mmcv | OpenMMLab Computer Vision Foundation | Computer Vision library
kandi X-RAY | mmcv Summary
kandi X-RAY | mmcv Summary
MMCV is a foundational library for computer vision research and supports many research projects as below:. It provides the following functionalities. See the documentation for more features and usage. Note: MMCV requires Python 3.6+.
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Top functions reviewed by kandi - BETA
- Forward the convolution layer
- Calculate the output size
- Calculate output size
- Fault convolution
- Collect environment variables
- Return the CUDA_HOME
- Check if pytorch is a pytorch
- Get build configuration
- Crop the given bounding box
- Register a module
- Forward computation
- Get a list of supported extensions
- Convenience function for forward computation
- Create a function for interpolation
- Decorator for registering a backend
- Forward convolutional layer
- Forward k - points
- Load object from file
- Visualize bounding boxes
- Plot learning rate curve
- Resize image to multiple dimensions
- Returns a list of files in the given directory or directory
- Get a logger
- Parse a requirements file
- Display a list of bounding boxes
- Create a progress bar for the given function
mmcv Key Features
mmcv Examples and Code Snippets
Lines of Code : 47License : Permissive (Apache-2.0)
Copymodel = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', model = dict(
type='MaskRCNN',
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', # Create a conda virtual environment and activate it
> conda create -n open-mmlab python=3.7 -y
> conda activate open-mmlab
# If you have CUDA 10.1 installed under /usr/local/cuda and would like to install PyTorch 1.5,you need to install the """Prepare the Kinetics400 dataset
==================================
`Kinetics400 `_ is an action recognition dataset
of realistic action videos, collected from YouTube. With 306,245 short trimmed videos
from 400 action categories, it is one of th """Prepare the HMDB51 Dataset
=============================
`HMDB51 `_ is an action recognition dataset,
collected from various sources, mostly from movies, and a small proportion from public databases such as the Prelinger archive,
YouTube and Goo """Prepare the UCF101 dataset
=============================
`UCF101 `_ is an action recognition dataset
of realistic action videos, collected from YouTube. With 13,320 short trimmed videos
from 101 action categories, it is one of the most widely us Community Discussions
Trending Discussions on mmcv
QUESTION
I am using MMSegmentainon library to train my model for instance image segmentation, during the traingin, I craete the model(Vision Transformer) and when I try to fit the model using this:
I get this error:
RuntimeError:CaughtRuntimeErrorinDataLoaderworkerprocess0.OriginalTraceback(mostrecentcalllast): File"/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/worker.py",line287,in _worker_loop data=fetcher.fetch(index) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/fetch.py", line 47, infetch returnself.collate_fn(data) File "/usr/local/lib/python3.7/dist-packages/mmcv/parallel/collate.py", line 81, in collateforkeyinbatch[0] File"/usr/local/lib/python3.7/dist-packages/mmcv/parallel/collate.py",line81,in forkey in batch[0] File"/usr/local/lib/python3.7/dist-packages/mmcv/parallel/collate.py",line59,incollatestacked.append(default_collate(padded_samples)) File "/usr/local/lib/python3.7/dist-packages/torch/utils/data/_utils/collate.py", line 56, indefault_collate returntorch.stack(batch,0,out=out)
RuntimeError: stack expects each tensor to be equal size, but got [1, 256, 256, 256] at entry0 and[1,256,256] at entry3
** I must also mention that I have tested my own dataset with other models available in their library but all of them works properly.
tried :
...ANSWER
Answered 2022-Apr-05 at 08:16It seems that images in your dataset might not have the same size, as in the VIT model https://arxiv.org/abs/2010.11929, you are using an MLP model,
if it was not the case, it is worth checking if your labels are all in the expected dimension. presumably, MMsegmentattion expects the output to be just the annotation map (a 2D array). It is recommended that you revise your dataset and prepare the annotation map.
QUESTION
i tried to imstall mmcv for cuda 11 by using this command
...ANSWER
Answered 2021-Apr-16 at 14:19Remove the spaces around ==:
QUESTION
**env:**ubuntu16.04 anaconda3 python3.7.8 cuda10.0 gcc5.5
command:
...ANSWER
Answered 2020-Nov-04 at 03:33I have solved this problem! Firstly,find the file:
QUESTION
I am trying to build a docker image for a python script that I would like to deploy. This is the first time I am using docker so I'm probably doing something wrong but I have no clue what.
My System:
...ANSWER
Answered 2020-Oct-22 at 13:20EDIT: this answer just tells you how to verify what's happening in your docker image. Unfortunately I'm unable to figure out why it is happening.
How to check it?
At each step of the docker build, you can see the various layers being generated. You can use that ID to create a temporary image to check what's happening. e.g.
QUESTION
Below code is part of my code
...ANSWER
Answered 2020-Apr-27 at 12:29The problem was due to version issues as discussed in this Github issue
Can you try the following?
pip install Pillow==6.1
Also, removing and reinstalling Pillow might help.
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install mmcv
mmcv-full: comprehensive, with full features and various CUDA ops out of box. It takes longer time to build.
mmcv: lite, without CUDA ops but all other features, similar to mmcv<1.0.0. It is useful when you do not need those CUDA ops.
Check here for detailed instruction.
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