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axmodel_inf.py

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+"""
+DEIMv2: Real-Time Object Detection Meets DINOv3
+Copyright (c) 2025 The DEIMv2 Authors. All Rights Reserved.
+---------------------------------------------------------------------------------
+Modified from D-FINE (https://github.com/Peterande/D-FINE)
+Copyright (c) 2024 The D-FINE Authors. All Rights Reserved.
+"""
+
+
+import cv2
+import numpy as np
+import axengine as ort
+import torch
+import torchvision
+import torchvision.transforms as T
+from PIL import Image, ImageDraw
+from torch import nn
+import torch.nn.functional as F
+
+
+def mod(a, b):
+    out = a - a // b * b
+    return out
+
+
+class PostProcessor(nn.Module):
+    __share__ = [
+        'num_classes',
+        'use_focal_loss',
+        'num_top_queries',
+        'remap_mscoco_category'
+    ]
+
+    def __init__(
+        self,
+        num_classes=80,
+        use_focal_loss=True,
+        num_top_queries=300,
+        remap_mscoco_category=False
+    ) -> None:
+        super().__init__()
+        self.use_focal_loss = use_focal_loss
+        self.num_top_queries = num_top_queries
+        self.num_classes = int(num_classes)
+        self.remap_mscoco_category = remap_mscoco_category
+        self.deploy_mode = False
+
+    def extra_repr(self) -> str:
+        return f'use_focal_loss={self.use_focal_loss}, num_classes={self.num_classes}, num_top_queries={self.num_top_queries}'
+
+    # def forward(self, outputs, orig_target_sizes):
+    def forward(self, outputs, orig_target_sizes: torch.Tensor):
+        logits, boxes = outputs['pred_logits'], outputs['pred_boxes']
+        # orig_target_sizes = torch.stack([t["orig_size"] for t in targets], dim=0)
+
+        bbox_pred = torchvision.ops.box_convert(boxes, in_fmt='cxcywh', out_fmt='xyxy')
+        bbox_pred *= orig_target_sizes.repeat(1, 2).unsqueeze(1)
+
+        if self.use_focal_loss:
+            scores = F.sigmoid(logits)
+            scores, index = torch.topk(scores.flatten(1), self.num_top_queries, dim=-1)
+            # labels = index % self.num_classes
+            labels = mod(index, self.num_classes)
+            index = index // self.num_classes
+            boxes = bbox_pred.gather(dim=1, index=index.unsqueeze(-1).repeat(1, 1, bbox_pred.shape[-1]))
+
+        else:
+            scores = F.softmax(logits)[:, :, :-1]
+            scores, labels = scores.max(dim=-1)
+            if scores.shape[1] > self.num_top_queries:
+                scores, index = torch.topk(scores, self.num_top_queries, dim=-1)
+                labels = torch.gather(labels, dim=1, index=index)
+                boxes = torch.gather(boxes, dim=1, index=index.unsqueeze(-1).tile(1, 1, boxes.shape[-1]))
+
+        if self.deploy_mode:
+            return labels, boxes, scores
+
+        if self.remap_mscoco_category:
+            from ..data.dataset import mscoco_label2category
+            labels = torch.tensor([mscoco_label2category[int(x.item())] for x in labels.flatten()])\
+                .to(boxes.device).reshape(labels.shape)
+
+        results = []
+        for lab, box, sco in zip(labels, boxes, scores):
+            result = dict(labels=lab, boxes=box, scores=sco)
+            results.append(result)
+
+        return results
+
+
+    def deploy(self, ):
+        self.eval()
+        self.deploy_mode = True
+        return self
+
+
+def resize_with_aspect_ratio(image, size, interpolation=Image.BILINEAR):
+    """Resizes an image while maintaining aspect ratio and pads it."""
+    original_width, original_height = image.size
+    ratio = min(size / original_width, size / original_height)
+    new_width = int(original_width * ratio)
+    new_height = int(original_height * ratio)
+    image = image.resize((new_width, new_height), interpolation)
+
+    # Create a new image with the desired size and paste the resized image onto it
+    new_image = Image.new("RGB", (size, size))
+    new_image.paste(image, ((size - new_width) // 2, (size - new_height) // 2))
+    return new_image, ratio, (size - new_width) // 2, (size - new_height) // 2
+
+
+def draw(images, labels, boxes, scores, ratios, paddings, thrh=0.4):
+    result_images = []
+    for i, im in enumerate(images):
+        draw = ImageDraw.Draw(im)
+        scr = scores[i]
+        lab = labels[i][scr > thrh]
+        box = boxes[i][scr > thrh]
+        scr = scr[scr > thrh]
+
+        ratio = ratios[i]
+        pad_w, pad_h = paddings[i]
+
+        for lbl, bb in zip(lab, box):
+            # Adjust bounding boxes according to the resizing and padding
+            bb = [
+                (bb[0] - pad_w) / ratio,
+                (bb[1] - pad_h) / ratio,
+                (bb[2] - pad_w) / ratio,
+                (bb[3] - pad_h) / ratio,
+            ]
+            draw.rectangle(bb, outline='red')
+            draw.text((bb[0], bb[1]), text=str(lbl), fill='blue')
+
+        result_images.append(im)
+    return result_images
+
+
+def process_image(sess, im_pil, size=640, model_size='s'):
+    post_processor = PostProcessor().deploy()
+    # Resize image while preserving aspect ratio
+    resized_im_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(im_pil, size)
+    orig_size = torch.tensor([[resized_im_pil.size[1], resized_im_pil.size[0]]])
+
+    transforms = T.Compose([
+            T.ToTensor(),
+            T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 
+                if model_size not in ['atto', 'femto', 'pico', 'n'] 
+                else T.Lambda(lambda x: x)
+        ])
+    im_data = transforms(resized_im_pil).unsqueeze(0)
+
+    output = sess.run(
+        output_names=None,
+        input_feed={'images': im_data.numpy()}
+    )
+    
+    output = {"pred_logits": torch.from_numpy(output[0]), "pred_boxes": torch.from_numpy(output[1])}
+    output = post_processor(output, orig_size)
+    labels, boxes, scores = output[0].numpy(), output[1].numpy(), output[2].numpy()
+
+    result_images = draw(
+        [im_pil], labels, boxes, scores,
+        [ratio], [(pad_w, pad_h)]
+    )
+    result_images[0].save('result.jpg')
+    print("Image processing complete. Result saved as 'result.jpg'.")
+
+
+def process_video(sess, video_path, size=640, model_size='s'):
+    cap = cv2.VideoCapture(video_path)
+
+    # Get video properties
+    fps = cap.get(cv2.CAP_PROP_FPS)
+    orig_w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+    orig_h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+
+    # Define the codec and create VideoWriter object
+    fourcc = cv2.VideoWriter_fourcc(*'mp4v')
+    out = cv2.VideoWriter('onnx_result.mp4', fourcc, fps, (orig_w, orig_h))
+
+    frame_count = 0
+    print("Processing video frames...")
+    while cap.isOpened():
+        ret, frame = cap.read()
+        if not ret:
+            break
+
+        # Convert frame to PIL image
+        frame_pil = Image.fromarray(cv2.cvtColor(frame, cv2.COLOR_BGR2RGB))
+
+        # Resize frame while preserving aspect ratio
+        resized_frame_pil, ratio, pad_w, pad_h = resize_with_aspect_ratio(frame_pil, size)
+        orig_size = torch.tensor([[resized_frame_pil.size[1], resized_frame_pil.size[0]]])
+
+        transforms = T.Compose([
+                T.ToTensor(),
+                T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) 
+                    if model_size not in ['atto', 'femto', 'pico', 'n'] 
+                    else T.Lambda(lambda x: x)
+            ])
+        im_data = transforms(resized_frame_pil).unsqueeze(0)
+
+        output = sess.run(
+            output_names=None,
+            input_feed={'images': im_data.numpy(), "orig_target_sizes": orig_size.numpy()}
+        )
+
+        labels, boxes, scores = output
+
+        # Draw detections on the original frame
+        result_images = draw(
+            [frame_pil], labels, boxes, scores,
+            [ratio], [(pad_w, pad_h)]
+        )
+        frame_with_detections = result_images[0]
+
+        # Convert back to OpenCV image
+        frame = cv2.cvtColor(np.array(frame_with_detections), cv2.COLOR_RGB2BGR)
+
+        # Write the frame
+        out.write(frame)
+        frame_count += 1
+
+        if frame_count % 10 == 0:
+            print(f"Processed {frame_count} frames...")
+
+    cap.release()
+    out.release()
+    print("Video processing complete. Result saved as 'result.mp4'.")
+
+
+def main(args):
+    """Main function."""
+    # Load the ONNX model
+    sess = ort.InferenceSession(args.axmodel)
+    size = sess.get_inputs()[0].shape[2]
+
+    input_path = args.input
+
+    try:
+        # Try to open the input as an image
+        im_pil = Image.open(input_path).convert('RGB')
+        process_image(sess, im_pil, size, args.model_size)
+    except IOError:
+        # Not an image, process as video
+        process_video(sess, input_path, size, args.model_size)
+
+
+if __name__ == '__main__':
+    import argparse
+    parser = argparse.ArgumentParser()
+    parser.add_argument('--axmodel', type=str, default="compiled.axmodel", help='Path to the axmodel model file.')
+    parser.add_argument('--input', type=str, required=True, help='Path to the input image or video file.')
+    parser.add_argument('-ms', '--model-size', type=str, required=True, choices=['atto', 'femto', 'pico', 'n', 's', 'm', 'l', 'x'], 
+                        help='Model size')
+    args = parser.parse_args()
+    main(args)