"\ 5r!"SS\RD5r#"SS\RD5r$S4S\RDS\RJS\RJS\RJS\\RJS\&S\&4Sjjr'"SS\RD5r("SS\RD5r)"SS \RD5r*"S!S"\RD5r+"S#S$\RD5r,"S%S&\5r-"S'S(\RD5r."S)S*\RD5r/\"S+S,\55r0\"S-S.\055r1\"S/S09"S1S2\055r2/S3Qr3g)5zPyTorch ViViT model.)CallableOptionalUnionN)nn)CrossEntropyLossMSELoss)ACT2FN)GradientCheckpointingLayer)BaseModelOutputBaseModelOutputWithPoolingImageClassifierOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer)auto_docstringlogging torch_int) VivitConfigc>^\rSrSrSrU4SjrSS\4SjjrSrU=r $)VivitTubeletEmbeddings$az Construct Vivit Tubelet embeddings. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size[0]) * (height // tubelet_size[1]) * (width // tubelet_size[2]). c>[TU]5 URUlURUlURUlURUR S-URUR S--URUR S--UlURUl[R"URURURURS9Ul g)Nrr) kernel_sizestride) super__init__ num_frames image_size tubelet_size patch_size num_patches hidden_size embed_dimrConv3d num_channels projectionselfconfig __class__s `/var/www/html/shao/venv/lib/python3.13/site-packages/transformers/models/vivit/modeling_vivit.pyr VivitTubeletEmbeddings.__init__/s  ++ ++ -- __ 2 2$//!"44 6$//!"44 6   ++))   !3!3ATAT]c]p]p interpolate_pos_encodingc bURup4pVnU(dPX`R:wdXpR:wa2[SUSUSURSSURSS3 5eURSSSSS 5nUR U5nUR S5R SS5nU$) NzImage image size (*z) doesn't match model (rrz).rr )shaper" ValueErrorpermuter*flatten transpose) r, pixel_valuesr2 batch_sizer!r)heightwidthxs r/forwardVivitTubeletEmbeddings.forward?s>J>P>P; e'V-F%SbSbJb$VHAeW4KDOO\]L^K__`aeapapqras`ttvw  $++Aq!Q: OOL ) IIaL " "1a (r1)r'r"r!r%r$r*F) __name__ __module__ __qualname____firstlineno____doc__r boolr@__static_attributes__ __classcell__r.s@r/rr$s dr1rc^\rSrSrSrU4SjrS\RS\S\S\R4Sjr S S \ 4S jjr S r U=r $) VivitEmbeddingsPz| Vivit Embeddings. Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings. c>[TU]5 [R"[R "SSUR 55Ul[U5Ul [R"[R "SURRS-UR 55Ul [R"UR5UlURSSUlXlg)Nr)rr r Parametertorchzerosr& cls_tokenrpatch_embeddingsr%position_embeddingsDropouthidden_dropout_probdropoutr#r$r-r+s r/r VivitEmbeddings.__init__Ws ekk!Q8J8J&KL 6v >#%<< KK400<returncURSS- nURRSS- n[RR 5(dXE:XaX#:Xa UR$URSS2SS24nURSS2SS24nURSnX R S-n X0R S-n [ US-5n URSXU5nURSSSS5n[RRUX4SS S 9nURSSSS5RSSU5n[R"Xg4SS 9$) a This method allows to interpolate the pre-trained position encodings, to be able to use the model on higher resolution images. This method is also adapted to support torch.jit tracing. Adapted from: - https://github.com/facebookresearch/dino/blob/de9ee3df6cf39fac952ab558447af1fa1365362a/vision_transformer.py#L174-L194, and - https://github.com/facebookresearch/dinov2/blob/e1277af2ba9496fbadf7aec6eba56e8d882d1e35/dinov2/models/vision_transformer.py#L179-L211 rNrg?r rbicubicF)sizemode align_cornersdim)r6rUrQjit is_tracingr$rreshaper8r functional interpolateviewcat) r,rZr=r>r% num_positionsclass_pos_embedpatch_pos_embedrc new_height new_widthsqrt_num_positionss r/r2(VivitEmbeddings.interpolate_pos_encodinges]!&&q)A- 0066q9A= yy##%%+*F6?++ +221bqb59221ab59r"q11 __Q// &}c'9:)11!5G]`a)11!Q1=--33 ( 4 *11!Q1=BB1b#Nyy/;CCr1r2c"URup4pVnURXS9nURRUSS/5n [R "X4SS9nU(aXR XU5-nOXR-nURU5nU$)Nr2rrb) r6rTrStilerQrjr2rUrX) r,r;r2r<r!r)r=r>rZ cls_tokenss r/r@VivitEmbeddings.forwards>J>P>P; e**<*k ^^((*a);< YY 7Q?  $#&C&CJX]&^^J#&>&>>J\\*- r1)rSr-rXrTr$rUrB)rCrDrErFrGr rQTensorintr2rHr@rIrJrKs@r/rMrMPsQ &D5<<&D&DUX&D]b]i]i&DPdr1rMmodulequerykeyvalueattention_maskscalingrXc [R"XRSS55U-n[RR US[R S9RUR5n[RRXURS9nUbX-n[R"X5n U RSS5R5n X4$)Nr])rcdtype)ptrainingrr) rQmatmulr:rrgsoftmaxfloat32torrXr contiguous) ryrzr{r|r}r~rXkwargs attn_weights attn_outputs r/eager_attention_forwardrs<<}}R'<=GL==((2U]](SVVW\WbWbcL==((6??([L!#4 ,,|3K''1-88:K  $$r1c ^\rSrSrS\SS4U4SjjrS S\\RS\ S\ \ \R\R4\ \R44Sjjr S r U=r$) VivitSelfAttentionr-r[Nc0>[TU]5 URUR-S:wa7[ US5(d&[ SURSURS35eXlURUl[URUR- 5UlURUR-Ul URUl URS-Ul SUl [R"URURUR S9Ul[R"URURUR S9Ul[R"URURUR S9Ulg) Nrembedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .gF)bias)rr r&num_attention_headshasattrr7r-rxattention_head_size all_head_sizeattention_probs_dropout_prob dropout_probr~ is_causalrLinearqkv_biasrzr{r|r+s r/r VivitSelfAttention.__init__sG     : : :a ?PVXhHiHi"6#5#5"67334A7   #)#=#= #&v'9'9FO##L '>dkk>^>^&_#)<     nnLL#}}C$2C2C * & #0"4"4"6s";t?Q?Q>S"S%--.EF 6G=2O\M]r1) rrr-rrr{rrzr~r|NF)rCrDrErFrr rrQrwrHrtupler@rIrJrKs@r/rrsw]{]t].-1"' 1ELL)1 1 uU\\5<</0% 2EE F 11r1rc^\rSrSrSrS\SS4U4SjjrS\RS\RS\R4S jr S r U=r $) VivitSelfOutputi z The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the layernorm applied before each block. r-r[Nc>[TU]5 [R"URUR5Ul[R "UR5UlgN) rr rrr&denserVrWrXr+s r/r VivitSelfOutput.__init__sB YYv1163E3EF zz&"<"<= r1r input_tensorcJURU5nURU5nU$rrrXr,rrs r/r@VivitSelfOutput.forwards$ =1  ]3 r1r) rCrDrErFrGrr rQrwr@rIrJrKs@r/rr sI >{>t> U\\RWR^R^r1rc ^\rSrSrS\SS4U4SjjrS\\SS4SjrS S\ RS \ \ RS \ S\ \\ R\ R4\\ R44S jjrS rU=r$)VivitAttentionir-r[Nc>[TU]5 [U5Ul[ U5Ul[ 5Ulgr)rr r attentionroutputset pruned_headsr+s r/r VivitAttention.__init__s0 +F3%f- Er1headsc6[U5S:Xag[XRRURRUR 5up[ URRU5URl[ URRU5URl[ URRU5URl [ URRUSS9URl URR[U5- URlURRURR-URl UR RU5Ulg)Nrrrb)lenrrrrrrrzr{r|rrrunion)r,rindexs r/ prune_headsVivitAttention.prune_heads#s u:? 7 >>55t~~7Y7Y[_[l[l  2$..2F2FN/0B0BEJ1$..2F2FN.t{{/@/@%QO .2^^-O-ORUV[R\-\*'+~~'I'IDNNLnLn'n$ --33E:r1rrrcfURXU5nURUSU5nU4USS-nU$)Nrr)rr)r,rrr self_outputsattention_outputrs r/r@VivitAttention.forward5sC ~~m@QR ;;|A F#% QR(88r1)rrrr)rCrDrErFrr rrxrrQrwrrHrrr@rIrJrKs@r/rrs"{"t" ;S;d;*-1"' || ELL)   uU\\5<</0% 2EE F  r1rc.^\rSrSrU4SjrSrSrU=r$)VivitIntermediateiCc^>[TU]5 [R"URUR 5Ul[R"UR5Ul [UR[5(a[URUlgURUlgr)rr rrr&intermediate_sizerrVrWrX isinstance hidden_actstrr intermediate_act_fnr+s r/r VivitIntermediate.__init__Dsv YYv1163K3KL zz&"<"<= f'' - -'-f.?.?'@D $'-'8'8D $r1clURU5nURU5nURU5nU$r)rrrX)r,rs r/r@VivitIntermediate.forwardMs4 =1 00?  ]3 r1)rrXrrCrDrErFr r@rIrJrKs@r/rrCs9r1rc.^\rSrSrU4SjrSrSrU=r$) VivitOutputiUc>[TU]5 [R"URUR 5Ul[R"UR5Ul gr) rr rrrr&rrVrWrXr+s r/r VivitOutput.__init__VsB YYv779K9KL zz&"<"<= r1cRURU5nURU5nX-nU$rrrs r/r@VivitOutput.forward[s, =1  ]3 %4 r1rrrKs@r/rrUs> r1rc6^\rSrSrSrU4SjrSSjrSrU=r$) VivitLayeriezNThis corresponds to the EncoderBlock class in the scenic/vivit implementation.cj>[TU]5 URUlSUl[ U5Ul[ U5Ul[U5Ul [R"URURS9Ul[R"URURS9Ulg)Nreps)rr chunk_size_feed_forward seq_len_dimrrr intermediaterrr LayerNormr&layer_norm_epslayernorm_beforelayernorm_afterr+s r/r VivitLayer.__init__hs '-'E'E$'/-f5!&) " V-?-?VEZEZ [!||F,>,>FDYDYZr1cURURU5UUS9nUSnUSSnXQ-nURU5nURU5nUR Xq5nU4U-nU$)N)rrr)rrrrr)r,rrrself_attention_outputsrr layer_outputs r/r@VivitLayer.forwardrs!%  ! !- 0 / "0"  2!4(,)8 ++M: ((6 {{<? 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' ' )  & & + + 1 1 31r1rN)rCrDrErFr__annotations__base_model_prefixmain_input_namesupports_gradient_checkpointing_no_split_modules_supports_sdpa_supports_flash_attn_supports_flex_attn_supports_attention_backendr*rIrr1r/rrs; $O&*#N"&4r1rc^\rSrSrSU4SjjrSrSr\SS\\ RS\\ RS\\ S\\ S \ S \\ S \ \ \ R\44S jj5rS rU=r$) VivitModelic>[TU]U5 Xl[U5Ul[ U5Ul[R"URURS9Ul U(a [U5OSUl UR5 g)z^ add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer rN)rr r-rMrZrencoderrrr&r layernormrpooler post_init)r,r-add_pooling_layerr.s r/r VivitModel.__init__si   )&1#F+ f&8&8f>S>ST->k&)D  r1c.URR$r)rZrT)r,s r/get_input_embeddingsVivitModel.get_input_embeddingss///r1cUR5H7up#URRURR U5 M9 g)zy Prunes heads of the model. Args: heads_to_prune: dict of {layer_num: list of heads to prune in this layer} N)itemsr8rrr)r,heads_to_prunerrs r/ _prune_headsVivitModel._prune_headss<+002LE LL  u % / / ; ;E B3r1r;rrr r2r r[cUbUOURRnUbUOURRnUbUOURRnUc [ S5eUR X RR 5nURXS9nURUUUUUS9nUSn URU 5n URbURU 5OSn U(d X4USS-$[U U URURS9$)a( Examples: ```python >>> import av >>> import numpy as np >>> from transformers import VivitImageProcessor, VivitModel >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def read_video_pyav(container, indices): ... ''' ... Decode the video with PyAV decoder. ... Args: ... container (`av.container.input.InputContainer`): PyAV container. ... indices (`list[int]`): List of frame indices to decode. ... Returns: ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). ... ''' ... frames = [] ... container.seek(0) ... start_index = indices[0] ... end_index = indices[-1] ... for i, frame in enumerate(container.decode(video=0)): ... if i > end_index: ... break ... if i >= start_index and i in indices: ... frames.append(frame) ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... ''' ... Sample a given number of frame indices from the video. ... Args: ... clip_len (`int`): Total number of frames to sample. ... frame_sample_rate (`int`): Sample every n-th frame. ... seg_len (`int`): Maximum allowed index of sample's last frame. ... Returns: ... indices (`list[int]`): List of sampled frame indices ... ''' ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> container = av.open(file_path) >>> # sample 32 frames >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=1, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container=container, indices=indices) >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") >>> model = VivitModel.from_pretrained("google/vivit-b-16x2-kinetics400") >>> # prepare video for the model >>> inputs = image_processor(list(video), return_tensors="pt") >>> # forward pass >>> outputs = model(**inputs) >>> last_hidden_states = outputs.last_hidden_state >>> list(last_hidden_states.shape) [1, 3137, 768] ```Nz You have to specify pixel_valuesrs)rrr r rr)r pooler_outputrr)r-rr use_return_dictr7 get_head_maskrrZr8r9r:r rr) r,r;rrr r2r embedding_outputencoder_outputssequence_outputrs r/r@VivitModel.forwards#h2C1N-TXT_T_TqTq$8$D $++JjJj &1% s 'tn s #' s d^s  uU&&')CC Ds s r1r6a ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for Kinetics-400. Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. ) custom_introc^\rSrSrU4Sjr\S S\\RS\\RS\\RS\\ S\\ S\ S \\ S \ \ \R\ 44S jj5rS rU=r$)VivitForVideoClassificationi~c.>[TU]U5 URUl[USS9UlURS:a+[ R "URUR5O[ R"5Ul UR5 g)NF)r<r) rr num_labelsr6rrrr&Identity classifierr;r+s r/r $VivitForVideoClassification.__init__ss   ++%@ OUN_N_bcNc"))F$6$68I8IJikititiv r1r;rlabelsrr r2r r[c UbUOURRnURUUUUUUS9nUSn URU SS2SSS245n Sn Ub~URS:Xa2[ 5n U "U R S5UR S55n O<[5n U "U R SUR5UR S55n U(dU 4USS-n U bU 4U -$U $[U U URURS9$)a labels (`torch.LongTensor` of shape `(batch_size,)`, *optional*): Labels for computing the image classification/regression loss. Indices should be in `[0, ..., config.num_labels - 1]`. If `config.num_labels == 1` a regression loss is computed (Mean-Square loss), If `config.num_labels > 1` a classification loss is computed (Cross-Entropy). Examples: ```python >>> import av >>> import numpy as np >>> import torch >>> from transformers import VivitImageProcessor, VivitForVideoClassification >>> from huggingface_hub import hf_hub_download >>> np.random.seed(0) >>> def read_video_pyav(container, indices): ... ''' ... Decode the video with PyAV decoder. ... Args: ... container (`av.container.input.InputContainer`): PyAV container. ... indices (`list[int]`): List of frame indices to decode. ... Returns: ... result (np.ndarray): np array of decoded frames of shape (num_frames, height, width, 3). ... ''' ... frames = [] ... container.seek(0) ... start_index = indices[0] ... end_index = indices[-1] ... for i, frame in enumerate(container.decode(video=0)): ... if i > end_index: ... break ... if i >= start_index and i in indices: ... frames.append(frame) ... return np.stack([x.to_ndarray(format="rgb24") for x in frames]) >>> def sample_frame_indices(clip_len, frame_sample_rate, seg_len): ... ''' ... Sample a given number of frame indices from the video. ... Args: ... clip_len (`int`): Total number of frames to sample. ... frame_sample_rate (`int`): Sample every n-th frame. ... seg_len (`int`): Maximum allowed index of sample's last frame. ... Returns: ... indices (`list[int]`): List of sampled frame indices ... ''' ... converted_len = int(clip_len * frame_sample_rate) ... end_idx = np.random.randint(converted_len, seg_len) ... start_idx = end_idx - converted_len ... indices = np.linspace(start_idx, end_idx, num=clip_len) ... indices = np.clip(indices, start_idx, end_idx - 1).astype(np.int64) ... return indices >>> # video clip consists of 300 frames (10 seconds at 30 FPS) >>> file_path = hf_hub_download( ... repo_id="nielsr/video-demo", filename="eating_spaghetti.mp4", repo_type="dataset" ... ) >>> container = av.open(file_path) >>> # sample 32 frames >>> indices = sample_frame_indices(clip_len=32, frame_sample_rate=4, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container=container, indices=indices) >>> image_processor = VivitImageProcessor.from_pretrained("google/vivit-b-16x2-kinetics400") >>> model = VivitForVideoClassification.from_pretrained("google/vivit-b-16x2-kinetics400") >>> inputs = image_processor(list(video), return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) ... logits = outputs.logits >>> # model predicts one of the 400 Kinetics-400 classes >>> predicted_label = logits.argmax(-1).item() >>> print(model.config.id2label[predicted_label]) LABEL_116 ```N)rrr r2r rrr]r)losslogitsrr) r-rHrrUrSrrirrrr)r,r;rrWrr r2r rrLrZrYloss_fctrs r/r@#VivitForVideoClassification.forwardsz&1% ~ 'tn ~ #'~ d^~  uU&&')>> ?~ ~ r1rQ)r6rrQ)r)4rGtypingrrrrQtorch.utils.checkpointrtorch.nnrr activationsr modeling_layersr modeling_outputsr r rmodeling_utilsrr pytorch_utilsrrutilsrrrconfiguration_vivitr get_loggerrCrModulerrMrwfloatrrrrrrrrrrr6rQ__all__rr1r/rls,, .!9bbFQ77,   H %)RYY)XLbiiLn% II% <<% % << % U\\* %  %%>FFTbii&$RYY$N $ "))  $+$N( 299( V ")) 4?44>T %T T n  L "6L  L ^ Pr1