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import os
from queue import Empty
import random
import cv2
import numpy as np
import torch
import torch.nn.functional as F
from pycocotools import mask
from transformers import CLIPImageProcessor
import transformers
from .muse import CustomSegDataset
from model.llava.mm_utils import tokenizer_image_token
from model.segment_anything.utils.transforms import ResizeLongestSide, ResizeShortestSide
from .data_processing import get_mask_from_json
from .reason_seg_dataset import ReasonSegDataset
from .refer import REFER
from .refer_seg_dataset import ReferSegDataset
from .sem_seg_dataset import SemSegDataset
from .vqa_dataset import VQADataset
from .multi_reason_seg_dataset import MultiReasonSegDataset
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
from model.llava import conversation as conversation_lib
from .utils import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_TOKEN)
def collate_fn(
batch, tokenizer=None, conv_type="llava_v1", use_mm_start_end=True, local_rank=-1
):
image_path_list = []
images_list = []
images_clip_list = []
conversation_list = []
masks_list = []
label_list = []
resize_list = []
questions_list = []
sampled_classes_list = []
clip_resize_list = []
offset_list = [0]
cnt = 0
inferences = []
multi_reasons = []
categories = []
answers_list = []
for item in batch:
(image_path, images, images_clip, conversations, masks, label,
resize, clip_resize, questions, sampled_classes, *rest) = item
multi_reason = False
inference = False
category = 'unknown'
answers = None
if len(rest) >= 1:
multi_reason = rest[0]
if len(rest) >= 2:
if isinstance(rest[1], (bool, np.bool_)) if 'np' in globals() else isinstance(rest[1], bool):
inference = rest[1]
if len(rest) >= 3:
category = rest[2] if isinstance(rest[2], str) else 'unknown'
if len(rest) >= 4:
answers = rest[3]
else:
category = rest[1] if isinstance(rest[1], str) else 'unknown'
if len(rest) >= 3:
answers = rest[2]
image_path_list.append(image_path)
images_list.append(images)
images_clip_list.append(images_clip)
conversation_list.extend(conversations)
label_list.append(label)
masks_list.append(masks.float())
resize_list.append(resize)
clip_resize_list.append(clip_resize)
questions_list.append(questions)
sampled_classes_list.append(sampled_classes)
cnt += len(conversations)
offset_list.append(cnt)
inferences.append(inference)
multi_reasons.append(multi_reason)
categories.append(category)
answers_list.append(answers)
if use_mm_start_end:
for i in range(len(conversation_list)):
replace_token = DEFAULT_IMAGE_TOKEN
replace_token = (
DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN
)
conversation_list[i] = conversation_list[i].replace(
DEFAULT_IMAGE_TOKEN, replace_token
)
input_ids = [
tokenizer_image_token(prompt, tokenizer, return_tensors="pt")
for prompt in conversation_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)
conv = conversation_lib.conv_templates['chatml'].copy() if conv_type == "chatml" else conversation_lib.default_conversation.copy()
targets = input_ids.clone()
if conv_type == "llava_v1" or "chatml":
sep = conv.sep + conv.roles[1] + ": "
else:
sep = "[/INST] "
for conversation, target in zip(conversation_list, targets):
total_len = int(target.ne(tokenizer.pad_token_id).sum())
if conv.sep2 not in conversation:
break
rounds = conversation.split(conv.sep2)
cur_len = 1
target[:cur_len] = IGNORE_INDEX
for i, rou in enumerate(rounds):
if rou == "":
break
if conv_type == "chatml":
if DEFAULT_IMAGE_TOKEN in conversation:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(rou+sep, tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(rou+sep).input_ids) - 2
if i == 0:
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
else:
parts = rou.split(sep)
assert len(parts) == 2, (len(parts), rou)
parts[0] += sep
if DEFAULT_IMAGE_TOKEN in conversation:
round_len = len(tokenizer_image_token(rou, tokenizer))
instruction_len = len(tokenizer_image_token(parts[0], tokenizer)) - 2
else:
round_len = len(tokenizer(rou).input_ids)
instruction_len = len(tokenizer(parts[0]).input_ids) - 2
target[cur_len : cur_len + instruction_len] = IGNORE_INDEX
cur_len += round_len
if conv_type == "chatml":
cur_len = total_len
target[cur_len:] = IGNORE_INDEX
if False:
z = target.clone()
z = torch.where(z == IGNORE_INDEX, tokenizer.unk_token_id, z)
if local_rank == 0:
print(
"conversation: ",
conversation,
"tokenizer.decode(z): ",
tokenizer.decode(z),
)
if cur_len < tokenizer.model_max_length:
assert cur_len == total_len
if inferences[0] == False:
truncate_len = tokenizer.model_max_length - 255
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_clip": torch.stack(images_clip_list, dim=0),
"input_ids": input_ids,
"labels": targets,
"attention_masks": attention_masks,
"masks_list": masks_list,
"label_list": label_list,
"resize_list": resize_list,
"clip_resize_list": clip_resize_list,
"offset": torch.LongTensor(offset_list),
"questions_list": questions_list,
"sampled_classes_list": sampled_classes_list,
"inference": inferences[0],
"conversation_list": conversation_list,
"multi_reason_list": multi_reasons,
"categories": categories,
"answers_list": answers_list,
}
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,
vision_tower,
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||vqa||reason_seg",
sample_rate=[9, 3, 3, 1],
sem_seg_data="ade20k||cocostuff||partimagenet||pascal_part||paco_lvis||mapillary",
refer_seg_data="refclef||refcoco||refcoco+||refcocog",
vqa_data="llava_instruct_150k",
reason_seg_data="ReasonSeg|train",
explanatory=0.1,
seg_token_num=1,
num_classes_per_question=1,
pad_train_clip_images=False,
masks_process_with_clip=False,
preprocessor_config='',
use_expand_question_list=False,
):
self.pad_train_clip_images = pad_train_clip_images
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.seg_token_num = seg_token_num
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,
vision_tower,
samples_per_epoch,
precision,
image_size,
num_classes_per_sample,
exclude_val,
sem_seg_data,
num_classes_per_question,
seg_token_num,
pad_train_clip_images,
masks_process_with_clip,
preprocessor_config,
use_expand_question_list,
)
)
elif dataset == "refer_seg":
self.all_datasets.append(
ReferSegDataset(
base_image_dir,
tokenizer,
vision_tower,
samples_per_epoch,
precision,
image_size,
num_classes_per_sample,
exclude_val,
refer_seg_data,
num_classes_per_question,
seg_token_num,
pad_train_clip_images,
masks_process_with_clip,
preprocessor_config,
use_expand_question_list,
)
)
elif dataset == "vqa":
self.all_datasets.append(
VQADataset(
base_image_dir,
tokenizer,
vision_tower,
samples_per_epoch,
precision,
image_size,
num_classes_per_sample,
exclude_val,
vqa_data,
pad_train_clip_images,
masks_process_with_clip,
preprocessor_config,
)
)
elif dataset == "reason_seg":
self.all_datasets.append(
ReasonSegDataset(
base_image_dir,
tokenizer,
vision_tower,
samples_per_epoch,
precision,
image_size,
num_classes_per_sample,
exclude_val,
reason_seg_data,
explanatory,
num_classes_per_question,
seg_token_num,
pad_train_clip_images,
masks_process_with_clip,
preprocessor_config,
use_expand_question_list,
)
)
elif dataset == "multi_reason_seg":
self.all_datasets.append(
MultiReasonSegDataset(
base_image_dir,
tokenizer,
vision_tower,
samples_per_epoch,
precision,
image_size,
num_classes_per_sample,
exclude_val,
reason_seg_data,
explanatory,
num_classes_per_question,
seg_token_num,
pad_train_clip_images,
masks_process_with_clip,
preprocessor_config,
use_expand_question_list
)
)
elif dataset == "custom_seg":
self.all_datasets.append(
CustomSegDataset(
os.path.join(base_image_dir, "CODrone/DRtrain"),
tokenizer,
vision_tower,
os.path.join(base_image_dir, "labels/DRSeg_train.json"),
samples_per_epoch=samples_per_epoch,
precision=precision,
image_size=image_size,
num_classes_per_sample=num_classes_per_sample,
exclude_val=exclude_val,
seg_token_num=seg_token_num,
pad_train_clip_images=pad_train_clip_images,
masks_process_with_clip=masks_process_with_clip,
preprocessor_config=preprocessor_config,
)
)
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
output = data[0]
if isinstance(data, (MultiReasonSegDataset,CustomSegDataset)) :
return *output, inference
else:
return *output, False, inference
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,
vision_tower,
val_dataset,
image_size=1024,
seg_token_num=1,
pad_val_clip_images=False,
masks_process_with_clip=False,
preprocessor_config='',
):
self.seg_token_num=seg_token_num
self.base_image_dir = base_image_dir
self.pad_val_clip_images = pad_val_clip_images
self.masks_process_with_clip = masks_process_with_clip
self.multiseg_inference = False
splits = val_dataset.split("|")
if len(splits) == 2:
ds, split = splits
if ds == "custom_seg":
self.data_type = "custom_seg"
from .muse import CustomSegDataset
if split == "val":
json_file_path = os.path.join(base_image_dir, "labels/DRSeg_val.json")
elif split == "test":
json_file_path = os.path.join(base_image_dir, "labels/DRSeg_test.json")
else:
json_file_path = os.path.join(base_image_dir, "labels/DRSeg_train.json")
if split == "test":
eval_image_dir = os.path.join(base_image_dir, "CODrone/DRtest")
else:
eval_image_dir = os.path.join(base_image_dir, "CODrone/DRval")
temp_dataset = CustomSegDataset(
base_image_dir=eval_image_dir,
tokenizer=tokenizer,
vision_tower=vision_tower,
json_file_path=json_file_path,
samples_per_epoch=0,
precision="bf16",
image_size=image_size,
num_classes_per_sample=1,
seg_token_num=seg_token_num,
pad_train_clip_images=pad_val_clip_images,
masks_process_with_clip=masks_process_with_clip,
preprocessor_config=preprocessor_config,
inference=True
)
self.custom_dataset = temp_dataset
self.images = list(range(len(temp_dataset)))
else:
images = glob.glob(
os.path.join(self.base_image_dir, "reason_seg", ds, split, "*.jpg")
)
self.images = images
self.data_type = "reason_seg"
elif len(splits) == 3:
ds, splitBy, split = splits
if 'multi' in ds:
self.multiseg_inference = True
ds = ds.split('multi')[-1]
if ds == 'rs_reason' or ds == 'rrsisd':
refer_api = REFER(self.base_image_dir, ds, splitBy)
else:
refer_api = REFER(self.base_image_dir+'/refer_seg/', 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, "refer_seg/images/saiapr_tc-12", item["file_name"]
)
elif ds in ["refcoco", "refcoco+", "refcocog", "grefcoco"]:
item["file_name"] = os.path.join(
base_image_dir,
"refer_seg/images/mscoco/images/train2014",
item["file_name"],
)
elif ds == 'rrsisd':
item["file_name"] = os.path.join(
base_image_dir,
"rrsisd/images/rrsisd/JPEGImages",
item["file_name"],
)
refer_seg_ds["images"].append(item)
refer_seg_ds["annotations"] = refer_api.Anns
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"
self.ds = ds
self.image_size = image_size
self.tokenizer = tokenizer
self.transform = ResizeLongestSide(image_size)
self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) if preprocessor_config == '' else CLIPImageProcessor.from_pretrained(preprocessor_config)
self.transform_clip = ResizeLongestSide(self.clip_image_processor.size['shortest_edge'])
def __len__(self):
if self.data_type == "refer_seg":
return len(self.refer_seg_ds["images"])
elif self.data_type == "custom_seg":
return len(self.custom_dataset)
else:
return len(self.images)
def preprocess(self, x: torch.Tensor, decoder_image_size) -> torch.Tensor:
"""Normalize pixel values and pad to a square input."""
x = (x - self.pixel_mean) / self.pixel_std
h, w = x.shape[-2:]
padh = decoder_image_size - h
padw = decoder_image_size - w
x = F.pad(x, (0, padw, 0, padh))
return x
def __getitem__(self, idx):
if self.data_type == "custom_seg":
return self.custom_dataset[idx]
elif 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
else:
image_path = self.images[idx]
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
json_path = image_path.replace(".jpg", ".json")
mask_json, sampled_sents, is_sentence = get_mask_from_json(json_path, image)
sampled_sents = [sampled_sents[0]]
conversations = []
conv = conversation_lib.default_conversation.copy()
i = 0
_seg = "[SEG]" if self.seg_token_num == 1 else ' '.join(["[SEG{}]".format(i) for i in range(self.seg_token_num)])
multi_sample_num = [6, 5, 4]
multi_sample_index = 0
while i < len(sampled_sents):
conv.messages = []
if self.multiseg_inference:
sample_num = multi_sample_num[multi_sample_index]
texts = [sampled_sents[k].strip() for k in range(i, i+sample_num)] if len(sampled_sents) - i >= sample_num else [sampled_sents[k].strip() for k in range(i, len(sampled_sents))]
text = ', '.join(texts[:-1]) + ' and {}'.format(texts[-1]) if len(texts) > 1 else texts[0]
else:
text = sampled_sents[i].strip()
if is_sentence:
conv.append_message(
conv.roles[0],
DEFAULT_IMAGE_TOKEN
+ "\n {} Please output segmentation mask.".format(text),
)
conv.append_message(conv.roles[1], "{}.".format(_seg))
else:
conv.append_message(
conv.roles[0],
DEFAULT_IMAGE_TOKEN
+ "\n What is {} in this image? Please output segmentation mask.".format(
text
),
)
if self.multiseg_inference:
answer = [_seg] * len(texts)
answer = ', '.join(answer[:-1]) + ' and ' + answer[-1] + '.' if len(answer) > 1 else answer[0]
conv.append_message(conv.roles[1], answer)
else:
conv.append_message(conv.roles[1], "{}.".format(_seg))
conversations.append(conv.get_prompt())
if self.multiseg_inference:
i += sample_num
multi_sample_index = (multi_sample_index + 1) % len(multi_sample_num)
else:
i += 1
if self.pad_val_clip_images:
image_clip = self.transform_clip.apply_image(image)
clip_resize = image_clip.shape[:2]
image_clip = self.preprocess(torch.from_numpy(image_clip).permute(2, 0, 1).contiguous(), self.clip_image_processor.size['shortest_edge'])
else:
image_clip = self.clip_image_processor.preprocess(image, return_tensors="pt")[
"pixel_values"
][0]
clip_resize = image_clip.shape[-2:]
image = self.transform.apply_image(image)
resize = image.shape[:2]
image = self.preprocess(torch.from_numpy(image).permute(2, 0, 1).contiguous(), self.img_size)
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:
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
)
m = m.astype(np.uint8)
masks.append(m)
else:
masks = [mask_json]
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
if self.masks_process_with_clip:
mask_shape = image_clip.shape[-1]
if len(masks) == 0:
masks = torch.zeros(0, mask_shape, mask_shape)
else:
masks = transform_mask(masks, mask_shape)
return (
image_path,
image,
image_clip,
conversations,
masks,
labels,
resize,
clip_resize,
None,
sampled_sents,
False,
inference,
)
def transform_mask(masks, size):
height, width = masks.shape[-2:]
short, long = (width, height) if width <= height else (height, width)
requested_new_short = size
new_short, new_long = requested_new_short, int(requested_new_short * long / short)
new_shape = (new_long, new_short) if width <= height else (new_short, new_long)
masks = F.interpolate(masks[None].float(), size=new_shape, mode="nearest")[0].bool()
orig_height, orig_width = new_shape
crop_height, crop_width = size, size
crop_height, crop_width = int(crop_height), int(crop_width)
top = (orig_height - crop_height) // 2
bottom = top + crop_height
left = (orig_width - crop_width) // 2
right = left + crop_width
assert top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width
masks = masks[..., top:bottom, left:right]
return masks
def center_crop_image(image, size):
orig_height, orig_width = image.shape[:2]
crop_height, crop_width = size, size
crop_height, crop_width = int(crop_height), int(crop_width)
top = (orig_height - crop_height) // 2
bottom = top + crop_height
left = (orig_width - crop_width) // 2
right = left + crop_width
assert top >= 0 and bottom <= orig_height and left >= 0 and right <= orig_width
image = image[top:bottom, left:right]
return image
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