Bienqq
/
car-detection-bayes
2
1
Fork 0
Code Issues Pull Requests Releases Wiki Activity

updates

This commit is contained in:
Glenn Jocher 2019-08-24 21:20:25 +02:00
parent 790e25592f
commit ca38c9050f
1 changed files with 20 additions and 17 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

17
train.py
View File

@ -186,7 +186,6 @@ def train():
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification' results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time() t0 = time.time()
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train() model.train()
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size')) print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
@ -267,9 +266,12 @@ def train():
mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB) mem = torch.cuda.memory_cached() / 1E9 if torch.cuda.is_available() else 0 # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ( s = ('%10s' * 2 + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size) '%g/%g' % (epoch, epochs - 1), '%.3gG' % mem, *mloss, len(targets), img_size)
pbar.set_description(s) pbar.set_description(s) # end batch -----------------------------------------------------------------------
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave) if opt.prebias:
print_model_biases(model)
else:
# Calculate mAP (always test final epoch, skip first 10 if opt.nosave)
final_epoch = epoch + 1 == epochs final_epoch = epoch + 1 == epochs
if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch: if not (opt.notest or (opt.nosave and epoch < 10)) or final_epoch:
with torch.no_grad(): with torch.no_grad():
@ -293,7 +295,7 @@ def train():
for xi, title in zip(x, titles): for xi, title in zip(x, titles):
tb_writer.add_scalar(title, xi, epoch) tb_writer.add_scalar(title, xi, epoch)
# Update best map # Update best mAP
fitness = results[2] # mAP fitness = results[2] # mAP
if fitness > best_fitness: if fitness > best_fitness:
best_fitness = fitness best_fitness = fitness
@ -324,7 +326,7 @@ def train():
torch.save(chkpt, wdir + 'backup%g.pt' % epoch) torch.save(chkpt, wdir + 'backup%g.pt' % epoch)
# Delete checkpoint # Delete checkpoint
del chkpt del chkpt # end epoch -------------------------------------------------------------------------------------
# Report time # Report time
plot_results() # save as results.png plot_results() # save as results.png
@ -373,9 +375,10 @@ if __name__ == '__main__':
train() # transfer-learn yolo biases for 1 epoch train() # transfer-learn yolo biases for 1 epoch
create_backbone('weights/last.pt') # saved results as backbone.pt create_backbone('weights/last.pt') # saved results as backbone.pt
opt.weights = 'weights/backbone.pt' # assign backbone opt.weights = 'weights/backbone.pt' # assign backbone
opt.prebias = False # disable prebias and train normally opt.prebias = False # disable prebias
print(opt) # display options
train() train() # train normally
else: # Evolve hyperparameters (optional) else: # Evolve hyperparameters (optional)
opt.notest = True # only test final epoch opt.notest = True # only test final epoch