer
[ -0.00009699767542770132, 0.01536651886999607, 0.013863272964954376, 0.020711395889520645, -0.0001035221794154495, 0.028728710487484932, 0.029619522392749786, -0.03251466527581215, 0.0021852748468518257, 0.030064929276704788, 0.024497348815202713, 0.008407045155763626, 0.0016702738357707858, ...
[ "F", "GradientCheckpointingLayer", "LayerNorm", "ModelMLPBlock", "ModelVisionLayer", "Model_VISION_ATTENTION_CLASSES", "__init__", "_attn_implementation", "attn", "attn_weights", "batch_size", "channel", "class", "config", "contiguous", "def", "eps", "forward", "height", "hidde...
sam/modeling_sam.py:SamVisionNeck
[ -0.00006912002572789788, 0.03223370388150215, 0.018145546317100525, 0.007833016104996204, 0, 0.03786896914243698, 0.014257215894758701, -0.04756161943078041, 0.019498011097311974, 0.037418145686388016, 0.013186515308916569, 0.021752115339040756, 0.005860673729330301, -0.012172168120741844,...
[ "Conv2d", "ModelLayerNorm", "ModelVisionNeck", "Module", "__init__", "channels_first", "class", "config", "conv1", "conv2", "data_format", "def", "forward", "hidden_size", "hidden_states", "kernel_size", "layer_norm1", "layer_norm2", "nn", "output_channels", "padding", "per...
sam/modeling_sam.py:SamPreTrainedModel
[ -0.00021509319776669145, 0.048719048500061035, -0.0017844768008217216, 0.020960520952939987, -0.0009205634705722332, 0.017674818634986877, 0.017561517655849457, -0.00393717922270298, -0.001926101977005601, 0.01586201600730419, -0.0017349080881103873, 0.011160061694681644, -0.0010763511527329...
[ "Model", "ModelConfig", "ModelPositionalEmbedding", "ModelPreTrainedModel", "ModelVisionAttention", "ModelVisionEncoder", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "_supports_sdpa", "base_model_prefix", "class", "config", "def", "elif", "if", "image", "init...
sam/modeling_sam.py:SamVisionEncoder
[ 0.00007003513019299135, 0.030703401193022728, 0.0016038045287132263, 0.03608210012316704, 0.000297649297863245, 0.023980028927326202, 0.028238164260983467, -0.02588498406112194, 0.0063311755657196045, 0.020842455327510834, 0.008908468298614025, 0.02319563552737236, 0.00026263174368068576, ...
[ "False", "ModelPatchEmbeddings", "ModelPreTrainedModel", "ModelVisionAttention", "ModelVisionEncoder", "ModelVisionEncoderOutput", "ModelVisionLayer", "ModelVisionNeck", "ModuleList", "None", "Parameter", "__init__", "_can_record_outputs", "append", "attentions", "check_model_inputs", ...
sam/modeling_sam.py:SamVisionModel
[ 0.00008263205381808802, 0.03668159618973732, -0.008607798255980015, 0.04860874265432358, 0.0004448065592441708, 0.021491365507245064, 0.048158660531044006, -0.0168780367821455, 0.010408122092485428, 0.028242580592632294, 0.024641932919621468, 0.005569751840084791, -0.0008474180358462036, -...
[ "ModelPreTrainedModel", "ModelVisionConfig", "ModelVisionEncoder", "ModelVisionModel", "None", "__init__", "auto_docstring", "class", "config", "def", "forward", "get_input_embeddings", "kwargs", "main_input_name", "patch_embed", "pixel_values", "post_init", "return", "self", "...
sam/modeling_sam.py:SamModel
[ -0.0001184461943921633, 0.03593745455145836, -0.008085926994681358, 0.011342758312821388, -0.00030532796517945826, 0.04761712625622749, 0.02021481841802597, -0.03436518833041191, 0.003341060131788254, 0.009770494885742664, 0.016733376309275627, 0.018867162987589836, -0.003509516827762127, ...
[ "Got", "ModelImageSegmentationOutput", "ModelMaskDecoder", "ModelModel", "ModelPositionalEmbedding", "ModelPreTrainedModel", "ModelPromptEncoder", "ModelTwoWayAttentionBlock", "ModelVisionEncoder", "Modele", "None", "Of", "OutputRecorder", "The", "True", "ValueError", "You", "__ini...
owlvit/modeling_owlvit.py:contrastive_loss
[ 0.00008122165309032425, 0.037291333079338074, 0.037291333079338074, -0.0017656880663707852, 0.00034784057061187923, 0.061926212161779404, 0.017854638397693634, 0.025877924636006355, 0.023052824661135674, -0.006780242547392845, 0.030059074983000755, -0.031415123492479324, 0.002161202253773808...
[ "Model_loss", "arange", "cross_entropy", "def", "device", "functional", "len", "logits", "nn", "return", "torch" ]
owlvit/modeling_owlvit.py:owlvit_loss
[ 0.00016533616872038692, 0.03712017834186554, 0.07967258244752884, -0.018786432221531868, 0.0008841506205499172, 0.04391045495867729, -0.004498558584600687, -0.012165912427008152, 0.026595251634716988, 0.01935228891670704, 0.033272355794906616, -0.005517099983990192, 0.0037346521858125925, ...
[ "Model_loss", "caption_loss", "contrastive_loss", "def", "image_loss", "return", "similarity", "t" ]
owlvit/modeling_owlvit.py:OwlViTOutput
[ -0.00009819567640079185, 0.01683354377746582, 0.015711307525634766, 0.0037594917230308056, -0.0005225412896834314, 0.030075933784246445, 0.04623613506555557, -0.01363517064601183, 0.016272425651550293, 0.004965895786881447, 0.02379141002893448, 0.021210266277194023, -0.0035911560989916325, ...
[ "ModelOutput", "None", "class", "def", "else", "for", "getattr", "if", "image_embeds", "in", "k", "keys", "logits_per_image", "logits_per_text", "loss", "not", "r", "return", "self", "text_embeds", "text_model_output", "to_tuple", "tuple", "vision_model_output" ]
owlvit/modeling_owlvit.py:_upcast
[ -0.0003424626193009317, 0.023878658190369606, 0.021110117435455322, -0.023763300850987434, -0.001405899180099368, 0.011074160225689411, 0.013438954949378967, -0.032530345022678375, -0.003085768548771739, 0.042681656777858734, -0.0220329649746418, -0.04314308241009712, -0.004700750112533569, ...
[ "_upcast", "def", "dtype", "else", "float", "float32", "float64", "if", "in", "int", "int64", "is_floating_point", "return", "t", "torch" ]
owlvit/modeling_owlvit.py:box_area
[ 0.00032100427779369056, -0.029818546026945114, 0.015613648109138012, -0.008863386698067188, 0.0017682750476524234, 0.04202771559357643, 0.03357521444559097, 0.007982918061316013, 0.029231566935777664, 0.017844170331954956, 0.004461042582988739, 0.011974376626312733, -0.002920221770182252, ...
[ "Model_area", "Modeles", "_upcast", "def", "return" ]
owlvit/modeling_owlvit.py:box_iou
[ 0.00011689333041431382, 0.012335369363427162, 0.0266124177724123, -0.011421638540923595, 0.0008530535851605237, 0.04088946431875229, 0.02706928178668022, -0.028896745294332504, 0.029010960832238197, -0.00947995949536562, 0.05528073012828827, -0.014105723239481449, -0.00174179975874722, -0....
[ "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/modeling_owlvit.py:generalized_box_iou
[ 0.00010878764442168176, -0.007103921379894018, 0.031472355127334595, -0.038038529455661774, 0.0005908141611143947, 0.023208029568195343, -0.005688797682523727, -0.023208029568195343, 0.02207593061029911, -0.01267950888723135, 0.025132598355412483, -0.01879284344613552, 0.00026356682064943016...
[ "Model_box_iou", "None", "ValueError", "all", "area", "be", "bottom_right", "box_iou", "boxes1", "boxes2", "but", "clamp", "corner", "def", "f", "format", "got", "if", "in", "iou", "max", "min", "must", "not", "raise", "return", "top_left", "torch", "union", ...
owlvit/modeling_owlvit.py:OwlViTObjectDetectionOutput
[ -0.00022811484814155847, 0.004362696316093206, 0.0007271160720847547, -0.002167091006413102, -0.0008732521091587842, 0.04607919976115227, 0.058853629976511, -0.029084641486406326, 0.015511808916926384, -0.0012760173995047808, 0.012375229969620705, 0.031023617833852768, -0.003992009907960892,...
[ "ModelObjectDetectionOutput", "ModelOutput", "None", "class", "class_embeds", "def", "else", "for", "getattr", "if", "image_embeds", "in", "k", "keys", "logits", "loss", "loss_dict", "not", "pred_boxes", "r", "return", "self", "text_embeds", "text_model_output", "to_t...
owlvit/modeling_owlvit.py:OwlViTImageGuidedObjectDetectionOutput
[ -0.00023544378927908838, 0.002286141272634268, -0.016563739627599716, -0.001955441664904356, -0.0011933945352211595, 0.052911948412656784, 0.05889330059289932, -0.025420740246772766, 0.014493272639811039, 0.009489643387496471, 0.007045341189950705, 0.03496789559721947, -0.006211402826011181,...
[ "ModelImageGuidedObjectDetectionOutput", "ModelOutput", "None", "class", "class_embeds", "def", "else", "for", "getattr", "if", "image_embeds", "in", "k", "keys", "logits", "not", "query_image_embeds", "query_pred_boxes", "r", "return", "self", "target_pred_boxes", "text_...
owlvit/modeling_owlvit.py:OwlViTVisionEmbeddings
[ -0.00015198385517578572, 0.017767177894711494, 0.011132597923278809, 0.022040296345949173, -0.0006465903134085238, 0.006465903017669916, 0.030136730521917343, -0.010289219208061695, 0.0023474039044231176, 0.020803339779376984, 0.02428930439054966, 0.03688376024365425, -0.0005060271942056715,...
[ "Conv2d", "Embedding", "False", "ModelVisionEmbeddings", "Module", "Parameter", "_", "__init__", "align_corners", "and", "arange", "batch_size", "bicubic", "cat", "class", "class_embedding", "class_embeds", "class_pos_embed", "config", "def", "dim", "else", "embed_dim", ...
owlvit/modeling_owlvit.py:OwlViTTextEmbeddings
[ -0.00015322833496611565, 0.022334136068820953, 0.015531862154603004, -0.00901301484555006, -0.0009282271494157612, 0.038319483399391174, 0.039679937064647675, -0.002139882417395711, 0.012527523562312126, -0.01655220240354538, 0.02618875913321972, 0.029249783605337143, -0.0005066277808509767,...
[ "Embedding", "False", "ModelTextEmbeddings", "Module", "None", "__init__", "arange", "class", "config", "def", "else", "embeddings", "expand", "forward", "hidden_size", "if", "input_ids", "inputs_embeds", "is", "max_position_embeddings", "nn", "not", "persistent", "posi...
owlvit/modeling_owlvit.py:OwlViTAttention
[ -0.0000686713246977888, 0.04031050205230713, 0.03941471502184868, -0.013436834327876568, -0.00029393075965344906, 0.02049117349088192, 0.026985643431544304, -0.016908016055822372, 0.001847564708441496, 0.029337089508771896, 0.01713196374475956, 0.006382496561855078, -0.001847564708441496, ...
[ "Attention", "False", "Linear", "ModelAttention", "Module", "None", "ValueError", "__init__", "_shape", "and", "attention_dropout", "attention_mask", "attn_output", "attn_probs", "attn_weights", "attn_weights_reshaped", "be", "bmm", "bsz", "but", "by", "causal_attention_mas...
owlvit/modeling_owlvit.py:OwlViTMLP
[ -0.00016017387679312378, 0.027564143761992455, 0.04351034387946129, 0.019363241270184517, -0.0006371361087076366, 0.04783859848976135, 0.03325921669602394, -0.028019750490784645, 0.005125564057379961, -0.009169064462184906, 0.04282693564891815, -0.03348701819777489, 0.0012315592030063272, ...
[ "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "activation_fn", "class", "config", "def", "fc1", "fc2", "forward", "hidden_act", "hidden_size", "hidden_states", "intermediate_size", "nn", "return", "self", "super" ]
owlvit/modeling_owlvit.py:OwlViTEncoderLayer
[ -0.00003186961112078279, 0.025657452642917633, 0.03781098499894142, 0.016092175617814064, -0.00009099322778638452, 0.04748879745602608, 0.025882519781589508, -0.01744256727397442, 0.007652223110198975, 0.010296741500496864, 0.009677811525762081, 0.02149374410510063, -0.00014945748262107372, ...
[ "False", "GradientCheckpointingLayer", "LayerNorm", "ModelAttention", "ModelEncoderLayer", "ModelMLP", "__init__", "attention_mask", "attn_weights", "causal_attention_mask", "class", "config", "def", "embed_dim", "eps", "forward", "hidden_size", "hidden_states", "if", "layer_no...
owlvit/modeling_owlvit.py:OwlViTPreTrainedModel
[ -0.00018734551849775016, 0.04863913729786873, 0.005712270736694336, 0.021378597244620323, -0.0008943097200244665, 0.03868508338928223, 0.016175340861082077, -0.0043266210705041885, 0.0037610495928674936, 0.015835998579859734, 0.009897499345242977, 0.004354899749159813, -0.004298342391848564,...
[ "LayerNorm", "Linear", "Model", "ModelAttention", "ModelConfig", "ModelEncoderLayer", "ModelForObjectDetection", "ModelMLP", "ModelModel", "ModelPreTrainedModel", "ModelTextEmbeddings", "ModelVisionEmbeddings", "None", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", ...
owlvit/modeling_owlvit.py:OwlViTEncoder
[ -0.00005490372495842166, 0.022488566115498543, 0.024962307885289192, 0.03328307718038559, -0.00009355595102533698, 0.03778079152107239, 0.009726304560899734, -0.025974294170737267, 0.008376991376280785, 0.0032748975791037083, 0.01326825376600027, 0.002825126051902771, -0.00174286391120404, ...
[ "BaseModelOutput", "False", "ModelEncoder", "ModelEncoderLayer", "Module", "ModuleList", "None", "_", "__init__", "all_attentions", "attention_mask", "attentions", "causal_attention_mask", "class", "config", "def", "else", "encoder_layer", "encoder_states", "for", "forward", ...
owlvit/modeling_owlvit.py:OwlViTTextTransformer
[ -0.00015208779950626194, 0.05761402100324631, 0.036233820021152496, 0.016654053702950478, -0.0007454939186573029, 0.027794264256954193, 0.02284305915236473, -0.014966142363846302, 0.010746365413069725, 0.009958673268556595, 0.04028480499982834, 0.018116910010576248, 0.0006259335787035525, ...
[ "BaseModelOutputWithPooling", "LayerNorm", "ModelEncoder", "ModelTextEmbeddings", "ModelTextTransformer", "Module", "None", "__init__", "_create_4d_causal_attention_mask", "_prepare_4d_attention_mask", "arange", "argmax", "attention_mask", "attentions", "auto_docstring", "causal_attent...
owlvit/modeling_owlvit.py:OwlViTTextModel
[ 0.00013635653886012733, 0.0595213808119297, 0.029984453693032265, -0.0010419038590043783, 0.0004510254948399961, 0.01991504803299904, 0.03132704272866249, -0.004139645025134087, 0.0025313368532806635, 0.002237645909190178, 0.02237645909190178, 0.007719878107309341, -0.003971821162849665, 0...
[ "ModelPreTrainedModel", "ModelTextConfig", "ModelTextModel", "ModelTextTransformer", "None", "__init__", "attention_mask", "auto_docstring", "class", "config", "def", "embeddings", "forward", "get_input_embeddings", "input_ids", "input_modalities", "kwargs", "output_attentions", ...
owlvit/modeling_owlvit.py:OwlViTVisionTransformer
[ -0.00016215104551520199, 0.05910757929086685, 0.019627325236797333, 0.043315477669239044, -0.0004617779632098973, 0.018386518582701683, 0.04760190472006798, -0.026846572756767273, 0.003666023490950465, 0.028425782918930054, 0.026282567530870438, 0.010716068558394909, -0.0005640035960823298, ...
[ "BaseModelOutputWithPooling", "False", "LayerNorm", "ModelEncoder", "ModelVisionEmbeddings", "ModelVisionTransformer", "Module", "None", "__init__", "attentions", "auto_docstring", "class", "config", "def", "dtype", "else", "embeddings", "encoder", "encoder_outputs", "eps", "...
owlvit/modeling_owlvit.py:OwlViTVisionModel
[ 0.00005382284871302545, 0.04906543344259262, 0.0014912867918610573, 0.03562285006046295, 0.0005531063652597368, 0.01131417602300644, 0.04794522002339363, -0.00918576680123806, 0.005209001712501049, 0.030245816335082054, 0.020836006850004196, 0.006833313964307308, -0.0007911521242931485, 0....
[ "False", "ModelPreTrainedModel", "ModelVisionConfig", "ModelVisionModel", "ModelVisionTransformer", "None", "__init__", "auto_docstring", "class", "config", "def", "embeddings", "forward", "get_input_embeddings", "image", "input_modalities", "interpolate_pos_encoding", "kwargs", ...
owlvit/modeling_owlvit.py:OwlViTModel
[ -0.000054212348914006725, 0.05395282432436943, 0.02664199471473694, 0.01950773596763611, -0.00018549767264630646, 0.03923841938376427, 0.02318633906543255, -0.008527668192982674, 0.006103134714066982, 0.018838899210095406, 0.041021984070539474, 0.015940608456730843, -0.002145851030945778, ...
[ "False", "Linear", "ModelConfig", "ModelModel", "ModelOutput", "ModelPreTrainedModel", "ModelTextConfig", "ModelTextTransformer", "ModelVisionConfig", "ModelVisionTransformer", "Model_loss", "None", "Parameter", "True", "TypeError", "__init__", "attention_mask", "auto_docstring", ...
owlvit/modeling_owlvit.py:OwlViTBoxPredictionHead
[ -0.000302949600154534, -0.008040638640522957, 0.015967225655913353, 0.022810323163866997, -0.0009551823022775352, 0.023722736164927483, 0.04858598858118057, -0.03444358706474304, -0.0022525193635374308, 0.02007308416068554, 0.0010193362832069397, 0.015967225655913353, -0.003378779161721468, ...
[ "GELU", "Linear", "ModelBoxPredictionHead", "Module", "__init__", "class", "config", "def", "dense0", "dense1", "dense2", "forward", "gelu", "hidden_size", "image_features", "nn", "out_dim", "output", "return", "self", "super", "vision_config", "width" ]
owlvit/modeling_owlvit.py:OwlViTClassPredictionHead
[ -0.0003084673953708261, 0.030071984976530075, 0.02123403549194336, 0.03305622935295105, -0.001370169222354889, 0.017905456945300102, 0.03305622935295105, -0.005451981909573078, -0.0019081936916336417, 0.028350306674838066, 0.021807927638292313, 0.026054736226797104, -0.002855116967111826, ...
[ "ELU", "Linear", "ModelClassPredictionHead", "Module", "None", "True", "__init__", "batch_size", "class", "config", "def", "dense0", "device", "dim", "dtype", "einsum", "elu", "finfo", "float32", "forward", "hidden_size", "if", "image_class_embeds", "image_embeds", "i...
owlvit/modeling_owlvit.py:OwlViTForObjectDetection
[ -0.00019404376507736742, 0.015764504671096802, -0.011171250604093075, 0.009980406612157822, -0.0009711048915050924, 0.031302183866500854, 0.04173624515533447, -0.020868122577667236, 0.010320647619664669, 0.015424264594912529, 0.01349623128771782, 0.04445817321538925, -0.0037710056640207767, ...
[ "False", "LayerNorm", "Model", "ModelBoxPredictionHead", "ModelClassPredictionHead", "ModelConfig", "ModelForObjectDetection", "ModelImageGuidedObjectDetectionOutput", "ModelModel", "ModelObjectDetectionOutput", "ModelPreTrainedModel", "None", "Sigmoid", "True", "_", "__init__", "all...
apertus/modeling_apertus.py:ApertusMLP
[ -0.00020942425180692226, 0.029429899528622627, 0.027262581512331963, 0.03444895148277283, -0.0004990535089746118, 0.05726282671093941, 0.025095263496041298, -0.01608378253877163, -0.006074194330722094, -0.01391646359115839, 0.033308256417512894, -0.05133121833205223, -0.00019694790535140783,...
[ "ACT2CLS", "ACT2FN", "Linear", "ModelMLP", "Module", "__init__", "act_fn", "class", "config", "def", "down_proj", "dtype", "forward", "hidden_act", "hidden_size", "if", "intermediate_size", "nn", "return", "self", "super", "up_proj", "x", "xielu" ]
apertus/modeling_apertus.py:ApertusRMSNorm
[ -0.00009884802420856431, 0.04179859161376953, 0.03230918198823929, 0.053095508366823196, -0.00046952810953371227, 0.03931327164173126, 0.022028988227248192, -0.02937198430299759, 0.007964326068758965, 0.042476408183574677, 0.019995542243123055, 0.006975846365094185, 0.0028524715453386307, ...
[ "ModelRMSNorm", "Module", "Parameter", "True", "__init__", "class", "def", "eps", "extra_repr", "f", "float32", "forward", "hidden_size", "hidden_states", "keepdim", "mean", "nn", "ones", "pow", "return", "rsqrt", "self", "shape", "super", "to", "torch", "tuple", ...
apertus/modeling_apertus.py:ApertusRotaryEmbedding
[ -0.00029968636226840317, 0.04852752387523651, 0.0020364229567348957, -0.005228263325989246, -0.0013792794197797775, 0.04136393964290619, 0.040670689195394516, 0.0062681385315954685, -0.0026430170983076096, 0.021144136786460876, 0.0021808501332998276, -0.004043960478156805, -0.001364836702123...
[ "False", "ModelRotaryEmbedding", "Module", "None", "ROPE_INIT_FUNCTIONS", "Tensor", "__init__", "and", "arange", "attention_factor", "attention_scaling", "base", "cat", "class", "clone", "compute_default_rope_parameters", "config", "cos", "cpu", "def", "default", "device", ...
apertus/modeling_apertus.py:rotate_half
[ 0.00002049485374300275, 0.013860220089554787, 0.03434192016720772, 0.002974055241793394, 0.0003507140791043639, 0.028506038710474968, 0.01975221559405327, -0.02008890174329281, 0.014477476477622986, 0.018742159008979797, -0.001487027620896697, -0.01217679213732481, 0.00022445700597018003, ...
[ "Model_half", "cat", "def", "dim", "return", "shape", "torch", "x", "x1", "x2" ]
apertus/modeling_apertus.py:apply_rotary_pos_emb
[ -0.0001444592053303495, 0.027112245559692383, 0.028019767254590988, 0.0028360087890177965, -0.0005707467789761722, 0.021667107939720154, 0.046510547399520874, -0.002183726755902171, 0.01293220091611147, 0.03652779385447502, 0.007884104736149311, 0.0017654155381023884, -0.0007834474672563374,...
[ "Model_rotary_pos_emb", "cos", "def", "k", "k_embed", "q", "q_embed", "return", "rotate_half", "sin", "unsqueeze", "unsqueeze_dim" ]
apertus/modeling_apertus.py:repeat_kv
[ -0.00025096136960200965, -0.0023088448215276003, -0.004072744864970446, -0.009292741306126118, -0.000559285341296345, 0.03143470734357834, 0.009694280102849007, -0.058739304542541504, 0.011185707524418831, 0.05162634328007698, 0.005736260209232569, -0.02317449077963829, 0.0006775957299396396...
[ "Model_kv", "None", "batch", "def", "expand", "head_dim", "hidden_states", "if", "n_rep", "num_key_value_heads", "reshape", "return", "shape", "slen" ]
apertus/modeling_apertus.py:eager_attention_forward
[ 0, 0.021594731137156487, 0.01775064319372177, -0.018768195062875748, -0.00008832923776935786, 0.039797618985176086, 0.05359111353754997, -0.035049039870500565, 0.02069023996591568, 0.011645326390862465, 0.029395969584584236, 0.022499222308397293, 0.002741739386692643, -0.014584923163056374...
[ "Model_attention_forward", "None", "attention_mask", "attn_output", "attn_weights", "causal_mask", "contiguous", "def", "dim", "dropout", "dtype", "float32", "functional", "if", "is", "key", "key_states", "kwargs", "matmul", "module", "nn", "not", "num_key_value_groups", ...
apertus/modeling_apertus.py:ApertusAttention
[ -0.00004590257958625443, 0.040707021951675415, 0.025076424703001976, 0.005987980403006077, -0.00029342510970309377, 0.03373510017991066, 0.037783313542604446, -0.012819338589906693, 0.003935761749744415, 0.023277219384908676, 0.018216954544186592, 0.03216079622507095, 0.00006457108975155279,...
[ "ALL_ATTENTION_FUNCTIONS", "Linear", "ModelAttention", "ModelRMSNorm", "Module", "None", "Tensor", "True", "__init__", "_attn_implementation", "apply_rotary_pos_emb", "attention_dropout", "attention_interface", "attention_mask", "attn_output", "attn_weights", "cache_kwargs", "cache...
apertus/modeling_apertus.py:ApertusDecoderLayer
[ -0.0001445422094548121, 0.04670476168394089, 0.015004186891019344, -0.0025383022148162127, -0.0005711180274374783, 0.03722843527793884, 0.04512537270784378, -0.03925907611846924, 0.008968668058514595, -0.003948470111936331, -0.00406128354370594, 0.026849597692489624, -0.0007368127699010074, ...
[ "False", "GradientCheckpointingLayer", "ModelAttention", "ModelDecoderLayer", "ModelMLP", "ModelRMSNorm", "None", "Tensor", "_", "__init__", "attention_layernorm", "attention_mask", "cache_position", "class", "config", "def", "eps", "feedforward_layernorm", "forward", "hidden_s...
apertus/modeling_apertus.py:ApertusPreTrainedModel
[ -0.00034975787275470793, 0.027630871161818504, 0.016904963180422783, 0.011250545270740986, -0.0018799485405907035, 0.029612833634018898, 0.03077869303524494, -0.03241089731454849, -0.00408050836995244, 0.003993069287389517, 0.006383080966770649, 0.004459412768483162, -0.004255387466400862, ...
[ "ModelAttention", "ModelConfig", "ModelDecoderLayer", "ModelPreTrainedModel", "PreTrainedModel", "True", "_can_compile_fullgraph", "_can_record_outputs", "_no_split_modules", "_skip_keys_device_placement", "_supports_attention_backend", "_supports_flash_attn", "_supports_flex_attn", "_supp...
apertus/modeling_apertus.py:ApertusModel
[ -0.00009990988473873585, 0.048900384455919266, -0.007661811541765928, -0.004140758421272039, -0.000760547467507422, 0.04078787937760353, 0.04033718630671501, -0.016675706952810287, 0.012732128612697124, 0.011943412013351917, 0.016901055350899696, -0.0022112212609499693, -0.000880263280123472...
[ "BaseModelOutputWithPast", "DynamicCache", "Embedding", "False", "ModelDecoderLayer", "ModelModel", "ModelPreTrainedModel", "ModelRMSNorm", "ModelRotaryEmbedding", "ModuleList", "None", "ValueError", "You", "__init__", "and", "arange", "attention_mask", "auto_docstring", "cache_p...
apertus/modeling_apertus.py:ApertusForCausalLM
[ -0.00028262686100788414, 0.03526614233851433, 0.008361488580703735, -0.001301150070503354, -0.0012087186332792044, 0.02741658128798008, 0.0389065183699131, -0.007394513580948114, 0.003640376031398773, 0.026734011247754097, 0.025823917239904404, 0.0026734010316431522, 0.0009385344455949962, ...
[ "CausalLMOutputWithPast", "GenerationMixin", "Linear", "ModelForCausalLM", "ModelModel", "ModelPreTrainedModel", "None", "__init__", "_pp_plan", "_tied_weights_keys", "_tp_plan", "attention_mask", "attentions", "auto_docstring", "cache_position", "can_return_tuple", "class", "colwi...
apertus/modeling_apertus.py:ApertusForTokenClassification
[ -0.00016310509818140417, 0.024394849315285683, -0.0069496952928602695, -0.024848707020282745, -0.0007446102099493146, 0.035854753106832504, 0.0356278270483017, -0.007885776460170746, 0.0015317696379497647, -0.018381234258413315, 0.040166404098272324, 0.024394849315285683, -0.0042832815088331...
[ "GenericForTokenClassification", "ModelForTokenClassification", "ModelPreTrainedModel", "class", "pass" ]
focalnet/modeling_focalnet.py:FocalNetEncoderOutput
[ -0.0002142470475519076, 0.024096069857478142, 0.037406280636787415, 0.024325555190443993, -0.0012836894020438194, 0.04222549498081207, 0.03465244174003601, -0.05186391994357109, 0.01336758118122816, 0.01336758118122816, -0.01158906240016222, 0.01789993792772293, -0.002079720376059413, 0.01...
[ "ModelEncoderOutput", "ModelOutput", "None", "class", "hidden_states", "last_hidden_state", "r", "reshaped_hidden_states" ]
focalnet/modeling_focalnet.py:FocalNetModelOutput
[ -0.00020303489873185754, 0.021981434896588326, 0.031140366569161415, 0.02747679315507412, -0.0010947785340249538, 0.047397468239068985, 0.03480393812060356, -0.023813221603631973, 0.01476877648383379, 0.0015169479884207249, -0.006210900377482176, -0.00253301695920527, -0.001037535141222179, ...
[ "ModelModelOutput", "ModelOutput", "None", "class", "hidden_states", "last_hidden_state", "pooler_output", "r", "reshaped_hidden_states" ]
focalnet/modeling_focalnet.py:FocalNetMaskedImageModelingOutput
[ -0.0002087996108457446, 0.038667913526296616, 0.03502858057618141, -0.003724629757925868, -0.0011657237773761153, 0.05458999425172806, 0.010974863544106483, -0.05140557512640953, 0.014159279875457287, -0.012737665325403214, -0.011088592000305653, 0.02376939356327057, -0.0022461507469415665, ...
[ "ModelMaskedImageModelingOutput", "ModelOutput", "None", "class", "hidden_states", "loss", "r", "reconstruction", "reshaped_hidden_states" ]
focalnet/modeling_focalnet.py:FocalNetImageClassifierOutput
[ -0.0002232617698609829, 0.0344744510948658, 0.03266000747680664, 0.02370118536055088, -0.0010844143107533455, 0.036288898438215256, 0.044453900307416916, -0.023247575387358665, 0.010546460747718811, -0.014855767600238323, 0.021092921495437622, 0.01304132305085659, -0.0035154870711266994, 0...
[ "ModelImageClassifierOutput", "ModelOutput", "None", "class", "hidden_states", "logits", "loss", "r", "reshaped_hidden_states" ]
focalnet/modeling_focalnet.py:FocalNetEmbeddings
[ -0.00014873150212224573, 0.030369555577635765, 0.011841860599815845, 0.009235518053174019, -0.0008853065664879978, 0.028216490522027016, 0.0125217754393816, -0.026290062814950943, 0.006940803490579128, -0.0009348837193101645, 0.025723466649651527, 0.018697673454880714, -0.0006020084256306291...
[ "Dropout", "False", "LayerNorm", "ModelEmbeddings", "ModelPatchEmbeddings", "Module", "None", "Parameter", "True", "_", "__init__", "batch_size", "bool_masked_pos", "class", "config", "def", "dropout", "else", "embed_dim", "embeddings", "eps", "expand", "forward", "grid...
focalnet/modeling_focalnet.py:FocalNetPatchEmbeddings
[ -0.00005924738070461899, 0.016066133975982666, 0.014043823815882206, 0.018313145264983177, -0.0001018177208607085, -0.0002001244865823537, -0.0034266929142177105, -0.01011155266314745, 0.0019099599448964, 0.00662868469953537, 0.02426772750914097, 0.019324300810694695, -0.0008566732285544276,...
[ "Conv2d", "False", "Iterable", "LayerNorm", "Make", "ModelPatchEmbeddings", "Module", "None", "ValueError", "_", "__init__", "abc", "add_norm", "channel", "class", "collections", "config", "configuration", "def", "dimension", "else", "embed_dim", "embeddings", "eps", ...
focalnet/modeling_focalnet.py:drop_path
[ 0, 0.017753854393959045, 0.03775503486394882, -0.02123720571398735, 0, 0.043598074465990067, 0.0339345820248127, -0.027304979041218758, 0.014832334592938423, 0.0017486985307186842, 0.020113544538617134, -0.04944111406803131, 0.0016574009787291288, -0.018315685912966728, -0.05034004524350...
[ "False", "Model_path", "Model_prob", "def", "device", "div", "dtype", "floor_", "if", "input", "keep_prob", "ndim", "not", "or", "output", "rand", "random_tensor", "return", "shape", "torch", "training" ]
focalnet/modeling_focalnet.py:FocalNetDropPath
[ -0.00011107319733127952, 0.018094833940267563, 0.03596488758921623, -0.025063030421733856, -0.00047414645086973906, 0.031918834894895554, 0.033717080950737, -0.026861274614930153, 0.0066872211173176765, -0.002430439693853259, 0.009721758775413036, -0.033717080950737, -0.000818341679405421, ...
[ "ModelDropPath", "Module", "None", "__init__", "class", "def", "drop_path", "drop_prob", "extra_repr", "f", "forward", "hidden_states", "nn", "p", "return", "self", "super", "training" ]
focalnet/modeling_focalnet.py:FocalNetModulation
[ -0.0001993429468711838, 0.03684358298778534, 0.006493395660072565, -0.014016580767929554, -0.0005327730905264616, 0.04302231967449188, 0.022083265706896782, -0.01556126493960619, 0.0010011843405663967, 0.0073229484260082245, 0.03981852903962135, -0.0019737633410841227, -0.0008796119363978505...
[ "Conv2d", "Dropout", "GELU", "LayerNorm", "Linear", "ModelModulation", "Model_factor", "Model_layers", "Model_level", "Model_levels", "Model_window", "Model_windows", "Module", "ModuleList", "Sequential", "True", "__init__", "activation", "append", "class", "config", "conti...
focalnet/modeling_focalnet.py:FocalNetMlp
[ -0.00028909469256177545, 0.023673444986343384, 0.03516540676355362, 0.020340774208307266, -0.0010486416285857558, 0.04780656844377518, 0.04114122688770294, -0.02792547084391117, -0.00042735738679766655, -0.003102830145508051, 0.03332669287919998, -0.034475889056921005, 0.0006931090028956532,...
[ "ACT2FN", "Dropout", "Linear", "ModelMlp", "Module", "None", "__init__", "activation", "class", "config", "def", "drop", "fc1", "fc2", "forward", "hidden_act", "hidden_features", "hidden_state", "in_features", "nn", "or", "out_features", "return", "self", "super" ]
focalnet/modeling_focalnet.py:FocalNetLayer
[ -0.00017519036191515625, 0.025042438879609108, 0.02117224410176277, 0.014513231813907623, -0.0004980030353181064, 0.050084877759218216, 0.004951572977006435, -0.016846731305122375, 0.001863954239524901, 0.015936097130179405, 0.02686370722949505, -0.018212683498859406, 0.001017349073663354, ...
[ "Identity", "LayerNorm", "ModelDropPath", "ModelLayer", "ModelMlp", "ModelModulation", "Module", "Parameter", "True", "_", "__init__", "batch_size", "class", "config", "def", "dim", "drop", "drop_path", "else", "eps", "forward", "gamma_1", "gamma_2", "height", "hidden...
focalnet/modeling_focalnet.py:FocalNetStage
[ -0.00016175673226825893, -0.0013294104719534516, -0.004667079541832209, 0.019573448225855827, -0.00009855669486569241, 0.04254113510251045, 0.022854546085000038, -0.02941674366593361, 0.002093114424496889, 0.010295860469341278, 0.020365437492728233, -0.025683078914880753, -0.0021355424541980...
[ "False", "GradientCheckpointingLayer", "ModelLayer", "ModelPatchEmbeddings", "ModelStage", "ModuleList", "None", "True", "__init__", "add_norm", "class", "config", "cpu", "def", "depths", "device", "dim", "downsample", "dpr", "drop_path", "drop_path_rate", "else", "embed_...
focalnet/modeling_focalnet.py:FocalNetEncoder
[ -0.00019567199342418462, 0.013599646277725697, 0.010029739700257778, 0.030145883560180664, -0.0006906070630066097, 0.020399469882249832, 0.005043202079832554, -0.05575855076313019, 0.0037115702871233225, 0.03581240400671959, 0.011503034271299839, -0.015299602411687374, -0.0026065988931804895...
[ "False", "ModelEncoder", "ModelEncoderOutput", "ModelStage", "Module", "ModuleList", "None", "True", "_", "__init__", "all_hidden_states", "all_reshaped_hidden_states", "and", "batch_size", "class", "config", "def", "depths", "elif", "else", "enumerate", "for", "forward",...
focalnet/modeling_focalnet.py:FocalNetPreTrainedModel
[ -0.00020100086112506688, 0.032272979617118835, -0.008124666288495064, 0.02459968440234661, -0.0008075297810137272, 0.029790444299578667, 0.017603443935513496, -0.005388233810663223, -0.0028492752462625504, 0.014556693844497204, 0.012694790959358215, -0.003272435162216425, -0.0016926388489082...
[ "Model", "ModelConfig", "ModelEmbeddings", "ModelLayer", "ModelPreTrainedModel", "ModelStage", "None", "PreTrainedModel", "True", "_init_weights", "_no_split_modules", "base_model_prefix", "class", "config", "constant_", "def", "elif", "gamma_2", "if", "init", "is", "isinst...