ambiguity of the from index "f"

[ping28198]

Exist ambiguity of the from index "f" in function "parse_model" and member function "forward_once" of class "Model"

def parse_model(md, 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'] 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 m = eval(m) if isinstance(m, str) else m # eval strings for j, a in enumerate(args): try: args[j] = eval(a) if isinstance(a, str) else a # eval strings except: pass n = max(round(n * gd), 1) if n > 1 else n # depth gain if m in [nn.Conv2d, Conv, Bottleneck, SPP, DWConv, MixConv2d, Focus, CrossConv, BottleneckCSP, C3]: c1, c2 = ch[f], args[0]

def forward_once(self, x, profile=False):

y, dt = [], [] # outputs for m in self.model: if m.f != -1: # if not from previous layer x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers if profile: try: import thop o = thop.profile(m, inputs=(x,), verbose=False)[0] / 1E9 2 # FLOPS except: o = 0 t = torch_utils.timesynchronized() for in range(10): _ = m(x) dt.append((torch_utils.time_synchronized() - t) 100) print('%10.1f%10.0f%10.1fms %-40s' % (o, m.np, dt[-1], m.type)) x = m(x) # run y.append(x if m.i in self.save else None) # save output

In the function "parse model" , the "f" include the original input, yes the raw image which has channel numble 3. However, In the member function "forward_once", the "f" do not include the original input. I am confused by this.

Environment

If applicable, add screenshots to help explain your problem.

• OS: [e.g. Windows]
• GPU [e.g. 2080 Ti]

[github-actions[bot]]

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[glenn-jocher]

@ping28198 I don't understand the question. If you are proposing improvements please show your proposed code or submit a PR please. Thank you.

[github-actions[bot]]

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