a h-@s,dZddlmZddlmZmZddlZddlZddlmZddl m Z ddl m Z dd l mZdd lmZmZmZmZdd lmZdd lmZd dlmZeeZeeddGdddeZeGddde ZdddZGdddej Z!Gdddej Z"eddGdddeZ#ddgZ$dS) zPyTorch VitPose model.) dataclass)OptionalUnionN)nn)BackboneOutput)PreTrainedModel)Unpack) ModelOutputTransformersKwargsauto_docstringlogging) load_backbone)can_return_tuple) VitPoseConfigz6 Class for outputs of pose estimation models. )Z custom_introc@sjeZdZUdZdZeejed<dZ eejed<dZ ee ejdfed<dZ ee ejdfed<dS)VitPoseEstimatorOutputaH loss (`torch.FloatTensor` of shape `(1,)`, *optional*, returned when `labels` is provided): Loss is not supported at this moment. See https://github.com/ViTAE-Transformer/ViTPose/tree/main/mmpose/models/losses for further detail. heatmaps (`torch.FloatTensor` of shape `(batch_size, num_keypoints, height, width)`): Heatmaps as predicted by the model. hidden_states (`tuple(torch.FloatTensor)`, *optional*, returned when `output_hidden_states=True` is passed or when `config.output_hidden_states=True`): Tuple of `torch.FloatTensor` (one for the output of the embeddings, if the model has an embedding layer, + one for the output of each stage) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states (also called feature maps) of the model at the output of each stage. Nlossheatmaps. hidden_states attentions) __name__ __module__ __qualname____doc__rrtorchZ FloatTensor__annotations__rrtuplerrrh/var/www/html/assistant/venv/lib/python3.9/site-packages/transformers/models/vitpose/modeling_vitpose.pyr&s  rc@s@eZdZUeed<dZdZdZee j e j e j fdddZ dS) VitPosePreTrainedModelconfigZvit pixel_valuesT)modulecCst|tjtjfrZtjj|jjt j d|j j d|jj |j_|jdur|jjn&t|tjr|jj|jjddS)zInitialize the weightsg)meanZstdNg?) isinstancerLinearConv2dinitZ trunc_normal_weightdatatorZfloat32r!Zinitializer_rangeZdtypebiasZzero_ LayerNormZfill_)selfr#rrr _init_weightsEs   z$VitPosePreTrainedModel._init_weightsN)rrrrrZbase_model_prefixZmain_input_nameZsupports_gradient_checkpointingrrr&r'r-r/rrrrr >s r gaussian-heatmapc Cs|dvrtd|jdkr"td|j\}}}}d}|dkrnd}|dddddd f |dddddd f<||d |||}|}|D]H\} } |dd| d f|dd| d f<|dd| d f|dd| d f<q|||||f}|d }|S) aFlip the flipped heatmaps back to the original form. Args: output_flipped (`torch.tensor` of shape `(batch_size, num_keypoints, height, width)`): The output heatmaps obtained from the flipped images. flip_pairs (`torch.Tensor` of shape `(num_keypoints, 2)`): Pairs of keypoints which are mirrored (for example, left ear -- right ear). target_type (`str`, *optional*, defaults to `"gaussian-heatmap"`): Target type to use. Can be gaussian-heatmap or combined-target. gaussian-heatmap: Classification target with gaussian distribution. combined-target: The combination of classification target (response map) and regression target (offset map). Paper ref: Huang et al. The Devil is in the Details: Delving into Unbiased Data Processing for Human Pose Estimation (CVPR 2020). Returns: torch.Tensor: heatmaps that flipped back to the original image )r0combined-targetz9target_type should be gaussian-heatmap or combined-targetzCoutput_flipped should be [batch_size, num_keypoints, height, width]rr1rN.) ValueErrorndimshapereshapeclonetolistflip) Zoutput_flipped flip_pairsZ target_type batch_sizeZ num_keypointsheightwidthZchannelsZoutput_flipped_backleftrightrrr flip_backTs" . " rAcsDeZdZdZedfdd Zd ejeejejdddZ Z S) VitPoseSimpleDecoderz Simple decoding head consisting of a ReLU activation, 4x upsampling and a 3x3 convolution, turning the feature maps into heatmaps. r!csHtt|_tj|jddd|_tj|j j |j dddd|_ dS)NZbilinearF) scale_factormodeZ align_cornersrr kernel_sizestridepadding) super__init__rReLU activationZUpsamplerD upsamplingr'backbone_config hidden_size num_labelsconvr.r! __class__rrrKs   zVitPoseSimpleDecoder.__init__N) hidden_stater;returncCs4||}||}||}|dur0t||}|SN)rMrNrRrAr.rVr;rrrrforwards     zVitPoseSimpleDecoder.forward)N rrrrrrKrTensorrrZ __classcell__rrrTrrB|s rBcs@eZdZdZedfdd Zd ejeejdddZ Z S) VitPoseClassicDecoderz Classic decoding head consisting of a 2 deconvolutional blocks, followed by a 1x1 convolution layer, turning the feature maps into heatmaps. rCcsttj|jjdddddd|_td|_t |_ tjddddddd|_ td|_ t |_ tjd|jdddd|_dS) Nr2rF)rGrHrIr,rrF)rJrKrZConvTranspose2drOrPdeconv1Z BatchNorm2d batchnorm1rLrelu1deconv2 batchnorm2relu2r'rQrRrSrTrrrKs     zVitPoseClassicDecoder.__init__N)rVr;cCs\||}||}||}||}||}||}||}|durXt||}|SrX)rarbrcrdrerfrRrArYrrrrZs        zVitPoseClassicDecoder.forward)Nr[rrrTrr^sr^z? The VitPose model with a pose estimation head on top. c s\eZdZedfdd Zeedeje eje eje eje e e dddZ ZS) VitPoseForPoseEstimationrCcs|t|t||_t|jjds,tdt|jjdsBtdt|jjdsXtd|jrft|nt ||_ | dS)NrPz0The backbone should have a hidden_size attribute image_sizez0The backbone should have an image_size attribute patch_sizez/The backbone should have a patch_size attribute) rJrKrbackbonehasattrr!r4Zuse_simple_decoderrBr^headZ post_initrSrTrrrKs  z!VitPoseForPoseEstimation.__init__N)r" dataset_indexr;labelskwargsrWc Ksd}|durtd|jj|fd|i|}|jd}|jd} |jjjd|jjjd} |jjjd|jjjd} | ddd}| | d| |  }|j ||d} t || |j|jd S) ac dataset_index (`torch.Tensor` of shape `(batch_size,)`): Index to use in the Mixture-of-Experts (MoE) blocks of the backbone. This corresponds to the dataset index used during training, e.g. For the single dataset index 0 refers to the corresponding dataset. For the multiple datasets index 0 refers to dataset A (e.g. MPII) and index 1 refers to dataset B (e.g. CrowdPose). flip_pairs (`torch.tensor`, *optional*): Whether to mirror pairs of keypoints (for example, left ear -- right ear). Examples: ```python >>> from transformers import AutoImageProcessor, VitPoseForPoseEstimation >>> import torch >>> from PIL import Image >>> import requests >>> processor = AutoImageProcessor.from_pretrained("usyd-community/vitpose-base-simple") >>> model = VitPoseForPoseEstimation.from_pretrained("usyd-community/vitpose-base-simple") >>> url = "http://images.cocodataset.org/val2017/000000039769.jpg" >>> image = Image.open(requests.get(url, stream=True).raw) >>> boxes = [[[412.8, 157.61, 53.05, 138.01], [384.43, 172.21, 15.12, 35.74]]] >>> inputs = processor(image, boxes=boxes, return_tensors="pt") >>> with torch.no_grad(): ... outputs = model(**inputs) >>> heatmaps = outputs.heatmaps ```NzTraining is not yet supportedrmr3rrr`)r;)rrrr)NotImplementedErrorrjZforward_with_filtered_kwargsZ feature_mapsr6r!rOrhriZpermuter7 contiguousrlrrr) r.r"rmr;rnroroutputsZsequence_outputr<Z patch_heightZ patch_widthrrrrrZs.'  z VitPoseForPoseEstimation.forward)NNN)rrrrrKrr rr\rr r rrZr]rrrTrrgsrg)r0)%r dataclassesrtypingrrrZtorch.utils.checkpointrZmodeling_outputsrZmodeling_utilsrZprocessing_utilsr utilsr r r r Zutils.backbone_utilsrZ utils.genericrZconfiguration_vitposerZ get_loggerrloggerrr rAModulerBr^rg__all__rrrrs8          (&V