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Zero
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# -*- coding: utf-8 -*-
# Max-Planck-Gesellschaft zur Förderung der Wissenschaften e.V. (MPG) is
# holder of all proprietary rights on this computer program.
# You can only use this computer program if you have closed
# a license agreement with MPG or you get the right to use the computer
# program from someone who is authorized to grant you that right.
# Any use of the computer program without a valid license is prohibited and
# liable to prosecution.
#
# Copyright©2023 Max-Planck-Gesellschaft zur Förderung
# der Wissenschaften e.V. (MPG). acting on behalf of its Max Planck Institute
# for Intelligent Systems. All rights reserved.
#
# Contact: mica@tue.mpg.de
import os
import subprocess
from copy import deepcopy
from datetime import datetime
import numpy as np
import torch
from loguru import logger
from torch.utils.data import DataLoader
import datasets
from utils import util
from utils.best_model import BestModel
class Validator(object):
def __init__(self, trainer):
self.trainer = trainer
self.device = self.trainer.device
self.nfc = self.trainer.nfc
self.cfg = deepcopy(self.trainer.cfg)
self.device = trainer.device
# Create a separate instance only for predictions
# nfc = util.find_model_using_name(model_dir='nfclib.models', model_name=self.cfg.model.name)(self.cfg, self.device)
# self.tester = Tester(nfc, self.cfg, self.device)
# self.tester.render_mesh = False
self.embeddings_lyhm = {}
self.best_model = BestModel(trainer)
self.prepare_data()
def prepare_data(self):
self.val_dataset, total_images = datasets.build_val(self.cfg.dataset, self.device)
self.val_dataloader = DataLoader(
self.val_dataset,
batch_size=2,
shuffle=False,
num_workers=4,
pin_memory=True,
drop_last=False)
self.val_iter = iter(self.val_dataloader)
logger.info(f'[VALIDATOR] Validation dataset is ready with {len(self.val_dataset)} actors and {total_images} images.')
def state_dict(self):
return {
'embeddings_lyhm': self.embeddings_lyhm,
'best_model': self.best_model.state_dict(),
}
def load_state_dict(self, dict):
self.embeddings_lyhm = dict['embeddings_lyhm']
self.best_model.load_state_dict(dict['best_model'])
def update_embeddings(self, actors, arcface):
B = len(actors)
for i in range(B):
actor = actors[i]
if actor not in self.embeddings_lyhm:
self.embeddings_lyhm[actor] = []
self.embeddings_lyhm[actor].append(arcface[i].data.cpu().numpy())
def run(self):
with torch.no_grad():
# In the case of using multiple GPUs
if self.trainer.device != 0:
return
self.nfc.eval()
optdicts = []
while True:
try:
batch = next(self.val_iter)
except Exception as e:
print(e)
self.val_iter = iter(self.val_dataloader)
break
actors = batch['imagename']
dataset = batch['dataset']
images = batch['image'].cuda()
images = images.view(-1, images.shape[-3], images.shape[-2], images.shape[-1])
arcface = batch['arcface'].cuda()
arcface = arcface.view(-1, arcface.shape[-3], arcface.shape[-2], arcface.shape[-1]).to(self.device)
flame = batch['flame']
codedict = self.nfc.encode(images, arcface)
codedict['flame'] = flame
opdict = self.nfc.decode(codedict, self.trainer.epoch)
self.update_embeddings(actors, opdict['faceid'])
loss = self.nfc.compute_losses(None, None, opdict)['pred_verts_shape_canonical_diff']
optdicts.append((opdict, images, dataset, actors, loss))
# Calculate averages
weighted_average = 0.
average = 0.
avg_per_dataset = {}
for optdict in optdicts:
opdict, images, dataset, actors, loss = optdict
name = dataset[0]
average += loss
if name not in avg_per_dataset:
avg_per_dataset[name] = (loss, 1.)
else:
l, i = avg_per_dataset[name]
avg_per_dataset[name] = (l + loss, i + 1.)
average = average.item() / len(optdicts)
loss_info = f"Step: {self.trainer.global_step}, Time: {datetime.now().strftime('%Y-%m-%d-%H:%M:%S')} \n"
loss_info += f' validation loss (average) : {average:.5f} \n'
logger.info(loss_info)
self.trainer.writer.add_scalar('val/average', average, global_step=self.trainer.global_step)
for key in avg_per_dataset.keys():
l, i = avg_per_dataset[key]
avg = l.item() / i
self.trainer.writer.add_scalar(f'val/average_{key}', avg, global_step=self.trainer.global_step)
# Save best model
smoothed_weighted, smoothed = self.best_model(weighted_average, average)
self.trainer.writer.add_scalar(f'val/smoothed_average', smoothed, global_step=self.trainer.global_step)
# self.now()
# Print embeddings every nth validation step
if self.trainer.global_step % (self.cfg.train.val_steps * 5) == 0:
lyhm_keys = list(self.embeddings_lyhm.keys())
embeddings = {**{key: self.embeddings_lyhm[key] for key in lyhm_keys}}
# util.save_embedding_projection(embeddings, os.path.join(self.cfg.output_dir, self.cfg.train.val_vis_dir, f'{self.trainer.global_step:08}_embeddings.jpg'))
self.embeddings_lyhm = {}
# Render predicted meshes
if self.trainer.global_step % self.cfg.train.val_save_img != 0:
return
pred_canonical_shape_vertices = torch.empty(0, 3, 512, 512).cuda()
flame_verts_shape = torch.empty(0, 3, 512, 512).cuda()
input_images = torch.empty(0, 3, 224, 224).cuda()
for i in np.random.choice(range(0, len(optdicts)), size=4, replace=False):
opdict, images, _, _, _ = optdicts[i]
n = np.random.randint(0, len(images) - 1)
rendering = self.nfc.render.render_mesh(opdict['pred_canonical_shape_vertices'][n:n + 1, ...])
pred_canonical_shape_vertices = torch.cat([pred_canonical_shape_vertices, rendering])
rendering = self.nfc.render.render_mesh(opdict['flame_verts_shape'][n:n + 1, ...])
flame_verts_shape = torch.cat([flame_verts_shape, rendering])
input_images = torch.cat([input_images, images[n:n + 1, ...]])
visdict = {
"pred_canonical_shape_vertices": pred_canonical_shape_vertices,
"flame_verts_shape": flame_verts_shape,
"input": input_images
}
savepath = os.path.join(self.cfg.output_dir, self.cfg.train.val_vis_dir, f'{self.trainer.global_step:08}.jpg')
util.visualize_grid(visdict, savepath, size=512)
def now(self):
logger.info(f'[Validator] NoW testing has begun...')
# self.tester.test_now('', 'training', self.nfc.model_dict())
root = os.path.abspath(os.path.join(os.path.dirname(__file__), '../'))
path = f'{root}{self.cfg.output_dir[1:]}/now_test_training/predicted_meshes'
cmd = f'./now_validation.sh {path}'
subprocess.call(cmd, shell=True)
errors = np.load(f'{path}/results/_computed_distances.npy', allow_pickle=True, encoding="latin1").item()['computed_distances']
median = np.median(np.hstack(errors))
mean = np.mean(np.hstack(errors))
std = np.std(np.hstack(errors))
self.best_model.now(median, mean, std)
self.trainer.writer.add_scalar(f'val/now_mean', mean, global_step=self.trainer.global_step)
logger.info(f'[Validator] NoW testing has ended...')
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