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SAE: remove rare sample booster. DSSIM->MSE transition now between 5-20k epochs.

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iperov 2019-02-11 17:18:01 +04:00
parent f8e63970d2
commit 470fb9287a
2 changed files with 24 additions and 21 deletions
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44
models/Model_SAE/Model.py
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@ -302,32 +302,34 @@ class SAEModel(ModelBase):
warped_src, target_src, target_src_mask, src_sample_idxs = generators_samples[0]
warped_dst, target_dst, target_dst_mask, dst_sample_idxs = generators_samples[1]
dssim_pixel_alpha = np.clip ( self.epoch / 15000.0, 0.0, 1.0 ) #smooth transition between DSSIM and MSE in 15k epochs
dssim_pixel_alpha = np.clip ( (self.epoch - 5000) / 15000.0, 0.0, 1.0 ) #smooth transition between DSSIM and MSE in 5-20k epochs
dssim_pixel_alpha = np.repeat( dssim_pixel_alpha, (self.batch_size,) )
dssim_pixel_alpha = np.expand_dims(dssim_pixel_alpha,-1)
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])
#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) ] )[:,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) ] )[:,0] ]).astype(np.uint)
generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
# '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) ] )[:,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) ] )[:,0] ]).astype(np.uint)
# generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
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])