518 lines
25 KiB
Python
518 lines
25 KiB
Python
import os
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os.environ["CUDA_VISIBLE_DEVICES"] = "2"
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import tensorflow as tf
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gpus = tf.config.experimental.list_physical_devices('GPU')
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if gpus:
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try:
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# Currently, memory growth needs to be the same across GPUs
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for gpu in gpus:
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tf.config.experimental.set_memory_growth(gpu, True)
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logical_gpus = tf.config.experimental.list_logical_devices('GPU')
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print(len(gpus), "Physical GPUs,", len(logical_gpus), "Logical GPUs")
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except RuntimeError as e:
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# Memory growth must be set before GPUs have been initialized
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print(e)
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import time
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import argparse
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import wandb
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import random
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import numpy as np
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import scipy as sp
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from custom_utils.custom_utils import UV2Mesh, maskFacesBatch, _maskFaces
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import plotly.graph_objects as go
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import plotly.express as ex
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from PIL import Image
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from utils import str_to_bool
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from tf_utils import check_tf_version, allow_memory_growth, split_gpu_for_testing
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from load_models import load_generator, load_discriminator
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from losses import d_logistic, d_logistic_r1_reg, g_logistic_non_saturating, g_logistic_ns_pathreg, edge_loss, uv_mask_loss, bend_loss
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# from losses import collision_penalty as collision_penalty
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from losses import collision_loss as collision_penalty
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from custom_utils.custom_utils import batch_to_dict, load_model
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.smpl import SMPL
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from models.cvae_pyramid_model import Pyramid
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name='cvae_pyr_3_d32'
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def init_wandb():
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run = wandb.init(project='vae', name=name,
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notes='learn offsets. smaller flattened latent vectors. with 25-85k pretraining. smaller batchsize. fixed balancing of reconst and kl loss for baseline. \
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new implementation for kl loss. no lr schedule. 25 jumpsuits, average reconst_loss before calculation, linearly increasing balancing, up to 1 from 0.1. \
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offsets over average unposed garment. remove global orientation (in posed body input and in pose_shape parameters. \
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retrained body encoder with deoriented bodies \
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use mean l2 loss. small batch. l2 * 1000, balancing to 1 from 0.01\
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fixed gender issue and retrained body encoder\
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remove shape from unposed garment\
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training on 100 garments with offsets over the average of these\
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l2 loss on both 128 and full res, bottom predicts base, high res predicts offsets over this\
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low and high res both use same full res condition encoders, but low res also gets low res versions for main encoder input\
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not using filled in versions, but using custom MLP upscaling')
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('cvae_pyramid.py')
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artifact.add_file('models/cvae_model.py')
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artifact.add_file('models/cvae_pyramid_model.py')
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artifact.add_file('models/upscaler.py')
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artifact.add_file('dataset.py')
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artifact.add_file('losses.py')
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artifact.add_file('convnext/models/convnext_tf.py')
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256)
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def plot_3d(mesh_gt, mesh_pred_unposed, mesh_pred_unposed_low, faces, collision, collision_faces, collision_color):
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mesh_pred_unposed[:,2] += 1
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mesh_pred_unposed_low[:,2] += 2
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collision[:,2] += 3
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fig = go.Figure(data=[
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go.Mesh3d(
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x=mesh_gt[:,0],
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y=mesh_gt[:,2],
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z=mesh_gt[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2]
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),
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go.Mesh3d(
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x=mesh_pred_unposed[:,0],
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y=mesh_pred_unposed[:,2],
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z=mesh_pred_unposed[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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),
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go.Mesh3d(
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x=mesh_pred_unposed_low[:,0],
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y=mesh_pred_unposed_low[:,2],
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z=mesh_pred_unposed_low[:,1],
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i = faces[:,0],
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j = faces[:,1],
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k = faces[:,2],
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),
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go.Mesh3d(
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x=collision[:,0],
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y=collision[:,2],
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z=collision[:,1],
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i = collision_faces[:,0],
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j = collision_faces[:,1],
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k = collision_faces[:,2],
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vertexcolor=collision_color
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)
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])
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fig.update_layout(scene_aspectmode='manual',
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scene_aspectratio=dict(x=1, y=3, z=1))
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fig.update_layout(
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scene = dict(
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xaxis = dict(nticks=4, range=[-1,1],),
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yaxis = dict(nticks=4, range=[-1,5],),
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zaxis = dict(nticks=4, range=[-1,1],),)
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)
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return fig
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def log_normal_pdf(sample, mean, logvar, raxis=1):
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log2pi = tf.math.log(2. * np.pi)
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res = tf.reduce_sum(-.5 * ((sample - mean) ** 2. * tf.exp(-logvar) + logvar + log2pi), axis=raxis)
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return res
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def kl_loss(mean, logvar):
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kl_loss = 1 + logvar - tf.square(mean) - tf.exp(logvar)
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kl_loss = tf.reduce_mean(kl_loss)
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kl_loss *= -0.5
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return kl_loss
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@tf.function
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def train_step(batch):
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with tf.GradientTape() as tape:
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
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pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'],
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batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low, batch['uv_mask' + resolution_modifier],])
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mesh_pred_full = uv2mesh(pred_full)
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mesh_pred_posed_full = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_full)
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mesh_pred_low = uv2mesh(pred_low)
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mesh_pred_posed_low = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_low)
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mesh_orig = batch['mesh_unposed']
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
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mesh_pred_posed_full = mesh_pred_posed_full + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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mesh_pred_posed_low = mesh_pred_posed_low + shape_offsets * batch['mesh_mask'][:,:,tf.newaxis]
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e_loss = tf.reduce_mean(edge_loss(mesh_pred_full, mesh_orig, batch['mesh_mask']))
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c_loss = tf.reduce_mean(collision_penalty(mesh_pred_posed_full, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred_full)), axis=-1), axis=[1, 2])
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l2_loss = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred_full) ** 2), axis=-1), axis=[1, 2])
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ms_ssim = tf.image.ssim_multiscale(pred_full + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3)
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kl = kl_loss(mean_full, logvar_full)
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pred_low = pred_low * batch['uv_mask' + resolution_modifier][:,:,:,tf.newaxis]
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e_loss_low = tf.reduce_mean(edge_loss(mesh_pred_low, mesh_orig, batch['mesh_mask']))
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c_loss_low = tf.reduce_mean(collision_penalty(mesh_pred_posed_low, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss_low = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low)), axis=-1), axis=[1, 2])
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l2_loss_low = 1e3 * tf.reduce_mean(tf.reduce_sum(((tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) - pred_low) ** 2), axis=-1), axis=[1, 2])
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ms_ssim_low = tf.image.ssim(pred_low + 1, tf.transpose(batch['uv_unposed' + resolution_modifier], [0,2,3,1]) + 1, max_val=3)
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kl_low = kl_loss(mean_full, logvar_full)
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reconstr_loss = l2_loss + l2_loss_low
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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loss = reconstr_loss + balancing_coeff * (kl + kl_low)
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ms_ssim = tf.reduce_mean(ms_ssim)
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l1_loss = tf.reduce_mean(l1_loss)
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l2_loss = tf.reduce_mean(l2_loss)
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mean = tf.reduce_mean(mean_full)
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logvar = tf.reduce_mean(logvar_full)
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ms_ssim_low = tf.reduce_mean(ms_ssim_low)
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l1_loss_low = tf.reduce_mean(l1_loss_low)
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l2_loss_low = tf.reduce_mean(l2_loss_low)
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mean_low = tf.reduce_mean(mean_low)
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logvar_low = tf.reduce_mean(logvar_low)
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mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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metric_low = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred_low) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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metric_2 = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred_full) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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gradients = tape.gradient(loss, model.trainable_variables)
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optimizer.apply_gradients(zip(gradients, model.trainable_variables))
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return { 'train/loss': loss, 'train/edge_loss': e_loss, 'train/collision_loss': c_loss, 'train/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim,
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'train/euclid_error_reconst': metric_2, 'train/reconstr_loss': reconstr_loss, 'train/mean': mean, 'train/logvar': logvar, 'train/kl_loss': kl,
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'train/var': np.e ** logvar, 'train/reg_coeff': reg_coeff, 'train/l2_loss': l2_loss,
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'train/edge_loss' + resolution_modifier: e_loss_low, 'train/collision_loss' + resolution_modifier: c_loss_low, 'train/l1_loss' + resolution_modifier: l1_loss_low, 'train/euclid_error' + resolution_modifier: metric_low, 'train/ms_ssim' + resolution_modifier: ms_ssim_low,
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'train/mean' + resolution_modifier: mean_low, 'train/logvar' + resolution_modifier: logvar_low, 'train/kl_loss' + resolution_modifier: kl_low,
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'train/var' + resolution_modifier: np.e ** logvar_low, 'train/l2_loss' + resolution_modifier: l2_loss_low }
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@tf.function
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def valid_step(batch):
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred, mean, logvar, z = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'], batch['uv_shaped_body']])
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mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2]))
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mesh_orig = batch['mesh_unposed']
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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c_loss = 1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss = tf.reduce_sum(tf.reduce_sum((tf.math.abs(tf.transpose(batch['uv_unposed'], [0,2,3,1]) - pred)), axis=-1) * batch['uv_mask'], axis=[1, 2])
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b_loss = 0 #1e-3 * bend_loss(mesh_pred, smpl_faces, laplacian_loss, batch['mesh_mask'])
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# ms_ssim = tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=2)
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ms_ssim = tf.image.ssim_multiscale(pred + 1, tf.transpose(batch['uv_unposed'], [0,2,3,1]) + 1, max_val=3)
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fft_loss = 0 #tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(tf.transpose(batch['uv_unposed'], [0,2,3,1]), tf.zeros_like(tf.transpose(batch['uv_unposed'], [0,2,3,1]))))), axis=[1,2,3])
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# mesh_orig_2 = uv2mesh(batch['uv_unposed'])
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metric = tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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metric_2 = 0 #tf.reduce_sum(tf.math.sqrt(tf.reduce_sum(batch['mesh_mask'][:,:,tf.newaxis] * (mesh_orig_2 - mesh_pred) ** 2, axis=-1))) / tf.reduce_sum(batch['mesh_mask'])
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reconstr_loss = l1_loss
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# logpx_z = -reconstr_loss
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# logpz = log_normal_pdf(z[:,:model.latent_dim], 0., 0.)
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# logqz_x = log_normal_pdf(z[:,:model.latent_dim], mean, logvar)
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# loss = -tf.reduce_mean(logpx_z + balancing_coeff * (logpz - logqz_x))
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kl = kl_loss(mean, logvar)
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# loss = reconstr_loss / (2 * (gamma ** 2)) + kl
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loss = reconstr_loss + balancing_coeff * kl
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ms_ssim = tf.reduce_mean(ms_ssim)
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l1_loss = tf.reduce_mean(l1_loss)
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fft_loss = tf.reduce_mean(fft_loss)
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b_loss = tf.reduce_mean(b_loss)
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reconstr_loss = tf.reduce_mean(reconstr_loss)
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mean = tf.reduce_mean(mean)
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logvar = tf.reduce_mean(logvar)
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return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/collision_loss': c_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim,
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'valid/fft_loss': fft_loss, 'valid/euclid_error_reconst': metric_2, 'train/bend_loss': b_loss, 'train/reconstr_loss': reconstr_loss,
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'valid/mean': mean, 'valid/logvar': logvar, 'valid/kl_loss': kl, 'valid/var': np.e ** logvar }
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def plot_everything(batch: dict, type: str):
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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uv_curvature_low = tf.one_hot(batch['uv_curvature' + resolution_modifier], 7)
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pred_full, mean_full, logvar_full, pred_low, mean_low, logvar_low, = model([batch['uv_unposed'], batch['uv_static'], batch['uv_body_posed'], uv_curvature, batch['uv_mask'],
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batch['uv_unposed' + resolution_modifier], batch['uv_static' + resolution_modifier], batch['uv_body_posed' + resolution_modifier], uv_curvature_low, batch['uv_mask' + resolution_modifier]])
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mesh_pred_full = uv2mesh(pred_full)
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mesh_pred_low = uv2mesh(pred_low)
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mesh_orig = batch['mesh_unposed']
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orig_img = tf.transpose(batch['uv_unposed'], [0,2,3,1])
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uv_static_img = tf.transpose(batch['uv_static'], [0,2,3,1])
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uv_body_posed_img = tf.transpose(batch['uv_body_posed'], [0,2,3,1])
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pred_img = pred_full
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pred_low_up = tf.image.resize(pred_low, (512, 256), tf.image.ResizeMethod.LANCZOS5)
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img = tf.concat((uv_static_img, uv_body_posed_img, orig_img, pred_img, pred_low_up), axis=2)
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imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
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shape = batch['pose_shape'][:, 72:]
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gender = tf.cast(tf.greater_equal(shape[:, 10] , 0.5), tf.int32)
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shape_offsets = tf.stack([tf.gather(tf.tensordot(shape[i,:10][tf.newaxis, :], smpl.shapedirs[gender[i]], axes=[[1], [2]])[0,:,:], smpl.to_keep, axis=0) for i in range(shape.shape[0])])
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F = np.load("custom_utils/smpl_faces.npy")
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mask = batch['mesh_mask'].numpy().astype(bool)
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garment_unposed, faces = maskFacesBatch(mesh_pred_full.numpy(), F, mask)
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garment_unposed_low, faces = maskFacesBatch(mesh_pred_low.numpy(), F, mask)
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garment_unposed = [garment_unposed[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])]
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garment_unposed_low = [garment_unposed_low[i] + shape_offsets.numpy()[i, mask[i], :] for i in range(mask.shape[0])]
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gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred_full.shape[0])]
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body = batch['mesh_body_posed'].numpy()
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mesh_posed_full = smpl.run_garment(batch['pose_shape'][:, :72], batch['pose_shape'][:, 72:], mesh_pred_full).numpy()
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garment_posed = [mesh_posed_full[i, mask[i], :] + shape_offsets.numpy()[i, mask[i], :] for i in range(body.shape[0])]
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collision = [np.concatenate((body[i], garment_posed[i]), axis=0) for i in range(len(garment_posed))]
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collision_faces = [np.concatenate((F, faces[i] + 14475), axis=0) for i in range(len(garment_posed))]
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collision_color = [np.concatenate((np.ones_like(body[i], dtype=np.int32)*100, np.ones_like(garment_posed[i], dtype=np.int32)*230), axis=0) for i in range(len(garment_posed))]
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figs = { type + "/garment_" + str(i): plot_3d(gt[i], garment_unposed[i], garment_unposed_low[i], faces[i], collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
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mean_hist = wandb.Histogram(sequence=mean_full.numpy().mean(axis=0))
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logvar_hist = wandb.Histogram(sequence=logvar_full.numpy().mean(axis=0))
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return { **figs, **{ type + '/images': imgs, type + '/mean_hist': mean_hist, type + '/logvar_hist': logvar_hist } }
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def sample(folder):
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# run = wandb.init(project='vae_plots', name=folder.split('/')[-1])
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load_model(model,folder)
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count = 0
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for batch in dataset_train:
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batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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nr_of_samples=2
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pred = model.sample(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples)
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pose, shape = batch['pose_shape'][0, :72], batch['pose_shape'][0, 72:]
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pose, shape = tf.tile(pose[tf.newaxis,:], [nr_of_samples,1]), tf.tile(shape[tf.newaxis,:], [nr_of_samples,1])
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mesh_pred = uv2mesh(tf.transpose(pred, [0,3,1,2]))
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mesh_posed = smpl.run_garment(pose, shape, mesh_pred).numpy()
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mesh_orig = batch['mesh_posed'][0].numpy()
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mesh_orig_reposed = smpl.run_garment(pose[:1], shape[:1], batch['mesh_unposed'][:1]).numpy()[0]
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|
|
body = batch['mesh_body_posed'][0].numpy()
|
|
body_color = np.ones_like(body, dtype=np.int32)*100
|
|
|
|
F = np.load("custom_utils/smpl_faces.npy")
|
|
mask = batch['mesh_mask'].numpy().astype(bool)[0]
|
|
_, faces = _maskFaces(np.ones((14475, 3)), F, mask)
|
|
|
|
|
|
pred_zero = model.sample(batch['uv_static'][0], batch['uv_body_posed'][0], uv_curvature[0], batch['uv_mask'][0], nr_of_samples=nr_of_samples, zero_latent=True)
|
|
mesh_pred_zero = uv2mesh(tf.transpose(pred_zero, [0,3,1,2]))
|
|
mesh_posed_zero = smpl.run_garment(pose, shape, mesh_pred_zero).numpy()
|
|
|
|
|
|
|
|
|
|
meshes = []
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=mesh_orig_reposed[mask,0],
|
|
y=mesh_orig_reposed[mask,2],
|
|
z=mesh_orig_reposed[mask,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
for i in range(nr_of_samples):
|
|
x = mesh_posed[i][mask, :].copy()
|
|
# x[:, 2] += 1 * (i + 1)
|
|
x[:, 2] += 1
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=x[:,0],
|
|
y=x[:,2],
|
|
z=x[:,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
|
|
for i in range(nr_of_samples):
|
|
x = mesh_posed_zero[i][mask, :].copy()
|
|
# x[:, 2] += 1 * (i + 1)
|
|
x[:, 2] += 2
|
|
meshes.append(go.Mesh3d(
|
|
# 8 vertices of a cube
|
|
x=x[:,0],
|
|
y=x[:,2],
|
|
z=x[:,1],
|
|
# i, j and k give the vertices of triangles
|
|
i = faces[:,0],
|
|
j = faces[:,1],
|
|
k = faces[:,2]
|
|
))
|
|
|
|
# for i in range(5):
|
|
# x = body.copy()
|
|
# x[:, 2] += 1 * i
|
|
# meshes.append(go.Mesh3d(
|
|
# # 8 vertices of a cube
|
|
# x=x[:,0],
|
|
# y=x[:,2],
|
|
# z=x[:,1],
|
|
# # i, j and k give the vertices of triangles
|
|
# i = F[:,0],
|
|
# j = F[:,1],
|
|
# k = F[:,2],
|
|
# vertexcolor=body_color
|
|
# ))
|
|
|
|
|
|
fig = go.Figure(data=meshes)
|
|
|
|
fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=2, z=1))
|
|
# fig.update_layout(scene_aspectmode='manual', scene_aspectratio=dict(x=1, y=nr_of_samples-1, z=1))
|
|
fig.update_layout(
|
|
scene = dict(
|
|
xaxis = dict(nticks=4, range=[-1,1],),
|
|
# yaxis = dict(nticks=4, range=[-1,nr_of_samples+1],),
|
|
yaxis = dict(nticks=4, range=[-1,3],),
|
|
zaxis = dict(nticks=4, range=[-1,1],),)
|
|
)
|
|
|
|
# wandb.log({'plot_' + str(count): fig})
|
|
fig.show()
|
|
|
|
pred = pred.numpy()
|
|
frames = [Image.fromarray(((pred[i] + 1) * 128).astype(np.int8), mode='RGB') for i in range(pred.shape[0])]
|
|
frames[0].save("temp/garment_1.gif", format="GIF", append_images=frames[1:],
|
|
save_all=True, duration=500, loop=0)
|
|
|
|
|
|
if count > 10:
|
|
break
|
|
|
|
print(count)
|
|
count += 1
|
|
|
|
print()
|
|
|
|
|
|
if __name__ == '__main__':
|
|
init_wandb()
|
|
allow_memory_growth()
|
|
parser = argparse.ArgumentParser(description='')
|
|
parser.add_argument('--name', type=str)
|
|
args = vars(parser.parse_args())
|
|
|
|
batch_size = 4
|
|
low_resolution = 32
|
|
resolution_modifier = '_' + str(low_resolution)
|
|
|
|
smpl = SMPL()
|
|
|
|
factory = DatasetFactory()
|
|
signature_dynamic = { 'id': (),
|
|
'pose_shape': (72 + 11,),
|
|
# 'uv_posed': (3, 512, 256,),
|
|
'uv_body_posed': (3, 512, 256,),
|
|
'uv_body_posed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
|
|
'mesh_body_posed': (14475, 3,),
|
|
'mesh_posed': (14475, 3,),
|
|
'mesh_unposed': (14475, 3,),
|
|
'uv_unposed': (3, 512, 256,),
|
|
'uv_unposed' + resolution_modifier: (3, low_resolution, low_resolution//2,),
|
|
'uv_curvature': (512, 256),
|
|
'uv_curvature' + resolution_modifier: (low_resolution, low_resolution//2),
|
|
}
|
|
signature_static = { 'uv_static': (3, 512, 256,),
|
|
'uv_static' + resolution_modifier: (3, low_resolution, low_resolution//2,),
|
|
# 'mesh_static': (14475, 3,),
|
|
'mesh_mask': (14475,),
|
|
'uv_mask': (512, 256,),
|
|
'uv_mask' + resolution_modifier: (low_resolution, low_resolution//2,),
|
|
# 'uv_shaped_body': (3, 512, 256,),
|
|
}
|
|
|
|
dataset_train = factory.get_dataset(folder='/home/workspace/local-data/train_100', batch_size=batch_size, signature=signature_dynamic, use_filled=False, resolutions=[512, low_resolution])
|
|
static_dataset_train = StaticDataset(folder='/home/workspace/local-data/train_100', signature=signature_static, use_filled=False, resolutions=[512, low_resolution])
|
|
dataset_valid = factory.get_dataset(folder='/home/workspace/local-data/jumpsuit_20/valid', batch_size=batch_size, signature=signature_dynamic)
|
|
static_dataset_valid = StaticDataset(folder='/home/workspace/local-data/jumpsuit_20/valid', signature=signature_static)
|
|
|
|
|
|
model = Pyramid(latent_dim=128,
|
|
low_res=low_resolution,
|
|
freeze_encoders=True,
|
|
static_model='checkpoints/ae_static_4',
|
|
body_model='checpoints/ae_body_7',
|
|
curvature_model='checpoints/ae_curvature_7',
|
|
upscaler_model='checkpoints/up_2_3')
|
|
|
|
# lr_schedule = tf.keras.optimizers.schedules.ExponentialDecay(0.0001, decay_steps=30000, decay_rate=0.1, staircase=False)
|
|
optimizer = tf.optimizers.Adam(learning_rate=0.0001)
|
|
balancing_coeff = 0.0001
|
|
reg_coeff = 1
|
|
folder = 'models/ae_logs/'
|
|
valid_iterator = iter(dataset_valid)
|
|
gamma = 1
|
|
balancing_activated_step = 0
|
|
# avg_jumpsuit = np.load('custom_data/jumpsuit_20_avg_uv.npy')
|
|
|
|
|
|
# sample('checkpoints/cvae_13')
|
|
|
|
|
|
ckpt = tf.train.Checkpoint(model=model)
|
|
manager = tf.train.CheckpointManager(ckpt, 'checkpoints/' + name, max_to_keep=3)
|
|
|
|
step = 0
|
|
for batch in dataset_train:
|
|
batch = static_dataset_train.expand_batch(batch_to_dict(batch, signature_dynamic))
|
|
|
|
train_loss = train_step(batch)
|
|
|
|
# data_valid = valid_iterator.get_next()
|
|
# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, signature_dynamic))
|
|
# valid_loss = valid_step(data_valid)
|
|
valid_loss = {}
|
|
|
|
to_log = { **train_loss, **valid_loss, **{ 'current_step': step} }
|
|
if step % 2500 == 0:
|
|
train_figs = plot_everything(batch, 'train')
|
|
# valid_figs = plot_everything(data_valid, 'valid')
|
|
valid_figs = {}
|
|
to_log = { **to_log, **train_figs, **valid_figs }
|
|
|
|
if step % 10000 == 0:
|
|
manager.save(checkpoint_number=step)
|
|
|
|
if train_loss['train/euclid_error'] < 0.01:
|
|
if balancing_activated_step == 0:
|
|
balancing_activated_step = step
|
|
balancing_coeff = balancing_coeff + 1 / 15000
|
|
# if train_loss['train/euclid_error'] < 0.015:
|
|
# reg_coeff = reg_coeff + 100 / 15000
|
|
# if step > 15000 and balancing_coeff <= 1e-3:
|
|
# balancing_coeff = 1e-2
|
|
# if step > 20000 and balancing_coeff <= 1e-3:
|
|
# balancing_coeff = 1e-2
|
|
# if step > 25000 and balancing_coeff <= 1e-2:
|
|
# balancing_coeff = 1e-1
|
|
# if step > 30000 and balancing_coeff <= 1e-1:
|
|
# balancing_coeff = 1
|
|
|
|
# gamma = 0.99 * gamma + 0.01 * train_loss['train/reconstr_loss']
|
|
|
|
to_log = { **to_log, **{ 'train/balancing_coeff': balancing_coeff, 'train/gamma': gamma } }
|
|
|
|
wandb.log(to_log, step = step)
|
|
|
|
print(step)
|
|
|
|
step += 1 |