Merge d21e071 into 7e4502b

open-mmlab · Feb 6, 2023 · 16d4a6d · 16d4a6d
2 parents 7e4502b + d21e071
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diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
@@ -5,7 +5,7 @@ repos:
     hooks:
       - id: flake8
   - repo: https://github.com/PyCQA/isort
-    rev: 5.10.1
+    rev: 5.11.5
     hooks:
       - id: isort
   - repo: https://github.com/pre-commit/mirrors-yapf

diff --git a/demo/image_demo.py b/demo/image_demo.py
@@ -4,23 +4,37 @@
 from mmengine.fileio import dump
 from rich import print_json
 
-from mmcls.apis import inference_model, init_model
+from mmcls.apis import ImageClassificationInferencer
 
 
 def main():
     parser = ArgumentParser()
     parser.add_argument('img', help='Image file')
-    parser.add_argument('config', help='Config file')
-    parser.add_argument('checkpoint', help='Checkpoint file')
+    parser.add_argument('model', help='Model name or config file path')
+    parser.add_argument('--checkpoint', help='Checkpoint file path.')
     parser.add_argument(
-        '--device', default='cuda:0', help='Device used for inference')
+        '--show',
+        action='store_true',
+        help='Whether to show the prediction result in a window.')
+    parser.add_argument(
+        '--show-dir',
+        type=str,
+        help='The directory to save the visualization image.')
+    parser.add_argument('--device', help='Device used for inference')
     args = parser.parse_args()
 
     # build the model from a config file and a checkpoint file
-    model = init_model(args.config, args.checkpoint, device=args.device)
-    # test a single image
-    result = inference_model(model, args.img)
+    try:
+        inferencer = ImageClassificationInferencer(args.model, args.checkpoint)
+    except ValueError:
+        raise ValueError(
+            f'Unavailable model "{args.model}", you can specify find a model '
+            'name or a config file or find a model name from '
+            'https://mmclassification.readthedocs.io/en/1.x/modelzoo_statistics.html#all-checkpoints'  # noqa: E501
+        )
+    result = inferencer(args.img, show=args.show, show_dir=args.show_dir)[0]
     # show the results
+    result.pop('pred_scores')  # pred_scores is too verbose for a demo.
     print_json(dump(result, file_format='json', indent=4))
 
 

diff --git a/docs/en/_templates/classtemplate.rst → docs/en/_templates/autosummary/class.rst b/docs/en/_templates/classtemplate.rst → docs/en/_templates/autosummary/class.rst
@@ -8,7 +8,6 @@
 .. autoclass:: {{ name }}
     :members:
 
-
 ..
-  autogenerated from source/_templates/classtemplate.rst
+  autogenerated from _templates/autosummary/class.rst
   note it does not have :inherited-members:
diff --git a/docs/en/_templates/callable.rst b/docs/en/_templates/callable.rst
@@ -0,0 +1,14 @@
+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:
+    :special-members: __call__
+
+..
+  autogenerated from _templates/callable.rst
+  note it does not have :inherited-members:
diff --git a/docs/en/_templates/data_transform.rst b/docs/en/_templates/data_transform.rst
@@ -0,0 +1,13 @@
+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members: transform
+
+..
+  autogenerated from _templates/callable.rst
+  note it does not have :inherited-members:
diff --git a/docs/en/api/apis.rst b/docs/en/api/apis.rst
@@ -16,14 +16,26 @@ These are some high-level APIs for classification tasks.
 Model
 ------------------
 
-.. autofunction:: list_models
-
-.. autofunction:: get_model
-
-.. autofunction:: init_model
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
 
+   list_models
+   get_model
+   init_model
 
 Inference
 ------------------
 
-.. autofunction:: inference_model
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+   :template: callable.rst
+
+   ImageClassificationInferencer
+
+.. autosummary::
+   :toctree: generated
+   :nosignatures:
+
+   inference_model
diff --git a/docs/en/api/data_process.rst b/docs/en/api/data_process.rst
@@ -55,8 +55,8 @@ Processing and Augmentation
 
 .. autosummary::
    :toctree: generated
-   :template: classtemplate.rst
    :nosignatures:
+   :template: data_transform.rst
 
    Albumentations
    ColorJitter
@@ -75,8 +75,8 @@ augmentation transforms, such as ``AutoAugment`` and ``RandAugment``.
 
 .. autosummary::
    :toctree: generated
-   :template: classtemplate.rst
    :nosignatures:
+   :template: data_transform.rst
 
    AutoAugment
    RandAugment
@@ -106,7 +106,7 @@ And you can also configure a group of policies manually by selecting from the be
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
+   :template: data_transform.rst
 
    AutoContrast
    Brightness
@@ -129,8 +129,8 @@ Formatting
 
 .. autosummary::
    :toctree: generated
-   :template: classtemplate.rst
    :nosignatures:
+   :template: data_transform.rst
 
    Collect
    PackClsInputs
@@ -213,7 +213,6 @@ Note that the ``model.data_preprocessor`` has higher priority than ``data_prepro
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    ClsDataPreprocessor
 
@@ -257,7 +256,7 @@ Here is a list of batch augmentations can be used in MMClassification.
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
+   :template: callable.rst
 
    Mixup
    CutMix

diff --git a/docs/en/api/engine.rst b/docs/en/api/engine.rst
@@ -26,7 +26,6 @@ Hooks
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    ClassNumCheckHook
    PreciseBNHook
@@ -43,6 +42,5 @@ Optimizers
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    Lamb
diff --git a/docs/en/api/evaluation.rst b/docs/en/api/evaluation.rst
@@ -19,7 +19,6 @@ Single Label Metric
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    Accuracy
    SingleLabelMetric
@@ -29,7 +28,6 @@ Multi Label Metric
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    AveragePrecision
    MultiLabelMetric

diff --git a/docs/en/api/models.rst b/docs/en/api/models.rst
@@ -40,7 +40,6 @@ Classifiers
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
     BaseClassifier
     ImageClassifier
@@ -55,7 +54,6 @@ Backbones
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    AlexNet
    BEiT
@@ -123,7 +121,6 @@ Necks
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    GlobalAveragePooling
    GeneralizedMeanPooling
@@ -137,7 +134,6 @@ Heads
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    ClsHead
    LinearClsHead
@@ -159,7 +155,6 @@ Losses
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    CrossEntropyLoss
    LabelSmoothLoss
@@ -182,7 +177,6 @@ Common Components
 .. autosummary::
    :toctree: generated
    :nosignatures:
-   :template: classtemplate.rst
 
    InvertedResidual
    SELayer

diff --git a/docs/en/get_started.md b/docs/en/get_started.md
@@ -74,18 +74,10 @@ pip install -U openmim && mim install "mmcls>=1.0.0rc0"
 
 To verify whether MMClassification is installed correctly, we provide some sample codes to run an inference demo.
 
-**Step 1.** We need to download config and checkpoint files.
-
-```shell
-mim download mmcls --config resnet50_8xb32_in1k --dest .
-```
-
-**Step 2.** Verify the inference demo.
-
 Option (a). If you install mmcls from the source, just run the following command:
 
 ```shell
-python demo/image_demo.py demo/demo.JPEG resnet50_8xb32_in1k.py resnet50_8xb32_in1k_20210831-ea4938fc.pth --device cpu
+python demo/image_demo.py demo/demo.JPEG resnet18_8xb32_in1k --device cpu
 ```
 
 You will see the output result dict including `pred_label`, `pred_score` and `pred_class` in your terminal.
@@ -101,6 +93,11 @@ inference_model(model, 'demo/demo.JPEG')
 
 You will see a dict printed, including the predicted label, score and category name.
 
+```{note}
+The `resnet18_8xb32_in1k` is the model name, and you can use [`mmcls.list_models`](mmcls.apis.list_models) to
+explore all models, or search them on the [Model Zoo Summary](./modelzoo_statistics.md)
+```
+
 ## Customize Installation
 
 ### CUDA versions

diff --git a/docs/en/migration.md b/docs/en/migration.md
@@ -121,7 +121,7 @@ test_dataloader = val_dataloader
 
 Changes in **`pipeline`**:
 
-- The original formatting transforms **`ToTensor`**、**`ImageToTensor`**、**`Collect`** are combined as [`PackClsInputs`](mmcls.datasets.transforms.PackClsInputs).
+- The original formatting transforms **`ToTensor`**, **`ImageToTensor`** and **`Collect`** are combined as [`PackClsInputs`](mmcls.datasets.transforms.PackClsInputs).
 - We don't recommend to do **`Normalize`** in the dataset pipeline. Please remove it from pipelines and set it in the `data_preprocessor` field.
 - The argument `flip_prob` in [**`RandomFlip`**](mmcv.transforms.RandomFlip) is renamed to `flip`.
 - The argument `size` in [**`RandomCrop`**](mmcls.datasets.transforms.RandomCrop) is renamed to `crop_size`.
@@ -182,7 +182,7 @@ Changes in **`evaluation`**:
   The `interval` is moved to `train_cfg.val_interval`, see [the schedule settings](./user_guides/config.md#schedule-settings) and the `save_best`
   is moved to `default_hooks.checkpoint.save_best`, see [the runtime settings](./user_guides/config.md#runtime-settings).
 - The 'accuracy' metric is renamed to [`Accuracy`](mmcls.evaluation.Accuracy).
-- The 'precision'，'recall'，'f1-score' and 'support' are combined as [`SingleLabelMetric`](mmcls.evaluation.SingleLabelMetric), and use `items` argument to specify to calculate which metric.
+- The 'precision', 'recall', 'f1-score' and 'support' are combined as [`SingleLabelMetric`](mmcls.evaluation.SingleLabelMetric), and use `items` argument to specify to calculate which metric.
 - The 'mAP' is renamed to [`AveragePrecision`](mmcls.evaluation.AveragePrecision).
 - The 'CP', 'CR', 'CF1', 'OP', 'OR', 'OF1' are combined as [`MultiLabelMetric`](mmcls.evaluation.MultiLabelMetric), and use `items` and `average` arguments to specify to calculate which metric.
 
@@ -370,7 +370,7 @@ test_cfg = dict()  # Use the default test loop.
 </table>
 
 In fact, in OpenMMLab 2.0, we introduced `Loop` to control the behaviors in training, validation and test. And
-the functionalities of `Runner` are also changed. You can find more details in {external+mmengine:doc}`the MMEngine tutorials <tutorials/runner>`.
+the functionalities of `Runner` are also changed. You can find more details in {external+mmengine:doc}`the MMEngine tutorials <design/runner>`.
 
 ### Runtime settings
 
@@ -489,16 +489,16 @@ New field **`default_scope`**: The start point to search module for all registri
 
 The documentation can be found [here](mmcls.apis).
 
-|       Function       | Changes                                         |
-| :------------------: | :---------------------------------------------- |
-|     `init_model`     | No changes                                      |
-|  `inference_model`   | No changes                                      |
-|    `train_model`     | Removed, use `runner.train` to train.           |
-|   `multi_gpu_test`   | Removed, use `runner.test` to test.             |
-|  `single_gpu_test`   | Removed, use `runner.test` to test.             |
-| `show_result_pyplot` | Waiting for support.                            |
-|  `set_random_seed`   | Removed, use `mmengine.runner.set_random_seed`. |
-|  `init_random_seed`  | Removed, use `mmengine.dist.sync_random_seed`.  |
+|       Function       | Changes                                                                                                                                |
+| :------------------: | :------------------------------------------------------------------------------------------------------------------------------------- |
+|     `init_model`     | No changes                                                                                                                             |
+|  `inference_model`   | No changes. But we recommend to use [`mmcls.ImageClassificationInferencer`](mmcls.apis.ImageClassificationInferencer) instead.         |
+|    `train_model`     | Removed, use `runner.train` to train.                                                                                                  |
+|   `multi_gpu_test`   | Removed, use `runner.test` to test.                                                                                                    |
+|  `single_gpu_test`   | Removed, use `runner.test` to test.                                                                                                    |
+| `show_result_pyplot` | Removed, use [`mmcls.ImageClassificationInferencer`](mmcls.apis.ImageClassificationInferencer) to inference model and show the result. |
+|  `set_random_seed`   | Removed, use `mmengine.runner.set_random_seed`.                                                                                        |
+|  `init_random_seed`  | Removed, use `mmengine.dist.sync_random_seed`.                                                                                         |
 
 ### `mmcls.core`
 
@@ -572,15 +572,15 @@ Changes in [heads](mmcls.models.heads):
 
 ### `mmcls.utils`
 
-|           Function           | Changes                                                       |
-| :--------------------------: | :------------------------------------------------------------ |
-|        `collect_env`         | No changes                                                    |
-|      `get_root_logger`       | Removed, use `mmengine.MMLogger.get_current_instance`         |
-|       `load_json_log`        | Waiting for support                                           |
-|   `setup_multi_processes`    | Removed, use `mmengine.utils.dl_utils.setup_multi_processes`. |
-| `wrap_non_distributed_model` | Removed, we auto wrap the model in the runner.                |
-|   `wrap_distributed_model`   | Removed, we auto wrap the model in the runner.                |
-|     `auto_select_device`     | Removed, we auto select the device in the runner.             |
+|           Function           | Changes                                                                                                         |
+| :--------------------------: | :-------------------------------------------------------------------------------------------------------------- |
+|        `collect_env`         | No changes                                                                                                      |
+|      `get_root_logger`       | Removed, use [`mmengine.logging.MMLogger.get_current_instance`](mmengine.logging.MMLogger.get_current_instance) |
+|       `load_json_log`        | Waiting for support                                                                                             |
+|   `setup_multi_processes`    | Removed, use [`mmengine.utils.dl_utils.set_multi_processing`](mmengine.utils.dl_utils.set_multi_processing).    |
+| `wrap_non_distributed_model` | Removed, we auto wrap the model in the runner.                                                                  |
+|   `wrap_distributed_model`   | Removed, we auto wrap the model in the runner.                                                                  |
+|     `auto_select_device`     | Removed, we auto select the device in the runner.                                                               |
 
 ### Other changes
 

diff --git a/docs/zh_CN/_templates/classtemplate.rst → docs/zh_CN/_templates/autosummary/class.rst b/docs/zh_CN/_templates/classtemplate.rst → docs/zh_CN/_templates/autosummary/class.rst
@@ -8,7 +8,6 @@
 .. autoclass:: {{ name }}
     :members:
 
-
 ..
-  autogenerated from source/_templates/classtemplate.rst
+  autogenerated from _templates/autosummary/class.rst
   note it does not have :inherited-members:
diff --git a/docs/zh_CN/_templates/callable.rst b/docs/zh_CN/_templates/callable.rst
@@ -0,0 +1,14 @@
+.. role:: hidden
+    :class: hidden-section
+.. currentmodule:: {{ module }}
+
+
+{{ name | underline}}
+
+.. autoclass:: {{ name }}
+    :members:
+    :special-members: __call__
+
+..
+  autogenerated from _templates/callable.rst
+  note it does not have :inherited-members: