o }oiE. @sdZddlZddlZddlmZddlZddlZddlZddl m Z m Z ej dZ ZeeejjZdZdZdDded ed ejjfd d Z  dEd ejjejjBded dfddZd eefddZdFdeded efddZded ejfddZ ded ejfddZ!dFdejde"d ejfddZ#dFdejde"d ejfdd Z$dedejfd!d"Z%dGdedejd$e"d dfd%d&Z&e ed'fd(ejd)ej'd ed*e"d ej(f d+d,Z)dHd-ej(d*e"d ejfd.d/Z*efd0ejd1e"d e+ejee"ffd2d3Z,eefd0ejd4e"d1e"d e+ejee"ffd5d6Z-d0ejd7ee"d ejfd8d9Z.d0ejd7ee"d ejfd:d;Z/dEdedZ0ded|p tj|d}|dtj|}tj|dd|S)z9Returns the output filepath for the given input filepath.z/reconstructions/T)exist_ok)ospathdirnamebasenamemakedirs)rroutput_filepathrrrget_output_filepathAsr'cCsJt|}|jdkrtj|gddd}|jddkr#|dddf}|S)zReads an image from a filepath. Args: filepath: The filepath to the image. Returns: The image as a numpy array, layout HxWxC, range [0..255], uint8 dtype. axis.N)media read_imagendimnpstackshaperimagerrrr/Is r/cCsJt|}|jdkrtj|gddd}|jddkr#|dddf}|S)zReads a video from a filepath. Args: filepath: The filepath to the video. Returns: The video as a numpy array, layout TxHxWxC, range [0..255], uint8 dtype. r)r*r+r-.N)r. read_videor0r1r2r3)rvideorrrr6]s  r6r5 short_sizecC|dur|S|jdd\}}||kr-|t|||d}}|ddkr(|n|d}nt|||d|}}|ddkrB|n|d}tj|||fdS) zResizes an image to have the short side of `short_size`. Args: image: The image to resize, layout HxWxC, of any range. short_size: The size of the short side. Returns: The resized image. Nr*?r(rr3)r3intr. resize_image)r5r8heightwidth height_new width_newrrrr?p r?r7cCr9) zResizes a video to have the short side of `short_size`. Args: video: The video to resize, layout TxHxWxC, of any range. short_size: The size of the short side. Returns: The resized video. Nr:r*r;r(rr<r=)r3r>r. resize_video)r7r8r@rArBrCrrrrErDrEcCs t||S)zWrites an image to a filepath.)r. write_imager4rrrrFs rFfpscCstj|||dS)zWrites a video to a filepath.)rH)r. write_video)rr7rHrrrrIsrIr* input_imagedtype range_mincCsr|j}ttd|ddttd|dd}|dt|t}|dkr.d|d}t|||S)zConverts image(dtype=np.uint8) to `dtype` in range [0..255]. Args: input_image: A batch of images in range [0..255], BxHxWx3 layout. Returns: A torch.Tensor of layout Bx3xHxW in range [-1..1], dtype. r<r*Nr@?) r0rrange transposetuple _UINT8_MAX_Fr from_numpyr)rJrKr rLr0indicesr5rrr numpy2tensors  , rV input_tensorcCsb|dkr |dd}|j}|dd}|dttd|d}|td  t j S) zConverts tensor in [-1,1] to image(dtype=np.uint8) in range [0..255]. Args: input_tensor: Input image tensor of Bx3xHxW layout, range [-1..1]. Returns: A numpy image of layout BxHxWx3, range [0..255], uint8 dtype. r*rOrNrr<rMr()r<r;) floatr0clampcpunumpyrQrRrPrSastyper1uint8)rWrLr0 output_imagerrr tensor2numpys r_batch spatial_aligncCs|jdd\}}|}||dkr|||nd}||dkr%|||nd}|d?|d?||d?||d?g}tj|d|d?||d?f|d?||d?fdfdd}||fS)zPads a batch of images to be divisible by `spatial_align`. Args: batch: The batch of images to pad, layout BxHxWx3, in any range. align: The alignment to pad to. Returns: The padded batch and the crop region. r<r)rrrconstantmoder3r1pad)r`rar@rAalign height_to_pad width_to_pad crop_regionrrrpad_image_batchs&    rltemporal_alignc Cs|jdd\}}}|}||dkr|||nd}||dkr&|||nd}|}|d|dkr:||d|nd} | d?|d?|d?|| d?||d?||d?g} tj|dd|d?||d?f|d?||d?fdfdd}tj|d| d?| | d?fdddfdd}|| fS) a[Pads a batch of videos to be divisible by `temporal_align` or `spatial_align`. Zero pad spatially. Reflection pad temporally to handle causality better. Args: batch: The batch of videos to pad., layout BxFxHxWx3, in any range. align: The alignment to pad to. Returns: The padded batch and the crop region. r*rr<rbrcrdedgerf) r`rmra num_framesr@rArhrirj frames_to_padrkrrrpad_video_batchsD$     rrrkcCsFt|dks Jd|\}}}}}}|d||||||ddfS)a Unpads video with `crop_region`. Args: batch: A batch of numpy videos, layout BxFxHxWxC. crop_region: [f1,y1,x1,f2,y2,x2] first, top, left, last, bot, right crop indices. Returns: np.ndarray: Cropped numpy video, layout BxFxHxWxC. zcrop_region should be len of 6..Nlen)r`rkf1y1x1f2y2x2rrrunpad_video_batch's "r|cCs<t|dks Jd|\}}}}|d||||ddfS)zUnpads image with `crop_region`. Args: batch: A batch of numpy images, layout BxHxWxC. crop_region: [y1,x1,y2,x2] top, left, bot, right crop indices. Returns: np.ndarray: Cropped numpy image, layout BxHxWxC. r-zcrop_region should be len of 4..Nrt)r`rkrwrxrzr{rrrunpad_image_batch6s r}tokenizer_configcCs0|d}t|jdi|}tj|}||fS)Nnamer)rvaluer r r )rr~tokenizer_namerckptsrrrget_pytorch_modelEs r model_typecCsZt||\}}|dkr|}n|dkr|}|j|dd||d|S)z(Loads a torch.nn.Module from a filepath.encdecF)strictrK)r encoder_jit decoder_jitload_state_dict state_dictrr)rr~rr rrrrrload_pytorch_modelLs rcCsXtd|}|r*|\}}}t|j}|tt|d|tt|d|SdS)z+return tokeinzer config from tokenizer namez#Cosmos-Tokenizer-(\D+)(\d+)x(\d+).*)spatial_compression)temporal_compressionN)rematchgroupsrrupdatedictr>)tokenizer_typerrtemporalspatialr~rrrget_tokenizer_configWs  r)Nr)NN)N)rG)r*)4__doc__r!rrmediapyr.r[r1r 1nemo.collections.common.video_tokenizers.networksrrbfloat16_DTYPE_DEVICErXiinfor]maxrS_SPATIAL_ALIGN_TEMPORAL_ALIGNrr ScriptModulerRecursiveScriptModulerrrr'ndarrayr/r6r>r?rErFrIrKTensorrVr_rRrlrrr|r}rrrrrrrrs|     *# 7