添加mmclassification-0.24.1代码，删除mmclassification-speed-benchmark

openmmlab_test/mmclassification-speed-benchmark/demo/image_demo.py → openmmlab_test/mmclassification-0.24.1/demo/image_demo.py

+# Copyright (c) OpenMMLab. All rights reserved.
 from argparse import ArgumentParser
 
+import mmcv
+
 from mmcls.apis import inference_model, init_model, show_result_pyplot
 
 
@@ -8,6 +11,10 @@ def main():
     parser.add_argument('img', help='Image file')
     parser.add_argument('config', help='Config file')
     parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument(
+        '--show',
+        action='store_true',
+        help='Whether to show the predict results by matplotlib.')
     parser.add_argument(
         '--device', default='cuda:0', help='Device used for inference')
     args = parser.parse_args()
@@ -17,7 +24,9 @@ def main():
     # test a single image
     result = inference_model(model, args.img)
     # show the results
-    show_result_pyplot(model, args.img, result)
+    print(mmcv.dump(result, file_format='json', indent=4))
+    if args.show:
+        show_result_pyplot(model, args.img, result)
 
 
 if __name__ == '__main__':

openmmlab_test/mmclassification-0.24.1/demo/ipu_train_example.sh

+
+
+# get SOTA accuracy 81.2 for 224 input ViT fine-tuning, reference is below:
+# https://github.com/google-research/vision_transformer#available-vit-models
+# cfg: vit-base-p16_ft-4xb544_in1k-224_ipu train model in fp16 precision
+# 8 epoch, 2176 batch size, 16 IPUs, 4 replicas, model Tput = 5600 images, training time 0.6 hour roughly
+cfg_name=vit-base-p16_ft-4xb544_in1k-224_ipu
+python3 tools/train.py configs/vision_transformer/${cfg_name}.py --ipu-replicas 4 --no-validate &&
+python3 tools/test.py configs/vision_transformer/${cfg_name}.py work_dirs/${cfg_name}/latest.pth --metrics accuracy --device ipu

openmmlab_test/mmclassification-0.24.1/docker/Dockerfile

+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ENV TORCH_CUDA_ARCH_LIST="6.0 6.1 7.0+PTX"
+ENV TORCH_NVCC_FLAGS="-Xfatbin -compress-all"
+ENV CMAKE_PREFIX_PATH="(dirname(which conda))/../"
+
+RUN apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && apt-get clean \
+    && rm -rf /var/lib/apt/lists/*
+
+# Install MMCV
+RUN pip install openmim
+RUN mim install mmcv-full
+
+# Install MMClassification
+RUN conda clean --all
+RUN git clone https://github.com/open-mmlab/mmclassification.git
+WORKDIR ./mmclassification
+RUN pip install --no-cache-dir -e .

openmmlab_test/mmclassification-0.24.1/docker/serve/Dockerfile

+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+ARG MMCV="1.7.0"
+ARG MMCLS="0.24.1"
+
+ENV PYTHONUNBUFFERED TRUE
+
+RUN apt-get update && \
+    DEBIAN_FRONTEND=noninteractive apt-get install --no-install-recommends -y \
+    ca-certificates \
+    g++ \
+    openjdk-11-jre-headless \
+    # MMDet Requirements
+    ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 \
+    && rm -rf /var/lib/apt/lists/*
+
+ENV PATH="/opt/conda/bin:$PATH"
+RUN export FORCE_CUDA=1
+
+# TORCHSEVER
+RUN pip install torchserve torch-model-archiver
+
+# MMLAB
+ARG PYTORCH
+ARG CUDA
+RUN ["/bin/bash", "-c", "pip install mmcv-full==${MMCV} -f https://download.openmmlab.com/mmcv/dist/cu${CUDA//./}/torch${PYTORCH}/index.html"]
+RUN pip install mmcls==${MMCLS}
+
+RUN useradd -m model-server \
+    && mkdir -p /home/model-server/tmp
+
+COPY entrypoint.sh /usr/local/bin/entrypoint.sh
+
+RUN chmod +x /usr/local/bin/entrypoint.sh \
+    && chown -R model-server /home/model-server
+
+COPY config.properties /home/model-server/config.properties
+RUN mkdir /home/model-server/model-store && chown -R model-server /home/model-server/model-store
+
+EXPOSE 8080 8081 8082
+
+USER model-server
+WORKDIR /home/model-server
+ENV TEMP=/home/model-server/tmp
+ENTRYPOINT ["/usr/local/bin/entrypoint.sh"]
+CMD ["serve"]

openmmlab_test/mmclassification-0.24.1/docs/en/_static/css/readthedocs.css

+.header-logo {
+    background-image: url("../image/mmcls-logo.png");
+    background-size: 204px 40px;
+    height: 40px;
+    width: 204px;
+}
+
+pre {
+    white-space: pre;
+}
+
+article.pytorch-article section code {
+  padding: .2em .4em;
+  background-color: #f3f4f7;
+  border-radius: 5px;
+}
+
+/* Disable the change in tables */
+article.pytorch-article section table code {
+  padding: unset;
+  background-color: unset;
+  border-radius: unset;
+}
+
+table.autosummary td {
+  width: 50%
+}

openmmlab_test/mmclassification-0.24.1/docs/en/_static/js/custom.js

+var collapsedSections = ['Model zoo'];

openmmlab_test/mmclassification-0.24.1/docs/en/_templates/classtemplate.rst

+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:
+
+
+..
+  autogenerated from source/_templates/classtemplate.rst
+  note it does not have :inherited-members:

openmmlab_test/mmclassification-0.24.1/docs/en/api/apis.rst

+.. role:: hidden
+    :class: hidden-section
+
+mmcls.apis
+===================================
+
+These are some high-level APIs for classification tasks.
+
+.. contents:: mmcls.apis
+   :depth: 2
+   :local:
+   :backlinks: top
+
+.. currentmodule:: mmcls.apis
+
+Train
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   init_random_seed
+   set_random_seed
+   train_model
+
+Test
+------------------
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   single_gpu_test
+   multi_gpu_test
+
+Inference
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   init_model
+   inference_model
+   show_result_pyplot

openmmlab_test/mmclassification-0.24.1/docs/en/api/core.rst

+.. role:: hidden
+    :class: hidden-section
+
+mmcls.core
+===================================
+
+This package includes some runtime components. These components are useful in
+classification tasks but not supported by MMCV yet.
+
+.. note::
+
+   Some components may be moved to MMCV in the future.
+
+.. contents:: mmcls.core
+   :depth: 2
+   :local:
+   :backlinks: top
+
+.. currentmodule:: mmcls.core
+
+Evaluation
+------------------
+
+Evaluation metrics calculation functions
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   precision
+   recall
+   f1_score
+   precision_recall_f1
+   average_precision
+   mAP
+   support
+   average_performance
+   calculate_confusion_matrix
+
+Hook
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   ClassNumCheckHook
+   PreciseBNHook
+   CosineAnnealingCooldownLrUpdaterHook
+   MMClsWandbHook
+
+
+Optimizers
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   Lamb

openmmlab_test/mmclassification-0.24.1/docs/en/api/datasets.rst

+.. role:: hidden
+    :class: hidden-section
+
+mmcls.datasets
+===================================
+
+The ``datasets`` package contains several usual datasets for image classification tasks and some dataset wrappers.
+
+.. currentmodule:: mmcls.datasets
+
+Custom Dataset
+--------------
+
+.. autoclass:: CustomDataset
+
+ImageNet
+--------
+
+.. autoclass:: ImageNet
+
+.. autoclass:: ImageNet21k
+
+CIFAR
+-----
+
+.. autoclass:: CIFAR10
+
+.. autoclass:: CIFAR100
+
+MNIST
+-----
+
+.. autoclass:: MNIST
+
+.. autoclass:: FashionMNIST
+
+VOC
+---
+
+.. autoclass:: VOC
+
+StanfordCars Cars
+-----------------
+
+.. autoclass:: StanfordCars
+
+Base classes
+------------
+
+.. autoclass:: BaseDataset
+
+.. autoclass:: MultiLabelDataset
+
+Dataset Wrappers
+----------------
+
+.. autoclass:: ConcatDataset
+
+.. autoclass:: RepeatDataset
+
+.. autoclass:: ClassBalancedDataset

openmmlab_test/mmclassification-0.24.1/docs/en/api/models.rst

+.. role:: hidden
+    :class: hidden-section
+
+mmcls.models
+===================================
+
+The ``models`` package contains several sub-packages for addressing the different components of a model.
+
+- :ref:`classifiers`: The top-level module which defines the whole process of a classification model.
+- :ref:`backbones`: Usually a feature extraction network, e.g., ResNet, MobileNet.
+- :ref:`necks`: The component between backbones and heads, e.g., GlobalAveragePooling.
+- :ref:`heads`: The component for specific tasks. In MMClassification, we provides heads for classification.
+- :ref:`losses`: Loss functions.
+
+.. currentmodule:: mmcls.models
+
+.. autosummary::
+    :toctree: generated
+    :nosignatures:
+
+    build_classifier
+    build_backbone
+    build_neck
+    build_head
+    build_loss
+
+.. _classifiers:
+
+Classifier
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   BaseClassifier
+   ImageClassifier
+
+.. _backbones:
+
+Backbones
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   AlexNet
+   CSPDarkNet
+   CSPNet
+   CSPResNeXt
+   CSPResNet
+   Conformer
+   ConvMixer
+   ConvNeXt
+   DenseNet
+   DistilledVisionTransformer
+   EfficientNet
+   HRNet
+   LeNet5
+   MlpMixer
+   MobileNetV2
+   MobileNetV3
+   PCPVT
+   PoolFormer
+   RegNet
+   RepMLPNet
+   RepVGG
+   Res2Net
+   ResNeSt
+   ResNeXt
+   ResNet
+   ResNetV1c
+   ResNetV1d
+   ResNet_CIFAR
+   SEResNeXt
+   SEResNet
+   SVT
+   ShuffleNetV1
+   ShuffleNetV2
+   SwinTransformer
+   T2T_ViT
+   TIMMBackbone
+   TNT
+   VAN
+   VGG
+   VisionTransformer
+   EfficientFormer
+   HorNet
+
+.. _necks:
+
+Necks
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   GlobalAveragePooling
+   GeneralizedMeanPooling
+   HRFuseScales
+
+.. _heads:
+
+Heads
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   ClsHead
+   LinearClsHead
+   StackedLinearClsHead
+   MultiLabelClsHead
+   MultiLabelLinearClsHead
+   VisionTransformerClsHead
+   DeiTClsHead
+   ConformerHead
+
+.. _losses:
+
+Losses
+------------------
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: classtemplate.rst
+
+   Accuracy
+   AsymmetricLoss
+   CrossEntropyLoss
+   LabelSmoothLoss
+   FocalLoss
+   SeesawLoss

openmmlab_test/mmclassification-0.24.1/docs/en/api/models.utils.augment.rst

+.. role:: hidden
+    :class: hidden-section
+
+Batch Augmentation
+===================================
+
+Batch augmentation is the augmentation which involve multiple samples, such as Mixup and CutMix.
+
+In MMClassification, these batch augmentation is used as a part of :ref:`classifiers`. A typical usage is as below:
+
+.. code-block:: python
+
+   model = dict(
+       backbone = ...,
+       neck = ...,
+       head = ...,
+       train_cfg=dict(augments=[
+           dict(type='BatchMixup', alpha=0.8, prob=0.5, num_classes=num_classes),
+           dict(type='BatchCutMix', alpha=1.0, prob=0.5, num_classes=num_classes),
+       ]))
+   )
+
+.. currentmodule:: mmcls.models.utils.augment
+
+Mixup
+-----
+.. autoclass:: BatchMixupLayer
+
+CutMix
+------
+.. autoclass:: BatchCutMixLayer
+
+ResizeMix
+---------
+.. autoclass:: BatchResizeMixLayer