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License: BSD 3-Clause "New" or "Revised" License
用来训练自己的数据,启动后发现CPU占用很高,GPU占用仅仅50%左右
hello, any one knows the model graph? Which tool could plot it?
Right now I only want to try to infer to compare with other models but I don't have a gpu in my computer, how I should change the code to make it?
Hi,
Does anybody have such issue when comiling core/models/py_utils/_cpools?
CornerNet_Lite) lhu@LAB00100W:/mnt/hdd1_6tb/experiment/CornerNet_Lite/core/models/py_utils/_cpools$ python setup.py install --user
running install
running bdist_egg
running egg_info
writing cpools.egg-info/PKG-INFO
writing dependency_links to cpools.egg-info/dependency_links.txt
writing top-level names to cpools.egg-info/top_level.txt
reading manifest file 'cpools.egg-info/SOURCES.txt'
writing manifest file 'cpools.egg-info/SOURCES.txt'
installing library code to build/bdist.linux-x86_64/egg
running install_lib
running build_ext
building 'top_pool' extension
gcc -pthread -B /home/lhu/anaconda3/envs/CornerNet_Lite/compiler_compat -Wl,--sysroot=/ -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include -I/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/TH -I/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/THC -I/home/lhu/anaconda3/envs/CornerNet_Lite/include/python3.6m -c src/top_pool.cpp -o build/temp.linux-x86_64-3.6/src/top_pool.o -DTORCH_EXTENSION_NAME=top_pool -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++11
cc1plus: warning: command line option \u2018-Wstrict-prototypes\u2019 is valid for C/ObjC but not for C++
src/top_pool.cpp: In function \u2018std::vectorat::Tensor top_pool_backward(at::Tensor, at::Tensor)\u2019:
src/top_pool.cpp:39:20: error: \u2018zeros\u2019 is not a member of \u2018torch\u2019
auto max_val = torch::zeros({batch, channel, width}, at::device(at::kCUDA).dtype(at::kFloat));
^~~~~
src/top_pool.cpp:39:20: note: suggested alternatives:
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:4374:22: note: \u2018at::zeros\u2019
static inline Tensor zeros(const Type & dtype, IntList size) {
^~~~~
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:11:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/NativeFunctions.h:548:15: note: \u2018at::native::zeros\u2019
AT_API Tensor zeros(IntList size, const TensorOptions & options={});
^~~~~
src/top_pool.cpp:40:20: error: \u2018zeros\u2019 is not a member of \u2018torch\u2019
auto max_ind = torch::zeros({batch, channel, width}, at::device(at::kCUDA).dtype(at::kLong));
^~~~~
src/top_pool.cpp:40:20: note: suggested alternatives:
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:4374:22: note: \u2018at::zeros\u2019
static inline Tensor zeros(const Type & dtype, IntList size) {
^~~~~
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:11:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/NativeFunctions.h:548:15: note: \u2018at::native::zeros\u2019
AT_API Tensor zeros(IntList size, const TensorOptions & options={});
^~~~~
src/top_pool.cpp:52:23: error: \u2018zeros\u2019 is not a member of \u2018torch\u2019
auto gt_mask = torch::zeros({batch, channel, width}, at::device(at::kCUDA).dtype(at::kByte));
^~~~~
src/top_pool.cpp:52:23: note: suggested alternatives:
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:4374:22: note: \u2018at::zeros\u2019
static inline Tensor zeros(const Type & dtype, IntList size) {
^~~~~
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:11:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/NativeFunctions.h:548:15: note: \u2018at::native::zeros\u2019
AT_API Tensor zeros(IntList size, const TensorOptions & options={});
^~~~~
src/top_pool.cpp:53:23: error: \u2018zeros\u2019 is not a member of \u2018torch\u2019
auto max_temp = torch::zeros({batch, channel, width}, at::device(at::kCUDA).dtype(at::kFloat));
^~~~~
src/top_pool.cpp:53:23: note: suggested alternatives:
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:4374:22: note: \u2018at::zeros\u2019
static inline Tensor zeros(const Type & dtype, IntList size) {
^~~~~
In file included from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/Functions.h:11:0,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/ATen.h:14,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/csrc/utils/pybind.h:5,
from /home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/torch/torch.h:6,
from src/top_pool.cpp:1:
/home/lhu/anaconda3/envs/CornerNet_Lite/lib/python3.6/site-packages/torch/lib/include/ATen/NativeFunctions.h:548:15: note: \u2018at::native::zeros\u2019
AT_API Tensor zeros(IntList size, const TensorOptions & options={});
^~~~~
error: command 'gcc' failed with exit status 1
(CornerNet_Lite) lhu@LAB00100W:/mnt/hdd1_6tb/experiment/CornerNet_Lite/core/models/py_utils/_cpools$
This error occurs when I run demo.py. Is this a problem with pytorch? However, the environment is executed according to the configuration file. thank you
loading from /test/CornerNet-Lite/core/../cache/nnet/CornerNet_Saccade/CornerNet_Saccade_500000.pkl
/opt/conda/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/nn/functional.py:2423: UserWarning: Default upsampling behavior when mode=bilinear is changed to align_corners=False since 0.4.0. Please specify align_corners=True if the old behavior is desired. See the documentation of nn.Upsample for details.
"See the documentation of nn.Upsample for details.".format(mode))
Hi,
I noticed that in the paper you mentioned the model can be trained with 4 GTX 1080-Ti.
I only have 2 GTX-1080-Ti, can I train it?
Thanks and best regards,
Liming
File "./code/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "./code/CornerNet-Lite/core/sample/cornernet.py", line 139, in cornernet
tl_regrs[b_ind, tag_ind, :] = [fxtl - xtl, fytl - ytl]
IndexError: index 128 is out of bounds for axis 1 with size 128
Solution:
bounding boxes number of current image exceed 128
set max_tag_len in cornetnet.py = 500 or max number of boxes in images
and i want to visualize the network ,how to do it?? can you help me
What is the attention map? Is it the same as a feature map? What is the gt attention?
Traceback (most recent call last):
File "train.py", line 253, in
main(1, ngpus_per_node, args)
File "train.py", line 236, in main
train(training_dbs, validation_db, system_config, model, args)
File "train.py", line 186, in train
nnet.set_lr(learning_rate)
File "/usr/lib/python3.5/contextlib.py", line 77, in exit
self.gen.throw(type, value, traceback)
File "/data/mj/CornerNet-Lite/core/utils/tqdm.py", line 23, in stdout_to_tqdm
raise exc
File "/data/mj/CornerNet-Lite/core/utils/tqdm.py", line 21, in stdout_to_tqdm
yield save_stdout
File "train.py", line 168, in train
training_loss = nnet.train(**training)
File "/data/mj/CornerNet-Lite/core/nnet/py_factory.py", line 93, in train
loss = self.network(xs, ys)
File "/home/mj/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 489, in call
result = self.forward(*input, **kwargs)
File "/data/mj/CornerNet-Lite/core/models/py_utils/data_parallel.py", line 66, in forward
inputs, kwargs = self.scatter(inputs, kwargs, self.device_ids, self.chunk_sizes)
File "/data/mj/CornerNet-Lite/core/models/py_utils/data_parallel.py", line 77, in scatter
return scatter_kwargs(inputs, kwargs, device_ids, dim=self.dim, chunk_sizes=self.chunk_sizes)
File "/data/mj/CornerNet-Lite/core/models/py_utils/scatter_gather.py", line 30, in scatter_kwargs
inputs = scatter(inputs, target_gpus, dim, chunk_sizes) if inputs else []
File "/data/mj/CornerNet-Lite/core/models/py_utils/scatter_gather.py", line 25, in scatter
return scatter_map(inputs)
File "/data/mj/CornerNet-Lite/core/models/py_utils/scatter_gather.py", line 18, in scatter_map
return list(zip(map(scatter_map, obj)))
File "/data/mj/CornerNet-Lite/core/models/py_utils/scatter_gather.py", line 20, in scatter_map
return list(map(list, zip(map(scatter_map, obj))))
File "/data/mj/CornerNet-Lite/core/models/py_utils/scatter_gather.py", line 15, in scatter_map
return Scatter.apply(target_gpus, chunk_sizes, dim, obj)
File "/home/mj/.local/lib/python3.5/site-packages/torch/nn/parallel/_functions.py", line 89, in forward
outputs = comm.scatter(input, target_gpus, chunk_sizes, ctx.dim, streams)
File "/home/mj/.local/lib/python3.5/site-packages/torch/cuda/comm.py", line 148, in scatter
return tuple(torch._C._scatter(tensor, devices, chunk_sizes, dim, streams))
RuntimeError: CUDA error: invalid device ordinal (exchangeDevice at /pytorch/aten/src/ATen/cuda/detail/CUDAGuardImpl.h:28)
frame #0: std::function<std::string ()>::operator()() const + 0x11 (0x7f08e8ee5021 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libc10.so)
frame #1: c10::Error::Error(c10::SourceLocation, std::string const&) + 0x2a (0x7f08e8ee48ea in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libc10.so)
frame #2: + 0x4e426f (0x7f09235f526f in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch_python.so)
frame #3: + 0x8cdfa2 (0x7f08e99c3fa2 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #4: + 0xa14ae5 (0x7f08e9b0aae5 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #5: at::TypeDefault::copy(at::Tensor const&, bool, c10::optionalc10::Device) const + 0x56 (0x7f08e9c47c76 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #6: + 0x977f47 (0x7f08e9a6df47 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #7: at::native::to(at::Tensor const&, at::TensorOptions const&, bool, bool) + 0x295 (0x7f08e9a6faf5 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #8: at::TypeDefault::to(at::Tensor const&, at::TensorOptions const&, bool, bool) const + 0x17 (0x7f08e9c0e4f7 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libcaffe2.so)
frame #9: torch::autograd::VariableType::to(at::Tensor const&, at::TensorOptions const&, bool, bool) const + 0x17a (0x7f08e814ebaa in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch.so.1)
frame #10: torch::cuda::scatter(at::Tensor const&, c10::ArrayRef, c10::optional<std::vector<long, std::allocator > > const&, long, c10::optional<std::vector<c10::optionalat::cuda::CUDAStream, std::allocator<c10::optionalat::cuda::CUDAStream > > > const&) + 0x391 (0x7f09235f75f1 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch_python.so)
frame #11: + 0x4ebd4f (0x7f09235fcd4f in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch_python.so)
frame #12: + 0x11642e (0x7f092322742e in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch_python.so)
frame #15: python() [0x53fc97]
frame #18: python() [0x4ec2e3]
frame #21: THPFunction_apply(_object, _object) + 0x581 (0x7f0923423ab1 in /home/mj/.local/lib/python3.5/site-packages/torch/lib/libtorch_python.so)
frame #25: python() [0x4ec2e3]
frame #27: python() [0x535f0e]
frame #32: python() [0x4ec2e3]
frame #34: python() [0x535f0e]
frame #38: python() [0x5401ef]
frame #40: python() [0x5401ef]
frame #42: python() [0x53fc97]
frame #46: python() [0x4ec3f7]
frame #50: python() [0x4ec2e3]
frame #52: python() [0x4fbfce]
frame #54: python() [0x574db6]
frame #58: python() [0x4ec3f7]
frame #62: python() [0x5401ef]
what is wrong?
Based on CornerNet, detects each object as a triplet, rather than a pair, of keypoints, CenterNet has made great progress. Do you have a test comparison between CenterNet and CornerNet-Lite? Thank you!
https://github.com/Duankaiwen/CenterNet
CenterNet: Keypoint Triplets for Object Detection CenterNet:用于对象检测的关键点三元组
CenterNet is an one-stage detector which gets trained from scratch. On the MS-COCO dataset, CenterNet achieves an AP of 47.0%, which surpasses all known one-stage detectors, and even gets very close to the top-performance two-stage detectors.
Description of their paper: “We build our framework upon a representative one-stage keypoint-based detector named CornerNet. Our approach, named CenterNet, detects each object as a triplet, rather than a pair, of keypoints, which improves both precision and recall. Accordingly, we design two customized modules named cascade corner pooling and center pooling, which play the roles of enriching information collected by both top-left and bottom-right corners and providing more recognizable information at the central regions, respectively.”
run demo.py
after "loading from /content/ai/hdy_conornet/CornerNet-Lite/core/../cache/nnet/CornerNet_Squeeze/CornerNet_Squeeze_500000.pkl"
I want to tran the model for my dataset which has 9 categories.
I just modify the coco.py for the data. Beside I set the CornerNet_Saccade.py
#TODO 80_>9
tl_heats = nn.ModuleList([self._pred_mod(9) for _ in range(stacks)])
br_heats = nn.ModuleList([self._pred_mod(9) for _ in range(stacks)])
So how to use your pretrained model for the train process ?
Hi @heilaw
I use default input size 511 to train and test, test_scales is 1 and test_flipped is false.
When I evaluate, I find input image size is the original image size not the input size, is that correct? I think input image should be prepocessed to 512 before input into network to do inference.
resize
./data/ch/val/JPEGImages/10016d000d0d6e0e8.jpg
torch.Size([3, 511, 767])
./data/ch/val/JPEGImages/1028c8000e8c388b9.jpg
torch.Size([3, 1023, 1407])
./data/ch/val/JPEGImages/1010cc000f912334d.jpg
torch.Size([3, 767, 1151])
./data/ch/val/JPEGImages/10034e000b214be47.jpg
torch.Size([3, 1023, 1407])
./data/ch/val/JPEGImages/101ae50008d3ac841.jpg
torch.Size([3, 4351, 3327])
I couldn't find where the ground truth code input into the model. And ground truth shape is?
In losses.py, when calcalating ae_loss, the push loss mentioned as:
dist = dist - 1 / (num + 1e-4)
I couldn't understand it? If you want to subtract the j=k in the formula, may be not use this method?
Looking forward your reply. thks
Why I cannot download modules? The link is not valid?
when I run the python demo.py , OOM occurred like below:
RuntimeError: CUDA out of memory. Tried to allocate 44.00 MiB (GPU 0; 1.95 GiB total capacity; 1.23 GiB already allocated; 2.62 MiB free; 98.86 MiB cached)
so where is bitch_size?
thx ^_^
when run python demo.py, I met this question:
`python demo.py
total parameters: 116969339
loading from /home/qing/ngs/CornerNet-Lite/core/../cache/nnet/CornerNet_Saccade/CornerNet_Saccade_500000.pkl
Traceback (most recent call last):
File "demo.py", line 7, in
detector = CornerNet_Saccade()
File "/home/qing/ngs/CornerNet-Lite/core/detectors.py", line 49, in init
super(CornerNet_Saccade, self).init(coco, cornernet, cornernet_saccade_inference, model=model_path)
File "/home/qing/ngs/CornerNet-Lite/core/base.py", line 14, in init
self._nnet.load_pretrained_params(model)
File "/home/qing/ngs/CornerNet-Lite/core/nnet/py_factory.py", line 123, in load_pretrained_params
params = torch.load(f)
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 367, in load
return _load(f, map_location, pickle_module)
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 538, in _load
result = unpickler.load()
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 504, in persistent_load
data_type(size), location)
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 113, in default_restore_location
result = fn(storage, location)
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 94, in _cuda_deserialize
device = validate_cuda_device(location)
File "/home/qing/anaconda3/envs/CornerNet_Lite/lib/python3.7/site-packages/torch/serialization.py", line 78, in validate_cuda_device
raise RuntimeError('Attempting to deserialize object on a CUDA '
RuntimeError: Attempting to deserialize object on a CUDA device but torch.cuda.is_available() is False. If you are running on a CPU-only machine, please use torch.load with map_location='cpu' to map your storages to the CPU.
`
CUDA Version 8.0.44
I have 4 Nvidia P40 GPUs
/data/coco/images/trainval2014 ===========>改用/data/coco/images/train2017
/data/coco/images/minival2014 ===========>改用/data/coco/images/val2017
/data/coco/images/testdev2017 ===========>改用/data/coco/images/test2017
/data/coco/annotations/instances_trainval2014.json ======>/data/coco/annotations/instances_train2017.json
/data/coco/annotations/instances_minival2014.json ===========>/data/coco/annotations/instances_val2017.json
/data/coco/annotations/instances_testdev2017.json ===========>/data/coco/annotations/instances_testdev2017.json
===========
D:\Tensorflow\pytorch\CornerNet-Lite>python train.py CornerNet_Saccade
Process 0: loading all datasets...
Process 0: using 4 workers
Traceback (most recent call last):
File "train.py", line 249, in
main(None, ngpus_per_node, args)
File "train.py", line 220, in main
training_dbs = [datasets[dataset](config["db"], split=train_split, sys_config=system_config) for _ in range(workers)]
File "train.py", line 220, in
training_dbs = [datasets[dataset](config["db"], split=train_split, sys_config=system_config) for _ in range(workers)]
File "D:\Tensorflow\pytorch\CornerNet-Lite\core\dbs\coco.py", line 67, in init
}[split]
KeyError: 'trainval'
Thank you for your open source, but I can't use the provided trained model to test, and the following error occurred:RuntimeError: Error(s) in loading state_dict for DummyModule.
An error occurred during training Can you show your dataset tree directory? Thank you (the following is my structure according to the document)
---CornerNet-Lite
------data
----------coco
---------------images
---------------------trainval2014
---------------------------------train2014.zip
---------------------minival2014
---------------------------------val2014.zip
---------------------testdev2017
---------------------------------test2017.zip
run python train.py CornerNet_Saccade --workers=1
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
Process Process-1:
Traceback (most recent call last):
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "train.py", line 59, in prefetch_data
raise e
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
setting learning rate to: 0.00025
training start...
start prefetching data...
shuffling indices...
Traceback (most recent call last):
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
Process Process-2:
Traceback (most recent call last):
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "train.py", line 59, in prefetch_data
raise e
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
In core/sample/util.py, there is a funtion called 'gaussian_radius'.
It can dynamic choose the radius of gaussian heatmap.
But I can't understand what does it mean.
Could you pls give me some advices? Thanks a lot.
I want to train my own dataset.
I see you use the eigenvalue and eigenvector during data sampling.
Could you tell me the method you use in details ?
请问下作者,1.为什末两个阶段要共享一个模型和参数呢?
2.预测时先用255*255的图预测location,再使用location的高清图输入网络,训练时是如何保证这两类图片都有被喂给网络呢?
Excuse me?
Where is the GPU setup?
Running shows that GPU = None, in fact, I have a gpu, cuda10 installation training is no problem. thank you.
请问一下,gpu在哪里设置?运行显示 gpu=None,实际我gpu是有的,cuda10都安装训练没问题。方便加QQ沟通一下吗? QQ2737499951,谢谢
================
args= Namespace(cfg_file='CornerNet_Saccade', dist_backend='nccl', dist_url=None, distributed=False, gpu=None, initialize=False, rank=0, start_iter=0, workers=2, world_size=-1)
train tart_iter 0 distributed False world_size -1 initialize False gpu None
Process 0: 创建模型 building model...
total parameters: 116969339
启动预取数据 start prefetching data...
CornerNet-Lite数据集
链接:https://pan.baidu.com/s/141G-JZuF2EHypJwIgbicbw
提取码:7z3k
**CornerNet_Squeeze_500000.pkl (122M)
CornerNet_Saccade_500000.pkl (447M)
CornerNet_500000.pkl (768M)
annotations.zip(157.5M)
**
Put the CornerNet-Saccade model under /cache/nnet/CornerNet_Saccade/, CornerNet-Squeeze model under /cache/nnet/CornerNet_Squeeze/ and CornerNet model under /cache/nnet/CornerNet/. (* Note we use underscore instead of dash in both the directory names for CornerNet-Saccade and CornerNet-Squeeze.)
==========
将CornerNet-Saccade模型放在下面/cache/nnet/CornerNet_Saccade/,将CornerNet-Squeeze模型放在下面/cache/nnet/CornerNet_Squeeze/,使用CornerNet模型/cache/nnet/CornerNet/。(*注意我们在CornerNet-Saccade和CornerNet-Squeeze的目录名中使用下划线而不是破折号。)
注意:CornerNet模型与原始CornerNet存储库中的模型相同。我们刚将它移植到这个新的仓库。
Can it support in Windows10?
Hi,
since it took so long to train (around two and a half week to train with 4 1080Ti GPUs.). is there any tool to monitor the training process?
Hi, author!
I wanna ask some questions about the training speed of your CornerNet_Squeeze with the default hyper-parameters in your configuration.
How long did you take to get the results (on the coco dataset) in the paper compared with yolo_v3?
I get more than 200.0s/it.
It`s too slow for training a detection model.
An error occurred during training Can you show your dataset tree directory? Thank you (the following is my structure according to the document)
---CornerNet-Lite
---data
---coco
--- images
---trainval2014
---train2014.zip
---minival2014
---val2014.zip
---testdev2017
---test2017.zip
run python train.py CornerNet_Saccade --workers=1
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
Process Process-1:
Traceback (most recent call last):
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "train.py", line 59, in prefetch_data
raise e
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
setting learning rate to: 0.00025
training start...
start prefetching data...
shuffling indices...
Traceback (most recent call last):
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
Process Process-2:
Traceback (most recent call last):
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 297, in _bootstrap
self.run()
File "/opt/conda/envs/CornerNet_Lite/lib/python3.7/multiprocessing/process.py", line 99, in run
self._target(*self._args, **self._kwargs)
File "train.py", line 59, in prefetch_data
raise e
File "train.py", line 55, in prefetch_data
data, ind = sample_data(system_config, db, ind, data_aug=data_aug)
File "/test/CornerNet-Lite/core/sample/init.py", line 5, in data_sampling_func
return globals()[sys_configs.sampling_function](sys_configs, db, k_ind, data_aug, debug)
File "/test/CornerNet-Lite/core/sample/cornernet_saccade.py", line 169, in cornernet_saccade
border = [0, image.shape[0], 0, image.shape[1]]
AttributeError: 'NoneType' object has no attribute 'shape'
Because of the lack of compution resource, so I have to train it on cpu,is it feasible?What part of code should I change?
I find the push loss is too large in my datasets, it almost 3.1 and difficultly to optimize . Anyone has the same problem?
I change input image size to 800,800 in configs/CornerNet_Saccade.json,but it is wrong.
I want to use CornerNet_Squeeze.pkl on Mobile Phone of Android or IOS .
Could you tell me the method ?
Hello,
Thanks a lot for the excellent job from Princeton-VL, our system is based on the Nvidia-GPU driver of CUDA-9.0.
Can I just directly try the open source code on my system with the configuration file change.
Or should I upgrade my driver?
Thanks a lot!
调用这些函数时出现未定义的符号,求解答
Any speed enhancement in lite version?
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