From 00862e47ef0822914b1458eaad9d5909482ba6f8 Mon Sep 17 00:00:00 2001 From: Glenn Jocher Date: Wed, 19 Feb 2020 15:16:00 -0800 Subject: [PATCH] updates --- models.py | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/models.py b/models.py index 5585c1d2..3e5947f4 100755 --- a/models.py +++ b/models.py @@ -254,7 +254,7 @@ class Darknet(nn.Module): def forward(self, x, var=None): img_size = x.shape[-2:] - output, layer_outputs = [], [] + yolo_out, out = [], [] verbose = False if verbose: print('0', x.shape) @@ -265,34 +265,34 @@ class Darknet(nn.Module): x = module(x) elif mtype == 'shortcut': # sum if verbose: - print('shortcut/add %s + %s' % (x.shape, [layer_outputs[i].shape for i in module.layers])) - x = module(x, layer_outputs) # weightedFeatureFusion() + print('shortcut/add %s + %s' % (list(x.shape), [list(out[i].shape) for i in module.layers])) + x = module(x, out) # weightedFeatureFusion() elif mtype == 'route': # concat layers = [int(x) for x in mdef['layers'].split(',')] if verbose: - print('route/concatenate %s' % ([layer_outputs[i].shape for i in layers])) + print('route/concatenate %s + %s' % (list(x.shape), [list(out[i].shape) for i in layers])) if len(layers) == 1: - x = layer_outputs[layers[0]] + x = out[layers[0]] else: try: - x = torch.cat([layer_outputs[i] for i in layers], 1) + x = torch.cat([out[i] for i in layers], 1) except: # apply stride 2 for darknet reorg layer - layer_outputs[layers[1]] = F.interpolate(layer_outputs[layers[1]], scale_factor=[0.5, 0.5]) - x = torch.cat([layer_outputs[i] for i in layers], 1) - # print(''), [print(layer_outputs[i].shape) for i in layers], print(x.shape) + out[layers[1]] = F.interpolate(out[layers[1]], scale_factor=[0.5, 0.5]) + x = torch.cat([out[i] for i in layers], 1) + # print(''), [print(out[i].shape) for i in layers], print(x.shape) elif mtype == 'yolo': - output.append(module(x, img_size)) - layer_outputs.append(x if i in self.routs else []) + yolo_out.append(module(x, img_size)) + out.append(x if i in self.routs else []) if verbose: - print(i, x.shape) + print(i, list(x.shape)) if self.training: # train - return output + return yolo_out elif ONNX_EXPORT: # export - x = [torch.cat(x, 0) for x in zip(*output)] + x = [torch.cat(x, 0) for x in zip(*yolo_out)] return x[0], torch.cat(x[1:3], 1) # scores, boxes: 3780x80, 3780x4 else: # test - io, p = zip(*output) # inference output, training output + io, p = zip(*yolo_out) # inference output, training output return torch.cat(io, 1), p def fuse(self):