Uh|dZddlmZmZmZmZmZddlZddlZddlm Z ddl m Z m Z ddl mZddlmZmZmZdd lmZmZdd lmZmZdd lmZmZmZd d lmZej>e Z!Gdde jDZ#Gdde jDZ$ d3de jDdejJdejJdejJdeejJde&de&fdZ'Gdde jDZ(Gdde jDZ)Gdde jDZ*Gd d!e jDZ+Gd"d#e jDZ,Gd$d%e jDZ-Gd&d'e jDZ.Gd(d)e jDZ/eGd*d+eZ0eGd,d-e0Z1ed./Gd0d1e0Z2gd2Z3y)4zPyTorch ViViT model.)CallableOptionalSetTupleUnionN)nn)CrossEntropyLossMSELoss)ACT2FN)BaseModelOutputBaseModelOutputWithPoolingImageClassifierOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer)auto_docstringlogging torch_int) VivitConfigc0eZdZdZfdZddefdZxZS)VivitTubeletEmbeddingsa Construct Vivit Tubelet embeddings. This module turns a batch of videos of shape (batch_size, num_frames, num_channels, height, width) into a tensor of shape (batch_size, seq_len, hidden_size) to be consumed by a Transformer encoder. The seq_len (the number of patches) equals (number of frames // tubelet_size[0]) * (height // tubelet_size[1]) * (width // tubelet_size[2]). ct||j|_|j|_|j|_|j|j dz|j|j dzz|j|j dzz|_|j|_tj|j|j|j|j|_ y)Nrr) kernel_sizestride) super__init__ num_frames image_size tubelet_size patch_size num_patches hidden_size embed_dimrConv3d num_channels projectionselfconfig __class__s z/var/www/catia.catastroantioquia-mas.com/valormas/lib/python3.12/site-packages/transformers/models/vivit/modeling_vivit.pyr zVivitTubeletEmbeddings.__init__.s  ++ ++ -- __ 2 2$//!"44 6$//!"44 6   ++))   !3!3ATAT]c]p]p interpolate_pos_encodingc \|j\}}}}}|sP||jk7s||jk7r2td|d|d|jdd|jdd |jddddd }|j |}|j dj dd}|S) NzImage image size (*z) doesn't match model (rrz).rr )shaper" ValueErrorpermuter*flatten transpose) r, pixel_valuesr1 batch_sizer!r)heightwidthxs r/forwardzVivitTubeletEmbeddings.forward>s>J>P>P; J fe'Vt-F%SWSbSbJb$VHAeW4KDOO\]L^K__`aeapapqras`ttvw  $++Aq!Q: OOL ) IIaL " "1a (r0F)__name__ __module__ __qualname____doc__r boolr? __classcell__r.s@r/rr#s dr0rcpeZdZdZfdZdej dededej fdZd de fd Z xZ S) VivitEmbeddingsz Vivit Embeddings. 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Falling back to eager attention. This warning can be removed using the argument `attn_implementation="eager"` when loading the model.)rrvrSrx)rrsrtrrrr-_attn_implementationlogger warning_oncerrrvr{rrYrr`) r, hidden_statesrr key_layer value_layer query_layerattention_interface context_layerattention_probsnew_context_layer_shapeoutputss r/r?zVivitSelfAttention.forwards=--dhh}.EF // =0IJ // =0IJ (? ;; + +w 6{{//69>O##L '>dkk>^>^&_#)<     nnLL#}}C$2C2C * & #0"4"4"6s";t?Q?Q>S"S%--.EF 6G=/2O\M]r0NF)rArBrCrr rLrorrrErrr?rFrGs@r/rrs]{]t](%ell%u||% bg!(0(>!Z^! uU\\5<</0% 2EE F!r0rc|eZdZdZdeddffd ZdejdejdejfdZxZ S) VivitSelfOutputz The residual connection is defined in VivitLayer instead of here (as is the case with other models), due to the layernorm applied before each block. r-rUNct|tj|j|j|_tj |j|_yN) rr rrr&denserQrRrSr+s r/r zVivitSelfOutput.__init__sB YYv1163E3EF zz&"<"<= r0r input_tensorcJ|j|}|j|}|SrrrSr,rrs r/r?zVivitSelfOutput.forwards$ =1  ]3 r0) rArBrCrDrr rLror?rFrGs@r/rrsD >{>t> U\\RWR^R^r0rc eZdZdeddffd ZdeeddfdZ d dejde ejd e de e ejejfe ejffd ZxZS) VivitAttentionr-rUNct|t||_t ||_t |_yr)rr r attentionroutputset pruned_headsr+s r/r zVivitAttention.__init__s0 +F3%f- Er0headsc>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\)lenrrrrrrrrrsrtrrrunion)r,rindexs r/ prune_headszVivitAttention.prune_headss u:? 7 4>>55t~~7Y7Y[_[l[l u 2$..2F2FN/0B0BEJ1$..2F2FN.t{{/@/@%QO .2^^-O-ORUV[R\-\*'+~~'I'IDNNLnLn'n$ --33E:r0rrrch|j|||}|j|d|}|f|ddz}|S)Nrr)rr)r,rrr self_outputsattention_outputrs r/r?zVivitAttention.forward)sE ~~mY@QR ;;|A F#% QR(88r0r)rArBrCrr rrprrLrorrErrr?rFrGs@r/rrs"{"t" ;S;d;*-1"' || ELL)   uU\\5<</0% 2EE F r0rc$eZdZfdZdZxZS)VivitIntermediatecPt|tj|j|j |_tj|j|_ t|jtrt|j|_y|j|_yr)rr rrr&intermediate_sizerrQrRrS isinstance hidden_actstrr intermediate_act_fnr+s r/r zVivitIntermediate.__init__8ss YYv1163K3KL zz&"<"<= f'' -'-f.?.?'@D $'-'8'8D $r0cl|j|}|j|}|j|}|Sr)rrrS)r,rs r/r?zVivitIntermediate.forwardAs4 =1 00?  ]3 r0rArBrCr r?rFrGs@r/rr7s 9r0rc$eZdZfdZdZxZS) VivitOutputct|tj|j|j |_tj|j|_ yr) rr rrrr&rrQrRrSr+s r/r zVivitOutput.__init__JsB YYv779K9KL zz&"<"<= r0cT|j|}|j|}||z}|Srrrs r/r?zVivitOutput.forwardOs. =1  ]3 % 4 r0rrGs@r/rrIs > r0rc*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)rr chunk_size_feed_forward seq_len_dimrrr intermediaterrr LayerNormr&layer_norm_epslayernorm_beforelayernorm_afterr+s r/r zVivitLayer.__init__\s '-'E'E$'/-f5!&) " V-?-?VEZEZ [!||F,>,>FDYDYZr0c|j|j|||}|d}|dd}||z}|j|}|j|}|j ||}|f|z}|S)N)rrr)rrrrr)r,rrrself_attention_outputsrr layer_outputs r/r?zVivitLayer.forwardfs!%  ! !- 0 / "0"  2!4(,)=8 ++M: ((6 {{<? /G+r0r)rArBrCrDr r?rFrGs@r/rrYsX[r0rc.eZdZfdZ ddZxZS) VivitEncoderct|||_tjt |j Dcgc] }t|c}|_d|_ ycc}wr) rr r-r ModuleListrangenum_hidden_layersrlayergradient_checkpointing)r,r-_r.s r/r 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 r/ 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 r/r?zVivitEncoder.forwards#7BD$5b4(4 POA|#$58H$H!.7.CilO**t}} $ A A ))!#% ! !-]OM^ _ )!,M &9]1=M.  - KK   " " $ MM   $ $S )  0    ! ! ' ' )  & & + + 1 1 31r0N) rArBrCr config_classbase_model_prefixmain_input_namesupports_gradient_checkpointing_no_split_modules_supports_sdpa_supports_flash_attn_2rrr0r/rrs.L$O&*#N!4r0rceZdZd fd ZdZdZe d deejdeejdee dee de d ee d e e eje ffd ZxZS) VivitModelct||||_t||_t ||_tj|j|j|_ |r t|nd|_ |jy)zv add_pooling_layer (bool, *optional*, defaults to `True`): Whether to add a pooling layer rN)rr r-rIrTrencoderrrr&r layernormrpooler post_init)r,r-add_pooling_layerr.s r/r zVivitModel.__init__sk   )&1#F+ f&8&8f>S>ST->k&)D  r0c.|jjSr)rTrO)r,s r/get_input_embeddingszVivitModel.get_input_embeddingss///r0c|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)itemsrrrr)r,heads_to_prunerrs r/ _prune_headszVivitModel._prune_headssE+002 CLE5 LL  u % / / ; ;E B Cr0r:rrrr1rrUc||n|jj}||n|jj}||n|jj}| t d|j ||jj }|j||}|j|||||}|d} |j| } |j|j| nd} |s | | f|ddzSt| | |j|jS)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_valuesrl)rrrrrr)r pooler_outputrr)r-rruse_return_dictr6 get_head_maskrrTrrr rrr) r,r:rrrr1rembedding_outputencoder_outputssequence_outputrs r/r?zVivitModel.forwards*h2C1N-TXT_T_TqTq$8$D $++JjJj &1% s 'tn s #' s d^s  uU&&')CC Ds s r0ra ViViT Transformer model with a video classification head on top (a linear layer on top of the final hidden state of the [CLS] token) e.g. for Kinetics-400. Note that it's possible to fine-tune ViT on higher resolution images than the ones it has been trained on, by setting `interpolate_pos_encoding` to `True` in the forward of the model. This will interpolate the pre-trained position embeddings to the higher resolution. ) custom_introceZdZfdZe d deejdeejdeejdee dee de dee d e e eje ffd Z xZS) VivitForVideoClassificationc.t|||j|_t|d|_|jdkDr*t j |j|jnt j|_ |jy)NF)r"r) rr num_labelsrrrrr&Identity classifierr!r+s r/r z$VivitForVideoClassification.__init__ss   ++%@ OUN_N_bcNc"))F$6$68I8IJikititiv r0r:rlabelsrrr1rrUc||n|jj}|j||||||}|d} |j| dddddf} d} |}|jdk(r2t } | | j d|j d} n 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)rrrr1rrrrWr)losslogitsrr) r-r+rr7r5r rcr rrr)r,r:rr8rrr1rrr/r;r:loss_fctrs r/r?z#VivitForVideoClassification.forwardsz&1% ~ 'tn ~ #'~ d^~  uU&&')>> ?~ ~ r0r3)rrr3)r)4rDtypingrrrrrrLtorch.utils.checkpointrtorch.nnr r activationsr modeling_outputsr rrmodeling_utilsrr pytorch_utilsrrutilsrrrconfiguration_vivitr get_loggerrArModulerrIrofloatrrrrrrrrrrrr3__all__rr0r/rKs88 .!bbFQ77,   H %)RYY)XLbiiLn% II% <<% % << % U\\* %  %%>;;~bii&$RYY$N $ "))  $$N0 2990 f ")) 4?44:T %T T n  L "6L  L ^ Pr0