373 lines
22 KiB
Python
373 lines
22 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 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, AEEncoderNew
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from models.pyramid_level import PyramidLevel, PyramidLevelNew
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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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if config['use_curvature']:
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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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if config['use_curvature']:
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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'], activation='tanh'),])
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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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# if config['is_cape']:
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# if len(config['pyramid']['resolutions']) == 2:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# if len(config['pyramid']['resolutions']) == 3:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# if len(config['pyramid']['resolutions']) == 4:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# else:
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if len(config['pyramid']['resolutions']) == 2:
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self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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if len(config['pyramid']['resolutions']) == 3:
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self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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if len(config['pyramid']['resolutions']) == 4:
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self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
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self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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if len(config['pyramid']['resolutions']) == 5:
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self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
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self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
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self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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self.add_noise = config['pyramid']['add_noise']
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self.noise_amount = config['pyramid']['noise_amount']
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self.use_offsets = config['use_offsets']
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self.use_curvature = config['use_curvature']
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self.upscale_enhancer = None
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if config['pyramid']['final_upscale_enhancer'] != '':
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k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
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self.upscale_enhancer : tf.keras.Model = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
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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):
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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, training):
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z_static = self.static_enc(uv_template, training)
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z_body = self.body_enc(uv_body_posed, training)
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if self.use_curvature:
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z_curv = self.curv_enc(uv_curv, training=training)
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z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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else:
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z_cond = tf.concat([z_static, z_body], axis=-1)
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z_cond = self.cond_encoder(z_cond, training=training)
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return z_cond
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def call(self, inputs, training):
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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, training)
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if self.add_noise == True and training == True:
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z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
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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 self.use_offsets:
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pred = pred + levels_input[0][2] # uv_body_posed_32
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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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return out_levels
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# # 32
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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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# # 32 64
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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 = tf.image.resize(pred_0, (self.levels[1].size, self.levels[1].size//2), tf.image.ResizeMethod.LANCZOS5)
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# pred = pred_0 * levels_input[1][-2][:,:,:,tf.newaxis]
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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 + pred_1
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# pred_1 = self.enhance(pred_1, levels_input[-1][-2], training=training)
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# pred_1 = tf.image.resize(pred_1, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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# pred_1 = pred_1 * levels_input[-1][-1][:,:,:,tf.newaxis] # full res uv mask
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# return [ [pred_0, mean_0, logvar_0], [pred_1, mean_1, logvar_1] ]
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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):
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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):
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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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class PyramidNew(tf.keras.Model):
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def __init__(self, config):
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super(PyramidNew, self).__init__()
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self.static_enc : AEEncoderNew = AEEncoderNew(config['cond_ae_static']['encoder_depths'], config['cond_ae_static']['encoder_dims'], config['cond_ae_static']['kernel_size'], config['cond_ae_static']['activation'])
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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 : AEEncoderNew = AEEncoderNew(config['cond_ae_body']['encoder_depths'], config['cond_ae_body']['encoder_dims'], 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 : 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'])
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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.levels = [PyramidLevelNew({ **config['pyramid']['levels'][i] }) for i in range(len(config['pyramid']['levels']))]
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# if len(config['pyramid']['resolutions']) == 2:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# if len(config['pyramid']['resolutions']) == 3:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# if len(config['pyramid']['resolutions']) == 4:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
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# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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# if len(config['pyramid']['resolutions']) == 5:
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# self.levels[-1].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100000)])
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# self.levels[-2].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 10000)])
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# self.levels[-3].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 1000)])
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# self.levels[-4].decoder.decoder.last = tf.keras.Sequential([tf.keras.layers.Dense(3), tf.keras.layers.Lambda(lambda x: x / 100)])
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self.add_noise = config['pyramid']['add_noise']
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self.noise_amount = config['pyramid']['noise_amount']
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self.use_offsets = config['use_offsets']
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self.use_curvature = config['use_curvature']
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self.upscale_enhancer = None
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if config['pyramid']['final_upscale_enhancer'] != '':
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k_size = config['pyramid']['final_upscale_enhancer_kernel_size'] if 'final_upscale_enhancer_kernel_size' in config['pyramid'].keys() else ((3 if config['pyramid']['resolutions'][-1] <= 64 else 5))
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self.upscale_enhancer = UpscalerBasicConv(low_res=config['pyramid']['resolutions'][-1], kernel_size=k_size)
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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):
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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, training):
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z_static = self.static_enc(uv_template, training)
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z_body = self.body_enc(uv_body_posed, training)
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if self.use_curvature:
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z_curv = self.curv_enc(uv_curv, training=training)
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z_cond = tf.concat([z_static, z_body, z_curv], axis=-1)
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else:
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z_cond = tf.concat([z_static, z_body], axis=-1)
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return z_cond
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def call(self, inputs, training):
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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, training)
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if self.add_noise == True and training == True:
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z_cond = z_cond + (tf.random.uniform(z_cond.shape, maxval=self.noise_amount * 2) - self.noise_amount)
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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 self.use_offsets:
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pred = pred + levels_input[0][2] # uv_body_posed_32
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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.BILINEAR)
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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 = 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, (512, 256), tf.image.ResizeMethod.BILINEAR)
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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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input = levels_input[i]
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input[0] = input[0] - pred
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input_min, input_max = tf.reduce_min(input[0], axis=[1,2,3]), tf.reduce_max(input[0], axis=[1,2,3])
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input[0] = (input[0] - input_min[:,None,None,None]) / (input_max - input_min)[:,None,None,None]
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input[0] = input[0] * input[-2][:,:,:,None] # uv_mask_res
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pred_level, mean, logvar = self.levels[i](input, z_cond, training=training)
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pred_level = ( (pred_level * (input_max - input_min)[:,None,None,None]) + input_min[:,None,None,None] ) * input[-2][:,:,:,None] # uv_mask_res
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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.BILINEAR)
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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 = self.levels[-1].enhance(pred, levels_input[-1][-2], training=training)
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pred = tf.image.resize(pred, (512, 256), tf.image.ResizeMethod.BILINEAR)
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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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|
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return out_levels |