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"""
Copyright (c) 2022, salesforce.com, inc.
All rights reserved.
SPDX-License-Identifier: BSD-3-Clause
For full license text, see the LICENSE_Lavis file in the repo root or https://opensource.org/licenses/BSD-3-Clause
"""
import re
from .registry import registry
from .base_processor import BaseProcessor
from .randaugment import RandomAugment
from omegaconf import OmegaConf
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
print("blip_processors.py is imported")
class BlipImageBaseProcessor(BaseProcessor):
def __init__(self, mean=None, std=None):
if mean is None:
mean = (0.48145466, 0.4578275, 0.40821073)
if std is None:
std = (0.26862954, 0.26130258, 0.27577711)
segment_mean = (0.485, 0.456, 0.406)
segment_std = (0.229, 0.224, 0.225)
self.normalize = transforms.Normalize(segment_mean, segment_std)
@registry.register_processor("blip_caption")
class BlipCaptionProcessor(BaseProcessor):
def __init__(self, prompt="", max_words=50):
self.prompt = prompt
self.max_words = max_words
def __call__(self, caption):
caption = self.prompt + self.pre_caption(caption)
return caption
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
prompt = cfg.get("prompt", "")
max_words = cfg.get("max_words", 50)
return cls(prompt=prompt, max_words=max_words)
def pre_caption(self, caption):
caption = re.sub(
r"([.!\"()*#:;~])",
" ",
caption.lower(),
)
caption = re.sub(
r"\s{2,}",
" ",
caption,
)
caption = caption.rstrip("\n")
caption = caption.strip(" ")
# truncate caption
caption_words = caption.split(" ")
if len(caption_words) > self.max_words:
caption = " ".join(caption_words[: self.max_words])
return caption
@registry.register_processor("blip2_image_train")
class Blip2ImageTrainProcessor(BlipImageBaseProcessor):
def __init__(self, image_size=224, mean=None, std=None, min_scale=0.5, max_scale=1.0):
super().__init__(mean=mean, std=std)
# self.transform = transforms.Compose(
# [
# transforms.RandomResizedCrop(
# image_size,
# scale=(min_scale, max_scale),
# interpolation=InterpolationMode.BICUBIC,
# ),
# transforms.ToTensor(),
# self.normalize,
# ]
# )
self.transform = transforms.Compose([
transforms.Resize(
(image_size, image_size), interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
self.normalize,
]
)
# ### segment anything
# '''
# x = (x - self.pixel_mean) / self.pixel_std
# # Pad
# h, w = x.shape[-2:]
# padh = self.image_encoder.img_size - h
# padw = self.image_encoder.img_size - w
# x = F.pad(x, (0, padw, 0, padh))
# '''
def __call__(self, item):
return self.transform(item)
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
image_size = cfg.get("image_size", 224)
mean = cfg.get("mean", None)
std = cfg.get("std", None)
min_scale = cfg.get("min_scale", 0.5)
max_scale = cfg.get("max_scale", 1.0)
return cls(
image_size=image_size,
mean=mean,
std=std,
min_scale=min_scale,
max_scale=max_scale,
)
@registry.register_processor("blip2_image_eval")
class Blip2ImageEvalProcessor(BlipImageBaseProcessor):
def __init__(self, image_size=224, mean=None, std=None):
super().__init__(mean=mean, std=std)
self.transform = transforms.Compose(
[
transforms.Resize(
(image_size, image_size), interpolation=InterpolationMode.BICUBIC
),
transforms.ToTensor(),
self.normalize,
]
)
def __call__(self, item):
return self.transform(item)
@classmethod
def from_config(cls, cfg=None):
if cfg is None:
cfg = OmegaConf.create()
image_size = cfg.get("image_size", 224)
mean = cfg.get("mean", None)
std = cfg.get("std", None)
return cls(image_size=image_size, mean=mean, std=std) |