pyramid model without for loops
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import os
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import time
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import random
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import tensorflow as tf
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import numpy as np
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# import plotly.graph_objects as go
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# import plotly.express as ex
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from custom_utils.custom_utils import load_model
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from models.base import AEEncoder
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from models.cvae_model import CVAE
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from models.pyramid_level import PyramidLevel
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from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew
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class Pyramid(tf.keras.Model):
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def __init__(self, config):
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super(Pyramid, self).__init__()
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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'])
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if config['pyramid']['static_model'] != '':
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load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
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self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['latent_dim'], config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], config['cond_ae_body']['bottleneck_size'], config['cond_ae_body']['kernel_size'])
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if config['pyramid']['body_model'] != '':
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load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
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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'])
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if config['pyramid']['curvature_model'] != '':
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load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
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if config['pyramid']['freeze_encoders']:
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self.static_enc.trainable = False
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self.body_enc.trainable = False
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self.curv_enc.trainable = False
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self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
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tf.keras.layers.Dense(256, activation='relu'),
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tf.keras.layers.Dense(128, activation='relu'),
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tf.keras.layers.Dense(config['pyramid']['latent_dim']),])
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self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
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self.upscale_enhancer = None
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if config['pyramid']['final_upscale_enhancer'] != '':
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self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1])
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load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
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self.upscale_enhancer.trainable = False
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def enhance(self, low, mask, training=None):
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x = self.upscale_enhancer([low, mask], training=training)
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return x
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def encode_conds(self, uv_template, uv_body_posed, uv_curv):
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z_static = self.static_enc(uv_template)
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z_body = self.body_enc(uv_body_posed)
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z_curv = self.curv_enc(uv_curv)
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z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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z_cond = self.cond_encoder(z_cond)
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return z_cond
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def call(self, inputs, training=None):
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levels_input, conds = inputs
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uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
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z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
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# out_levels = []
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# pred, mean, logvar = self.levels[0](levels_input[0], z_cond, training=training)
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# if 1 != len(self.levels):
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# pred = self.levels[0].enhance(pred, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
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# pred = tf.image.resize(pred, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
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# out_levels.append([pred, mean, logvar])
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# pred = pred * levels_input[1][-2][:,:,:,tf.newaxis]
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# else:
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# pred = self.enhance(pred, levels_input[0][-2], training=training)
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# pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
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# out_levels.append([pred, mean, logvar])
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# for i in range(1, len(self.levels)):
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# pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
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# pred = pred + pred_level
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# if i != len(self.levels) - 1:
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# pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
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# out_levels.append([pred, mean, logvar])
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# pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
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# pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
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# else:
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# if self.upscale_enhancer is not None:
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# pred = self.enhance(pred, levels_input[-1][-2], training=training)
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# pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
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# out_levels.append([pred, mean, logvar])
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pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
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pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
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pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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pred_0 = pred_0 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
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return [ [pred_0, mean_0, logvar_0] ]
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# class Pyramid(tf.keras.Model):
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# def __init__(self, config):
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# super(Pyramid, self).__init__()
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# 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'])
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# if config['pyramid']['static_model'] != '':
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# load_model(self.static_enc, config['pyramid']['static_model'] + '_enc')
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# 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'])
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# if config['pyramid']['body_model'] != '':
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# load_model(self.body_enc, config['pyramid']['body_model'] + '_enc')
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# 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'])
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# if config['pyramid']['curvature_model'] != '':
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# load_model(self.curv_enc, config['pyramid']['curvature_model'] + '_enc')
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# if config['pyramid']['freeze_encoders']:
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# self.static_enc.trainable = False
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# self.body_enc.trainable = False
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# self.curv_enc.trainable = False
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# self.cond_encoder = tf.keras.Sequential([tf.keras.layers.Dense(512, activation='relu'),
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# tf.keras.layers.Dense(256, activation='relu'),
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# tf.keras.layers.Dense(128, activation='relu'),
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# tf.keras.layers.Dense(config['pyramid']['latent_dim']),])
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# self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae': config['cond_ae']} }) for i in range(len(config['pyramid']['levels']))]
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# self.upscale_enhancer = None
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# if config['pyramid']['final_upscale_enhancer'] != '':
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# self.upscale_enhancer = UpscalerBasicNew(low_res=config['pyramid']['resolutions'][-1])
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# load_model(self.upscale_enhancer, config['pyramid']['final_upscale_enhancer'])
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# self.upscale_enhancer.trainable = False
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# def enhance(self, low, mask, training=None):
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# x = self.upscale_enhancer([low, mask], training=training)
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# return x
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# def encode_conds(self, uv_template, uv_body_posed, uv_curv):
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# z_static = self.static_enc(uv_template)
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# z_body = self.body_enc(uv_body_posed)
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# z_curv = self.curv_enc(uv_curv)
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# z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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# z_cond = self.cond_encoder(z_cond)
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# return z_cond
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# def call(self, inputs, training=None):
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# levels_input, conds = inputs
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# uv_static, uv_body, uv_curv = conds[0], conds[1], conds[2]
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# z_cond = self.encode_conds(uv_static, uv_body, uv_curv)
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# out_levels = []
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# pred_0, mean_0, logvar_0 = self.levels[0](levels_input[0], z_cond, training=training)
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# pred_0 = self.levels[0].enhance(pred_0, levels_input[0][-2], training=training) # levels_input[i][-2]=uv_mask_res
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# # pred_0 = self.enhance(pred_0, levels_input[0][-2], training=training)
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# ##################
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# # if more than 1 level
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# # x = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
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# # x = x * levels_input[1][-2][:,:,:,tf.newaxis]
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# # if only 1 level
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# pred_0 = tf.image.resize(pred_0, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# ##################
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# # pred_1, mean_1, logvar_1 = self.levels[1](levels_input[1], z_cond, training=training)
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# # pred_1 = pred_0 + pred_1
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# # pred_1 = self.levels[1].enhance(pred, levels_input[1][-2], training=training)
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# # for i in range(1, len(self.levels)):
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# # pred_level, mean, logvar = self.levels[i](levels_input[i], z_cond, training=training)
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# # pred = pred + pred_level
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# # if i != len(self.levels) - 1:
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# # pred = self.levels[i].enhance(pred, levels_input[i][-2], training=training)
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# # out_levels.append([pred, mean, logvar])
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# # pred = tf.image.resize(pred, (self.levels[i+1].size, self.levels[i+1].size//2), tf.image.ResizeMethod.LANCZOS5)
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# # pred = pred * levels_input[i+1][-2][:,:,:,tf.newaxis]
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# # else:
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# # if self.upscale_enhancer is not None:
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# # pred = self.enhance(pred, levels_input[-1][-2], training=training)
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# # pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# # out_levels.append([pred, mean, logvar])
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# # pred = pred * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
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# out_levels = [
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# [pred_0, mean_0, logvar_0]
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# ]
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# return out_levels
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