o ZhX@srdZddlZddlmZddlmZddlmZm Z m Z m Z m Z ddl ZddlZddlZddlmZddlmZmZmZdd lmZdd lmZmZdd lmZmZdd lmZm Z dd l!m"Z"m#Z#m$Z$ddl%m&Z&m'Z'ddl(m)Z)e$*e+Z,eGddde"Z-eGddde"Z.ddZ/Gdddej0Z1Gdddej0Z2 dAdej0dej3dej3dej3d e ej3d!e4d"e4fd#d$Z5Gd%d&d&ej0Z6Gd'd(d(ej0Z7Gd)d*d*ej0Z8Gd+d,d,ej0Z9Gd-d.d.ej0Z:Gd/d0d0ej0Z;Gd1d2d2ej0ZGd7d8d8ej0Z?e#d9d:Gd;d<dd?d?e=ZAgd@ZBdS)Bz,PyTorch VideoMAE (masked autoencoder) model.N)deepcopy) dataclass)CallableOptionalSetTupleUnion)nn)BCEWithLogitsLossCrossEntropyLossMSELoss)ACT2FN)BaseModelOutputImageClassifierOutput)ALL_ATTENTION_FUNCTIONSPreTrainedModel) find_pruneable_heads_and_indicesprune_linear_layer) ModelOutputauto_docstringlogging)IMAGENET_DEFAULT_MEANIMAGENET_DEFAULT_STD)VideoMAEConfigc@sPeZdZUdZdZeejed<dZ ee ejed<dZ ee ejed<dS)VideoMAEDecoderOutputaO Class for VideoMAEDecoder's outputs, with potential hidden states and attentions. Args: logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. 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 + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. Nlogits hidden_states attentions) __name__ __module__ __qualname____doc__rrtorch FloatTensor__annotations__rrrr'r']/var/www/auris/lib/python3.10/site-packages/transformers/models/videomae/modeling_videomae.pyr,s rc@sbeZdZUdZdZeejed<dZ eejed<dZ ee ejed<dZ ee ejed<dS)VideoMAEForPreTrainingOutputa Class for VideoMAEForPreTraining's outputs, with potential hidden states and attentions. Args: loss (`torch.FloatTensor` of shape `(1,)`): Pixel reconstruction loss. logits (`torch.FloatTensor` of shape `(batch_size, patch_size ** 2 * num_channels)`): Pixel reconstruction logits. 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 + one for the output of each layer) of shape `(batch_size, sequence_length, hidden_size)`. Hidden-states of the model at the output of each layer plus the initial embedding outputs. attentions (`tuple(torch.FloatTensor)`, *optional*, returned when `output_attentions=True` is passed or when `config.output_attentions=True`): Tuple of `torch.FloatTensor` (one for each layer) of shape `(batch_size, num_heads, sequence_length, sequence_length)`. Attentions weights after the attention softmax, used to compute the weighted average in the self-attention heads. 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Indicates which patches are masked (1) and which aren't (0). Each video in the batch must have the same number of masked patches. If `None`, then all patches are considered. Sequence length is `(num_frames // tubelet_size) * (image_size // patch_size) ** 2`. Examples: ```python >>> import av >>> import numpy as np >>> from transformers import AutoImageProcessor, VideoMAEModel >>> 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 16 frames >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container, indices) >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEModel.from_pretrained("MCG-NJU/videomae-base") >>> # 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, 1568, 768] ```Nrrrrrrr) rDrruse_return_dictZ get_head_maskrrTrrrrr) rFrRrSrrrrZembedding_outputZencoder_outputssequence_outputr'r'r(rXs.Y   zVideoMAEModel.forward)NNNNN)r r!r"r>rrrr$r%r BoolTensorrrrrrrXrZr'r'rGr(rs2  rcs,eZdZfddZ   dddZZS)VideoMAEDecodercst|j|j|jd}t||j_|j_ |j _ |j _ tfddt|jD|_t|j|_|dkrFt|j|nt|_d|_||_dS)Nr+crr'rrZdecoder_configr'r(r1r5z,VideoMAEDecoder.__init__..rF)r=r>rWr^r\rdecoder_hidden_sizerBZdecoder_num_hidden_layersrZdecoder_num_attention_headsrZdecoder_intermediate_sizerr rr2decoder_layersrnormrIdentityheadrrD)rFrDrAZdecoder_num_labelsrGrr(r>s  zVideoMAEDecoder.__init__FTc Cs|rdnd}|r dnd}t|jD]/\}} |r||f}|jr,|jr,|| j|d|} n| |d|d} | d}|r@|| df}q|rH||f}|dkrW|dd| df}||}||} |sotdd| ||fDSt | ||dS)Nr')rrrrcsrr<r'rr'r'r(rrz*VideoMAEDecoder.forward..)rrr) rrrrzrrrrrr) rFrZreturn_token_numrrrrrrrrrr'r'r(rXs4      zVideoMAEDecoder.forward)FFT)r r!r"r>rXrZr'r'rGr(rs  rzb The VideoMAE Model transformer with the decoder on top for self-supervised pre-training. )Z custom_introcsneZdZfddZe    d dejdejdeej dee dee d ee d e e e ffd d ZZS)VideoMAEForPreTrainingcs~t|||_t||_tj|j|jdd|_ t t dd|j|_ t|jjj|j|_t||jjjd|_|dS)NFrr)rA)r=r>rDrrr rrBrencoder_to_decoderrr$r mask_tokenr9rTrArCrdecoderrrErGr'r(r>s    zVideoMAEForPreTraining.__init__NrRrSrrrrrc# Csj|dur|n|jj}|j||||||d}|d}||}|j\} } } |dur,td|j| dd|} | j |j dd} | | | d| } | | | d| }t j|| |j|gdd }|||jd}|j}d}t  |jjd kr|}n2|j }|j}t tj ||d ddddddf}t tj ||d ddddddf}|||}|j\} }} }}|jj|jj}}|jjr*|| |||| ||||||}|ddd d dddd }|| |||||||||| }||jddd|jdddd d}|| |||||||||| }nB|jjd kr5td|| |||| ||||||}|ddd d dddd }|| |||||||||| }|j\} }} || | d| } Wdn 1swYt!}!|!|| }|s|f|dd}"|dur|f|"S|"St"|||j#|j$dS)a bool_masked_pos (`torch.BoolTensor` of shape `(batch_size, sequence_length)`): Boolean masked positions. Indicates which patches are masked (1) and which aren't (0). Each video in the batch must have the same number of masked patches. Sequence length is `(num_frames // tubelet_size) * (image_size // patch_size) ** 2`. Examples: ```python >>> from transformers import AutoImageProcessor, VideoMAEForPreTraining >>> import numpy as np >>> import torch >>> num_frames = 16 >>> video = list(np.random.randint(0, 256, (num_frames, 3, 224, 224))) >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base") >>> model = VideoMAEForPreTraining.from_pretrained("MCG-NJU/videomae-base") >>> pixel_values = image_processor(video, return_tensors="pt").pixel_values >>> num_patches_per_frame = (model.config.image_size // model.config.patch_size) ** 2 >>> seq_length = (num_frames // model.config.tubelet_size) * num_patches_per_frame >>> bool_masked_pos = torch.randint(0, 2, (1, seq_length)).bool() >>> outputs = model(pixel_values, bool_masked_pos=bool_masked_pos) >>> loss = outputs.loss ```N)rSrrrrrz!One must provided a boolean mask rLTrIrrr )rJrxrgr+rv)rwkeepdim)rwZunbiasedrgư>zQCan't unnormalize non-RGB images. 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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 torch >>> import numpy as np >>> from transformers import AutoImageProcessor, VideoMAEForVideoClassification >>> 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 16 frames >>> indices = sample_frame_indices(clip_len=16, frame_sample_rate=1, seg_len=container.streams.video[0].frames) >>> video = read_video_pyav(container, indices) >>> image_processor = AutoImageProcessor.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> model = VideoMAEForVideoClassification.from_pretrained("MCG-NJU/videomae-base-finetuned-kinetics") >>> 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]) eating spaghetti ```NrrrZ regressionZsingle_label_classificationZmulti_label_classificationrLr)rDrrr rr Z problem_typerrxr$longrcr Zsqueezer rr rrr) rFrRrrrrrrrrr*rrr'r'r(rXsR\     "       z&VideoMAEForVideoClassification.forward)NNNNNN)r r!r"r>rrr$rrrrrrXrZr'r'rGr(rs0   r)rrrr)rn)Cr#collections.abcr`rKr dataclassesrtypingrrrrrnumpyr,r$Ztorch.utils.checkpointr Ztorch.nnr r r Z activationsrZmodeling_outputsrrZmodeling_utilsrrZ pytorch_utilsrrutilsrrrZutils.constantsrrZconfiguration_videomaerZ get_loggerr rrr)r9Moduler;r?rfloatrrrrrrrrrrrrr__all__r'r'r'r(s      != J(+3D2 !