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create end2end laplacian pyramid

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luni
2023-01-26 20:39:54 +00:00
parent 8c0436cea8
commit 0dfe7f7659
5 changed files with 133 additions and 116 deletions
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aaa_helpers.txt
+3
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@@ -1,3 +1,6 @@
nohup python cvae_pyramid.py --name="lhunor_pyr_abl_32_64_e2e_r3" > nohup/review/pyr_abl_32_64_e2e_r3.out 2>&1 &
one final convolutional layer for smoothing, on the final coordinates
skirts!!!!
configs/config_abl_pyr_32.py
+8 -4
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@@ -1,8 +1,9 @@
config = dict()
config['gpu'] = '0'
config['name'] = 'pyr_abl_32_r1'
config['name'] = 'pyr_abl_32_64_e2e_r3'
config['notes'] = ''
config['tags'] = ['review_3']
# DON'T FORGET TO RESET SSIM LOSS USAGE
# DON'T FORGET TO CHECK MODEL LOADING
@@ -21,9 +22,12 @@ config['use_offsets'] = False
config['pyramid'] = dict()
config['pyramid']['resolutions'] = [32]
config['pyramid']['resolutions'] = [32, 64]
config['pyramid']['pretrained_pyramid'] = ''
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/32_512_4'
config['pyramid']['final_upscale_enhancer'] = 'checkpoints/64_512_2'
config['pretrained_model'] = ''
config['pretrained_model_iteration'] = ''
config['train_dir'] = '/home/workspace/local-data/train'
config['valid_dir'] = '/home/workspace/local-data/valid'
@@ -149,7 +153,7 @@ for res in config['pyramid']['resolutions']:
level['decoder_depths'] = [9, 9]
level['decoder_dims'] = [256, 256]
level['avg_garment_path'] = ''
level['upscale_enhancer'] = ''#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer'] = 'checkpoints/32_512_4'#'checkpoints/up_3_d32_64_2'
level['upscale_enhancer_freeze'] = True
config['pyramid']['levels'].append(level)
else:
custom_utils/custom_utils.py
+1 -1
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@@ -45,7 +45,7 @@ class UV2Mesh(tf.keras.Model):
if uv.shape[1] == 3:
uv = tf.transpose(uv, [0,2,3,1])
if uv.shape[1] != 512:
uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.LANCZOS5)
uv = tf.image.resize(uv, (512, 256), tf.image.ResizeMethod.BILINEAR)
batch_size = uv.shape[0]
cvae_pyramid.py
+113 -106
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@@ -1,6 +1,6 @@
import os
import sys
from configs.config_abl_pyr_32_skirt import config
from configs.config_abl_pyr_32 import config
os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
import tensorflow as tf
@@ -210,73 +210,77 @@ def plot_3d(mesh_gt, mesh_pred_unposed_levels, faces, mesh_posed, collision, col
return fig
# @tf.function
@tf.function
def train_step(batch):
levels_input, conds = group_inputs_for_levels(batch, config)
with tf.GradientTape() as tape:
out_levels = model([levels_input, conds], training=True)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
output = dict()
reconstr_loss_total = 0
kl_total = 0
#for i, res in enumerate(config['pyramid']['resolutions']):
i = -1
res = config['pyramid']['resolutions'][-1]
res_modifier = '_' + str(res) if res != 512 else ''
pred, mean, logvar = out_levels[i]
for i, res in enumerate(config['pyramid']['resolutions']):
# i = -1
# res = config['pyramid']['resolutions'][-1]
if res != 512:
if i == len(config['pyramid']['resolutions']) - 1:
res_modifier = ''
else:
res_modifier = '_' + str(res)
else:
res_modifier = ''
mesh_pred = uv2mesh(pred)
pred, mean, logvar = out_levels[i]
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred = uv2mesh(pred)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
# is the output already masked?
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
kl = kl_loss(mean, logvar)
# is the output already masked?
if config['use_extra_losses']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
if not config['is_cape']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
else:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2]))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
kl = kl_loss(mean, logvar)
# if config['use_norm_loss'] and step > 2000:
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
kl_total += kl
if config['use_extra_losses']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
if not config['is_cape']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
else:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
mean = tf.reduce_mean(mean)
logvar = tf.reduce_mean(logvar)
# if config['use_norm_loss'] and step > 2000:
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
kl_total += kl
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
mean = tf.reduce_mean(mean)
logvar = tf.reduce_mean(logvar)
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
output = { **output, **{ 'train/edge_loss' + res_modifier: e_loss, 'train/collision_loss' + res_modifier: c_loss,'train/euclid_error' + res_modifier: metric,
'train/mean' + res_modifier: mean, 'train/logvar' + res_modifier: logvar, 'train/kl_loss' + res_modifier: kl,
'train/var' + res_modifier: np.e ** logvar, 'train/l2_loss' + res_modifier: l2_loss, 'train/normal_loss': n_loss,
'train/euclid_error': metric, 'train/l2_mesh_loss': l2_mesh_loss } } # 'train/ms_ssim' + res_modifier: ms_ssim,
loss = reconstr_loss_total + balancing_coeff * kl_total
@@ -290,66 +294,69 @@ def valid_step(batch):
levels_input, conds = group_inputs_for_levels(batch, config)
out_levels = model([levels_input, conds], training=False)
if config['use_skinning']:
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
else:
mesh_orig, uv_orig = batch['mesh_posed'], batch['uv_posed']
mesh_orig, uv_orig = batch['mesh_unposed'], batch['uv_unposed']
output = dict()
reconstr_loss_total = 0
kl_total = 0
#for i, res in enumerate(config['pyramid']['resolutions']):
i = -1
res = config['pyramid']['resolutions'][-1]
res_modifier = '_' + str(res) if res != 512 else ''
pred, mean, logvar = out_levels[i]
for i, res in enumerate(config['pyramid']['resolutions']):
# i = -1
# res = config['pyramid']['resolutions'][-1]
if res != 512:
if i == len(config['pyramid']['resolutions']) - 1:
res_modifier = ''
else:
res_modifier = '_' + str(res)
else:
res_modifier = ''
mesh_pred = uv2mesh(pred)
pred, mean, logvar = out_levels[i]
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
mesh_pred = uv2mesh(pred)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
# is the output already masked?
if config['use_skinning']:
shape = batch['pose_shape'][:, 72:]
gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((uv_orig - pred) ** 2), axis=-1), axis=[1, 2]))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
kl = kl_loss(mean, logvar)
mesh_pred_shaped = mesh_pred + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
mesh_pred_posed = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_shaped)
else:
mesh_pred_posed = mesh_pred
# is the output already masked?
if config['use_extra_losses']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
if not config['is_cape']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
else:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
# if config['use_norm_loss'] and step > 2000:
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
e_loss = tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
c_loss, c_ratio = collision_penalty(mesh_pred_posed, batch['mesh_body_posed'], batch['mesh_mask'])
c_loss, c_ratio = tf.reduce_mean(c_loss), tf.reduce_mean(c_ratio)
l2_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(((batch['uv_unposed' + res_modifier] - pred) ** 2), axis=-1), axis=[1, 2]))
# ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 5, batch['uv_unposed'] + 5, max_val=10, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
# ms_ssim = tf.reduce_mean(tf.image.ssim(pred + 1, batch['uv_unposed'] + 1, max_val=3))
l2_mesh_loss = tf.reduce_mean(1e3 * tf.reduce_mean(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (batch['mesh_posed'] - mesh_pred_posed) ** 2, axis=-1), axis=-1))
n_loss = normal_loss(mesh_pred_posed, batch['mesh_posed'], batch['mesh_mask'], is_skirt=config['is_skirt'])
kl = kl_loss(mean, logvar)
if config['use_extra_losses']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss + l2_mesh_loss)
if not config['is_cape']:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (e_loss + c_loss) * config['reg_loss_coeff']
else:
reconstr_loss_total = reconstr_loss_total + config['pyramid']['loss_weights'][i] * (l2_loss)
kl_total += kl
# if config['use_norm_loss'] and step > 2000:
# reconstr_loss_total = reconstr_loss_total + n_loss * config['reg_loss_coeff']
kl_total += kl
mean = tf.reduce_mean(mean)
logvar = tf.reduce_mean(logvar)
mean = tf.reduce_mean(mean)
logvar = tf.reduce_mean(logvar)
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
output = { **output, **{ 'valid/edge_loss' + res_modifier: e_loss, 'valid/collision_loss' + res_modifier: c_loss,'valid/euclid_error' + res_modifier: metric,
'valid/mean' + res_modifier: mean, 'valid/logvar' + res_modifier: logvar, 'valid/kl_loss' + res_modifier: kl,
'valid/var' + res_modifier: np.e ** logvar, 'valid/l2_loss' + res_modifier: l2_loss, 'valid/normal_loss': n_loss,
'valid/euclid_error': metric, 'valid/l2_mesh_loss': l2_mesh_loss } } # 'valid/ms_ssim' + res_modifier: ms_ssim,
loss = reconstr_loss_total + balancing_coeff * kl_total
@@ -733,7 +740,7 @@ def speed_step(levels_input, conds):
if __name__ == '__main__':
# init_wandb()
init_wandb()
parser = argparse.ArgumentParser(description='')
parser.add_argument('--name', type=str, default='lhunor')
args = vars(parser.parse_args())
@@ -800,16 +807,16 @@ if __name__ == '__main__':
# assert len(config['pyramid']['resolutions']) == 1
load_model(model, config['pretrained_model'], iteration=config['pretrained_model_iteration'])
if len(config['pyramid']['resolutions']) == 1:
model.static_enc.trainable = False
model.body_enc.trainable = False
model.curv_enc.trainable = False
else:
load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
model.static_enc.trainable = False
model.body_enc.trainable = False
model.curv_enc.trainable = False
model.cond_encoder.trainable = False
model.static_enc.trainable = False
model.body_enc.trainable = False
model.curv_enc.trainable = False
# if len(config['pyramid']['resolutions']) == 1:
# else:
# load_model(model, config['pyramid']['pretrained_pyramid'], iteration=(config['pyramid']['pretrained_pyramid_iteration'] if 'pretrained_pyramid_iteration' in config.keys() else ''))
# model.static_enc.trainable = False
# model.body_enc.trainable = False
# model.curv_enc.trainable = False
# model.cond_encoder.trainable = False
# load_model(model.levels[0].upscale_enhancer, config['pyramid']['levels'][0]['upscale_enhancer'])
# model.levels[0].upscale_enhancer.trainable = False
@@ -822,8 +829,8 @@ if __name__ == '__main__':
load_model(model.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
model.upscale_enhancer.trainable = False
for i in range(len(model.levels)-1):
model.levels[i].trainable = False
# for i in range(len(model.levels)-1):
# model.levels[i].trainable = False
# load_model(model.cond_encoder, config['pyramid']['pretrained_pyramid'])
# load_model(model.levels[0], config['pyramid']['pretrained_pyramid'])
models/pyramid_model_optimized.py
+8 -5
View File
@@ -107,27 +107,30 @@ class Pyramid(tf.keras.Model):
pred = pred + levels_input[0][2] # uv_body_posed_32
if 1 != len(self.levels):
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
out_levels.append([pred, mean, logvar])
pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.BILINEAR)
pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
else:
pred = self.enhance(pred, levels_input[0][-2], training=training)
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
out_levels.append([pred, mean, logvar])
for i in range(1, len(self.levels)):
pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
input = levels_input[i]
input[0] = pred # use previous level output as input for next level
pred_level, mean, logvar = self.levels[i](input, z_cond, training=training)
pred = pred + pred_level
if i != len(self.levels) - 1:
pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
out_levels.append([pred, mean, logvar])
pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.BILINEAR)
pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
else:
if self.upscale_enhancer is not None:
pred = self.enhance(pred, levels_input[-1][-2], training=training)
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
out_levels.append([pred, mean, logvar])