Spaces:
Runtime error
Runtime error
File size: 6,056 Bytes
8e5cc83 |
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 |
import json
import numpy as np
import torch
import torchvision.transforms as transforms
from pycocotools import mask as mask_utils
from skimage import io
from torch.utils.data import DataLoader, Dataset
from torch.utils.data.distributed import DistributedSampler
from efficientvit.apps.data_provider import DataProvider
from efficientvit.samcore.data_provider.utils import (
Normalize_and_Pad,
RandomHFlip,
ResizeLongestSide,
SAMDistributedSampler,
)
__all__ = ["SAMDataProvider"]
class OnlineDataset(Dataset):
def __init__(self, root, train=True, num_masks=64, transform=None):
self.root = root
self.train = train
self.num_masks = num_masks
self.transform = transform
self.data = open(f"{self.root}/sa_images_ids.txt", "r").read().splitlines()
if self.train:
self.data = self.data[: int(len(self.data) * 0.99)]
else:
self.data = self.data[int(len(self.data) * 0.99) :]
def __len__(self):
return len(self.data)
def __getitem__(self, idx):
"""
Note: We provide the simplest data organization here. You can modify the code according to your data organization.
"""
index = int(self.data[idx])
image_path = f"{self.root}/images/sa_{index}.jpg"
image = io.imread(image_path)
json_path = f"{self.root}/masks/sa_{index}.json"
annotations = json.load(open(json_path))["annotations"]
if self.train:
if len(annotations) > self.num_masks:
r = np.random.choice(len(annotations), size=self.num_masks, replace=False)
else:
repeat, residue = self.num_masks // len(annotations), self.num_masks % len(annotations)
r = np.random.choice(len(annotations), size=residue, replace=False)
r = np.concatenate([np.arange(len(annotations)) for _ in range(repeat)] + [r], axis=0)
else:
if len(annotations) > self.num_masks:
r = np.arange(self.num_masks)
else:
repeat, residue = self.num_masks // len(annotations), self.num_masks % len(annotations)
r = np.arange(residue)
r = np.concatenate([np.arange(len(annotations)) for _ in range(repeat)] + [r], axis=0)
masks = np.stack([mask_utils.decode(annotations[i]["segmentation"]) for i in r])
points = np.stack([annotations[i]["point_coords"][0] for i in r])
bboxs = np.stack([annotations[i]["bbox"] for i in r])
image = torch.tensor(image, dtype=torch.float32)
image = torch.transpose(torch.transpose(image, 1, 2), 0, 1)
masks = torch.tensor(masks, dtype=torch.float32)
points = torch.tensor(points, dtype=torch.float32)
bboxs = torch.tensor(bboxs, dtype=torch.float32)
sample = {
"image": image,
"masks": masks,
"points": points,
"bboxs": bboxs,
"shape": torch.tensor(image.shape[-2:]),
}
if self.transform:
sample = self.transform(sample)
return sample
class SAMDataProvider(DataProvider):
name = "sam"
def __init__(
self,
root: str,
sub_epochs_per_epoch: int,
num_masks: int,
train_batch_size: int,
test_batch_size: int,
valid_size: int or float or None = None,
n_worker=8,
image_size: int = 1024,
num_replicas: int or None = None,
rank: int or None = None,
train_ratio: float or None = None,
drop_last: bool = False,
):
self.root = root
self.num_masks = num_masks
self.sub_epochs_per_epoch = sub_epochs_per_epoch
super().__init__(
train_batch_size,
test_batch_size,
valid_size,
n_worker,
image_size,
num_replicas,
rank,
train_ratio,
drop_last,
)
def build_train_transform(self):
train_transforms = [
RandomHFlip(),
ResizeLongestSide(target_length=self.image_size[0]),
Normalize_and_Pad(target_length=self.image_size[0]),
]
return transforms.Compose(train_transforms)
def build_valid_transform(self):
valid_transforms = [
ResizeLongestSide(target_length=self.image_size[0]),
Normalize_and_Pad(target_length=self.image_size[0]),
]
return transforms.Compose(valid_transforms)
def build_datasets(self) -> tuple[any, any, any]:
train_transform = self.build_train_transform()
valid_transform = self.build_valid_transform()
train_dataset = OnlineDataset(root=self.root, train=True, num_masks=self.num_masks, transform=train_transform)
val_dataset = OnlineDataset(root=self.root, train=False, num_masks=2, transform=valid_transform)
test_dataset = None
return train_dataset, val_dataset, test_dataset
def build_dataloader(self, dataset: any or None, batch_size: int, n_worker: int, drop_last: bool, train: bool):
if dataset is None:
return None
if train:
sampler = SAMDistributedSampler(dataset, sub_epochs_per_epoch=self.sub_epochs_per_epoch)
dataloader = DataLoader(dataset, batch_size, sampler=sampler, drop_last=True, num_workers=n_worker)
return dataloader
else:
sampler = DistributedSampler(dataset, shuffle=False)
dataloader = DataLoader(dataset, batch_size, sampler=sampler, drop_last=False, num_workers=n_worker)
return dataloader
def set_epoch_and_sub_epoch(self, epoch: int, sub_epoch: int) -> None:
if isinstance(self.train.sampler, SAMDistributedSampler):
self.train.sampler.set_epoch_and_sub_epoch(epoch, sub_epoch)
|