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Bump version to v1.2.0 (#1860)
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* [Fix] Fix resize mix argument bug.

* Bump version to v1.2.0

* Fix UT
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mzr1996 authored Jan 4, 2024
1 parent 9ac4b31 commit 17a886c
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5 changes: 5 additions & 0 deletions README.md
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Expand Up @@ -86,6 +86,11 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351

## What's new

🌟 v1.2.0 was released in 04/01/2023

- Support LLaVA 1.5.
- Implement of RAM with a gradio interface.

🌟 v1.1.0 was released in 12/10/2023

- Support Mini-GPT4 training and provide a Chinese model (based on Baichuan-7B)
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5 changes: 5 additions & 0 deletions README_zh-CN.md
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Expand Up @@ -84,6 +84,11 @@ https://github.com/open-mmlab/mmpretrain/assets/26739999/e4dcd3a2-f895-4d1b-a351

## 更新日志

🌟 2024/01/04 发布了 v1.2.0 版本

- 支持了 LLaVA 1.5
- 实现了一个 RAM 模型的 gradio 推理例程

🌟 2023/10/12 发布了 v1.1.0 版本

- 支持 Mini-GPT4 训练并提供一个基于 Baichuan-7B 的中文模型
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2 changes: 1 addition & 1 deletion docker/serve/Dockerfile
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Expand Up @@ -3,7 +3,7 @@ ARG CUDA="11.7"
ARG CUDNN="8"
FROM pytorch/torchserve:latest-gpu

ARG MMPRE="1.1.1"
ARG MMPRE="1.2.0"

ENV PYTHONUNBUFFERED TRUE

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11 changes: 11 additions & 0 deletions docs/en/notes/changelog.md
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@@ -1,5 +1,16 @@
# Changelog (MMPreTrain)

## v1.2.0(04/01/2024)

### New Features

- [Feature] Support LLaVA 1.5 ([#1853](https://github.com/open-mmlab/mmpretrain/pull/1853))
- [Feature] Implement of RAM with a gradio interface. ([#1802](https://github.com/open-mmlab/mmpretrain/pull/1802))

### Bug Fix

- [Fix] Fix resize mix argument bug.

## v1.1.0(12/10/2023)

### New Features
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3 changes: 2 additions & 1 deletion docs/en/notes/faq.md
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Expand Up @@ -16,7 +16,8 @@ and make sure you fill in all required information in the template.

| MMPretrain version | MMEngine version | MMCV version |
| :----------------: | :---------------: | :--------------: |
| 1.1.1 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.2.0 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.1.1 | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.0.0 | mmengine >= 0.8.0 | mmcv >= 2.0.0 |
| 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
| 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
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3 changes: 2 additions & 1 deletion docs/zh_CN/notes/faq.md
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Expand Up @@ -13,7 +13,8 @@

| MMPretrain 版本 | MMEngine 版本 | MMCV 版本 |
| :-------------: | :---------------: | :--------------: |
| 1.1.1 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.2.0 (main) | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.1.1 | mmengine >= 0.8.3 | mmcv >= 2.0.0 |
| 1.0.0 | mmengine >= 0.8.0 | mmcv >= 2.0.0 |
| 1.0.0rc8 | mmengine >= 0.7.1 | mmcv >= 2.0.0rc4 |
| 1.0.0rc7 | mmengine >= 0.5.0 | mmcv >= 2.0.0rc4 |
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2 changes: 1 addition & 1 deletion mmpretrain/__init__.py
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Expand Up @@ -7,7 +7,7 @@
from .version import __version__

mmcv_minimum_version = '2.0.0'
mmcv_maximum_version = '2.2.0'
mmcv_maximum_version = '2.4.0'
mmcv_version = digit_version(mmcv.__version__)

mmengine_minimum_version = '0.8.3'
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2 changes: 1 addition & 1 deletion mmpretrain/models/utils/batch_augments/resizemix.py
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Expand Up @@ -87,7 +87,7 @@ def mix(self, batch_inputs: torch.Tensor,
(y1, y2, x1, x2), lam = self.cutmix_bbox_and_lam(img_shape, lam)
batch_inputs[:, :, y1:y2, x1:x2] = F.interpolate(
batch_inputs[index],
size=(y2 - y1, x2 - x1),
size=(int(y2 - y1), int(x2 - x1)),
mode=self.interpolation,
align_corners=False)
mixed_scores = lam * batch_scores + (1 - lam) * batch_scores[index, :]
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2 changes: 1 addition & 1 deletion mmpretrain/version.py
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@@ -1,6 +1,6 @@
# Copyright (c) OpenMMLab. All rights reserved

__version__ = '1.1.1'
__version__ = '1.2.0'


def parse_version_info(version_str):
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2 changes: 1 addition & 1 deletion requirements/mminstall.txt
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@@ -1,2 +1,2 @@
mmcv>=2.0.0,<2.3.0
mmcv>=2.0.0,<2.4.0
mmengine>=0.8.3,<1.0.0
2 changes: 1 addition & 1 deletion requirements/optional.txt
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@@ -1,4 +1,4 @@
albumentations>=0.3.2 --no-binary qudida,albumentations # For Albumentations data transform
grad-cam >= 1.3.7 # For CAM visualization
grad-cam >= 1.3.7,<1.5.0 # For CAM visualization
requests # For torchserve
scikit-learn # For t-SNE visualization and unit tests.
3 changes: 2 additions & 1 deletion tests/test_models/test_backbones/test_repmlp.py
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Expand Up @@ -169,4 +169,5 @@ def test_deploy_(self):

assert len(feats_) == len(feats__)
for i in range(len(feats)):
self.assertTrue(torch.allclose(feats__[i], feats_[i]))
self.assertTrue(
torch.allclose(feats__[i], feats_[i], rtol=0.01, atol=0.01))

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