o *j=@sddlZddlmZmZddlZddlmZmZddlm Z ddl m Z ddl m Z ddlmZdd lmZdd lmZd e_de_de_Gd d d eZdS)N)AnyDict)Image ImageFile)create_transform) transforms) load_image)ModeKeys)OfaBasePreprocessor) RandomAugmentTcseZdZdZejffdd Zdeee fdeee ffddZ deee fdeee ffdd Z deee fdeee ffd d Z Z S) "OfaImageClassificationPreprocessorz9 OFA preprocessor for image classification task. cstt|j|||g|Ri||jtjkr9tddtj|j |j ftj j dt tj |j|jdg|_dSt|j ddddd d d |j|jd |_ttd dddg|jjdd|jjdgtdddgddg|jjddg|_dS)zpreprocess the data Args: cfg(modelscope.utils.config.ConfigDict) : model config model_dir (str): model path, mode: preprocessor mode (model mode) cS |dSNRGBconvertimagerr/var/www/html/Deteccion_Ine/venv/lib/python3.10/site-packages/modelscope/preprocessors/ofa/image_classification.py+ z=OfaImageClassificationPreprocessor.__init__..) interpolation)meanstdTg?zrand-m9-mstd0.5-inc1Zbicubicg?Zpixelr ) Z input_sizeZ is_trainingZ color_jitterZ auto_augmentrZre_probZre_modeZre_countrrcSs||SNr)xyrrrr?scSrrrrrrrrArN) ZIdentityZ AutoContrastZEqualizeZ BrightnessZ SharpnessZShearXZShearYZ TranslateXZ TranslateYZRotate)ZisPILZaugs)superr __init__moder TRAINrZComposeZResizeZpatch_image_sizeZInterpolationModeZBICUBICZToTensorZ Normalizerrpatch_resize_transformr functoolsreducer )selfcfgZ model_dirr$argskwargs __class__rrr#s^         z+OfaImageClassificationPreprocessor.__init__datareturncCs |jtjkr ||S||Sr)r$r r%_build_train_sample_build_infer_sample)r)r/rrr__call__Ss   z+OfaImageClassificationPreprocessor.__call__cCs||}d|d}|ddi|d<|j|dd|d<t|j|ddd g|d <|jduritt|d t|j f }t t|d D]}|d d|d  }|j |}d |||<qJ||d <|S)a Building training samples. step 1. Preprocess the data using the logic of `_build_infer_sample` and make sure the label data in the result. step 2. Preprocess the label data. Contains: - add ` ` before the label value and add `ref_dict` value - tokenize the label as `target` value without `bos` token. - add `bos` token and remove `eos` token of `target` as `prev_output_tokens`. - add constraints mask. Args: data (`Dict[str, Any]`): Input data, should contains the key of `image`, `prompt` and `label`, `image` refers the image input data, `prompt` refers the text input data the `label` is the supervised data for training. Return: A dict object, contains source, image, mask, label, target tokens, and previous output tokens data. z {}labelg?Zref_dictF)Zadd_bostargetNZprev_output_tokensr Tconstraint_mask)r2format tokenize_texttorchcatbos_itemZconstraint_trieZzeroslenZtgt_dictboolrangetolistZget_next_layer)r)r/sampler5r7iZconstraint_prefix_tokenZconstraint_nodesrrrr1Ys6   z6OfaImageClassificationPreprocessor._build_train_samplecCs~|||jd}||}|jjdd}||}||tdg|j d}d|jvr=|jd|vr=||jd|d<|S)az Building inference samples. step 1. Get the pillow image. step 2. Do some transforms for the pillow image as the image input, such as resize, normalize, to tensor etc. step 3. Tokenize the prompt as text input. step 4. Determine Whether or not to add labels to the sample. Args: data (`Dict[str, Any]`): Input data, should contains the key of `image` and `prompt`, the former refers the image input data, and the later refers the text input data. Return: A dict object, contains source, image, mask and label data. rpromptz what does the image describe?T)source patch_imageZ patch_maskZdecoder_prompttextr4) Z get_img_pilZ column_mapr&r*modelgetr9r:Ztensorr<)r)r/rrErCZinputsrArrrr2s   z6OfaImageClassificationPreprocessor._build_infer_sample)__name__ __module__ __qualname____doc__r Z INFERENCEr#rstrrr3r1r2 __classcell__rrr-rr s":"*(r )r'typingrrr:ZPILrrZ timm.datarZ torchvisionrZmodelscope.preprocessors.imagerZmodelscope.utils.constantr baser Zutils.vision_helperr ZLOAD_TRUNCATED_IMAGESZMAX_IMAGE_PIXELSr rrrrs