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[Fix] Fix mmcv version compatible in get_started.md #658

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merged 2 commits into from
Jul 1, 2021

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Junjun2016
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@Junjun2016 Junjun2016 commented Jun 30, 2021

Motivation

init_weight in BaseModule has been changed to init_weights when MMCV >= V1.3.2
Fix #653

Modification

get_started.md

BC-breaking (Optional)

Does the modification introduce changes that break the backward-compatibility of the downstream repos?
No

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codecov bot commented Jun 30, 2021

Codecov Report

Merging #658 (764a4ed) into master (170a9d1) will not change coverage.
The diff coverage is 100.00%.

❗ Current head 764a4ed differs from pull request most recent head 3b6f766. Consider uploading reports for the commit 3b6f766 to get more accurate results
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@@           Coverage Diff           @@
##           master     #658   +/-   ##
=======================================
  Coverage   85.82%   85.82%           
=======================================
  Files         103      103           
  Lines        5304     5304           
  Branches      856      856           
=======================================
  Hits         4552     4552           
  Misses        581      581           
  Partials      171      171           
Flag Coverage Δ
unittests 85.80% <100.00%> (ø)

Flags with carried forward coverage won't be shown. Click here to find out more.

Impacted Files Coverage Δ
mmseg/__init__.py 73.68% <100.00%> (ø)

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@Junjun2016 Junjun2016 requested a review from xvjiarui July 1, 2021 08:47
@xvjiarui xvjiarui merged commit 0997224 into open-mmlab:master Jul 1, 2021
bowenroom pushed a commit to bowenroom/mmsegmentation that referenced this pull request Feb 25, 2022
* fix mmcv version compatible

* update version compatible
aravind-h-v pushed a commit to aravind-h-v/mmsegmentation that referenced this pull request Mar 27, 2023
* Changes for VQ-diffusion VQVAE

Add specify dimension of embeddings to VQModel:
`VQModel` will by default set the dimension of embeddings to the number
of latent channels. The VQ-diffusion VQVAE has a smaller
embedding dimension, 128, than number of latent channels, 256.

Add AttnDownEncoderBlock2D and AttnUpDecoderBlock2D to the up and down
unet block helpers. VQ-diffusion's VQVAE uses those two block types.

* Changes for VQ-diffusion transformer

Modify attention.py so SpatialTransformer can be used for
VQ-diffusion's transformer.

SpatialTransformer:
- Can now operate over discrete inputs (classes of vector embeddings) as well as continuous.
- `in_channels` was made optional in the constructor so two locations where it was passed as a positional arg were moved to kwargs
- modified forward pass to take optional timestep embeddings

ImagePositionalEmbeddings:
- added to provide positional embeddings to discrete inputs for latent pixels

BasicTransformerBlock:
- norm layers were made configurable so that the VQ-diffusion could use AdaLayerNorm with timestep embeddings
- modified forward pass to take optional timestep embeddings

CrossAttention:
- now may optionally take a bias parameter for its query, key, and value linear layers

FeedForward:
- Internal layers are now configurable

ApproximateGELU:
- Activation function in VQ-diffusion's feedforward layer

AdaLayerNorm:
- Norm layer modified to incorporate timestep embeddings

* Add VQ-diffusion scheduler

* Add VQ-diffusion pipeline

* Add VQ-diffusion convert script to diffusers

* Add VQ-diffusion dummy objects

* Add VQ-diffusion markdown docs

* Add VQ-diffusion tests

* some renaming

* some fixes

* more renaming

* correct

* fix typo

* correct weights

* finalize

* fix tests

* Apply suggestions from code review

Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>

* Apply suggestions from code review

Co-authored-by: Pedro Cuenca <pedro@huggingface.co>

* finish

* finish

* up

Co-authored-by: Patrick von Platen <patrick.v.platen@gmail.com>
Co-authored-by: Anton Lozhkov <aglozhkov@gmail.com>
Co-authored-by: Pedro Cuenca <pedro@huggingface.co>
sibozhang pushed a commit to sibozhang/mmsegmentation that referenced this pull request Mar 22, 2024
…en-mmlab#658)

* add faq for how to fix stages of backbone when finetuning a model

* add bounus info
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error in train.py
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