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"""
Pydantic models for request/response validation.
"""
import enum
from typing import Optional
import pydantic
class ImageData(pydantic.BaseModel):
"""Image data model for base64 encoded images."""
mediaType: str
data: str
class ImageRequest(pydantic.BaseModel):
"""Request model for image classification."""
image: ImageData
class Labels(enum.IntEnum):
Natural = 0
FullySynthesized = 1
LocallyEdited = 2
LocallySynthesized = 3
class LocalizationMask(pydantic.BaseModel):
"""A bit mask indicating which pixels are manipulated / synthesized.
A bit value of ``1`` means that the model believes the corresponding pixel
has been edited or synthesized (i.e., its label would be non-zero).
A bit value of ``0`` means that the model believes the pixel is unaltered.
The mask ``.width`` and ``.height`` should be the same as the input image.
Extra bits at the end of ``.bitsRowMajor`` after the first
``width * height`` bits are **ignored**; for simplicity/efficiency,
you should encode your bit mask into a byte array and not worry if the
final byte isn't "full", then convert the byte array to base64.
"""
width: int = pydantic.Field(
description="The width of the mask."
)
height: int = pydantic.Field(
description="The height of the mask."
)
bitsRowMajor: str = pydantic.Field(
description="A base64 string encoding the bit mask in row-major order.",
# Canonical base64 encoding
# https://stackoverflow.com/a/64467300/3709935
pattern=r"^(?:[A-Za-z0-9+/]{4})*(?:[A-Za-z0-9+/][AQgw]==|[A-Za-z0-9+/]{2}[AEIMQUYcgkosw048]=)?$",
)
class PredictionResponse(pydantic.BaseModel):
"""Response model for synthetic image classification results.
Detector models will be scored primarily on their ability to classify the
entire image into 1 of the 4 label categories::
0: (Natural) The image is natural / unaltered.
1: (FullySynthesized) The entire image was synthesized by e.g., a
generative image model.
2: (LocallyEdited) The image is a natural image where a portion has
been edited using traditional photo editing techniques such as
splicing.
3: (LocallySynthesized) The image is a natural image where a portion
has been replaced by synthesized content.
"""
logprobs: list[float] = pydantic.Field(
description="The log-probabilities for each of the 4 possible labels.",
min_length=4,
max_length=4,
)
localizationMask: Optional[LocalizationMask] = pydantic.Field(
description="A bit mask localizing predicted edits. Models that are"
" not capable of localization may omit this field. It may also be"
" omitted if the predicted label is ``0`` or ``1``, in which case the"
" mask will be assumed to be all 0's or all 1's, as appropriate."
) |