124 lines
4.0 KiB
Python
124 lines
4.0 KiB
Python
import os
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import sys
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import argparse
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import warnings
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import onnx
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import torch
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import torch.nn as nn
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from yolov6.utils.checkpoint import load_checkpoint
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from yolov6.layers.common import RepVGGBlock, SiLU
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from yolov6.models.effidehead import Detect
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try:
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from yolov6.layers.common import ConvModule
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except ImportError:
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from yolov6.layers.common import Conv as ConvModule
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class DeepStreamOutput(nn.Module):
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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[:, :, :4]
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objectness = x[:, :, 4:5]
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scores, classes = torch.max(x[:, :, 5:], 2, keepdim=True)
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scores *= objectness
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classes = classes.float()
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return boxes, scores, classes
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def suppress_warnings():
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warnings.filterwarnings('ignore', category=torch.jit.TracerWarning)
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warnings.filterwarnings('ignore', category=UserWarning)
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warnings.filterwarnings('ignore', category=DeprecationWarning)
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def yolov6_export(weights, device):
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model = load_checkpoint(weights, map_location=device, inplace=True, fuse=True)
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for layer in model.modules():
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if isinstance(layer, RepVGGBlock):
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layer.switch_to_deploy()
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elif isinstance(layer, nn.Upsample) and not hasattr(layer, 'recompute_scale_factor'):
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layer.recompute_scale_factor = None
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model.eval()
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for k, m in model.named_modules():
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if isinstance(m, ConvModule):
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if hasattr(m, 'act') and isinstance(m.act, nn.SiLU):
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m.act = SiLU()
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elif isinstance(m, Detect):
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m.inplace = False
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return model
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def main(args):
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suppress_warnings()
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print('\nStarting: %s' % args.weights)
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print('Opening YOLOv6 model\n')
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device = torch.device('cpu')
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model = yolov6_export(args.weights, device)
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model = nn.Sequential(model, DeepStreamOutput())
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img_size = args.size * 2 if len(args.size) == 1 else args.size
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if img_size == [640, 640] and args.p6:
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img_size = [1280] * 2
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onnx_input_im = torch.zeros(args.batch, 3, *img_size).to(device)
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onnx_output_file = os.path.basename(args.weights).split('.pt')[0] + '.onnx'
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dynamic_axes = {
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'input': {
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0: 'batch'
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},
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'boxes': {
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0: 'batch'
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},
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'scores': {
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0: 'batch'
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},
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'classes': {
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0: 'batch'
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}
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}
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print('\nExporting the model to ONNX')
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torch.onnx.export(model, onnx_input_im, onnx_output_file, verbose=False, opset_version=args.opset,
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do_constant_folding=True, input_names=['input'], output_names=['boxes', 'scores', 'classes'],
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dynamic_axes=dynamic_axes if args.dynamic else None)
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if args.simplify:
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print('Simplifying 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('Done: %s\n' % onnx_output_file)
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def parse_args():
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parser = argparse.ArgumentParser(description='DeepStream YOLOv6 conversion')
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parser.add_argument('-w', '--weights', required=True, help='Input weights (.pt) file path (required)')
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parser.add_argument('-s', '--size', nargs='+', type=int, default=[640], help='Inference size [H,W] (default [640])')
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parser.add_argument('--p6', action='store_true', help='P6 model')
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parser.add_argument('--opset', type=int, default=13, 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='Static 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('Invalid weights file')
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if args.dynamic and args.batch > 1:
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raise SystemExit('Cannot set dynamic batch-size and static batch-size at same time')
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return args
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if __name__ == '__main__':
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args = parse_args()
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sys.exit(main(args))
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