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rtdetrv2_pytorch/references/deploy/rtdetrv2_torch.py

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+"""Copyright(c) 2023 lyuwenyu. All Rights Reserved.
+"""
+
+import torch
+import torch.nn as nn 
+import torchvision.transforms as T
+
+import numpy as np 
+from PIL import Image, ImageDraw
+
+from src.core import YAMLConfig
+
+
+def draw(images, labels, boxes, scores, thrh = 0.6):
+    for i, im in enumerate(images):
+        draw = ImageDraw.Draw(im)
+
+        scr = scores[i]
+        lab = labels[i][scr > thrh]
+        box = boxes[i][scr > thrh]
+        scrs = scores[i][scr > thrh]
+
+        for j,b in enumerate(box):
+            draw.rectangle(list(b), outline='red',)
+            draw.text((b[0], b[1]), text=f"{lab[j].item()} {round(scrs[j].item(),2)}", fill='blue', )
+
+        im.save(f'results_{i}.jpg')
+
+
+def main(args, ):
+    """main
+    """
+    cfg = YAMLConfig(args.config, resume=args.resume)
+
+    if args.resume:
+        checkpoint = torch.load(args.resume, map_location='cpu') 
+        if 'ema' in checkpoint:
+            state = checkpoint['ema']['module']
+        else:
+            state = checkpoint['model']
+    else:
+        raise AttributeError('Only support resume to load model.state_dict by now.')
+
+    # NOTE load train mode state -> convert to deploy mode
+    cfg.model.load_state_dict(state)
+
+    class Model(nn.Module):
+        def __init__(self, ) -> None:
+            super().__init__()
+            self.model = cfg.model.deploy()
+            self.postprocessor = cfg.postprocessor.deploy()
+            
+        def forward(self, images, orig_target_sizes):
+            outputs = self.model(images)
+            outputs = self.postprocessor(outputs, orig_target_sizes)
+            return outputs
+
+    model = Model().to(args.device)
+
+    im_pil = Image.open(args.im_file).convert('RGB')
+    w, h = im_pil.size
+    orig_size = torch.tensor([w, h])[None].to(args.device)
+
+    transforms = T.Compose([
+        T.Resize((640, 640)),
+        T.ToTensor(),
+    ])
+    im_data = transforms(im_pil)[None].to(args.device)
+
+    output = model(im_data, orig_size)
+    labels, boxes, scores = output
+
+    draw([im_pil], labels, boxes, scores)
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('-c', '--config', type=str, )
+    parser.add_argument('-r', '--resume', type=str, )
+    parser.add_argument('-f', '--im-file', type=str, )
+    parser.add_argument('-d', '--device', type=str, default='cpu')
+    args = parser.parse_args()
+    main(args)