From 46e3343494d38caa183c49ce54ed3fdcd10a0e97 Mon Sep 17 00:00:00 2001
From: Glenn Jocher <glenn.jocher@ultralytics.com>
Date: Thu, 21 Feb 2019 16:16:35 +0100
Subject: [PATCH] updates

---
 models.py | 17 ++++++++---------
 1 file changed, 8 insertions(+), 9 deletions(-)

diff --git a/models.py b/models.py
index 7a659404..1ecfdc18 100755
--- a/models.py
+++ b/models.py
@@ -135,9 +135,10 @@ class YOLOLayer(nn.Module):
         # p.view(bs, 255, 13, 13) -- > (bs, 3, 13, 13, 80)  # (bs, anchors, grid, grid, classes + xywh)
         p = p.view(bs, self.nA, self.nC + 5, nG, nG).permute(0, 1, 3, 4, 2).contiguous()  # prediction
 
-        # Width and height
-        wh = p[..., 2:4]  # yolo method
-        # wh = torch.sigmoid(p[..., 2:4])  # power method
+        # xy, width and height
+        xy = torch.sigmoid(p[..., 0:2])
+        wh = p[..., 2:4]  # wh (yolo method)
+        # wh = torch.sigmoid(p[..., 2:4])  # wh (power method)
 
         # Training
         if targets is not None:
@@ -146,7 +147,6 @@ class YOLOLayer(nn.Module):
             CrossEntropyLoss = nn.CrossEntropyLoss()
 
             # Get outputs
-            xy = torch.sigmoid(p[..., 0:2])
             p_conf = p[..., 4]  # Conf
             p_cls = p[..., 5:]  # Class
 
@@ -160,7 +160,6 @@ class YOLOLayer(nn.Module):
             nT = sum([len(x) for x in targets])  # number of targets
             nM = mask.sum().float()  # number of anchors (assigned to targets)
             k = 1  # nM / bs
-
             if nM > 0:
                 lxy = k * MSELoss(xy[mask], txy[mask])
                 lwh = k * MSELoss(wh[mask], twh[mask])
@@ -184,14 +183,14 @@ class YOLOLayer(nn.Module):
                 anchor_wh = self.anchor_wh.repeat((1, 1, nG, nG, 1)).view((1, -1, 2)) / nG
 
                 # p = p.view(-1, 85)
-                # xy = torch.sigmoid(p[:, 0:2]) + self.grid_xy[0]  # x, y
-                # wh = torch.exp(p[:, 2:4]) * self.anchor_wh[0]  # width, height
+                # xy = xy + self.grid_xy[0]  # x, y
+                # wh = torch.exp(wh) * self.anchor_wh[0]  # width, height
                 # p_conf = torch.sigmoid(p[:, 4:5])  # Conf
                 # p_cls = F.softmax(p[:, 5:85], 1) * p_conf  # SSD-like conf
                 # return torch.cat((xy / nG, wh, p_conf, p_cls), 1).t()
 
                 p = p.view(1, -1, 85)
-                xy = torch.sigmoid(p[..., 0:2]) + grid_xy  # x, y
+                xy = xy + grid_xy  # x, y
                 wh = torch.exp(p[..., 2:4]) * anchor_wh  # width, height
                 p_conf = torch.sigmoid(p[..., 4:5])  # Conf
                 p_cls = p[..., 5:85]
@@ -202,7 +201,7 @@ class YOLOLayer(nn.Module):
                 p_cls = p_cls.permute(2, 1, 0)
                 return torch.cat((xy / nG, wh, p_conf, p_cls), 2).squeeze().t()
 
-            p[..., 0:2] = torch.sigmoid(p[..., 0:2]) + self.grid_xy  # xy
+            p[..., 0:2] = xy + self.grid_xy  # xy
             p[..., 2:4] = torch.exp(wh) * self.anchor_wh  # wh yolo method
             # p[..., 2:4] = ((wh * 2) ** 2) * self.anchor_wh  # wh power method
             p[..., 4] = torch.sigmoid(p[..., 4])  # p_conf