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car-detection-bayes
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updates

This commit is contained in:
Glenn Jocher 2019-02-19 16:11:18 +01:00
parent 3157049c60
commit 0dd791b7ad
1 changed files with 20 additions and 21 deletions
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37
models.py
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@ -110,7 +110,9 @@ class YOLOLayer(nn.Module):
self.nA = nA # number of anchors (3)
self.nC = nC # number of classes (80)
self.bbox_attrs = 5 + nC
self.img_dim = img_dim # from hyperparams in cfg file, NOT from parser
self.img_dim = img_dim # TODO: from hyperparams in cfg file, NOT from parser. Make dynamic
self.initialized = False
# self.weights = class_weights()
if anchor_idxs[0] == (nA * 2): # 6
stride = 32
@ -124,34 +126,24 @@ class YOLOLayer(nn.Module):
# Build anchor grids
nG = int(self.img_dim / stride) # number grid points
self.nG = nG
self.stride = stride
self.grid_x = torch.arange(nG).repeat((nG, 1)).view((1, 1, nG, nG)).float()
self.grid_y = torch.arange(nG).repeat((nG, 1)).t().view((1, 1, nG, nG)).float()
self.anchor_wh = torch.FloatTensor([(a_w / stride, a_h / stride) for a_w, a_h in anchors]) # scale anchors
self.anchor_w = self.anchor_wh[:, 0].view((1, nA, 1, 1))
self.anchor_h = self.anchor_wh[:, 1].view((1, nA, 1, 1))
self.weights = class_weights()
self.loss_means = torch.ones(6)
self.yolo_layer = anchor_idxs[0] / nA # 2, 1, 0
self.stride = stride
self.nG = nG
if ONNX_EXPORT: # use fully populated and reshaped tensors
self.anchor_w = self.anchor_w.repeat((1, 1, nG, nG)).view(1, -1, 1)
self.anchor_h = self.anchor_h.repeat((1, 1, nG, nG)).view(1, -1, 1)
self.grid_x = self.grid_x.repeat(1, nA, 1, 1).view(1, -1, 1)
self.grid_y = self.grid_y.repeat(1, nA, 1, 1).view(1, -1, 1)
self.grid_xy = torch.cat((self.grid_x, self.grid_y), 2)
self.anchor_wh = torch.cat((self.anchor_w, self.anchor_h), 2) / nG
def forward(self, p, targets=None, var=None):
bs = 1 if ONNX_EXPORT else p.shape[0] # batch size
nG = self.nG if ONNX_EXPORT else p.shape[-1] # number of grid points
if p.is_cuda and not self.weights.is_cuda:
if not self.initialized:
self.initialized = True
if p.is_cuda:
self.grid_x, self.grid_y = self.grid_x.cuda(), self.grid_y.cuda()
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(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
@ -212,6 +204,13 @@ class YOLOLayer(nn.Module):
else:
if ONNX_EXPORT:
anchor_w = self.anchor_w.repeat((1, 1, nG, nG)).view(1, -1, 1)
anchor_h = self.anchor_h.repeat((1, 1, nG, nG)).view(1, -1, 1)
grid_x = self.grid_x.repeat(1, self.nA, 1, 1).view(1, -1, 1)
grid_y = self.grid_y.repeat(1, self.nA, 1, 1).view(1, -1, 1)
grid_xy = torch.cat((grid_x, grid_y), 2)
anchor_wh = torch.cat((anchor_w, anchor_h), 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
@ -220,8 +219,8 @@ class YOLOLayer(nn.Module):
# return torch.cat((xy / nG, wh, p_conf, p_cls), 1).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
xy = torch.sigmoid(p[..., 0:2]) + 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]
# Broadcasting only supported on first dimension in CoreML. See onnx-coreml/_operators.py