luni/cvpr
1
0
Fork 0
Code Issues Pull Requests Packages Projects Releases Wiki Activity

more training

Browse Source
This commit is contained in:
luni
2023-03-02 14:59:51 +00:00
parent 424f8d2fcc
commit 25112a7ff9
17 changed files with 1484 additions and 17 deletions
Download Patch File Download Diff File Expand all files Collapse all files
models/pyramid_model_optimized.py
+12 -7
View File
@@ -264,14 +264,16 @@ class PyramidNew(tf.keras.Model):
if config['pyramid']['body_model'] != '':
load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
if config['pyramid']['curvature_model'] != '':
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
if config['use_curvature']:
self.curv_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_curv']['encoder_depths'], config['cond_ae_curv']['encoder_dims'], config['cond_ae_curv']['kernel_size'], config['cond_ae_curv']['activation'])
if config['pyramid']['curvature_model'] != '':
load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
if config['pyramid']['freeze_encoders']:
self.static_enc.trainable = False
self.body_enc.trainable = False
self.curv_enc.trainable = False
if config['use_curvature']:
self.curv_enc.trainable = False
self.levels = [PyramidLevelNew({ **config['pyramid']['levels'][i] }) for i in range(len(config['pyramid']['levels']))]
@@ -294,6 +296,7 @@ class PyramidNew(tf.keras.Model):
self.add_noise = config['pyramid']['add_noise']
self.noise_amount = config['pyramid']['noise_amount']
self.use_offsets = config['use_offsets']
self.use_curvature = config['use_curvature']
self.upscale_enhancer = None
if config['pyramid']['final_upscale_enhancer'] != '':
@@ -309,9 +312,11 @@ class PyramidNew(tf.keras.Model):
def encode_conds(self, uv_template, uv_body_posed, uv_curv, training):
z_static = self.static_enc(uv_template, training)
z_body = self.body_enc(uv_body_posed, training)
z_curv = self.curv_enc(uv_curv, training)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
if self.use_curvature:
z_curv = self.curv_enc(uv_curv, training)
z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
else:
z_cond = tf.concat([z_static, z_body], axis=-1)
return z_cond