169 lines
7.9 KiB
Python
169 lines
7.9 KiB
Python
import os
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from configs.config_ae_body import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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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 cv2
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import numpy as np
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import tensorflow as tf
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from custom_utils.custom_utils import UV2Mesh, load_model, maskFacesBatch
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import plotly.graph_objects as go
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import plotly.express as ex
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from tf_utils import check_tf_version, allow_memory_growth
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from losses import edge_loss, uv_mask_loss
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from dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict, plot_3d
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from models.cond_encoder import AutoEncoder
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def init_wandb():
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run = wandb.init(project='vae', name=config['name'], notes=config['notes'])
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('auto_encoder_body.py')
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artifact.add_file('losses.py')
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artifact.add_file('configs/config_base.py')
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artifact.add_file('configs/config_ae_body.py')
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artifact.add_file('models/base.py')
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artifact.add_file('models/cond_encoder.py')
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artifact.add_file('dataset.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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@tf.function
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def train_step(batch):
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with tf.GradientTape() as tape:
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pred = model([batch['uv_body_posed' + resolution_modifier], uv_mask_body], training=True)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_body_posed']
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, tf.ones((pred.shape[0], 14475))))
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l1_loss = tf.reduce_mean(tf.reduce_sum(((tf.math.abs(batch['uv_body_posed' + resolution_modifier] - pred))), axis=1))
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_body_posed' + resolution_modifier] + 1, max_val=2, power_factors=(0.5, 0.2, 0.1, 0.1, 0.1)))
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loss = 1e3 * (1 - ms_ssim)
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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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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/l1_loss': l1_loss, 'train/euclid_error': metric, 'train/ms_ssim': ms_ssim, }
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# UPDATE!
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@tf.function
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def valid_step(batch):
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pred = model([batch['uv_body_posed'], batch['uv_mask']])
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_body_posed']
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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 = 0 #1e-2 * tf.reduce_mean(collision_penalty(mesh_pred, batch['mesh_body_posed'], batch['mesh_mask']))
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_body_posed'] - pred)), axis=1) * batch['uv_mask'])
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(tf.transpose(pred, [0,2,3,1]) + 1, tf.transpose(batch['uv_body_posed'], [0,2,3,1]) + 1, max_val=2))
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fft_loss = 0 #tf.reduce_mean(tf.reduce_sum(tf.math.abs(tf.signal.fft3d(tf.complex(pred, tf.zeros_like(pred))) - tf.signal.fft3d(tf.complex(batch['uv_body_posed'], tf.zeros_like(batch['uv_body_posed'])))), axis=[1,2,3]))
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loss = 1e3 * (1 - ms_ssim)
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mesh_orig_2 = uv2mesh(batch['uv_body_posed'])
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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 = 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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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
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}
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_body_posed' + resolution_modifier], uv_mask_body], training=False)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_body_posed']
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orig_img = batch['uv_body_posed' + resolution_modifier]
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pred_img = pred
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img = tf.concat((orig_img, pred_img), axis=2)
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imgs = [wandb.Image((img[i] + 1) / 2.0) for i in range(4)]
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F = np.load("custom_utils/smpl_faces.npy")
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garment_unposed, faces = mesh_pred.numpy(), F
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gt = [mesh_orig[i].numpy()[:, :] for i in range(pred.shape[0])]
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body = uv2mesh(batch['uv_body_posed' + resolution_modifier]).numpy()
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garment_posed = garment_unposed
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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 + 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], faces, collision[i], collision_faces[i], collision_color[i]) for i in range(4) }
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return { **figs, **{ type + '/images': imgs } }
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def train():
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valid_iterator = iter(dataset_valid)
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step = 0
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for batch in dataset_train:
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batch = static_dataset_train.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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train_loss = train_step(batch)
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# data_valid = valid_iterator.get_next()
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# data_valid = static_dataset_valid.expand_batch(batch_to_dict(data_valid, config['signature_dynamic']))
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valid_loss = {}#valid_step(data_valid)
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if step % 2500 == 0:
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train_figs = plot_everything(batch, 'train')
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valid_figs = {}#plot_everything(data_valid, 'valid')
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wandb.log({ **train_loss, **valid_loss, **train_figs, **valid_figs }, step=step)
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else:
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wandb.log({ **train_loss, **valid_loss }, step=step)
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if step % 10000 == 0:
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manager_enc.save(checkpoint_number=step)
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manager_dec.save(checkpoint_number=step)
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print(step)
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step += 1
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if __name__ == '__main__':
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init_wandb()
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allow_memory_growth()
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parser = argparse.ArgumentParser(description='')
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parser.add_argument('--name', type=str)
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args = vars(parser.parse_args())
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# TODO: different resolutions not working yet, model should be ready though
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resolution_modifier = ''
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if config['resolution'] != 512:
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resolution_modifier = '_' + str(config['resolution'])
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uv_mask_body = np.load('custom_utils/mask_256x512.npy')
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uv_mask_body = cv2.dilate(uv_mask_body.astype(np.float32), cv2.getStructuringElement(cv2.MORPH_RECT, (2 * 2 + 1, 2 * 2 + 1), (2, 2)), iterations=2)
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factory = DatasetFactory()
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dataset_train = factory.get_dataset(folder=config['train_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'])
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static_dataset_train = StaticDataset(folder=config['train_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']])
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dataset_valid = factory.get_dataset(folder=config['valid_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=config['dataset_in_memory'])
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static_dataset_valid = StaticDataset(folder=config['valid_dir'], signature=config['signature_static'])
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model = AutoEncoder(config['cond_ae'])
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optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
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ckpt_enc = tf.train.Checkpoint(model=model.encoder)
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manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/' + config['name'] + '_enc', max_to_keep=3)
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ckpt_dec = tf.train.Checkpoint(model=model.decoder)
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manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/' + config['name'] + '_dec', max_to_keep=3)
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train() |