pytorch-lightning-bolts | Toolbox of models , callbacks , and datasets | Machine Learning library
kandi X-RAY | pytorch-lightning-bolts Summary
kandi X-RAY | pytorch-lightning-bolts Summary
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QUESTION
I'm using the CIFAR-10 pre-trained VAE from lightning-bolts. It should be able to regenerate images with the quality shown on this picture taken from the docs (LHS are the real images, RHS are the generated)
However, when I write a simple script that loads the model, the weights, and tests it over the training set, I get a much worse reconstruction (top row are real images, bottom row are the generated ones):
Here is a link to a self-contained colab notebook that reproduces the steps I've followed to produce the pictures.
Am I doing something wrong on my inference process? Could it be that the weights are not as "good" as the docs claim?
Thanks!
...ANSWER
Answered 2022-Feb-01 at 20:11First, the image from the docs you show is for the AE, not the VAE. The results for the VAE look much worse:
https://pl-bolts-weights.s3.us-east-2.amazonaws.com/vae/vae-cifar10/vae_output.png
Second, the docs state "Both input and generated images are normalized versions as the training was done with such images." So when you load the data you should specify normalize=True. When you plot your data, you will need to 'unnormalize' the data as well:
QUESTION
This is a specific instance of a general problem that I run into when updating packages using conda. I have an environment that is working great on machine A. I want to transfer it to machine B. But, machine A has GTX1080 gpus, and due to configuration I cannot control, requires cudatoolkit 10.2. Machine B has A100 gpus, and due to configuration I cannot control, requires cudatoolkit 11.1
I can easily export Machine A's environment to yml, and create a new environment on Machine B using that yml. However, I cannot seem to update cudatoolkit to 11.1 on that environment on Machine B. I try
...ANSWER
Answered 2021-Mar-22 at 03:02I'd venture the issue is that recreating from a YAML that includes versions and builds will establish those versions and builds as explicit specifications for that environment moving forward. That is, Conda will regard explicit specifications as hard requirements that it cannot mutate and so if even a single one of the dependencies of cudatoolkit
also needs to be updated in order to use version 11, Conda will not know how to satisfy it without violating those previously specified constraints.
Specifically, this is what I see when searching (assuming linux-64 platform):
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