[Enhancement] Add a visualize script for inference result comparation.

docs/en/02-how-to-run/useful_tools.md

@@ -203,6 +203,23 @@ And the output look like this:
 +--------+------------+---------+
 ```
 
+## visualize
+
+This tool can be used to visualize model inference results in different backend.
+
+### Usage
+
+```bash
+python tools/visualize.py \
+    --deploy-cfg {DEPLOY_CFG} \
+    --model-cfg {MODEL_CFG} \
+    --deploy-path {DEPLOY_PATH} \
+    --test-img {TEST_IMGS} \
+    --checkpoint {CHECKPOINTS} \
+    --save-dir {SAVE_DIR} \
+    --device {DEVICE}
+```
+
 ## generate_md_table
 
 This tool can be used to generate supported-backends markdown table.
@@ -244,3 +261,30 @@ And the output look like this:
 | [SAR](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/sar)           | TextRecognition |      Y      |    N     |      Y      |   N   |    N     |  N   |
 | [SATRN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/satrn)       | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
 | [ABINet](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/abinet)     | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+
+## visualize
+
+This tool can be used to visualize model inference results in different backend.
+
+### Usage
+
+```bash
+python tools/visualize.py \
+    --deploy-cfg {DEPLOY_CFG} \
+    --model-cfg {MODEL_CFG} \
+    --deploy-path {DEPLOY_PATH} \
+    --test-img {TEST_IMGS} \
+    --checkpoint {CHECKPOINTS} \
+    --save-dir {SAVE_DIR} \
+    --device {DEVICE}
+```
+
+### Description of all arguments
+
+- `deploy-cfg` : The path of the deploy config file in MMDeploy codebase.
+- `model-cfg` : The path of model config file in OpenMMLab codebase.
+- `deploy-path` : The path of the model to be tested, if the backend contains multiple files, you can use it multiple times.
+- `test-img` : The path of the images to be tested, you can use it multiple times.
+- `checkpoint` : The path of the checkpoint to be tested, if it is used, the result will be cancated to right part.
+- `save-dir` : The path to save the visualization results, if it not specified, it will be set to '.'.
+- `device` : The device type. If not specified, it will be set to `cpu`.

docs/zh_cn/02-how-to-run/useful_tools.md

@@ -244,3 +244,30 @@ python tools/generate_md_table.py tests/regression/mmocr.yml tests/regression/mm
 | [SAR](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/sar)           | TextRecognition |      Y      |    N     |      Y      |   N   |    N     |  N   |
 | [SATRN](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/satrn)       | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
 | [ABINet](https://github.com/open-mmlab/mmocr/tree/main/configs/textrecog/abinet)     | TextRecognition |      Y      |    Y     |      Y      |   N   |    N     |  N   |
+
+## visualize
+
+这个工具可以用来可视化不同推理引擎的推理结果。
+
+### 用法
+
+```bash
+python tools/visualize.py \
+    --deploy-cfg {DEPLOY_CFG} \
+    --model-cfg {MODEL_CFG} \
+    --deploy-path {DEPLOY_PATH} \
+    --test-img {TEST_IMGS} \
+    --checkpoint {CHECKPOINTS} \
+    --save-dir {SAVE_DIR} \
+    --device {DEVICE}
+```
+
+### 参数说明
+
+- `deploy-cfg` : 输入的配置文件路径
+- `model-cfg` : 输入的模型配置文件路径
+- `deploy-path` : 测试的模型文件路径，如果部分模型含有多个文件，请多次使用该参数
+- `test-img` : 测试的图片路径，可以多次使用测试测试多张图片
+- `checkpoint` : PyTorch的权重文件，如果使用这个参数，推理的结果会被拼接到图像的右侧
+- `save-dir` : 保存可视化结果，如果没有指定则会被设为当前目录
+- `device` : 运行的设备类型，如果没有指定则会默认设置为`cpu`

tools/visualize.py

@@ -0,0 +1,103 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import logging
+import os
+import os.path as osp
+import time
+
+import mmcv
+import mmengine
+import numpy as np
+from tqdm import tqdm
+
+from mmdeploy.apis import visualize_model
+from mmdeploy.utils import Backend, get_backend, get_root_logger, load_config
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Model inference visualization.')
+    parser.add_argument('--deploy-cfg', help='deploy config path')
+    parser.add_argument('--model-cfg', help='model config path')
+    parser.add_argument(
+        '--deploy-path', type=str, nargs='+', help='deploy model path')
+    parser.add_argument(
+        '--checkpoint', default=None, help='model checkpoint path')
+    parser.add_argument(
+        '--test-img',
+        default=None,
+        type=str,
+        nargs='+',
+        help='image used to test model')
+    parser.add_argument(
+        '--save-dir',
+        default=os.getcwd(),
+        help='the dir to save inference results')
+    parser.add_argument('--device', help='device to run model', default='cpu')
+    parser.add_argument(
+        '--log-level',
+        help='set log level',
+        default='INFO',
+        choices=list(logging._nameToLevel.keys()))
+    parser.add_argument(
+        '--uri',
+        default='192.168.1.1:60000',
+        help='Remote ipv4:port or ipv6:port for inference on edge device.')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    logger = get_root_logger()
+    log_level = logging.getLevelName(args.log_level)
+    logger.setLevel(log_level)
+
+    deploy_cfg_path = args.deploy_cfg
+    model_cfg_path = args.model_cfg
+    checkpoint_path = args.checkpoint
+    deploy_model_path = args.deploy_path
+    if not isinstance(deploy_model_path, list):
+        deploy_model_path = [deploy_model_path]
+
+    # load deploy_cfg
+    deploy_cfg = load_config(deploy_cfg_path)[0]
+
+    # create save_dir or generate default save_dir
+    current_time = time.localtime()
+    save_dir = osp.join(os.getcwd(),
+                        time.strftime('%Y_%m_%d_%H_%M_%S', current_time))
+    mmengine.mkdir_or_exist(save_dir)
+
+    # get backend info
+    backend = get_backend(deploy_cfg)
+    extra = dict()
+    if backend == Backend.SNPE:
+        extra['uri'] = args.uri
+
+    # iterate single_img
+    for single_img in tqdm(args.test_img):
+        filename = osp.basename(single_img)
+        output_file = osp.join(save_dir, filename)
+        visualize_model(model_cfg_path, deploy_cfg_path, deploy_model_path,
+                        single_img, args.device, backend, output_file, False,
+                        **extra)
+
+        if checkpoint_path:
+            pytorch_output_file = osp.join(save_dir, 'pytorch_out.jpg')
+            visualize_model(model_cfg_path, deploy_cfg_path, [checkpoint_path],
+                            single_img, args.device, Backend.PYTORCH,
+                            pytorch_output_file, False)
+
+            # concat pytorch result and backend result
+            backend_result = mmcv.imread(output_file)
+            pytorch_result = mmcv.imread(pytorch_output_file)
+            result = np.concatenate((backend_result, pytorch_result), axis=1)
+            mmcv.imwrite(result, output_file)
+
+            # remove temp pytorch result
+            os.remove(osp.join(save_dir, pytorch_output_file))
+
+
+if __name__ == '__main__':
+    main()