From 45ce01f8597dfb8d26a6d41966c88e1e346234a2 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Mon, 17 Feb 2020 15:28:11 -0800
Subject: [PATCH] updates

---
 models.py             | 19 ++++++++++---------
 utils/parse_config.py |  2 +-
 2 files changed, 11 insertions(+), 10 deletions(-)

diff --git a/models.py b/models.py
index a48f35d2..f8ed9449 100755
--- a/models.py
+++ b/models.py
@@ -67,9 +67,9 @@ def create_modules(module_defs, img_size, arc):
             #     modules = nn.Upsample(scale_factor=1/float(mdef[i+1]['stride']), mode='nearest')  # reorg3d
 
         elif mdef['type'] == 'shortcut':  # nn.Sequential() placeholder for 'shortcut' layer
-            layer = int(mdef['from'])
-            filters = output_filters[layer]
-            routs.extend([i + layer if layer < 0 else layer])
+            layers = [int(x) for x in mdef['from'].split(',')]
+            filters = output_filters[layers[0]]
+            routs.extend([i + l if l < 0 else l for l in layers])
 
         elif mdef['type'] == 'reorg3d':  # yolov3-spp-pan-scale
             # torch.Size([16, 128, 104, 104])
@@ -239,10 +239,10 @@ class Darknet(nn.Module):
             mtype = mdef['type']
             if mtype in ['convolutional', 'upsample', 'maxpool']:
                 x = module(x)
-            elif mtype == 'route':
+            elif mtype == 'route':  # concat
                 layers = [int(x) for x in mdef['layers'].split(',')]
                 if verbose:
-                    print('route concatenating %s' % ([layer_outputs[i].shape for i in layers]))
+                    print('route/concatenate %s' % ([layer_outputs[i].shape for i in layers]))
                 if len(layers) == 1:
                     x = layer_outputs[layers[0]]
                 else:
@@ -252,11 +252,12 @@ class Darknet(nn.Module):
                         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)
-            elif mtype == 'shortcut':
-                j = int(mdef['from'])
+            elif mtype == 'shortcut':  # sum
+                layers = [int(x) for x in mdef['from'].split(',')]
                 if verbose:
-                    print('shortcut adding layer %g-%s to %g-%s' % (j, layer_outputs[j].shape, i - 1, x.shape))
-                x = x + layer_outputs[j]
+                    print('shortcut/add %s' % ([layer_outputs[i].shape for i in layers]))
+                for j in layers:
+                    x = x + layer_outputs[j]
             elif mtype == 'yolo':
                 output.append(module(x, img_size))
             layer_outputs.append(x if i in self.routs else [])
diff --git a/utils/parse_config.py b/utils/parse_config.py
index 5d3c20fb..2516388a 100644
--- a/utils/parse_config.py
+++ b/utils/parse_config.py
@@ -33,7 +33,7 @@ def parse_model_cfg(path):
     # Check all fields are supported
     supported = ['type', 'batch_normalize', 'filters', 'size', 'stride', 'pad', 'activation', 'layers', 'groups',
                  'from', 'mask', 'anchors', 'classes', 'num', 'jitter', 'ignore_thresh', 'truth_thresh', 'random',
-                 'stride_x', 'stride_y']
+                 'stride_x', 'stride_y', 'weights_type', 'weights_normalization']
 
     f = []  # fields
     for x in mdefs[1:]: