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added --cpu-only mode for all stages. Upd readme. Added requirements-cpu.txt

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iperov 2018-12-21 16:54:22 +04:00
parent d04e8b1d91
commit 0d7387165a
6 changed files with 91 additions and 35 deletions
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38
models/ModelBase.py
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@ -118,10 +118,13 @@ class ModelBase(object):
print ("== |== %s : %s" % (key, self.options[key]) )
print ("== Running on:")
for idx in self.gpu_config.gpu_idxs:
print ("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)) )
if self.gpu_config.cpu_only:
print ("== |== [CPU]")
else:
for idx in self.gpu_config.gpu_idxs:
print ("== |== [%d : %s]" % (idx, gpufmkmgr.getDeviceName(idx)) )
if self.gpu_total_vram_gb == 2:
if not self.gpu_config.cpu_only and self.gpu_total_vram_gb == 2:
print ("==")
print ("== WARNING: You are using 2GB GPU. Result quality may be significantly decreased.")
print ("== If training does not start, close all programs and try again.")
@ -264,7 +267,10 @@ class ModelBase(object):
self.epoch += 1
#............."Saving...
loss_string = "Training [#{0:06d}][{1:04d}ms]".format ( self.epoch, int(epoch_time*1000) % 10000 )
if epoch_time >= 10000:
loss_string = "Training [#{0:06d}][{1:03d}s]".format ( self.epoch, epoch_time / 1000 )
else:
loss_string = "Training [#{0:06d}][{1:04d}ms]".format ( self.epoch, int(epoch_time*1000) % 10000 )
for (loss_name, loss_value) in losses:
loss_string += " %s:%.3f" % (loss_name, loss_value)
@ -301,14 +307,18 @@ class ModelBase(object):
#example d = {2:2,3:4,4:8,5:16,6:32,7:32,8:32,9:48}
keys = [x for x in d.keys()]
if self.gpu_total_vram_gb < keys[0]:
raise Exception ('Sorry, this model works only on %dGB+ GPU' % ( keys[0] ) )
if self.batch_size == 0:
for x in keys:
if self.gpu_total_vram_gb <= x:
self.batch_size = d[x]
break
if self.gpu_config.cpu_only:
if self.batch_size == 0:
self.batch_size = d[ keys[-1] ]
self.batch_size = 2
else:
if self.gpu_total_vram_gb < keys[0]:
raise Exception ('Sorry, this model works only on %dGB+ GPU' % ( keys[0] ) )
if self.batch_size == 0:
for x in keys:
if self.gpu_total_vram_gb <= x:
self.batch_size = d[x]
break
if self.batch_size == 0:
self.batch_size = d[ keys[-1] ]