This repository contains the implementation for "D²Conv3D: Dynamic Dilated Convolutions for Object Segmentation in Videos" by Christian Schmidt, Ali Athar, Sabarinath Mahadevan, and Bastian Leibe (Accepted to WACV 2022).
Code coming soon.
Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut
An infinite sum of Poisson-weighted convolutions Kyle Cranmer, Aug 2018 If viewing on GitHub, this looks better with nbviewer: click here Consider a v
R2Plus1D-PyTorch PyTorch implementation of the R2Plus1D convolution based ResNet architecture described in the paper "A Closer Look at Spatiotemporal
RepMLP: Re-parameterizing Convolutions into Fully-connected Layers for Image Recognition (PyTorch) Paper: https://arxiv.org/abs/2105.01883 Citation: @
KPAC: Kernel-Sharing Parallel Atrous Convolutional block This repository contains the official Tensorflow implementation of the following paper: Singl
revisiting-sepconv This is a reference implementation of Revisiting Adaptive Convolutions for Video Frame Interpolation [1] using PyTorch. Given two f
pytorch-inpainting-with-partial-conv Official implementation is released by the authors. Note that this is an ongoing re-implementation and I cannot f
Created by Hugues THOMAS Introduction Update 27/04/2020: New PyTorch implementation available. With SemanticKitti, and Windows supported. This reposit
AdaConv — Simple TensorFlow Implementation [Paper] : Adaptive Convolutions for Structure-Aware Style Transfer (CVPR 2021) Note This repository does no
I follow the instruction to download the backbone files in https://github.com/facebookresearch/VMZ/blob/main/c2/tutorials/model_zoo.md, but the snapshot file of this site is '.pkl' file and your weights path in config/model/backbone/irCSN152.yaml is a '.pth' file,
then:
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![[ICCV 2021] Official Tensorflow Implementation for](https://github.com/HyeongseokSon1/KPAC/raw/main/assets/KPAC.jpg)
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Traceback (most recent call last):
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/main.py", line 51, in main
model = SaliencyModel(cfg)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/experiments/saliency.py", line 24, in __init__
self.model = build_network(hparams.model)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/construct.py", line 44, in build_network
return retrieve("network", cfg.name)(cfg)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/network/segmentation/encoder_decoder.py", line 17, in __init__
backbone = build_backbone(cfg.backbone)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/construct.py", line 62, in build_backbone
load_weights(
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/util.py", line 144, in load_weights
state_dict = torch.load(weights_path, map_location="cpu", encoding="utf-8")
File "/home/wilson/anaconda3/envs/d2conv3d/lib/python3.8/site-packages/torch/serialization.py", line 713, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/wilson/anaconda3/envs/d2conv3d/lib/python3.8/site-packages/torch/serialization.py", line 920, in _legacy_load
magic_number = pickle_module.load(f, **pickle_load_args)
UnicodeDecodeError: 'utf-8' codec can't decode byte 0xde in position 0: invalid continuation byte
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
and I have tried to revise the line 84 in utils/util.py:
state_dict = torch.load(weights_path, map_location="cpu")
to
state_dict = torch.load(weights_path, map_location="cpu",, encoding="latin1")
something wrong:
Traceback (most recent call last):
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/main.py", line 51, in main
model = SaliencyModel(cfg)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/experiments/saliency.py", line 24, in __init__
self.model = build_network(hparams.model)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/construct.py", line 44, in build_network
return retrieve("network", cfg.name)(cfg)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/network/segmentation/encoder_decoder.py", line 17, in __init__
backbone = build_backbone(cfg.backbone)
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/construct.py", line 62, in build_backbone
load_weights(
File "/media/wilson/SD_Data/DE/video_shadow/d2conv3d-main/utils/util.py", line 144, in load_weights
state_dict = torch.load(weights_path, map_location="cpu", encoding="latin1")
File "/home/wilson/anaconda3/envs/d2conv3d/lib/python3.8/site-packages/torch/serialization.py", line 713, in load
return _legacy_load(opened_file, map_location, pickle_module, **pickle_load_args)
File "/home/wilson/anaconda3/envs/d2conv3d/lib/python3.8/site-packages/torch/serialization.py", line 922, in _legacy_load
raise RuntimeError("Invalid magic number; corrupt file?")
RuntimeError: Invalid magic number; corrupt file?
Set the environment variable HYDRA_FULL_ERROR=1 for a complete stack trace.
It seems can be only read by
with open(weights_path, "rb") as f:
caffe2_checkpoint = pickle.load(f, encoding="latin1")
How can I use this '.pkl' file?
Thanks for your excellent work! However, when I tried to train the model, the error occurs as shown below.
I follow the instruction in README.md to build the environment with docker nvidia/cuda:11.1-cudnn8-devel.
Thanks for your time to solve my problem!
Stereo-Waterdrop-Removal-with-Row-wise-Dilated-Attention This repository includes official codes for "Stereo Waterdrop Removal with Row-wise Dilated A
Study-CSRNet-pytorch This is the PyTorch version repo for CSRNet: Dilated Convolutional Neural Networks for Understanding the Highly Congested Scenes
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Dynamic View Synthesis from Dynamic Monocular Video Project Website | Video | Paper Dynamic View Synthesis from Dynamic Monocular Video Chen Gao, Ayus
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CvT: Introducing Convolutions to Vision Transformers Pytorch implementation of CvT: Introducing Convolutions to Vision Transformers Usage: img = torch
Introduction This is an official implementation of CvT: Introducing Convolutions to Vision Transformers. We present a new architecture, named Convolut
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