106 lines
3.6 KiB
Python
106 lines
3.6 KiB
Python
import os
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import sys
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import onnx
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import paddle
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import paddle.nn as nn
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from ppdet.core.workspace import load_config, merge_config
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from ppdet.utils.check import check_version, check_config
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from ppdet.utils.cli import ArgsParser
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from ppdet.engine import Trainer
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from ppdet.slim import build_slim_model
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from ppdet.data.source.category import get_categories
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class DeepStreamOutput(nn.Layer):
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def __init__(self):
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super().__init__()
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def forward(self, x):
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boxes = x['bbox']
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x['bbox_num'] = x['bbox_num'].transpose([0, 2, 1])
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scores = paddle.max(x['bbox_num'], 2, keepdim=True)
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classes = paddle.argmax(x['bbox_num'], 2, keepdim=True)
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return boxes, scores, classes
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def ppyoloe_export(FLAGS):
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cfg = load_config(FLAGS.config)
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FLAGS.opt['weights'] = FLAGS.weights
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FLAGS.opt['exclude_nms'] = True
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merge_config(FLAGS.opt)
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if FLAGS.slim_config:
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cfg = build_slim_model(cfg, FLAGS.slim_config, mode='test')
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merge_config(FLAGS.opt)
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check_config(cfg)
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check_version()
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trainer = Trainer(cfg, mode='test')
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trainer.load_weights(cfg.weights)
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trainer.model.eval()
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if not os.path.exists('.tmp'):
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os.makedirs('.tmp')
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static_model, _ = trainer._get_infer_cfg_and_input_spec('.tmp')
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os.system('rm -r .tmp')
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return trainer.cfg, static_model
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def main(FLAGS):
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print('\nStarting: %s' % FLAGS.weights)
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print('\nOpening PPYOLOE model\n')
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paddle.set_device('cpu')
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cfg, model = ppyoloe_export(FLAGS)
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anno_file = cfg['TestDataset'].get_anno()
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if os.path.isfile(anno_file):
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_, catid2name = get_categories(cfg['metric'], anno_file, 'detection_arch')
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print('\nCreating labels.txt file')
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f = open('labels.txt', 'w')
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for name in catid2name.values():
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f.write(str(name) + '\n')
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f.close()
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model = nn.Sequential(model, DeepStreamOutput())
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img_size = [cfg.eval_height, cfg.eval_width]
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onnx_input_im = {}
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onnx_input_im['image'] = paddle.static.InputSpec(shape=[FLAGS.batch, 3, *img_size], dtype='float32', name='image')
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onnx_input_im['scale_factor'] = paddle.static.InputSpec(shape=[FLAGS.batch, 2], dtype='float32', name='scale_factor')
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onnx_output_file = cfg.filename + '.onnx'
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print('\nExporting the model to ONNX\n')
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paddle.onnx.export(model, cfg.filename, input_spec=[onnx_input_im], opset_version=FLAGS.opset)
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if FLAGS.simplify:
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print('\nSimplifying the ONNX model')
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import onnxsim
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model_onnx = onnx.load(onnx_output_file)
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model_onnx, _ = onnxsim.simplify(model_onnx)
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onnx.save(model_onnx, onnx_output_file)
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print('\nDone: %s\n' % onnx_output_file)
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def parse_args():
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parser = ArgsParser()
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pdparams) file path (required)')
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parser.add_argument('--slim_config', default=None, type=str, help='Slim configuration file of slim method')
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parser.add_argument('--opset', type=int, default=11, help='ONNX opset version')
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parser.add_argument('--simplify', action='store_true', help='ONNX simplify model')
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parser.add_argument('--dynamic', action='store_true', help='Dynamic batch-size')
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parser.add_argument('--batch', type=int, default=1, help='Implicit batch-size')
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args = parser.parse_args()
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if not os.path.isfile(args.weights):
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raise SystemExit('\nInvalid weights file')
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if args.dynamic and args.batch > 1:
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raise SystemExit('\nCannot set dynamic batch-size and implicit batch-size at same time')
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elif args.dynamic:
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args.batch = None
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return args
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if __name__ == '__main__':
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FLAGS = parse_args()
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sys.exit(main(FLAGS))
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