add resources part

docs/en/useful_tools/complexity_analysis.md

+# Model Complexity Analysis
+
+## Get the FLOPs and params (experimental)
+
+We provide a script adapted from [MMEngine](https://github.com/open-mmlab/mmengine/blob/main/mmengine/analysis/complexity_analysis.py) to compute the FLOPs and params of a given model.
+
+```shell
+python tools/analysis_tools/get_flops.py ${CONFIG_FILE} [--shape ${INPUT_SHAPE}]
+```
+
+Description of all arguments:
+
+- `config`: The path of the model config file.
+- `--shape`: Input size, support single value or double value parameter, such as `--shape 256` or `--shape 224 256`. If not set, default to be `224 224`.
+
+Example:
+
+```shell
+python tools/analysis_tools/get_flops.py configs/resnet/resnet50_8xb32_in1k.py
+```
+
+You will get the final result like this.
+
+```text
+==============================
+Input shape: (3, 224, 224)
+Flops: 4.109G
+Params: 25.557M
+Activation: 11.114M
+==============================
+```
+
+Also, you will get the detailed complexity information of each layer like this:
+
+```text
++--------------------------+----------------------+-----------+--------------+
+| module                   | #parameters or shape | #flops    | #activations |
++--------------------------+----------------------+-----------+--------------+
+| model                    | 25.557M              | 4.109G    | 11.114M      |
+|  backbone                |  23.508M             |  4.109G   |  11.114M     |
+|   backbone.conv1         |   9.408K             |   0.118G  |   0.803M     |
+|    backbone.conv1.weight |    (64, 3, 7, 7)     |           |              |
+|   backbone.bn1           |   0.128K             |   1.606M  |   0          |
+|    backbone.bn1.weight   |    (64,)             |           |              |
+|    backbone.bn1.bias     |    (64,)             |           |              |
+|   backbone.layer1        |   0.216M             |   0.677G  |   4.415M     |
+|    backbone.layer1.0     |    75.008K           |    0.235G |    2.007M    |
+|    backbone.layer1.1     |    70.4K             |    0.221G |    1.204M    |
+|    backbone.layer1.2     |    70.4K             |    0.221G |    1.204M    |
+|   backbone.layer2        |   1.22M              |   1.034G  |   3.111M     |
+|    backbone.layer2.0     |    0.379M            |    0.375G |    1.305M    |
+|    backbone.layer2.1     |    0.28M             |    0.22G  |    0.602M    |
+|    backbone.layer2.2     |    0.28M             |    0.22G  |    0.602M    |
+|    backbone.layer2.3     |    0.28M             |    0.22G  |    0.602M    |
+|   backbone.layer3        |   7.098M             |   1.469G  |   2.158M     |
+|    backbone.layer3.0     |    1.512M            |    0.374G |    0.652M    |
+|    backbone.layer3.1     |    1.117M            |    0.219G |    0.301M    |
+|    backbone.layer3.2     |    1.117M            |    0.219G |    0.301M    |
+|    backbone.layer3.3     |    1.117M            |    0.219G |    0.301M    |
+|    backbone.layer3.4     |    1.117M            |    0.219G |    0.301M    |
+|    backbone.layer3.5     |    1.117M            |    0.219G |    0.301M    |
+|   backbone.layer4        |   14.965M            |   0.81G   |   0.627M     |
+|    backbone.layer4.0     |    6.04M             |    0.373G |    0.326M    |
+|    backbone.layer4.1     |    4.463M            |    0.219G |    0.151M    |
+|    backbone.layer4.2     |    4.463M            |    0.219G |    0.151M    |
+|  head.fc                 |  2.049M              |           |              |
+|   head.fc.weight         |   (1000, 2048)       |           |              |
+|   head.fc.bias           |   (1000,)            |           |              |
+|  neck.gap                |                      |  0.1M     |  0           |
++--------------------------+----------------------+-----------+--------------+
+```
+
+```{warning}
+This tool is still experimental and we do not guarantee that the number is correct. You may well use the result for simple comparisons, but double-check it before you adopt it in technical reports or papers.
+- FLOPs are related to the input shape while parameters are not. The default input shape is (1, 3, 224, 224).
+- Some operators are not counted into FLOPs like custom operators. Refer to [`mmengine.analysis.complexity_analysis._DEFAULT_SUPPORTED_FLOP_OPS`](https://github.com/open-mmlab/mmengine/blob/main/mmengine/analysis/complexity_analysis.py) for details.
+```

docs/en/useful_tools/print_config.md

+# How to Get the Complete Config
+
+We also provide the `print_config.py` tools to print the complete configuration of the given experiment.
+You can check each item of the config before the training by using the following command.
+
+## Description
+
+`tools/misc/print_config.py` prints the whole config verbatim, expanding all its imports.
+
+```shell
+python tools/misc/print_config.py ${CONFIG} [--cfg-options ${CFG_OPTIONS}]
+```
+
+Description of all arguments:
+
+- `config` : The path of the model config file.
+- `--cfg-options`: If specified, the key-value pair config will be merged into the config file, for more details please refer to [Learn about Configs](../user_guides/config.md)
+
+## Examples
+
+```shell
+# Print a complete config
+python tools/misc/print_config.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py
+
+# Save the complete config to a independent config file.
+python tools/misc/print_config.py configs/t2t_vit/t2t-vit-t-14_8xb64_in1k.py > final_config.py
+```

docs/en/useful_tools/scheduler_visualization.md

+# Hyper-parameter Scheduler Visualization
+
+This tool aims to help the user to check the hyper-parameter scheduler of the optimizer (without training), which support the "learning rate" or "momentum"
+
+## Introduce the scheduler visualization tool
+
+```bash
+python tools/visualization/vis_scheduler.py \
+    ${CONFIG_FILE} \
+    [-p, --parameter ${PARAMETER_NAME}] \
+    [-d, --dataset-size ${DATASET_SIZE}] \
+    [-n, --ngpus ${NUM_GPUs}] \
+    [-s, --save-path ${SAVE_PATH}] \
+    [--title ${TITLE}] \
+    [--style ${STYLE}] \
+    [--window-size ${WINDOW_SIZE}] \
+    [--cfg-options]
+```
+
+**Description of all arguments**：
+
+- `config`: The path of a model config file.
+- **`-p, --parameter`**: The param to visualize its change curve, choose from "lr" and "momentum". Default to use "lr".
+- **`-d, --dataset-size`**: The size of the datasets. If set，`build_dataset` will be skipped and `${DATASET_SIZE}` will be used as the size. Default to use the function `build_dataset`.
+- **`-n, --ngpus`**: The number of GPUs used in training, default to be 1.
+- **`-s, --save-path`**: The learning rate curve plot save path, default not to save.
+- `--title`: Title of figure. If not set, default to be config file name.
+- `--style`: Style of plt. If not set, default to be `whitegrid`.
+- `--window-size`: The shape of the display window. If not specified, it will be set to `12*7`. If used, it must be in the format `'W*H'`.
+- `--cfg-options`: Modifications to the configuration file, refer to [Learn about Configs](../user_guides/config.md).
+
+```{note}
+Loading annotations maybe consume much time, you can directly specify the size of the dataset with `-d, dataset-size` to save time.
+```
+
+## How to plot the learning rate curve without training
+
+You can use the following command to plot the step learning rate schedule used in the config `configs/swin_transformer/swin-base_16xb64_in1k.py`:
+
+```bash
+python tools/visualization/vis_scheduler.py configs/swin_transformer/swin-base_16xb64_in1k.py --dataset-size 1281167 --ngpus 16
+```
+
+<div align=center><img src="https://user-images.githubusercontent.com/26739999/226544329-cf3a3d45-6ab3-48aa-8972-2c2a58c35e62.png" style=" width: auto; height: 40%; "></div>

docs/zh_CN/_static/image/confusion-matrix.png

+../../../en/_static/image/confusion-matrix.png
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