# Copyright (C) 2023 Intel Corporation
#
# SPDX-License-Identifier: MIT
import os
import numpy as np
from datumaro.components.annotation import HashKey
from datumaro.components.dataset import Dataset
from datumaro.components.media import MediaElement
templates = [
"a photo of a {}.",
]
cifar10_templates = [
"a photo of a {}.",
"a blurry photo of a {}.",
"a black and white photo of a {}.",
"a low contrast photo of a {}.",
"a high contrast photo of a {}.",
"a bad photo of a {}.",
"a good photo of a {}.",
"a photo of a small {}.",
"a photo of a big {}.",
"a photo of the {}.",
"a blurry photo of the {}.",
"a black and white photo of the {}.",
"a low contrast photo of the {}.",
"a high contrast photo of the {}.",
"a bad photo of the {}.",
"a good photo of the {}.",
"a photo of the small {}.",
"a photo of the big {}.",
]
cifar100_templates = [
"a photo of a {}.",
"a blurry photo of a {}.",
"a black and white photo of a {}.",
"a low contrast photo of a {}.",
"a high contrast photo of a {}.",
"a bad photo of a {}.",
"a good photo of a {}.",
"a photo of a small {}.",
"a photo of a big {}.",
"a photo of the {}.",
"a blurry photo of the {}.",
"a black and white photo of the {}.",
"a low contrast photo of the {}.",
"a high contrast photo of the {}.",
"a bad photo of the {}.",
"a good photo of the {}.",
"a photo of the small {}.",
"a photo of the big {}.",
]
caltech101_templates = [
"a photo of a {}.",
"a painting of a {}.",
"a plastic {}.",
"a sculpture of a {}.",
"a sketch of a {}.",
"a tattoo of a {}.",
"a toy {}.",
"a rendition of a {}.",
"a embroidered {}.",
"a cartoon {}.",
"a {} in a video game.",
"a plushie {}.",
"a origami {}.",
"art of a {}.",
"graffiti of a {}.",
"a drawing of a {}.",
"a doodle of a {}.",
"a photo of the {}.",
"a painting of the {}.",
"the plastic {}.",
"a sculpture of the {}.",
"a sketch of the {}.",
"a tattoo of the {}.",
"the toy {}.",
"a rendition of the {}.",
"the embroidered {}.",
"the cartoon {}.",
"the {} in a video game.",
"the plushie {}.",
"the origami {}.",
"art of the {}.",
"graffiti of the {}.",
"a drawing of the {}.",
"a doodle of the {}.",
]
eurosat_templates = [
"a centered satellite photo of {}.",
"a centered satellite photo of a {}.",
"a centered satellite photo of the {}.",
]
flowers101_templates = [
"a photo of a {}, a type of flower.",
]
food101_templates = [
"a photo of {}, a type of food.",
]
kitti_templates = [
"{}",
]
kinetics_templates = [
"a photo of {}.",
"a photo of a person {}.",
"a photo of a person using {}.",
"a photo of a person doing {}.",
"a photo of a person during {}.",
"a photo of a person performing {}.",
"a photo of a person practicing {}.",
"a video of {}.",
"a video of a person {}.",
"a video of a person using {}.",
"a video of a person doing {}.",
"a video of a person during {}.",
"a video of a person performing {}.",
"a video of a person practicing {}.",
"a example of {}.",
"a example of a person {}.",
"a example of a person using {}.",
"a example of a person doing {}.",
"a example of a person during {}.",
"a example of a person performing {}.",
"a example of a person practicing {}.",
"a demonstration of {}.",
"a demonstration of a person {}.",
"a demonstration of a person using {}.",
"a demonstration of a person doing {}.",
"a demonstration of a person during {}.",
"a demonstration of a person performing {}.",
"a demonstration of a person practicing {}.",
]
mnist_templates = [
'a photo of the number: "{}".',
]
format_templates = {
"cifar10": cifar10_templates,
"cifar100": cifar100_templates,
"caltech101": caltech101_templates,
"eurosat": eurosat_templates,
"flowers101": flowers101_templates,
"food101": food101_templates,
"kitti": kitti_templates,
"kinetics": kinetics_templates,
"mnist": mnist_templates,
}
[docs]
def select_uninferenced_dataset(dataset):
uninferenced_dataset = Dataset(media_type=MediaElement, ann_types=set())
for item in dataset:
if not any(isinstance(annotation, HashKey) for annotation in item.annotations):
uninferenced_dataset.put(item)
return uninferenced_dataset
[docs]
def calculate_hamming(B1, B2):
"""
:param B1: vector [n]
:param B2: vector [r*n]
:return: hamming distance [r]
"""
return np.count_nonzero(B1 != B2, axis=1)
[docs]
def match_query_subset(query_id, dataset, subset=None):
if subset:
return dataset.get(query_id, subset)
subset_names = dataset.subsets().keys()
for subset_name in subset_names:
try:
query_datasetitem = dataset.get(query_id, subset_name)
if query_datasetitem:
return query_datasetitem
except Exception:
pass
return None
[docs]
def check_and_convert_to_list(paths):
if isinstance(paths, str):
paths = [paths]
elif not isinstance(paths, list):
raise ValueError("Invalid value type. Expected str or list.")
valid_paths = []
for path in paths:
if os.path.exists(path):
valid_paths.append(path)
else:
raise ValueError(f"Invalid path: {path}")
return valid_paths
