181 lines
6.3 KiB
Python
181 lines
6.3 KiB
Python
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 scipy as sp
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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, VariationalEncoder, Decoder, VariationalEncoderNew, DecoderNew
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from models.upscaler import UpscalerBasic, UpscalerBasicConv, UpscalerBasicNew
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class PyramidLevel(tf.keras.Model):
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def __init__(self, config):
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super(PyramidLevel, self).__init__()
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self.latent_dim = config['latent_dim']
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self.size = config['resolution']
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self.encoder = VariationalEncoder(config['latent_dim'], config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'])
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self.decoder = Decoder(config['decoder_input'], config['decoder_depths'], config['decoder_dims'], config['kernel_size'])
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self.upscale_enhancer = None
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if config['upscale_enhancer'] != '':
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k_size = config['upscale_enhancer_kernel_size'] if 'upscale_enhancer_kernel_size' in config.keys() else ((3 if config['resolution'] <= 64 else 5))
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self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=k_size)
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load_model(self.upscale_enhancer, config['upscale_enhancer'])
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self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']
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if config['avg_garment_path'] != '':
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self.avg_garment = np.load(config['avg_garment_path'])
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else:
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self.avg_garment = None
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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 sample(self, z_cond, uv_mask, noise=None):
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if noise is None:
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eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
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else:
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eps = noise
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z = tf.concat([eps, z_cond], axis=-1)
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x = self.decoder(z)
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if self.avg_garment is not None:
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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def reparameterize(self, mean, logvar, training, fix_noise=False):
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if training == True:
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if not fix_noise:
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eps = tf.random.normal(shape=mean.shape)
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else:
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eps = tf.random.normal(shape=(1, mean.shape[1]))
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eps = tf.tile(eps, [mean.shape[0],1])
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out = eps * tf.exp(logvar * .5) + mean
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else:
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out = tf.exp(logvar * .5) + mean
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return out
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def call(self, inputs, z_cond, training=None):
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uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
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x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
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mean, logvar = self.encoder(x, training=training)
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z = self.reparameterize(mean, logvar, training)
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z = tf.concat([z, z_cond], axis=-1)
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x = self.decoder(z, training=training)
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if self.avg_garment is not None:
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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# if self.upscale_enhancer is not None:
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# pred_low = self.enhance(x, uv_mask, training=training)
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# x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# x = x * uv_mask_high[:,:,:,tf.newaxis]
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return x, mean, logvar
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class PyramidLevelNew(tf.keras.Model):
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def __init__(self, config):
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super(PyramidLevelNew, self).__init__()
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self.size = config['resolution']
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self.encoder = VariationalEncoderNew(config['encoder_depths'], config['encoder_dims'], kernel_size=config['kernel_size'])
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self.decoder = DecoderNew(config['decoder_depths'], config['decoder_dims'], config['kernel_size'])
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self.upscale_enhancer = None
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if config['upscale_enhancer'] != '':
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k_size = config['upscale_enhancer_kernel_size'] if 'upscale_enhancer_kernel_size' in config.keys() else ((3 if config['resolution'] <= 64 else 5))
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self.upscale_enhancer = UpscalerBasicConv(config['resolution'], kernel_size=k_size)
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load_model(self.upscale_enhancer, config['upscale_enhancer'])
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self.upscale_enhancer.trainable = not config['upscale_enhancer_freeze']
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if config['avg_garment_path'] != '':
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self.avg_garment = np.load(config['avg_garment_path'])
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else:
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self.avg_garment = None
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self.reshape = tf.keras.layers.Reshape((8, 4, 64))
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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 sample(self, z_cond, uv_mask, noise=None):
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if noise is None:
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eps = tf.random.normal(shape=(uv_mask.shape[0], self.latent_dim))
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else:
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eps = noise
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z = tf.concat([eps, z_cond], axis=-1)
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x = self.decoder(z)
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if self.avg_garment is not None:
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x
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def reparameterize(self, mean, logvar, training, fix_noise=False):
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if training == True:
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if not fix_noise:
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eps = tf.random.normal(shape=mean.shape)
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else:
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eps = tf.random.normal(shape=(1, mean.shape[1]))
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eps = tf.tile(eps, [mean.shape[0],1])
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out = eps * tf.exp(logvar * .5) + mean
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else:
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out = tf.exp(logvar * .5) + mean
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return out
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def call(self, inputs, z_cond, training=None):
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uv_unposed, uv_template, uv_body_posed, uv_curv, uv_mask, uv_mask_high = inputs
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x = tf.concat([uv_unposed, uv_template, uv_body_posed, uv_curv], axis=-1)
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mean, logvar = self.encoder(x, training=training)
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z = self.reparameterize(mean, logvar, training)
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z = self.reshape(z)
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z = tf.concat([z, z_cond], axis=-1)
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x = self.decoder(z, training=training)
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if self.avg_garment is not None:
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x = x + self.avg_garment[tf.newaxis,:,:,:]
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x = x * uv_mask[:,:,:,tf.newaxis]
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# if self.upscale_enhancer is not None:
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# pred_low = self.enhance(x, uv_mask, training=training)
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# x = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# x = x * uv_mask_high[:,:,:,tf.newaxis]
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return x, mean, logvar |