[Feature] Add deploy demo (#343)

* [Feature] Add deploy demo

* [Feature] Add deploy demo

* [Feature] Add deploy demo

* [Feature] Add deploy demo

demo/deploy_demo.py

@@ -0,0 +1,120 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Deploy demo for mmdeploy.
+
+This script help user to run mmdeploy demo after convert the
+checkpoint to backends.
+
+Usage:
+    python deploy_demo.py img \
+                          config \
+                          checkpoint \
+                          [--deploy-cfg DEPLOY_CFG] \
+                          [--device DEVICE] \
+                          [--out-dir OUT_DIR] \
+                          [--show] \
+                          [--score-thr SCORE_THR]
+
+Example:
+    python deploy_demo.py \
+        ${MMYOLO_PATH}/data/cat/images \
+        ./yolov5_s-v61_syncbn_fast_1xb32-100e_cat.py \
+        ./end2end.engine \
+        --deploy-cfg ./detection_tensorrt-fp16_dynamic-192x192-960x960.py \
+        --out-dir ${MMYOLO_PATH}/work_dirs/deploy_predict_out \
+        --device cuda:0 \
+        --score-thr 0.5
+"""
+import argparse
+import os
+
+import torch
+from mmengine import ProgressBar
+
+from mmyolo.utils.misc import get_file_list
+
+try:
+    from mmdeploy.apis.utils import build_task_processor
+    from mmdeploy.utils import get_input_shape, load_config
+except ImportError:
+    raise ImportError(
+        'mmdeploy is not installed, please see '
+        'https://mmdeploy.readthedocs.io/en/1.x/01-how-to-build/build_from_source.html'  # noqa
+    )
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='For mmdeploy predict')
+    parser.add_argument(
+        'img', help='Image path, include image file, dir and URL.')
+    parser.add_argument('config', help='model config root')
+    parser.add_argument('checkpoint', help='checkpoint backend model path')
+    parser.add_argument('--deploy-cfg', help='deploy config path')
+    parser.add_argument(
+        '--device', default='cuda:0', help='device used for conversion')
+    parser.add_argument(
+        '--out-dir', default='./output', help='Path to output file')
+    parser.add_argument(
+        '--show', action='store_true', help='Show the detection results')
+    parser.add_argument(
+        '--score-thr', type=float, default=0.3, help='Bbox score threshold')
+    args = parser.parse_args()
+    return args
+
+
+# TODO Still need to refactor to not building dataset.
+def main():
+    args = parse_args()
+
+    if not os.path.exists(args.out_dir) and not args.show:
+        os.mkdir(args.out_dir)
+
+    # read deploy_cfg and config
+    deploy_cfg, model_cfg = load_config(args.deploy_cfg, args.config)
+
+    # build task and backend model
+    task_processor = build_task_processor(model_cfg, deploy_cfg, args.device)
+    model = task_processor.build_backend_model([args.checkpoint])
+
+    # get model input shape
+    input_shape = get_input_shape(deploy_cfg)
+
+    # get file list
+    files, source_type = get_file_list(args.img)
+
+    # start detector inference
+    progress_bar = ProgressBar(len(files))
+    for file in files:
+        # process input image
+        model_inputs, _ = task_processor.create_input(file, input_shape)
+
+        # do model inference
+        with torch.no_grad():
+            result = model.test_step(model_inputs)
+
+        if source_type['is_dir']:
+            filename = os.path.relpath(file, args.img).replace('/', '_')
+        else:
+            filename = os.path.basename(file)
+        out_file = None if args.show else os.path.join(args.out_dir, filename)
+
+        # filter score
+        result = result[0]
+        result.pred_instances = result.pred_instances[
+            result.pred_instances.scores > args.score_thr]
+
+        # visualize results
+        task_processor.visualize(
+            image=file,
+            model=model,
+            result=result,
+            show_result=args.show,
+            window_name=os.path.basename(filename),
+            output_file=out_file)
+
+        progress_bar.update()
+
+    print('All done!')
+
+
+if __name__ == '__main__':
+    main()