256 lines
11 KiB
Python
256 lines
11 KiB
Python
import os
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import sys
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from configs.iccv.config_ae_static_25p import config
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os.environ["CUDA_VISIBLE_DEVICES"] = config['gpu']
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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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from custom_utils.custom_utils import UV2Mesh, get_summary_html, load_model, maskFacesBatch, get_summary_str
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import plotly.graph_objects as go
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import plotly.express as ex
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if config['is_skirt']:
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from losses import edge_loss_skirt as edge_loss
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else:
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from losses import edge_loss
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from tf_utils import check_tf_version, allow_memory_growth
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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 AutoEncoderNew as AutoEncoder
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from custom_utils.custom_utils import Accumulator
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def init_wandb():
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run = wandb.init(project='ae', name=config['name'], notes=config['notes'], dir='/tmp', tags=config['tags'])
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artifact = wandb.Artifact('code', type='code')
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artifact.add_file('auto_encoder_static.py')
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artifact.add_file('losses.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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config_file_names = [key for key in sys.modules.keys() if key.startswith('configs.')]
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for config_file_name in config_file_names:
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file_name = '/'.join(config_file_name.split('.')) + '.py'
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if os.path.exists(file_name):
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artifact.add_file(file_name)
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run.log_artifact(artifact)
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uv2mesh = UV2Mesh(res=256, is_skirt=config['is_skirt'], is_smpl_orig=config['is_cape'])
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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_static'], batch['uv_mask']], training=True)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_static']
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 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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@tf.function
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def valid_step(batch):
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pred = model([batch['uv_static'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_static']
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e_loss = 1e-2 * tf.reduce_mean(edge_loss(mesh_pred, mesh_orig, batch['mesh_mask']))
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l1_loss = tf.reduce_mean(tf.reduce_sum((tf.math.abs(batch['uv_static'] - pred)), axis=1))
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ms_ssim = tf.reduce_mean(tf.image.ssim_multiscale(pred + 1, batch['uv_static'] + 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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return { 'valid/loss': loss, 'valid/edge_loss': e_loss, 'valid/l1_loss': l1_loss, 'valid/euclid_error': metric, 'valid/ms_ssim': ms_ssim, }
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_static'], batch['uv_mask']], training=False)
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_static']
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orig_img = batch['uv_static']
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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" + skirt_modifier + ("_orig" if config['is_cape'] else "") + ".npy")
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mask = batch['mesh_mask'].numpy().astype(bool)
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garment_static, faces = maskFacesBatch(mesh_pred.numpy(), F, mask)
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gt = [mesh_orig[i].numpy()[mask[i], :] for i in range(pred.shape[0])]
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body = gt
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garment_posed = garment_static
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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((faces[i], faces[i] + np.sum(mask[i])), 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_static[i], faces[i], 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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train_accumulator.update(train_loss)
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if step % 25 == 0:
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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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valid_accumulator.update(valid_loss)
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if step % 1000 == 0 or (step < 1000 and step % 100 == 0):
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to_log = dict({ **train_loss, **valid_loss, **{ 'current_step': step } })
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if step % 5000 == 0 or (step < 5000 and step % 1000 == 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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to_log = { **to_log, **train_figs, **valid_figs }
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if step == 0:
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to_log = { **to_log, **{ 'model/summary': wandb.Html(get_summary_html(model)) } }
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wandb.log(to_log, step=step)
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train_accumulator.reset()
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valid_accumulator.reset()
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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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def test():
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load_model(model.encoder, 'checkpoints/' + config['name'] + '_enc')
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load_model(model.decoder, 'checkpoints/' + config['name'] + '_dec')
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dataset_test = factory.get_dataset(folder=config['test_dir'], batch_size=config['batch_size'], signature=config['signature_dynamic'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False, do_repeat=False)
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static_dataset_test = StaticDataset(folder=config['test_dir'], signature=config['signature_static'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=False)
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avg_error = 0
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count = 0
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for batch in dataset_test:
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batch = static_dataset_test.expand_batch(batch_to_dict(batch, config['signature_dynamic']))
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pred = model([batch['uv_static'], batch['uv_mask']], training=False)
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# orig_img = batch['uv_static']
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# pred_img = pred
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# img = tf.concat((orig_img, pred_img), axis=2).numpy()
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# ex.imshow((img[0] + 1) / 2).show()
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mesh_pred = uv2mesh(pred)
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mesh_orig = batch['mesh_static']
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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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avg_error += metric
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count += 1
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print(count)
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print()
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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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skirt_modifier = '_skirt' if config['is_skirt'] else ''
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factory = DatasetFactory()
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dataset_train = factory.get_dataset(folder=config['train_dir'],
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batch_size=config['batch_size'],
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signature=config['signature_dynamic'],
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resolutions=[config['resolution']],
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in_memory=config['dataset_in_memory'],
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use_no_orient=config['remove_global_orientation'],
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frames_file=config['frames_file'] if 'frames_file' in config.keys() else None,
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is_cape=config['is_cape'])
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static_dataset_train = StaticDataset(folder=config['train_dir'],
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signature=config['signature_static'],
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resolutions=[config['resolution']],
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in_memory=True,
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is_cape=config['is_cape'])
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dataset_valid = factory.get_dataset(folder=config['valid_dir'],
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batch_size=config['batch_size'],
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signature=config['signature_dynamic'],
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resolutions=[config['resolution']],
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in_memory=config['dataset_in_memory'],
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use_no_orient=config['remove_global_orientation'],
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frames_file=config['frames_file_valid'] if 'frames_file_valid' in config.keys() else None,
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is_cape=config['is_cape'])
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static_dataset_valid = StaticDataset(folder=config['valid_dir'],
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signature=config['signature_static'],
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resolutions=[config['resolution']],
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in_memory=True,
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is_cape=config['is_cape'])
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model : AutoEncoder = AutoEncoder(config['cond_ae'])
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optimizer = tf.optimizers.Adam(learning_rate=config['lr'])
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train_accumulator = Accumulator()
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valid_accumulator = Accumulator()
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if config['pretrained_model'] != '':
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load_model(model.encoder, config['pretrained_model'] + '_enc')
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load_model(model.decoder, config['pretrained_model'] + '_dec')
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ckpt_enc = tf.train.Checkpoint(model=model.encoder)
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manager_enc = tf.train.CheckpointManager(ckpt_enc, 'checkpoints/iccv/' + config['name'] + '_enc', max_to_keep=20)
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ckpt_dec = tf.train.Checkpoint(model=model.decoder)
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manager_dec = tf.train.CheckpointManager(ckpt_dec, 'checkpoints/iccv/' + config['name'] + '_dec', max_to_keep=20)
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# test()
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train() |