scripts/eval_handal_oursmetric.py

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

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 psalm.eval.segmentation_evaluation import openseg_classes
import utils

COLOR_MAP = openseg_classes.ADE20K_150_CATEGORIES
@dataclass
class DataCollatorForCOCODatasetV2(object):
    """Collate examples for supervised fine-tuning."""

    tokenizer: transformers.PreTrainedTokenizer

    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

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):
    def __getitem__(self, idx):
        data = self.data[idx]
        image_file = data['image']
        image_folder = self.data_args.image_folder


        data_dict = {}
        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']
        data_dict['image_id'] = data['new_img_id']
        data_dict['annotations'] = data['anns']
        data_dict['vp_annotations'] = data['first_frame_anns']
        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
            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']

        if isinstance(self.data_args.image_processor,dict):
            processor = self.data_args.image_processor['instance']
        else:
            processor = self.data_args.image_processor
        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('||')
        data_dict = processor.preprocess(data_dict,region_mask_type=region_mask_type,mask_format='bitmask')

        num_target = len(data_dict['instances'])
        prefix_inst = 'This is an image <image>, Please segment by given regions'
        regions_inst = ' <region>,' * (num_target - 1) + ' <region>.'
        sources_value = f'\nThis is all regions: {regions_inst}\n'

        sources = [
            [{'from': 'human', 'value': prefix_inst + sources_value},
             {'from': 'gpt', 'value': '\n[SEG]<seg>'}]]

        text_dict = self.preprocess_llama2(sources, self.tokenizer)
        input_ids = text_dict['input_ids'][0]
        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,fill_number_list):
    fused_mask = np.zeros_like(mask_list[0])
    for mask, fill_number in zip(mask_list,fill_number_list):
        fill_number = int(fill_number)
        fused_mask[mask == 1] = fill_number
    return fused_mask


IoUs = []
def evaluation():
    parser = transformers.HfArgumentParser(DataArguments)
    data_args = parser.parse_args_into_dataclasses()[0]
    disable_torch_init()
    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')

    data_args.image_processor = image_processor
    data_args.is_multimodal = True
    conversation_lib.default_conversation = conversation_lib.conv_templates[data_args.version]

    eval_dataset = DAVIS_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 DAVIS_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,'mask_predictions')  #for psalm
    #save_dir = os.path.join(save_dir,'DAVIS-PSALMModel-from-PSALMPretrained-Epoch5-20250118')  #for davis

    device = 'cuda' if torch.cuda.is_available() else 'cpu'

    model.to(device=device,dtype=torch.float).eval()
    prev_image = None
    prev_mask_list = None
    prev_fill_number_list = None
    prev_video = None
    prev_transformer = None



    with torch.no_grad():
        for idx, inputs in tqdm(enumerate(eval_dataloader), total=len(eval_dataloader)):
            inputs = {k: v.to(device) if torch.is_tensor(v) else v for k, v in inputs.items()}
            video_name = inputs['seg_info'][0]['file_name'].split('/')[-2]

            
            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).squeeze(0)
            # print(type(gt_mask))
            # print(gt_mask.shape)
            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
                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,fill_number_list)
            h, w = fused_pred_mask.shape
            #print(f'fused_pred_mask shape: {fused_pred_mask.shape}')
            #TODO: 找到正确的gt-mask

            gt_mask = cv2.resize(gt_mask, (w, h), interpolation=cv2.INTER_NEAREST)
            iou, shape_acc = utils.eval_mask(gt_mask, fused_pred_mask)
            IoUs.append(iou)

           



            
    print("data_len:",len(IoUs))
    print(f'IOU:', np.mean(IoUs))

if __name__ == '__main__':
    evaluation()