* implement training and evaluation on IPU
* fp16 SOTA
* Tput reaches 5600
* 123
* add poptorch dataloder
* change ipu_replicas to ipu-replicas
* add noqa to config long line(website)
* remove ipu dataloder test code
* del one blank line in test_builder
* refine the dataloder initialization
* fix a typo
* refine args for dataloder
* remove an annoted line
* process one more conflict
* adjust code structure in mmcv.ipu
* adjust ipu code structure in mmcv
* IPUDataloader to IPUDataLoader
* align with mmcv
* adjust according to mmcv
* mmcv code structre fixed
Co-authored-by: hudi <dihu@graphcore.ai>
* [Enhance] Imporve efficiency of precision, recall, f1_score and support.
* Fix bugs
* Use np.maximum since torch doesn't have maximum before torch 1.7
* Fix bug
* Add API inference in the docs and fix readthedocs config.
* Replace some relative link in docs.
* Format docstring for reStructuredText syntax.
* Fix vit paper link
* Fix docstring of `show_results` function in `BaseClassifier`.
* support thr
* replace thrs with thr
* fix docstring
* minor change
* revise according to comments
* revised according to comments
* revise according to comments
* rewrite basedataset.evaluate to avoid duplicate calculation
* minor change
* change thr to thrs
* add more unit test
* add model inference on single image
* rm --eval
* revise doc
* add inference tool and demo
* fix linting
* rename inference_image to inference_model
* infer pred_label and pred_score
* fix linting
* add docstr for inference
* add remove_keys
* add doc for inference
* dump results rather than outputs
* add class_names
* add related infer scripts
* add demo image and the first part of colab tutorial
* conduct evaluation in dataset
* return lst in simple_test
* compuate topk accuracy with numpy
* return outputs in test api
* merge inference and evaluation tool
* fix typo
* rm gt_labels in test conifg
* get gt_labels during evaluation
* sperate the ipython notebook to another PR
* return tensor for onnx_export
* detach var in simple_test
* rm inference script
* rm inference script
* construct data dict to replace LoadImage
* print first predicted result if args.out is None
* modify test_pipeline in inference
* refactor class_names of imagenet
* set class_to_idx as a property in base dataset
* output pred_class during inference
* remove unused docstr