engine/crog_engine_test.py

import os
import time
from tqdm import tqdm

import cv2
import numpy as np
import torch
import torch.cuda.amp as amp
import torch.distributed as dist
import torch.nn.functional as F
import wandb
from loguru import logger
from utils.wandb_utils import fix_metric_names  # Import metric name fixer
from utils.dataset import tokenize
from utils.misc import (AverageMeter, ProgressMeter, concat_all_gather, trainMetricGPU, get_seg_image)
from utils.grasp_eval import (detect_grasps, calculate_iou, calculate_max_iou, calculate_jacquard_index, visualization)

def train_with_grasp(train_loader, model, optimizer, scheduler, scaler, epoch, args):
    batch_time = AverageMeter('Batch', ':2.2f')
    data_time = AverageMeter('Data', ':2.2f')
    lr = AverageMeter('Lr', ':1.6f')
    loss_meter = AverageMeter('Loss', ':2.4f')
    qua_loss_metter = AverageMeter('Loss_qua', ':2.4f')
    sin_loss_metter = AverageMeter('Loss_sin', ':2.4f')
    cos_loss_metter = AverageMeter('Loss_cos', ':2.4f')
    wid_loss_metter = AverageMeter('Loss_wid', ':2.4f')
    iou_meter = AverageMeter('IoU', ':2.2f')
    pr_meter = AverageMeter('Prec@50', ':2.2f')
    progress = ProgressMeter(
        len(train_loader),
        [
            batch_time, data_time, lr, loss_meter, 
            qua_loss_metter, sin_loss_metter, cos_loss_metter, wid_loss_metter, 
            iou_meter, pr_meter
        ],
        prefix="Training: Epoch=[{}/{}] ".format(epoch, args.epochs))

    model.train()
    time.sleep(2)
    end = time.time()

    # size_list = [320, 352, 384, 416, 448, 480, 512]
    # idx = np.random.choice(len(size_list))
    # new_size = size_list[idx]

    for i, data in enumerate(train_loader):
        # image, target, text = data
        # ins_mask, grasp_quality_mask, grasp_sin_mask, grasp_cos_mask, grasp_width_mask = target
        
        image = data["img"]
        text = data["word_vec"] if not getattr(args, 'use_raw_text', False) else data["sentence"]
        ins_mask = data["mask"]
        grasp_qua_mask = data["grasp_masks"]["qua"]
        grasp_sin_mask = data["grasp_masks"]["sin"]
        grasp_cos_mask = data["grasp_masks"]["cos"]
        grasp_wid_mask = data["grasp_masks"]["wid"]
        
        
        data_time.update(time.time() - end)
        # data
        image = image.cuda(non_blocking=True)
        # Only move text to CUDA if it's a tensor (tokenized), not raw text
        if isinstance(text, torch.Tensor):
            text = text.cuda(non_blocking=True)
        ins_mask = ins_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_qua_mask = grasp_qua_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_sin_mask = grasp_sin_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_cos_mask = grasp_cos_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_wid_mask = grasp_wid_mask.cuda(non_blocking=True).unsqueeze(1)

        # # multi-scale training
        # image = F.interpolate(image, size=(new_size, new_size), mode='bilinear')

        # forward
        with amp.autocast():
            pred, target, loss, loss_dict = model(image, text, ins_mask, grasp_qua_mask, grasp_sin_mask, grasp_cos_mask, grasp_wid_mask)
        
        ins_mask_pred = pred[0]
        ins_mask_target = target[0]

        # backward
        optimizer.zero_grad()
        scaler.scale(loss).backward()
        if args.max_norm:
            torch.nn.utils.clip_grad_norm_(model.parameters(), args.max_norm)
        scaler.step(optimizer)
        scaler.update()
        
        # Update scheduler after each batch for OneCycleLR and warmup schedulers
        if hasattr(scheduler, '_scheduler_type'):
            if scheduler._scheduler_type == 'OneCycleLR':
                scheduler.step()
            elif hasattr(scheduler, '_warmup_steps') and scheduler._step_count < scheduler._warmup_steps:
                # Step warmup scheduler during warmup phase
                scheduler.step()
        elif hasattr(scheduler, '__class__') and scheduler.__class__.__name__ == 'OneCycleLR':
            scheduler.step()

        # metric
        iou, pr5 = trainMetricGPU(ins_mask_pred, ins_mask_target, 0.35, 0.5)
        dist.all_reduce(loss.detach())
        dist.all_reduce(iou)
        dist.all_reduce(pr5)
        loss = loss / dist.get_world_size()
        iou = iou / dist.get_world_size()
        pr5 = pr5 / dist.get_world_size()

        loss_meter.update(loss.item(), image.size(0))
        qua_loss_metter.update(loss_dict["m_qua"], image.size(0))
        sin_loss_metter.update(loss_dict["m_sin"], image.size(0))
        cos_loss_metter.update(loss_dict["m_cos"], image.size(0))
        wid_loss_metter.update(loss_dict["m_wid"], image.size(0))
        iou_meter.update(iou.item(), image.size(0))
        pr_meter.update(pr5.item(), image.size(0))
        lr.update(scheduler.get_last_lr()[-1])
        batch_time.update(time.time() - end)
        end = time.time()

        if (i + 1) % args.print_freq == 0:
            progress.display(i + 1)
            if dist.get_rank() in [-1, 0]:
                # Enhanced metric logging with proper naming
                metrics = {
                    "time/batch": batch_time.val,
                    "time/data": data_time.val,
                    "training/lr": lr.val,
                    "training/loss": loss_meter.val,
                    "training/loss_qua": qua_loss_metter.val,
                    "training/loss_sin": sin_loss_metter.val,
                    "training/loss_cos": cos_loss_metter.val,
                    "training/loss_wid": wid_loss_metter.val,
                    "training/iou": iou_meter.val,
                    "training/prec@50": pr_meter.val,
                }
                
                # Also log learning rates for each parameter group if available
                if hasattr(optimizer, 'param_groups'):
                    for idx, param_group in enumerate(optimizer.param_groups):
                        metrics[f"lr/param_group_{idx}"] = param_group['lr']
                
                global_step = (epoch - 1) * len(train_loader) + i
                wandb.log(metrics, step=global_step)


@torch.no_grad()
def validate_with_grasp(val_loader, model, epoch, args):
    def inverse(img, mat, w, h):
        img = np.asarray(img)
        if img.dtype != np.float32:
            img = img.astype(np.float32)
        inv_img = cv2.warpAffine(img, mat, (w, h),
                                flags=cv2.INTER_CUBIC,
                                borderValue=0.)
        return inv_img

    iou_list = []
    num_correct_grasps = 0
    num_total_grasps = 0
    model.eval()
    time.sleep(2)

    num_grasps = [1,5]
    num_correct_grasps = [0, 0]
    num_total_grasps = [0, 0]

    pbar = tqdm(val_loader)
    for data in pbar:
        # data
        image = data["img"]
        text = data["word_vec"] if not getattr(args, 'use_raw_text', False) else data["sentence"]
        ins_mask = data["mask"]
        grasp_qua_mask = data["grasp_masks"]["qua"]
        grasp_sin_mask = data["grasp_masks"]["sin"]
        grasp_cos_mask = data["grasp_masks"]["cos"]
        grasp_wid_mask = data["grasp_masks"]["wid"]
        inverse_matrix = data["inverse"]
        ori_sizes = data["ori_size"]
        grasp_targets = data["grasps"]
        
        image = image.cuda(non_blocking=True)
        # Only move text to CUDA if it's a tensor (tokenized), not raw text
        if isinstance(text, torch.Tensor):
            text = text.cuda(non_blocking=True)
        ins_mask = ins_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_qua_mask = grasp_qua_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_sin_mask = grasp_sin_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_cos_mask = grasp_cos_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_wid_mask = grasp_wid_mask.cuda(non_blocking=True).unsqueeze(1)
        
        # inference & get predictions from model
        with torch.no_grad():
            with amp.autocast():
                pred, target = model(image, text, ins_mask, grasp_qua_mask, grasp_sin_mask, grasp_cos_mask, grasp_wid_mask)
        
        # predictions
        ins_mask_preds = pred[0]
        grasp_qua_mask_preds = pred[1]
        grasp_sin_mask_preds = pred[2]
        grasp_cos_mask_preds = pred[3]
        grasp_wid_mask_preds = pred[4]
        
        # targets
        ins_mask_targets = target[0]
        grasp_qua_mask_targets = target[1]
        grasp_sin_mask_targets = target[2]
        grasp_cos_mask_targets = target[3]
        grasp_wid_mask_targets = target[4]
        
        # Interpolate the predicted ins mask to the same size of input image
        ins_mask_preds = torch.sigmoid(ins_mask_preds)
        grasp_qua_mask_preds = torch.sigmoid(grasp_qua_mask_preds)
        grasp_wid_mask_preds = torch.sigmoid(grasp_wid_mask_preds)
        
        if ins_mask_preds.shape[-2:] != image.shape[-2:]:
            ins_mask_preds = F.interpolate(ins_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)

            grasp_qua_mask_preds = F.interpolate(grasp_qua_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
            
            grasp_sin_mask_preds = F.interpolate(grasp_sin_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
            
            grasp_cos_mask_preds = F.interpolate(grasp_cos_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
            
            grasp_wid_mask_preds = F.interpolate(grasp_wid_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
        else:
            ins_mask_preds = ins_mask_preds.squeeze(1)
            grasp_qua_mask_preds = grasp_qua_mask_preds.squeeze(1)
            grasp_sin_mask_preds = grasp_sin_mask_preds.squeeze(1)
            grasp_cos_mask_preds = grasp_cos_mask_preds.squeeze(1)
            grasp_wid_mask_preds = grasp_wid_mask_preds.squeeze(1)
        
        # iterate over the whole batch
        for idx in range(ins_mask_preds.shape[0]):
            inv_mat = inverse_matrix[idx]
            ori_size = ori_sizes[idx]
            h, w = ori_size
            
            ins_mask_pred = ins_mask_preds[idx].cpu().numpy()
            grasp_qua_mask_pred = grasp_qua_mask_preds[idx].squeeze().cpu().numpy()
            grasp_sin_mask_pred = grasp_sin_mask_preds[idx].squeeze().cpu().numpy()
            grasp_cos_mask_pred = grasp_cos_mask_preds[idx].squeeze().cpu().numpy()
            grasp_wid_mask_pred = grasp_wid_mask_preds[idx].squeeze().cpu().numpy()
            
            ins_mask_target = ins_mask_targets[idx].squeeze().cpu().numpy()
            grasp_target = grasp_targets[idx]
            grasp_qua_mask_target = grasp_qua_mask_targets[idx].squeeze().cpu().numpy()
            grasp_sin_mask_target = grasp_sin_mask_targets[idx].squeeze().cpu().numpy()
            grasp_cos_mask_target = grasp_cos_mask_targets[idx].squeeze().cpu().numpy()
            grasp_wid_mask_target = grasp_wid_mask_targets[idx].squeeze().cpu().numpy()
            
            # Inverse to original size
            ins_mask_pred = inverse(ins_mask_pred, inv_mat, w, h)
            ins_mask_pred = (ins_mask_pred > 0.35)
            grasp_qua_mask_pred = inverse(grasp_qua_mask_pred, inv_mat, w, h)
            grasp_sin_mask_pred = inverse(grasp_sin_mask_pred, inv_mat, w, h)
            grasp_cos_mask_pred = inverse(grasp_cos_mask_pred, inv_mat, w, h)
            grasp_wid_mask_pred = inverse(grasp_wid_mask_pred, inv_mat, w, h)
            
            ins_mask_target = inverse(ins_mask_target, inv_mat, w, h)
            grasp_qua_mask_target = inverse(grasp_qua_mask_target, inv_mat, w, h)
            grasp_sin_mask_target = inverse(grasp_sin_mask_target, inv_mat, w, h)
            grasp_cos_mask_target = inverse(grasp_cos_mask_target, inv_mat, w, h)
            grasp_wid_mask_target = inverse(grasp_wid_mask_target, inv_mat, w, h)
            
            # Calculate IoU between predicted instance mask and gt
            inter = np.logical_and(ins_mask_pred, ins_mask_target)
            union = np.logical_or(ins_mask_pred, ins_mask_target)
            
            iou = np.sum(inter) / (np.sum(union) + 1e-6)
            iou_list.append(iou)
            
            # Calculate grasp configurations
            for i in range(len(num_grasps)):
                num_g = num_grasps[i]
                grasp_preds, _ = detect_grasps(grasp_qua_mask_pred, grasp_sin_mask_pred, grasp_cos_mask_pred, grasp_wid_mask_pred, num_g)

                j_index = calculate_jacquard_index(grasp_preds, grasp_target)
                
                num_correct_grasps[i] += j_index
                num_total_grasps[i] += 1
    
    J_index = [0, 0]
    for i in range(len(num_grasps)):
        J_index[i] = num_correct_grasps[i]/num_total_grasps[i]
            
    iou_list = np.stack(iou_list)
    iou_list = torch.from_numpy(iou_list).to(image.device)
    iou_list = concat_all_gather(iou_list)
    prec_list = []
    for thres in torch.arange(0.5, 1.0, 0.1):
        tmp = (iou_list > thres).float().mean()
        prec_list.append(tmp)
    iou = iou_list.mean()
    prec = {}
    temp = '  '
    for i, thres in enumerate(range(5, 10)):
        key = 'Pr@{}'.format(thres * 10)
        value = prec_list[i].item()
        prec[key] = value
        temp += "{}: {:.2f}  ".format(key, 100. * value)
    head = 'Evaluation: Epoch=[{}/{}]  IoU={:.2f}  J_index@1: {:.2f}  J_index@5: {:.2f}'.format(
        epoch, args.epochs, 100. * iou.item(), 100. * J_index[0], 100. * J_index[1])
    logger.info(head + temp)
    # if dist.get_rank() in [-1, 0]:
    #     wandb.log({
    #         "validation/iou": iou.item(),
    #         "validation/J_index@1": J_index[0],
    #         "validation/J_index@5": J_index[1],
    #         **{f"validation/{key}": value for key, value in prec.items()}
    #     }, step=epoch)
    return iou.item(), prec, J_index


@torch.no_grad()
def validate_without_grasp(val_loader, model, epoch, args):
    def inverse(img, mat, w, h):
        img = np.asarray(img)
        if img.dtype != np.float32:
            img = img.astype(np.float32)
        inv_img = cv2.warpAffine(img, mat, (w, h),
                                flags=cv2.INTER_CUBIC,
                                borderValue=0.)
        return inv_img

    iou_list = []
    num_correct_grasps = 0
    num_total_grasps = 0
    model.eval()
    time.sleep(2)

    num_grasps = [1,5]
    num_correct_grasps = [0, 0]
    num_total_grasps = [0, 0]

    pbar = tqdm(val_loader)
    for data in pbar:
        # data
        image = data["img"]
        text = data["word_vec"]
        ins_mask = data["mask"]
        grasp_qua_mask = data["grasp_masks"]["qua"]
        grasp_sin_mask = data["grasp_masks"]["sin"]
        grasp_cos_mask = data["grasp_masks"]["cos"]
        grasp_wid_mask = data["grasp_masks"]["wid"]
        inverse_matrix = data["inverse"]
        ori_sizes = data["ori_size"]
        grasp_targets = data["grasps"]
        
        image = image.cuda(non_blocking=True)
        text = text.cuda(non_blocking=True)
        ins_mask = ins_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_qua_mask = grasp_qua_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_sin_mask = grasp_sin_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_cos_mask = grasp_cos_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_wid_mask = grasp_wid_mask.cuda(non_blocking=True).unsqueeze(1)
        
        # inference & get predictions from model
        with torch.no_grad():
            with amp.autocast():
                pred, ins_mask_targets = model(image, text, ins_mask, grasp_qua_mask, grasp_sin_mask, grasp_cos_mask, grasp_wid_mask)
        
        # Interpolate the predicted ins mask to the same size of input image
        ins_mask_preds = torch.sigmoid(pred)
        if ins_mask_preds.shape[-2:] != image.shape[-2:]:
            ins_mask_preds = F.interpolate(ins_mask_preds,
                                  size=image.shape[-2:],
                                  mode='bicubic',
                                  align_corners=True).squeeze(1)
        
        # iterate over the whole batch
        for idx in range(ins_mask_preds.shape[0]):
            inv_mat = inverse_matrix[idx]
            ori_size = ori_sizes[idx]
            h, w = ori_size
            
            ins_mask_pred = ins_mask_preds[idx].squeeze().cpu().numpy()
            ins_mask_target = ins_mask_targets[idx].squeeze().cpu().numpy()
            
            # Inverse to original size
            ins_mask_pred = inverse(ins_mask_pred, inv_mat, w, h)
            ins_mask_pred = (ins_mask_pred > 0.35)
            
            ins_mask_target = inverse(ins_mask_target, inv_mat, w, h)
            
            # Calculate IoU between predicted instance mask and gt
            inter = np.logical_and(ins_mask_pred, ins_mask_target)
            union = np.logical_or(ins_mask_pred, ins_mask_target)
            
            iou = np.sum(inter) / (np.sum(union) + 1e-6)
            iou_list.append(iou)
    
    J_index = [0, 0]
    
    iou_list = np.stack(iou_list)
    iou_list = torch.from_numpy(iou_list).to(image.device)
    iou_list = concat_all_gather(iou_list)
    prec_list = []
    for thres in torch.arange(0.5, 1.0, 0.1):
        tmp = (iou_list > thres).float().mean()
        prec_list.append(tmp)
    iou = iou_list.mean()
    prec = {}
    temp = '  '
    for i, thres in enumerate(range(5, 10)):
        key = 'Pr@{}'.format(thres * 10)
        value = prec_list[i].item()
        prec[key] = value
        temp += "{}: {:.2f}  ".format(key, 100. * value)
    head = 'Evaluation: Epoch=[{}/{}]  IoU={:.2f}  J_index@1: {:.2f}  J_index@5: {:.2f}'.format(
        epoch, args.epochs, 100. * iou.item(), 100. * J_index[0], 100. * J_index[1])
    logger.info(head + temp)
    return iou.item(), prec, J_index



@torch.no_grad()
def inference_with_grasp(test_loader, model, args):
    def inverse(img, mat, w, h):
        img = np.asarray(img)
        if img.dtype != np.float32:
            img = img.astype(np.float32)
        inv_img = cv2.warpAffine(img, mat, (w, h),
                                 flags=cv2.INTER_CUBIC,
                                 borderValue=0.)
        return inv_img

    iou_list = []
    model.eval()
    time.sleep(2)

    num_grasps = [1, 5]
    num_correct_grasps = [0, 0]
    num_total_grasps = [0, 0]

    pbar = tqdm(test_loader)
    sample_count = 0

    for data in pbar:
        image = data["img"]
        text = data["word_vec"] if not getattr(args, 'use_raw_text', False) else data["sentence"]
        ins_mask = data["mask"]
        grasp_qua_mask = data["grasp_masks"]["qua"]
        grasp_sin_mask = data["grasp_masks"]["sin"]
        grasp_cos_mask = data["grasp_masks"]["cos"]
        grasp_wid_mask = data["grasp_masks"]["wid"]
        inverse_matrix = data["inverse"]
        ori_sizes = data["ori_size"]
        grasp_targets = data["grasps"]

        # 新增：取样本信息
        sentences = data.get("sentence", [""])
        scene_ids = data.get("scene_id", [""])
        sent_ids = data.get("sent_id", [""])
        targets = data.get("target", [""])
        img_paths = data.get("img_path", [""])
        bboxes = data.get("bbox", [""])

        image = image.cuda(non_blocking=True)
        if isinstance(text, torch.Tensor):
            text = text.cuda(non_blocking=True)
        ins_mask = ins_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_qua_mask = grasp_qua_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_sin_mask = grasp_sin_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_cos_mask = grasp_cos_mask.cuda(non_blocking=True).unsqueeze(1)
        grasp_wid_mask = grasp_wid_mask.cuda(non_blocking=True).unsqueeze(1)

        with amp.autocast():
            pred, target = model(image, text, ins_mask, grasp_qua_mask, grasp_sin_mask, grasp_cos_mask, grasp_wid_mask)

        ins_mask_preds = pred[0]
        grasp_qua_mask_preds = pred[1]
        grasp_sin_mask_preds = pred[2]
        grasp_cos_mask_preds = pred[3]
        grasp_wid_mask_preds = pred[4]

        ins_mask_targets = target[0]
        grasp_qua_mask_targets = target[1]
        grasp_sin_mask_targets = target[2]
        grasp_cos_mask_targets = target[3]
        grasp_wid_mask_targets = target[4]

        ins_mask_preds = torch.sigmoid(ins_mask_preds)
        grasp_qua_mask_preds = torch.sigmoid(grasp_qua_mask_preds)
        grasp_wid_mask_preds = torch.sigmoid(grasp_wid_mask_preds)

        if ins_mask_preds.shape[-2:] != image.shape[-2:]:
            ins_mask_preds = F.interpolate(
                ins_mask_preds,
                size=image.shape[-2:],
                mode='bicubic',
                align_corners=True
            ).squeeze(1)

            grasp_qua_mask_preds = F.interpolate(
                grasp_qua_mask_preds,
                size=image.shape[-2:],
                mode='bicubic',
                align_corners=True
            ).squeeze(1)

            grasp_sin_mask_preds = F.interpolate(
                grasp_sin_mask_preds,
                size=image.shape[-2:],
                mode='bicubic',
                align_corners=True
            ).squeeze(1)

            grasp_cos_mask_preds = F.interpolate(
                grasp_cos_mask_preds,
                size=image.shape[-2:],
                mode='bicubic',
                align_corners=True
            ).squeeze(1)

            grasp_wid_mask_preds = F.interpolate(
                grasp_wid_mask_preds,
                size=image.shape[-2:],
                mode='bicubic',
                align_corners=True
            ).squeeze(1)
        else:
            ins_mask_preds = ins_mask_preds.squeeze(1)
            grasp_qua_mask_preds = grasp_qua_mask_preds.squeeze(1)
            grasp_sin_mask_preds = grasp_sin_mask_preds.squeeze(1)
            grasp_cos_mask_preds = grasp_cos_mask_preds.squeeze(1)
            grasp_wid_mask_preds = grasp_wid_mask_preds.squeeze(1)

        for idx in range(ins_mask_preds.shape[0]):
            sample_count += 1

            inv_mat = inverse_matrix[idx]
            ori_size = ori_sizes[idx]
            h, w = ori_size

            ins_mask_pred = ins_mask_preds[idx].cpu().numpy()
            grasp_qua_mask_pred = grasp_qua_mask_preds[idx].squeeze().cpu().numpy()
            grasp_sin_mask_pred = grasp_sin_mask_preds[idx].squeeze().cpu().numpy()
            grasp_cos_mask_pred = grasp_cos_mask_preds[idx].squeeze().cpu().numpy()
            grasp_wid_mask_pred = grasp_wid_mask_preds[idx].squeeze().cpu().numpy()

            ins_mask_target = ins_mask_targets[idx].squeeze().cpu().numpy()
            grasp_target = grasp_targets[idx]
            grasp_qua_mask_target = grasp_qua_mask_targets[idx].squeeze().cpu().numpy()
            grasp_sin_mask_target = grasp_sin_mask_targets[idx].squeeze().cpu().numpy()
            grasp_cos_mask_target = grasp_cos_mask_targets[idx].squeeze().cpu().numpy()
            grasp_wid_mask_target = grasp_wid_mask_targets[idx].squeeze().cpu().numpy()

            ins_mask_pred = inverse(ins_mask_pred, inv_mat, w, h)
            ins_mask_pred = (ins_mask_pred > 0.35)
            grasp_qua_mask_pred = inverse(grasp_qua_mask_pred, inv_mat, w, h)
            grasp_sin_mask_pred = inverse(grasp_sin_mask_pred, inv_mat, w, h)
            grasp_cos_mask_pred = inverse(grasp_cos_mask_pred, inv_mat, w, h)
            grasp_wid_mask_pred = inverse(grasp_wid_mask_pred, inv_mat, w, h)

            ins_mask_target = inverse(ins_mask_target, inv_mat, w, h)
            grasp_qua_mask_target = inverse(grasp_qua_mask_target, inv_mat, w, h)
            grasp_sin_mask_target = inverse(grasp_sin_mask_target, inv_mat, w, h)
            grasp_cos_mask_target = inverse(grasp_cos_mask_target, inv_mat, w, h)
            grasp_wid_mask_target = inverse(grasp_wid_mask_target, inv_mat, w, h)

            inter = np.logical_and(ins_mask_pred, ins_mask_target)
            union = np.logical_or(ins_mask_pred, ins_mask_target)

            iou = np.sum(inter) / (np.sum(union) + 1e-6)
            iou_list.append(iou)

            # 每个样本单独算 J-index
            sample_j = []
            for i in range(len(num_grasps)):
                num_g = num_grasps[i]
                grasp_preds, _ = detect_grasps(
                    grasp_qua_mask_pred,
                    grasp_sin_mask_pred,
                    grasp_cos_mask_pred,
                    grasp_wid_mask_pred,
                    num_g
                )

                j_index = calculate_jacquard_index(grasp_preds, grasp_target)
                sample_j.append(j_index)

                num_correct_grasps[i] += j_index
                num_total_grasps[i] += 1

            # 新增：打印每个样本详细信息
            gt_mask_sum = float(np.sum(ins_mask_target > 0.5))
            pred_mask_sum = float(np.sum(ins_mask_pred))
            inter_sum = float(np.sum(inter))
            union_sum = float(np.sum(union))

            logger.info(
                f"[Sample {sample_count:05d}] "
                f"scene_id={scene_ids[idx]} | "
                f"sent_id={sent_ids[idx]} | "
                f"target={targets[idx]} | "
                f"IoU={iou:.4f} | "
                f"J@1={sample_j[0]:.4f} | "
                f"J@5={sample_j[1]:.4f} | "
                f"GTsum={gt_mask_sum:.1f} | "
                f"Predsum={pred_mask_sum:.1f} | "
                f"Inter={inter_sum:.1f} | "
                f"Union={union_sum:.1f} | "
                f"bbox={bboxes[idx]} | "
                f"img={img_paths[idx]} | "
                f"text={sentences[idx]}"
            )

    J_index = [0, 0]
    for i in range(len(num_grasps)):
        J_index[i] = num_correct_grasps[i] / max(num_total_grasps[i], 1)

    iou_list = np.stack(iou_list)
    iou_list = torch.from_numpy(iou_list).to(image.device)

    prec_list = []
    for thres in torch.arange(0.5, 1.0, 0.1):
        tmp = (iou_list > thres).float().mean()
        prec_list.append(tmp)

    iou = iou_list.mean()
    prec = {}
    temp = '  '
    for i, thres in enumerate(range(5, 10)):
        key = 'Pr@{}'.format(thres * 10)
        value = prec_list[i].item()
        prec[key] = value
        temp += "{}: {:.2f}  ".format(key, 100. * value)

    head = 'Evaluation: Epoch=[{}/{}]  IoU={:.2f}  J_index@1: {:.2f}  J_index@5: {:.2f}'.format(
        0, getattr(args, 'epochs', 0), 100. * iou.item(), 100. * J_index[0], 100. * J_index[1]
    )
    logger.info(head + temp)

    return iou.item(), prec, J_index