I was having some problems when running the following line ,

Write the train prototxt in a file

f = open(MODEL_FOLDER + 'train_segnet.prototxt', 'w') f.write(str(net_arch.to_proto()))

I got the error: 'LayerParameter' object has no attribute 'scale'

I don't know what cause the error,how can i resolve this problem? the following error message is showed: File "training.py", line 213, in f.write(str(net_arch.to_proto())) File "/home/tukrin1/caffe/python/caffe/net_spec.py", line 189, in to_proto top._to_proto(layers, names, autonames) File "/home/tukrin1/caffe/python/caffe/net_spec.py", line 97, in _to_proto return self.fn._to_proto(layers, names, autonames) File "/home/tukrin1/caffe/python/caffe/net_spec.py", line 158, in _to_proto assign_proto(layer, k, v) File "/home/tukrin1/caffe/python/caffe/net_spec.py", line 64, in assign_proto is_repeated_field = hasattr(getattr(proto, name), 'extend') AttributeError: 'LayerParameter' object has no attribute 'scale'

Could anyone help me with this? I appreciate your help!

Hello Sir, Please I would like to change the ISPRS Postdam dataset by another one but I always receive this error in the training Concerning the error, please refer to the attached screenshot Thank you in advance for your help Best regards

I use the potsdam data ,when i run the create_lmdb.py i got this error:

Traceback (most recent call last):
  File "create_lmdb.py", line 143, in <module>
    create_image_lmdb(target_folder, samples, bgr=True)
  File "create_lmdb.py", line 92, in create_image_lmdb
    sample = sample[:,:,::-1]
IndexError: too many indices for array

but i test the image independently it runs well:

sample = io.imread('/home/tukrin1/Breeze/rs/Potsdam/RELEASE_FOLDER/Potsdam/potsdam_128_128_32/irrg_train/5411.png')
sample = sample[:,:,::-1]
print sample.shape
>> (128, 128, 3)

i'm so confused about the error ,any idea about what to do ? thanks

Hi, @nshaud what changes should be made to use the CPU rather than the GPU? You have to remove the calls to cuda () definitely, but with what to replace them? Could you explicitly define all the lines to be modified to work on cpu?

Thanks in advance

I downloaded the data. when i create the label lmdb i got error because the code assume that the label are 1dim and i have 3dim you make something to the original label ? i use this path for the label - "~/ISPRS/Vaihingen/ISPRS_semantic_labeling_Vaihingen/gts_for_participants" so i change the code for read the label to lmdb. But of course i have a problem in the loss layer because againe the net expect for 1dim and not 3dim . you have solution for me? thenk you

@nshaud
I just run the default PyTorch code SegNet_PyTorch_v2.ipynb. However, the accuracy of Vaihingen is about 86%, not as good as your paper. Then I read your paper again, and I found that you have made some change. Do you have any plan to share the code in the BeyondRGB paper?

Hello, I am using the pytorch version of your code to detect wetlands from a 3-band geotiff made up of topographic indices. My verification data is made up of manually surveyed wetland delineations, so it has an irregular shape. I use the wetland surveys as ground truth wetland and nonwetland areas for training and testing purposes by splitting the surveyed area into separate tiles and randomly splitting these into the two groups. Of course, some of the training tiles end up having no data pixels, representing land cover that is not known to be either wetland or nonwetland because it extends further than the limits of the original wetland surveys. If I limit my training set to only those tiles without nodata pixels, I get reasonable predictions. However, I'm already VERY limited in my training data size, relative to most deep learning applications, and would rather include all training data possible by letting the code just ignore these sets of pixels.

So far I've tried:

1. setting the nodata pixels to a value outside of the range(len(LABELS)) i.e., nonwetlands = 0, wetlands = 1 and nodata >1 or < 0
2. including nodata value in the list of target labels i.e., nodata=0, nonwetlands=1, wetlands=2 and then adjusting the WEIGHTS parameter to give nodata a weight of 0.
3. Both 1 and 2 PLUS setting ignore_index = [nodata value] when calling cross_entropy()

These attempts either cause loss = nan or produce poor results after a couple of epochs (predict everything as nonwetland)

Do you have other ideas for signaling to pytorch to ignore a certain type of pixel? I'm very new to deep learning and pytorch so any insight would be very helpful! Thanks! Example training image and corresponding label tile that have nodata pixels

after the installation, I tried to run the inference_patches.py, an error "ImportError: No module named _caffe" occurred

sys.path.insert(0,CAFFE_ROOT+"/python")
import caffe

I read some other notes with similar problems, which says _caffe.so should be in the /python folder after make pycaffe

I can find the libcaffe.so and libcaffe.so.1.0.0-rc4 in the /caffe/build/lib, but in the /python folder i could find any _caffe.so. to make _caffe.so as suggested by some other, I tried ' make pycaffe ' inside /python folder, but a 'no rule to make target pycaffe ' error occored

Dear, In the segnet_isprs_vaihingen_irrg.prototxt the value of the outputs is equal to 6, however the real number of outputs of the dataset is 7 ('imp_surfaces', 'building', 'low_vegetation', 'tree', 'car', 'clutter', 'unclassified').

So, I think that I don't understand somethin, because, I try to adapt your example to my dataset with 3 labels (A,B and 'unclassified), setting the value of the outputs to 2, and I obtain an error: 'error == cudaSuccess (77 vs. 0) an illegal memory access was encountered'

I try setting 3 in the outputs, and It works...but this is not what I want, because in this case I'm training the net with the unclassfied values, and I don't want to do this. I only want to train the net with A and B labels.

I try to create the dataset with nan values, but it crash in the lmdb creation.

Is there any way to avoid the unclassified values in the net? Can you help me? Thanks in advance

Dear Dr. Audebert @nshaud :

Thank you for your wonderful work "Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks" [1]. However, I can't find the nDSM of Vaihingen Dataset. Could you provide it ? Thank you very much !

[1] Audebert, Nicolas, Bertrand Le Saux, and Sébastien Lefèvre. "Beyond RGB: Very high resolution urban remote sensing with multimodal deep networks." ISPRS Journal of Photogrammetry and Remote Sensing 140 (2018): 20-32.

Best,

Hi, In the notebook, the parser to load the Potsdam data must be modified : Original : all_ids = ["".join(f.split('')[3:5]) for f in all_files] What works : all_ids = ["".join(f.split('')[5:7]) for f in all_files]

And there is a typo in DATA_FOLDER for Potsdam : it begins with a "3" not with a "Y".

Hello Sir, Please I tried to apply the Segnet-Pytorch code you developed using the SEMCITY TOULOUSE dataset (http://rs.ipb.uni-bonn.de/data/semcity-toulouse/)

I want to inform you that I have changed only the classes at the code level. Please find below the results I obtained: impervious surface = nan building = 0.99 pervious surface = nan high vegetation = nan car = nan water = nan sport venues = nan

Please I would like to know why the nan in the all classes except the Building class Thank you for your help

Since PyTorch 4.0, some things have been deprecated and should be fixed:

• [ ] Variable does not exist anymore
• [ ] loss.data[0] should now be loss.item()
• [ ] volatile flag does not do anything, use no_grad instead
• [ ] .to() method allows us to write device-agnostic code