Add support CO-DETR (MMDetection)

utils/export_codetr.py

@@ -0,0 +1,149 @@
+import os
+import types
+import onnx
+import torch
+import torch.nn as nn
+from copy import deepcopy
+
+from projects import *
+from mmengine.registry import MODELS
+from mmdeploy.utils import load_config
+from mmdet.utils import register_all_modules
+from mmengine.model import revert_sync_batchnorm
+from mmengine.runner.checkpoint import load_checkpoint
+
+
+class DeepStreamOutput(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        boxes = []
+        scores = []
+        labels = []
+        for det in x:
+            boxes.append(det.bboxes)
+            scores.append(det.scores.unsqueeze(-1))
+            labels.append(det.labels.unsqueeze(-1))
+        boxes = torch.stack(boxes, dim=0)
+        scores = torch.stack(scores, dim=0)
+        labels = torch.stack(labels, dim=0)
+        return torch.cat([boxes, scores, labels.to(boxes.dtype)], dim=-1)
+
+
+def forward_deepstream(self, batch_inputs, batch_data_samples):
+    b, _, h, w = batch_inputs.shape
+    batch_data_samples = [{'batch_input_shape': (h, w), 'img_shape': (h, w)} for _ in range(b)]
+    img_feats = self.extract_feat(batch_inputs)
+    return self.predict_query_head(img_feats, batch_data_samples, rescale=False)
+
+
+def query_head_predict_deepstream(self, feats, batch_data_samples, rescale=False):
+    with torch.no_grad():
+        outs = self.forward(feats, batch_data_samples)
+        predictions = self.predict_by_feat(
+            *outs, batch_img_metas=batch_data_samples, rescale=rescale)
+    return predictions
+
+
+def codetr_export(weights, config, device):
+    register_all_modules()
+    model_cfg = load_config(config)[0]
+    model = deepcopy(model_cfg.model)
+    model.pop('pretrained', None)
+    for key in model['train_cfg']:
+        if 'rpn_proposal' in key:
+            key['rpn_proposal'] = {}
+    model['test_cfg'] = [{}, {'rpn': {}, 'rcnn': {}}, {}]
+    preprocess_cfg = deepcopy(model_cfg.get('preprocess_cfg', {}))
+    preprocess_cfg.update(deepcopy(model_cfg.get('data_preprocessor', {})))
+    model.setdefault('data_preprocessor', preprocess_cfg)
+    model = MODELS.build(model)
+    load_checkpoint(model, weights, map_location=device)
+    model = revert_sync_batchnorm(model)
+    if hasattr(model, 'backbone') and hasattr(model.backbone, 'switch_to_deploy'):
+        model.backbone.switch_to_deploy()
+    if hasattr(model, 'switch_to_deploy') and callable(model.switch_to_deploy):
+        model.switch_to_deploy()
+    model = model.to(device)
+    model.eval()
+    del model.data_preprocessor
+    model._forward = types.MethodType(forward_deepstream, model)
+    model.query_head.predict = types.MethodType(query_head_predict_deepstream, model.query_head)
+    return model
+
+
+def suppress_warnings():
+    import warnings
+    warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
+    warnings.filterwarnings('ignore', category=UserWarning)
+    warnings.filterwarnings('ignore', category=DeprecationWarning)
+    warnings.filterwarnings('ignore', category=FutureWarning)
+    warnings.filterwarnings('ignore', category=ResourceWarning)
+
+
+def main(args):
+    suppress_warnings()
+
+    print(f'\nStarting: {args.weights}')
+
+    print('Opening CO-DETR model')
+
+    device = torch.device('cpu')
+    model = codetr_export(args.weights, args.config, device)
+
+    model = nn.Sequential(model, DeepStreamOutput())
+
+    img_size = args.size * 2 if len(args.size) == 1 else args.size
+
+    onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
+    onnx_output_file = f'{args.weights}.onnx'
+
+    dynamic_axes = {
+        'input': {
+            0: 'batch'
+        },
+        'output': {
+            0: 'batch'
+        }
+    }
+
+    print('Exporting the model to ONNX')
+    torch.onnx.export(
+        model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset, do_constant_folding=True,
+        input_names=['input'], output_names=['output'], dynamic_axes=dynamic_axes if args.dynamic else None
+    )
+
+    if args.simplify:
+        print('Simplifying the ONNX model')
+        import onnxslim
+        model_onnx = onnx.load(onnx_output_file)
+        model_onnx = onnxslim.slim(model_onnx)
+        onnx.save(model_onnx, onnx_output_file)
+
+    print(f'Done: {onnx_output_file}\n')
+
+
+def parse_args():
+    import argparse
+    parser = argparse.ArgumentParser(description='DeepStream CO-DETR conversion')
+    parser.add_argument('-w', '--weights', required=True, type=str, help='Input weights (.pth) file path (required)')
+    parser.add_argument('-c', '--config', required=True, help='Input config (.py) file path (required)')
+    parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
+    parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
+    parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
+    parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
+    parser.add_argument('--batch', type=int, default=1, help='Static batch-size')
+    args = parser.parse_args()
+    if not os.path.isfile(args.weights):
+        raise SystemExit('Invalid weights file')
+    if not os.path.isfile(args.config):
+        raise SystemExit('Invalid config file')
+    if args.dynamic and args.batch > 1:
+        raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+    main(args)