o .wi.@sJddlmZddlmZmZmZmZmZmZddl Z ddl m Z ddl m Z m Z mZmZmZddlmZddlmZmZddlmZdd lmZmZmZdd lmZmZesZd d gZes_d gZer}er}dd l m!Z"ddl m#Z$dddZ%ee%s|d d gZnd d gZGdd d eZ&ere&Z'e'dde'dde'dde'dddSdS))Sequence) TYPE_CHECKINGAnyListLiteralOptionalUnionN)Tensor)_clip_iqa_compute_clip_iqa_format_prompts_clip_iqa_get_anchor_vectors_clip_iqa_update!_get_clip_iqa_model_and_processor)Metric)_SKIP_SLOW_DOCTEST_try_proceed_with_timeout) dim_zero_cat)_MATPLOTLIB_AVAILABLE_PIQ_GREATER_EQUAL_0_8 _TRANSFORMERS_GREATER_EQUAL_4_10)_AX_TYPE_PLOT_OUT_TYPECLIPImageQualityAssessmentCLIPImageQualityAssessment.plot) CLIPModel) CLIPProcessorreturncCs tjdddtjddddS)Nopenai/clip-vit-large-patch14T)resume_download) _CLIPModelfrom_pretrained_CLIPProcessorr"r"]/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/torchmetrics/multimodal/clip_iqa.py_download_clip_iqa_metric/sr$c seZdZUdZdZeed<dZeed<dZeed<dZ dZ e ed <e e ed <d Z eed <  d#deddedeeeeeeffdfdeddf fdd Zde ddfddZdee eee fffddZd$dee ee dfd eedefd!d"ZZS)%ra>Calculates `CLIP-IQA`_, that can be used to measure the visual content of images. The metric is based on the `CLIP`_ model, which is a neural network trained on a variety of (image, text) pairs to be able to generate a vector representation of the image and the text that is similar if the image and text are semantically similar. The metric works by calculating the cosine similarity between user provided images and pre-defined prompts. The prompts always comes in pairs of "positive" and "negative" such as "Good photo." and "Bad photo.". By calculating the similartity between image embeddings and both the "positive" and "negative" prompt, the metric can determine which prompt the image is more similar to. The metric then returns the probability that the image is more similar to the first prompt than the second prompt. Build in prompts are: * quality: "Good photo." vs "Bad photo." * brightness: "Bright photo." vs "Dark photo." * noisiness: "Clean photo." vs "Noisy photo." * colorfullness: "Colorful photo." vs "Dull photo." * sharpness: "Sharp photo." vs "Blurry photo." * contrast: "High contrast photo." vs "Low contrast photo." * complexity: "Complex photo." vs "Simple photo." * natural: "Natural photo." vs "Synthetic photo." * happy: "Happy photo." vs "Sad photo." * scary: "Scary photo." vs "Peaceful photo." * new: "New photo." vs "Old photo." * warm: "Warm photo." vs "Cold photo." * real: "Real photo." vs "Abstract photo." * beautiful: "Beautiful photo." vs "Ugly photo." * lonely: "Lonely photo." vs "Sociable photo." * relaxing: "Relaxing photo." vs "Stressful photo." As input to ``forward`` and ``update`` the metric accepts the following input - ``images`` (:class:`~torch.Tensor`): tensor with images feed to the feature extractor with shape ``(N,C,H,W)`` As output of `forward` and `compute` the metric returns the following output - ``clip_iqa`` (:class:`~torch.Tensor` or dict of tensors): tensor with the CLIP-IQA score. If a single prompt is provided, a single tensor with shape ``(N,)`` is returned. If a list of prompts is provided, a dict of tensors is returned with the prompt as key and the tensor with shape ``(N,)`` as value. Args: model_name_or_path: string indicating the version of the CLIP model to use. Available models are: - `"clip_iqa"`, model corresponding to the CLIP-IQA paper. - `"openai/clip-vit-base-patch16"` - `"openai/clip-vit-base-patch32"` - `"openai/clip-vit-large-patch14-336"` - `"openai/clip-vit-large-patch14"` data_range: The maximum value of the input tensor. For example, if the input images are in range [0, 255], data_range should be 255. The images are normalized by this value. prompts: A string, tuple of strings or nested tuple of strings. If a single string is provided, it must be one of the available prompts (see above). Else the input is expected to be a tuple, where each element can be one of two things: either a string or a tuple of strings. If a string is provided, it must be one of the available prompts (see above). If tuple is provided, it must be of length 2 and the first string must be a positive prompt and the second string must be a negative prompt. kwargs: Additional keyword arguments, see :ref:`Metric kwargs` for more info. .. hint:: If using the default `clip_iqa` model, the package `piq` must be installed. Either install with `pip install piq` or `pip install torchmetrics[image]`. Raises: ModuleNotFoundError: If transformers package is not installed or version is lower than 4.10.0 ValueError: If `prompts` is a tuple and it is not of length 2 ValueError: If `prompts` is a string and it is not one of the available prompts ValueError: If `prompts` is a list of strings and not all strings are one of the available prompts Example:: Single prompt: >>> from torch import randint >>> from torchmetrics.multimodal import CLIPImageQualityAssessment >>> imgs = randint(255, (2, 3, 224, 224)).float() >>> metric = CLIPImageQualityAssessment() >>> metric(imgs) tensor([0.8894, 0.8902]) Example:: Multiple prompts: >>> from torch import randint >>> from torchmetrics.multimodal import CLIPImageQualityAssessment >>> imgs = randint(255, (2, 3, 224, 224)).float() >>> metric = CLIPImageQualityAssessment(prompts=("quality", "brightness")) >>> metric(imgs) {'quality': tensor([0.8693, 0.8705]), 'brightness': tensor([0.5722, 0.4762])} Example:: Custom prompts. Must always be a tuple of length 2, with a positive and negative prompt. >>> from torch import randint >>> from torchmetrics.multimodal import CLIPImageQualityAssessment >>> imgs = randint(255, (2, 3, 224, 224)).float() >>> metric = CLIPImageQualityAssessment(prompts=(("Super good photo.", "Super bad photo."), "brightness")) >>> metric(imgs) {'user_defined_0': tensor([0.9578, 0.9654]), 'brightness': tensor([0.5495, 0.5764])} Fis_differentiableThigher_is_betterfull_state_updateggY@anchors probs_listmodelfeature_networkclip_iqa?qualitymodel_name_or_path)r,zopenai/clip-vit-base-patch16zopenai/clip-vit-base-patch32z!openai/clip-vit-large-patch14-336r data_rangeprompts.kwargsrNc stjdi|t|ttfr|dkstd||_t|\}}||_||_ t |\|_ |_ ||_ tt||j |j |j|j}Wdn1sMwY|d||jdgdddS)Nrz2Argument `data_range` should be a positive number.r(r)cat)dist_reduce_fxr")super__init__ isinstanceintfloat ValueErrorr1r prompts_list prompts_namerr* processorr0torchinference_moder deviceregister_buffer add_state)selfr0r1r2r3r<r=r( __class__r"r#r7s     z#CLIPImageQualityAssessment.__init__imagescCs|t0t|j||j|j|j|j}t||j |j dd}t |t s&t d|j|WddS1s7wYdS)z"Update metric state with new data.F)format_as_dictzOutput probs should be a tensorN)r?r@r r0r*r>r1rAr r(r=r8r r;r)append)rDrG img_featuresprobsr"r"r#updates  "z!CLIPImageQualityAssessment.updatecs8t|jt|jdkrSfddt|jDS)zCompute metric.cs"i|] \}}|dd|fqS)Nr").0iprKr"r# s"z6CLIPImageQualityAssessment.compute..)rr)lenr=squeeze enumerate)rDr"rQr#computes z"CLIPImageQualityAssessment.computevalaxcCs |||S)a>Plot a single or multiple values from the metric. Args: val: Either a single result from calling `metric.forward` or `metric.compute` or a list of these results. If no value is provided, will automatically call `metric.compute` and plot that result. ax: An matplotlib axis object. If provided will add plot to that axis Returns: Figure and Axes object Raises: ModuleNotFoundError: If `matplotlib` is not installed .. plot:: :scale: 75 >>> # Example plotting a single value >>> import torch >>> from torchmetrics.multimodal.clip_iqa import CLIPImageQualityAssessment >>> metric = CLIPImageQualityAssessment() >>> metric.update(torch.rand(1, 3, 224, 224)) >>> fig_, ax_ = metric.plot() .. plot:: :scale: 75 >>> # Example plotting multiple values >>> import torch >>> from torchmetrics.multimodal.clip_iqa import CLIPImageQualityAssessment >>> metric = CLIPImageQualityAssessment() >>> values = [ ] >>> for _ in range(10): ... values.append(metric(torch.rand(1, 3, 224, 224))) >>> fig_, ax_ = metric.plot(values) )_plot)rDrWrXr"r"r#plots &r)r,r-r.)NN)__name__ __module__ __qualname____doc__r%bool__annotations__r&r'plot_lower_boundplot_upper_boundr rr+strrr:tuplerrr7rLdictrVrrrrrZ __classcell__r"r"rEr#r9s:  h         ! 2) colorfullness)r2)r/ brightness noisiness)r/rhrirg))zPhoto of a catzPhoto of a dogr/)zColorful photozBlack and white photo)rN)(collections.abcrtypingrrrrrrr?r +torchmetrics.functional.multimodal.clip_iqar r r r rtorchmetrics.metricrtorchmetrics.utilities.checksrrtorchmetrics.utilities.datartorchmetrics.utilities.importsrrrtorchmetrics.utilities.plotrr__doctest_skip__ transformersrrrr!r$rfr"r"r"r#sF       P