diff --git a/.dev_scripts/benchmark_valid_flops.py b/.dev_scripts/benchmark_valid_flops.py
new file mode 100644
index 000000000..a02e9056a
--- /dev/null
+++ b/.dev_scripts/benchmark_valid_flops.py
@@ -0,0 +1,282 @@
+import logging
+import re
+import tempfile
+from argparse import ArgumentParser
+from collections import OrderedDict
+from functools import partial
+from pathlib import Path
+
+import numpy as np
+import pandas as pd
+import torch
+from mmengine import Config, DictAction
+from mmengine.analysis import get_model_complexity_info
+from mmengine.analysis.print_helper import _format_size
+from mmengine.fileio import FileClient
+from mmengine.logging import MMLogger
+from mmengine.model import revert_sync_batchnorm
+from mmengine.registry import init_default_scope
+from mmengine.runner import Runner
+from modelindex.load_model_index import load
+from rich.console import Console
+from rich.table import Table
+from rich.text import Text
+from tqdm import tqdm
+
+from mmocr.registry import MODELS
+
+console = Console()
+MMOCR_ROOT = Path(__file__).absolute().parents[1]
+
+
+def parse_args():
+    parser = ArgumentParser(description='Valid all models in model-index.yml')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[1280, 800],
+        help='input image size')
+    parser.add_argument(
+        '--checkpoint_root',
+        help='Checkpoint file root path. If set, load checkpoint before test.')
+    parser.add_argument('--img', default='demo/demo.jpg', help='Image file')
+    parser.add_argument('--models', nargs='+', help='models name to inference')
+    parser.add_argument(
+        '--batch-size',
+        type=int,
+        default=1,
+        help='The batch size during the inference.')
+    parser.add_argument(
+        '--flops', action='store_true', help='Get Flops and Params of models')
+    parser.add_argument(
+        '--flops-str',
+        action='store_true',
+        help='Output FLOPs and params counts in a string form.')
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
+    parser.add_argument(
+        '--size_divisor',
+        type=int,
+        default=32,
+        help='Pad the input image, the minimum size that is divisible '
+        'by size_divisor, -1 means do not pad the image.')
+    args = parser.parse_args()
+    return args
+
+
+def inference(config_file, checkpoint, work_dir, args, exp_name):
+    logger = MMLogger.get_instance(name='MMLogger')
+    logger.warning('if you want test flops, please make sure torch>=1.12')
+    cfg = Config.fromfile(config_file)
+    cfg.work_dir = work_dir
+    cfg.load_from = checkpoint
+    cfg.log_level = 'WARN'
+    cfg.experiment_name = exp_name
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+    init_default_scope(cfg.get('default_scope', 'mmocr'))
+
+    # forward the model
+    result = {'model': config_file.stem}
+
+    if args.flops:
+
+        if len(args.shape) == 1:
+            h = w = args.shape[0]
+        elif len(args.shape) == 2:
+            h, w = args.shape
+        else:
+            raise ValueError('invalid input shape')
+        divisor = args.size_divisor
+        if divisor > 0:
+            h = int(np.ceil(h / divisor)) * divisor
+            w = int(np.ceil(w / divisor)) * divisor
+
+        input_shape = (3, h, w)
+        result['resolution'] = input_shape
+
+        try:
+            cfg = Config.fromfile(config_file)
+            if args.cfg_options is not None:
+                cfg.merge_from_dict(args.cfg_options)
+
+            model = MODELS.build(cfg.model)
+            input = torch.rand(1, *input_shape)
+            if torch.cuda.is_available():
+                model.cuda()
+                input = input.cuda()
+            model = revert_sync_batchnorm(model)
+            inputs = (input, )
+            model.eval()
+            outputs = get_model_complexity_info(
+                model, input_shape, inputs, show_table=False, show_arch=False)
+            flops = outputs['flops']
+            params = outputs['params']
+            activations = outputs['activations']
+            result['Get Types'] = 'Random input'
+        except:  # noqa 772
+            logger = MMLogger.get_instance(name='MMLogger')
+            logger.warning(
+                'Direct get flops failed, try to get flops with data')
+            cfg = Config.fromfile(config_file)
+            data_loader = Runner.build_dataloader(cfg.val_dataloader)
+            data_batch = next(iter(data_loader))
+            model = MODELS.build(cfg.model)
+            if torch.cuda.is_available():
+                model = model.cuda()
+            model = revert_sync_batchnorm(model)
+            model.eval()
+            _forward = model.forward
+            data = model.data_preprocessor(data_batch)
+            del data_loader
+            model.forward = partial(
+                _forward, data_samples=data['data_samples'])
+            outputs = get_model_complexity_info(
+                model,
+                input_shape,
+                data['inputs'],
+                show_table=False,
+                show_arch=False)
+            flops = outputs['flops']
+            params = outputs['params']
+            activations = outputs['activations']
+            result['Get Types'] = 'Dataloader'
+
+        if args.flops_str:
+            flops = _format_size(flops)
+            params = _format_size(params)
+            activations = _format_size(activations)
+
+        result['flops'] = flops
+        result['params'] = params
+
+    return result
+
+
+def show_summary(summary_data, args):
+    table = Table(title='Validation Benchmark Regression Summary')
+    table.add_column('Model')
+    table.add_column('Validation')
+    table.add_column('Resolution (c, h, w)')
+    if args.flops:
+        table.add_column('Flops', justify='right', width=11)
+        table.add_column('Params', justify='right')
+
+    for model_name, summary in summary_data.items():
+        row = [model_name]
+        valid = summary['valid']
+        color = 'green' if valid == 'PASS' else 'red'
+        row.append(f'[{color}]{valid}[/{color}]')
+        if valid == 'PASS':
+            row.append(str(summary['resolution']))
+            if args.flops:
+                row.append(str(summary['flops']))
+                row.append(str(summary['params']))
+        table.add_row(*row)
+
+    console.print(table)
+    table_data = {
+        x.header: [Text.from_markup(y).plain for y in x.cells]
+        for x in table.columns
+    }
+    table_pd = pd.DataFrame(table_data)
+    table_pd.to_csv('./mmocr_flops.csv')
+
+
+# Sample test whether the inference code is correct
+def main(args):
+    model_index_file = MMOCR_ROOT / 'model-index.yml'
+    model_index = load(str(model_index_file))
+    model_index.build_models_with_collections()
+    models = OrderedDict({model.name: model for model in model_index.models})
+
+    logger = MMLogger(
+        'validation',
+        logger_name='validation',
+        log_file='benchmark_test_image.log',
+        log_level=logging.INFO)
+
+    if args.models:
+        patterns = [
+            re.compile(pattern.replace('+', '_')) for pattern in args.models
+        ]
+        filter_models = {}
+        for k, v in models.items():
+            k = k.replace('+', '_')
+            if any([re.match(pattern, k) for pattern in patterns]):
+                filter_models[k] = v
+        if len(filter_models) == 0:
+            print('No model found, please specify models in:')
+            print('\n'.join(models.keys()))
+            return
+        models = filter_models
+
+    summary_data = {}
+    tmpdir = tempfile.TemporaryDirectory()
+    for model_name, model_info in tqdm(models.items()):
+
+        if model_info.config is None:
+            continue
+
+        model_info.config = model_info.config.replace('%2B', '+')
+        config = Path(model_info.config)
+
+        try:
+            config.exists()
+        except:  # noqa 722
+            logger.error(f'{model_name}: {config} not found.')
+            continue
+
+        logger.info(f'Processing: {model_name}')
+
+        http_prefix = 'https://download.openmmlab.com/mmocr/'
+        if args.checkpoint_root is not None:
+            root = args.checkpoint_root
+            if 's3://' in args.checkpoint_root:
+                from petrel_client.common.exception import AccessDeniedError
+                file_client = FileClient.infer_client(uri=root)
+                checkpoint = file_client.join_path(
+                    root, model_info.weights[len(http_prefix):])
+                try:
+                    exists = file_client.exists(checkpoint)
+                except AccessDeniedError:
+                    exists = False
+            else:
+                checkpoint = Path(root) / model_info.weights[len(http_prefix):]
+                exists = checkpoint.exists()
+            if exists:
+                checkpoint = str(checkpoint)
+            else:
+                print(f'WARNING: {model_name}: {checkpoint} not found.')
+                checkpoint = None
+        else:
+            checkpoint = None
+
+        try:
+            # build the model from a config file and a checkpoint file
+            result = inference(MMOCR_ROOT / config, checkpoint, tmpdir.name,
+                               args, model_name)
+            result['valid'] = 'PASS'
+        except Exception:  # noqa 722
+            import traceback
+            logger.error(f'"{config}" :\n{traceback.format_exc()}')
+            result = {'valid': 'FAIL'}
+
+        summary_data[model_name] = result
+
+    tmpdir.cleanup()
+    show_summary(summary_data, args)
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)
diff --git a/docs/en/user_guides/useful_tools.md b/docs/en/user_guides/useful_tools.md
index 9ba6a49dd..27c028704 100644
--- a/docs/en/user_guides/useful_tools.md
+++ b/docs/en/user_guides/useful_tools.md
@@ -29,7 +29,7 @@ python tools/visualizations/browse_dataset.py \
 | -t, --task          | `auto`, `textdet`, `textrecog`        | Specify the task type of the dataset. If `auto`, the task type will be inferred from the config. If the script is unable to infer the task type, you need to specify it manually. Defaults to `auto`. |
 | -n, --show-number   | int                                   | The number of samples to visualized. If not specified, display all images in the dataset.                                                        |
 | -i, --show-interval | float                                 | Interval of visualization (s), defaults to 2.                                                                                                    |
-| --cfg-options       | float                                 | Override configs. [Example](./config.md#command-line-modification)                                                                               |
+| --cfg-options       | str                                   | Override configs. [Example](./config.md#command-line-modification)                                                                               |
 
 #### Examples
 
@@ -110,19 +110,15 @@ python tools/analysis_tools/offline_eval.py configs/textdet/psenet/psenet_r50_fp
 
 In addition, based on this tool, users can also convert predictions obtained from other libraries into MMOCR-supported formats, then use MMOCR's built-in metrics to evaluate them.
 
-| ARGS          | Type  | Description                                                        |
-| ------------- | ----- | ------------------------------------------------------------------ |
-| config        | str   | (required) Path to the config.                                     |
-| pkl_results   | str   | (required) The saved predictions.                                  |
-| --cfg-options | float | Override configs. [Example](./config.md#command-line-modification) |
+| ARGS          | Type | Description                                                        |
+| ------------- | ---- | ------------------------------------------------------------------ |
+| config        | str  | (required) Path to the config.                                     |
+| pkl_results   | str  | (required) The saved predictions.                                  |
+| --cfg-options | str  | Override configs. [Example](./config.md#command-line-modification) |
 
 ### Calculate FLOPs and the Number of Parameters
 
-We provide a method to calculate the FLOPs and the number of parameters, first we install the dependencies using the following command.
-
-```shell
-pip install fvcore
-```
+We provide a method to calculate the FLOPs and the number of parameters.
 
 The usage of the script to calculate FLOPs and the number of parameters is as follows.
 
@@ -130,10 +126,11 @@ The usage of the script to calculate FLOPs and the number of parameters is as fo
 python tools/analysis_tools/get_flops.py ${config} --shape ${IMAGE_SHAPE}
 ```
 
-| ARGS    | Type | Description                                                                               |
-| ------- | ---- | ----------------------------------------------------------------------------------------- |
-| config  | str  | (required) Path to the config.                                                            |
-| --shape | int  | Image size to use when calculating FLOPs, such as `--shape 320 320`. Default is `640 640` |
+| ARGS          | Type          | Description                                                                                                                                                                    |
+| ------------- | ------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------ |
+| config        | str           | (required) Path to the config.                                                                                                                                                 |
+| --shape       | int * \[1-3\] | Image size to use when calculating FLOPs, such as `--shape 320 320`. It can accept 1 to 3 arguments, representing `H&W`, `H, W` and `C, H, W` respectively (C = 3 by default). Default is `640 640` |
+| --cfg-options | str           | Override configs. [Example](./config.md#command-line-modification)                                                                                                             |
 
 For example, you can run the following command to get FLOPs and the number of parameters of `dbnet_resnet18_fpnc_100k_synthtext.py`:
 
@@ -144,51 +141,13 @@ python tools/analysis_tools/get_flops.py configs/textdet/dbnet/dbnet_resnet18_fp
 The output is as follows:
 
 ```shell
-input shape is  (1, 3, 1024, 1024)
-| module                    | #parameters or shape | #flops  |
-| :------------------------ | :------------------- | :------ |
-| model                     | 12.341M              | 63.955G |
-| backbone                  | 11.177M              | 38.159G |
-| backbone.conv1            | 9.408K               | 2.466G  |
-| backbone.conv1.weight     | (64, 3, 7, 7)        |         |
-| backbone.bn1              | 0.128K               | 83.886M |
-| backbone.bn1.weight       | (64,)                |         |
-| backbone.bn1.bias         | (64,)                |         |
-| backbone.layer1           | 0.148M               | 9.748G  |
-| backbone.layer1.0         | 73.984K              | 4.874G  |
-| backbone.layer1.1         | 73.984K              | 4.874G  |
-| backbone.layer2           | 0.526M               | 8.642G  |
-| backbone.layer2.0         | 0.23M                | 3.79G   |
-| backbone.layer2.1         | 0.295M               | 4.853G  |
-| backbone.layer3           | 2.1M                 | 8.616G  |
-| backbone.layer3.0         | 0.919M               | 3.774G  |
-| backbone.layer3.1         | 1.181M               | 4.842G  |
-| backbone.layer4           | 8.394M               | 8.603G  |
-| backbone.layer4.0         | 3.673M               | 3.766G  |
-| backbone.layer4.1         | 4.721M               | 4.837G  |
-| neck                      | 0.836M               | 14.887G |
-| neck.lateral_convs        | 0.246M               | 2.013G  |
-| neck.lateral_convs.0.conv | 16.384K              | 1.074G  |
-| neck.lateral_convs.1.conv | 32.768K              | 0.537G  |
-| neck.lateral_convs.2.conv | 65.536K              | 0.268G  |
-| neck.lateral_convs.3.conv | 0.131M               | 0.134G  |
-| neck.smooth_convs         | 0.59M                | 12.835G |
-| neck.smooth_convs.0.conv  | 0.147M               | 9.664G  |
-| neck.smooth_convs.1.conv  | 0.147M               | 2.416G  |
-| neck.smooth_convs.2.conv  | 0.147M               | 0.604G  |
-| neck.smooth_convs.3.conv  | 0.147M               | 0.151G  |
-| det_head                  | 0.329M               | 10.909G |
-| det_head.binarize         | 0.164M               | 10.909G |
-| det_head.binarize.0       | 0.147M               | 9.664G  |
-| det_head.binarize.1       | 0.128K               | 20.972M |
-| det_head.binarize.3       | 16.448K              | 1.074G  |
-| det_head.binarize.4       | 0.128K               | 83.886M |
-| det_head.binarize.6       | 0.257K               | 67.109M |
-| det_head.threshold        | 0.164M               |         |
-| det_head.threshold.0      | 0.147M               |         |
-| det_head.threshold.1      | 0.128K               |         |
-| det_head.threshold.3      | 16.448K              |         |
-| det_head.threshold.4      | 0.128K               |         |
-| det_head.threshold.6      | 0.257K               |         |
+
+==============================
+Compute type: Random input
+Input shape: torch.Size([1024, 1024])
+Flops: 63.737G
+Params: 12.341M
+==============================
 !!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
+
 ```
diff --git a/docs/zh_cn/user_guides/useful_tools.md b/docs/zh_cn/user_guides/useful_tools.md
index f72322cef..c6b688b98 100644
--- a/docs/zh_cn/user_guides/useful_tools.md
+++ b/docs/zh_cn/user_guides/useful_tools.md
@@ -29,7 +29,7 @@ python tools/visualizations/browse_dataset.py \
 | -t, --task          | `auto`, `textdet`, `textrecog`        | 用于指定可视化数据集的任务类型。`auto`：自动模式，将依据给定的配置文件自动选择合适的任务类型，如果无法自动获取任务类型，则需要用户手动指定为 `textdet` 文本检测任务 或 `textrecog` 文本识别任务。默认采用 `auto` 自动模式。 |
 | -n, --show-number   | int                                   | 指定需要可视化的样本数量。若该参数缺省则默认将可视化全部图片。                                                                                   |
 | -i, --show-interval | float                                 | 可视化图像间隔时间，默认为 2 秒。                                                                                                                |
-| --cfg-options       | float                                 | 用于覆盖配置文件中的参数，详见[示例](./config.md#command-line-modification)。                                                                    |
+| --cfg-options       | str                                   | 用于覆盖配置文件中的参数，详见[示例](./config.md#command-line-modification)。                                                                    |
 
 #### 用法示例
 
@@ -110,11 +110,11 @@ python tools/analysis_tools/offline_eval.py configs/textdet/psenet/psenet_r50_fp
 
 此外，基于此工具，用户也可以将其他算法库获取的预测结果转换成 MMOCR 支持的格式，从而使用 MMOCR 内置的评估指标来对其他算法库的模型进行评测。
 
-| 参数          | 类型  | 说明                                                             |
-| ------------- | ----- | ---------------------------------------------------------------- |
-| config        | str   | （必须）配置文件路径。                                           |
-| pkl_results   | str   | （必须）预先保存的预测结果文件。                                 |
-| --cfg-options | float | 用于覆写配置文件中的指定参数。[示例](./config.md#命令行修改配置) |
+| 参数          | 类型 | 说明                                                             |
+| ------------- | ---- | ---------------------------------------------------------------- |
+| config        | str  | （必须）配置文件路径。                                           |
+| pkl_results   | str  | （必须）预先保存的预测结果文件。                                 |
+| --cfg-options | str  | 用于覆写配置文件中的指定参数。[示例](./config.md#命令行修改配置) |
 
 ### 计算 FLOPs 和参数量
 
@@ -130,10 +130,11 @@ pip install fvcore
 python tools/analysis_tools/get_flops.py ${config} --shape ${IMAGE_SHAPE}
 ```
 
-| 参数    | 类型   | 说明                                                               |
-| ------- | ------ | ------------------------------------------------------------------ |
-| config  | str    | （必须) 配置文件路径。                                             |
-| --shape | int\*2 | 计算 FLOPs 使用的图片尺寸，如 `--shape 320 320`。 默认为 `640 640` |
+| 参数          | 类型          | 说明                                                                                                                                                 |
+| ------------- | ------------- | ---------------------------------------------------------------------------------------------------------------------------------------------------- |
+| config        | str           | （必须) 配置文件路径。                                                                                                                               |
+| --shape       | int * \[1-3\] | 计算 FLOPs 使用的图片尺寸，例如 `--shape 320 320`。它可以接受 1 到 3 个参数，分别表示 `H&W`，`H, W` 和 `C, H, W`（C = 3 为默认值）。默认为 `640 640` |
+| --cfg-options | str           | 用于覆写配置文件中的指定参数。[示例](./config.md#命令行修改配置)                                                                                     |
 
 获取 `dbnet_resnet18_fpnc_100k_synthtext.py` FLOPs 和参数量的示例命令如下。
 
@@ -144,51 +145,13 @@ python tools/analysis_tools/get_flops.py configs/textdet/dbnet/dbnet_resnet18_fp
 输出如下：
 
 ```shell
-input shape is  (1, 3, 1024, 1024)
-| module                    | #parameters or shape | #flops  |
-| :------------------------ | :------------------- | :------ |
-| model                     | 12.341M              | 63.955G |
-| backbone                  | 11.177M              | 38.159G |
-| backbone.conv1            | 9.408K               | 2.466G  |
-| backbone.conv1.weight     | (64, 3, 7, 7)        |         |
-| backbone.bn1              | 0.128K               | 83.886M |
-| backbone.bn1.weight       | (64,)                |         |
-| backbone.bn1.bias         | (64,)                |         |
-| backbone.layer1           | 0.148M               | 9.748G  |
-| backbone.layer1.0         | 73.984K              | 4.874G  |
-| backbone.layer1.1         | 73.984K              | 4.874G  |
-| backbone.layer2           | 0.526M               | 8.642G  |
-| backbone.layer2.0         | 0.23M                | 3.79G   |
-| backbone.layer2.1         | 0.295M               | 4.853G  |
-| backbone.layer3           | 2.1M                 | 8.616G  |
-| backbone.layer3.0         | 0.919M               | 3.774G  |
-| backbone.layer3.1         | 1.181M               | 4.842G  |
-| backbone.layer4           | 8.394M               | 8.603G  |
-| backbone.layer4.0         | 3.673M               | 3.766G  |
-| backbone.layer4.1         | 4.721M               | 4.837G  |
-| neck                      | 0.836M               | 14.887G |
-| neck.lateral_convs        | 0.246M               | 2.013G  |
-| neck.lateral_convs.0.conv | 16.384K              | 1.074G  |
-| neck.lateral_convs.1.conv | 32.768K              | 0.537G  |
-| neck.lateral_convs.2.conv | 65.536K              | 0.268G  |
-| neck.lateral_convs.3.conv | 0.131M               | 0.134G  |
-| neck.smooth_convs         | 0.59M                | 12.835G |
-| neck.smooth_convs.0.conv  | 0.147M               | 9.664G  |
-| neck.smooth_convs.1.conv  | 0.147M               | 2.416G  |
-| neck.smooth_convs.2.conv  | 0.147M               | 0.604G  |
-| neck.smooth_convs.3.conv  | 0.147M               | 0.151G  |
-| det_head                  | 0.329M               | 10.909G |
-| det_head.binarize         | 0.164M               | 10.909G |
-| det_head.binarize.0       | 0.147M               | 9.664G  |
-| det_head.binarize.1       | 0.128K               | 20.972M |
-| det_head.binarize.3       | 16.448K              | 1.074G  |
-| det_head.binarize.4       | 0.128K               | 83.886M |
-| det_head.binarize.6       | 0.257K               | 67.109M |
-| det_head.threshold        | 0.164M               |         |
-| det_head.threshold.0      | 0.147M               |         |
-| det_head.threshold.1      | 0.128K               |         |
-| det_head.threshold.3      | 16.448K              |         |
-| det_head.threshold.4      | 0.128K               |         |
-| det_head.threshold.6      | 0.257K               |         |
+
+==============================
+Compute type: Random input
+Input shape: torch.Size([1024, 1024])
+Flops: 63.737G
+Params: 12.341M
+==============================
 !!!Please be cautious if you use the results in papers. You may need to check if all ops are supported and verify that the flops computation is correct.
+
 ```
diff --git a/tools/analysis_tools/get_flops.py b/tools/analysis_tools/get_flops.py
index caa97203a..43351bd6f 100644
--- a/tools/analysis_tools/get_flops.py
+++ b/tools/analysis_tools/get_flops.py
@@ -1,54 +1,153 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import argparse
+import tempfile
+from functools import partial
+from pathlib import Path
 
 import torch
-from fvcore.nn import FlopCountAnalysis, flop_count_table
-from mmengine import Config
+from mmengine.config import Config, DictAction
+from mmengine.logging import MMLogger
+from mmengine.model import revert_sync_batchnorm
 from mmengine.registry import init_default_scope
+from mmengine.runner import Runner
 
 from mmocr.registry import MODELS
 
+try:
+    from mmengine.analysis import get_model_complexity_info
+    from mmengine.analysis.print_helper import _format_size
+except ImportError:
+    raise ImportError('Please upgrade to mmengine>=0.6.0')
+
 
 def parse_args():
-    parser = argparse.ArgumentParser(description='Train a detector')
+    parser = argparse.ArgumentParser(description='Get model flops')
     parser.add_argument('config', help='train config file path')
     parser.add_argument(
         '--shape',
         type=int,
         nargs='+',
         default=[640, 640],
-        help='input image size')
+        help='Image size to use when calculating FLOPs, e.g. '
+        '`--shape 320 320`. It can accept 1 to 3 arguments, representing'
+        ' `H&W`, `H, W` and `C, H, W` respectively (C = 3 by default).'),
+    parser.add_argument(
+        '--cfg-options',
+        nargs='+',
+        action=DictAction,
+        help='override some settings in the used config, the key-value pair '
+        'in xxx=yyy format will be merged into config file. If the value to '
+        'be overwritten is a list, it should be like key="[a,b]" or key=a,b '
+        'It also allows nested list/tuple values, e.g. key="[(a,b),(c,d)]" '
+        'Note that the quotation marks are necessary and that no white space '
+        'is allowed.')
     args = parser.parse_args()
     return args
 
 
-def main():
+def inference(args, logger):
 
-    args = parse_args()
+    config_name = Path(args.config)
+    if not config_name.exists():
+        logger.error(f'{config_name} not found.')
+
+    cfg = Config.fromfile(args.config)
+    cfg.work_dir = tempfile.TemporaryDirectory().name
+    cfg.log_level = 'WARN'
+    if args.cfg_options is not None:
+        cfg.merge_from_dict(args.cfg_options)
+
+    init_default_scope(cfg.get('default_scope', 'mmocr'))
 
+    c = 3
     if len(args.shape) == 1:
         h = w = args.shape[0]
     elif len(args.shape) == 2:
         h, w = args.shape
+    elif len(args.shape) == 3:
+        c, h, w = args.shape
     else:
-        raise ValueError('invalid input shape, please use --shape h w')
+        raise ValueError('invalid input shape')
+    result = {}
+
+    # Supports two ways to calculate flops,
+    # 1. randomly generate a picture
+    # 2. load a picture from the dataset
+    try:
+        model = MODELS.build(cfg.model)
+        if torch.cuda.is_available():
+            model.cuda()
+        model = revert_sync_batchnorm(model)
+        data_batch = {'inputs': [torch.rand(c, h, w)], 'batch_samples': [None]}
+        data = model.data_preprocessor(data_batch)
+        result['ori_shape'] = (h, w)
+        result['pad_shape'] = data['inputs'].shape[-2:]
+        model.eval()
+        outputs = get_model_complexity_info(
+            model,
+            None,
+            inputs=data['inputs'],
+            show_table=False,
+            show_arch=False)
+        flops = outputs['flops']
+        params = outputs['params']
+        result['compute_type'] = 'Random input'
+
+    except TypeError:
+        logger.warning(
+            'Failed to directly get FLOPs, try to get flops with real data')
+        data_loader = Runner.build_dataloader(cfg.val_dataloader)
+        data_batch = next(iter(data_loader))
+        model = MODELS.build(cfg.model)
+        if torch.cuda.is_available():
+            model = model.cuda()
+        model = revert_sync_batchnorm(model)
+        model.eval()
+        _forward = model.forward
+        data = model.data_preprocessor(data_batch)
+        result['ori_shape'] = data['data_samples'][0].ori_shape
+        result['pad_shape'] = data['data_samples'][0].pad_shape
+
+        del data_loader
+        model.forward = partial(_forward, data_samples=data['data_samples'])
+        outputs = get_model_complexity_info(
+            model,
+            None,
+            inputs=data['inputs'],
+            show_table=False,
+            show_arch=False)
+        flops = outputs['flops']
+        params = outputs['params']
+        result['compute_type'] = 'Dataloader'
+
+    flops = _format_size(flops)
+    params = _format_size(params)
+    result['flops'] = flops
+    result['params'] = params
+
+    return result
 
-    input_shape = (1, 3, h, w)
-
-    cfg = Config.fromfile(args.config)
-    init_default_scope(cfg.get('default_scope', 'mmocr'))
-    model = MODELS.build(cfg.model)
-
-    flops = FlopCountAnalysis(model, torch.ones(input_shape))
-
-    # params = parameter_count_table(model)
-    flops_data = flop_count_table(flops)
-
-    print(flops_data)
 
+def main():
+    args = parse_args()
+    logger = MMLogger.get_instance(name='MMLogger')
+    result = inference(args, logger)
+    split_line = '=' * 30
+    ori_shape = result['ori_shape']
+    pad_shape = result['pad_shape']
+    flops = result['flops']
+    params = result['params']
+    compute_type = result['compute_type']
+
+    if pad_shape != ori_shape:
+        print(f'{split_line}\nUse size divisor set input shape '
+              f'from {ori_shape} to {pad_shape}')
+    print(f'{split_line}\nCompute type: {compute_type}\n'
+          f'Input shape: {pad_shape}\nFlops: {flops}\n'
+          f'Params: {params}\n{split_line}')
     print('!!!Please be cautious if you use the results in papers. '
-          'You may need to check if all ops are supported and verify that the '
-          'flops computation is correct.')
+          'You may need to check if all ops are supported and verify '
+          'that the flops computation is correct.')
 
 
 if __name__ == '__main__':