Spaces:
Runtime error
Runtime error
File size: 5,578 Bytes
e4bd7f9 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 |
import os
import logging
import warnings
from bubogpt.common.registry import registry
from bubogpt.datasets.builders.image_base_dataset_builder import ImageBaseDatasetBuilder
from bubogpt.datasets.datasets.image_caption.laion_dataset import LaionDataset
from bubogpt.datasets.datasets.image_caption.cc_sbu_dataset import CCSBUDataset, \
CCSBUAlignDatasetImageImageCaptionDataset, CCDataset
from bubogpt.datasets.datasets.image_caption.llava_dataset import LlavaInstruct150Dataset
@registry.register_builder("cc_sbu")
class CCSBUBuilderImage(ImageBaseDatasetBuilder):
train_dataset_cls = CCSBUDataset
DATASET_CONFIG_DICT = {"default": "configs/datasets/cc_sbu/defaults.yaml"}
def _download_ann(self):
pass
def _download_vis(self):
pass
def build(self):
self.build_processors()
build_info = self.config.build_info
datasets = dict()
split = "train"
# create datasets
# [NOTE] return inner_datasets (wds.DataPipeline)
dataset_cls = self.train_dataset_cls
datasets[split] = dataset_cls(
vision_processor=self.vis_processors[split],
text_processor=self.text_processors[split],
location=build_info.storage,
).inner_dataset
return datasets
@registry.register_builder("laion")
class LaionBuilderImage(ImageBaseDatasetBuilder):
train_dataset_cls = LaionDataset
DATASET_CONFIG_DICT = {"default": "configs/datasets/laion/defaults.yaml"}
def _download_ann(self):
pass
def _download_vis(self):
pass
def build(self):
self.build_processors()
build_info = self.config.build_info
datasets = dict()
split = "train"
# create datasets
# [NOTE] return inner_datasets (wds.DataPipeline)
dataset_cls = self.train_dataset_cls
datasets[split] = dataset_cls(
vision_processor=self.vis_processors[split],
text_processor=self.text_processors[split],
location=build_info.storage,
).inner_dataset
return datasets
@registry.register_builder("cc_sbu_align")
class CCSBUAlignBuilderImage(ImageBaseDatasetBuilder):
train_dataset_cls = CCSBUAlignDatasetImageImageCaptionDataset
DATASET_CONFIG_DICT = {
"default": "configs/datasets/cc_sbu/align.yaml",
}
def build_datasets(self):
# at this point, all the annotations and image/videos should be all downloaded to the specified locations.
logging.info("Building datasets...")
self.build_processors()
build_info = self.config.build_info
storage_path = build_info.storage
datasets = dict()
if not os.path.exists(storage_path):
warnings.warn("storage path {} does not exist.".format(storage_path))
# create datasets
dataset_cls = self.train_dataset_cls
datasets['train'] = dataset_cls(
vision_processor=self.vis_processors["train"],
text_processor=self.text_processors["train"],
ann_paths=[os.path.join(storage_path, 'filter_cap.json')],
vis_root=os.path.join(storage_path, 'image'),
)
return datasets
@registry.register_builder("cc12m")
class CC12MBuilder(ImageBaseDatasetBuilder):
train_dataset_cls = CCDataset
DATASET_CONFIG_DICT = {"default": "configs/datasets/cc12m/defaults.yaml"}
def _download_ann(self):
pass
def _download_vis(self):
pass
def build(self):
self.build_processors()
build_info = self.config.build_info
datasets = dict()
split = "train"
# create datasets
# [NOTE] return inner_datasets (wds.DataPipeline)
dataset_cls = self.train_dataset_cls
datasets[split] = dataset_cls(
vis_processor=self.vis_processors[split],
text_processor=self.text_processors[split],
location=build_info.storage,
).inner_dataset
return datasets
@registry.register_builder("llava_instruct150")
class LlavaInstruct150Builder(ImageBaseDatasetBuilder):
train_dataset_cls = LlavaInstruct150Dataset
DATASET_CONFIG_DICT = {"default": None}
def _download_ann(self):
pass
def _download_vis(self):
pass
def build(self):
self.build_processors()
datasets = dict()
split = "train"
dataset_cls = self.train_dataset_cls
datasets[split] = dataset_cls(
vis_processor=self.vis_processors[split],
text_processor=self.text_processors[split],
vis_root="/path/to/dataset/COCO_2014",
ann_paths=[os.path.join("/path/to/dataset/llava/annotations", subset + '.json')
for subset in ["complex_reasoning_77k", "conversation_58k", "detail_23k"]],
)
return datasets
# from bubogpt.datasets.builders.image_text_pair_builder import LlavaInstruct150Builder
if __name__ == "__main__":
from omegaconf import OmegaConf
from itertools import islice
data_cfg = OmegaConf.create({
"vis_processor": {"train": {"name": "imagebind_vision_train", "image_size": 224}},
"text_processor": {"train": {"name": "imagebind_caption"}},
"data_type": "image",
})
builder = LlavaInstruct150Builder(data_cfg)
datasets = builder.build_datasets()
datasets["train"].check_existence()
for sample in islice(datasets["train"], 10):
print(sample["vision"].shape, sample["prompt"], sample["text_input"])
|