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Running
on
Zero
File size: 9,446 Bytes
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import os
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from pycocotools import mask
from model.segment_anything.utils.transforms import ResizeLongestSide
from .grefer import G_REFER
from .refer import REFER
from torchvision import transforms
class ReferSegDataset(torch.utils.data.Dataset):
pixel_mean = torch.Tensor([123.675, 116.28, 103.53]).view(-1, 1, 1)
pixel_std = torch.Tensor([58.395, 57.12, 57.375]).view(-1, 1, 1)
img_size = 1024
ignore_label = 255
def __init__(
self,
base_image_dir,
tokenizer,
samples_per_epoch=500 * 8 * 2 * 10,
precision: str = "fp32",
image_size: int = 224,
num_classes_per_sample: int = 3,
exclude_val=False,
refer_seg_data="refclef||refcoco||refcoco+||refcocog",
model_type="ori",
transform=ResizeLongestSide(1024),
):
self.model_type = model_type
self.exclude_val = exclude_val
self.samples_per_epoch = samples_per_epoch
self.num_classes_per_sample = num_classes_per_sample
self.base_image_dir = base_image_dir
self.tokenizer = tokenizer
self.precision = precision
self.transform = transform
self.image_preprocessor = transforms.Compose([
transforms.ToTensor(),
transforms.Resize((image_size, image_size), interpolation=3),
transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
])
DATA_DIR = os.path.join(base_image_dir, "refer_seg")
self.refer_seg_ds_list = refer_seg_data.split(
"||"
) # ['refclef', 'refcoco', 'refcoco+', 'refcocog']
self.refer_seg_data = {}
for ds in self.refer_seg_ds_list:
if ds == "refcocog":
splitBy = "umd"
else:
splitBy = "unc"
if ds == "grefcoco":
refer_api = G_REFER(DATA_DIR, ds, splitBy)
else:
refer_api = REFER(DATA_DIR, ds, splitBy)
ref_ids_train = refer_api.getRefIds(split="train")
images_ids_train = refer_api.getImgIds(ref_ids=ref_ids_train)
refs_train = refer_api.loadRefs(ref_ids=ref_ids_train)
refer_seg_ds = {}
refer_seg_ds["images"] = []
loaded_images = refer_api.loadImgs(image_ids=images_ids_train)
for item in loaded_images:
item = item.copy()
if ds == "refclef":
item["file_name"] = os.path.join(
DATA_DIR, "images/saiapr_tc-12", item["file_name"]
)
else:
item["file_name"] = os.path.join(
DATA_DIR, "images/mscoco/images/train2014", item["file_name"]
)
refer_seg_ds["images"].append(item)
refer_seg_ds["annotations"] = refer_api.Anns # anns_train
print(
"dataset {} (refs {}) (train split) has {} images and {} annotations.".format(
ds,
splitBy,
len(refer_seg_ds["images"]),
len(refer_seg_ds["annotations"]),
)
)
img2refs = {}
for ref in refs_train:
image_id = ref["image_id"]
img2refs[image_id] = img2refs.get(image_id, []) + [
ref,
]
refer_seg_ds["img2refs"] = img2refs
self.refer_seg_data[ds] = refer_seg_ds
def __len__(self):
return self.samples_per_epoch
def preprocess(self, x: torch.Tensor) -> torch.Tensor:
"""Normalize pixel values and pad to a square input."""
if self.model_type=="hq":
h, w = x.shape[-2:]
padh = self.img_size - h
padw = self.img_size - w
x = F.pad(x, (0, padw, 0, padh), value=128)
# Normalize colors
x = (x - self.pixel_mean) / self.pixel_std
if self.model_type=="effi":
x = F.interpolate(x.unsqueeze(0), (self.img_size, self.img_size), mode="bilinear").squeeze(0)
else:
# Pad
h, w = x.shape[-2:]
padh = self.img_size - h
padw = self.img_size - w
x = F.pad(x, (0, padw, 0, padh))
return x
def __getitem__(self, idx):
ds = random.randint(0, len(self.refer_seg_ds_list) - 1)
ds = self.refer_seg_ds_list[ds]
refer_seg_ds = self.refer_seg_data[ds]
images = refer_seg_ds["images"]
annotations = refer_seg_ds["annotations"]
img2refs = refer_seg_ds["img2refs"]
idx = random.randint(0, len(images) - 1)
image_info = images[idx]
image_path = image_info["file_name"]
image_id = image_info["id"]
refs = img2refs[image_id]
if len(refs) == 0:
return self.__getitem__(0)
sents = []
ann_ids = []
for ref in refs:
for sent in ref["sentences"]:
text = sent["sent"]
sents.append(text)
ann_ids.append(ref["ann_id"])
if len(sents) >= self.num_classes_per_sample:
sampled_inds = np.random.choice(
list(range(len(sents))), size=self.num_classes_per_sample, replace=False
)
else:
sampled_inds = list(range(len(sents)))
sampled_sents = np.vectorize(sents.__getitem__)(sampled_inds).tolist()
# sampled_ann_ids = np.vectorize(ann_ids.__getitem__)(sampled_inds).tolist()
sampled_ann_ids = [ann_ids[ind] for ind in sampled_inds]
sampled_classes = sampled_sents
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
# preprocess image for evf
image_evf = self.image_preprocessor(image)
image = self.transform.apply_image(image) # preprocess image for sam
resize = image.shape[:2]
image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous())
flag = False
masks = []
for ann_id in sampled_ann_ids:
if isinstance(ann_id, list):
flag = True
if -1 in ann_id:
assert len(ann_id) == 1
m = np.zeros((image_info["height"], image_info["width"])).astype(
np.uint8
)
else:
m_final = np.zeros(
(image_info["height"], image_info["width"])
).astype(np.uint8)
for ann_id_i in ann_id:
ann = annotations[ann_id_i]
if len(ann["segmentation"]) == 0:
m = np.zeros(
(image_info["height"], image_info["width"])
).astype(np.uint8)
else:
if type(ann["segmentation"][0]) == list: # polygon
rle = mask.frPyObjects(
ann["segmentation"],
image_info["height"],
image_info["width"],
)
else:
rle = ann["segmentation"]
for i in range(len(rle)):
if not isinstance(rle[i]["counts"], bytes):
rle[i]["counts"] = rle[i]["counts"].encode()
m = mask.decode(rle)
m = np.sum(
m, axis=2
) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
m_final = m_final | m
m = m_final
masks.append(m)
continue
ann = annotations[ann_id]
if len(ann["segmentation"]) == 0:
m = np.zeros((image_info["height"], image_info["width"])).astype(
np.uint8
)
masks.append(m)
continue
if type(ann["segmentation"][0]) == list: # polygon
rle = mask.frPyObjects(
ann["segmentation"], image_info["height"], image_info["width"]
)
else:
rle = ann["segmentation"]
for i in range(len(rle)):
if not isinstance(rle[i]["counts"], bytes):
rle[i]["counts"] = rle[i]["counts"].encode()
m = mask.decode(rle)
m = np.sum(
m, axis=2
) # sometimes there are multiple binary map (corresponding to multiple segs)
m = m.astype(np.uint8) # convert to np.uint8
masks.append(m)
masks = np.stack(masks, axis=0)
masks = torch.from_numpy(masks)
label = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label
return (
image_path,
image,
image_evf,
masks,
label,
resize,
sampled_classes,
)
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