Update yolo.py channel array (#2223)

models/yolo.py

@@ -2,7 +2,6 @@
 import logging
 import sys
 from copy import deepcopy
-from pathlib import Path
 
 sys.path.append('./')  # to run '$ python *.py' files in subdirectories
 logger = logging.getLogger(__name__)
@@ -213,43 +212,27 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
         if m in [Conv, GhostConv, Bottleneck, GhostBottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP,
                  C3]:
             c1, c2 = ch[f], args[0]
-
-            # Normal
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1.75  # exponential (default 2.0)
-            #     e = math.log(c2 / ch[1]) / math.log(2)
-            #     c2 = int(ch[1] * ex ** e)
-            # if m != Focus:
-
-            c2 = make_divisible(c2 * gw, 8) if c2 != no else c2
-
-            # Experimental
-            # if i > 0 and args[0] != no:  # channel expansion factor
-            #     ex = 1 + gw  # exponential (default 2.0)
-            #     ch1 = 32  # ch[1]
-            #     e = math.log(c2 / ch1) / math.log(2)  # level 1-n
-            #     c2 = int(ch1 * ex ** e)
-            # if m != Focus:
-            #     c2 = make_divisible(c2, 8) if c2 != no else c2
+            if c2 != no:  # if not output
+                c2 = make_divisible(c2 * gw, 8)
 
             args = [c1, c2, *args[1:]]
             if m in [BottleneckCSP, C3]:
-                args.insert(2, n)
+                args.insert(2, n)  # number of repeats
                 n = 1
         elif m is nn.BatchNorm2d:
             args = [ch[f]]
         elif m is Concat:
-            c2 = sum([ch[x if x < 0 else x + 1] for x in f])
+            c2 = sum([ch[x] for x in f])
         elif m is Detect:
-            args.append([ch[x + 1] for x in f])
+            args.append([ch[x] for x in f])
             if isinstance(args[1], int):  # number of anchors
                 args[1] = [list(range(args[1] * 2))] * len(f)
         elif m is Contract:
-            c2 = ch[f if f < 0 else f + 1] * args[0] ** 2
+            c2 = ch[f] * args[0] ** 2
         elif m is Expand:
-            c2 = ch[f if f < 0 else f + 1] // args[0] ** 2
+            c2 = ch[f] // args[0] ** 2
         else:
-            c2 = ch[f if f < 0 else f + 1]
+            c2 = ch[f]
 
         m_ = nn.Sequential(*[m(*args) for _ in range(n)]) if n > 1 else m(*args)  # module
         t = str(m)[8:-2].replace('__main__.', '')  # module type
@@ -258,6 +241,8 @@ def parse_model(d, ch):  # model_dict, input_channels(3)
         logger.info('%3s%18s%3s%10.0f  %-40s%-30s' % (i, f, n, np, t, args))  # print
         save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1)  # append to savelist
         layers.append(m_)
+        if i == 0:
+            ch = []
         ch.append(c2)
     return nn.Sequential(*layers), sorted(save)