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2 changed files with 28 additions and 24 deletions
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@ -305,29 +305,6 @@ class SAEModel(ModelBase):
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dssim_pixel_alpha = np.expand_dims(dssim_pixel_alpha,-1)
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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])
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# 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
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#
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##gathering array of sample_losses
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#self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
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#self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
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#
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#if len(self.src_sample_losses) >= 48: #array is big enough
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# #fetching idxs which losses are bigger than average
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# x = np.array (self.src_sample_losses)
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# self.src_sample_losses = []
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# b = x[:,1]
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# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
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# generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
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#
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#
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#if len(self.dst_sample_losses) >= 48: #array is big enough
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# #fetching idxs which losses are bigger than average
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# x = np.array (self.dst_sample_losses)
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# self.dst_sample_losses = []
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# b = x[:,1]
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# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
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# generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
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if self.options['learn_mask']:
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src_mask_loss, dst_mask_loss, = self.src_dst_mask_train ([warped_src, target_src_mask, warped_dst, target_dst_mask])
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@ -574,4 +551,30 @@ class SAEModel(ModelBase):
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return func
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Model = SAEModel
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Model = SAEModel
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# 'worst' sample booster gives no good result, or I dont know how to filter worst samples properly.
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#
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##gathering array of sample_losses
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#self.src_sample_losses += [[src_sample_idxs[i], src_sample_losses[i]] for i in range(self.batch_size) ]
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#self.dst_sample_losses += [[dst_sample_idxs[i], dst_sample_losses[i]] for i in range(self.batch_size) ]
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#
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#if len(self.src_sample_losses) >= 128: #array is big enough
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# #fetching idxs which losses are bigger than average
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# x = np.array (self.src_sample_losses)
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# self.src_sample_losses = []
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# b = x[:,1]
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# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
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# generators_list[0].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
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# print ("src repeated %d" % (len(idxs)) )
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#
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#if len(self.dst_sample_losses) >= 128: #array is big enough
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# #fetching idxs which losses are bigger than average
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# x = np.array (self.dst_sample_losses)
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# self.dst_sample_losses = []
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# b = x[:,1]
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# idxs = (x[:,0][ np.argwhere ( b [ b > (np.mean(b)+np.std(b)) ] )[:,0] ]).astype(np.uint)
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# generators_list[1].repeat_sample_idxs(idxs) #ask generator to repeat these sample idxs
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# print ("dst repeated %d" % (len(idxs)) )
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