Cannot train on custom dataset

hoangthienan95 commented 4 years ago

Hi I'm pretty new to CV and DL. I'm trying to get my dataset to work with YOLACT to compare with Mask R-CNN. I followed the steps in the repo and my folder is here on github with only 10 train and 1 test images as sample. Would you mind help pointing me to what I need to do?

**My `config.py` changes**

```python IMAT_CLASSES = ('shirt, blouse', 'top, t-shirt, sweatshirt', 'sweater', 'cardigan', 'jacket', 'vest', 'pants', 'shorts', 'skirt', 'coat', 'dress', 'jumpsuit', 'cape', 'glasses', 'hat', 'headband, head covering, hair accessory', 'tie', 'glove', 'watch', 'belt', 'leg warmer', 'tights, stockings', 'sock', 'shoe', 'bag, wallet', 'scarf', 'umbrella', 'hood', 'collar', 'lapel', 'epaulette', 'sleeve', 'pocket', 'neckline', 'buckle', 'zipper', 'applique', 'bead', 'bow', 'flower', 'fringe', 'ribbon', 'rivet', 'ruffle', 'sequin', 'tassel') imaterialist_dataset = dataset_base.copy({ 'name': 'imaterialist', 'train_images': '../coco_format_train', 'train_info': '../fashion_train_dataset_10k.json', 'valid_images': '../coco_format_test', 'valid_info': '../fashion_test_dataset_10k.json', 'has_gt': True, 'class_names': IMAT_CLASSES, }) ... yolact_base_config = coco_base_config.copy({ 'name': 'yolact_base', # Dataset stuff 'dataset': imaterialist_dataset,#coco2017_dataset, 'num_classes': len(imaterialist_dataset.class_names) + 1 ... ```

Try to go from pretrained YOLACT weights on COCO: python train.py --config=yolact_base_config --resume=weights/yolact_resnet50_54_800000.pth --start_iter=-1

Error

```python ... index created! Resuming training, loading weights/yolact_resnet50_54_800000.pth... Traceback (most recent call last): File "train.py", line 501, in train() File "train.py", line 204, in train yolact_net.load_weights(args.resume) File "/home/ec2-user/cv-exp/for_vivek_train/yolact/yolact.py", line 527, in load_weights self.load_state_dict(state_dict) File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/nn/modules/module.py", line 839, in load_state_dict self.__class__.__name__, "\n\t".join(error_msgs))) RuntimeError: Error(s) in loading state_dict for Yolact: Missing key(s) in state_dict: "backbone.layers.2.6.conv1.weight", "backbone.layers.2.6.bn1.weight", "backbone.layers.2.6.bn1.bias", "backbone.layers.2.6.bn1.running_mean", "backbone.layers.2.6.bn1.running_var", "backbone.layers.2.6.conv2.weight", "backbone.layers.2.6.bn2.weight", "backbone.layers.2.6.bn2.bias", "backbone.layers.2.6.bn2.running_mean", "backbone.layers.2.6.bn2.running_var", "backbone.layers.2.6.conv3.weight", "backbone.layers.2.6.bn3.weight", "backbone.layers.2.6.bn3.bias", "backbone.layers.2.6.bn3.running_mean", "backbone.layers.2.6.bn3.running_var", "backbone.layers.2.7.conv1.weight", "backbone.layers.2.7.bn1.weight", "backbone.layers.2.7.bn1.bias", "backbone.layers.2.7.bn1.running_mean", "backbone.layers.2.7.bn1.running_var", "backbone.layers.2.7.conv2.weight", "backbone.layers.2.7.bn2.weight", "backbone.layers.2.7.bn2.bias", "backbone.layers.2.7.bn2.running_mean", "backbone.layers.2.7.bn2.running_var", "backbone.layers.2.7.conv3.weight", "backbone.layers.2.7.bn3.weight", "backbone.layers.2.7.bn3.bias", "backbone.layers.2.7.bn3.running_mean", "backbone.layers.2.7.bn3.running_var", "backbone.layers.2.8.conv1.weight", "backbone.layers.2.8.bn1.weight", "backbone.layers.2.8.bn1.bias", "backbone.layers.2.8.bn1.running_mean", "backbone.layers.2.8.bn1.running_var", "backbone.layers.2.8.conv2.weight", "backbone.layers.2.8.bn2.weight", "backbone.layers.2.8.bn2.bias", "backbone.layers.2.8.bn2.running_mean", "backbone.layers.2.8.bn2.running_var", "backbone.layers.2.8.conv3.weight", "backbone.layers.2.8.bn3.weight", "backbone.layers.2.8.bn3.bias", "backbone.layers.2.8.bn3.running_mean", "backbone.layers.2.8.bn3.running_var", "backbone.layers.2.9.conv1.weight", "backbone.layers.2.9.bn1.weight", "backbone.layers.2.9.bn1.bias", "backbone.layers.2.9.bn1.running_mean", "backbone.layers.2.9.bn1.running_var", "backbone.layers.2.9.conv2.weight", "backbone.layers.2.9.bn2.weight", "backbone.layers.2.9.bn2.bias", "backbone.layers.2.9.bn2.running_mean", "backbone.layers.2.9.bn2.running_var", "backbone.layers.2.9.conv3.weight", "backbone.layers.2.9.bn3.weight", "backbone.layers.2.9.bn3.bias", "backbone.layers.2.9.bn3.running_mean", "backbone.layers.2.9.bn3.running_var", "backbone.layers.2.10.conv1.weight", "backbone.layers.2.10.bn1.weight", "backbone.layers.2.10.bn1.bias", "backbone.layers.2.10.bn1.running_mean", "backbone.layers.2.10.bn1.running_var", "backbone.layers.2.10.conv2.weight", "backbone.layers.2.10.bn2.weight", "backbone.layers.2.10.bn2.bias", "backbone.layers.2.10.bn2.running_mean", "backbone.layers.2.10.bn2.running_var", "backbone.layers.2.10.conv3.weight", "backbone.layers.2.10.bn3.weight", "backbone.layers.2.10.bn3.bias", "backbone.layers.2.10.bn3.running_mean", "backbone.layers.2.10.bn3.running_var", "backbone.layers.2.11.conv1.weight", "backbone.layers.2.11.bn1.weight", "backbone.layers.2.11.bn1.bias", "backbone.layers.2.11.bn1.running_mean", "backbone.layers.2.11.bn1.running_var", "backbone.layers.2.11.conv2.weight", "backbone.layers.2.11.bn2.weight", "backbone.layers.2.11.bn2.bias", "backbone.layers.2.11.bn2.running_mean", "backbone.layers.2.11.bn2.running_var", "backbone.layers.2.11.conv3.weight", "backbone.layers.2.11.bn3.weight", "backbone.layers.2.11.bn3.bias", "backbone.layers.2.11.bn3.running_mean", "backbone.layers.2.11.bn3.running_var", "backbone.layers.2.12.conv1.weight", "backbone.layers.2.12.bn1.weight", "backbone.layers.2.12.bn1.bias", "backbone.layers.2.12.bn1.running_mean", "backbone.layers.2.12.bn1.running_var", "backbone.layers.2.12.conv2.weight", "backbone.layers.2.12.bn2.weight", "backbone.layers.2.12.bn2.bias", "backbone.layers.2.12.bn2.running_mean", "backbone.layers.2.12.bn2.running_var", "backbone.layers.2.12.conv3.weight", "backbone.layers.2.12.bn3.weight", "backbone.layers.2.12.bn3.bias", "backbone.layers.2.12.bn3.running_mean", "backbone.layers.2.12.bn3.running_var", "backbone.layers.2.13.conv1.weight", "backbone.layers.2.13.bn1.weight", "backbone.layers.2.13.bn1.bias", "backbone.layers.2.13.bn1.running_mean", "backbone.layers.2.13.bn1.running_var", "backbone.layers.2.13.conv2.weight", "backbone.layers.2.13.bn2.weight", "backbone.layers.2.13.bn2.bias", "backbone.layers.2.13.bn2.running_mean", "backbone.layers.2.13.bn2.running_var", "backbone.layers.2.13.conv3.weight", "backbone.layers.2.13.bn3.weight", "backbone.layers.2.13.bn3.bias", "backbone.layers.2.13.bn3.running_mean", "backbone.layers.2.13.bn3.running_var", "backbone.layers.2.14.conv1.weight", "backbone.layers.2.14.bn1.weight", "backbone.layers.2.14.bn1.bias", "backbone.layers.2.14.bn1.running_mean", "backbone.layers.2.14.bn1.running_var", "backbone.layers.2.14.conv2.weight", "backbone.layers.2.14.bn2.weight", "backbone.layers.2.14.bn2.bias", "backbone.layers.2.14.bn2.running_mean", "backbone.layers.2.14.bn2.running_var", "backbone.layers.2.14.conv3.weight", "backbone.layers.2.14.bn3.weight", "backbone.layers.2.14.bn3.bias", "backbone.layers.2.14.bn3.running_mean", "backbone.layers.2.14.bn3.running_var", "backbone.layers.2.15.conv1.weight", "backbone.layers.2.15.bn1.weight", "backbone.layers.2.15.bn1.bias", "backbone.layers.2.15.bn1.running_mean", "backbone.layers.2.15.bn1.running_var", "backbone.layers.2.15.conv2.weight", "backbone.layers.2.15.bn2.weight", "backbone.layers.2.15.bn2.bias", "backbone.layers.2.15.bn2.running_mean", "backbone.layers.2.15.bn2.running_var", "backbone.layers.2.15.conv3.weight", "backbone.layers.2.15.bn3.weight", "backbone.layers.2.15.bn3.bias", "backbone.layers.2.15.bn3.running_mean", "backbone.layers.2.15.bn3.running_var", "backbone.layers.2.16.conv1.weight", "backbone.layers.2.16.bn1.weight", "backbone.layers.2.16.bn1.bias", "backbone.layers.2.16.bn1.running_mean", "backbone.layers.2.16.bn1.running_var", "backbone.layers.2.16.conv2.weight", "backbone.layers.2.16.bn2.weight", "backbone.layers.2.16.bn2.bias", "backbone.layers.2.16.bn2.running_mean", "backbone.layers.2.16.bn2.running_var", "backbone.layers.2.16.conv3.weight", "backbone.layers.2.16.bn3.weight", "backbone.layers.2.16.bn3.bias", "backbone.layers.2.16.bn3.running_mean", "backbone.layers.2.16.bn3.running_var", "backbone.layers.2.17.conv1.weight", "backbone.layers.2.17.bn1.weight", "backbone.layers.2.17.bn1.bias", "backbone.layers.2.17.bn1.running_mean", "backbone.layers.2.17.bn1.running_var", "backbone.layers.2.17.conv2.weight", "backbone.layers.2.17.bn2.weight", "backbone.layers.2.17.bn2.bias", "backbone.layers.2.17.bn2.running_mean", "backbone.layers.2.17.bn2.running_var", "backbone.layers.2.17.conv3.weight", "backbone.layers.2.17.bn3.weight", "backbone.layers.2.17.bn3.bias", "backbone.layers.2.17.bn3.running_mean", "backbone.layers.2.17.bn3.running_var", "backbone.layers.2.18.conv1.weight", "backbone.layers.2.18.bn1.weight", "backbone.layers.2.18.bn1.bias", "backbone.layers.2.18.bn1.running_mean", "backbone.layers.2.18.bn1.running_var", "backbone.layers.2.18.conv2.weight", "backbone.layers.2.18.bn2.weight", "backbone.layers.2.18.bn2.bias", "backbone.layers.2.18.bn2.running_mean", "backbone.layers.2.18.bn2.running_var", "backbone.layers.2.18.conv3.weight", "backbone.layers.2.18.bn3.weight", "backbone.layers.2.18.bn3.bias", "backbone.layers.2.18.bn3.running_mean", "backbone.layers.2.18.bn3.running_var", "backbone.layers.2.19.conv1.weight", "backbone.layers.2.19.bn1.weight", "backbone.layers.2.19.bn1.bias", "backbone.layers.2.19.bn1.running_mean", "backbone.layers.2.19.bn1.running_var", "backbone.layers.2.19.conv2.weight", "backbone.layers.2.19.bn2.weight", "backbone.layers.2.19.bn2.bias", "backbone.layers.2.19.bn2.running_mean", "backbone.layers.2.19.bn2.running_var", "backbone.layers.2.19.conv3.weight", "backbone.layers.2.19.bn3.weight", "backbone.layers.2.19.bn3.bias", "backbone.layers.2.19.bn3.running_mean", "backbone.layers.2.19.bn3.running_var", "backbone.layers.2.20.conv1.weight", "backbone.layers.2.20.bn1.weight", "backbone.layers.2.20.bn1.bias", "backbone.layers.2.20.bn1.running_mean", "backbone.layers.2.20.bn1.running_var", "backbone.layers.2.20.conv2.weight", "backbone.layers.2.20.bn2.weight", "backbone.layers.2.20.bn2.bias", "backbone.layers.2.20.bn2.running_mean", "backbone.layers.2.20.bn2.running_var", "backbone.layers.2.20.conv3.weight", "backbone.layers.2.20.bn3.weight", "backbone.layers.2.20.bn3.bias", "backbone.layers.2.20.bn3.running_mean", "backbone.layers.2.20.bn3.running_var", "backbone.layers.2.21.conv1.weight", "backbone.layers.2.21.bn1.weight", "backbone.layers.2.21.bn1.bias", "backbone.layers.2.21.bn1.running_mean", "backbone.layers.2.21.bn1.running_var", "backbone.layers.2.21.conv2.weight", "backbone.layers.2.21.bn2.weight", "backbone.layers.2.21.bn2.bias", "backbone.layers.2.21.bn2.running_mean", "backbone.layers.2.21.bn2.running_var", "backbone.layers.2.21.conv3.weight", "backbone.layers.2.21.bn3.weight", "backbone.layers.2.21.bn3.bias", "backbone.layers.2.21.bn3.running_mean", "backbone.layers.2.21.bn3.running_var", "backbone.layers.2.22.conv1.weight", "backbone.layers.2.22.bn1.weight", "backbone.layers.2.22.bn1.bias", "backbone.layers.2.22.bn1.running_mean", "backbone.layers.2.22.bn1.running_var", "backbone.layers.2.22.conv2.weight", "backbone.layers.2.22.bn2.weight", "backbone.layers.2.22.bn2.bias", "backbone.layers.2.22.bn2.running_mean", "backbone.layers.2.22.bn2.running_var", "backbone.layers.2.22.conv3.weight", "backbone.layers.2.22.bn3.weight", "backbone.layers.2.22.bn3.bias", "backbone.layers.2.22.bn3.running_mean", "backbone.layers.2.22.bn3.running_var". size mismatch for prediction_layers.0.conf_layer.weight: copying a param with shape torch.Size([243, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([141, 256, 3, 3]). size mismatch for prediction_layers.0.conf_layer.bias: copying a param with shape torch.Size([243]) from checkpoint, the shape in current model is torch.Size([141]). size mismatch for semantic_seg_conv.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([46, 256, 1, 1]). size mismatch for semantic_seg_conv.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([46]). ```

Try to train from scratch: python train.py --config=yolact_base_config (I think this error is because of malformatted json file but I couldn't find where I went wrong...)

Error

```python ... index created! Initializing weights... Begin training! Traceback (most recent call last): File "train.py", line 502, in train() File "train.py", line 268, in train for datum in data_loader: File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 819, in __next__ return self._process_data(data) File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 846, in _process_data data.reraise() File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/_utils.py", line 385, in reraise raise self.exc_type(msg) KeyError: Caught KeyError in DataLoader worker process 0. Original Traceback (most recent call last): File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/worker.py", line 178, in _worker_loop data = fetcher.fetch(index) File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in fetch data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/fetch.py", line 44, in data = [self.dataset[idx] for idx in possibly_batched_index] File "/home/ec2-user/cv-exp/for_vivek_train/yolact/data/coco.py", line 91, in __getitem__ im, gt, masks, h, w, num_crowds = self.pull_item(index) File "/home/ec2-user/cv-exp/for_vivek_train/yolact/data/coco.py", line 130, in pull_item file_name = self.coco.loadImgs(img_id)[0]['file_name'] File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/coco.py", line 231, in loadImgs return [self.imgs[id] for id in ids] File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/coco.py", line 231, in return [self.imgs[id] for id in ids] KeyError: 'd' ```

ljjyxz123 commented 4 years ago

@hoangthienan95

Did you create your own dataset according to COCO annotation format? It means that in your annotation file, you should generate it with the same format as COCO.
Did you change the directory to your own dataset?

hoangthienan95 commented 4 years ago

@ljjyxz123 1.yes I did. My json is here: https://raw.githubusercontent.com/hoangthienan95/computer-vision-project/master/an/for_vivek_train/fashion_test_dataset_10k.json

Yes, my config.py changes are shown above, and my entire folder structure is here, if you want to reproduce the error: https://github.com/hoangthienan95/computer-vision-project/tree/master/an/for_vivek_train

dbolya commented 4 years ago

Your image ids should be numbers--pretty sure that's why you're getting the second error (right now you have image ids as file names). Preferably sequential starting from 1, but I'm not sure what the acceptable range of values is for pycocotools. Other things may be wrong, but that's what immediately stood out to me.

As for the first error, apply the change in #36. Those errors are expected because the number of classes changed, and you can ignore them with the code from that issue.

hoangthienan95 commented 4 years ago

Hi @dbolya thanks so much for the quick reply! I recreated the json file but ran into another error: Command: python train.py --config=yolact_base_config --batch_size 1 (have to put batch size =1 due to only 2 test images or else division by zero error)

Initializing weights...
Begin training!
ERROR: Unexpected segmentation fault encountered in worker.
Traceback (most recent call last):
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 724, in _try_get_data
    data = self._data_queue.get(timeout=timeout)
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/queue.py", line 173, in get
    self.not_empty.wait(remaining)
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/threading.py", line 299, in wait
    gotit = waiter.acquire(True, timeout)
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/_utils/signal_handling.py", line 66, in handler
    _error_if_any_worker_fails()
RuntimeError: DataLoader worker (pid 5348) is killed by signal: Segmentation fault. 

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "train.py", line 501, in <module>
    train()
  File "train.py", line 267, in train
    for datum in data_loader:
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 804, in __next__
    idx, data = self._get_data()
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 761, in _get_data
    success, data = self._try_get_data()
  File "/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 737, in _try_get_data
    raise RuntimeError('DataLoader worker (pid(s) {}) exited unexpectedly'.format(pids_str))
RuntimeError: DataLoader worker (pid(s) 5348) exited unexpectedly

When I change --numworkers to 0: python train.py --config=yolact_base_config --batch_size 1 --num_workers 0

Initializing weights...
Begin training!

Segmentation fault

I'm using AWS g3s.xlarge instance so there should be plenty of GPU memory for one image. Do you have any insights?

dbolya commented 4 years ago

You're using all the GPUs on the system with a batch size of 1. That means 1 GPU is is getting 1 image and the rest are getting 0 (hence the div by 0 / segfaults). To train with all your GPUs, just change the batch size to 6-8 * num_gpus (whatever fits). Otherwise, train with one GPU by running "export CUDA_VISIBLE_DEVICES=0" (or the idx of the desired GPU) first.

hoangthienan95 commented 4 years ago

Thank you, I did "export CUDA_VISIBLE_DEVICES=0" since my instance only have 1 GPU and still have errors: python train.py --config=yolact_base_config --num_workers 0 and python train.py --config=yolact_base_config

Error

```python loading annotations into memory... Done (t=0.06s) creating index... index created! loading annotations into memory... Done (t=0.02s) creating index... index created! Initializing weights... Begin training! *** Error in `python': malloc(): memory corruption: 0x0000563e5e6d6150 *** ======= Backtrace: ========= /lib64/libc.so.6(+0x82d06)[0x7fd3ea22bd06] /lib64/libc.so.6(+0x83226)[0x7fd3ea22c226] /lib64/libc.so.6(__libc_memalign+0x75)[0x7fd3ea22eb05] /lib64/libc.so.6(posix_memalign+0x4c)[0x7fd3ea23078c] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x2058f)[0x7fd3e24f458f] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x658b8)[0x7fd3e25398b8] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x126a4d)[0x7fd3e25faa4d] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x1000ae)[0x7fd3e25d40ae] python(PyCFunction_Call+0xc6)[0x563e0916f9f6] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so(+0xc13e)[0x7fd37e7a613e] python(_PyCFunction_FastCallDict+0x19a)[0x563e0916cbda] python(_PyObject_FastCallDict+0x2bf)[0x563e0916cfaf] python(+0x91656)[0x563e090ef656] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x64c55)[0x7fd3e2538c55] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x68dc2)[0x7fd3e253cdc2] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x6921c)[0x7fd3e253d21c] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x695af)[0x7fd3e253d5af] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/core/multiarray.cpython-36m-x86_64-linux-gnu.so(+0x10825a)[0x7fd3e25dc25a] python(PyCFunction_Call+0xc6)[0x563e0916f9f6] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so(+0x9bb9)[0x7fd37e7a3bb9] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so(+0x5f09)[0x7fd37e79ff09] /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/pycocotools/_mask.cpython-36m-x86_64-linux-gnu.so(+0x1a5dd)[0x7fd37e7b45dd] python(_PyCFunction_FastCallDict+0x115)[0x563e0916cb55] python(+0x19e67c)[0x563e091fc67c] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(+0x19870b)[0x563e091f670b] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(+0x19870b)[0x563e091f670b] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(+0x197c26)[0x563e091f5c26] python(+0x198941)[0x563e091f6941] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(+0x197dae)[0x563e091f5dae] python(+0x198941)[0x563e091f6941] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(_PyFunction_FastCallDict+0x11b)[0x563e091f6d7b] python(_PyObject_FastCallDict+0x26f)[0x563e0916cf5f] python(_PyObject_Call_Prepend+0x63)[0x563e09171a03] python(PyObject_Call+0x3e)[0x563e0916c99e] python(+0x16b439)[0x563e091c9439] python(_PyEval_EvalFrameDefault+0x862)[0x563e0921f222] python(+0x197c26)[0x563e091f5c26] python(+0x198941)[0x563e091f6941] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(+0x197dae)[0x563e091f5dae] python(+0x198941)[0x563e091f6941] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] python(_PyFunction_FastCallDict+0x11b)[0x563e091f6d7b] python(_PyObject_FastCallDict+0x26f)[0x563e0916cf5f] python(_PyObject_Call_Prepend+0x63)[0x563e09171a03] python(_PyObject_FastCallDict+0x8b)[0x563e0916cd7b] python(+0x16b72f)[0x563e091c972f] python(_PyEval_EvalFrameDefault+0x776)[0x563e0921f136] python(+0x197dae)[0x563e091f5dae] python(+0x198941)[0x563e091f6941] python(+0x19e755)[0x563e091fc755] python(_PyEval_EvalFrameDefault+0x2fa)[0x563e0921ecba] ======= Memory map: ======== 200000000-200100000 rw-s 00000000 00:06 12944 /dev/nvidiactl 200100000-200300000 rw-s 00000000 00:06 12944 /dev/nvidiactl 200300000-202b00000 rw-s 00000000 00:06 12944 /dev/nvidiactl 202b00000-202c00000 rw-s 00000000 00:06 12944 /dev/nvidiactl 202c00000-202d00000 rw-s 00000000 00:05 22454 /dev/zero (deleted) 202d00000-202e00000 rw-s 00000000 00:06 12944 /dev/nvidiactl 202e00000-202f00000 rw-s 00000000 00:05 22455 /dev/zero (deleted) 202f00000-203000000 rw-s 00000000 00:06 12944 /dev/nvidiactl 203000000-203120000 ---p 00000000 00:00 0 203120000-203220000 rw-s 00000000 00:06 12944 /dev/nvidiactl 203220000-203300000 rw-s 00000000 00:06 12944 /dev/nvidiactl 203300000-203400000 rw-s 00000000 00:05 22458 /dev/zero (deleted) 203400000-203500000 rw-s 00000000 00:05 22459 /dev/zero (deleted) 203500000-e00000000 ---p 00000000 00:00 0 563e0905e000-563e0931d000 r-xp 00000000 ca:01 3690005 /home/ec2-user/anaconda3/envs/pytorch_p36/bin/python3.6 563e0951c000-563e0951f000 r--p 002be000 ca:01 3690005 /home/ec2-user/anaconda3/envs/pytorch_p36/bin/python3.6 563e0951f000-563e09582000 rw-p 002c1000 ca:01 3690005 /home/ec2-user/anaconda3/envs/pytorch_p36/bin/python3.6 563e09582000-563e095b3000 rw-p 00000000 00:00 0 563e0a7f9000-563e647c6000 rw-p 00000000 00:00 0 [heap] 7fd358000000-7fd358021000 rw-p 00000000 00:00 0 7fd358021000-7fd35c000000 ---p 00000000 00:00 0 7fd35c8c6000-7fd35e463000 rw-p 00000000 00:00 0 7fd360000000-7fd360001000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360001000-7fd360002000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360002000-7fd360003000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360003000-7fd360004000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360004000-7fd360005000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360005000-7fd360006000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360006000-7fd360007000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360007000-7fd360008000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360008000-7fd360009000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360009000-7fd36000a000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000a000-7fd36000b000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000b000-7fd36000c000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000c000-7fd36000d000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000d000-7fd36000e000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000e000-7fd36000f000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd36000f000-7fd360010000 rw-s 00000000 00:06 12945 /dev/nvidia0 7fd360010000-7fd370000000 ---p 00000000 00:00 0 7fd370057000-7fd371256000 r-xp 00000000 ca:01 2913250 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/libnvrtc.so.10.1.243 7fd371256000-7fd371455000 ---p 011ff000 ca:01 2913250 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/libnvrtc.so.10.1.243 7fd371455000-7fd3716d8000 r--p 011fe000 ca:01 2913250 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/libnvrtc.so.10.1.243 7fd3716d8000-7fd37171f000 rw-p 01481000 ca:01 2913250 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/libnvrtc.so.10.1.243 7fd37171f000-7fd3717c7000 rw-p 00000000 00:00 0 7fd3717c7000-7fd3717c8000 rw-p 014c8000 ca:01 2913250 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/libnvrtc.so.10.1.243 7fd3717c8000-7fd371f22000 rw-p 00000000 00:00 0 7fd37267c000-7fd372d29000 rw-p 00000000 00:00 0 7fd3733d6000-7fd3739eb000 rw-p 00000000 00:00 0 7fd374000000-7fd374021000 rw-p 00000000 00:00 0 7fd374021000-7fd378000000 ---p 00000000 00:00 0 7fd378150000-7fd3784ee000 rw-p 00000000 00:00 0 7fd378689000-7fd37868a000 r-xp 00000000 ca:01 4473588 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/lib/libcaffe2_nvrtc.so 7fd37868a000-7fd37888a000 ---p 00001000 ca:01 4473588 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/lib/libcaffe2_nvrtc.so 7fd37888a000-7fd37888b000 r--p 00001000 ca:01 4473588 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/lib/libcaffe2_nvrtc.so 7fd37888b000-7fd37888c000 rw-p 00002000 ca:01 4473588 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/torch/lib/libcaffe2_nvrtc.so 7fd37888c000-7fd378c24000 rw-p 00000000 00:00 0 7fd378dbc000-7fd378fbc000 rw-p 00000000 00:00 0 7fd378fbc000-7fd3790bc000 rw-s 00000000 00:05 22456 /dev/zero (deleted) 7fd3790bc000-7fd3790bd000 ---p 00000000 00:00 0 7fd3790bd000-7fd37a13d000 rw-p 00000000 00:00 0 7fd37a15b000-7fd37a79b000 rw-p 00000000 00:00 0 7fd37a79b000-7fd37a7a0000 r-xp 00000000 ca:01 2887996 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_lsprof.cpython-36m-x86_64-linux-gnu.so 7fd37a7a0000-7fd37a99f000 ---p 00005000 ca:01 2887996 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_lsprof.cpython-36m-x86_64-linux-gnu.so 7fd37a99f000-7fd37a9a0000 r--p 00004000 ca:01 2887996 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_lsprof.cpython-36m-x86_64-linux-gnu.so 7fd37a9a0000-7fd37a9a1000 rw-p 00005000 ca:01 2887996 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_lsprof.cpython-36m-x86_64-linux-gnu.so 7fd37a9a1000-7fd37a9e1000 rw-p 00000000 00:00 0 7fd37a9e1000-7fd37a9e2000 ---p 00000000 00:00 0 7fd37a9e2000-7fd37b1e2000 rw-p 00000000 00:00 0 7fd37b1e2000-7fd37b229000 r-xp 00000000 ca:01 20768 /usr/lib64/libnvidia-fatbinaryloader.so.418.87.01 7fd37b229000-7fd37b429000 ---p 00047000 ca:01 20768 /usr/lib64/libnvidia-fatbinaryloader.so.418.87.01 7fd37b429000-7fd37b42b000 rw-p 00047000 ca:01 20768 /usr/lib64/libnvidia-fatbinaryloader.so.418.87.01 7fd37b42b000-7fd37b430000 rw-p 00000000 00:00 0 7fd37b430000-7fd37c21e000 r-xp 00000000 ca:01 20762 /usr/lib64/libcuda.so.418.87.01 7fd37c21e000-7fd37c41d000 ---p 00dee000 ca:01 20762 /usr/lib64/libcuda.so.418.87.01 7fd37c41d000-7fd37c597000 rw-p 00ded000 ca:01 20762 /usr/lib64/libcuda.so.418.87.01 7fd37c597000-7fd37c5a7000 rw-p 00000000 00:00 0 7fd37c5b4000-7fd37c5f4000 rw-p 00000000 00:00 0 7fd37c5f4000-7fd37c601000 r--p 00000000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c601000-7fd37c63f000 r-xp 0000d000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c63f000-7fd37c645000 r--p 0004b000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c645000-7fd37c646000 ---p 00051000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c646000-7fd37c647000 r--p 00051000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c647000-7fd37c648000 rw-p 00052000 ca:01 4343329 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/backends/_backend_agg.cpython-36m-x86_64-linux-gnu.so 7fd37c648000-7fd37c749000 rw-p 00000000 00:00 0 7fd37c749000-7fd37c754000 r--p 00000000 ca:01 4343330 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_qhull.cpython-36m-x86_64-linux-gnu.so 7fd37c754000-7fd37c78f000 r-xp 0000b000 ca:01 4343330 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_qhull.cpython-36m-x86_64-linux-gnu.so 7fd37c78f000-7fd37c7a8000 r--p 00046000 ca:01 4343330 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_qhull.cpython-36m-x86_64-linux-gnu.so 7fd37c7a8000-7fd37c7aa000 r--p 0005e000 ca:01 4343330 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_qhull.cpython-36m-x86_64-linux-gnu.so 7fd37c7aa000-7fd37c7ab000 rw-p 00060000 ca:01 4343330 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_qhull.cpython-36m-x86_64-linux-gnu.so 7fd37c7ab000-7fd37c7ad000 rw-p 00000000 00:00 0 7fd37c7ad000-7fd37c7b5000 r--p 00000000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7b5000-7fd37c7c1000 r-xp 00008000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7c1000-7fd37c7c5000 r--p 00014000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7c5000-7fd37c7c6000 ---p 00018000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7c6000-7fd37c7c7000 r--p 00018000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7c7000-7fd37c7c8000 rw-p 00019000 ca:01 4343326 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_tri.cpython-36m-x86_64-linux-gnu.so 7fd37c7c8000-7fd37c808000 rw-p 00000000 00:00 0 7fd37c808000-7fd37c80f000 r-xp 00000000 ca:01 2888104 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_csv.cpython-36m-x86_64-linux-gnu.so 7fd37c80f000-7fd37ca0f000 ---p 00007000 ca:01 2888104 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_csv.cpython-36m-x86_64-linux-gnu.so 7fd37ca0f000-7fd37ca10000 r--p 00007000 ca:01 2888104 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_csv.cpython-36m-x86_64-linux-gnu.so 7fd37ca10000-7fd37ca12000 rw-p 00008000 ca:01 2888104 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/lib-dynload/_csv.cpython-36m-x86_64-linux-gnu.so 7fd37ca12000-7fd37cb52000 rw-p 00000000 00:00 0 7fd37cb52000-7fd37cb5c000 r--p 00000000 ca:01 4343328 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_image.cpython-36m-x86_64-linux-gnu.so 7fd37cb5c000-7fd37cb86000 r-xp 0000a000 ca:01 4343328 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/matplotlib/_image.cpython-36m-x86_64-linux-gnu.so 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/home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/_mklinit.cpython-36m-x86_64-linux-gnu.so 7fd3ea9a7000-7fd3ea9a8000 r--p 00002000 ca:01 4607685 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/_mklinit.cpython-36m-x86_64-linux-gnu.so 7fd3ea9a8000-7fd3ea9a9000 r--p 00002000 ca:01 4607685 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/_mklinit.cpython-36m-x86_64-linux-gnu.so 7fd3ea9a9000-7fd3ea9aa000 rw-p 00003000 ca:01 4607685 /home/ec2-user/anaconda3/envs/pytorch_p36/lib/python3.6/site-packages/numpy/_mklinit.cpython-36m-x86_64-linux-gnu.so 7fd3ea9aa000-7fd3ea9ab000 rwxp 00000000 00:00 0 7fd3ea9ab000-7fd3ea9b2000 r--s 00000000 ca:01 3250 /usr/lib64/gconv/gconv-modules.cache 7fd3ea9b2000-7fd3ea9b3000 rw-p 00000000 00:00 0 7fd3ea9b3000-7fd3ea9b4000 r--p 00021000 ca:01 262733 /lib64/ld-2.17.so 7fd3ea9b4000-7fd3ea9b5000 rw-p 00022000 ca:01 262733 /lib64/ld-2.17.so 7fd3ea9b5000-7fd3ea9b6000 rw-p 00000000 00:00 0 7ffcae235000-7ffcae257000 rw-p 00000000 00:00 0 [stack] 7ffcae39c000-7ffcae39f000 r--p 00000000 00:00 0 [vvar] 7ffcae39f000-7ffcae3a1000 r-xp 00000000 00:00 0 [vdso] ffffffffff600000-ffffffffff601000 r-xp 00000000 00:00 0 [vsyscall] Aborted ```

hoangthienan95 commented 4 years ago

I will try with a larger EC2 instance but I doubt that this is caused by the images being too big.

dbolya commented 4 years ago

Sorry I misinterpreted your previous comment. Actually given that these seem to be issues with C (segfault, bad mallocs), maybe your environment is corrupt or something? Can you try setting up your environment again from scratch?

oggyfaker commented 4 years ago

@dbolya I train my custom data in yolact plus resnet50 , but it costs a lot of memory of my Ram :( , 32GB Ram is not enough for training. So do you have some way to solve this problem ? i changed batch size = 4 , workers = 1 , but not efficient . Please help me :(

NTS-wen commented 3 years ago

@oggyfaker Could you share your a json file in your dataset for me? I encountered the second problem similar to the above: Error(s) in loading state_dict for Yolact: size mismatch for maskiou_net.maskiou_net.10.weight: copying a param with shape torch.Size([80, 128, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 128, 1, 1]). size mismatch for maskiou_net.maskiou_net.10.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). size mismatch for prediction_layers.0.conf_layer.weight: copying a param with shape torch.Size([729, 256, 3, 3]) from checkpoint, the shape in current model is torch.Size([18, 256, 3, 3]). size mismatch for prediction_layers.0.conf_layer.bias: copying a param with shape torch.Size([729]) from checkpoint, the shape in current model is torch.Size([18]). size mismatch for semantic_seg_conv.weight: copying a param with shape torch.Size([80, 256, 1, 1]) from checkpoint, the shape in current model is torch.Size([1, 256, 1, 1]). size mismatch for semantic_seg_conv.bias: copying a param with shape torch.Size([80]) from checkpoint, the shape in current model is torch.Size([1]). Thanks a lot !

dbolya / yolact

Cannot train on custom dataset #236