vid2vid | Pytorch implementation of our method for high-resolution | Machine Learning library
kandi X-RAY | vid2vid Summary
kandi X-RAY | vid2vid Summary
Pytorch implementation for high-resolution (e.g., 2048x1024) photorealistic video-to-video translation. It can be used for turning semantic label maps into photo-realistic videos, synthesizing people talking from edge maps, or generating human motions from poses. The core of video-to-video translation is image-to-image translation. Some of our work in that space can be found in pix2pixHD and SPADE. Video-to-Video Synthesis Ting-Chun Wang1, Ming-Yu Liu1, Jun-Yan Zhu2, Guilin Liu1, Andrew Tao1, Jan Kautz1, Bryan Catanzaro1 1NVIDIA Corporation, 2MIT CSAIL In Neural Information Processing Systems (NeurIPS) 2018.
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Top functions reviewed by kandi - BETA
- Train the model
- Prepare data
- Parse ids
- Create and return the model object
- Parse arguments
- Forward computation
- Generate a grid of inputs
- Resample an image
- Create a 2d triangular grid
- Compute the loss of the loss function
- Read a PNG file
- Create a list of images from a directory
- Generate the inference
- Create a dataset
- Compute the flow
- Saves images
- Parse tensorflow tensorflow distributions
- Parse weights and bias
- Load pretrained model
- Parse flatetfusion module
- Saves all of the tensors to disk
- Parse tensors
- Load the first frame in the network
- Label colormap
- Create and return a model object
- Adds arguments for the given module
- Performs the forward computation
vid2vid Key Features
vid2vid Examples and Code Snippets
Community Discussions
Trending Discussions on vid2vid
QUESTION
I’d like to use my own dataset created from the FaceForensics footage with few-show-vid2vid
. So I generated image sequences with ffmpeg
and keypoints with dlib
. When I try to start the training script, I get the following error. What exactly is the problem? The provided small dataset was working for me.
ANSWER
Answered 2020-Mar-03 at 01:13for i in range(67):
This is incorrect, you should be using range(68) for 68 face landmarks. You can verify this with python -c "for i in range(67): print(i)"
which will only count from 0 to 66 (67 total numbers). python -c "for i in range(68): print(i)"
will count from 0 to 67 (68 items) and get the whole face landmark set.
QUESTION
I am training some deep learning code from this repository on a Google Colab notebook. The training is ongoing and seems like it is going to take a day or two.
I am new to deep learning, but my question:
Once the Google Colab notebook has finished running the training script, does this mean that the resulting weights and biases will be hard written to a model somewhere (in the repository folder that I have on my Google Drive), and therefore I can then run the code on any test data I like at any point in the future? Or, once I close the Google Colab notebook, do I lose the weight and bias information and would have to run the training script again if I wanted to use the neural network?
I realise that this might depend on the details of the script (again, the repository is here), but I thought that there might be a general way that these things work also.
Any help in understanding would be greatly appreciated.
...ANSWER
Answered 2020-Jan-07 at 15:31No; Colab comes with no built-in checkpointing; any saving must be done by the user - so unless the repository code does so, it's up to you.
Note that the repo would need to figure out how to connect to a remote server (or connect to your local device) for data transfer; skimming through its train.py, there's no such thing.
How to save model? See this SO; for a minimal version - the most common, and a reliable option is to "mount" your Google Drive onto Colab, and point save/load paths to direct
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install vid2vid
If you plan to train with face datasets, please install dlib.
If you plan to train with pose datasets, please install DensePose and/or OpenPose.
Clone this repo:
Docker Image If you have difficulty building the repo, a docker image can be found in the docker folder.
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