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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-21 16:16:35 +01:00
parent af853f604c
commit 46e3343494
1 changed files with 8 additions and 9 deletions
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17
models.py
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@ -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.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 p = p.view(bs, self.nA, self.nC + 5, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Width and height # xy, width and height
wh = p[..., 2:4] # yolo method xy = torch.sigmoid(p[..., 0:2])
# wh = torch.sigmoid(p[..., 2:4]) # power method wh = p[..., 2:4] # wh (yolo method)
# wh = torch.sigmoid(p[..., 2:4]) # wh (power method)
# Training # Training
if targets is not None: if targets is not None:
@ -146,7 +147,6 @@ class YOLOLayer(nn.Module):
CrossEntropyLoss = nn.CrossEntropyLoss() CrossEntropyLoss = nn.CrossEntropyLoss()
# Get outputs # Get outputs
xy = torch.sigmoid(p[..., 0:2])
p_conf = p[..., 4] # Conf p_conf = p[..., 4] # Conf
p_cls = p[..., 5:] # Class p_cls = p[..., 5:] # Class
@ -160,7 +160,6 @@ class YOLOLayer(nn.Module):
nT = sum([len(x) for x in targets]) # number of targets nT = sum([len(x) for x in targets]) # number of targets
nM = mask.sum().float() # number of anchors (assigned to targets) nM = mask.sum().float() # number of anchors (assigned to targets)
k = 1 # nM / bs k = 1 # nM / bs
if nM > 0: if nM > 0:
lxy = k * MSELoss(xy[mask], txy[mask]) lxy = k * MSELoss(xy[mask], txy[mask])
lwh = k * MSELoss(wh[mask], twh[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 anchor_wh = self.anchor_wh.repeat((1, 1, nG, nG, 1)).view((1, -1, 2)) / nG
# p = p.view(-1, 85) # p = p.view(-1, 85)
# xy = torch.sigmoid(p[:, 0:2]) + self.grid_xy[0] # x, y # xy = xy + self.grid_xy[0] # x, y
# wh = torch.exp(p[:, 2:4]) * self.anchor_wh[0] # width, height # wh = torch.exp(wh) * self.anchor_wh[0] # width, height
# p_conf = torch.sigmoid(p[:, 4:5]) # Conf # p_conf = torch.sigmoid(p[:, 4:5]) # Conf
# p_cls = F.softmax(p[:, 5:85], 1) * p_conf # SSD-like 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() # return torch.cat((xy / nG, wh, p_conf, p_cls), 1).t()
p = p.view(1, -1, 85) 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 wh = torch.exp(p[..., 2:4]) * anchor_wh # width, height
p_conf = torch.sigmoid(p[..., 4:5]) # Conf p_conf = torch.sigmoid(p[..., 4:5]) # Conf
p_cls = p[..., 5:85] p_cls = p[..., 5:85]
@ -202,7 +201,7 @@ class YOLOLayer(nn.Module):
p_cls = p_cls.permute(2, 1, 0) p_cls = p_cls.permute(2, 1, 0)
return torch.cat((xy / nG, wh, p_conf, p_cls), 2).squeeze().t() 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] = torch.exp(wh) * self.anchor_wh # wh yolo method
# p[..., 2:4] = ((wh * 2) ** 2) * self.anchor_wh # wh power method # p[..., 2:4] = ((wh * 2) ** 2) * self.anchor_wh # wh power method
p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf p[..., 4] = torch.sigmoid(p[..., 4]) # p_conf