nashory/delf-pytorch

PyTorch Implementation of "Large-Scale Image Retrieval with Attentive Deep Local Features"

delf-pytorch's Issues

Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)

In the step2: Feature Extraction of DeLF
After the step1, I have get the fix.pth.tar model file, the config in extractor.py show here:

MODE = 'pca'           # either "delf" or "pca"
GPU_ID = 4
IOU_THRES = 0.98
ATTN_THRES = 0.37
TOP_K = 1000
USE_PCA = False
PCA_DIMS = 40
SCALE_LIST = [0.25, 0.3535, 0.5, 0.7071, 1.0, 1.4142, 2.0]
ARCH = 'resnet50'
EXPR = 'dummy'
TARGET_LAYER = 'layer3'
MODEL_NAME = 'res18_mix_debase_2'
LOAD_FROM = '../train/repo/res18_mix_debase_1/keypoint/ckpt/fix.pth.tar'
PCA_PARAMETERS_PATH = './output/pca/{}/pca.h5'.format(MODEL_NAME)

however, I get the result in console:

Process finished with exit code 139 (interrupted by signal 11: SIGSEGV)

And regardless what I type print() method to test. there is not anything return.

hyper-parameters for training keypoint stage && training result of finetune stage

@nashory hi, I noticed that, when training the keypoint stage, you set use_l2_normalized_feature = True, what's the reason of setting this parameters? And what's more, I noticed that you set target_layer = layer3 by default, have you tried target_layer = layer4? If yes, which one is better?

Another question that confused me is, when I train finetune stage directly on google-landmark-dataset-top1k, I got acc1 over 97.5, how about your result on this stage?

Thank you and wait for your answer.

Training datasets

many image download links are invalid.

The code of “Benchmark Result on Oxf5k

extractor

hello,when i run extractor.py,it happened,what should I do?

How can i get the model pretrained on imagenet?

I want to extract delf for a dataset of common things like book, bottle, picture. But i found the matching performance of the offered model finetuned on the Romelandmark dataset is bad. So i want to known how to get the model pretrained on imagenet? Or do you have any suggestions for extracting delf for a dataset of common things? Looking forward to your early reply. Thank you very much!

Original DELF model by authors has BN for spatial attention

See here the definition of the model in Tensorflow by authors :
https://github.com/tensorflow/models/blob/master/research/delf/delf/python/training/model/delf_model.py

They use BatchNorm between conv1 and conv2 of the spatial attention module.

multi-gpu training?

Thanks for the great pytorch code. It seems that this repo does not support multi-gpu training.

Hardcoded arguments for "Feature Extraction of DeLF"

Thanks for the implementation!

In the readme, examples are provided of arguments being passed into the (1) train PCA and (2) extract dimension reduced DeLF steps. However, all the values are hardcoded in extract.py. Thus, the steps in the main README are somewhat misleading.

Is there specific resoning for hardcoding the extract parameters, but passing the train parameters?

I feel that it would be quite convenient to pass all extract parameters from cli.

datasets

Hello! I am always looking at your beneficial repository.

I'm wondering what difference between the dataset used in the pretrain process and finetuning process.

One uses full and one uses clean, what is the difference between this two datasets?

No module named 'progress'

CUDA out of memory when running notebook/visualize.ipynb

Hi @nashory and everyone,

I have the following error when running get_result(myfeeder, query) in the visualize.ipynb notebook. Could you show me how to fix this? I've tried to reduce workers = 1 to no avail. Thank you in advance!

RuntimeError: CUDA out of memory. Tried to allocate 374.00 MiB (GPU 0; 10.73 GiB total capacity; 9.57 GiB already allocated; 330.62 MiB free; 66.62 MiB cached)

”bar“ is missing from “utils”

Lack of the function "bar",i will appreciated if you can upload it. Have a nice day!

error message:floor_vml_cpu not implemented for 'Long'

What's mean of receptive field, stride and padding of FeatureExtractor class in extractor.py?

I wanna extract delf with another network.How to set receptive field, stride and padding of FeatureExtractor class in extractor.py?

Model file seems missing

There is no file 'archive/model/ldmk/keypoint/ckpt/fix.pth.tar' in the repo, is it missing? How can I get it?

what is the difference between the finetune dataset and the keypoint dataset?

The value of receptive field

Hi ! Thank you for your great work.
I have a little question. The value of receptive field in layer4 is 483 in your code. But I find you use resnet50 from torchvision model zoo, it is different with resnet50 from tensorflow slim. The value of receptive field in layer4 may be 427 ?

len() of a 0-d tensor

Hi, Thanks for releasing this code
I try to train the model on my dataset
but sometimes when I run the visualize notebook I get an error:

/home/ubuntu/DeLF-pytorch/helper/delf_helper.pyc in GetDelfFeatureFromSingleScale(x, model, scale, pca_mean, pca_vars, pca_matrix, pca_dims, rf, stride, padding, attn_thres, use_pca)
    283     # use attention score to select feature.
    284     indices = None
--> 285     while(indices is None or len(indices) == 0):
    286         indices = torch.gt(scaled_scores, attn_thres).nonzero().squeeze()
    287         attn_thres = attn_thres * 0.5   # use lower threshold if no indexes are found.

/usr/local/lib/python2.7/dist-packages/torch/tensor.pyc in __len__(self)
    368     def __len__(self):
    369         if self.dim() == 0:
--> 370             raise TypeError("len() of a 0-d tensor")
    371         return self.shape[0]

any idea why ?

need help! speed infer time

I test the infer time of pytorch, 7scales used 1---2s，
while the infer time of tensorflow ,7 scales just used 0.1--0.2s

how can i speed it?

AttributeError: module 'utils.misc' has no attribute 'get_mean_and_std'

question about visualize.ipynb

I try to match two pictures(with your provided trained model), but I meet something wrong like
"UserWarning: An output with one or more elements was resized since it had shape [999], which does not match the required output shape [998].This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.index_select(x1, 0, idx, out=xx1)"
and it cycle and cycle until
"UserWarning: An output with one or more elements was resized since it had shape [2], which does not match the required output shape [1].This behavior is deprecated, and in a future PyTorch release outputs will not be resized unless they have zero elements. You can explicitly reuse an out tensor t by resizing it, inplace, to zero elements with t.resize_(0). (Triggered internally at ../aten/src/ATen/native/Resize.cpp:24.)
torch.index_select(x1, 0, idx, out=xx1)"
@nashory I am confused and would like your reply please

Hello, what is the training data set? Where can I download it?

Hello, what is the training data set? Where can I download it?
For example, the default '../../data/landmarks/landmarks_full_val' in the code.

train PCA

error

bar“ is missing frombar函数

No module 'progress'

Did you not upload completely? Missing module'progress' ，I would be very grateful if you could respond.

from feeder import Feeder

when running the code, in visualize.ipynb, "from feeder import Feeder", I did't search it, I'd want to know what the feeder packet is and where can I load it? Thank you for your sharing

evaluation about benchmark set

Hi! I appreciate you(@nashory) releasing the code.
I am very curious about the way you evaluated the benchmark set.
Could you explain to me in detail?
And, if you have that evaluation code, can you release the code?

Some functions in utils/misc.py are not implemented

Functions declared in var __all__ in utils/misc.py, which are get_mean_and_std and init_param, are not implemented. To start training, those two need to be removed from __all__.

inference for test

Hi, how run inference for retrieval test image?

How to prepare my own dataset to train the keypoint stage?

I wanna train delf model for product searching, however what kind of datasets should i provide for keypoint training? Is there any off-th-shelf datasets can i use for training?

extractor

when i extractor.py in pca stage ,this problem hanppend?what should I do?

No module named 'skimage'

I will be very grateful if someone can tell me the answer or upload this module.

error:floor_vml_cpu not implemented for 'Long'

In train PCA, the error "floor_vml_cpu not implemented for 'Long'" ocur.

Pretrained weights trained on landmark dataset

I share the pretrained weight trained on landmarkd dataset.
Please download it from the following url:

https://drive.google.com/open?id=1dbdaDyVeIb53iGh4Uk5kA4in9-uURoLM

Pre-trained landmark problem

@nashory Thanks for sharing such a great work. I had downloaded your shared pre-trained weights on landmarks dataset but it seems there's error with the file that I cant read or extract as well.
Can you fix the link or file ?

How to Compute and Sort the Similarity between Retrieved Pictures and Base Pictures

How to calculate and sort the similarity after extracting the features of the retrieved image and the base image.

license?

Hi,

Thank you for the cool re-implementation! May I ask you about the license on your code?
I would like to port to to the kornia local features https://kornia.readthedocs.io/en/latest/feature.html

--
Best, Dmytro

Train Data Format

Hi, I'm trying to train DeLF on my custom dataset but I'm not able to understand how to arrange the data set. Where should i place my data and in what format should it be. Could anyone please help me.
Any help would be great.

Is there any way to install feeder or its alternative