Is it Python2 or pytorch version specific?

python3 eval.py --image-data-path ~/hpatches-sequences-release Traceback (most recent call last): File "eval.py", line 8, in import torchvision.transforms as transforms File "/usr/local/lib/python3.6/dist-packages/torchvision/init.py", line 1, in from torchvision import models File "/usr/local/lib/python3.6/dist-packages/torchvision/models/init.py", line 12, in from . import detection File "/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/init.py", line 1, in from .faster_rcnn import * File "/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/faster_rcnn.py", line 13, in from .rpn import AnchorGenerator, RPNHead, RegionProposalNetwork File "/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/rpn.py", line 8, in from . import _utils as det_utils File "/usr/local/lib/python3.6/dist-packages/torchvision/models/detection/_utils.py", line 74, in @torch.jit.script File "/usr/local/lib/python3.6/dist-packages/torch/jit/init.py", line 364, in script graph = _script_graph(fn, _frames_up=_frames_up + 1) File "/usr/local/lib/python3.6/dist-packages/torch/jit/init.py", line 359, in _script_graph ast = get_jit_ast(fn) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 132, in get_jit_ast return build_def(SourceRangeFactory(source), py_ast.body[0]) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 151, in build_def build_stmts(ctx, body)) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 123, in build_stmts stmts = [build_stmt(ctx, s) for s in stmts] File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 123, in stmts = [build_stmt(ctx, s) for s in stmts] File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 140, in call return method(ctx, node) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 205, in build_Assign rhs = build_expr(ctx, stmt.value) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 140, in call return method(ctx, node) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 314, in build_Call func = build_expr(ctx, expr.func) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 140, in call return method(ctx, node) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 300, in build_Attribute value = build_expr(ctx, expr.value) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 140, in call return method(ctx, node) File "/usr/local/lib/python3.6/dist-packages/torch/jit/frontend.py", line 422, in build_Subscript raise NotSupportedError(base.range(), "slicing multiple dimensions at the same time isn't supported yet") torch.jit.frontend.NotSupportedError: slicing multiple dimensions at the same time isn't supported yet proposals (Tensor): boxes to be encoded """

# perform some unpacking to make it JIT-fusion friendly
wx = weights[0]
wy = weights[1]
ww = weights[2]
wh = weights[3]

proposals_x1 = proposals[:, 0].unsqueeze(1)
               ~~~~~~~~~ <--- HERE
proposals_y1 = proposals[:, 1].unsqueeze(1)
proposals_x2 = proposals[:, 2].unsqueeze(1)
proposals_y2 = proposals[:, 3].unsqueeze(1)

reference_boxes_x1 = reference_boxes[:, 0].unsqueeze(1)
reference_boxes_y1 = reference_boxes[:, 1].unsqueeze(1)
reference_boxes_x2 = reference_boxes[:, 2].unsqueeze(1)
reference_boxes_y2 = reference_boxes[:, 3].unsqueeze(1)

Bumps opencv-python from 4.0.0.21 to 4.1.1.26.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

DGC-Net$ python3.7 eval.py --image-data-path ~/hpatches-sequences-release 0%| | 0/59 [00:00<?, ?it/s]torch.Size([1, 225, 15, 15])

It outputs a torch.size with 0% progress bar by running the first test from the Readme. Probably not quite right, is it? It reported

GC-Net$ python3.7 eval.py --image-data-path ~/hpatches-sequences-release 0%| | 0/59 [00:00<?, ?it/s]torch.Size([1, 225, 15, 15]) Traceback (most recent call last): File "eval.py", line 93, in device) File "/home/ubuntu/sticker_detection/DGC-Net/utils/evaluate.py", line 61, in calculate_epe_hpatches estimates_grid, estimates_mask = net(source_img, target_img) File "/home/ubuntu/anaconda3/envs/match/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(*input, **kwargs) File "/home/ubuntu/anaconda3/envs/match/lib/python3.7/site-packages/torch/nn/parallel/data_parallel.py", line 150, in forward return self.module(*inputs[0], **kwargs[0]) File "/home/ubuntu/anaconda3/envs/match/lib/python3.7/site-packages/torch/nn/modules/module.py", line 541, in call result = self.forward(*input, **kwargs) File "/home/ubuntu/sticker_detection/DGC-Net/model/net.py", line 68, in forward sys.exit() NameError: name 'sys' is not defined

before, which I fixed by importing sys obviously.

Hi man, great job! Do you have some script for demo? I mean, the code for getting as input two images (source and target) and generating at the output the transformed image? If so, could you please provide it? Thanks!

Bumps opencv-python from 4.0.0.21 to 4.2.0.32.

Dependabot will resolve any conflicts with this PR as long as you don't alter it yourself. You can also trigger a rebase manually by commenting @dependabot rebase.

Hi Melekhov. I find that the H parameters are fixed in the csv file. I'm interested in the homography augmentation parameters you used. Could you please provide some guidelines?

Apologies if my question sounds dumb!

I would like to know if it makes sense to use netvlad/dgc-net for place recognition of problems dealing with sequences of images (SLAM) over seasonal changes (no specific viewpoint changes)?

What I have in mind is Nordland dataset in which images rather suffer from illumination changes over seasons.

Cheers,

model/net.py

 for k in reversed(range(4)):  ####3，2，1，0
        p1, p2 = target_pyr[k], source_pyr[k]
        est_map = F.interpolate(input=estimates_grid[-1], scale_factor=2, mode='bilinear', align_corners=False)

        p1_w = F.grid_sample(p1, est_map.transpose(1,2).transpose(2,3))
        est_map = self.__dict__['_modules']['reg_' + str(k)](x1=p1_w, x2=p2, x3=est_map)
        estimates_grid.append(est_map)

In my opinion :

   p1, p2 = target_pyr[k], source_pyr[k]

should be: p1,p2 = source_pyr[k],target_pyr[k]

is it right? if i was wrong. pls explain it briefly. I'm confused.... thanks very much!!

Additional, I think your work is outstanding .Maybe my graduation project will be closely relate to your excellent work. So I 'd like to learn about any details.

Forgive me for my poor English can’t fully express the feeling of gratitude.

Thanks for your excellent work!!!!

I'm very interested in your research,but i can't get the tokyo time machine dataset with your links....

maybe my question is native,,but i'm eager to get your reply!

Datasets Place recognition datasets: Tokyo Time Machine: available on request. The train/val splits are provided with our code. Tokyo 24/7: available on request here. Pittsburgh 250k: available on request here. The train/val/test splits are provided with our code. Tiny subset of Tokyo Time Machine (21 MB). Contains 360 images, just to be used to validate if the NetVLAD code is set up correctly.