387 lines
13 KiB
Python
387 lines
13 KiB
Python
import glob
|
|
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 .data_processing import get_mask_from_json
|
|
from .refer import REFER
|
|
from .refer_seg_dataset import ReferSegDataset
|
|
from .sem_seg_dataset import SemSegDataset
|
|
from torchvision import transforms
|
|
import json
|
|
from PIL import Image
|
|
|
|
def collate_fn(
|
|
batch, tokenizer=None, local_rank=-1
|
|
):
|
|
image_path_list = []
|
|
images_list = []
|
|
images_evf_list = []
|
|
masks_list = []
|
|
label_list = []
|
|
resize_list = []
|
|
sampled_classes_list = []
|
|
offset_list = [0]
|
|
cnt = 0
|
|
inferences = []
|
|
for (
|
|
image_path,
|
|
images,
|
|
images_evf,
|
|
masks,
|
|
label,
|
|
resize,
|
|
sampled_classes,
|
|
inference,
|
|
) in batch:
|
|
image_path_list.append(image_path)
|
|
images_list.append(images)
|
|
images_evf_list.append(images_evf)
|
|
label_list.append(label)
|
|
masks_list.append(masks.float())
|
|
resize_list.append(resize)
|
|
sampled_classes_list.extend(sampled_classes)
|
|
cnt += len(sampled_classes)
|
|
offset_list.append(cnt)
|
|
inferences.append(inference)
|
|
|
|
input_ids = [
|
|
tokenizer(prompt, return_tensors="pt").input_ids[0]
|
|
for prompt in sampled_classes_list
|
|
]
|
|
|
|
input_ids = torch.nn.utils.rnn.pad_sequence(
|
|
input_ids, batch_first=True, padding_value=tokenizer.pad_token_id
|
|
)
|
|
attention_masks = input_ids.ne(tokenizer.pad_token_id)
|
|
|
|
if inferences[0] == False:
|
|
truncate_len = tokenizer.model_max_length
|
|
|
|
if input_ids.shape[1] > truncate_len:
|
|
input_ids = input_ids[:, :truncate_len]
|
|
targets = targets[:, :truncate_len]
|
|
attention_masks = attention_masks[:, :truncate_len]
|
|
|
|
return {
|
|
"image_paths": image_path_list,
|
|
"images": torch.stack(images_list, dim=0),
|
|
"images_evf": torch.stack(images_evf_list, dim=0),
|
|
"input_ids": input_ids,
|
|
"attention_masks": attention_masks,
|
|
"masks_list": masks_list,
|
|
"label_list": label_list,
|
|
"resize_list": resize_list,
|
|
"offset": torch.LongTensor(offset_list),
|
|
"sampled_classes_list": sampled_classes_list,
|
|
"inference": inferences[0],
|
|
}
|
|
|
|
|
|
class HybridDataset(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,
|
|
dataset="sem_seg||refer_seg",
|
|
sample_rate=[9, 3, 3, 1],
|
|
sem_seg_data="ade20k||cocostuff||pascal_part||mapillary",
|
|
refer_seg_data="refclef||refcoco||refcoco+||refcocog",
|
|
explanatory=-1,
|
|
model_type="ori",
|
|
transform=ResizeLongestSide(1024),
|
|
):
|
|
self.transform=transform
|
|
self.model_type = model_type
|
|
self.exclude_val = exclude_val
|
|
self.dataset = dataset
|
|
self.samples_per_epoch = samples_per_epoch
|
|
self.explanatory = explanatory
|
|
self.num_classes_per_sample = num_classes_per_sample
|
|
sample_rate = np.array(sample_rate)
|
|
self.sample_rate = sample_rate / sample_rate.sum()
|
|
|
|
self.base_image_dir = base_image_dir
|
|
self.image_size = image_size
|
|
self.tokenizer = tokenizer
|
|
self.precision = precision
|
|
|
|
self.datasets = dataset.split("||")
|
|
|
|
self.all_datasets = []
|
|
for dataset in self.datasets:
|
|
if dataset == "sem_seg":
|
|
self.all_datasets.append(
|
|
SemSegDataset(
|
|
base_image_dir,
|
|
tokenizer,
|
|
samples_per_epoch,
|
|
precision,
|
|
image_size,
|
|
num_classes_per_sample,
|
|
exclude_val,
|
|
sem_seg_data,
|
|
self.model_type,
|
|
self.transform
|
|
)
|
|
)
|
|
elif dataset == "refer_seg":
|
|
self.all_datasets.append(
|
|
ReferSegDataset(
|
|
base_image_dir,
|
|
tokenizer,
|
|
samples_per_epoch,
|
|
precision,
|
|
image_size,
|
|
num_classes_per_sample,
|
|
exclude_val,
|
|
refer_seg_data,
|
|
self.model_type,
|
|
self.transform
|
|
)
|
|
)
|
|
|
|
def __len__(self):
|
|
return self.samples_per_epoch
|
|
|
|
def __getitem__(self, idx):
|
|
ind = np.random.choice(list(range(len(self.datasets))), p=self.sample_rate)
|
|
data = self.all_datasets[ind]
|
|
inference = False
|
|
return *data[0], inference
|
|
|
|
|
|
def init_ade20k(base_image_dir):
|
|
with open("utils/ade20k_classes.json", "r") as f:
|
|
ade20k_classes = json.load(f)
|
|
ade20k_classes = np.array(ade20k_classes)
|
|
image_ids = sorted(
|
|
os.listdir(os.path.join(base_image_dir, "ade20k/images", "validation"))
|
|
)
|
|
ade20k_image_ids = []
|
|
for x in image_ids:
|
|
if x.endswith(".jpg"):
|
|
ade20k_image_ids.append(x[:-4])
|
|
ade20k_images = []
|
|
for image_id in ade20k_image_ids: # self.descriptions:
|
|
ade20k_images.append(
|
|
os.path.join(
|
|
base_image_dir,
|
|
"ade20k",
|
|
"images",
|
|
"validation",
|
|
"{}.jpg".format(image_id),
|
|
)
|
|
)
|
|
ade20k_labels = [
|
|
x.replace(".jpg", ".png").replace("images", "annotations")
|
|
for x in ade20k_images
|
|
]
|
|
print("ade20k: ", len(ade20k_images))
|
|
return ade20k_classes, ade20k_images, ade20k_labels
|
|
|
|
|
|
class ValDataset(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,
|
|
val_dataset,
|
|
image_size=224,
|
|
model_type="ori"
|
|
):
|
|
self.model_type = model_type
|
|
self.base_image_dir = base_image_dir
|
|
splits = val_dataset.split("|")
|
|
if len(splits) == 3:
|
|
ds, splitBy, split = splits
|
|
base_image_dir = os.path.join(base_image_dir, "refer_seg")
|
|
refer_api = REFER(base_image_dir, ds, splitBy)
|
|
ref_ids_val = refer_api.getRefIds(split=split)
|
|
images_ids_val = refer_api.getImgIds(ref_ids=ref_ids_val)
|
|
refs_val = refer_api.loadRefs(ref_ids=ref_ids_val)
|
|
refer_seg_ds = {}
|
|
refer_seg_ds["images"] = []
|
|
loaded_images = refer_api.loadImgs(image_ids=images_ids_val)
|
|
for item in loaded_images:
|
|
item = item.copy()
|
|
if ds == "refclef":
|
|
item["file_name"] = os.path.join(
|
|
base_image_dir, "images/saiapr_tc-12", item["file_name"]
|
|
)
|
|
elif ds in ["refcoco", "refcoco+", "refcocog", "grefcoco"]:
|
|
item["file_name"] = os.path.join(
|
|
base_image_dir,
|
|
"images/mscoco/images/train2014",
|
|
item["file_name"],
|
|
)
|
|
refer_seg_ds["images"].append(item)
|
|
refer_seg_ds["annotations"] = refer_api.Anns # anns_val
|
|
|
|
img2refs = {}
|
|
for ref in refs_val:
|
|
image_id = ref["image_id"]
|
|
img2refs[image_id] = img2refs.get(image_id, []) + [
|
|
ref,
|
|
]
|
|
refer_seg_ds["img2refs"] = img2refs
|
|
self.refer_seg_ds = refer_seg_ds
|
|
self.data_type = "refer_seg"
|
|
elif val_dataset=="ade":
|
|
ds = "ade"
|
|
self.classes, self.images, self.labels = init_ade20k(base_image_dir)
|
|
self.data_type = "sem_seg"
|
|
|
|
|
|
self.ds = ds
|
|
self.tokenizer = tokenizer
|
|
self.transform = ResizeLongestSide(1024)
|
|
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))
|
|
])
|
|
def __len__(self):
|
|
if self.data_type == "refer_seg":
|
|
return len(self.refer_seg_ds["images"])
|
|
else:
|
|
return len(self.images)
|
|
|
|
def preprocess(self, x: torch.Tensor) -> torch.Tensor:
|
|
"""Normalize pixel values and pad to a square input."""
|
|
# Normalize colors
|
|
x = (x - self.pixel_mean) / self.pixel_std
|
|
|
|
if self.model_type=="effi" or self.model_type=="sam2":
|
|
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):
|
|
if self.data_type == "refer_seg":
|
|
refer_seg_ds = self.refer_seg_ds
|
|
images = refer_seg_ds["images"]
|
|
annotations = refer_seg_ds["annotations"]
|
|
img2refs = refer_seg_ds["img2refs"]
|
|
|
|
image_info = images[idx]
|
|
image_path = image_info["file_name"]
|
|
image_id = image_info["id"]
|
|
|
|
refs = img2refs[image_id]
|
|
if len(refs) == 0:
|
|
raise ValueError("image {} has no refs".format(image_id))
|
|
|
|
sents = []
|
|
ann_ids = []
|
|
for ref in refs:
|
|
for sent in ref["sentences"]:
|
|
sents.append(sent["sent"].strip().lower())
|
|
ann_ids.append(ref["ann_id"])
|
|
|
|
sampled_sents = sents
|
|
sampled_ann_ids = ann_ids
|
|
image = cv2.imread(image_path)
|
|
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
|
|
is_sentence = False
|
|
|
|
elif self.data_type == "sem_seg":
|
|
image_path = self.images[idx]
|
|
label_path = self.labels[idx]
|
|
label = Image.open(label_path)
|
|
label = np.array(label)
|
|
label[label == 0] = 255
|
|
label -= 1
|
|
label[label == 254] = 255
|
|
unique_label = np.unique(label).tolist()
|
|
if 255 in unique_label:
|
|
unique_label.remove(255)
|
|
|
|
sampled_sents = [self.classes[class_id] for class_id in unique_label]
|
|
|
|
img = cv2.imread(image_path)
|
|
image = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)
|
|
class_ids = unique_label
|
|
label = torch.from_numpy(label).long()
|
|
masks = []
|
|
for class_id in class_ids:
|
|
masks.append(label == class_id)
|
|
masks = torch.stack(masks, dim=0)
|
|
|
|
# preprocess image for evf
|
|
image_evf = self.image_preprocessor(image)
|
|
|
|
# preprocess image for sam
|
|
image = self.transform.apply_image(image)
|
|
resize = image.shape[:2]
|
|
image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous())
|
|
|
|
if self.data_type == "refer_seg":
|
|
masks = []
|
|
for i, ann_id in enumerate(sampled_ann_ids):
|
|
ann = annotations[ann_id]
|
|
if len(ann["segmentation"]) == 0 and sampled_sents[i] != "":
|
|
m = np.zeros((image_info["height"], image_info["width"], 1))
|
|
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
|
|
masks.append(m)
|
|
|
|
if not isinstance(masks, torch.Tensor):
|
|
masks = np.stack(masks, axis=0)
|
|
masks = torch.from_numpy(masks)
|
|
labels = torch.ones(masks.shape[1], masks.shape[2]) * self.ignore_label
|
|
inference = True
|
|
|
|
return (
|
|
image_path,
|
|
image,
|
|
image_evf,
|
|
masks,
|
|
labels,
|
|
resize,
|
|
sampled_sents,
|
|
inference,
|
|
)
|