93 lines
3.7 KiB
Python
93 lines
3.7 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.cvae_model import CVAE, CVAE128, CVAE32
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from models.upscaler import UpscalerBasic as Upscaler
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class Pyramid(tf.keras.Model):
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def __init__(self, low_res=32,
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latent_dim=128,
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avg_garment_path='',
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static_model='',
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body_model='',
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curvature_model='',
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upscaler_model='',
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freeze_encoders=False,
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use_full_res_encoders=True):
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super(Pyramid, self).__init__()
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if low_res == 32:
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self.model_low = CVAE32(latent_dim=latent_dim,
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static_model=static_model,
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body_model=body_model,
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curvature_model=curvature_model,
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freeze_encoders=freeze_encoders,
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use_full_res_encoders=use_full_res_encoders)
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if low_res == 128:
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self.model_low = CVAE128(latent_dim=latent_dim,
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static_model=static_model,
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body_model=body_model,
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curvature_model=curvature_model,
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freeze_encoders=freeze_encoders,
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use_full_res_encoders=use_full_res_encoders)
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self.model_full = CVAE(latent_dim=latent_dim,
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static_model=static_model,
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body_model=body_model,
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curvature_model=curvature_model,
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freeze_encoders=freeze_encoders)
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self.upscale_enhancer = None
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if upscaler_model != '':
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self.upscale_enhancer = Upscaler(512, low_res)
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load_model(self.upscale_enhancer, upscaler_model)
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self.upscale_enhancer = tf.keras.Sequential([
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self.upscale_enhancer.flatten,
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self.upscale_enhancer.seq,
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self.upscale_enhancer.reshape
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])
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self.upscale_enhancer.trainable = False
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self.use_full_res_encoders = use_full_res_encoders
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if avg_garment_path != '':
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self.avg_garment = np.load(avg_garment_path)
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else:
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self.avg_garment = None
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def call(self, inputs, training=None):
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uv_unposed, uv_static, uv_body_posed, uv_curv, uv_mask, uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low, uv_mask_low = inputs
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if self.use_full_res_encoders:
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pred_low, mean_low, logvar_low, _ = self.model_low([uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low, uv_static, uv_body_posed, uv_curv], training=training)
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else:
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pred_low, mean_low, logvar_low, _ = self.model_low([uv_unposed_low, uv_static_low, uv_body_posed_low, uv_curv_low], training=training)
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pred_low = pred_low * uv_mask_low[:,:,:,tf.newaxis]
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if self.upscale_enhancer is not None:
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x = self.upscale_enhancer(pred_low, training=training)
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x = x * (1 - uv_mask_low[:,:,:,tf.newaxis])
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pred_low = x + pred_low
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x = tf.image.resize(x, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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x = x * uv_mask[:,:,:,tf.newaxis]
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pred_full, mean_full, logvar_full, _ = self.model_full([uv_unposed, uv_static, uv_body_posed, uv_curv], training=training)
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x = x + pred_full
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x = x * uv_mask[:,:,:,tf.newaxis]
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return x, mean_full, logvar_full, pred_low, mean_low, logvar_low,
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