hello fyu, I am using code for some tests. I download the model following your instructions.But where I run the python predict.py images/dog.jpg --gpu 0 I always meet this problem.

upgrade_proto.cpp:86] Check failed: ReadProtoFromBinaryFile(param_file, param) Failed to parse NetParameter file: ./pretrained/dilated_convolution_context_coco.caffemodel

So, is there something wrong with you caffemodel? Or there exist some problem else?

Thank you~

I want to train the context module, but get the following error:

F0911 01:01:41.267956 15432 softmax_loss_layer.cpp:47] Check failed: outer_num_ * inner_num_ == bottom[1]->count() (3276800 vs. 435600) Number of labels must match number of predictions; e.g., if softmax axis == 1 and prediction shape is (N, C, H, W), label count (number of labels) must be N_H_W, with integer values in {0, 1, ..., C-1}.

I followed the documentation "training.md". First, i had trained the front-end module and than generated the .bin files from test.py (and the feats.txt). I use the following to start the training:

python train.py context \
--train_image /home/timo/dilation/feat/train/feats.txt \
--train_label /home/timo/Cityscapes/gtFine/train/train_city_gt.txt \
--test_image /home/timo/dilation/feat/val/feats.txt \
--test_label /home/timo/Cityscapes/gtFine/val/val_city_gt.txt \
--train_batch 100 \
--test_batch 10 \
--caffe /home/timo/dilation/caffe-dilation/build_master_release/tools/caffe \
--classes 19 \
--layers 10 \
--label_shape 66 66
--lr 0.0001
--momentum 0.99

I am grateful for every tip.

hi,

When I

make all make test make pycaffe

there is no _caffe.so in caffe-dilation/python/caffe

and I try the predict.py there is an error ImportError: libcaffe.so.1.0.0-rc3: cannot open shared object file: No such file or directory

I have already added the build_master/python into my PYTHONPATH

Does anyone know how to solve this problem?

Cheers, Mao

Hi Fisher

Thanks for that great repository! Could you still tell me, how to convert the color images to label images, where each pixel has an ID that represents the ground truth label? Is there already a script? Thanks in advance!

Best Timo

Hi Fisher, since great results have been reported by using Dilated CNN, I'm thinking to use it as the segmentation engine for my research. After reading your paper, today I started to play around with your code. Strangely, I kept getting the out of memory errors when tried the predict script. After checking the closed issues, I changed the Caffe back to the commit 08c5df but got the same errors.

I tried camvid, kitti and cityscapes. On camvid it worked, but not on the other two. Since GTX has 12GB memory, "out of memory" seems very weird to me. Is there any hint from your side?

Does anybody else get the similar errors?

cheers Rui

Thanks for sharing your pre-trained models. Could you also share your training prototxt files and the solver configuration, so that we can easily try to reproduce and/or use your architecture on other datasets ?

Hi there,

First of all, great work ! :)

I noticed there is an upconvolution layer at the end of Dilated10, (see here).

Correct me if I'm wrong but I don't remember seeing this mentioned in the paper. Is this the model which led to the results presented in the paper ? Or perhaps it is a new one ? If so, could you kindly advise on whether you kept the same training procedure ? Thanks ! Cheers,

Pauline

the python code should import caffe but when I make pycaffe, an error occurs:

rsync -a --include '/' --include '.py' --exclude '*'
python/caffe/ build_master_release/python/caffe

What' s the problem? I cloned the caffe from your link fyu/caffe-dilation

I want to train the model with my own dataset. I'm confused about the input and output forms of the image data. Could you help me? Could you please share the training prototxt files and the solver configuration? Thanks.

Hi, I want to use the ResNet50 with dilation, and I don't know which layer's dilation parameter should be added. Is there any suggestion for me?

Thanks.

hi，Thanks for your sharing!

The dilation is used for dense prediction,such as Semantic Segmentation. I have a naive idea, can we apply the dilation conv to the image classification task，such as imagenet?

Do you know the work about this? Do you think the dilation will work for image classification?Is it worthy trying?

Thanks for your kindly help and nice work!

I'm training for my own dataset, but not quite sure what to put in training/testing image/label text files. As far as I understood, the contents as follows: train_image: <the list of paths of the original images> train_label: <the list of paths of the images that is inversed in black and white where I want them to detect as the area (the correct, expected result)> test_image: <the list of paths of the images I want to test> test_label: <?>

What to put in the test_label? Also, please correct me if I'm wrong.

I download the CityScapesDataset dataset and run the code but I get an error

in init pretrained=pretrained, num_classes=1000) in drn_c_26 model = DRN(BasicBlock, [1, 1, 2, 2, 2, 2, 1, 1], arch='C', **kwargs) in init self.relu = nn.ReLU(inplace=False) init_ super(ReLU, self).init(0, 0, inplace)

TypeError: super(type, obj): obj must be an instance or subtype of type

How much GPU memory is required/recommended to run the demo? I am trying to run the demo on my Nvidia Jetson TX1 with 4 GB of RAM and the program terminates ("Killed") and/or reboots the machine.

cuda 8 cudnn 6 ubuntu 16

#1: python predict.py pascal_voc images/dog.jpg --gpu 0

#2: python predict.py kitti images/example_kitti.png --gpu 0

nvidia@tegra-ubuntu:~/cviz/dilation$ python predict.py kitti images/example_kitti.png --gpu 0 I0213 00:39:05.451731 2439 gpu_memory.cpp:159] GPUMemory::Manager initialized with Caching (CUB) GPU Allocator I0213 00:39:05.451974 2439 gpu_memory.cpp:161] Total memory: 4174815232, Free: 1888354304, dev_info[0]: total=4174815232 free=1888354304 I0213 00:39:05.452177 2439 gpu_memory.cpp:159] GPUMemory::Manager initialized with Caching (CUB) GPU Allocator I0213 00:39:05.452195 2439 gpu_memory.cpp:161] Total memory: 4174815232, Free: 1888354304, dev_info[0]: total=4174815232 free=1888354304 Using GPU 0 I0213 00:39:05.463042 2439 upgrade_proto.cpp:66] Attempting to upgrade input file specified using deprecated input fields: models/dilation7_kitti_deploy.prototxt I0213 00:39:05.463099 2439 upgrade_proto.cpp:69] Successfully upgraded file specified using deprecated input fields. W0213 00:39:05.463116 2439 upgrade_proto.cpp:71] Note that future Caffe releases will only support input layers and not input fields. I0213 00:39:05.463681 2439 net.cpp:70] Initializing net from parameters: state {

-How can i launch the Context Train on CPU ? -And how can i launch my training on my edited network architecture ? As it seems for train.py file takes only the weights and i have to trick it by generating the arch weights on other module and input them here.