Bienqq
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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-18 15:51:35 +01:00
parent 6deda82384
commit c535a8699a
2 changed files with 24 additions and 17 deletions
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39
models.py
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@ -145,7 +145,6 @@ class YOLOLayer(nn.Module):
self.anchor_wh = torch.cat((self.anchor_w, self.anchor_h), 2) / nG
def forward(self, p, targets=None, var=None):
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
bs = 1 if ONNX_EXPORT else p.shape[0] # batch size
nG = self.nG # number of grid points
@ -154,7 +153,7 @@ class YOLOLayer(nn.Module):
self.anchor_w, self.anchor_h = self.anchor_w.cuda(), self.anchor_h.cuda()
self.weights, self.loss_means = self.weights.cuda(), self.loss_means.cuda()
# p.view(12, 255, 13, 13) -- > (12, 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.bbox_attrs, nG, nG).permute(0, 1, 3, 4, 2).contiguous() # prediction
# Training
@ -201,6 +200,7 @@ class YOLOLayer(nn.Module):
lcls = (k / 4) * CrossEntropyLoss(p_cls[mask], torch.argmax(tcls, 1))
# lcls = (k * 10) * BCEWithLogitsLoss(p_cls[mask], tcls.float())
else:
FT = torch.cuda.FloatTensor if p.is_cuda else torch.FloatTensor
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
lconf = (k * 64) * BCEWithLogitsLoss(p_conf, mask.float())
@ -212,19 +212,26 @@ class YOLOLayer(nn.Module):
else:
if ONNX_EXPORT:
p = p.view(1, -1, 85)
xy = torch.sigmoid(p[..., 0:2]) + self.grid_xy # x, y
width_height = torch.exp(p[..., 2:4]) * self.anchor_wh # width, height
p_conf = torch.sigmoid(p[..., 4:5]) # Conf
p_cls = p[..., 5:85]
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
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)
# Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
# p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
p_cls = torch.exp(p_cls).permute((2, 1, 0))
p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
p_cls = p_cls.permute(2, 1, 0)
return torch.cat((xy / nG, width_height, p_conf, p_cls), 2).squeeze().t()
# p = p.view(1, -1, 85)
# xy = torch.sigmoid(p[..., 0:2]) + self.grid_xy # x, y
# wh = torch.exp(p[..., 2:4]) * self.anchor_wh # width, height
# p_conf = torch.sigmoid(p[..., 4:5]) # Conf
# p_cls = p[..., 5:85]
#
# # Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py
# # p_cls = F.softmax(p_cls, 2) * p_conf # SSD-like conf
# p_cls = torch.exp(p_cls).permute((2, 1, 0))
# p_cls = p_cls / p_cls.sum(0).unsqueeze(0) * p_conf.permute((2, 1, 0)) # F.softmax() equivalent
# p_cls = p_cls.permute(2, 1, 0)
#
# return torch.cat((xy / nG, wh, p_conf, p_cls), 2).squeeze().t()
p[..., 0] = torch.sigmoid(p[..., 0]) + self.grid_x # x
p[..., 1] = torch.sigmoid(p[..., 1]) + self.grid_y # y
@ -285,8 +292,8 @@ class Darknet(nn.Module):
self.losses['nT'] /= 3
if ONNX_EXPORT:
output = torch.cat(output, 1) # merge the 3 layers 85 x (507, 2028, 8112) to 85 x 10647
return output[5:85].t(), output[:4].t() # ONNX scores, boxes
output = torch.cat(output, 0) # merge the 3 layers 85 x (507, 2028, 8112) to 85 x 10647
return output[:, 5:85], output[:, :4] # ONNX scores, boxes
return sum(output) if is_training else torch.cat(output, 1)

2
utils/utils.py
View File

@ -443,7 +443,7 @@ def plot_results():
plt.figure(figsize=(16, 8))
s = ['X', 'Y', 'Width', 'Height', 'Confidence', 'Classification', 'Total Loss', 'mAP', 'Recall', 'Precision']
files = sorted(glob.glob('results.txt'))
files = sorted(glob.glob('results*.txt'))
for f in files:
results = np.loadtxt(f, usecols=[2, 3, 4, 5, 6, 7, 8, 11, 12, 13]).T # column 13 is mAP
n = results.shape[1]