it will be appricated if the training code is completed!

[Glorainow]

The evaluate_crowd_no_overlap function is not implemented. When would this be completed?

Look forward to try APGCC!

[Yu-Zhouz]

Hello, recently I am also doing research work related to crowd counting, this is the evaluate_crowd_no_overlap function part that I provided for reference

`@torch.no_grad()
def evaluate_crowd_no_overlap(model, val_dl, device):
model.to(device)
model.eval()
# Inference is performed on all images to compute the MAE
maes = []
mses = []
counts_pred, counts_true = [], [] # Storage of projected and real numbers
img_id = [] # 创建储存图片id的列表
# Iterate through the validation dataset
for samples, targets in val_dl:
samples = samples.to(device)
outputs = model(samples)

    # Assuming that the model output contains the predicted points and corresponding scores
    outputs_points = outputs['pred_points'][0]
    outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]

    # Getting a count of real targets
    gt_cnt = targets[0]['point'].shape[0]

    # Filtering prediction points based on score thresholds
    threshold = 0.5
    points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
    predict_cnt = int((outputs_scores>threshold).sum())
    # accumulate MAE, MSE
    mae = abs(predict_cnt - gt_cnt)
    mse = (predict_cnt - gt_cnt) * (predict_cnt - gt_cnt)
    maes.append(float(mae))
    mses.append(float(mse))
    # Storage of projected and real numbers
    counts_pred.append(predict_cnt)
    counts_true.append(gt_cnt)
    img_id.append(int(targets[0]['image_id']))
# calc MAE, MSE
mae = np.mean(maes)
mse = np.sqrt(np.mean(mses))
# save_counts_to_file_sorted(f"{mse_dir}/counting_person_{mae:.2f}.txt", img_id, counts_pred)
return mae, mse`

[nice98k]

Hello, recently I am also doing research work related to crowd counting, this is the evaluate_crowd_no_overlap function part that I provided for reference

`@torch.no_grad() def evaluate_crowd_no_overlap(model, val_dl, device): model.to(device) model.eval() # Inference is performed on all images to compute the MAE maes = [] mses = [] counts_pred, counts_true = [], [] # Storage of projected and real numbers img_id = [] # 创建储存图片id的列表 # Iterate through the validation dataset for samples, targets in val_dl: samples = samples.to(device) outputs = model(samples)

    # Assuming that the model output contains the predicted points and corresponding scores
    outputs_points = outputs['pred_points'][0]
    outputs_scores = torch.nn.functional.softmax(outputs['pred_logits'], -1)[:, :, 1][0]

    # Getting a count of real targets
    gt_cnt = targets[0]['point'].shape[0]

    # Filtering prediction points based on score thresholds
    threshold = 0.5
    points = outputs_points[outputs_scores > threshold].detach().cpu().numpy().tolist()
    predict_cnt = int((outputs_scores>threshold).sum())
    # accumulate MAE, MSE
    mae = abs(predict_cnt - gt_cnt)
    mse = (predict_cnt - gt_cnt) * (predict_cnt - gt_cnt)
    maes.append(float(mae))
    mses.append(float(mse))
    # Storage of projected and real numbers
    counts_pred.append(predict_cnt)
    counts_true.append(gt_cnt)
    img_id.append(int(targets[0]['image_id']))
# calc MAE, MSE
mae = np.mean(maes)
mse = np.sqrt(np.mean(mses))
# save_counts_to_file_sorted(f"{mse_dir}/counting_person_{mae:.2f}.txt", img_id, counts_pred)
return mae, mse`

请问，你这部分代码能跑吗？结果如何？

[little-seasalt]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

[Yu-Zhouz]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

[nice98k]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

你好，能提供一下你跑通的代码吗？谢谢

[Yu-Zhouz]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

你好，能提供一下你跑通的代码吗？谢谢

在上述回答中，我已经提供了作者代码缺失的部分，可以尝试运行，如果有其他问题，可以再次联系我

[nice98k]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

你好，能提供一下你跑通的代码吗？谢谢

在上述回答中，我已经提供了作者代码缺失的部分，可以尝试运行，如果有其他问题，可以再次联系我
请问你跑的效果和作者相差多少?

[susu-source]

您好！
请问跑通代码都需要做那些修改呢

[UntrainedButLoveCode]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

你好，我也能跑通，但是作者是不是并没有完全实现auxiliary point的部分。并不是论文描述的代码吧？

[Indraa145]

请问大家，有人能跑通训练代码吗？可以交流一下吗？

我已经成功跑通，不过使用作者的参数得到的效果并不好，未达到论文中的效果

你好，我也能跑通，但是作者是不是并没有完全实现auxiliary point的部分。并不是论文描述的代码吧？

Without the auxiliary point code, the auxiliary loss cannot be calculated (loss_auxiliary) unless we ignore the loss like in the testing part. But if we ignore it during training, will the result be good? Did anyone try to train the model?