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car-detection-bayes
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ONNX export compatability updates

This commit is contained in:
Glenn Jocher 2018-12-23 12:51:02 +01:00
parent 69963ff1f5
commit c50df0d1db
2 changed files with 27 additions and 27 deletions
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4
detect.py
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@ -66,7 +66,9 @@ def detect(
# Get detections # Get detections
with torch.no_grad(): with torch.no_grad():
pred = model(torch.from_numpy(img).unsqueeze(0).to(device)) img = torch.from_numpy(img).unsqueeze(0).to(device)
# pred = torch.onnx._export(model, img, 'weights/model.onnx', verbose=True,); return # ONNX export
pred = model(img)
pred = pred[pred[:, :, 4] > conf_thres] pred = pred[pred[:, :, 4] > conf_thres]
if len(pred) > 0: if len(pred) > 0:

50
models.py
View File

@ -133,6 +133,8 @@ class YOLOLayer(nn.Module):
# Get outputs # Get outputs
x = torch.sigmoid(p[..., 0]) # Center x x = torch.sigmoid(p[..., 0]) # Center x
y = torch.sigmoid(p[..., 1]) # Center y y = torch.sigmoid(p[..., 1]) # Center y
p_conf = p[..., 4] # Conf
p_cls = p[..., 5:] # Class
# Width and height (yolo method) # Width and height (yolo method)
w = p[..., 2] # Width w = p[..., 2] # Width
@ -146,28 +148,25 @@ class YOLOLayer(nn.Module):
# width = ((w.data * 2) ** 2) * self.anchor_w # width = ((w.data * 2) ** 2) * self.anchor_w
# height = ((h.data * 2) ** 2) * self.anchor_h # height = ((h.data * 2) ** 2) * self.anchor_h
# Add offset and scale with anchors (in grid space, i.e. 0-13)
pred_boxes = FT(bs, self.nA, nG, nG, 4)
pred_conf = p[..., 4] # Conf
pred_cls = p[..., 5:] # Class
# Training # Training
if targets is not None: if targets is not None:
MSELoss = nn.MSELoss() MSELoss = nn.MSELoss()
BCEWithLogitsLoss = nn.BCEWithLogitsLoss() BCEWithLogitsLoss = nn.BCEWithLogitsLoss()
CrossEntropyLoss = nn.CrossEntropyLoss() CrossEntropyLoss = nn.CrossEntropyLoss()
p_boxes = None
if batch_report: if batch_report:
# Predictd boxes: add offset and scale with anchors (in grid space, i.e. 0-13)
gx = self.grid_x[:, :, :nG, :nG] gx = self.grid_x[:, :, :nG, :nG]
gy = self.grid_y[:, :, :nG, :nG] gy = self.grid_y[:, :, :nG, :nG]
pred_boxes[..., 0] = x.data + gx - width / 2 p_boxes = torch.stack((x.data + gx - width / 2,
pred_boxes[..., 1] = y.data + gy - height / 2 y.data + gy - height / 2,
pred_boxes[..., 2] = x.data + gx + width / 2 x.data + gx + width / 2,
pred_boxes[..., 3] = y.data + gy + height / 2 y.data + gy + height / 2), 4) # x1y1x2y2
tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \ tx, ty, tw, th, mask, tcls, TP, FP, FN, TC = \
build_targets(pred_boxes, pred_conf, pred_cls, targets, self.scaled_anchors, self.nA, self.nC, nG, build_targets(p_boxes, p_conf, p_cls, targets, self.scaled_anchors, self.nA, self.nC, nG, batch_report)
batch_report)
tcls = tcls[mask] tcls = tcls[mask]
if x.is_cuda: if x.is_cuda:
tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda() tx, ty, tw, th, mask, tcls = tx.cuda(), ty.cuda(), tw.cuda(), th.cuda(), mask.cuda(), tcls.cuda()
@ -194,15 +193,15 @@ class YOLOLayer(nn.Module):
# import matplotlib.pyplot as plt # import matplotlib.pyplot as plt
# plt.hist(self.x) # plt.hist(self.x)
# lconf = k * BCEWithLogitsLoss(pred_conf[mask], mask[mask].float()) # lconf = k * BCEWithLogitsLoss(p_conf[mask], mask[mask].float())
lcls = (k / 4) * CrossEntropyLoss(pred_cls[mask], torch.argmax(tcls, 1)) lcls = (k / 4) * CrossEntropyLoss(p_cls[mask], torch.argmax(tcls, 1))
# lcls = (k * 10) * BCEWithLogitsLoss(pred_cls[mask], tcls.float()) # lcls = (k * 10) * BCEWithLogitsLoss(p_cls[mask], tcls.float())
else: else:
lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0]) lx, ly, lw, lh, lcls, lconf = FT([0]), FT([0]), FT([0]), FT([0]), FT([0]), FT([0])
# lconf += k * BCEWithLogitsLoss(pred_conf[~mask], mask[~mask].float()) # lconf += k * BCEWithLogitsLoss(p_conf[~mask], mask[~mask].float())
lconf = (k * 64) * BCEWithLogitsLoss(pred_conf, mask.float()) lconf = (k * 64) * BCEWithLogitsLoss(p_conf, mask.float())
# Sum loss components # Sum loss components
balance_losses_flag = False balance_losses_flag = False
@ -218,24 +217,23 @@ class YOLOLayer(nn.Module):
# Sum False Positives from unassigned anchors # Sum False Positives from unassigned anchors
FPe = torch.zeros(self.nC) FPe = torch.zeros(self.nC)
if batch_report: if batch_report:
i = torch.sigmoid(pred_conf[~mask]) > 0.5 i = torch.sigmoid(p_conf[~mask]) > 0.5
if i.sum() > 0: if i.sum() > 0:
FP_classes = torch.argmax(pred_cls[~mask][i], 1) FP_classes = torch.argmax(p_cls[~mask][i], 1)
FPe = torch.bincount(FP_classes, minlength=self.nC).float().cpu() # extra FPs FPe = torch.bincount(FP_classes, minlength=self.nC).float().cpu() # extra FPs
return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \ return loss, loss.item(), lx.item(), ly.item(), lw.item(), lh.item(), lconf.item(), lcls.item(), \
nT, TP, FP, FPe, FN, TC nT, TP, FP, FPe, FN, TC
else: else:
pred_boxes[..., 0] = x.data + self.grid_x
pred_boxes[..., 1] = y.data + self.grid_y
pred_boxes[..., 2] = width
pred_boxes[..., 3] = height
# If not in training phase return predictions # If not in training phase return predictions
output = torch.cat((pred_boxes.view(bs, -1, 4) * stride, p_boxes = torch.stack((x + self.grid_x, y + self.grid_y, width, height), 4) # xywh
torch.sigmoid(pred_conf.view(bs, -1, 1)), pred_cls.view(bs, -1, self.nC)), -1)
return output.data # output.shape = [1, 3, 13, 13, 85]
output = torch.cat((p_boxes * stride, torch.sigmoid(p_conf).unsqueeze(4), p_cls), 4)
# returns shape = [1, 507, 85]
return output.data.view(bs, -1, 5 + self.nC)
class Darknet(nn.Module): class Darknet(nn.Module):