import torch import os from enum import Enum from tqdm import tqdm import numpy as np from detectron2.structures import BitMasks from psalm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_TOKEN, DEFAULT_IM_START_TOKEN, \ DEFAULT_IM_END_TOKEN, DEFAULT_SEG_TOKEN, SEG_TOKEN_INDEX from psalm.model.builder import load_pretrained_model from psalm.utils import disable_torch_init from psalm.mm_utils import tokenizer_image_token, get_model_name_from_path, KeywordsStoppingCriteria import cv2 from torch.utils.data import Dataset, DataLoader from psalm import conversation as conversation_lib from psalm.train.train_datasets_eval import COCO_interactive_dataset #debug #from psalm.train.train_datasets import COCO_interactive_dataset import json from pycocotools.mask import encode, decode, frPyObjects from detectron2.structures import BoxMode from detectron2.data import MetadataCatalog, DatasetCatalog from typing import Dict, Optional, Sequence, List from dataclasses import dataclass, field import torch.distributed as dist import transformers from pathlib import Path # from segmentation_evaluation import openseg_classes from psalm.eval.segmentation_evaluation import openseg_classes COLOR_MAP = openseg_classes.ADE20K_150_CATEGORIES import re from psalm.constants import IGNORE_INDEX, IMAGE_TOKEN_INDEX, DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN, SEG_TOKEN_INDEX, CLS_TOKEN_INDEX, REGION_TOKEN_INDEX, REFER_TOKEN_INDEX class Multicondition_Dataset(COCO_interactive_dataset): #将ref instruction转化为整数tokens序列，并在末尾加上代表整个句子全部含义的[SEG]token def preprocess_referring_instruction(self,instruction, REFER_token='[SEG]'): tokenized = self.tokenizer.encode(instruction, add_special_tokens=False) tokenized = tokenized + [self.tokenizer.encode(REFER_token, add_special_tokens=False)[0]] token_refer_id = torch.tensor(tokenized) return token_refer_id # 相较于interatitive类，新增加了 def tokenizer_special_tokens(self, prompt, tokenizer, image_token_index=IMAGE_TOKEN_INDEX, seg_token_index=SEG_TOKEN_INDEX, cls_token_index=CLS_TOKEN_INDEX, region_token_index=REGION_TOKEN_INDEX,refer_token_index=REFER_TOKEN_INDEX, return_tensors=None): input_ids = [] special_token_map = {'': image_token_index, '': seg_token_index, '': cls_token_index, '':region_token_index, '':refer_token_index} prompt_chunks = re.split('(||||)', prompt) for chunk in prompt_chunks: if chunk in special_token_map: input_ids.append(special_token_map[chunk]) else: input_ids.extend(tokenizer.encode(chunk, add_special_tokens=False)) if return_tensors is not None: if return_tensors == 'pt': return torch.tensor(input_ids, dtype=torch.long).squeeze() raise ValueError(f'Unsupported tensor type: {return_tensors}') else: return input_ids #注意，这里所有的处理逻辑针对的都是一帧图像 def __getitem__(self, idx): data = self.data[idx] #图片的相对路径名称，like2017/trainval/JPEGImages/480p/bike-packing/00001.jpg image_file = data['image'] #image_folder是data_root根路径 在这里是data_segswap image_folder = self.data_args.image_folder data_dict = {} #file_name是图片的完整路径名称，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00001.jpg data_dict['file_name'] = os.path.join(image_folder, image_file) data_dict['height'] = data['image_info']['height'] data_dict['width'] = data['image_info']['width'] #image_id可以理解为计数器，编号 data_dict['image_id'] = data['new_img_id'] #annotations，本帧对应的注释，coco格式的分割mask，一张图片可能包含多个实例的mask data_dict['annotations'] = data['anns'] #vp_annotations，每段视频中第一帧的注释 data_dict['vp_annotations'] = data['first_frame_anns'] #vp_image，每段视频中第一帧的完整路径，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00000.jpg data_dict['vp_image'] = os.path.join(image_folder,data['first_frame_image']) #debug：这里没有把refdataset里的category_id处理搬过来，不知道有影响吗 for annotation in data_dict['annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS #边界框左上角和右下角的坐标都为原点，意思是将边界框置为空框 annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] #为了训练的时候instance能有region_mask属性而增设 # annotation['mask_visual_prompt_mask'] = annotation['segmentation'] for annotation in data_dict['vp_annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] #初始化processor，应该是个图像预处理器，再送进visual encoder之前，总体来说下面的一小段代码是对输入图像和mask的预处理 # print("self.data_args.image_processor", self.data_args.image_processor) if isinstance(self.data_args.image_processor,dict): #根据是否是对齐ego exo的size进行切换，图像预处理器 processor = self.data_args.image_processor['null_mask'] # processor = self.data_args.image_processor['instance_resize'] else: processor = self.data_args.image_processor #尝试从命令行参数中获取region_mask_type region_mask_type = getattr(self.data_args,'region_mask_type',None) if region_mask_type is not None: region_mask_type = region_mask_type.split('||') # print("region_mask_type:", region_mask_type) #根据region_mask_type和mask_format（这里是0、1掩码），对原始的data_dict进行预处理，将Detectron2格式的dataset dict转化为MaskFormer格式的 data_dict = processor.preprocess(data_dict,region_mask_type=region_mask_type,mask_format='bitmask') #debug: 目前为止和egodataset完全一样，除了上面增加的两个函数 sentences = data['instruction'] #num_target，本帧图像中有多少个对象 #下面的一小段代码，主要是利用llama处理输入的文本，生成对应的token num_target = len(data_dict['instances']) # 是一个特殊的占位符，表示图像的输入 #debug: 这里有个问题，使用哪种前缀提示词 # prefix_inst = 'This is an image , Please segment by given regions' # prefix_inst = 'This is an image , Please doing Referring Segmentation according to the following instruction:' #debug:自己创造的前缀词 prefix_inst = 'This is an image , Please segment by given regions and instruction' #debug: 提取一帧图像中所有的物体描述并拼接在一起 # instruction="a bag.a cup.a pencil" instruction = '' for sent in sentences: instruction += ' {}.'.format(sent['sent']) #debug: 这些特殊的站位符号本质上还是字符串 # 占位符来表示每个需要分割的区域，用逗号分隔，最后一个 以句号结束，例如，如果有 3 个区域，结果是 ' , , .' regions_inst = ' ,' * (num_target - 1) + ' .' sources_value = f'\nThis is all regions: {regions_inst}\n' #sources构建了一个人类和模型交互的对话格式，定义了来自人类的输入和来自模型的输出 #debug: vp_seg的对话形式 # sources = [ # [{'from': 'human', 'value': prefix_inst + sources_value}, # {'from': 'gpt', 'value': '\n[SEG]'}]] #debug: refseg的对话形式，看看怎么把两种任务的形式结合在一起 #[SEG]指的是代表整个句子的token，指的是代表mask token # sources = [[{'from': 'human', 'value': prefix_inst + '\n'}, # {'from': 'gpt', 'value': '\nSure, the segmentation result is '}]] #debug: 自己创造的对话形式，这里需要解决的是gpt返回的value是什么SEG] or sources = [[{'from': 'human', 'value': prefix_inst + sources_value + "and this is the instruction: " + '\n'}, {'from': 'gpt', 'value': '\n[SEG]'}]] #debug：sources的作用主要是输出text_dict text_dict = self.preprocess_llama2(sources, self.tokenizer) #input_ids是模型的实际输入，是由分词器将文本 sources 转换为的一系列数字标识（token IDs） input_ids = text_dict['input_ids'][0] #labels是模型训练时的token的真实标签，与input_ids对应 labels = text_dict['labels'][0] #debug: 这里为针对ref新增加的 # instruction在这里才用上 token_refer_id = self.preprocess_referring_instruction(instruction) refer_embedding_indices = torch.zeros_like(input_ids) refer_embedding_indices[input_ids == REFER_TOKEN_INDEX] = 1 # refer_embedding_indices[input_ids == 50256] = 1 #debug data_dict['input_ids'] = input_ids data_dict['labels'] = labels data_dict['dataset_type'] = 'referring_coco' #debug: 看看这里的dataset_type的设置有影响吗 # data_dict['dataset_type'] = 'region_coco' data_dict['token_refer_id'] = token_refer_id data_dict['refer_embedding_indices'] = refer_embedding_indices return data_dict #从eval_davis中的DataCollatorForCOCODatasetV2类中，可以看出DAVIS_Dataset类每一帧对应的字典有哪些键 @dataclass class DataCollatorForCOCODatasetV2(object): """Collate examples for supervised fine-tuning.""" tokenizer: transformers.PreTrainedTokenizer #sequence表示列表、元组等有序对象，instances的类型表示为字典组成的有序列表，其中一个字典表示一帧图像 def __call__(self, instances: Sequence[Dict]) -> Dict[str, torch.Tensor]: input_ids, labels = tuple([instance[key] for instance in instances] for key in ("input_ids", "labels")) input_ids = torch.nn.utils.rnn.pad_sequence( input_ids, batch_first=True, padding_value=self.tokenizer.pad_token_id) labels = torch.nn.utils.rnn.pad_sequence(labels, batch_first=True, padding_value=IGNORE_INDEX) input_ids = input_ids[:, :self.tokenizer.model_max_length] labels = labels[:, :self.tokenizer.model_max_length] batch = dict( input_ids=input_ids, labels=labels, attention_mask=input_ids.ne(self.tokenizer.pad_token_id), ) if 'image' in instances[0]: images = [instance['image'] for instance in instances] if all(x is not None and x.shape == images[0].shape for x in images): batch['images'] = torch.stack(images) else: batch['images'] = images if 'vp_image' in instances[0]: vp_images = [instance['vp_image'] for instance in instances] if all(x is not None and x.shape == vp_images[0].shape for x in vp_images): batch['vp_images'] = torch.stack(vp_images) else: batch['vp_images'] = vp_images for instance in instances: for key in ['input_ids', 'labels', 'image']: del instance[key] batch['seg_info'] = [instance for instance in instances] if 'dataset_type' in instances[0]: batch['dataset_type'] = [instance['dataset_type'] for instance in instances] if 'class_name_ids' in instances[0]: class_name_ids = [instance['class_name_ids'] for instance in instances] if any(x.shape != class_name_ids[0].shape for x in class_name_ids): batch['class_name_ids'] = torch.nn.utils.rnn.pad_sequence( class_name_ids, batch_first=True, padding_value=-1, ) else: batch['class_name_ids'] = torch.stack(class_name_ids, dim=0) if 'token_refer_id' in instances[0]: token_refer_id = [instance['token_refer_id'] for instance in instances] batch['token_refer_id'] = token_refer_id if 'cls_indices' in instances[0]: cls_indices = [instance['cls_indices'] for instance in instances] if any(x.shape != cls_indices[0].shape for x in cls_indices): batch['cls_indices'] = torch.nn.utils.rnn.pad_sequence( cls_indices, batch_first=True, padding_value=-1, ) else: batch['cls_indices'] = torch.stack(cls_indices, dim=0) if 'random_idx' in instances[0]: random_idxs = [instance['random_idx'] for instance in instances] batch['random_idx'] = torch.stack(random_idxs, dim=0) if 'class_name_embedding_indices' in instances[0]: class_name_embedding_indices = [instance['class_name_embedding_indices'] for instance in instances] class_name_embedding_indices = torch.nn.utils.rnn.pad_sequence( class_name_embedding_indices, batch_first=True, padding_value=0) batch['class_name_embedding_indices'] = class_name_embedding_indices if 'refer_embedding_indices' in instances[0]: refer_embedding_indices = [instance['refer_embedding_indices'] for instance in instances] refer_embedding_indices = torch.nn.utils.rnn.pad_sequence( refer_embedding_indices, batch_first=True, padding_value=0) batch['refer_embedding_indices'] = refer_embedding_indices return batch class Summary(Enum): NONE = 0 AVERAGE = 1 SUM = 2 COUNT = 3 class AverageMeter(object): """Computes and stores the average and current value""" def __init__(self, name, fmt=":f", summary_type=Summary.AVERAGE): self.name = name self.fmt = fmt self.summary_type = summary_type self.reset() def reset(self): self.val = 0 self.avg = 0 self.sum = 0 self.count = 0 def update(self, val, n=1): self.val = val self.sum += val * n self.count += n self.avg = self.sum / self.count def all_reduce(self): device = "cuda" if torch.cuda.is_available() else "cpu" if isinstance(self.sum, np.ndarray): total = torch.tensor( self.sum.tolist() + [ self.count, ], dtype=torch.float32, device=device, ) else: total = torch.tensor( [self.sum, self.count], dtype=torch.float32, device=device ) dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False) if total.shape[0] > 2: self.sum, self.count = total[:-1].cpu().numpy(), total[-1].cpu().item() else: self.sum, self.count = total.tolist() self.avg = self.sum / (self.count + 1e-5) def __str__(self): fmtstr = "{name} {val" + self.fmt + "} ({avg" + self.fmt + "})" return fmtstr.format(**self.__dict__) def summary(self): fmtstr = "" if self.summary_type is Summary.NONE: fmtstr = "" elif self.summary_type is Summary.AVERAGE: fmtstr = "{name} {avg:.3f}" elif self.summary_type is Summary.SUM: fmtstr = "{name} {sum:.3f}" elif self.summary_type is Summary.COUNT: fmtstr = "{name} {count:.3f}" else: raise ValueError("invalid summary type %r" % self.summary_type) return fmtstr.format(**self.__dict__) def intersectionAndUnionGPU(output, target, K, ignore_index=255): # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. assert output.dim() in [1, 2, 3] assert output.shape == target.shape output = output.view(-1) target = target.view(-1) output[target == ignore_index] = ignore_index intersection = output[output == target] area_intersection = torch.histc(intersection, bins=K, min=0, max=K - 1) area_output = torch.histc(output, bins=K, min=0, max=K - 1) area_target = torch.histc(target, bins=K, min=0, max=K - 1) area_union = area_output + area_target - area_intersection return area_intersection, area_union, area_target @dataclass class DataArguments: data_path: str = field(default=None, metadata={"help": "Path to the training data."}) lazy_preprocess: bool = False is_multimodal: bool = False image_folder: Optional[str] = field(default='/path/to/val2017') model_path: Optional[str] = field(default="/path/to/model") mask_config: Optional[str] = field(default="./psalm/mask_config/maskformer2_swin_base_384_bs16_50ep.yaml") image_aspect_ratio: str = 'square' image_grid_pinpoints: Optional[str] = field(default=None) json_path: str = '/path/to/coco' model_map_name: str = 'psalm_video' version: str = 'llava_phi' segmentation: bool = True eval_batch_size: int = 1 dataloader_num_workers: int = 4 seg_task: Optional[str] = field(default="region") region_mask_type: Optional[str] = field(default=None) with_memory: bool = False eval_type: str = 'with_text' def parse_outputs(outputs,gt_mask): res_list = [] for output in outputs: # gt = output['gt'].cpu().numpy().astype(np.uint8) pred_mask = output['instances'].pred_masks pred_mask = pred_mask.cpu().numpy() scores = output['instances'].scores.transpose(1,0).cpu().numpy() gt_mask = output['gt'].cpu().numpy().astype(np.uint8) try: pred_cls = output['instances'].pred_classes.cpu().numpy() except: pred_cls = None assert scores.shape[0] == gt_mask.shape[0] for i in range(gt_mask.shape[0]): res = { 'pred':pred_mask, 'gt': gt_mask[i], 'scores':scores[i], 'pred_cls':pred_cls } res_list.append(res) return res_list def compute_metric(intersection_meter,union_meter,acc_iou_meter, results_list): pred_list = [] gt_list = [] results_list = list(results_list) for results in results_list: gt = results['gt'] preds = results['pred'] scores = results['scores'] preds = preds.astype(np.uint8) # pick mask with maximum score topk_scores,idx = torch.topk(torch.tensor(scores),1) idx = idx.cpu().numpy() topk_preds = preds[idx,:] if results['pred_cls'] is not None: topk_pred_cls = results['pred_cls'][idx] max_acc_iou = -1 max_iou = 0 max_intersection = 0 max_union = 0 max_i = 0 # here topk=1, len(topk_preds)=1 for i,pred_ in enumerate(topk_preds): intersection, union, _ = intersectionAndUnionGPU( torch.tensor(pred_).int().cuda().contiguous().clone(), torch.tensor(gt).int().cuda().contiguous(), 2, ignore_index=255 ) intersection, union = intersection.cpu().numpy(), union.cpu().numpy() acc_iou = intersection / (union + 1e-5) acc_iou[union == 0] = 1.0 # no-object target fore_acc_iou = acc_iou[1] if fore_acc_iou > max_acc_iou: max_acc_iou = fore_acc_iou max_iou = acc_iou max_intersection = intersection max_union = union max_i = i intersection_meter.update(max_intersection) union_meter.update(max_union) acc_iou_meter.update(max_iou, n=1) pred_list.append(topk_preds[max_i]) gt_list.append(gt) return pred_list,gt_list class DAVIS_Dataset(COCO_interactive_dataset): #注意，这里所有的处理逻辑针对的都是一帧图像 def __getitem__(self, idx): data = self.data[idx] #图片的相对路径名称，like2017/trainval/JPEGImages/480p/bike-packing/00001.jpg image_file = data['image'] #image_folder是data_root根路径 在这里是data_segswap image_folder = self.data_args.image_folder data_dict = {} #file_name是图片的完整路径名称，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00001.jpg data_dict['file_name'] = os.path.join(image_folder, image_file) data_dict['height'] = data['image_info']['height'] data_dict['width'] = data['image_info']['width'] #image_id可以理解为计数器，编号 data_dict['image_id'] = data['new_img_id'] #annotations，本帧对应的注释，coco格式的分割mask，一张图片可能包含多个实例的mask data_dict['annotations'] = data['anns'] #vp_annotations，每段视频中第一帧的注释 data_dict['vp_annotations'] = data['first_frame_anns'] #vp_image，每段视频中第一帧的完整路径，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00000.jpg data_dict['vp_image'] = os.path.join(image_folder,data['first_frame_image']) for annotation in data_dict['annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS #边界框左上角和右下角的坐标都为原点，意思是将边界框置为空框 annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] for annotation in data_dict['vp_annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] #初始化processor，应该是个图像预处理器，再送进visual encoder之前，总体来说下面的一小段代码是对输入图像和mask的预处理 # print("self.data_args.image_processor", self.data_args.image_processor) if isinstance(self.data_args.image_processor,dict): #根据是否是对齐ego exo的size进行切换，图像预处理器 processor = self.data_args.image_processor['instance'] # processor = self.data_args.image_processor['instance_resize'] else: processor = self.data_args.image_processor #尝试从命令行参数中获取region_mask_type region_mask_type = getattr(self.data_args,'region_mask_type',None) if region_mask_type is not None: region_mask_type = region_mask_type.split('||') print("region_mask_type:", region_mask_type) #根据region_mask_type和mask_format（这里是0、1掩码），对原始的data_dict进行预处理，将Detectron2格式的dataset dict转化为MaskFormer格式的 data_dict = processor.preprocess(data_dict,region_mask_type=region_mask_type,mask_format='bitmask') #num_target，本帧图像中有多少个对象 #下面的一小段代码，主要是利用llama处理输入的文本，生成对应的token num_target = len(data_dict['instances']) # 是一个特殊的占位符，表示图像的输入 prefix_inst = 'This is an image , Please segment by given regions' # 占位符来表示每个需要分割的区域，用逗号分隔，最后一个 以句号结束，例如，如果有 3 个区域，结果是 ' , , .' regions_inst = ' ,' * (num_target - 1) + ' .' sources_value = f'\nThis is all regions: {regions_inst}\n' #sources构建了一个人类和模型交互的对话格式，定义了来自人类的输入和来自模型的输出 sources = [ [{'from': 'human', 'value': prefix_inst + sources_value}, {'from': 'gpt', 'value': '\n[SEG]'}]] text_dict = self.preprocess_llama2(sources, self.tokenizer) #input_ids是模型的实际输入，是由分词器将文本 sources 转换为的一系列数字标识（token IDs） input_ids = text_dict['input_ids'][0] #labels是模型训练时的token的真实标签，与input_ids对应 labels = text_dict['labels'][0] data_dict['input_ids'] = input_ids data_dict['labels'] = labels data_dict['dataset_type'] = 'region_coco' return data_dict class Ego_Train_Dataset(COCO_interactive_dataset): #注意，这里所有的处理逻辑针对的都是一帧图像 def __getitem__(self, idx): data = self.data[idx] #图片的相对路径名称，like2017/trainval/JPEGImages/480p/bike-packing/00001.jpg image_file = data['image'] #image_folder是data_root根路径 在这里是data_segswap image_folder = self.data_args.image_folder data_dict = {} #file_name是图片的完整路径名称，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00001.jpg data_dict['file_name'] = os.path.join(image_folder, image_file) data_dict['height'] = data['image_info']['height'] data_dict['width'] = data['image_info']['width'] #image_id可以理解为计数器，编号 data_dict['image_id'] = data['new_img_id'] #annotations，本帧对应的注释，coco格式的分割mask，一张图片可能包含多个实例的mask data_dict['annotations'] = data['anns'] #vp_annotations，每段视频中第一帧的注释 data_dict['vp_annotations'] = data['first_frame_anns'] #vp_image，每段视频中第一帧的完整路径，like /data/Davis/2017/trainval/JPEGImages/480p/bike-packing/00000.jpg data_dict['vp_image'] = os.path.join(image_folder,data['first_frame_image']) for annotation in data_dict['annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS #边界框左上角和右下角的坐标都为原点，意思是将边界框置为空框 annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] #为了训练的时候instance能有region_mask属性而增设 # annotation['mask_visual_prompt_mask'] = annotation['segmentation'] for annotation in data_dict['vp_annotations']: annotation['bbox_mode'] = BoxMode.XYXY_ABS annotation['bbox'] = [0,0,0,0] annotation['image_id'] = data['new_img_id'] #初始化processor，应该是个图像预处理器，再送进visual encoder之前，总体来说下面的一小段代码是对输入图像和mask的预处理 # print("self.data_args.image_processor", self.data_args.image_processor) if isinstance(self.data_args.image_processor,dict): #根据是否是对齐ego exo的size进行切换，图像预处理器 #print(self.data_args.image_processor.keys()) processor = self.data_args.image_processor['null_mask'] # processor = self.data_args.image_processor['instance_resize'] else: processor = self.data_args.image_processor #尝试从命令行参数中获取region_mask_type region_mask_type = getattr(self.data_args,'region_mask_type',None) if region_mask_type is not None: region_mask_type = region_mask_type.split('||') # print("region_mask_type:", region_mask_type) #根据region_mask_type和mask_format（这里是0、1掩码），对原始的data_dict进行预处理，将Detectron2格式的dataset dict转化为MaskFormer格式的 data_dict = processor.preprocess(data_dict,region_mask_type=region_mask_type,mask_format='bitmask') #num_target，本帧图像中有多少个对象 #下面的一小段代码，主要是利用llama处理输入的文本，生成对应的token num_target = len(data_dict['instances']) # 是一个特殊的占位符，表示图像的输入 prefix_inst = 'This is an image , Please segment by given regions' # 占位符来表示每个需要分割的区域，用逗号分隔，最后一个 以句号结束，例如，如果有 3 个区域，结果是 ' , , .' regions_inst = ' ,' * (num_target - 1) + ' .' sources_value = f'\nThis is all regions: {regions_inst}\n' #sources构建了一个人类和模型交互的对话格式，定义了来自人类的输入和来自模型的输出 sources = [ [{'from': 'human', 'value': prefix_inst + sources_value}, {'from': 'gpt', 'value': '\n[SEG]'}]] text_dict = self.preprocess_llama2(sources, self.tokenizer) #input_ids是模型的实际输入，是由分词器将文本 sources 转换为的一系列数字标识（token IDs） input_ids = text_dict['input_ids'][0] #labels是模型训练时的token的真实标签，与input_ids对应 labels = text_dict['labels'][0] data_dict['input_ids'] = input_ids data_dict['labels'] = labels data_dict['dataset_type'] = 'region_coco' return data_dict def fuse_davis_mask(mask_list): fused_mask = np.zeros_like(mask_list[0]) for mask in mask_list: fused_mask[mask == 1] = 1 return fused_mask import os import re import utils_metric def get_latest_checkpoint_path(model_path): # 正则表达式用于匹配 checkpoint 文件夹名称格式：checkpoint- checkpoint_pattern = re.compile(r"checkpoint-(\d+)") # 检查是否已经是具体的 checkpoint 路径 if os.path.basename(model_path).startswith("checkpoint-") and checkpoint_pattern.match(os.path.basename(model_path)): return model_path # 已经是具体的 checkpoint，直接返回 # 如果是目录路径，查找其中的最新 checkpoint elif os.path.isdir(model_path): checkpoints = [d for d in os.listdir(model_path) if checkpoint_pattern.match(d)] if not checkpoints: raise ValueError("No checkpoints found in the specified directory.") # 根据迭代次数找到最新的 checkpoint max_checkpoint = max(checkpoints, key=lambda x: int(checkpoint_pattern.match(x).group(1))) model_path = os.path.join(model_path, max_checkpoint) elif not os.path.exists(model_path): raise FileNotFoundError(f"The specified path '{model_path}' does not exist.") return model_path parser = transformers.HfArgumentParser(DataArguments) data_args = parser.parse_args_into_dataclasses()[0] # debug 需要修改 #file_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/ExoQuery_val_newprompt_all_instruction.json" #file_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/egoexo_val_framelevel_newprompt_all_instruction.json" pred_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap/mask_predictions/egofullmodel_smalljson" root_path = "/data/work-gcp-europe-west4-a/yuqian_fu/Ego/data_segswap" val_set = os.listdir(pred_path) with open(data_args.json_path, 'r') as f: datas = json.load(f) IoUs = [] ShapeAcc = [] ExistenceAcc = [] LocationScores = [] def evaluation(): # parser = transformers.HfArgumentParser(DataArguments) # data_args = parser.parse_args_into_dataclasses()[0] disable_torch_init() if data_args.eval_type == 'without_text': model_path = os.path.expanduser(data_args.model_path) model_name = get_model_name_from_path(model_path) print(f'current model is {model_path}') tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, model_args=data_args, mask_config=data_args.mask_config, device='cuda') elif data_args.eval_type == 'with_text': model_path = os.path.expanduser(data_args.model_path) model_path = get_latest_checkpoint_path(model_path) #xiugai: to adapt only input model path without sepcify the ckp path print('------------------------TESTING----------------- ckp:', model_path) model_name = get_model_name_from_path(model_path) print(f'current model is {model_path}') print('save model name:', model_name) # model_map_name = 'psalm' model_name = 'psalm_SSL_MultiCondition' print('now changed the model name to:', model_name) tokenizer, model, image_processor, context_len = load_pretrained_model(model_path, None, model_name, model_args=data_args, mask_config=data_args.mask_config, device='cuda') # ckpt = torch.load(os.path.join(model_path,'pytorch_model.bin')) # model.load_state_dict(ckpt,strict=True) data_args.image_processor = image_processor #print('image_processor:', image_processor) data_args.is_multimodal = True conversation_lib.default_conversation = conversation_lib.conv_templates[data_args.version] # eval_dataset = EGO4D_Dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) # eval_dataset = DAVIS_Dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) #eval_dataset = Ego_Train_Dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) if data_args.eval_type == 'with_text': eval_dataset = Multicondition_Dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) elif data_args.eval_type == 'without_text': eval_dataset = Ego_Train_Dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) data_collator = DataCollatorForCOCODatasetV2(tokenizer=tokenizer) dataloader_params = { "batch_size": data_args.eval_batch_size, "num_workers": data_args.dataloader_num_workers, } eval_dataloader = DataLoader(eval_dataset, batch_size=dataloader_params['batch_size'], collate_fn=data_collator, num_workers=dataloader_params['num_workers']) def load_ref_dataset(): return RefCOCO_dataset(json_path=data_args.json_path, tokenizer=tokenizer, data_args=data_args) DatasetCatalog.register('refcoco_dataset', load_ref_dataset) MetadataCatalog.get('refcoco_dataset').set(stuff_classes=['object'],) gt_json_path = data_args.json_path save_dir = os.path.dirname(gt_json_path) save_dir = os.path.join(save_dir,'predictions_memory') # evaluator = my_refcoco_evaluator('refcoco_dataset', output_dir='./output/instruction_segmentation', distributed=False) # evaluator.reset() device = 'cuda' if torch.cuda.is_available() else 'cpu' # device = 'cpu' model.to(device=device,dtype=torch.float).eval() # inference_on_dataset(model, eval_dataloader, evaluator) with torch.no_grad(): for idx, inputs in tqdm(enumerate(eval_dataloader), total=len(eval_dataloader)): if len(inputs) == 0: print('no data load') continue inputs = {k: v.to(device) if torch.is_tensor(v) else v for k, v in inputs.items()} if data_args.eval_type == 'with_text': inputs['token_refer_id'] = [ids.to(device) for ids in inputs['token_refer_id']] image_id = inputs['seg_info'][0]['image_id'] # print("image_id:", type(image_id)) data_json = datas[image_id] if data_args.eval_type == 'with_text': outputs = model.eval_video( input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], images=inputs['images'].float(), vp_images=inputs['vp_images'].float(), seg_info=inputs['seg_info'], token_refer_id = inputs['token_refer_id'], refer_embedding_indices=inputs['refer_embedding_indices'], labels=inputs['labels'] ) else: outputs = model.eval_video( input_ids=inputs['input_ids'], attention_mask=inputs['attention_mask'], images=inputs['images'].float(), vp_images=inputs['vp_images'].float(), seg_info=inputs['seg_info'], labels=inputs['labels'] ) if torch.cuda.is_available(): torch.cuda.synchronize() output = outputs[0] pred_mask = output['instances'].pred_masks pred_mask = pred_mask.cpu().numpy() scores = output['instances'].scores.transpose(1, 0).cpu().numpy() gt_mask = output['gt'].cpu().numpy().astype(np.uint8) assert len(scores) == len(inputs['seg_info'][0]['instances'].vp_fill_number) pred_mask_list = [] pred_score_list = [] fill_number_list = [] prev_idx = [] for i in range(len(scores)): cur_scores = scores[i] cur_fill_number = inputs['seg_info'][0]['instances'].vp_fill_number[i] max_score, idx = torch.topk(torch.tensor(cur_scores), 10, largest=True, sorted=True) idx = idx.cpu().numpy() for i in range(10): if idx[i] not in prev_idx: prev_idx.append(idx[i]) pick_idx = idx[i] pick_score = max_score[i] break #TODO这里curpred是单个物体的mask，可以在这里看看能不能提取种类id信息 cur_pred = pred_mask[pick_idx, :] pred_score_list.append(pick_score) pred_mask_list.append(cur_pred) fill_number_list.append(cur_fill_number) pred_mask_list = [tensor_.astype(np.uint8) for tensor_ in pred_mask_list] fused_pred_mask = fuse_davis_mask(pred_mask_list) gt_mask_list = [] for ann in data_json['anns']: gt_mask = decode(ann['segmentation']) gt_mask_list.append(gt_mask) fused_gt_mask = fuse_davis_mask(gt_mask_list) h, w = fused_pred_mask.shape gt_mask = cv2.resize(fused_gt_mask, (w, h), interpolation=cv2.INTER_NEAREST) iou, shape_acc = utils_metric.eval_mask(gt_mask, fused_pred_mask) ex_acc = utils_metric.existence_accuracy(gt_mask, fused_pred_mask) location_score = utils_metric.location_score(gt_mask, fused_pred_mask, size=(h, w)) IoUs.append(iou) ShapeAcc.append(shape_acc) ExistenceAcc.append(ex_acc) LocationScores.append(location_score) print(f'average IoU is {np.mean(IoUs)}') print(f'average ShapeAcc is {np.mean(ShapeAcc)}') print(f'average ExistenceAcc is {np.mean(ExistenceAcc)}') print(f'average LocationScores is {np.mean(LocationScores)}') print("data_len:", len(IoUs)) if __name__ == '__main__': evaluation()