T^(hdZddlmZmZmZmZmZddlZddlZddlm Z ddl m Z m Z ddl mZddlmZmZmZdd lmZmZdd lmZmZdd lmZmZmZmZmZd d lm Z ejBe"Z#dZ$dZ%Gdde jLZ'Gdde jLZ( d7de jLdejRdejRdejRdeejRde*de*fdZ+Gdde jLZ,Gdde jLZ-Gd d!e jLZ.Gd"d#e jLZ/Gd$d%e jLZ0Gd&d'e jLZ1Gd(d)e jLZ2Gd*d+e jLZ3Gd,d-eZ4d.Z5d/Z6ed0e5Gd1d2e4Z7ed3e5Gd4d5e4Z8gd6Z9y)8zPyTorch ViViT model.)CallableOptionalSetTupleUnionN)nn)CrossEntropyLossMSELoss)ACT2FN)BaseModelOutputBaseModelOutputWithPoolingImageClassifierOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer)add_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings torch_int) VivitConfigzgoogle/vivit-b-16x2-kinetics400rc0eZdZdZfdZddefdZxZS)VivitTubeletEmbeddingsa 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]). ct||j|_|j|_|j|_|j|j dz|j|j dzz|j|j dzz|_|j|_tj|j|j|j|j|_ y)Nrr) kernel_sizestride) super__init__ num_frames image_size tubelet_size patch_size num_patches hidden_size embed_dimrConv3d num_channels projectionselfconfig __class__s f/var/www/skyplay_api_hub/venv/lib/python3.12/site-packages/transformers/models/vivit/modeling_vivit.pyr"zVivitTubeletEmbeddings.__init__7s  ++ ++ -- __ 2 2$//!"44 6$//!"44 6   ++))   !3!3ATAT]c]p]p interpolate_pos_encodingc \|j\}}}}}|sP||jk7s||jk7r2td|d|d|jdd|jdd |jddddd }|j |}|j dj dd}|S) NzImage image size (*z) doesn't match model (rrz).rr )shaper$ ValueErrorpermuter,flatten transpose) r. pixel_valuesr3 batch_sizer#r+heightwidthxs r1forwardzVivitTubeletEmbeddings.forwardGs>J>P>P; J fe'Vt-F%SWSbSbJb$VHAeW4KDOO\]L^K__`aeapapqras`ttvw  $++Aq!Q: OOL ) IIaL " "1a (r2F)__name__ __module__ __qualname____doc__r"boolrA __classcell__r0s@r1rr,s dr2rcpeZdZdZfdZdej dededej fdZd de fd Z xZ S) VivitEmbeddingsz Vivit Embeddings. Creates embeddings from a video using VivitTubeletEmbeddings, adds CLS token and positional embeddings. ct|tjt j dd|j |_t||_ tjt j d|jjdz|j |_ tj|j|_|jdd|_||_y)Nr)r!r"r Parametertorchzerosr( cls_tokenrpatch_embeddingsr'position_embeddingsDropouthidden_dropout_probdropoutr%r&r/r-s r1r"zVivitEmbeddings.__init___s ekk!Q8J8J&KL 6v >#%<< KK400<r?returnc|jddz }|jjddz }tjj s||k(r||k(r |jS|jddddf}|jddddf}|jd}||j dz} ||j dz} t |dz} |jd| | |}|jdddd}tjj|| | fdd }|jddddjdd|}tj||fd S) 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)r7rRrNjit is_tracingr&rreshaper9r functional interpolateviewcat) r.rVr>r?r' num_positionsclass_pos_embedpatch_pos_embedr_ new_height new_widthsqrt_num_positionss r1r3z(VivitEmbeddings.interpolate_pos_encodingmsj!&&q)A- 0066q9A= yy##%+*F6UZ?++ +221bqb59221ab59r"tq11 T__Q// &}c'9:)11!5GI[]`a)11!Q1=--33 i( 4 *11!Q1=BB1b#Nyy/?;CCr2r3c0|j\}}}}}|j||}|jj|ddg} t j | |fd}|r||j |||z}n||jz}|j|}|S)Nr3rr^) r7rQrPtilerNrfr3rRrU) r.r<r3r=r#r+r>r?rV cls_tokenss r1rAzVivitEmbeddings.forwards>J>P>P; J fe**&>>J\\*- r2rB) rCrDrErFr"rNTensorintr3rGrArHrIs@r1rKrKXsL &D5<<&D&DUX&D]b]i]i&DPdr2rKmodulequerykeyvalueattention_maskscalingrUc tj||jdd|z}tjj |dtj j|j}tjj|||j}|||z}tj||} | jddj} | |fS)NrY)r_dtype)ptrainingrr) rNmatmulr;rrcsoftmaxfloat32tor{rUr} contiguous) rsrtrurvrwrxrUkwargs attn_weights attn_outputs r1eager_attention_forwardrs<<s}}R'<=GL==((2U]](SVVW\WbWbcL==((6??([L!#n4 ,,|U3K''1-88:K  $$r2c eZdZdeddffd Zdej dej fdZ d deej de de e ej ej fe ej ffd Z xZ S) VivitSelfAttentionr/rWNc2t||j|jzdk7r2t |ds&t d|jd|jd||_|j|_t|j|jz |_|j|jz|_ |j|_ |jdz|_ d|_ tj|j|j|j |_tj|j|j|j |_tj|j|j|j |_y) Nrembedding_sizezThe hidden size z4 is not a multiple of the number of attention heads .gF)bias)r!r"r(num_attention_headshasattrr8r/rrattention_head_size all_head_sizeattention_probs_dropout_prob dropout_probrx is_causalrLinearqkv_biasrtrurvr-s r1r"zVivitSelfAttention.__init__sF     : : :a ?PVXhHi"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]r2NF)rCrDrErr"rNrqrrrGrrrArHrIs@r1rrs]{]t](%ell%u||% bg!(0(>!Z^! uU\\5<</0% 2EE F!r2rc|eZdZdZdeddffd ZdejdejdejfdZxZ S) VivitSelfOutputz 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/rWNct|tj|j|j|_tj |j|_yN) r!r"rrr(denserSrTrUr-s r1r"zVivitSelfOutput.__init__ sB YYv1163E3EF zz&"<"<= r2r input_tensorcJ|j|}|j|}|SrrrUr.rrs r1rAzVivitSelfOutput.forwards$ =1  ]3 r2) rCrDrErFrr"rNrqrArHrIs@r1rrsD >{>t> U\\RWR^R^r2rc eZdZdeddffd ZdeeddfdZ d dejde ejd e de e ejejfe ejffd ZxZS) VivitAttentionr/rWNct|t||_t ||_t |_yr)r!r"r attentionroutputset pruned_headsr-s r1r"zVivitAttention.__init__s0 +F3%f- Er2headsc>t|dk(ryt||jj|jj|j \}}t |jj||j_t |jj||j_t |jj||j_ t |jj|d|j_ |jjt|z |j_|jj|jjz|j_ |j j||_y)Nrrr^)lenrrrrrrrtrurvrrrunion)r.rindexs r1 prune_headszVivitAttention.prune_heads s u:? 7 4>>55t~~7Y7Y[_[l[l u 2$..2F2FN/0B0BEJ1$..2F2FN.t{{/@/@%QO .2^^-O-ORUV[R\-\*'+~~'I'IDNNLnLn'n$ --33E:r2rrrch|j|||}|j|d|}|f|ddz}|S)Nrr)rr)r.rrr self_outputsattention_outputrs r1rAzVivitAttention.forward2sE ~~mY@QR ;;|A F#% QR(88r2r)rCrDrErr"rrrrrNrqrrGrrrArHrIs@r1rrs"{"t" ;S;d;*-1"' || ELL)   uU\\5<</0% 2EE F r2rc$eZdZfdZdZxZS)VivitIntermediatecPt|tj|j|j |_tj|j|_ t|jtrt|j|_y|j|_yr)r!r"rrr(intermediate_sizerrSrTrU isinstance hidden_actstrr intermediate_act_fnr-s r1r"zVivitIntermediate.__init__Ass YYv1163K3KL zz&"<"<= f'' -'-f.?.?'@D $'-'8'8D $r2cl|j|}|j|}|j|}|Sr)rrrU)r.rs r1rAzVivitIntermediate.forwardJs4 =1 00?  ]3 r2rCrDrEr"rArHrIs@r1rr@s 9r2rc$eZdZfdZdZxZS) VivitOutputct|tj|j|j |_tj|j|_ yr) r!r"rrrr(rrSrTrUr-s r1r"zVivitOutput.__init__SsB YYv779K9KL zz&"<"<= r2cT|j|}|j|}||z}|Srrrs r1rAzVivitOutput.forwardXs. =1  ]3 % 4 r2rrIs@r1rrRs > r2rc*eZdZdZfdZddZxZS) VivitLayerzNThis corresponds to the EncoderBlock class in the scenic/vivit implementation.crt||j|_d|_t ||_t ||_t||_ tj|j|j|_tj|j|j|_y)Nreps)r!r"chunk_size_feed_forward seq_len_dimrrr intermediaterrr LayerNormr(layer_norm_epslayernorm_beforelayernorm_afterr-s r1r"zVivitLayer.__init__es '-'E'E$'/-f5!&) " V-?-?VEZEZ [!||F,>,>FDYDYZr2c|j|j|||}|d}|dd}||z}|j|}|j|}|j ||}|f|z}|S)N)rrr)rrrrr)r.rrrself_attention_outputsrr layer_outputs r1rAzVivitLayer.forwardos!%  ! !- 0 / "0"  2!4(,)=8 ++M: ((6 {{<? /G+r2r)rCrDrErFr"rArHrIs@r1rrbsX[r2rc.eZdZfdZ ddZxZS) VivitEncoderct|||_tjt |j Dcgc] }t|c}|_d|_ ycc}wr) r!r"r/r ModuleListrangenum_hidden_layersrlayergradient_checkpointing)r.r/_r0s r1r"zVivitEncoder.__init__sN  ]]fF^F^@_#`1Jv$6#`a &+#$asA#ct|rdnd}|rdnd}t|jD]h\}} |r||fz}|||nd} |jr+|jr|j | j || |} n | || |} | d}|s`|| dfz}j|r||fz}|st d|||fDSt|||S)Nrrc3&K|] }|| ywrr).0vs r1 z'VivitEncoder.forward..smq_`_lms)last_hidden_stater attentions) enumeraterrr}_gradient_checkpointing_func__call__tupler ) r.rrroutput_hidden_states return_dictall_hidden_statesall_self_attentionsi layer_modulelayer_head_mask layer_outputss r1rAzVivitEncoder.forwards#7BD$5b4(4 POA|#$58H$H!.7.CilO**t}} $ A A ))!#% ! !-]OM^ _ )!,M &9]1=M.  - KK   " " $ MM   $ $S )  0    ! ! ' ' )  & & + + 1 1 31r2N) rCrDrErFr config_classbase_model_prefixmain_input_namesupports_gradient_checkpointing_no_split_modules_supports_sdpa_supports_flash_attn_2rrr2r1rrs5 L$O&*#N!4r2raG This model is a PyTorch [torch.nn.Module](https://pytorch.org/docs/stable/nn.html#torch.nn.Module) subclass. Use it as a regular PyTorch Module and refer to the PyTorch documentation for all matter related to general usage and behavior. Parameters: config ([`VivitConfig`]): Model configuration class with all the parameters of the model. Initializing with a config file does not load the weights associated with the model, only the configuration. Check out the [`~PreTrainedModel.from_pretrained`] method to load the model weights. a Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_frames, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`VivitImageProcessor`]. See [`VivitImageProcessor.preprocess`] for details. head_mask (`torch.FloatTensor` of shape `(num_heads,)` or `(num_layers, num_heads)`, *optional*): Mask to nullify selected heads of the self-attention modules. Mask values selected in `[0, 1]`: - 1 indicates the head is **not masked**, - 0 indicates the head is **masked**. output_attentions (`bool`, *optional*): Whether or not to return the attentions tensors of all attention layers. See `attentions` under returned tensors for more detail. output_hidden_states (`bool`, *optional*): Whether or not to return the hidden states of all layers. See `hidden_states` under returned tensors for more detail. interpolate_pos_encoding (`bool`, *optional*, `False`): Whether to interpolate the pre-trained position encodings. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. z_The bare ViViT Transformer model outputting raw hidden-states without any specific head on top.ceZdZd fd ZdZdZeeee e  dde e jde e jde ede ed ed e ed eee je ffd ZxZS) VivitModelct||||_t||_t ||_tj|j|j|_ |r t|nd|_ |jy)Nr)r!r"r/rKrVrencoderrrr(r layernormrpooler post_init)r.r/add_pooling_layerr0s r1r"zVivitModel.__init__si   )&1#F+ f&8&8f>S>ST->k&)D  r2c.|jjSr)rVrQ)r.s r1get_input_embeddingszVivitModel.get_input_embeddings#s///r2c|jD]7\}}|jj|jj |9y)z Prunes heads of the model. Args: heads_to_prune: dict of {layer_num: list of heads to prune in this layer} N)itemsr rrr)r.heads_to_prunerrs r1 _prune_headszVivitModel._prune_heads&sE+002 CLE5 LL  u % / / ; ;E B Cr2 output_typerr<rrrr3rrWc||n|jj}||n|jj}||n|jj}| t d|j ||jj }|j||}|j|||||}|d} |j| } |j|j| nd} |s | | f|ddzSt| | |j|jS)a Returns: 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_valuesrn)rrrrrr)r pooler_outputrr)r/rruse_return_dictr8 get_head_maskrrVr r!r"rrr) r.r<rrrr3rembedding_outputencoder_outputssequence_outputrs r1rAzVivitModel.forward1s*n2C1N-TXT_T_TqTq$8$D $++JjJj &1% u 'tn u #' u d^u  uU&&')CC Du eCu r2ra 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. c eZdZfdZeeeee d de e jde e jde e jde e de e de d e e d eee jeffd ZxZS) VivitForVideoClassificationc.t|||j|_t|d|_|jdkDr*t j |j|jnt j|_ |jy)NF)r$r) r!r" num_labelsrr rrr(Identity classifierr#r-s r1r"z$VivitForVideoClassification.__init__ss   ++%@ OUN_N_bcNc"))F$6$68I8IJikititiv r2r+r<rlabelsrrr3rrWc||n|jj}|j||||||}|d} |j| dddddf} d} |}|jdk(r2t } | | j d|j d} n 1` a classification loss is computed (Cross-Entropy). Returns: 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)rrrr3rrrrYr)losslogitsrr) r/r/r r<r:r rer rrr)r.r<rr=rrr3rrr3r@r?loss_fctrs r1rAz#VivitForVideoClassification.forwards@&1% @ 'tn @ #'@ d^@  uU&&')>> ?@ `C@ r2r8)rrr8)r):rFtypingrrrrrrNtorch.utils.checkpointrtorch.nnr r activationsr modeling_outputsr rrmodeling_utilsrr pytorch_utilsrrutilsrrrrrconfiguration_vivitr get_loggerrCr_CHECKPOINT_FOR_DOCr5ModulerrKrqfloatrrrrrrrrrrVIVIT_START_DOCSTRINGr4rr8__all__rr2r1rRs88 .!bbFQ-   H %7)RYY)XLbiiLn% II% <<% % << % U\\* %  %%>;;~bii&$RYY$N $ "))  $$N0 2990 f ")) 4?4D 2eS %S S l O "6O O d Pr2