Comments (21)
self.DIST_ENABLE
is necessary for multi-GPU training.
from aot-benchmark.
I meant that I am trying to test single GPU.
In many places cfg.DIST_ENABLE
is checked to safely go through the non distributed code path.
But in many other places not:
aot-benchmark/networks/managers/trainer.py
Lines 342 to 352 in 1c3a5ec
from aot-benchmark.
E.g. here instead the code is checking cfg.DIST_ENABLE
aot-benchmark/networks/managers/trainer.py
Lines 59 to 83 in 1c3a5ec
from aot-benchmark.
The distributed sampler is useless and meaningless for evaluation, where GPUs are asynchronous instead of synchronous. The video lengths are always different for different GPUs.
from aot-benchmark.
Is this not in the trainer?
I am trying to train a single GPU job with cfg.DIST_ENABLE=False
from aot-benchmark.
from aot-benchmark.
Yes it is what I meant. Are we not going to have issue if we don't conditional wrap torch.nn.parallel.DistributedDataParallel
in the trainer?
from aot-benchmark.
from aot-benchmark.
Isn't that one going to require init_process_group
? But it is conditional wrapped in the trainer.
aot-benchmark/networks/managers/trainer.py
Lines 59 to 64 in 1c3a5ec
from aot-benchmark.
from aot-benchmark.
E.g. with self.DIST_ENABLE = False
in configs/default.py
we are going to fail directly at:
aot-benchmark/networks/managers/trainer.py
Line 342 in 1c3a5ec
RuntimeError: Default process group has not been initialized, please make sure to call init_process_group.
Cause:
#36 (comment)
As we don't have completely covered cfg.DIST_ENABLE
safeguards and the relatrive alternative code path.
from aot-benchmark.
Sorry, I don't understand what you mean.
Is it clear now?
from aot-benchmark.
I have updated the trainer.py
, and it should be ok to set self.DIST_ENABLE = False
and train with a single GPU.
from aot-benchmark.
Thanks, I've checked your changes and they were the same I've done locally on my side in these days.
What do you think now about this (pytorch/pytorch#37444)?
Lines 78 to 79 in d5bd73f
from aot-benchmark.
Other then my previous comment I think that we still have now an issue with the keys
with DIST_ENABLE=False
:
aot-benchmark/networks/managers/trainer.py
Lines 677 to 682 in d5bd73f
for key in boards['image'].keys():
AttributeError: 'list' object has no attribute 'keys'
from aot-benchmark.
It should be ok now.
from aot-benchmark.
It seems that we have two issues:
- The first is that the trainer it seems to be "randomly" deadlocked on different runs same code
- images in
img_logs
seems to be not correct anymore as they are only the binary masks instead they was "composited" if I remember correctly.
from aot-benchmark.
For the first issue this is the stacktrace of one of the deadlock and it could be related to #36 (comment):
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 240, in spawn
return start_processes(fn, args, nprocs, join, daemon, start_method='spawn')
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 198, in start_processes
while not context.join():
File "/opt/conda/lib/python3.10/site-packages/torch/multiprocessing/spawn.py", line 109, in join
ready = multiprocessing.connection.wait(
File "/opt/conda/lib/python3.10/multiprocessing/connection.py", line 936, in wait
ready = selector.select(timeout)
File "/opt/conda/lib/python3.10/selectors.py", line 416, in select
fd_event_list = self._selector.poll(timeout)
from aot-benchmark.
I guess the first problem is due to torch.spawn
, and I have modified the code related to it. Please take a try, and hope this will work for you.
As to the second issue, img_logs
should be images where target objects are marked with colorful masks, referring to here.
from aot-benchmark.
I guess the first problem is due to torch.spawn, and I have modified the code related to it. Please take a try, and hope this will work for you.
Yes, it seems ok now.
As to the second issue, img_logs should be images where target objects are marked with colorful masks, referring to here.
Yes sorry this was a false positive related to the image range in the compositing phase, thanks for the double check.
from aot-benchmark.
The same need to be fixed in the PAOT
branch.
from aot-benchmark.
Related Issues (20)
- [Reproducing quantitative numbers of AOT and DeAOT] HOT 7
- YoutubeVOS quantitative numbers
- Would it help if I had more "ground truth" masks? HOT 2
- Object merging in tracking HOT 3
- Recreating the general examples HOT 2
- Optimizing Model Performance: Exploring ONNX Export and Engine Integration with TensorRT and OpenVino HOT 4
- Work Around for Pytorch-Correlation-extension HOT 5
- Demo.py with more then one GPU HOT 2
- package specified for utils HOT 1
- AUX loss WEIGHT and RATIO HOT 3
- [PAOT] swinv2b
- Evaluator Memory
- F.interpolate upsample gradient with amp HOT 1
- SDPA Attention
- How to train aot? Where is aot's train.py?
- Static image
- INT_MAX `local2global`
- How to reproduce the pre_dav_ytb result?
- To feed truth values into the model during the testing phase? HOT 4
- Does this model convert to onnx to run on edge device? HOT 1
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