StarMirror/DeepFaceLab
1
0
Fork 0
mirror of https://github.com/iperov/DeepFaceLab.git synced 2024-03-22 13:10:55 +08:00
Code Issues Projects Releases Wiki Activity

AMP: last high loss samples behaviour - same as SAEHD

Browse Source
This commit is contained in:
iperov 2021-07-28 08:58:32 +04:00
parent bfa88c5fd9
commit 4be135af60
1 changed files with 9 additions and 11 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

20
models/Model_AMP/Model.py
View File

@ -622,24 +622,22 @@ class AMPModel(ModelBase):
src_loss, dst_loss = self.train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
for i in range(bs):
self.last_src_samples_loss.append ( (src_loss[i], warped_src[i], target_src[i], target_srcm[i], target_srcm_em[i]) )
self.last_dst_samples_loss.append ( (dst_loss[i], warped_dst[i], target_dst[i], target_dstm[i], target_dstm_em[i]) )
self.last_src_samples_loss.append ( (src_loss[i], target_src[i], target_srcm[i], target_srcm_em[i]) )
self.last_dst_samples_loss.append ( (dst_loss[i], target_dst[i], target_dstm[i], target_dstm_em[i]) )
if len(self.last_src_samples_loss) >= bs*16:
src_samples_loss = sorted(self.last_src_samples_loss, key=operator.itemgetter(0), reverse=True)
dst_samples_loss = sorted(self.last_dst_samples_loss, key=operator.itemgetter(0), reverse=True)
warped_src = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
target_src = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
target_srcm = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
target_srcm_em = np.stack( [ x[4] for x in src_samples_loss[:bs] ] )
target_src = np.stack( [ x[1] for x in src_samples_loss[:bs] ] )
target_srcm = np.stack( [ x[2] for x in src_samples_loss[:bs] ] )
target_srcm_em = np.stack( [ x[3] for x in src_samples_loss[:bs] ] )
warped_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
target_dst = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
target_dstm = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
target_dstm_em = np.stack( [ x[4] for x in dst_samples_loss[:bs] ] )
target_dst = np.stack( [ x[1] for x in dst_samples_loss[:bs] ] )
target_dstm = np.stack( [ x[2] for x in dst_samples_loss[:bs] ] )
target_dstm_em = np.stack( [ x[3] for x in dst_samples_loss[:bs] ] )
src_loss, dst_loss = self.train (warped_src, target_src, target_srcm, target_srcm_em, warped_dst, target_dst, target_dstm, target_dstm_em)
src_loss, dst_loss = self.train (target_src, target_src, target_srcm, target_srcm_em, target_dst, target_dst, target_dstm, target_dstm_em)
self.last_src_samples_loss = []
self.last_dst_samples_loss = []