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191 lines
8.2 KiB
191 lines
8.2 KiB
4 days ago
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# Copyright 2025 Google Inc. HuggingFace Inc. team. All rights reserved.
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#
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from collections.abc import Mapping, Sequence
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from ...feature_extraction_utils import BatchFeature
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from ...image_utils import ImageInput
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from ...processing_utils import Unpack
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from ...utils import logging
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from ..gemma3.processing_gemma3 import Gemma3Processor, Gemma3ProcessorKwargs
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logger = logging.get_logger(__name__)
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DEFAULT_SHIELDGEMMA2_POLICIES: Mapping[str, str] = {
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"dangerous": (
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"No Dangerous Content: The image shall not contain content that"
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" facilitates or encourages activities that could cause real-world harm"
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" (e.g., building firearms and explosive devices, promotion of"
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" terrorism, instructions for suicide)."
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),
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"sexual": (
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"No Sexually Explicit content: The image shall not contain content that"
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" depicts explicit or graphic sexual acts (e.g., pornography, erotic"
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" nudity, depictions of rape or sexual assault)."
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),
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"violence": (
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"No Violence/Gore content: The image shall not contain content that"
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" depicts shocking, sensational, or gratuitous violence (e.g.,"
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" excessive blood and gore, gratuitous violence against animals,"
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" extreme injury or moment of death)."
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),
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}
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class ShieldGemma2ProcessorKwargs(Gemma3ProcessorKwargs, total=False):
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policies: Sequence[str] | None
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custom_policies: Mapping[str, str] | None
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_defaults = {
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"text_kwargs": {
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"padding": True,
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},
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"images_kwargs": {
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"do_pan_and_scan": False,
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},
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}
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class ShieldGemma2Processor(Gemma3Processor):
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def __init__(
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self, image_processor, tokenizer, chat_template=None, image_seq_length=256, policy_definitions=None, **kwargs
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):
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"""A processor for the ShieldGemma 2 model.
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Args:
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image_processor: The image processor to use, typically a `Gemma3ImageProcessorFast` instance.
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tokenizer: The tokenizer to use, typically a `GemmaTokenizerFast` instance.
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chat_template: The chat template to use with this processor. Typically, this is unset as the processor
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configuration on Hugging Face Hub includes this value already.
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image_seq_length: The number of soft tokens per image. Typically, this is unset as the processor
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configuration on Hugging Face Hub includes this value already.
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policy_definitions: A mapping from policy name to its description in text used as the default policies to
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classify images against. The policy descriptions are included in the text of the prompts generated by
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this processor. Typically, this is unset as the processor configuration on Hugging Face Hub includes
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the base policies ShieldGemma was trained on.
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"""
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super().__init__(image_processor, tokenizer, chat_template, image_seq_length, **kwargs)
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if policy_definitions is None:
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self.policy_definitions = DEFAULT_SHIELDGEMMA2_POLICIES
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else:
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self.policy_definitions = policy_definitions
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def __call__(
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self,
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images: ImageInput | None = None,
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text=None,
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**kwargs: Unpack[ShieldGemma2ProcessorKwargs],
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) -> BatchFeature:
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"""Generates a batch of inputs from the provided images.
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ShieldGemma was trained to classify image content for policy compliance using a specific prompt construction.
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This processor generates a batch of such prompts from the provided images by:
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1. Creating a list of conversations, one for each `<image, policy>` pair;
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2. Converting these conversations to text using `self.apply_chat_template()`; and
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3. Encoding the conversations and images using the same techniques as `Gemma3Processor`.
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Args:
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images: A single image or a list of images to include in the batch.
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text: Not supported.
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videos: Not supported.
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audio: Not supported.
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kwargs: An optional dictionary of keyword arguments to configure the
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processor. Possible values include:
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* `custom_policies`: Additional policy definitions that augment the `self.policy_definitions` passed
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into the constructor. Note that `custom_policies` that share a key with `self.policy_definitions`
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will override the policy description
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* `policies`: (Optional) a list of keys in the joint `self.policy_definitions | custom_policies`
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dictionary of specific interest for the provided images. If empty or None, prompts will be
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generated for every key in the joint dictionary.
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Returns:
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A `BatchFeature` containing `input_ids`, `pixel_values`, etc. where each Tensor is of shape
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`(len(images) * len(policies), )`, and the order within the batch will be
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img1_policy1, ... img1_policyN, ... imgM_policyN.
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"""
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if not images:
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raise ValueError("ShieldGemma 2 needs images to classify")
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elif not isinstance(images, Sequence):
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images = [images]
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if not self.chat_template:
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raise ValueError("ShieldGemma 2 requires the use of a specific chat template")
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common_kwargs = kwargs.setdefault("common_kwargs", {})
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if "return_tensors" in kwargs:
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common_kwargs["return_tensors"] = kwargs.pop("return_tensors")
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# Disable pan and scan
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images_kwargs = kwargs.setdefault("images_kwargs", {})
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if images_kwargs.get("do_pan_and_scan") is True:
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logger.warning_once("ShieldGemma2 does not support pan and scan.")
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images_kwargs["do_pan_and_scan"] = False
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# Enable padding on the batch during tokenization
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text_kwargs = kwargs.setdefault("text_kwargs", {})
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if "padding" not in text_kwargs:
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text_kwargs["padding"] = kwargs.pop("padding", True)
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text_kwargs["padding_side"] = kwargs.pop("padding_side", "left")
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policy_definitions: Mapping[str, str] = {
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**self.policy_definitions,
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**kwargs.get("custom_policies", {}),
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}
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if (policies := kwargs.get("policies")) is None:
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policies = list(policy_definitions.keys())
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# TODO(ryanmullins): Support images from PIL or URLs.
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messages = []
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expanded_images = []
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for img in images:
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if not isinstance(img, list):
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img = [img]
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elif len(img) > 1:
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raise ValueError(f"SheildGemma can process at most one image per sample, but got {len(img)} images")
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for policy in policies:
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if img:
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messages.append(
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[
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{
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"role": "user",
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"content": [
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{"type": "image"},
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{"type": "text", "text": policy_definitions[policy]},
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],
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}
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]
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)
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else:
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messages.append(
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[
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{
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"role": "user",
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"content": [
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{"type": "text", "text": policy_definitions[policy]},
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],
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}
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]
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)
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expanded_images.append(img)
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text = self.apply_chat_template(messages, tokenize=False)
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return super().__call__(images=expanded_images, text=text, **kwargs)
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__all__ = ["ShieldGemma2Processor"]
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