improved model.yaml source tracking

detect.py

@@ -128,7 +128,7 @@ def detect(save_img=False):
 
     if save_txt or save_img:
         print('Results saved to %s' % os.getcwd() + os.sep + out)
-        if platform == 'darwin':  # MacOS
+        if platform == 'darwin' and not opt.update:  # MacOS
             os.system('open ' + save_path)
 
     print('Done. (%.3fs)' % (time.time() - t0))

models/yolo.py

 import argparse
+from copy import deepcopy
 
 from models.experimental import *
 
@@ -43,20 +44,21 @@ class Detect(nn.Module):
 
 
 class Model(nn.Module):
-    def __init__(self, model_cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
+    def __init__(self, cfg='yolov5s.yaml', ch=3, nc=None):  # model, input channels, number of classes
         super(Model, self).__init__()
-        if type(model_cfg) is dict:
-            self.md = model_cfg  # model dict
+        if isinstance(cfg, dict):
+            self.yaml = cfg  # model dict
         else:  # is *.yaml
             import yaml  # for torch hub
-            with open(model_cfg) as f:
-                self.md = yaml.load(f, Loader=yaml.FullLoader)  # model dict
+            self.yaml_file = Path(cfg).name
+            with open(cfg) as f:
+                self.yaml = yaml.load(f, Loader=yaml.FullLoader)  # model dict
 
         # Define model
-        if nc and nc != self.md['nc']:
-            print('Overriding %s nc=%g with nc=%g' % (model_cfg, self.md['nc'], nc))
-            self.md['nc'] = nc  # override yaml value
-        self.model, self.save = parse_model(self.md, ch=[ch])  # model, savelist, ch_out
+        if nc and nc != self.yaml['nc']:
+            print('Overriding %s nc=%g with nc=%g' % (cfg, self.yaml['nc'], nc))
+            self.yaml['nc'] = nc  # override yaml value
+        self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch])  # model, savelist, ch_out
         # print([x.shape for x in self.forward(torch.zeros(1, ch, 64, 64))])
 
         # Build strides, anchors
@@ -148,17 +150,21 @@ class Model(nn.Module):
                 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)
+        self.info()
         return self
 
-def parse_model(md, ch):  # model_dict, input_channels(3)
+    def info(self):  # print model information
+        torch_utils.model_info(self)
+
+
+def parse_model(d, ch):  # model_dict, input_channels(3)
     print('\n%3s%18s%3s%10s  %-40s%-30s' % ('', 'from', 'n', 'params', 'module', 'arguments'))
-    anchors, nc, gd, gw = md['anchors'], md['nc'], md['depth_multiple'], md['width_multiple']
+    anchors, nc, gd, gw = d['anchors'], d['nc'], d['depth_multiple'], d['width_multiple']
     na = (len(anchors[0]) // 2)  # number of anchors
     no = na * (nc + 5)  # number of outputs = anchors * (classes + 5)
 
     layers, save, c2 = [], [], ch[-1]  # layers, savelist, ch out
-    for i, (f, n, m, args) in enumerate(md['backbone'] + md['head']):  # from, number, module, args
+    for i, (f, n, m, args) in enumerate(d['backbone'] + d['head']):  # from, number, module, args
         m = eval(m) if isinstance(m, str) else m  # eval strings
         for j, a in enumerate(args):
             try: