model fusion and onnx export

models/common.py

@@ -20,6 +20,9 @@ class Conv(nn.Module):  # standard convolution
     def forward(self, x):
         return self.act(self.bn(self.conv(x)))
 
+    def fuseforward(self, x):
+        return self.act(self.conv(x))
+
 
 class Bottleneck(nn.Module):
     def __init__(self, c1, c2, shortcut=True, g=1, e=0.5):  # ch_in, ch_out, shortcut, groups, expansion

models/onnx_export.py

-# Exports a pytorch *.pt model to *.onnx format. Example usage (run from ./yolov5 directory):
-# $ export PYTHONPATH="$PWD"
-# $ python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
+# Exports a pytorch *.pt model to *.onnx format
+# Example usage (run from ./yolov5 directory):
+# $ export PYTHONPATH="$PWD" && python models/onnx_export.py --weights ./weights/yolov5s.pt --img 640 --batch 1
 
 import argparse
 
@@ -10,10 +10,11 @@ from models.common import *
 
 if __name__ == '__main__':
     parser = argparse.ArgumentParser()
-    parser.add_argument('--weights', default='./weights/yolov5s.pt', help='weights path')
-    parser.add_argument('--img-size', default=640, help='inference size (pixels)')
-    parser.add_argument('--batch-size', default=1, help='batch size')
+    parser.add_argument('--weights', type=str, default='./weights/yolov5s.pt', help='weights path')
+    parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
+    parser.add_argument('--batch-size', type=int, default=1, help='batch size')
     opt = parser.parse_args()
+    print(opt)
 
     # Parameters
     f = opt.weights.replace('.pt', '.onnx')  # onnx filename
@@ -23,7 +24,7 @@ if __name__ == '__main__':
     google_utils.attempt_download(opt.weights)
     model = torch.load(opt.weights)['model']
     model.eval()
-    # model.fuse()  # optionally fuse Conv2d + BatchNorm2d layers TODO
+    # model.fuse()
 
     # Export to onnx
     model.model[-1].export = True  # set Detect() layer export=True

models/yolo.py

@@ -123,6 +123,15 @@ class Model(nn.Module):
             b = self.model[f].bias.detach().view(m.na, -1).T  # conv.bias(255) to (3,85)
             print(('%g Conv2d.bias:' + '%10.3g' * 6) % (f, *b[:5].mean(1).tolist(), b[5:].mean()))
 
+    def fuse(self):  # fuse model Conv2d() + BatchNorm2d() layers
+        print('Fusing layers...')
+        for m in self.model.modules():
+            if type(m) is Conv:
+                m.conv = torch_utils.fuse_conv_and_bn(m.conv, m.bn)  # update conv
+                m.bn = None  # remove batchnorm
+                m.forward = m.fuseforward  # update forward
+        torch_utils.model_info(self)
+
 
 def parse_model(md, ch):  # model_dict, input_channels(3)
     print('\n%3s%15s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))