jackroos / vl-bert Goto Github PK
View Code? Open in Web Editor NEWCode for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".
License: MIT License
Code for ICLR 2020 paper "VL-BERT: Pre-training of Generic Visual-Linguistic Representations".
License: MIT License
Excellent work!
I used the script and https://github.com/jackroos/VL-BERT/blob/master/cfgs/pretrain/base_e2e_16x16G_fp16.yaml to pretrain,but can't be sure if the loss and accuracy are good enough.
When using my pre-trained model to finetune in VCR task, I cannot get the high score of using the shared pre-trained model.
I will appreciate for your share of pre-trained logs.
Thanks.
hi!
i want fine-tuning for vcr task!
Doesn't it offer a fine-tuned VCR model?
thank you :)!
Thank you for your brilliant work.
dataset.py
by assigning add_image_as_a_box=True
(e.g. VQA). In my understanding, your code utilizes this first box (whole image) for Visual Feature Embedding of words other than image regions (with [IMG]
token). However, it seems your code didn't remove this full image box after that so there's one additional (full image) box at the front of boxes
?[END]
(e.g. in class VisualLinguisticBert)? It neither used predefined [END]
token like [SEP]
and [CLS]
nor used the full image box as Visual Feature embedding. Did I miss anything here?Thanks for your great work!
And I wonder that how to get the test result directly, must I generate prediction results on test set for leaderboard submission?
Dear contributor,
Fantastic work on VL task!
Now I have some trouble showing the visualization results between language and ROIs. Could you please hit me your code to generate the result as attention_viz.png showed if possible?
Many thanks.
Hi, my system has CUDA 10.1, so I followed your setup instructions, but using conda install pytorch torchvision cudatoolkit=10.1 -c pytorch
. The rest of the installation went fine, but I got the following message when trying to test refcoco+:
ImportError: VL-BERT/refcoco/../common/lib/roi_pooling/C_ROIPooling.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN6caffe26detail36_typeMetaDataInstance_preallocated_7E
I think this could be due to the CUDA version mismatch. Do you know if the compiling scripts have to be run differently for CUDA 10.1?
Hi, there. I read some of your codes and have some questions about class FastRCNN in file './common/faster_rcnn.py'. As we can see, it's forward method has args like (self, images, boxes, box_mask, im_info, classes=None, segms=None, mvrc_ops=None, mask_visual_embed=None)
, but isn't it Faster-RCNN's work to get the RoIs, so why is there parameters like 'boxes' and so on?
如题
When I try training on VCR dataset with the comand ./scripts/dist_run_single.sh 1 vcr/train_end2end.py ./cfgs/vcr/base_q2a_4x16G_fp32.yaml ./
, I got an error like this:
Traceback (most recent call last):
File "vcr/train_end2end.py", line 59, in <module>
main()
File "vcr/train_end2end.py", line 53, in main
rank, model = train_net(args, config)
File "/gruntdata/guimin.gm/vlbert/vcr/../vcr/function/train.py", line 87, in train_net
group_name='mtorch')
File "/home/guimin.gm/miniconda3/envs/pt/lib/python3.6/site-packages/torch/distributed/distributed_c10d.py", line 406, in init_process_group
store, rank, world_size = next(rendezvous(url))
File "/home/guimin.gm/miniconda3/envs/pt/lib/python3.6/site-packages/torch/distributed/rendezvous.py", line 95, in _tcp_rendezvous_handler
store = TCPStore(result.hostname, result.port, world_size, start_daemon)
RuntimeError: Address already in use
Traceback (most recent call last):
File "./scripts/launch.py", line 200, in <module>
main()
File "./scripts/launch.py", line 196, in main
cmd=process.args)
subprocess.CalledProcessError: Command '['/home/guimin.gm/miniconda3/envs/pt/bin/python', '-u', 'vcr/train_end2end.py', '--cfg', './cfgs/vcr/base_q2a_4x16G_fp32.yaml', '--model-dir', './', '--dist']' returned non-zero exit status 1.
I haven`t found the solution, is there anybody can help me? Thanks a lot.
It seems that the setup.py only compiles roipooling but not roialign.
use VL-Bert as a generator for text or image or both?
Dear Author,
When I try to run " generate_tsv_v2_1.py", I meet the problem "Check failed: error == cudaSuccess (2 vs. 0) out of memory".
My GPU is 11G Tesla K40m. I have changed the batch_size to 1, but the problem is still there
Do you know how to solve the problem? Thanks a lot!
Thank you for your work, I would love to reference your VL-Bert in my network. Is there a VQA dataset word vector file saved in txt format?
Hi,
I'd like to take a look at the pretraining but I was wondering if there was a way to have access to the train_frcnn.zip files without having to run the caffe model, are they available somewhere?
Best,
I follow the document to install apex, then run scripts:
pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
and then got the error as follow:
/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/commands/install.py:244: UserWarning: Disabling all use of wheels due to the use of --build-option / --global-option / --install-option.
cmdoptions.check_install_build_global(options)
Non-user install because site-packages writeable
Created temporary directory: /tmp/pip-ephem-wheel-cache-3gl_tl_j
Created temporary directory: /tmp/pip-req-tracker-ctu9auww
Initialized build tracking at /tmp/pip-req-tracker-ctu9auww
Created build tracker: /tmp/pip-req-tracker-ctu9auww
Entered build tracker: /tmp/pip-req-tracker-ctu9auww
Created temporary directory: /tmp/pip-install-y028bhtk
Looking in indexes: https://mirrors.aliyun.com/pypi/simple
Processing /home/qzhang/projects/image.clef.project/VL-BERT/apex
Created temporary directory: /tmp/pip-req-build-p9qpie0o
Added file:///home/qzhang/projects/image.clef.project/VL-BERT/apex to build tracker '/tmp/pip-req-tracker-ctu9auww'
Running setup.py (path:/tmp/pip-req-build-p9qpie0o/setup.py) egg_info for package from file:///home/qzhang/projects/image.clef.project/VL-BERT/apex
Running command python setup.py egg_info
torch.__version__ = 1.1.0
running egg_info
creating /tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info
writing /tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/PKG-INFO
writing dependency_links to /tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/dependency_links.txt
writing top-level names to /tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/top_level.txt
writing manifest file '/tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/SOURCES.txt'
reading manifest file '/tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/SOURCES.txt'
writing manifest file '/tmp/pip-req-build-p9qpie0o/pip-egg-info/apex.egg-info/SOURCES.txt'
Source in /tmp/pip-req-build-p9qpie0o has version 0.1, which satisfies requirement apex==0.1 from file:///home/qzhang/projects/image.clef.project/VL-BERT/apex
Removed apex==0.1 from file:///home/qzhang/projects/image.clef.project/VL-BERT/apex from build tracker '/tmp/pip-req-tracker-ctu9auww'
Skipping wheel build for apex, due to binaries being disabled for it.
Installing collected packages: apex
Created temporary directory: /tmp/pip-record-htjucn0b
Running command /home/qzhang/anaconda3/envs/vl-bert/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-htjucn0b/install-record.txt --single-version-externally-managed --compile --install-headers /home/qzhang/anaconda3/envs/vl-bert/include/python3.6m/apex
torch.__version__ = 1.1.0
Compiling cuda extensions with
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2019 NVIDIA Corporation
Built on Wed_Oct_23_19:24:38_PDT_2019
Cuda compilation tools, release 10.2, V10.2.89
from /usr/local/cuda/bin
Traceback (most recent call last):
File "<string>", line 1, in <module>
File "/tmp/pip-req-build-p9qpie0o/setup.py", line 64, in <module>
check_cuda_torch_binary_vs_bare_metal(torch.utils.cpp_extension.CUDA_HOME)
File "/tmp/pip-req-build-p9qpie0o/setup.py", line 54, in check_cuda_torch_binary_vs_bare_metal
"https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. "
RuntimeError: Cuda extensions are being compiled with a version of Cuda that does not match the version used to compile Pytorch binaries. Pytorch binaries were compiled with Cuda 9.0.176.
In some cases, a minor-version mismatch will not cause later errors: https://github.com/NVIDIA/apex/pull/323#discussion_r287021798. You can try commenting out this check (at your own risk).
Running setup.py install for apex ... error
Cleaning up...
Removing source in /tmp/pip-req-build-p9qpie0o
Removed build tracker: '/tmp/pip-req-tracker-ctu9auww'
ERROR: Command errored out with exit status 1: /home/qzhang/anaconda3/envs/vl-bert/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-htjucn0b/install-record.txt --single-version-externally-managed --compile --install-headers /home/qzhang/anaconda3/envs/vl-bert/include/python3.6m/apex Check the logs for full command output.
Exception information:
Traceback (most recent call last):
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/cli/base_command.py", line 186, in _main
status = self.run(options, args)
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/commands/install.py", line 404, in run
use_user_site=options.use_user_site,
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/req/__init__.py", line 71, in install_given_reqs
**kwargs
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/req/req_install.py", line 829, in install
scheme=scheme,
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/operations/install/legacy.py", line 72, in install
cwd=install_req.unpacked_source_directory,
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/utils/subprocess.py", line 275, in runner
spinner=spinner,
File "/home/qzhang/anaconda3/envs/vl-bert/lib/python3.6/site-packages/pip/_internal/utils/subprocess.py", line 242, in call_subprocess
raise InstallationError(exc_msg)
pip._internal.exceptions.InstallationError: Command errored out with exit status 1: /home/qzhang/anaconda3/envs/vl-bert/bin/python -u -c 'import sys, setuptools, tokenize; sys.argv[0] = '"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"'; __file__='"'"'/tmp/pip-req-build-p9qpie0o/setup.py'"'"';f=getattr(tokenize, '"'"'open'"'"', open)(__file__);code=f.read().replace('"'"'\r\n'"'"', '"'"'\n'"'"');f.close();exec(compile(code, __file__, '"'"'exec'"'"'))' --cpp_ext --cuda_ext install --record /tmp/pip-record-htjucn0b/install-record.txt --single-version-externally-managed --compile --install-headers /home/qzhang/anaconda3/envs/vl-bert/include/python3.6m/apex Check the logs for full command output.
1 location(s) to search for versions of pip:
* https://mirrors.aliyun.com/pypi/simple/pip/
Fetching project page and analyzing links: https://mirrors.aliyun.com/pypi/simple/pip/
Getting page https://mirrors.aliyun.com/pypi/simple/pip/
Found index url https://mirrors.aliyun.com/pypi/simple
Starting new HTTPS connection (1): mirrors.aliyun.com:443
https://mirrors.aliyun.com:443 "GET /pypi/simple/pip/ HTTP/1.1" 200 13652
Found link https://mirrors.aliyun.com/pypi/packages/18/ad/c0fe6cdfe1643a19ef027c7168572dac6283b80a384ddf21b75b921877da/pip-0.2.1.tar.gz#sha256=83522005c1266cc2de97e65072ff7554ac0f30ad369c3b02ff3a764b962048da (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 0.2.1
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Found link https://mirrors.aliyun.com/pypi/packages/45/ae/8a0ad77defb7cc903f09e551d88b443304a9bd6e6f124e75c0fbbf6de8f7/pip-18.1.tar.gz#sha256=c0a292bd977ef590379a3f05d7b7f65135487b67470f6281289a94e015650ea1 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 18.1
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Found link https://mirrors.aliyun.com/pypi/packages/c8/89/ad7f27938e59db1f0f55ce214087460f65048626e2226531ba6cb6da15f0/pip-19.0.1.tar.gz#sha256=e81ddd35e361b630e94abeda4a1eddd36d47a90e71eb00f38f46b57f787cd1a5 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0.1
Found link https://mirrors.aliyun.com/pypi/packages/d7/41/34dd96bd33958e52cb4da2f1bf0818e396514fd4f4725a79199564cd0c20/pip-19.0.2-py2.py3-none-any.whl#sha256=6a59f1083a63851aeef60c7d68b119b46af11d9d803ddc1cf927b58edcd0b312 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0.2
Found link https://mirrors.aliyun.com/pypi/packages/4c/4d/88bc9413da11702cbbace3ccc51350ae099bb351febae8acc85fec34f9af/pip-19.0.2.tar.gz#sha256=f851133f8b58283fa50d8c78675eb88d4ff4cde29b6c41205cd938b06338e0e5 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0.2
Found link https://mirrors.aliyun.com/pypi/packages/d8/f3/413bab4ff08e1fc4828dfc59996d721917df8e8583ea85385d51125dceff/pip-19.0.3-py2.py3-none-any.whl#sha256=bd812612bbd8ba84159d9ddc0266b7fbce712fc9bc98c82dee5750546ec8ec64 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0.3
Found link https://mirrors.aliyun.com/pypi/packages/36/fa/51ca4d57392e2f69397cd6e5af23da2a8d37884a605f9e3f2d3bfdc48397/pip-19.0.3.tar.gz#sha256=6e6f197a1abfb45118dbb878b5c859a0edbdd33fd250100bc015b67fded4b9f2 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0.3
Found link https://mirrors.aliyun.com/pypi/packages/11/31/c483614095176ddfa06ac99c2af4171375053b270842c7865ca0b4438dc1/pip-19.0.tar.gz#sha256=c82bf8bc00c5732f0dd49ac1dea79b6242a1bd42a5012e308ed4f04369b17e54 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.0
Found link https://mirrors.aliyun.com/pypi/packages/f9/fb/863012b13912709c13cf5cfdbfb304fa6c727659d6290438e1a88df9d848/pip-19.1-py2.py3-none-any.whl#sha256=8f59b6cf84584d7962d79fd1be7a8ec0eb198aa52ea864896551736b3614eee9 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.1
Found link https://mirrors.aliyun.com/pypi/packages/5c/e0/be401c003291b56efc55aeba6a80ab790d3d4cece2778288d65323009420/pip-19.1.1-py2.py3-none-any.whl#sha256=993134f0475471b91452ca029d4390dc8f298ac63a712814f101cd1b6db46676 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.1.1
Found link https://mirrors.aliyun.com/pypi/packages/93/ab/f86b61bef7ab14909bd7ec3cd2178feb0a1c86d451bc9bccd5a1aedcde5f/pip-19.1.1.tar.gz#sha256=44d3d7d3d30a1eb65c7e5ff1173cdf8f7467850605ac7cc3707b6064bddd0958 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.1.1
Found link https://mirrors.aliyun.com/pypi/packages/51/5f/802a04274843f634469ef299fcd273de4438386deb7b8681dd059f0ee3b7/pip-19.1.tar.gz#sha256=d9137cb543d8a4d73140a3282f6d777b2e786bb6abb8add3ac5b6539c82cd624 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*), version: 19.1
Found link https://mirrors.aliyun.com/pypi/packages/3a/6f/35de4f49ae5c7fdb2b64097ab195020fb48faa8ad3a85386ece6953c11b1/pip-19.2-py2.py3-none-any.whl#sha256=468c67b0b1120cd0329dc72972cf0651310783a922e7609f3102bd5fb4acbf17 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2
Found link https://mirrors.aliyun.com/pypi/packages/62/ca/94d32a6516ed197a491d17d46595ce58a83cbb2fca280414e57cd86b84dc/pip-19.2.1-py2.py3-none-any.whl#sha256=80d7452630a67c1e7763b5f0a515690f2c1e9ad06dda48e0ae85b7fdf2f59d97 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.1
Found link https://mirrors.aliyun.com/pypi/packages/8b/8a/1b2aadd922db1afe6bc107b03de41d6d37a28a5923383e60695fba24ae81/pip-19.2.1.tar.gz#sha256=258d702483dd749400aec59c23d638a5b2249ae28a0f478b6cab12ad45681a80 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.1
Found link https://mirrors.aliyun.com/pypi/packages/8d/07/f7d7ced2f97ca3098c16565efbe6b15fafcba53e8d9bdb431e09140514b0/pip-19.2.2-py2.py3-none-any.whl#sha256=4b956bd8b7b481fc5fa222637ff6d0823a327e5118178f1ec47618a480e61997 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.2
Found link https://mirrors.aliyun.com/pypi/packages/aa/1a/62fb0b95b1572c76dbc3cc31124a8b6866cbe9139eb7659ac7349457cf7c/pip-19.2.2.tar.gz#sha256=e05103825871e210d50a44c7e448587b0ed99dd775d3ef586304c58f40224a53 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.2
Found link https://mirrors.aliyun.com/pypi/packages/30/db/9e38760b32e3e7f40cce46dd5fb107b8c73840df38f0046d8e6514e675a1/pip-19.2.3-py2.py3-none-any.whl#sha256=340a0ba40fdeb16413914c0fcd8e0b4ebb0bf39a900ec80e11c05d836c05103f (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.3
Found link https://mirrors.aliyun.com/pypi/packages/00/9e/4c83a0950d8bdec0b4ca72afd2f9cea92d08eb7c1a768363f2ea458d08b4/pip-19.2.3.tar.gz#sha256=e7a31f147974362e6c82d84b91c7f2bdf57e4d3163d3d454e6c3e71944d67135 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2.3
Found link https://mirrors.aliyun.com/pypi/packages/41/13/b6e68eae78405af6e4e9a93319ae5bb371057786f1590b157341f7542d7d/pip-19.2.tar.gz#sha256=aa6fdd80d13caac75d92b5eced06778712859b1606ba92d62389c11be12b2dad (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.2
Found link https://mirrors.aliyun.com/pypi/packages/4a/08/6ca123073af4ebc4c5488a5bc8a010ac57aa39ce4d3c8a931ad504de4185/pip-19.3-py2.py3-none-any.whl#sha256=e100a7eccf085f0720b4478d3bb838e1c179b1e128ec01c0403f84e86e0e2dfb (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.3
Found link https://mirrors.aliyun.com/pypi/packages/00/b6/9cfa56b4081ad13874b0c6f96af8ce16cfbc1cb06bedf8e9164ce5551ec1/pip-19.3.1-py2.py3-none-any.whl#sha256=6917c65fc3769ecdc61405d3dfd97afdedd75808d200b2838d7d961cebc0c2c7 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.3.1
Found link https://mirrors.aliyun.com/pypi/packages/ce/ea/9b445176a65ae4ba22dce1d93e4b5fe182f953df71a145f557cffaffc1bf/pip-19.3.1.tar.gz#sha256=21207d76c1031e517668898a6b46a9fb1501c7a4710ef5dfd6a40ad9e6757ea7 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.3.1
Found link https://mirrors.aliyun.com/pypi/packages/af/7a/5dd1e6efc894613c432ce86f1011fcc3bbd8ac07dfeae6393b7b97f1de8b/pip-19.3.tar.gz#sha256=324d234b8f6124846b4e390df255cacbe09ce22791c3b714aa1ea6e44a4f2861 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 19.3
Found link https://mirrors.aliyun.com/pypi/packages/60/65/16487a7c4e0f95bb3fc89c2e377be331fd496b7a9b08fd3077de7f3ae2cf/pip-20.0-py2.py3-none-any.whl#sha256=eea07b449d969dbc8c062c157852cf8ed2ad1b8b5ac965a6b819e62929e41703 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0
Found link https://mirrors.aliyun.com/pypi/packages/57/36/67f809c135c17ec9b8276466cc57f35b98c240f55c780689ea29fa32f512/pip-20.0.1-py2.py3-none-any.whl#sha256=b7110a319790ae17e8105ecd6fe07dbcc098a280c6d27b6dd7a20174927c24d7 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0.1
Found link https://mirrors.aliyun.com/pypi/packages/28/af/2c76c8aa46ccdf7578b83d97a11a2d1858794d4be4a1610ade0d30182e8b/pip-20.0.1.tar.gz#sha256=3cebbac2a1502e09265f94e5717408339de846b3c0f0ed086d7b817df9cab822 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0.1
Found link https://mirrors.aliyun.com/pypi/packages/54/0c/d01aa759fdc501a58f431eb594a17495f15b88da142ce14b5845662c13f3/pip-20.0.2-py2.py3-none-any.whl#sha256=4ae14a42d8adba3205ebeb38aa68cfc0b6c346e1ae2e699a0b3bad4da19cef5c (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0.2
Found link https://mirrors.aliyun.com/pypi/packages/8e/76/66066b7bc71817238924c7e4b448abdb17eb0c92d645769c223f9ace478f/pip-20.0.2.tar.gz#sha256=7db0c8ea4c7ea51c8049640e8e6e7fde949de672bfa4949920675563a5a6967f (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0.2
Found link https://mirrors.aliyun.com/pypi/packages/8c/5c/c18d58ab5c1a702bf670e0bd6a77cd4645e4aeca021c6118ef850895cc96/pip-20.0.tar.gz#sha256=5128e9a9401f1d16c1d15b2ed766a79d7813db1538428d0b0ce74838249e3a41 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.0
Found link https://mirrors.aliyun.com/pypi/packages/54/2e/df11ea7e23e7e761d484ed3740285a34e38548cf2bad2bed3dd5768ec8b9/pip-20.1-py2.py3-none-any.whl#sha256=4fdc7fd2db7636777d28d2e1432e2876e30c2b790d461f135716577f73104369 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1
Found link https://mirrors.aliyun.com/pypi/packages/43/84/23ed6a1796480a6f1a2d38f2802901d078266bda38388954d01d3f2e821d/pip-20.1.1-py2.py3-none-any.whl#sha256=b27c4dedae8c41aa59108f2fa38bf78e0890e590545bc8ece7cdceb4ba60f6e4 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1.1
Found link https://mirrors.aliyun.com/pypi/packages/08/25/f204a6138dade2f6757b4ae99bc3994aac28a5602c97ddb2a35e0e22fbc4/pip-20.1.1.tar.gz#sha256=27f8dc29387dd83249e06e681ce087e6061826582198a425085e0bf4c1cf3a55 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1.1
Found link https://mirrors.aliyun.com/pypi/packages/d1/05/059c78cd5d740d2299266ffa15514dad6692d4694df571bf168e2cdd98fb/pip-20.1.tar.gz#sha256=572c0f25eca7c87217b21f6945b7192744103b18f4e4b16b8a83b227a811e192 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1
Found link https://mirrors.aliyun.com/pypi/packages/ec/05/82d3fababbf462d876883ebc36f030f4fa057a563a80f5a26ee63679d9ea/pip-20.1b1-py2.py3-none-any.whl#sha256=4cf0348b683937da883ccaae8c8bcfc9b4c7ba4c48b38cc2d89cd7b8d0b220d9 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1b1
Found link https://mirrors.aliyun.com/pypi/packages/cd/81/c1184456fe506bd50992571c9f8581907976ce71502e36741f033e2da1f1/pip-20.1b1.tar.gz#sha256=699880a47f6d306f4f9a87ca151ef33d41d2223b81ff343b786d38c297923a19 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.1b1
Found link https://mirrors.aliyun.com/pypi/packages/fe/3b/0fc5e63eb277d5a50a95ce5c896f742ef243be27382303a4a44dd0197e29/pip-20.2b1-py2.py3-none-any.whl#sha256=b4e230e2b8ece18c5a19b818f3c20a8d4eeac8172962779fd9898d7c4ceb1636 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.2b1
Found link https://mirrors.aliyun.com/pypi/packages/77/3e/6a1fd8e08a06e3e0f54182c7c937bba3f4e9cf1b26f54946d3915021ea2e/pip-20.2b1.tar.gz#sha256=dbf65ecb1c30d35d72f5fda052fcd2f1ea9aca8eaf03d930846d990f51d3f6f6 (from https://mirrors.aliyun.com/pypi/simple/pip/) (requires-python:>=2.7,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*), version: 20.2b1
Found link https://mirrors.aliyun.com/pypi/packages/dc/7c/21191b5944b917b66e4e4e06d74f668d814b6e8a3ff7acd874479b6f6b3d/pip-6.0-py2.py3-none-any.whl#sha256=5ec6732505bd8be49fe1f8ad557b88253ffb085736396df4d6bea753fc2a8f2c (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0
Found link https://mirrors.aliyun.com/pypi/packages/e9/7a/cdbc1a12ed52410d557e48d4646f4543e9e991ff32d2374dc6db849aa617/pip-6.0.1-py2.py3-none-any.whl#sha256=322aea7d1f7b9ee68ad87ac4704cad5df97f77e70668c0bd18f964c5daa78173 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.1
Found link https://mirrors.aliyun.com/pypi/packages/4d/c3/8675b90cd89b9b222062f4f6c7e9d48b0387f5b35cbf747a74403a883e56/pip-6.0.1.tar.gz#sha256=fa2f7c68da4a405d673aa38542f9df009d60026db4f532429ac9cbfbda1f959d (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.1
Found link https://mirrors.aliyun.com/pypi/packages/71/3c/b5a521e5e99cfff091e282231591f21193fd80de079ec5fb8ed9c6614044/pip-6.0.2-py2.py3-none-any.whl#sha256=7d17b0f267f7c9cd17cd2924bbbe2b4a3d407322c0e09084ca3f1295c1fed50d (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.2
Found link https://mirrors.aliyun.com/pypi/packages/4c/5a/f9e8e3de0153282c7cb54a9b991af225536ac914bac858ca664cf883bb3e/pip-6.0.2.tar.gz#sha256=6fa90667706a679e3dc75b27a51fddafa64401c45e96f8ae6c20978183290077 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.2
Found link https://mirrors.aliyun.com/pypi/packages/73/cb/3eebf42003791df29219a3dfa1874572aa16114b44c9b1b0ac66bf96e8c0/pip-6.0.3-py2.py3-none-any.whl#sha256=b72655b6ac6aef1c86dd07f51e8ace8d7aabd6a1c4ff88db87155276fa32a073 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.3
Found link https://mirrors.aliyun.com/pypi/packages/ce/63/8d99ae60d11ae1a65f5d4fc39a529a598bd3b8e067132210cb0c4d9e9f74/pip-6.0.3.tar.gz#sha256=b091a35f5fa0faffac0b27b97e1e1e93ffe63b463c2ea8dbde0c1fb987933614 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.3
Found link https://mirrors.aliyun.com/pypi/packages/c5/0e/c974206726542bc495fc7443dd97834a6d14c2f0cba183fcfcd01075225a/pip-6.0.4-py2.py3-none-any.whl#sha256=8dfd95de29a7a3bb1e7d368cc83d566938eb210b04d553ebfe5e3a422f4aec65 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.4
Found link https://mirrors.aliyun.com/pypi/packages/02/a1/c90f19910ee153d7a0efca7216758121118d7e93084276541383fe9ca82e/pip-6.0.4.tar.gz#sha256=1dbbff9c369e510c7468ab68ba52c003f68f83c99c2f8259acd51099e8799f1e (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.4
Found link https://mirrors.aliyun.com/pypi/packages/e9/1b/c6a375a337fb576784cdea3700f6c3eaf1420f0a01458e6e034cc178a84a/pip-6.0.5-py2.py3-none-any.whl#sha256=b2c20e3a2a43b2bbb1d19ad98be27eccc7b0f0ece016da602ccaa757a862b0e2 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.5
Found link https://mirrors.aliyun.com/pypi/packages/19/f2/58628768f618c8c9fea878e0fb97730c0b8a838d3ab3f325768bf12dac94/pip-6.0.5.tar.gz#sha256=3bf42d28be9085ab2e9aecfd69a6da2d31563fe833304bf71a620a30c38ab8a2 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.5
Found link https://mirrors.aliyun.com/pypi/packages/64/fc/4a49ccb18f55a0ceeb76e8d554bd4563217117492997825d194ed0017cc1/pip-6.0.6-py2.py3-none-any.whl#sha256=fb04f8afe1ba57626783f0c8e2f3d46bbaebaa446fcf124f434e968a2fee595e (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.6
Found link https://mirrors.aliyun.com/pypi/packages/f6/ce/d9e4e178b66c766c117f62ddf4fece019ef9d50127a8926d2f60300d615e/pip-6.0.6.tar.gz#sha256=3a14091299dcdb9bab9e9004ae67ac401f2b1b14a7c98de074ca74fdddf4bfa0 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.6
Found link https://mirrors.aliyun.com/pypi/packages/7a/8e/2bbd4fcf3ee06ee90ded5f39ec12f53165dfdb9ef25a981717ad38a16670/pip-6.0.7-py2.py3-none-any.whl#sha256=93a326304c7db749896bcef822bbbac1ab29dad5651c6d732e245975239890e6 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.7
Found link https://mirrors.aliyun.com/pypi/packages/52/85/b160ebdaa84378df6bb0176d4eed9f57edca662446174eead7a9e2e566d6/pip-6.0.7.tar.gz#sha256=35a5a43ac6b7af83ed47ea5731a365f43d350a3a7267e039e5f06b61d42ab3c2 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.7
Found link https://mirrors.aliyun.com/pypi/packages/63/65/55b71647adec1ad595bf0e5d76d028506dfc002df30c256f022ff7a660a5/pip-6.0.8-py2.py3-none-any.whl#sha256=3c22b0a8ff92727bd737a82f72700790591f177541df08c07bc1f90d6b72ac19 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.8
Found link https://mirrors.aliyun.com/pypi/packages/ef/8a/e3a980bc0a7f791d72c1302f65763ed300f2e14c907ac033e01b44c79e5e/pip-6.0.8.tar.gz#sha256=0d58487a1b7f5be2e5e965c11afbea1dc44ecec8069de03491a4d0d6c85f4551 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0.8
Found link https://mirrors.aliyun.com/pypi/packages/38/fd/065c66a88398f240e344fdf496b9707f92d75f88eedc3d10ff847b28a657/pip-6.0.tar.gz#sha256=6103897f1bb68d3f933edd60f3e3830c4ea6b8abf7a4b500db148921b11f6c9b (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.0
Found link https://mirrors.aliyun.com/pypi/packages/24/fb/8a56a46243514681e569bbafd8146fa383476c4b7c725c8598c452366f31/pip-6.1.0-py2.py3-none-any.whl#sha256=435a018f6d29e34d4f901bf4e6860d8a5fa1816b68d62008c18ca062a306db31 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.1.0
Found link https://mirrors.aliyun.com/pypi/packages/6c/84/432eb60bbcb414b9cdfcb135d5f4925e253c74e7d6916ada79990d6cc1a0/pip-6.1.0.tar.gz#sha256=89f120e2ab3d25ab70c36eb28ad4f280fc9ba71736e74d3055f609c1f9173768 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.1.0
Found link https://mirrors.aliyun.com/pypi/packages/67/f0/ba0fb41dbdbfc4aa3e0c16b40269aca6b9e3d59cacdb646218aa2e9b1d2c/pip-6.1.1-py2.py3-none-any.whl#sha256=a67e54aa0f26b6d62ccec5cc6735eff205dd0fed075f56ac3d3111e91e4467fc (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.1.1
Found link https://mirrors.aliyun.com/pypi/packages/bf/85/871c126b50b8ee0b9819e8a63b614aedd264577e73478caedcd447e8f28c/pip-6.1.1.tar.gz#sha256=89f3b626d225e08e7f20d85044afa40f612eb3284484169813dc2d0631f2a556 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 6.1.1
Found link https://mirrors.aliyun.com/pypi/packages/5a/9b/56d3c18d0784d5f2bbd446ea2dc7ffa7476c35e3dc223741d20cfee3b185/pip-7.0.0-py2.py3-none-any.whl#sha256=309c48399c7d68501a10ef206abd6e5c541fedbf84b95435d9063bd454b39df7 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.0
Found link https://mirrors.aliyun.com/pypi/packages/c6/16/6475b142927ca5d03e3b7968efa5b0edd103e4684ecfde181a25f6fa2505/pip-7.0.0.tar.gz#sha256=7b46bfc1b95494731de306a688e2a7bc056d7fa7ad27e026908fb2ae67fed23d (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.0
Found link https://mirrors.aliyun.com/pypi/packages/5a/10/bb7a32c335bceba636aa673a4c977effa1e73a79f88856459486d8d670cf/pip-7.0.1-py2.py3-none-any.whl#sha256=d26b8573ba1ac1ec99a9bdbdffee2ff2b06c7790815211d0eb4dc1462a089705 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.1
Found link https://mirrors.aliyun.com/pypi/packages/4a/83/9ae4362a80739657e0c8bb628ea3fa0214a9aba7c8590dacc301ea293f73/pip-7.0.1.tar.gz#sha256=cfec177552fdd0b2d12b72651c8e874f955b4c62c1c2c9f2588cbdc1c0d0d416 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.1
Found link https://mirrors.aliyun.com/pypi/packages/64/7f/7107800ae0919a80afbf1ecba21b90890431c3ee79d700adac3c79cb6497/pip-7.0.2-py2.py3-none-any.whl#sha256=83c869c5ab7113866e2d69641ec470d47f0faae68ca4550a289a4d3db515ad65 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.2
Found link https://mirrors.aliyun.com/pypi/packages/75/b1/66532c273bca0133e42c3b4540a1609289f16e3046f1830f18c60794d661/pip-7.0.2.tar.gz#sha256=ba28fa60b573a9444e7b78ccb3b0f261d1f66f46d20403f9dce37b18a6aed405 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.2
Found link https://mirrors.aliyun.com/pypi/packages/96/76/33a598ae42dd0554207d83c7acc60e3b166dbde723cbf282f1f73b7a127c/pip-7.0.3-py2.py3-none-any.whl#sha256=7b1cb03e827d58d2d05e68ea96a9e27487ed4b0afcd951ac6e40847ce94f0738 (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.3
Found link https://mirrors.aliyun.com/pypi/packages/35/59/5b23115758ba0f2fc465c459611865173ef006202ba83f662d1f58ed2fb8/pip-7.0.3.tar.gz#sha256=b4c598825a6f6dc2cac65968feb28e6be6c1f7f1408493c60a07eaa731a0affd (from https://mirrors.aliyun.com/pypi/simple/pip/), version: 7.0.3
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Given no hashes to check 141 links for project 'pip': discarding no candidates
Dear authors, thank you for your great work and making it available.
I'm currently using vl-bert and I'd like to visualize attention to make sure my model is learning from images and text jointly and not independently. I've tried using the visualization tool available in the notebook under ./viz however it only provides a birds-eye view of attention across all of the model’s layers and heads. I'd be interested in looking at something that looks more like what you show in the Figure 2 of the README with the text and ROI label on each side and focusing on one attention head and one layer at a time. Can this be plotted from some code I haven't found yet in the repo?
Hi. I have noticed that there are several config files in cfg/pretrain
, cfg/vqa
and cfg/refcoco
(like there are 3 base-model configs base_e2e_16x16G_fp16.yaml
, base_prec_4x16G_fp32.yaml
, base_prec_withouttextonly_4x16G_fp32.yaml
existing in cfg/pretrain
) Can you provide more details about the differences of these configs? If I want to reproduce the paper results, which configs among them should I use? Thank you!
Thanks for your great work!
Could you provide the MLMACC, including "val" and "train", on the pretraining stage based on the base-bert model and only conceptual caption dataset?
Additionally, it is easy to be stuck during the pretraining process due to the large I-O. I wonder if you could provide the specific config based on the "slurm setting", like using mc or not, the num_workers per GPU.
The training loss suddenly increases to 'nan' after training 2 epochs. Any methods to avoid this?
There is a list of fine-tined models in:
https://github.com/jackroos/VL-BERT/blob/master/model/pretrained_model/PREPARE_PRETRAINED_MODELS.md
But it looks like some of the fine-tuned model is not here, for example the VQA model. Is this intentional?
Thanks for your great code.
I try to train on the vcr task to see result.
when i did
python vcr/val.py \ --a-cfg ./cfgs/vcr/base_q2a_4x16G_fp32.yaml --r-cfg ./cfgs/vcr/base_qa2r_4x16G_fp32.yaml \ --a-ckpt ./output/base_q2a_4x16G_fp32.yaml --r-ckpt ./output/base_qa2r_4x16G_fp32.yaml \ --gpus 0 1 \ --result-path ./results/ --result-name eval_vcr
,
the mistake happened.
As follows:
warnings.warn('miss keys: {}'.format(miss_keys)) Warnings: Unexpected keys: ['cls.predictions.bias', 'cls.predictions.transform.dense.weight', 'cls.predictions.transform.dense.bias', 'cls.predictions.transform.LayerNorm.gamma', 'cls.predictions.transform.LayerNorm.beta', 'cls.predictions.decoder.weight', 'cls.seq_relationship.weight', 'cls.seq_relationship.bias']. Traceback (most recent call last): File "vcr/val.py", line 214, in <module> main() File "/home/songzijie/.conda/envs/vlbert/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 43, in decorate_no_grad return func(*args, **kwargs) File "vcr/val.py", line 114, in main a_ckpt = torch.load(args.a_ckpt, map_location=lambda storage, loc: storage) File "/home/songzijie/.conda/envs/vlbert/lib/python3.6/site-packages/torch/serialization.py", line 387, in load return _load(f, map_location, pickle_module, **pickle_load_args) File "/home/songzijie/.conda/envs/vlbert/lib/python3.6/site-packages/torch/serialization.py", line 564, in _load magic_number = pickle_module.load(f, **pickle_load_args) _pickle.UnpicklingError: invalid load key, '-'.
I hope can get some help to solve the problem.
Thanks a lot.
I would like to run the evaluation scrtipt on VCR dataset vcr/val.py
, but not sure about the checkpoint files --a-ckpt
& --r-ckpt
.
Could kindly assist me with this issue.
For the given template:
python vcr/val.py \
--a-cfg <cfg_of_q2a> --r-cfg <cfg_of_qa2r> \
--a-ckpt <checkpoint_of_q2a> --r-ckpt <checkpoint_of_qa2r> \
--gpus <indexes_of_gpus_to_use> \
--result-path <dir_to_save_result> --result-name <result_file_name>
This is what I am executing:
python3 vcr/val.py \
--a-cfg ./cfgs/vcr/base_q2a_4x16G_fp32.yaml \
--r-cfg ./cfgs/vcr/base_qa2r_4x16G_fp32.yaml \
--gpus 0 1 --result-path ./results/ --result-name eval_vcr \
--a-ckpt ./model/pretrained_model/vl-bert-base-e2e.model
However I am facing the following error:
Traceback (most recent call last):
File "vcr/val.py", line 218, in <module>
main()
File "/home/axe/VirtualEnvs/py3ve/lib/python3.6/site-packages/torch/autograd/grad_mode.py", line 49, in decorate_no_grad
return func(*args, **kwargs)
File "vcr/val.py", line 119, in main
smart_load_model_state_dict(answer_model, a_ckpt['state_dict'])
File "/home/axe/Projects/VL_BERT/common/utils/load.py", line 16, in smart_load_model_state_dict
raise ValueError('failed to match key of state dict smartly!')
ValueError: failed to match key of state dict smartly!
This is my ./models
directory:
└── pretrained_model
├── bert-base-uncased
│ ├── bert_config.json
│ ├── pytorch_model.bin
│ └── vocab.txt
├── bert-large-uncased
│ ├── bert_config.json
│ ├── pytorch_model.bin
│ └── vocab.txt
├── PREPARE_PRETRAINED_MODELS.md
├── resnet101-pt-vgbua-0000.model
└── vl-bert-base-e2e.model
Hi @jackroos and thanks for the great repo!
I was looking at the cfgs file for VQA and noticed different hyperparameters than in the appendix of the paper.
For instance, 5 epochs instead of 20, 500 warmup steps instead of 2000, smaller learning rate, ...
Should we follow -- in this and other tasks -- the values in the repository or the ones in the paper?
Also, are inputs not truncated to a maximum length during fine-tuning?
Thanks!
Hi, Thanks for the great repo.
I want to get an understanding of what happened during the pretraining, especially the train and validation loss change.
In other work like lxmert and my own experiment, The MLM loss is highly overfitted. So I wander is that the same thing of VL-BERT? Considering the specia mask operation of VL-BERT.
Is it possible that you can share the logfile of the pretraining process?
Hi, thank you for making this code available !
I can download the en_corpus .dioc files (bookcorpus, wikipedia) from my PC, but I want them on a server an have to use a bash wget command; when I use the "gdown" library, the command don't work because the permission level seems insufficient
`Maybe you need to change permission over 'Anyone with the link'?``
Can you please change the accessibility level or help me find a way to download the corpus from command line ? Thanks!
Hi, Thanks for the great repo.
I wonder that do we need to pretrain model again if I want to apply VL-BERT(base with pretraining) or VL-BERT large to vcr task? Thanks a lot!
when have trained a fine-tuned VCR model nearly approaching a day, the error happened.
Traceback (most recent call last): File "vcr/train_end2end.py", line 59, in <module> main() File "vcr/train_end2end.py", line 53, in main rank, model = train_net(args, config) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../vcr/function/train.py", line 337, in train_net gradient_accumulate_steps=config.TRAIN.GRAD_ACCUMULATE_STEPS) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../common/trainer.py", line 115, in train outputs, loss = net(*batch) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/parallel/distributed.py", line 376, in forward output = self.module(*inputs[0], **kwargs[0]) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../common/module.py", line 22, in forward return self.train_forward(*inputs, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../vcr/modules/resnet_vlbert_for_vcr.py", line 340, in train_forward output_text_and_object_separately=True) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../common/nlp/time_distributed.py", line 35, in forward reshaped_outputs = self._module(*reshaped_inputs, **kwargs) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../common/visual_linguistic_bert.py", line 140, in forward output_attention_probs=output_attention_probs) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../external/pytorch_pretrained_bert/modeling.py", line 410, in forward hidden_states = layer_module(hidden_states, attention_mask, output_attention_probs=output_attention_probs) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../external/pytorch_pretrained_bert/modeling.py", line 392, in forward intermediate_output = self.intermediate(attention_output) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/project/VLbert/VL-BERT-master/vcr/../external/pytorch_pretrained_bert/modeling.py", line 362, in forward hidden_states = self.dense(hidden_states) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in __call__ result = self.forward(*input, **kwargs) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/linear.py", line 92, in forward return F.linear(input, self.weight, self.bias) File "/home/songzijie/.conda/envs/vl-bert/lib/python3.6/site-packages/torch/nn/functional.py", line 1408, in linear output = input.matmul(weight.t()) RuntimeError: CUDA out of memory. Tried to allocate 24.00 MiB (GPU 1; 11.91 GiB total capacity; 10.43 GiB already allocated; 12.06 MiB free; 348.09 MiB cached)
I tryed training by 2 or 3 GPUs.
And tryed to reduced Batch by changing LOG_FREQUENT from 100 to 2, but no use.
The error still happened in one day train.
I hope can get some help for it.
Thank you for the code. I want to fine tune this model on refcoco dataset. I got a segmentation fault error when I run the non-distributed sh file. Please help.
[Partial Load] non pretrain keys: ['final_mlp.2.weight', 'final_mlp.2.bias']
PROGRESS: 0.00%
Segmentation fault
Hi,
Thanks for your code. I want to use VLBERT to fine-tune on VQA dataset. The pretrained weights have 12-layer parameters. But in config file of VQA task, the number of layers is only 4. Can you provide more details about how to load pretrained weights and fine-tune on VQA?
Thank you.
Hi, Thank you for sharing nice work.
It seems really good at vision and Language multi-grounding.
I wanna check the performance with other tasks.
What should i follow, if i wanna pre-train the model with another dataset?
Thank you.
Hello. Thanks for sharing your code
I downloaded trainval2014_resnet101_faster_rcnn_genome
file from the google drive link. I could only find image_id, image_w, image_h, num_boxes, boxes, features
inside it. But I can't find the predicted category for each box, in which you use in your masked ROI classification. May you tell me where are these located?
When I wanted to extract img feature from conceptual-captions following the instructions, I found some errors during inference with this file and it took me sometime to debug and I'd like to share with you:
python ./tools/generate_tsv_v2.py --gpu 0,1,2,3,4,5,6,7 --cfg experiments/cfgs/faster_rcnn_end2end_resnet.yml --def models/vg/ResNet-101/faster_rcnn_end2end_final/test.prototxt --net data/faster_rcnn_models/resnet101_faster_rcnn_final.caffemodel --split conceptual_captions_train --data_root {Conceptual_Captions_Root} --out {Conceptual_Captions_Root}/train_frcnn/
A little mistake in --def params
with open(os.path.join(data_root, 'utils/train.json')) as f: #Line46
with open(os.path.join(data_root, 'utils/val.json')) as f: #Line54
zip_image = ziphelper.imread(str("/".join(data_root, im_file)))
def generate_tsv(gpu_id, prototxt, weights, image_ids, data_root, outfolder):
And correspondly, in Line170, the same problem:
json.dump(get_detections_from_im(net, im_file, image_id, ziphelper, data_root), f)
Really thank you for your sharing of the code!
Hi, Thanks for your great work.
From the generate_tsv_v2.py
file, it is seemed that you have used the 10-36 boxes for each image?
However, in your paper, you have wrote "at most 100 RoIs are selected...."
Have I missed anything?
Thanks
Hi,
When I use your code to evaluate my own visual grounding dataset, could you give me some instructions for constructing vgbua_res101_precomputed files?
Thanks
I have such a data set, each of its records is like this: <text>,<image>,<label>. It is a simple classification task, I have the following questions, I hope you can answer:
1. Can you use this pre-trained model for fine-tuning? So how do you do it?
2. How do I load my dataset into the model?
3. How to get the output of the model?
Thank you for answering my question
I am trying to train VL-BERT for RefCOCO+ (python refcoco/train_end2end.py --cfg cfgs/refcoco/base_detected_regions_4x16G.yaml
). However, I am getting the following CUDA-related error.
THCudaCheck FAIL file=/project/ocean/tsriniva/VL-BERT/common/lib/roi_pooling/cuda/ROIAlign_cuda.cu line=297 error=98 : invalid device function
Traceback (most recent call last):
File "refcoco/train_end2end.py", line 60, in <module>
main()
File "refcoco/train_end2end.py", line 54, in main
rank, model = train_net(args, config)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../refcoco/function/train.py", line 323, in train_net
gradient_accumulate_steps=config.TRAIN.GRAD_ACCUMULATE_STEPS)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../common/trainer.py", line 115, in train
outputs, loss = net(*batch)
File "/home/tsriniva/anaconda2/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../common/module.py", line 22, in forward
return self.train_forward(*inputs, **kwargs)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../refcoco/modules/resnet_vlbert_for_refcoco.py", line 96, in train_forward
segms=None)
File "/home/tsriniva/anaconda2/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../common/fast_rcnn.py", line 149, in forward
roi_align_res = self.roi_align(img_feats['body4'], rois).type(images.dtype)
File "/home/tsriniva/anaconda2/envs/vl-bert/lib/python3.6/site-packages/torch/nn/modules/module.py", line 550, in __call__
result = self.forward(*input, **kwargs)
File "/project/ocean/tsriniva/VL-BERT/refcoco/../common/lib/roi_pooling/roi_align.py", line 69, in forward
input.float(), rois.float(), self.output_size, self.spatial_scale, self.sampling_ratio
File "/project/ocean/tsriniva/VL-BERT/refcoco/../common/lib/roi_pooling/roi_align.py", line 20, in forward
input, rois, spatial_scale, output_size[0], output_size[1], sampling_ratio
RuntimeError: cuda runtime error (98) : invalid device function at /project/ocean/tsriniva/VL-BERT/common/lib/roi_pooling/cuda/ROIAlign_cuda.cu:297
Segmentation fault (core dumped)
Is there any fix for this?
安装说明也没装成功,真的太郁闷了
I found a little mistake when loading the data from multimodal dataset, for example the CC. I'm not sure whether it will make a big difference.
Ref to you src paper, the pretrain input is in the format of [CLS] + Caption Tokens + [SEQ] + ROIs + [END] and I also noticed that when the input sequence is over long, the code will truncate the sequence to max_len, e.g. 64, at here. My question is, when the code do text truncation, like
text = text[:text_len_keep]
mlm_labels = mlm_labels[:text_len_keep]
The text has already added [CLS] & [SEQ] in the beginning and end, so this truncation op will definitely Move Out the [SEQ] Token. I don't know whether this small mistake matters. But to keep input format, it's better to truncate the input caption tokens first, then add [CLS] & [SEQ], then convert to ids..
Thank you for your reply in advance
Hi,
In Pre-training VL-BERT
section, you've highlighed some tasks on which model was trained.
I had some questions:
Traceback (most recent call last):
File "pretrain/train_end2end.py", line 51, in
main()
File "pretrain/train_end2end.py", line 47, in main
rank, model = train_net(args, config)
File "VL-BERT/pretrain/../pretrain/function/train.py", line 361, in train_net
gradient_accumulate_steps=config.TRAIN.GRAD_ACCUMULATE_STEPS)
File "VL-BERT/pretrain/../common/trainer.py", line 101, in train
for nbatch, batch in enumerate(train_loader):
File "VL-BERT/pretrain/../common/utils/multi_task_dataloader.py", line 35, in next
output_tuple = (*next(self.iters[0]), )
File "anaconda3/envs/vl-bert/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 582, in next
return self._process_next_batch(batch)
File "anaconda3/envs/vl-bert/lib/python3.6/site-packages/torch/utils/data/dataloader.py", line 608, in _process_next_batch
raise batch.exc_type(batch.exc_msg)
TypeError: init() missing 2 required positional arguments: 'doc' and 'pos'
Hi, recently I prepare to pretrain a similar model, I am wondering how much computing resources (the number of gpus, the type of gpu and amount of time). Hope you could answer this question to me.
cuda 9.0
pytorch 1.1.0
ubuntu 16.04
when running the command "./scripts/nondist_run.sh vcr/train_end2end.py ./cfgs/vcr/base_q2a_4x16G_fp32.yaml ./"
i have a problem.
i find this problem caused of mismatch in the cuda version installed on the system and cuda version working in my environment.
but my cuda version installed on the system is 9.0 and cuda version working in my environment is 9.0..
how do i do? help T^T
Traceback (most recent call last):
File "vcr/train_end2end.py", line 8, in <module>
from vcr.function.train import train_net
File "/home/ailab/VL-BERT/vcr/../vcr/function/train.py", line 26, in <module>
from vcr.modules import *
File "/home/ailab/VL-BERT/vcr/../vcr/modules/__init__.py", line 1, in <module>
from .resnet_vlbert_for_vcr import ResNetVLBERT
File "/home/ailab/VL-BERT/vcr/../vcr/modules/resnet_vlbert_for_vcr.py", line 7, in <module>
from common.fast_rcnn import FastRCNN
File "/home/ailab/VL-BERT/vcr/../common/fast_rcnn.py", line 10, in <module>
from common.lib.roi_pooling.roi_pool import ROIPool
File "/home/ailab/VL-BERT/vcr/../common/lib/roi_pooling/__init__.py", line 1, in <module>
from .roi_align import ROIAlign
File "/home/ailab/VL-BERT/vcr/../common/lib/roi_pooling/roi_align.py", line 8, in <module>
from . import C_ROIPooling
ImportError: /home/ailab/VL-BERT/vcr/../common/lib/roi_pooling/C_ROIPooling.cpython-36m-x86_64-linux-gnu.so: undefined symbol: __cudaPopCallConfiguration
when running the command "./scripts/dist_run_single.sh 4 vcr/train_end2end.py ./cfgs/vcr/base_q2a_4x16G_fp32.yaml ./"
ImportError: /data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/C_ROIPooling.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN6caffe26detail37_typeMetaDataInstance_preallocated_32E
Traceback (most recent call last):
File "./scripts/launch.py", line 200, in
main()
File "./scripts/launch.py", line 196, in main
cmd=process.args)
subprocess.CalledProcessError: Command '['/root/anaconda3/envs/vl-bert/bin/python', '-u', 'vcr/train_end2end.py', '--cfg', './cfgs/vcr/base_q2a_4x16G_fp32.yaml', '--model-dir', './', '--dist']' returned non-zero exit status 1.
(vl-bert) root@6cbe3808c2b8:/data/workspace/VL-BERT# Traceback (most recent call last):
File "vcr/train_end2end.py", line 8, in
from vcr.function.train import train_net
File "/data/workspace/VL-BERT/vcr/../vcr/function/train.py", line 26, in
from vcr.modules import *
File "/data/workspace/VL-BERT/vcr/../vcr/modules/init.py", line 1, in
from .resnet_vlbert_for_vcr import ResNetVLBERT
File "/data/workspace/VL-BERT/vcr/../vcr/modules/resnet_vlbert_for_vcr.py", line 7, in
from common.fast_rcnn import FastRCNN
File "/data/workspace/VL-BERT/vcr/../common/fast_rcnn.py", line 10, in
from common.lib.roi_pooling.roi_pool import ROIPool
File "/data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/init.py", line 1, in
from .roi_align import ROIAlign
File "/data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/roi_align.py", line 8, in
from . import C_ROIPooling
ImportError: /data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/C_ROIPooling.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN6caffe26detail37_typeMetaDataInstance_preallocated_32E
Traceback (most recent call last):
File "vcr/train_end2end.py", line 8, in
from vcr.function.train import train_net
File "/data/workspace/VL-BERT/vcr/../vcr/function/train.py", line 26, in
from vcr.modules import *
File "/data/workspace/VL-BERT/vcr/../vcr/modules/init.py", line 1, in
from .resnet_vlbert_for_vcr import ResNetVLBERT
File "/data/workspace/VL-BERT/vcr/../vcr/modules/resnet_vlbert_for_vcr.py", line 7, in
from common.fast_rcnn import FastRCNN
File "/data/workspace/VL-BERT/vcr/../common/fast_rcnn.py", line 10, in
from common.lib.roi_pooling.roi_pool import ROIPool
File "/data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/init.py", line 1, in
from .roi_align import ROIAlign
File "/data/workspace/VL-BERT/vcr/../common/lib/roi_pooling/roi_align.py", line 8, in
from . import C_ROIPooling
I think it may be caused by ROI_pooling.xx.so
But I've compiled C_ROIPooling.cpython-36m-x86_64-linux-gnu.so with "./scripts/init.sh"
Hi,
My gcc and Ubuntu versions match exactly those in README.md, but I have CUDA 10.1. I set up the environment exactly as in README.md.
When trying to run the code, I find CUDA 10.1 causing trouble:
Is there any fix other than having to install CUDA 9.0?
Hi,
I am looking at your code. Can you please point me out which part of the code is doing location vector normalization? I can see you pass 4-d raw location into the model, but I could find the place that you normalize it with image width and height.
Thanks!
In .\VL-BERT-master\data\conceptual-captions\ReadMe.txt there is a request in step 8(3): "Download pretrained model (https://www.dropbox.com/s/wqada4qiv1dz9dk/resnet101_faster_rcnn_final.caffemodel?dl=1)", which is not available now.
Could you please provide another solution?
Thx!
hi!
i want vcr training.
but i got this error!
(vl-bert) ailab@ailab:~/vl-bert/VL-BERT$ ./scripts/dist_run_single.sh 2 vcr/train_end2end.py ./cfgs/vcr/base_q2a_4x16G_fp32.yaml ./vcr/saves/q2a/
Traceback (most recent call last):
File "vcr/train_end2end.py", line 8, in <module>
from vcr.function.train import train_net
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/function/train.py", line 25, in <module>
from vcr.data.build import make_dataloader, build_dataset, build_transforms
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/build.py", line 5, in <module>
from .transforms.build import build_transforms
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/transforms/__init__.py", line 1, in <module>
from .transforms import Compose
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/transforms/transforms.py", line 6, in <module>
import torchvision
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/__init__.py", line 1, in <module>
from torchvision import models
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/__init__.py", line 11, in <module>
from . import detection
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/detection/__init__.py", line 1, in <module>
from .faster_rcnn import *
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/detection/faster_rcnn.py", line 7, in <module>
from torchvision.ops import misc as misc_nn_ops
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/ops/__init__.py", line 1, in <module>
from .boxes import nms, box_iou
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/ops/boxes.py", line 2, in <module>
from torchvision import _C
ImportError: /home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN3c107Warning4warnENS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE
Traceback (most recent call last):
File "vcr/train_end2end.py", line 8, in <module>
from vcr.function.train import train_net
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/function/train.py", line 25, in <module>
from vcr.data.build import make_dataloader, build_dataset, build_transforms
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/build.py", line 5, in <module>
from .transforms.build import build_transforms
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/transforms/__init__.py", line 1, in <module>
from .transforms import Compose
File "/home/ailab/vl-bert/VL-BERT/vcr/../vcr/data/transforms/transforms.py", line 6, in <module>
import torchvision
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/__init__.py", line 1, in <module>
from torchvision import models
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/__init__.py", line 11, in <module>
from . import detection
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/detection/__init__.py", line 1, in <module>
from .faster_rcnn import *
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/models/detection/faster_rcnn.py", line 7, in <module>
from torchvision.ops import misc as misc_nn_ops
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/ops/__init__.py", line 1, in <module>
from .boxes import nms, box_iou
File "/home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/ops/boxes.py", line 2, in <module>
from torchvision import _C
ImportError: /home/ailab/anaconda3/envs/vl-bert/lib/python3.6/site-packages/torchvision/_C.cpython-36m-x86_64-linux-gnu.so: undefined symbol: _ZN3c107Warning4warnENS_14SourceLocationENSt7__cxx1112basic_stringIcSt11char_traitsIcESaIcEEE
Traceback (most recent call last):
File "./scripts/launch.py", line 200, in <module>
main()
File "./scripts/launch.py", line 196, in main
cmd=process.args)
subprocess.CalledProcessError: Command '['/home/ailab/anaconda3/envs/vl-bert/bin/python', '-u', 'vcr/train_end2end.py', '--cfg', './cfgs/vcr/base_q2a_4x16G_fp32.yaml', '--model-dir', './vcr/saves/q2a/', '--dist']' returned non-zero exit status 1.
how do i do?
thank you!
Hi, I am wondering if there is anyway I can run it with GCC 7.5.0? I don't really know how to downgrade my GCC version. I am getting the ROI_pooling undefined symbol error, so I figure it might be something about gcc version...?
您好,我在学习您提供的参考工程,请问参考工程中的train_gcc-training.tsv以及validation_gcc_1.1.0-Validation.tsv必须从https://ai.google.com/research/ConceptualCaptions/download下载吗,我从国内好像访问不到这个数据,谢谢。
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