import os import random import cv2 import numpy as np import torch import torch.nn.functional as F from pycocotools import mask from transformers import CLIPImageProcessor from model.segment_anything.utils.transforms import ResizeLongestSide from .conversation import get_default_conv_template from .refer import REFER from .utils import ( ANSWER_LIST, DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IMAGE_TOKEN, SHORT_QUESTION_LIST, ) 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, 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, refer_seg_data="refclef||refcoco||refcoco+||refcocog", ): 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.image_size = image_size self.tokenizer = tokenizer self.precision = precision self.transform = ResizeLongestSide(image_size) self.clip_image_processor = CLIPImageProcessor.from_pretrained(vision_tower) self.short_question_list = SHORT_QUESTION_LIST self.answer_list = ANSWER_LIST 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" 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 (before excluding: {} images)".format( ds, splitBy, len(refer_seg_ds["images"]), len(refer_seg_ds["annotations"]), len(loaded_images), ) ) 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.""" # Normalize colors x = (x - self.pixel_mean) / self.pixel_std # 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 = images[idx] image_path = image["file_name"] image_id = image["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_classes = sampled_sents img = cv2.imread(image_path) images = cv2.cvtColor(img, cv2.COLOR_BGR2RGB) # preprocess images for clip images_clip = self.clip_image_processor.preprocess(images, return_tensors="pt")[ "pixel_values" ][0] image_token_len = (images_clip.shape[1] // 14) * ( images_clip.shape[2] // 14 ) # FIXME: 14 is hardcoded patch size images = self.transform.apply_image(images) # preprocess images for sam resize = images.shape[:2] questions = [] answers = [] for text in sampled_classes: text = text.strip() assert len(text.split("||")) == 1 question_template = random.choice(self.short_question_list) questions.append(question_template.format(class_name=text.lower())) answers.append(random.choice(self.answer_list)) conversations = [] conv = get_default_conv_template("vicuna").copy() roles = {"human": conv.roles[0], "gpt": conv.roles[1]} i = 0 while i < len(questions): conv.messages = [] conv.append_message(conv.roles[0], questions[i]) conv.append_message(conv.roles[1], answers[i]) conversations.append(conv.get_prompt()) i += 1 # replace token for i in range(len(conversations)): replace_token = DEFAULT_IMAGE_PATCH_TOKEN * image_token_len replace_token = ( DEFAULT_IM_START_TOKEN + replace_token + DEFAULT_IM_END_TOKEN ) conversations[i] = conversations[i].replace( DEFAULT_IMAGE_TOKEN, replace_token ) images = self.preprocess(torch.from_numpy(images).permute(2, 0, 1).contiguous()) masks = [] for ann_id in sampled_ann_ids: ann = annotations[ann_id] if len(ann["segmentation"]) == 0: m = np.zeros((image["height"], image["width"])).astype(np.uint8) masks.append(m) continue if type(ann["segmentation"][0]) == list: # polygon rle = mask.frPyObjects( ann["segmentation"], image["height"], image["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, images, images_clip, conversations, masks, label, resize, questions, sampled_classes, )