jaxnerf/eval.py

# 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)