# coding=utf-8 # Copyright 2021 The Google Research Authors. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. # Lint as: python3 """Evaluation script for Nerf.""" import functools from os import path from absl import app from absl import flags import flax from flax.metrics import tensorboard from flax.training import checkpoints import jax from jax import random import numpy as np import tensorflow as tf import tensorflow_hub as tf_hub #import wandb import glob import cv2 import os from jaxnerf.nerf import datasets from jaxnerf.nerf import models from jaxnerf.nerf import utils FLAGS = flags.FLAGS utils.define_flags() #LPIPS_TFHUB_PATH = "@neural-rendering/lpips/distance/1" def compute_lpips(image1, image2, model): """Compute the LPIPS metric.""" # The LPIPS model expects a batch dimension. return model( tf.convert_to_tensor(image1[None, Ellipsis]), tf.convert_to_tensor(image2[None, Ellipsis]))[0] def main(unused_argv): # Hide the GPUs and TPUs from TF so it does not reserve memory on them for # LPIPS computation or dataset loading. tf.config.experimental.set_visible_devices([], "GPU") tf.config.experimental.set_visible_devices([], "TPU") #wandb.init(project="hf-flax-clip-nerf", entity="wandb", sync_tensorboard=True) rng = random.PRNGKey(20200823) if FLAGS.config is not None: utils.update_flags(FLAGS) if FLAGS.train_dir is None: raise ValueError("train_dir must be set. None set now.") if FLAGS.data_dir is None: raise ValueError("data_dir must be set. None set now.") dataset = datasets.get_dataset("test", FLAGS) rng, key = random.split(rng) model, init_variables = models.get_model(key, dataset.peek(), FLAGS) optimizer = flax.optim.Adam(FLAGS.lr_init).create(init_variables) state = utils.TrainState(optimizer=optimizer) del optimizer, init_variables #lpips_model = tf_hub.load(LPIPS_TFHUB_PATH) # Rendering is forced to be deterministic even if training was randomized, as # this eliminates "speckle" artifacts. def render_fn(variables, key_0, key_1, rays): return jax.lax.all_gather( model.apply(variables, key_0, key_1, rays, False), axis_name="batch") # pmap over only the data input. render_pfn = jax.pmap( render_fn, in_axes=(None, None, None, 0), donate_argnums=3, axis_name="batch", ) # Compiling to the CPU because it's faster and more accurate. ssim_fn = jax.jit( functools.partial(utils.compute_ssim, max_val=1.), backend="cpu") last_step = 0 out_dir = path.join(FLAGS.train_dir, "path_renders" if FLAGS.render_path else "test_preds") if not FLAGS.eval_once: summary_writer = tensorboard.SummaryWriter( path.join(FLAGS.train_dir, "eval")) while True: state = checkpoints.restore_checkpoint(FLAGS.train_dir, state) step = int(state.optimizer.state.step) if step <= last_step: continue if FLAGS.save_output and (not utils.isdir(out_dir)): utils.makedirs(out_dir) psnr_values = [] ssim_values = [] #lpips_values = [] if not FLAGS.eval_once: showcase_index = np.random.randint(0, dataset.size) for idx in range(dataset.sizerender_image): print(f"Evaluating {idx + 1}/{dataset.size}") batch = next(dataset) pred_color, pred_disp, pred_acc = utils.render_image( functools.partial(render_pfn, state.optimizer.target), batch["rays"], rng, FLAGS.dataset == "llff", chunk=FLAGS.chunk) if jax.host_id() != 0: # Only record via host 0. continue if not FLAGS.eval_once and idx == showcase_index: showcase_color = pred_color showcase_disp = pred_disp showcase_acc = pred_acc if not FLAGS.render_path: showcase_gt = batch["pixels"] if not FLAGS.render_path: psnr = utils.compute_psnr(((pred_color - batch["pixels"]) ** 2).mean()) ssim = ssim_fn(pred_color, batch["pixels"]) #lpips = compute_lpips(pred_color, batch["pixels"], lpips_model) print(f"PSNR = {psnr:.4f}, SSIM = {ssim:.4f}") psnr_values.append(float(psnr)) ssim_values.append(float(ssim)) #lpips_values.append(float(lpips)) if FLAGS.save_output: utils.save_img(pred_color, path.join(out_dir, "{:03d}.png".format(idx))) utils.save_img(pred_disp[Ellipsis, 0], path.join(out_dir, "disp_{:03d}.png".format(idx))) if (not FLAGS.eval_once) and (jax.host_id() == 0): summary_writer.image("pred_color", showcase_color, step) summary_writer.image("pred_disp", showcase_disp, step) summary_writer.image("pred_acc", showcase_acc, step) if not FLAGS.render_path: summary_writer.scalar("psnr", np.mean(np.array(psnr_values)), step) summary_writer.scalar("ssim", np.mean(np.array(ssim_values)), step) #summary_writer.scalar("lpips", np.mean(np.array(lpips_values)), step) summary_writer.image("target", showcase_gt, step) if FLAGS.save_output and (not FLAGS.render_path) and (jax.host_id() == 0): with utils.open_file(path.join(out_dir, f"psnrs_{step}.txt"), "w") as f: f.write(" ".join([str(v) for v in psnr_values])) with utils.open_file(path.join(out_dir, f"ssims_{step}.txt"), "w") as f: f.write(" ".join([str(v) for v in ssim_values])) #with utils.open_file(path.join(out_dir, f"lpips_{step}.txt"), "w") as f: #f.write(" ".join([str(v) for v in lpips_values])) with utils.open_file(path.join(out_dir, "psnr.txt"), "w") as f: f.write("{}".format(np.mean(np.array(psnr_values)))) with utils.open_file(path.join(out_dir, "ssim.txt"), "w") as f: f.write("{}".format(np.mean(np.array(ssim_values)))) #with utils.open_file(path.join(out_dir, "lpips.txt"), "w") as f: #f.write("{}".format(np.mean(np.array(lpips_values)))) imglist = glob.glob(os.path.join(out_dir, "[0-9][0-9][0-9].png")) sorted_files = sorted(imglist, key=lambda x: int(x.split('/')[-1].split('.')[0])) imglist2 = glob.glob(os.path.join(out_dir, "disp_[0-9][0-9][0-9].png")) sorted_files2 = sorted(imglist2, key=lambda x: int(x.split('/')[-1].split('.')[0].split('_')[-1])) fourcc = cv2.VideoWriter_fourcc(*'MP4V') fps = 10.0 out = cv2.VideoWriter(os.path.join(out_dir, "rendering_video.mp4"), fourcc, fps, (2 * img.shape[1], img.shape[0])) for i in range(len(imglist)): img = cv2.imread(imglist[i], cv2.IMREAD_COLOR) img2 = cv2.imread(imglist2[i], cv2.IMREAD_COLOR) catimg = np.concatenate((img, img2), axis=1) out.write(catimg) out.release() if FLAGS.eval_once: break if int(step) >= FLAGS.max_steps: break last_step = step if __name__ == "__main__": app.run(main)