Mmpretrain

New Features

- Add `dim` argument for `GlobalAveragePooling`. (236)
- Add random noise to `RandAugment` magnitude. (240)
- Refine `new_dataset.md` and add Chinese translation of `finture.md`, `new_dataset.md`. (243)

Improvements
- Refactor arguments passing for Heads. (239)
- Allow more flexible `magnitude_range` in `RandAugment`. (249)
- Inherits MMCV registry so that in the future OpenMMLab repos like MMDet and MMSeg could directly use the backbones supported in MMCls. (252)

Bug Fixes
- Fix typo in `analyze_results.py`. (237)
- Fix typo in unittests. (238)
- Check if specified tmpdir exists when testing to avoid deleting existing data. (242; 258)
- Add missing config files in `MANIFEST.in`. (250; 255)
- Use temporary directory under shared directory to collect results to avoid unavailability of temporary directory for multi-node testing. (251)

New Features

- Support cutmix trick. (198)
- Add `simplify` option in `pytorch2onnx.py`. (200)
- Support random augmentation. (201)
- Add config and checkpoint for training ResNet on CIFAR-100. (208)
- Add `tools/deployment/test.py` as a ONNX runtime test tool. (212)
- Support ViT backbone and add training configs for ViT on ImageNet. (214)
- Add finetuning configs for ViT on ImageNet. (217)
- Add `device` option to support training on CPU. (219)
- Add Chinese `README.md` and some Chinese tutorials. (221)
- Add `metafile.yml` in configs to support interaction with paper with code(PWC) and MMCLI. (225)
- Upload configs and converted checkpoints for ViT fintuning on ImageNet. (230)

Improvements

- Fix `LabelSmoothLoss` so that label smoothing and mixup could be enabled at the same time. (203)
- Add `cal_acc` option in `ClsHead`. (206)
- Check `CLASSES` in checkpoint to avoid unexpected key error. (207)
- Check mmcv version when importing mmcls to ensure compatibility. (209)
- Update `CONTRIBUTING.md` to align with that in MMCV. (210)
- Change tags to html comments in configs README.md. (226)
- Clean codes in ViT backbone. (227)
- Reformat `pytorch2onnx.md` tutorial. (229)
- Update `setup.py` to support MMCLI. (232)

Bug Fixes

- Fix missing `cutmix_prob` in ViT configs. (220)
- Fix backend for resize in ResNeXt configs. (222)

New Features

- Add `Rotate` pipeline for data augmentation. (167)
- Add `Invert` pipeline for data augmentation. (168)
- Add `Color` pipeline for data augmentation. (171)
- Add `Solarize` and `Posterize` pipeline for data augmentation. (172)
- Support fp16 training. (178)
- Add tutorials for installation and basic usage of MMClassification.(176)
- Support `AutoAugmentation`, `AutoContrast`, `Equalize`, `Contrast`, `Brightness` and `Sharpness` pipelines for data augmentation. (179)

Improvements

- Support dynamic shape export to onnx. (175)
- Release training configs and update model zoo for fp16 (184)
- Use MMCV's EvalHook in MMClassification (182)

Bug Fixes

- Fix wrong naming in vgg config (181)

New Features

- Implement mixup and provide configs of training ResNet50 using mixup. (160)
- Add `Shear` pipeline for data augmentation. (163)
- Add `Translate` pipeline for data augmentation. (165)
- Add `tools/onnx2tensorrt.py` as a tool to create TensorRT engine from ONNX, run inference and verify outputs in Python. (153)


Improvements

- Add `--eval-options` in `tools/test.py` to support eval options override, matching the behavior of other open-mmlab projects. (158)
- Support showing and saving painted results in `mmcls.apis.test` and `tools/test.py`, matching the behavior of other open-mmlab projects. (162)


Bug Fixes

- Fix configs for VGG, replace checkpoints converted from other repos with the ones trained by ourselves and upload the missing logs in the model zoo. (161)

New Features

- Add evaluation metrics: mAP, CP, CR, CF1, OP, OR, OF1 for multi-label task. (123)
- Add BCE loss for multi-label task. (130)
- Add focal loss for multi-label task. (131)
- Support PASCAL VOC 2007 dataset for multi-label task. (134)
- Add asymmetric loss for multi-label task. (132)
- Add analyze_results.py to select images for success/fail demonstration. (142)
- Support new metric that calculates the total number of occurrences of each label. (143)
- Support class-wise evaluation results. (143)
- Add thresholds in eval_metrics. (146)
- Add heads and a baseline config for multilabel task. (145)


Improvements

- Remove the models with 0 checkpoint and ignore the repeated papers when counting papers to gain more accurate model statistics. (135)
- Add tags in README.md. (137)
- Fix optional issues in docstring. (138)
- Update stat.py to classify papers. (139)
- Fix mismatched columns in README.md. (150)
- Fix test.py to support more evaluation metrics. (155)


Bug Fixes

- Fix bug in VGG weight_init. (140)
- Fix bug in 2 ResNet configs in which outdated heads were used. (147)
- Fix bug of misordered height and width in `RandomCrop` and `RandomResizedCrop`. (151)
- Fix missing `meta_keys` in `Collect`. (149, 152)

New Features

- Add evaluation metrics: precision, recall, and F1 score. (93)
- Allow config override during testing and inference with `--options`. (91 & 96)


Improvements

- Remove installation of MMCV from requirements. (90)
- Use `build_runner` to make runners more flexible. (54)
- Support to get category ids in `BaseDataset`. (72)
- Allow `CLASSES` override during `BaseDateset` initialization. (85)
- Allow input image as `numpy.ndarray` during inference. (87)
- Optimize MNIST config. (98)
- Add config links in model zoo documentation. (99)
- Use functions from MMCV to collect environment. (103)
- Refactor config files so that they are now categorized by methods. (116)
- Add README in config directory. (117)
- Add model statistics. (119)
- Refactor documentation structures. (126)


Bug Fixes

- Add missing `CLASSES` argument to dataset wrappers. (66)
- Fix slurm evaluation error during training. (69)
- Resolve error caused by shape in `Accuracy`. (104)
- Fix bug caused by extremely insufficient data in distributed sampler.(108)
- Fix bug in `gpu_ids` in distributed training. (107)
- Fix bug caused by extremely insufficient data in collect results during testing. (114)