221 lines
9.0 KiB
Python
221 lines
9.0 KiB
Python
import os
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from configs.config_ae_curv 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, UV2MeshLabel, get_summary_html, 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 dataset import DatasetFactory, StaticDataset
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from custom_utils.custom_utils import batch_to_dict
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from models.cond_encoder import AutoEncoder
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testing = False
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def init_wandb():
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if not testing:
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run = wandb.init(project='ae', 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_curv.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_curv.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 = UV2MeshLabel(res=256, is_skirt=config['is_skirt'])
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g_mean_iou = tf.keras.metrics.MeanIoU(num_classes=7)
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def get_mean_iou(y_true, y_pred):
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g_mean_iou.reset_state()
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g_mean_iou.update_state(y_true, y_pred)
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return g_mean_iou.result()
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g_mean_iou_mesh = tf.keras.metrics.MeanIoU(num_classes=7)
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def get_mean_iou_mesh(y_true, y_pred):
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g_mean_iou_mesh.reset_state()
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g_mean_iou_mesh.update_state(y_true, y_pred)
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return g_mean_iou_mesh.result()
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# https://github.com/NifTK/NiftyNet/blob/dev/niftynet/layer/loss_segmentation.py
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def dice_dense(prediction, ground_truth, weight_map=None):
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"""
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Computing mean-class Dice similarity.
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:param prediction: last dimension should have ``num_classes``
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:param ground_truth: segmentation ground truth (encoded as a binary matrix)
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last dimension should be ``num_classes``
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:param weight_map:
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:return: ``1.0 - mean(Dice similarity per class)``
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"""
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if weight_map is not None:
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raise NotImplementedError
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prediction = tf.cast(prediction, dtype=tf.float32)
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ground_truth = tf.cast(ground_truth, dtype=tf.float32)
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ground_truth = tf.reshape(ground_truth, prediction.shape)
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# computing Dice over the spatial dimensions
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reduce_axes = list(range(len(prediction.shape) - 1))
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dice_numerator = 2.0 * tf.reduce_sum(
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prediction * ground_truth, axis=reduce_axes)
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dice_denominator = \
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tf.reduce_sum(tf.square(prediction), axis=reduce_axes) + \
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tf.reduce_sum(tf.square(ground_truth), axis=reduce_axes)
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epsilon = 0.00001
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dice_score = (dice_numerator + epsilon) / (dice_denominator + epsilon)
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return tf.reduce_mean(dice_score)
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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_curvature'], batch['uv_mask']], training=True)
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ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
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loss = ce_uv_loss
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pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)
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mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
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mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])
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dice = dice_dense(pred, batch['uv_curvature'])
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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/ce_uv_loss': ce_uv_loss, 'train/ssim': ms_ssim, 'train/mean_iou': mean_iou, 'train/mean_iou_mesh': mean_iou_mesh, 'train/dice': dice }
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@tf.function
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def valid_step(batch):
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pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
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ce_uv_loss = scce(tf.math.argmax(batch['uv_curvature'], axis=-1), pred)
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ms_ssim = tf.reduce_mean(tf.image.ssim(pred, batch['uv_curvature'], max_val=1))
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loss = ce_uv_loss
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pred_mesh = tf.math.argmax(uv2mesh(pred), axis=-1)
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mean_iou = get_mean_iou(tf.math.argmax(batch['uv_curvature'], axis=-1), tf.math.argmax(pred, axis=-1))
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mean_iou_mesh = get_mean_iou(tf.cast(batch['mesh_curvature'], tf.float32) * batch['mesh_mask'], tf.cast(pred_mesh, tf.float32) * batch['mesh_mask'])
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dice = dice_dense(pred, batch['uv_curvature'])
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return { 'valid/loss': loss, 'valid/ce_uv_loss': ce_uv_loss, 'valid/ssim': ms_ssim, 'valid/mean_iou': mean_iou, 'valid/mean_iou_mesh': mean_iou_mesh, 'valid/dice': dice }
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def plot_everything(batch: dict, type: str):
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pred = model([batch['uv_curvature'], batch['uv_mask']], training=False)
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orig_img = tf.math.argmax(batch['uv_curvature'], axis=-1, output_type=tf.dtypes.int32)
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pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
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img = tf.concat((orig_img, pred_img), axis=2).numpy()
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imgs = { type + '/images': [wandb.Image(img[i,:,:,np.newaxis] / 6.0) for i in range(img.shape[0])] }
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return 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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to_log = { **train_loss, **valid_loss }
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if step % 5000 == 0 or (step < 5000 and step % 500 == 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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print(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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step += 1
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def test():
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#TODO
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# ckpt.restore(manager.latest_checkpoint)
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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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uv_curvature = tf.one_hot(batch['uv_curvature'], 7)
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pred = model([uv_curvature, batch['uv_mask']])
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orig_img = batch['uv_curvature']
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pred_img = tf.math.argmax(pred, axis=-1, output_type=tf.dtypes.int32)
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img = tf.concat((orig_img, pred_img), axis=2).numpy()
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ex.imshow(img[0] / 6.0).show()
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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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scce = tf.keras.losses.SparseCategoricalCrossentropy()
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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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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']], in_memory=True)
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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'], use_filled=config['use_filled'], resolutions=[config['resolution']], in_memory=True)
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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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if testing:
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test()
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else:
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train() |