Comments (1)
also I met this following problem when run python run_train.py --gpu='0'
Detect #GPUS: 1
Using manual seed: 10
Dataset train: 1323
Dataset valid: 686
Model net
pretrained path: D:/hovernet/hover_net-master/hover_net-master/pretrained/ImageNet-ResNet50-Preact_pytorch.tar
Missing Variables:
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decoder.hv.u3.dense.units.7.preact_bna/bn.running_var', 'decoder.hv.u3.dense.units.7.conv1.weight', 'decoder.hv.u3.dense.units.7.conv1/bn.weight', 'decoder.hv.u3.dense.units.7.conv1/bn
.bias', 'decoder.hv.u3.dense.units.7.conv1/bn.running_mean', 'decoder.hv.u3.dense.units.7.conv1/bn.running_var', 'decoder.hv.u3.dense.units.7.conv2.weight', 'decoder.hv.u3.dense.blk_bn
a.bn.weight', 'decoder.hv.u3.dense.blk_bna.bn.bias', 'decoder.hv.u3.dense.blk_bna.bn.running_mean', 'decoder.hv.u3.dense.blk_bna.bn.running_var', 'decoder.hv.u3.convf.weight', 'decoder
.hv.u2.conva.weight', 'decoder.hv.u2.dense.units.0.preact_bna/bn.weight', 'decoder.hv.u2.dense.units.0.preact_bna/bn.bias', 'decoder.hv.u2.dense.units.0.preact_bna/bn.running_mean', 'd
ecoder.hv.u2.dense.units.0.preact_bna/bn.running_var', 'decoder.hv.u2.dense.units.0.conv1.weight', 'decoder.hv.u2.dense.units.0.conv1/bn.weight', 'decoder.hv.u2.dense.units.0.conv1/bn.
bias', 'decoder.hv.u2.dense.units.0.conv1/bn.running_mean', 'decoder.hv.u2.dense.units.0.conv1/bn.running_var', 'decoder.hv.u2.dense.units.0.conv2.weight', 'decoder.hv.u2.dense.units.1
.preact_bna/bn.weight', 'decoder.hv.u2.dense.units.1.preact_bna/bn.bias', 'decoder.hv.u2.dense.units.1.preact_bna/bn.running_mean', 'decoder.hv.u2.dense.units.1.preact_bna/bn.running_v
ar', 'decoder.hv.u2.dense.units.1.conv1.weight', 'decoder.hv.u2.dense.units.1.conv1/bn.weight', 'decoder.hv.u2.dense.units.1.conv1/bn.bias', 'decoder.hv.u2.dense.units.1.conv1/bn.runni
ng_mean', 'decoder.hv.u2.dense.units.1.conv1/bn.running_var', 'decoder.hv.u2.dense.units.1.conv2.weight', 'decoder.hv.u2.dense.units.2.preact_bna/bn.weight', 'decoder.hv.u2.dense.units
.2.preact_bna/bn.bias', 'decoder.hv.u2.dense.units.2.preact_bna/bn.running_mean', 'decoder.hv.u2.dense.units.2.preact_bna/bn.running_var', 'decoder.hv.u2.dense.units.2.conv1.weight', '
decoder.hv.u2.dense.units.2.conv1/bn.weight', 'decoder.hv.u2.dense.units.2.conv1/bn.bias', 'decoder.hv.u2.dense.units.2.conv1/bn.running_mean', 'decoder.hv.u2.dense.units.2.conv1/bn.ru
nning_var', 'decoder.hv.u2.dense.units.2.conv2.weight', 'decoder.hv.u2.dense.units.3.preact_bna/bn.weight', 'decoder.hv.u2.dense.units.3.preact_bna/bn.bias', 'decoder.hv.u2.dense.units
.3.preact_bna/bn.running_mean', 'decoder.hv.u2.dense.units.3.preact_bna/bn.running_var', 'decoder.hv.u2.dense.units.3.conv1.weight', 'decoder.hv.u2.dense.units.3.conv1/bn.weight', 'dec
oder.hv.u2.dense.units.3.conv1/bn.bias', 'decoder.hv.u2.dense.units.3.conv1/bn.running_mean', 'decoder.hv.u2.dense.units.3.conv1/bn.running_var', 'decoder.hv.u2.dense.units.3.conv2.wei
ght', 'decoder.hv.u2.dense.blk_bna.bn.weight', 'decoder.hv.u2.dense.blk_bna.bn.bias', 'decoder.hv.u2.dense.blk_bna.bn.running_mean', 'decoder.hv.u2.dense.blk_bna.bn.running_var', 'deco
der.hv.u2.convf.weight', 'decoder.hv.u1.conva.weight', 'decoder.hv.u0.bn.weight', 'decoder.hv.u0.bn.bias', 'decoder.hv.u0.bn.running_mean', 'decoder.hv.u0.bn.running_var', 'decoder.hv.u0.conv.weight', 'decoder.hv.u0.conv.bias']
Detected Unknown Variables:
[]
----------------EPOCH 1
Processing: | | 0/82[00:00<?,?it/s]Batch = nan|EMA = nanTraceback (most recent call last):
File "", line 1, in
Traceback (most recent call last):
File "run_train.py", line 305, in
trainer.run()
File "run_train.py", line 289, in run
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\spawn.py", line 105, in spawn_main
exitcode = _main(fd)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\spawn.py", line 114, in _main
phase_info, engine_opt, save_path, prev_log_dir=prev_save_path
File "run_train.py", line 265, in run_once
prepare(preparation_data)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\spawn.py", line 225, in prepare
main_runner.run(opt["nr_epochs"])
File "D:\hovernet\hover_net-master\hover_net-master\run_utils\engine.py", line 172, in run
_fixup_main_from_path(data['init_main_from_path'])
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\spawn.py", line 277, in _fixup_main_from_path
for data_batch in self.dataloader:
File "D:\anaconda33\envs\hovernet\lib\site-packages\torch\utils\data\dataloader.py", line 291, in iter
run_name="mp_main")
File "D:\anaconda33\envs\hovernet\lib\runpy.py", line 263, in run_path
return _MultiProcessingDataLoaderIter(self)
File "D:\anaconda33\envs\hovernet\lib\site-packages\torch\utils\data\dataloader.py", line 737, in init
pkg_name=pkg_name, script_name=fname)
File "D:\anaconda33\envs\hovernet\lib\runpy.py", line 96, in _run_module_code
w.start()
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\process.py", line 105, in start
mod_name, mod_spec, pkg_name, script_name)
File "D:\anaconda33\envs\hovernet\lib\runpy.py", line 85, in _run_code
self._popen = self._Popen(self)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\context.py", line 223, in _Popen
exec(code, run_globals)
File "D:\hovernet\hover_net-master\hover_net-master\run_train.py", line 30, in
return _default_context.get_context().Process._Popen(process_obj)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\context.py", line 322, in Popen
import torch
File "D:\anaconda33\envs\hovernet\lib\site-packages\torch_init.py", line 116, in
return Popen(process_obj)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\popen_spawn_win32.py", line 65, in init
raise err
OSError: [WinError 1455] 页面文件太小,无法完成操作。 Error loading "D:\anaconda33\envs\hovernet\lib\site-packages\torch\lib\caffe2_detectron_ops_gpu.dll" or one of its dependencies.
reduction.dump(process_obj, to_child)
File "D:\anaconda33\envs\hovernet\lib\multiprocessing\reduction.py", line 60, in dump
ForkingPickler(file, protocol).dump(obj)
BrokenPipeError: [Errno 32] Broken pipe
Processing: | | 0/82[00:10<?,?it/s]Batch = nan|EMA = nan
from hover_net.
Related Issues (20)
- Other papers say that the test set of CPM-17 has 8 images, but the test set I downloaded here has 32 images. which is correct? HOT 1
- Nuclei number of the Kumar dataset. HOT 1
- Hi, I am getting list index out of range error during training HOT 2
- Question about compute_states
- CoNSeP dataset HOT 6
- CoNic implied on Mask-RCNN HOT 2
- Instace segmentation HOT 1
- The solution of the possibly occurred "memoryError"
- Running compute_stats.py with mode=type results in an error.
- Where do the other four colors of images in the tnbc data set come from? HOT 3
- lack of nuclei number after inference HOT 2
- PanNuke trained weights HOT 1
- pannuke HOT 1
- Experiencing GPU issues while training
- CoNSep Dataset - compressed or not? HOT 2
- tiles are not adjacent in nuclei predictions (CAMELYON16) HOT 1
- Confusion about input size and output size and center crop? HOT 1
- Closing Issue - Opened by Mistake
- Inference error
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from hover_net.