gyfdZddlZddlZddlmZddlmZddlmZmZm Z m Z m Z m Z m Z ddlZddlZddlmZddlmZdd lmZmZdd lmZmZmZdd lmZmZmZmZmZm Z m!Z!d d l"m#Z#m$Z$m%Z%m&Z&m'Z'ej(e)Z*dZ+dZ,dZ-dZ.dZ/gdZ0dZ1dZ2e e'e%e&fZ3eGddeZ4eGddeZ5eGddeZ6Gddej7Z8Gddej7Z9Gdd ej7Z:Gd!d"ej7Z;Gd#d$ej7Z<Gd%d&ej7Z=Gd'd(ej7Z>Gd)d*ej7Z?Gd+d,ej7Z@Gd-d.ej7ZAGd/d0ej7ZBd1ZCd2ZDd3ZEeEeDzZFd4ZGeGeDzZHd5eDzZId6ZJeEeGzeDzeJzZKd7eGzeEzd8zeDzZLd9ZMGd:d;eZNedd?eNZPed@eCOd=GdAdBeNZQedCeCOd=GdDdEeNZRedFeCOdG=GdHdIeNZSGdJdKej7ZTGdLdMej7ZUGdNdOej7ZVedPeCOdQ=GdRdSeNZWGdTdUej7ZXGdVdWej7ZYGdXdYej7ZZGdZd[ej7Z[ed\eCOdG=eMzGd]d^eNZ\dS)_zPyTorch FLAVA model.N) OrderedDict) dataclass)AnyDictListOptionalSetTupleUnion)nn)ACT2FN)BaseModelOutputBaseModelOutputWithPooling)PreTrainedModel find_pruneable_heads_and_indicesprune_linear_layer) ModelOutputadd_code_sample_docstringsadd_start_docstrings%add_start_docstrings_to_model_forwardloggingreplace_return_docstrings torch_int) FlavaConfigFlavaImageCodebookConfigFlavaImageConfigFlavaMultimodalConfigFlavaTextConfigzfacebook/flava-fullzfacebook/flava-image-codebookrr r)rg$(~k@ceZdZUdZdZeejed<dZ ee ed<dZ eejed<dZ ee ed<dZ eejed<dZee ed<d eefd ZdS) FlavaModelOutputa Output from FlavaModel containing embeddings and outputs from individual encoders. Note that `image_embeddings` and `text_embeddigns` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. Nimage_embeddings image_outputtext_embeddings text_outputmultimodal_embeddingsmultimodal_outputreturnc^tfdDS)Nc3tK|]2}|dvr|n!t|V3dS))r(r&r*Ngetattrto_tuple).0kselfs d/var/www/html/ai-engine/env/lib/python3.11/site-packages/transformers/models/flava/modeling_flava.py z,FlavaModelOutput.to_tuple..bsc   TTTDGGZabfhiZjZjZsZsZuZu      tuplekeysr3s`r4r0zFlavaModelOutput.to_tupleasC    YY[[      r6)__name__ __module__ __qualname____doc__r%rtorch FloatTensor__annotations__r&rr'r(r)r*r rr0r6r4r$r$Bs,59hu018889=L(56===37OXe/07778Bx :;BBB %*      r6r$ceZdZUdZdZeejed<dZ eejed<dZ eejed<dZ eejed<dZ eejed<dZ eejed<d efd ZdS) FlavaLossesa"Class representing pretraining losses from FLAVA model Args: mim (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels` and `pixel_values` are present, `input_ids_masked` is absent and `mim_weight` > 0.: Masked Image Modeling loss as used in BeIT calculated only for unimodal image data. mlm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels` and `input_ids_masked` are present, `pixel_values` is absent and `mlm_weight` > 0.: Masked Language Modeling loss as used in BERT calculated only for unimodal text data. itm (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `itm_labels`, `input_ids_masked`, `pixel_values` are present and `itm_weight` > 0.: Image Text Matching (ITM) loss calculated for paired image-text data. Note that ITM loss is calculated on masked pairs in FLAVA. global_contrastive (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `input_ids` and `pixel_values` are present and `global_contrastive_weight` > 0.: Contrastive loss for image-text similarity similar to CLIP but calculated globally for paired image-text data. This is calculated on unmasked images and texts. mmm_image (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mim_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_image_weight` > 0.: Masked Multimodal Modeling loss's image component calculated on paired image-text data. mmm_text (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `mlm_labels`, `pixel_values` and `input_ids_masked` are present and `mmm_text_weight` > 0.: Masked Multimodal Modeling loss's text component calculated on paired image-text data. Nmimmlmitmglobal_contrastive mmm_imagemmm_textr+cDd}|D]}|d}n |S)NTF)values)r3all_nonevs r4rMzFlavaLosses.all_nones9  A} r6)r;r<r=r>rErr?r@rArFrGrHrIrJboolrMrBr6r4rDrDhs&(,C%# $+++'+C%# $+++'+C%# $+++6:!23:::-1Ix)*111,0Hhu()000$r6rDceZdZUdZdZeejed<dZ e ed<dZ eejed<dZ ee ed<dZeejed<dZee ed<dZeejed <dZee ed <dZeejed <dZee ed <dZeejed <dZee ed<dZeejed<dZee ed<dZeejed<dZeejed<dZeejed<dZeejed<dZeejed<dZeejed<dZeejed<dee fdZ!dS)FlavaForPreTrainingOutputa Output from FlavaForPreTraining containing embeddings, and outputs from individual encoders. Note that `image_embeddings` and `text_embeddings` returned are similar to pooled output returned from a transformer. If you want embeddings for contrastive loss or retrieval use a FLAVA model's `image_projection` and `text_projection` layers on `image_embeddings` and `text_embeddings` respectively. Args: loss (`torch.FloatTensor`, *optional*, returned when `return_loss` is True): Total loss calculated for this model. loss_info (`FlavaLosses`): Detailed info for FLAVA Pretraining losses. Check `FlavaLosses` class description for the information on the keys. image_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. image_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. text_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids` are present): The output of the [`FlavaTextModel`]. multimodal_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_output (`BaseModelOutputWithPooling`, returned when `input_ids` and `pixel_values` are present and `skip_unmasked_multimodal_encoder` is `None` or `False`): The output of the [`FlavaMultimodalModel`]. image_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `pixel_values` are present): The image embeddings which are basically the pooled output of [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. image_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `pixel_values` are present): The output of the [`FlavaImageModel`]. Uses `bool_masked_pos` to create masked images. text_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids_masked` are present): The text embeddings which are basically the pooled output of [`FlavaTextModel`]. text_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` are present): The output of the [`FlavaTextModel`]. multimodal_masked_embeddings (`torch.FloatTensor` of shape `(batch_size, output_dim)`, *optional*, returned when `input_ids` and `pixel_values` are present): The multimodal embeddings which are basically the pooled output of [`FlavaTextModel`]. multimodal_masked_output (`BaseModelOutputWithPooling`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The output of the [`FlavaMultimodalModel`]. mim_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape `(total_masked_patches, image_vocab_size)` , *optional*, returned when `pixel_values` are present and `input_ids_masked` are not): The logits for MIM unimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mlm_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(total_masked_seq_length, text_vocab_size)`, *optional*, returned when `input_ids_masked` are present and `pixel_values` are not): The logits for MLM unimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. itm_logits (`torch.FloatTensor` of shape `(batch_size, 2)`, *optional*, returned when `input_ids_masked` and `pixel_values` are present): The logits for ITM loss. Note that ITM loss is calculated on masked pairs in FLAVA. mmm_image_logits (`torch.FloatTensor` of shape `(batch_size, num_image_patches, image_vocab_size)` or of shape`(total_masked_patches, image_vocab_size)`, *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM image multimodal loss. Uses `book_masked_pos` to get masked patches. The flattened output is returned when `bool_masked_pos` has some of the patches masked. mmm_text_logits (`torch.FloatTensor` of shape `(batch_size, text_seq_length, text_vocab_size)` or of shape `(`(total_masked_seq_length, text_vocab_size)`), *optional*, returned when `pixel_values` and `input_ids_masked` are present): The logits for MMM text multimodal loss. The flattened output is returned when `input_ids_masked` has some of the tokens masked. contrastive_logits_per_image (`torch.FloatTensor` of shape `(image_batch_size, text_batch_size)`): The scaled dot product scores between `image_embeddings` and `text_embeddings` but passed through FLAVA's `image_projection` and `text_projection` layers respectively. This represents the image-text similarity scores. This is calculated on unmasked images and texts. contrastive_logits_per_text (`torch.FloatTensor` of shape `(text_batch_size, image_batch_size)`): The scaled dot product scores between `text_embeddings` and `image_embeddings` but passed through FLAVA's `text_projection` and `image_projection` layers respectively. This is calculated on unmasked images and texts. Nloss loss_infor%r&r'r(r)r*image_masked_embeddingsimage_masked_outputtext_masked_embeddingstext_masked_outputmultimodal_masked_embeddingsmultimodal_masked_output mim_logits mlm_logits itm_logitscontrastive_logits_per_imagecontrastive_logits_per_textmmm_image_logitsmmm_text_logitsr+cjgdtfdDS)N)r(r&r*rWrUrYc3tK|]2}|vr|n!t|V3dSNr.)r1r2r3transformer_outputss r4r5z5FlavaForPreTrainingOutput.to_tuple..sNssbc)< < rRrr?r@rArSrDr%r&rr'r(r)r*rTrUrVrWrXrYrZr[r\r]r^r_r`r rr0rBr6r4rQrQs9>>@)-D(5$ %,,,!I{!!!48hu018889=L(56===37OXe/07778Bx :;BBB;?Xe&78???@D"<=DDD:>HU%67>>>?C!;<CCC@D (5+<"=DDDEIh'ABIII.2J*+222.2J*+222.2J*+222@D (5+<"=DDD?C%*;!<CCC48hu0188837OXe/0777 t%* t t t t t tr6rQc eZdZdZddededdffd Zdejd e d e dejfd Z dd ejd e ej dedejfdZ xZS)FlavaImageEmbeddingszb Construct the CLS token, position and patch embeddings. Optionally, also the mask token. Fconfiguse_mask_tokenr+Ncft|p|j}tjt jdd|j|_|r-tjt jdd|jnd|_t|j |j |j |j|_ |j j}tjt jd|dz|j|_tj|j|_|j |_ ||_dS)Nr) image_size patch_size num_channels embed_dim)super__init__ mask_tokenr Parameterr?zeros hidden_size cls_tokenPatchEmbeddingsrjrkrlpatch_embeddings num_patchesposition_embeddingsDropouthidden_dropout_probdropoutrg)r3rgrhrw __class__s r4rozFlavaImageEmbeddings.__init__s '<6+<ek!Q8J&K&KLLQ_i",u{1a9K'L'LMMMei /((,( ! ! !  +7 #%< A{QPVPb0c0c#d#d z&"<==  + r6 embeddingsheightwidthc|jddz }|jjddz }tjs||kr ||kr|jS|jddddf}|jddddf}|jd}||jz} ||jz} t |dz} |d| | |}|dddd}tj || | fdd }|dddd dd|}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 rNg?rr bicubicF)sizemode align_cornersdim)shaperxr?jit is_tracingrkrreshapepermuter functional interpolateviewcat) r3r}r~rrw num_positionsclass_pos_embedpatch_pos_embedr new_height new_widthsqrt_num_positionss r4interpolate_pos_encodingz-FlavaImageEmbeddings.interpolate_pos_encoding sr!&q)A- 06q9A= y##%% ,+*F*F6UZ??+ +2111bqb592111abb59r"t. 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EL) )  ) # ) )  uo% &) ) ) ) ) ) ) ) r6r>c:eZdZdeffd ZdejfdZxZS) FlavaPoolerrgcttj|j|j|_tj|_dSrc)rnror rrsr Tanh activationrs r4rozFlavaPooler.__init__sC Yv163EFF '))r6rcr|dddf}||}||}|SNr)r r[)r3rfirst_token_tensor pooled_outputs r4rzFlavaPooler.forwards@+111a40 #566  66 r6r,rs@r4rXrXsb$3$$$$$$ U\r6rXaD 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 ([`{config}`]): 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 attention_mask (`torch.FloatTensor` of shape `({0})`, *optional*): Mask to avoid performing attention on padding token indices. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) 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. return_dict (`bool`, *optional*): Whether or not to return a [`~utils.ModelOutput`] instead of a plain tuple. a; Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Pixel values can be obtained using [`AutoImageProcessor`]. See [`FlavaImageProcessor.__call__`] for details. bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, image_num_patches)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). interpolate_pos_encoding (`bool`, *optional*): Whether to interpolate the pre-trained position encodings. a Args: input_ids (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. Indices can be obtained using [`AutoTokenizer`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) token_type_ids (`torch.LongTensor` of shape `({0})`, *optional*): Segment token indices to indicate first and second portions of the inputs. Indices are selected in `[0, 1]`: - 0 corresponds to a *sentence A* token, - 1 corresponds to a *sentence B* token. [What are token type IDs?](../glossary#token-type-ids) z Args: hidden_states (`torch.FloatTensor` of shape `(batch_size, image_num_patches + text_seq_len, hidden_size)`): The concatenated hidden states of unimodal encoders. z Args: skip_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder. Useful if multimodal encoding is not going to be used. a Args: input_ids_masked (`torch.LongTensor` of shape `({0})`): Indices of input sequence tokens in the vocabulary. These ones are the masked version of the original task to be used with MLM. Indices can be obtained using [`AutoTokenizer`] along with [`DataCollatorForMaskedLanguageModeling`]. See [`PreTrainedTokenizer.encode`] and [`PreTrainedTokenizer.__call__`] for details. [What are input IDs?](../glossary#input-ids) a image_attention_mask (`torch.FloatTensor` of shape `({1})`, *optional*): Mask to avoid performing attention on padding token indices specifically for images. Mask values selected in `[0, 1]`: - 1 for tokens that are **not masked**, - 0 for tokens that are **masked**. [What are attention masks?](../glossary#attention-mask) skip_unmasked_multimodal_encoder (*bool*, *optional*): Skip any calculations for multimodal encoder for unmasked inputs. FLAVA pretraining doesn't need unmasked multimodal embeddings or outputs as of now. mlm_labels (`torch.LongTensor` of shape `(batch_size, text_seq_len)`, *optional*): Labels for computing the left-to-right language and multimodal masked modeling loss (next word prediction). Indices should be in `[-100, 0, ..., text_config.vocab_size - 1]` (see `input_ids` docstring). Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., text_config.vocab_size - 1]`. mim_labels (`torch.LongTensor` of shape `(batch_size, image_num_patches)`, *optional*): Labels for computing the image and multimodal masked modeling loss. Indices should be in `[-100, 0, ..., image_config.vocab_size - 1]`. Tokens with indices set to `-100` are ignored (masked), the loss is only computed for the tokens with labels in `[0, ..., image_config.vocab_size - 1]`. If not passed, they are generated automatically using the image codebook assigned to the model. By default, it uses [`FlavaImageCodebook`]. See [`FlavaImageCodebook`] to understand how to generate mim_labels. itm_labels (`torch.LongTensor` of shape `(batch_size, 1)`, *optional*): Labels for computing the image-text matching loss. 0 means the pairs don't match and 1 means they match. The pairs with 0 will be skipped for calculation of MMM and global contrastive losses as well. return_loss (`bool`, *optional*, default to None): Whether to return calculated loss or not. z Parameters: image_codebook ([`nn.Module`]): If passed, the image codebook will be set to this. Otherwise. it will be initialized using the image_codebook_config defined in the config first as the first parameter. c^eZdZdZeZdZdZdee j e j e j fddfdZ dS)FlavaPreTrainedModelz An abstract class to handle weights initialization and a simple interface for downloading and loading pretrained models. flavaTmoduler+Nct|tjtjfrT|jjd|jj|j |j j dSdSt|tj r_|jjd|jj|j +|jj|j  dSdSt|tj r?|j j |jjddSdS)zInitialize the weightsg)meanstdNr)rr rrweightdatanormal_rginitializer_rangerzero_rrrfill_)r3rcs r4 _init_weightsz"FlavaPreTrainedModel._init_weights^s0 fry")4 5 5 * M  & &CT[5R & S S S{&  &&((((('&  - - * M  & &CT[5R & S S S!- "6#56<<>>>>>.-  - - * K  " " $ $ $ M  $ $S ) ) ) ) ) * *r6)r;r<r=r>r config_classbase_model_prefixsupports_gradient_checkpointingr r rrrrmrBr6r4raraTsf L&*#*E")RY *L$M*RV******r6razeThe bare FLAVA Image Model transformer outputting raw hidden-states without any specific head on top.)rgceZdZeZdZdZddedeffd Zde j fdZ d e j fd Z d e eeefdd fd Zeedeeeede ddeejdeejdeedeejdeejdeedeedeedeeeffdZxZ S)FlavaImageModelzflava.image_modelrTrgadd_pooling_layercJt|||_t||_t ||_tj|j |j |_ |rt|nd|_ |dSNr)rnrorgrfr}r>encoderr rrsr layernormrXpooler post_initr3rgrsr|s r4rozFlavaImageModel.__init__ys     .v66#F++ f&8f>STTT->Hk&)))D  r6r+c|jjSrcr}rvr:s r4get_input_embeddingsz$FlavaImageModel.get_input_embeddingss //r6rc||j_dSrcr|r3rs r4set_input_embeddingsz$FlavaImageModel.set_input_embeddingss+0(((r6heads_to_pruneNc|D]/\}}|jj|j|0dSz Prunes heads of the model. heads_to_prune: dict of {layer_num: list of heads to prune in this layer} See base class PreTrainedModel NitemsrvrErrr3rrErs r4 _prune_headszFlavaImageModel._prune_headsU +0022 C CLE5 L u % / ; 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Examples: ```python >>> from transformers import AutoProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = AutoProcessor.from_pretrained("{0}") >>> inputs = processor( ... text=["a photo of a cat", "a photo of a dog"], max_length=77, padding="max_length", return_tensors="pt" ... ) >>> text_features = model.get_text_features(**inputs) ```)rrrrrrGrHr)rrrr) r3rrrrrrGrH text_outputsr_ text_featuress r4get_text_featureszFlavaModel.get_text_featuressk "v)***))%/!5#'  %Q ,,];; r6rrrrrc dt|||||||||} | d} || } | S)Na Returns: image_features (`torch.FloatTensor` of shape `(batch_size, output_dim`): The image embeddings obtained by applying the projection layer to the pooled output of [`FlavaImageModel`]. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("{0}") >>> processor = AutoProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(images=image, return_tensors="pt") >>> image_features = model.get_image_features(**inputs) ```)rrrrrrGrrHr)rrrr) r3rrrrrrrGrH image_outputsr_image_featuress r4get_image_featureszFlavaModel.get_image_featuressq *v)***((%+)/!5%=#)   &a( ..}==r6rrrnTimage_attention_maskskip_multimodal_encoderc | | n |jj} | stdd} d} d}d}|F||||| | | }|d|d} } || d}d}d}d}d}|G|||||| | | }|d|d}}||d}d}d}|||s|Q|j\}}}|jj r|dz }tj |||j }tj ||gd }nd}tj ||gd }|||| }|d}| s| |||||fSt| ||||| S) a  Returns: Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoProcessor, FlavaModel >>> model = FlavaModel.from_pretrained("facebook/flava-full") >>> processor = AutoProcessor.from_pretrained("facebook/flava-full") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = processor(text=["a photo of a cat"], images=image, return_tensors="pt", padding=True) >>> outputs = model(**inputs) >>> image_embeddings = outputs.image_embeddings >>> text_embeddings = outputs.text_embeddings >>> multimodal_embeddings = outputs.multimodal_embeddings >>> outputs.image_embeddings.shape torch.Size([1, 197, 768]) >>> text_embeddings.shape torch.Size([1, 7, 768]) >>> multimodal_embeddings.shape torch.Size([1, 205, 768]) ``` NzRFLAVA model requires hidden states to work. Please set `output_hidden_states=True`)rrrrrGrHrrr)rrrrrrGrHrrr)rrH)r%r&r'r(r)r*)rgrHrrrrrrrrr?rrrr$)r3rrrrrrrrrrGrHr% image_statesimage_mm_projectionr&r' text_statestext_mm_projectionr(r)r*rrrattention_mask_imageattention_multimodalmultimodal_inputs r4rzFlavaModel.forward-s?j&1%;q>[O!%!;!;KO!L!L  $   */A/MVm/M))<)B& GQ(6!qLG',z*gNaNh'i'i'i$',y2F1W]^'_'_'_$$'+$$y*=?Q)RXYZZZ  $ 5 5 1ES^!6!! %6a$8 !  %!   -%+#"7/     r6)NNNNNNNr) NNNNNNNNNTN)r;r<r=rrnrorrrrr?rrOr@rrrrFLAVA_MODEL_INPUTS_DOCSTRINGrr$ LongTensorr r r.rrrs@r4rrsb L){))))))V+*+F+M+MNk+l+lmm-11515/3,0/3&*))EL))!.)!. ) u|, ) $D> )'tn)d^)  )))nm)V+*+G+N+NOn+o+opp046:3715,0,0/3&*//u|,/"%"23/#+4. / !. / EL) /$D>/'tn/d^/  ///qp/b+*$++,Z[[+;+VVV1548151526377;26,0%)&*z z E,-z u01z !. z !. z "%,/ z u/0z 'u|4z "*$z $D>z #z d^z  uk! "z z z WVz z z z z r6rcLeZdZdedeffd ZdejdejfdZxZS)FlavaImageCodebookResPathin_sizeout_sizec (t|dz}t}tj|d<tj||dd|d<tj|d<tj||dd|d<tj|d <tj||dd|d <tj|d <tj||dd |d <tj||_dS)Nrelu_1r rrpaddingconv_1relu_2conv_2relu_3conv_3relu_4rconv_4)rnrorr ReLUr Sequentialpath)r3rrkwargshid_sizerr|s r4roz"FlavaImageCodebookResPath.__init__s q=}}X7H!QOOOXX8X1aPPPXX8X1aPPPXX8X1aPPPXM$'' r6rr+c,||Src)rr3rs r4rz!FlavaImageCodebookResPath.forwardsyy||r6 r;r<r=rror?rrrrs@r4rrsq((s(((((( %,r6rcPeZdZdededeffd ZdejdejfdZxZS)FlavaImageCodebookBlockrr num_layersc td|dzz |_||krtj||dd|_ntj|_t|||_dS)Nrrrr) rnro post_gainr rid_pathIdentityrres_path)r3rrrrr|s r4roz FlavaImageCodebookBlock.__init__sr j!m, h  9WhAqQQQDLL;==DL1'8DD r6rr+ch|||j||zzSrc)rrrrs r4rzFlavaImageCodebookBlock.forwards*||A$--2B2B!BBBr6rrs@r4rrs E Es E E E E E E ECC%,CCCCCCCCr6rc ZeZdZd dededededef fd Zdejd ejfd ZxZ S) FlavaImageCodebookLayerGroupT num_blocksrrruse_poolcdtt}t|D]=}|dkrt ||||d|dz<#t ||||d|dz<>|rt jd|d<t j||_dS)Nrblock_rr)rpool) rnrorrCrr MaxPool2drgroup) r3rrrrr blocksrSr|s r4roz%FlavaImageCodebookLayerGroup.__init__s z"" a aAAvv)@(T^)_)_~!~~&&)@8U_)`)`~!~~&&  9\a888F6N]6** r6rr+c,||Src)rrs r4rz$FlavaImageCodebookLayerGroup.forwardszz!}}r6r) r;r<r=rrOror?rrrrs@r4rrs + +3 +C +# +QT +`d + + + + + +%,r6ra" The FLAVA's image codebook model inspired from DALL-E's original encoder. Outputs raw hidden states and can be used to generate image tokens for an image based on DALL-E's vocab. Used to generate labels for MIM. Use `get_codebook_indices` to get image tokens for an image. rceZdZdZeZdZdZdedeffd Z de j de j fdZ de j de j fd Z de jde j fd ZxZS) FlavaImageCodebookrFrgrc &t|||_|j|_|j|_|j|_|j|_|j|_|j|jz}t}tj |d<tj d|jz|jdd|d<t}tj |jd|jzdd|d <t|j|d|jzd|jz|d <t|j|d|jzd |jz|d <t|j|d |jzd |jz|d<t|j|d |jzd|jzd|d<tj ||d<tj ||_||jjr|D] }d|_ dSdS)Nrelurrrconvr inputgroup_1rgroup_2rgroup_3F)r group_4r)rnrorg num_groupsinput_channelsnum_blocks_per_grouprsrrr rrrrrryfreeze parameters requires_grad)r3rgrr output_blocksrparamr|s r4rozFlavaImageCodebook.__init__s&      +$3$*$?!!- +_t'@@ # " f " !d.>*>]^hi j j j f)D$7T=M9M[\fghhhw8  %z1t7G3GTM]I]  y9  %z1t7G3GTM]I]  y9  %z1t7G3GTM]I]  y9  %z1t7G3GTM]I]hm   y=77xmF++   ;  ,** , ,&+## , , , ,r6r+cdt||}tj|dS)Na Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing `return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoImageProcessor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> image_processor = AutoImageProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model.get_codebook_indices(**inputs) ``` r)axis)r_CHECKPOINT_FOR_CODEBOOK_DOCrr?argmaxr3rz_logitss r4get_codebook_indicesz'FlavaImageCodebook.get_codebook_indices"s@ . F/ 0 0 0;;|,,|H1----r6ch||}tjd|S)Nrr)rr Softmaxr*s r4get_codebook_probsz%FlavaImageCodebook.get_codebook_probs>s0;;|,, rza   ***r6c6dtt|jdkrt d|jd|jd|jkr%t d|jdd|j||S)Na Args: pixel_values (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)`): Pixel values. Codebook pixel values can be obtained using [`AutoImageProcessor`] by passing `return_codebook_pixels=True`. See [`FlavaImageProcessor.__call__`] for details. Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import AutoImageProcessor, FlavaImageCodebook >>> model = FlavaImageCodebook.from_pretrained("{0}") >>> image_processor = AutoImageProcessor.from_pretrained("{0}") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> inputs = image_processor([image], return_codebook_pixels=True, return_tensors="pt") >>> inputs = dict(pixel_values=inputs.codebook_pixel_values) >>> outputs = model(**inputs) >>> print(outputs.shape) (1, 196) ``` rz input shape z is not 4drz input has z channels but model built for )rr(rrrrr)r3rs r4rzFlavaImageCodebook.forwardBs 4 F/ 0 0 0 |! " "a ' 'JL,>JJJKK K  a D$7 7 7t,*STTTr6c||}||}||}|Src)r r4rr+s r4rz$FlavaPredictionHeadTransform.forwardos= =11 --m<< }55 r6r;r<r=rorrrs@r4r2r2esLUUUUUr6r2c,eZdZdfd ZdZdZxZS)FlavaMaskedPredictionHeadNcht||_t||_t j|j|jd|_ t j tj |j|_ | ||j _|j |j _ dS)NFr)rnrorgr2 transformr rrsrdecoderrqr?rrrrg)r3rgrgr|s r4roz"FlavaMaskedPredictionHead.__init__ws  5f==y!3V5FUSSS LV->!?!?@@  "(DL !I r6c(|j|j_dSrc)rr;r:s r4 _tie_weightsz&FlavaMaskedPredictionHead._tie_weightss I r6cZ||}||}|Src)r:r;rs r4rz!FlavaMaskedPredictionHead.forwards' NN1   LLOOr6rc)r;r<r=ror=rrrs@r4r8r8vs[ & & & & & &&&&r6r8c$eZdZfdZdZxZS) FlavaITMHeadct||_t||_t j|jd|_dS)Nr) rnrorgrXrxr rrsseq_relationshiprs r4rozFlavaITMHead.__init__sL  !&)) " &*.s$'e'e'ea(8(I(I'e'e'er6c8g|]}tjSrBrJ)r1rr%s r4rAz6FlavaGlobalContrastiveHead.forward..s%&e&e&eau'78H'I'I&e&e&er6r)r?exp distributed is_availableis_initializedrrrget_world_sizerGr r all_gatherrCget_rankrrr) r3r%r'r temperaturelabelsimage_embeddings_alltext_embeddings_alllocal_batch_size world_sizelogits_per_imagelogits_per_texts `` r4rz"FlavaGlobalContrastiveHead.forwards2i ,,  --// u7H7W7W7Y7Y \"2"7"7":":CSCZ[[[F$4#5 #2"3  /44Q77 *99;;J/ S(-'8';'F'Q'QRb'c'c$&+&7&:&E&P&PQ`&a&a##'e'e'e'eSXYcSdSd'e'e'e$&e&e&e&eSXYcSdSd&e&e&e#!,,-ACSTTT!,,-@/RRR%(9(B(B(D(DDu| )9)@HHHF %y)=>>#i(;<< <(8:M:W:WXY[\:]:]^^all,8L8V8VWXZ[8\8\]]`kk&88r6r6rs@r4rErEsLNNNNN 9999999r6rEzk The FLAVA model for pretraining which outputs losses, embeddings, logits and transformer outputs. c)eZdZgdZddedeejffd Zde j fdZ e e dd eee  d d ee jd ee jdee jdee jdee j dee j dee j dee jdee j dedee j dee j dee j deededeedeedeee j eff$dZxZS)!FlavaForPreTraining)zmmm_text_head.decoder.biaszmmm_image_head.decoder.biaszmlm_head.decoder.biaszmim_head.decoder.biasNrgimage_codebookct|t||_||_|j |jrt |j|_t|j |_ t|j |_ t||_t|j |_t|j |_t#||_|j j|_|j j|_|j|_|j|_|j|_|j|_|j|_|j|_|j|_|j|_|dSrc)rnrorrbr^ init_codebookrimage_codebook_configr8rmim_headrmlm_headr@itm_headmmm_image_head mmm_text_headrEglobal_contrastive_headrimage_vocab_sizetext_vocab_size mlm_weight mim_weightglobal_contrastive_weightce_ignore_index itm_weightmmm_image_weightmmm_text_weight skip_unmasked_multimodal_encoderry)r3rgr^r|s r4rozFlavaForPreTraining.__init__sH    '' ,   &6+? &"4V5Q"R"RD 2&2EFF 1&2DEE $V,, 78KLL6v7IJJ'A&'I'I$ & 3 >%1< + +)/)I&%5 + & 7%5060W- r6rc|dkr)||dd}|S)Nrrr)rrrrs r4 _resize_to_2dz!FlavaForPreTraining._resize_to_2ds5 5577Q;;qvvayy"%%Ar6zbatch_size, text_seq_lenrrTrinput_ids_maskedrcodebook_pixel_valuesrrrrrrq mlm_labels mim_labels itm_labelsrrGrH return_lossr+c||n |jj}||n |jj}| | n|j} ||td|}||||||| | ||d }|||||| |||d }d}|j}|j}|j}|j}|j }dx}x}x}x}x}x}} dx}!x}"x}#}$dx}%x}&}'||E| C|rA|j td|td|j |} |jdkr(|%|"|}(| || } ||}|j| |d<|(dd| d  dddf}(| |j})| |)}*|(|)ddf}(||(}!|rVt(j|!d |j|*d }||jz}n||(}!|jdkr|||}+| || } |+dd| d  dddf}+| |j})| |)},|+|)ddf}+||+}"|rVt(j|"d |j|,d }||jz}n||+}"|jdkr|||}%| | d}-t=j|- |-|-!dg}|r*t(j|%| } | |jz} |||}| | |} | | |} ||}|4|j"dkr(|}(|d d z }.|(ddd d |.zddf}(| || } ||}|j| |d<| |j})| |)}*|(|)ddf}(|#|(}$|rVt(j|$d |j|*d }||j"z}n|#|(}$||j$dkr|}+|+dd|d  dddf}+| || } | |j})| |)},|+|)ddf}+|%|+}#|rVt(j|#d |j|,d }||j$z}n|%|+}#|a|^|j&dkrR|j'|dddddf}/t(j(|/d }/|j)|dddddf}0t(j(|0d }0|jj*j+,tZt\|/|0|/|jj*\}&}'}1||&|}&|'|}'|1|}1|rRt(j|&|1}2t(j|'|1}3|2|3zd z }||j&z}ta||| ||| }4|r?|41s+ted|43D}|s||j4|j45nd||j6|j65nd|j |j7|j75nd||j4|j45nd||j6|j65nd||j7|j75nd|!|"|%|&|&|$|#f}5|r|41s||4f|5z}5tqd|5DStsd%id|d|4d|d|j4d|d|j6d|j d|j7d|d|j4d|d|j6d|d|j7d|!d|"d |%d!|&d"|'d#|$d$|#S)&ai Examples: ```python >>> from PIL import Image >>> import requests >>> from transformers import FlavaForPreTraining, AutoProcessor >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> model = FlavaForPreTraining.from_pretrained("facebook/flava-full") >>> processor = AutoProcessor.from_pretrained("facebook/flava-full") >>> text = ["a photo of a cat"] >>> inputs = processor( ... images=[image], ... text=text, ... return_masks=True, ... return_codebook_pixels=True, ... padding=True, ... max_length=77, ... return_tensors="pt", ... ) >>> output = model(**inputs) ``` Return: Nz`input_ids_masked` isn't passed which means MLM loss won't be calculated correctlySetting it to `input_ids` so that model can work. Please pass it if this is unintentional. This is usually OKAY if you are doing inference on unmasked text...T) rrrrrrrrrGrH) rrrrrrrrGrHz`return_loss` is set to True but the image codebook is not initialized and no `mim_labels` have been passed. Reinstantiate the model with `init_codebook` set to True or pass in your custom `mim_labels`z`codebook_pixel_value` are required to generate `mim_labels` if loss is expected. Call `AutoProcessor` with `return_codebook_pixels` set to Truerrrrr)rErFrGrHrIrJc3"K|] }||ndV dSr]rB)r1rRs r4r5z.FlavaForPreTraining.forward..s+__T%5TT1______r6c3K|]}||V dSrcrB)r1rs r4r5z.FlavaForPreTraining.forward.. s"88qaiiiii88r6rRrSr%r&r'r(r)r*rTrUrVrWrXrYrZr[r\r]r^r_r`rB):rgrryrqloggerwarningrbr%r'r)r^ RuntimeErrorrr,rkrsrmnerrbr r cross_entropyrrhrjrcrirnrdr?whereanynewrorerprfrlr normalizerrrhclamp_LOGIT_SCALE_CLAMP_MINLOGIT_SCALE_CLAMP_MAXrgrDrMsumrLr&r0r(r*r8rQ)6r3rrtrrurrrrrrqrvrwrxrrGrHry flava_outputflava_masked_outputpos_maskr%r'rTrVrX total_lossmim_lossmlm_loss mmm_text_lossmmm_image_lossgc_lossitm_lossrZr[r`r_r\rZr[sequence_for_image masked_tokensmim_labels_filteredsequence_for_textmlm_labels_filtered pos_pairs end_indextext_embeddingimage_embedding gc_labels gc_loss_image gc_loss_text flava_lossesrs6 r4rzFlavaForPreTraining.forwards p&1%eGV^GKK KZK/4D:>> >% # .2N2Z!k!&.&; )0$Y"0EEF[\\  ?Q  #:#FKgKo!8 %!// ;; "&"4"4_"E"E7;7K ?--d334%7JOOA> 0!}::"D,ABBDWD\D\]_D`D`  H/H!]]+=>>  ?Q  #9#EJfJn 6 %!// ;; $5aaa*//!:L:L9L9N9NPQPQPQ6Q$R! * d.B C C &0&?#$5mQQQ6F$G!!]]+<== 0!}::"D,@AACVC[C[\^C_C_  H/H!]]+<==  ?Q  #?#K'CDDJ%&MM!,,  ;y}} 9==RVQWCXCXYY0!}:::zRRH/H/;3OPX3Y0)!+H!5J)!+H!5J&5h&?O ( 38MPQ8Q8Q!= /44Q77!;I!3AAAq1y=7H!!!4K!L %!// ;; "&"4"4_"E"E7;7K ?--d334 * d.B C C &0&?#%7 qqq8H%I"#'#6#67I#J#J <%']%@%@(--b$2GHHJ]JbJbceJfJf&&N#d&;;N#'#6#67I#J#J  ( 38Lq8P8P <  1!!!6L6Q6QRS6T6T5T5V5VXYXYXY2Y Z %!// ;; * d.B C C &0&?#$5mQQQ6F$G!"&"4"45F"G"G:$&M$?$?',,R1EFFH[H`H`acHdHd%%M"T%99M"&"4"45F"G"G  'O,GDLjmnLnLn!Z771aaa8PQQN]44^4LLN"j99:J111aQRQRQR7:STTO m55o25NNO J " ' . ./DF[ \ \ \;?;W;W1G<< 8 oy ##3H#= "1(";%h/  : " ; ;
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