# 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 math import glob import os from os import path import functools 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 tensorflow as tf from tqdm import tqdm import cv2 import numpy as np from PIL import Image from nerf import datasets from nerf import models from nerf import utils from nerf import clip_utils FLAGS = flags.FLAGS utils.define_flags() 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 predict_to_image(pred_out): image_arr = np.array(np.clip(pred_out, 0., 1.) * 255.).astype(np.uint8) return Image.fromarray(image_arr) 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 state = checkpoints.restore_checkpoint(FLAGS.train_dir, state) # 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 model.apply(variables, key_0, key_1, rays, False) # 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") os.makedirs(out_dir, exist_ok=True) if FLAGS.save_output: print(f'eval output will be saved: {out_dir}') else: print(f'eval output will not be saved') if not FLAGS.eval_once: summary_writer = tensorboard.SummaryWriter( path.join(FLAGS.train_dir, "eval")) def generate_spinning_gif(radius, phi, output_dir, frame_n): _rng = random.PRNGKey(0) partial_render_fn = functools.partial(render_pfn, state.optimizer.target) gif_images = [] gif_images2 = [] for theta in tqdm(np.linspace(-math.pi, math.pi, frame_n)): camtoworld = np.array(clip_utils.pose_spherical(radius, theta, phi)) rays = dataset.camtoworld_matrix_to_rays(camtoworld, downsample=4) _rng, key0, key1 = random.split(_rng, 3) color, disp, _ = utils.render_image(partial_render_fn, rays, _rng, False, chunk=4096) image = predict_to_image(color) image2 = predict_to_image(disp[Ellipsis, 0]) gif_images.append(image) gif_images2.append(image2) gif_fn = os.path.join(output_dir, 'rgb_spinning.gif') gif_fn2 = os.path.join(output_dir, 'disp_spinning.gif') gif_images[0].save(gif_fn, save_all=True, append_images=gif_images, duration=100, loop=0) gif_images2[0].save(gif_fn2, save_all=True, append_images=gif_images2, duration=100, loop=0) #return gif_images, gif_images2 if FLAGS.generate_gif_only: print('generate GIF file only') _radius = 4. _phi = (30 * math.pi) / 180 generate_spinning_gif(_radius, _phi, out_dir, frame_n=30) print('GIF file for spinning views written)') return else: print('generate GIF file AND evaluate model performance') is_gif_written = False while True: 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.size): 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)))) print(f'performance metrics written as txt files: {out_dir}') 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])) fourcc = cv2.VideoWriter_fourcc(*'MP4V') fps = 10.0 img = cv2.imread(sorted_files[0], cv2.IMREAD_COLOR) video_fn = os.path.join(out_dir, "rendering_video.mp4") out = cv2.VideoWriter(video_fn, fourcc, fps, (img.shape[1], img.shape[0])) for i in range(len(sorted_files)): img = cv2.imread(sorted_files[i], cv2.IMREAD_COLOR) out.write(img) out.release() print(f'video file written: {video_fn}') # write gif file for spinning views of a scene if not is_gif_written: _radius = 4. _phi = (30 * math.pi) / 180 generate_spinning_gif(_radius, _phi, out_dir, frame_n=30) print(f'GIF file for spinning views written') is_gif_written = True if FLAGS.eval_once: break if int(step) >= FLAGS.max_steps: break last_step = step if __name__ == "__main__": app.run(main)