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update pyramid

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luni
2022-10-07 21:58:18 +00:00
parent 9d34c540d9
commit 01c8c3dc27
3 changed files with 179 additions and 19 deletions
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models/pyramid_model.py
+98 -2
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@@ -70,13 +70,15 @@ class Pyramid(tf.keras.Model):
out_levels = []
pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
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 = 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 = 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)):
@@ -91,10 +93,104 @@ class Pyramid(tf.keras.Model):
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)
out_levels.append([pred, mean, logvar])
pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
out_levels.append([pred, mean, logvar])
return out_levels
# class Pyramid(tf.keras.Model):
# def __init__(self, config):
# super(Pyramid, self).__init__()
# self.static_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
# if config['pyramid']['static_model'] != '':
# load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
# self.body_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
# if config['pyramid']['body_model'] != '':
# load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
# self.curv_enc : AEEncoder = AEEncoder(config['cond_ae']['latent_dim'], config['cond_ae']['encoder_depths'], config['cond_ae']['encoder_dims'], config['cond_ae']['bottleneck_size'], config['cond_ae']['kernel_size'])
# 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
# self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
# tf.keras.layers.Dense(256, activation='relu'),
# tf.keras.layers.Dense(128, activation='relu'),
# tf.keras.layers.Dense(config['pyramid']['latent_dim']),])
# self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
# self.upscale_enhancer = None
# if config['pyramid']['final_upscale_enhancer'] != '':
# self.upscale_enhancer = UpscalerBasicNew(low_res=config['pyramid']['resolutions'][-1])
# load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
# self.upscale_enhancer.trainable = False
# def enhance(self, low, mask, training=None):
# x = self.upscale_enhancer([low, mask], training=training)
# return x
# def encode_conds(self, uv_template, uv_body_posed, uv_curv):
# z_static = self.static_enc(uv_template)
# z_body = self.body_enc(uv_body_posed)
# z_curv = self.curv_enc(uv_curv)
# z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
# z_cond = self.cond_encoder(z_cond)
# return z_cond
# def call(self, inputs, training=None):
# levels_input, conds = inputs
# uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
# z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
# out_levels = []
# pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
# pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
# # pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
# ##################
# # if more than 1 level
# # x = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
# # x = x * levels_input[1][-2][:,:,:,tf.newaxis]
# # if only 1 level
# pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
# ##################
# # pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
# # pred_1 = pred_0 + pred_1
# # pred_1 = self.levels[1].enhance(pred, levels_input[1][-2], training=training)
# # for i in range(1, len(self.levels)):
# # pred_level, mean, logvar = self.levels[i](levels_input[i], 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 = 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)
# # out_levels.append([pred, mean, logvar])
# # pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
# out_levels = [
# [pred_0, mean_0, logvar_0]
# ]
# return out_levels