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iperov 2019-02-12 10:55:41 +04:00
parent 429e7e6aee
commit 535041f7bb
2 changed files with 28 additions and 24 deletions
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49
models/Model_SAE/Model.py
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@ -306,29 +306,6 @@ class SAEModel(ModelBase):
src_loss, dst_loss, src_sample_losses, dst_sample_losses = self.src_dst_train ([dssim_pixel_alpha, warped_src, target_src, target_src_mask, warped_dst, target_dst, target_dst_mask]) src_loss, dst_loss, src_sample_losses, dst_sample_losses = self.src_dst_train ([dssim_pixel_alpha, warped_src, target_src, target_src_mask, warped_dst, target_dst, target_dst_mask])
# 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
#
##gathering array of sample_losses
#self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
#self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
#
#if len(self.src_sample_losses) >= 48: #array is big enough
# #fetching idxs which losses are bigger than average
# x = np.array (self.src_sample_losses)
# self.src_sample_losses = []
# b = x[:,1]
# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
# generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
#
#
#if len(self.dst_sample_losses) >= 48: #array is big enough
# #fetching idxs which losses are bigger than average
# x = np.array (self.dst_sample_losses)
# self.dst_sample_losses = []
# b = x[:,1]
# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
# generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
if self.options['learn_mask']: if self.options['learn_mask']:
src_mask_loss, dst_mask_loss, = self.src_dst_mask_train ([warped_src, target_src_mask, warped_dst, target_dst_mask]) src_mask_loss, dst_mask_loss, = self.src_dst_mask_train ([warped_src, target_src_mask, warped_dst, target_dst_mask])
@ -575,3 +552,29 @@ class SAEModel(ModelBase):
return func return func
Model = SAEModel Model = SAEModel
# 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
#
##gathering array of sample_losses
#self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
#self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
#
#if len(self.src_sample_losses) >= 128: #array is big enough
# #fetching idxs which losses are bigger than average
# x = np.array (self.src_sample_losses)
# self.src_sample_losses = []
# b = x[:,1]
# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
# generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
# print ("src repeated %d" % (len(idxs)) )
#
#if len(self.dst_sample_losses) >= 128: #array is big enough
# #fetching idxs which losses are bigger than average
# x = np.array (self.dst_sample_losses)
# self.dst_sample_losses = []
# b = x[:,1]
# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
# generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
# print ("dst repeated %d" % (len(idxs)) )

1
samples/SampleGeneratorFace.py
View file

@ -55,6 +55,7 @@ class SampleGeneratorFace(SampleGeneratorBase):
return next(generator) return next(generator)
#forces to repeat these sample idxs as fast as possible #forces to repeat these sample idxs as fast as possible
#currently unused
def repeat_sample_idxs(self, idxs): # [ idx, ... ] def repeat_sample_idxs(self, idxs): # [ idx, ... ]
#send idxs list to all sub generators. #send idxs list to all sub generators.
for gen_sq in self.generators_sq: for gen_sq in self.generators_sq: