246 lines
15 KiB
Python
246 lines
15 KiB
Python
import os
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import time
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import random
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import torch
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from torch import nn
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import torch.nn.functional as F
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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 utils.checkpoints import load_model
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from models.base import AEEncoder
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from models.pyramid_level import PyramidLevel
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# class Pyramid(nn.Module):
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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_static']['encoder_block_settings'], kernel_size=config['cond_ae_static']['kernel_size'], activation=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 : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], kernel_size=config['cond_ae_body']['kernel_size'], activation=config['cond_ae_body']['activation'])
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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_curv']['encoder_block_settings'], kernel_size=config['cond_ae_curv']['kernel_size'], activation=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['freeze_encoders']:
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# for param in self.static_enc.parameters(): param.requires_grad = False
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# for param in self.body_enc.parameters(): param.requires_grad = False
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# for param in self.curv_enc.parameters(): param.requires_grad = False
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# self.levels = [PyramidLevel({ **config['pyramid']['levels'][i], **{ 'cond_ae_static': config['cond_ae_static'], 'cond_ae_body': config['cond_ae_body'], 'cond_ae_curv': config['cond_ae_curv'], } }) 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.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):
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# x = self.upscale_enhancer([low, mask])
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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 = torch.concat([z_static, z_body, z_curv], dim=-1)
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# return z_cond
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# def call(self, inputs):
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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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# # 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)
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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]) # levels_input[i][-2]=uv_mask_res
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# pred = F.interpolate(pred, size=(self.levels[1].size, self.levels[1].size//2), mode='bilinear')
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# out_levels.append([pred, mean, logvar])
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# pred = pred * levels_input[1][-2][:,None,:,:]
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# else:
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# pred = self.enhance(pred, levels_input[0][-2])
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# pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
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# pred = pred * levels_input[-1][-1][:,None,:,:] # 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)
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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])
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# out_levels.append([pred, mean, logvar])
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# pred = F.interpolate(pred, size=(self.levels[i+1].size, self.levels[i+1].size//2), mode='bilinear')
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# pred = pred * levels_input[i+1][-2][:,None,:,:]
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# else:
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# pred = self.enhance(pred, levels_input[i][-1])
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# pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
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# pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
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# out_levels.append([pred, mean, logvar])
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# return out_levels
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class Pyramid(nn.Module):
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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_static']['encoder_block_settings'], kernel_size=config['cond_ae_static']['kernel_size'], activation=config['cond_ae_static']['activation'])
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if config['static_model'] != '':
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load_model(self.static_enc, config['static_model'] + '_enc')
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self.body_enc : AEEncoder = AEEncoder(config['cond_ae_body']['encoder_block_settings'], kernel_size=config['cond_ae_body']['kernel_size'], activation=config['cond_ae_body']['activation'])
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if config['body_model'] != '':
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load_model(self.body_enc, config['body_model'] + '_enc')
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self.curv_enc : AEEncoder = AEEncoder(config['cond_ae_curv']['encoder_block_settings'], kernel_size=config['cond_ae_curv']['kernel_size'], activation=config['cond_ae_curv']['activation'], is_label=True, nr_of_labels=7)
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if config['curvature_model'] != '':
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load_model(self.curv_enc, config['curvature_model'] + '_enc')
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if config['freeze_encoders']:
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for param in self.static_enc.parameters(): param.requires_grad = False
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for param in self.body_enc.parameters(): param.requires_grad = False
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for param in self.curv_enc.parameters(): param.requires_grad = False
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self.levels = nn.ModuleList([PyramidLevel({ **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.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):
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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 = torch.concat([z_static, z_body, z_curv], dim=1)
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return z_cond
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def forward(self, inputs):
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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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# 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)
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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]) # levels_input[i][-2]=uv_mask_res
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pred = F.interpolate(pred, size=(self.levels[1].size, self.levels[1].size//2), mode='bilinear')
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out_levels.append([pred, mean, logvar])
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pred = pred * levels_input[1][-2][:,None,:,:]
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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]) # levels_input[i][-2]=uv_mask_res
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pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
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pred = pred * levels_input[-1][-1][:,None,:,:] # 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)
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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])
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out_levels.append([pred, mean, logvar])
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pred = F.interpolate(pred, size=(self.levels[i+1].size, self.levels[i+1].size//2), mode='bilinear')
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pred = pred * levels_input[i+1][-2][:,None,:,:]
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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])
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pred = F.interpolate(pred, size=(512, 256), mode='bilinear')
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pred = pred * levels_input[-1][-1][:,None,:,:] # full res uv mask
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out_levels.append([pred, mean, logvar])
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return out_levels |