deepalchemist / deep-person-search Goto Github PK

Traceback (most recent call last):
File "main.py", line 282, in
cfg = Config().parse()
File "/home/deep-person-search-master/lib/cfg/config.py", line 108, in parse
self.print_options(cfg)
File "/home/deep-person-search-master/lib/cfg/config.py", line 92, in print_options
shutil.copy("./run.sh", cfg.expr_dir)
File "/home/qd/anaconda3/envs/dps/lib/python3.6/shutil.py", line 245, in copy
copyfile(src, dst, follow_symlinks=follow_symlinks)
File "/home/qd/anaconda3/envs/dps/lib/python3.6/shutil.py", line 120, in copyfile
with open(src, 'rb') as fsrc:
FileNotFoundError: [Errno 2] No such file or directory: './run.sh'

I cannot find "run.sh" in your project, how to solve the problem?

Excuse me, in this code, the unrecognizable id is set to -1, and then these pictures are deleted during training. I found that during training, all the remaining frames (training and testing frames) are trained instead of The remaining frames in the training set. Why is that?

Hello! When I run the code, the CPU memory will keep increasing, and finally the memory will be exceeded, and the code will report an error. I tried to change the value of num_workers, but it didn't work.Do you know why this happens? What should I do?

When I train on the original PRW dataset, the same will appear
‘File "main.py", line 43, in train_one_epoch
_, losses = engine.optimize_parameters(cfg)
TypeError:'NoneType' object is not iterable
’
Why is that?
Looking forward to your reply

Hi,
during evaluation with default values I obtain the following result:

Experiment dir: /home/roberto/PersonReID/deep-person-search/20201004/test
Loaded dataset ssm_test for testing
6978 roidb entries
Backbone loading ImageNet pretrained weights from
./cache/pretrained_model/resnet50_caffe.pth
Engine [baseline] was created
Not loading checkpoint!
#ModelParams : 35.05 M
extracting probe features ...
im_exfeat: 2900/2900 0.008s
extracting gallery features ...
total time:1.297s, reid time:0.002s
evaluating detections
all detection:
recall = 0.00%
ap = 0.00%
/home/roberto/anaconda3/envs/dps/lib/python3.6/site-packages/sklearn/metrics/ranking.py:528: RuntimeWarning: invalid value encountered in true_divide
recall = tps / tps[-1]
labeled_only detection:
recall = 0.00%
ap = nan%
search ranking:
recall = 0.00%
mAP = 0.00%
top- 1 = 0.00%
top- 5 = 0.00%
top-10 = 0.00%
test time: 36.9min

The command used is:

CUDA_VISIBLE_DEVICES=0 python main.py --is_test --benchmark ssm --eval_batch_size 5 --backbone bsl --in_level C5 --cls_type oim

It is quite a strange result. What can be wrong?
Thanks

Is there any idea on the lower performance of NAE on PRW compared with the performance reported in original paper?

Hello, I would like to ask what is the main difference between the baseline you implemented and OIM and NAE?

Hi, I tested your pretrained model, and I got some amazing result.My test script was python main.py --is_test --benchmark prw --eval_batch_size 5 --backbone bsl --in_level C5 --cls_type oim --load_ckpt /workplace/.torch/checkpoints/prw-bsl.pth. And I got this.

Rank-1 is the top-1 in the picture? thanks.

因为数据集还在下载，还没测试

As the codes in lib/detector/getdet.py ln15, why do you set the conv1 froze?

# Fix blocks
for p in self.conv1.parameters(): p.requires_grad = False

Thanks for your explanation.

Hello! when I train the model with the option '-dis' ,the code will report an error

I have found a lot of solutions online but they didn't work.Do you know why this happens?
thanks!

OIM.py #152 detections, detector_losses = self.roi_heads(features, proposals, images.image_sizes, targets)

It seems that you forgot to return result when self.training=True
OIM.py #215

`
result, losses = [], {}
if self.training:
loss_detection, loss_box_reg = fastrcnn_loss(class_logits, box_regression,labels, regression_targets)

        loss_reid, acc = self.reid_loss(embeddings_, pids)

        losses = dict(loss_classifier=loss_detection,
                      loss_box_reg=loss_box_reg,
                      loss_ide=loss_reid)
    else:
        boxes, scores, embeddings, labels = \
            self._postprocess_detections(class_logits, box_regression, embeddings_,
                                         proposals, image_shapes)
        num_images = len(boxes)
        for i in range(num_images):
            result.append(
                dict(
                    boxes=boxes[i],
                    labels=labels[i],
                    scores=scores[i],
                    feats=embeddings[i],
                )
            )
    return result, losses

`

GPU:1080ti
CPU:64 core
pytorch 1.4.0
dataset: CUHK-SYSU

• #DetParams: 16.73M #reIDParams: 18.32M
  loading checkpoint ./checkpoints/20210409/ssmbsloimb5dowu/latest.pth

evaluating detections
all detection:
recall = 88.83%
ap = 84.76%
labeled_only detection:
recall = 99.20%
ap = 37.35%
search ranking:
recall = 99.18%
mAP = 90.06%
top- 1 = 90.83%
top- 5 = 96.62%
top-10 = 97.66%
Finished. Total elapsed time (h:m:s): 4:47:02
Experiment name: ssmbsloimb5dowu

The highest result of the benchmark network I trained was 90.83. When I train Baseline many times, most results are 89.* how to solve?