zipnerf/eval.py at main · Squadrones/zipnerf · GitHub

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# coding=utf-8

# 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.

"""Evaluation script for mipNeRF360."""

import functools

import gc

from os import path

import sys

import time

from absl import app

from absl import logging

import chex

import flax

from flax.metrics import tensorboard

from flax.training import checkpoints

import gin

from internal import alignment

from internal import camera_utils

from internal import configs

from internal import datasets

from internal import image_io

from internal import image_utils

from internal import models

from internal import ref_utils

from internal import train_utils

from internal import utils

from internal import vis

import jax

from jax import random

import jax.numpy as jnp

import jaxcam

import numpy as np

configs.define_common_flags()

jax.config.parse_flags_with_absl()

def plot_camera_metrics(

summary_writer,

camera_params,

train_cameras,

train_cameras_gt,

config,

step,

tag,

"""Plots camera statistics to TensorBoard."""

camera_delta = config.camera_delta_cls()

optimized_cameras: jaxcam.Camera = camera_delta.apply(

camera_params, train_cameras

)

diffs = camera_utils.compute_camera_metrics(

train_cameras_gt, optimized_cameras

)

reduce_fns = {

'mean': np.mean,

'max': np.max,

'std': np.std,

}

for reduce_name, reduce_fn in reduce_fns.items():

for stat_name, stat in diffs.items():

summary_writer.scalar(

f'eval_train_camera_{tag}_{reduce_name}/{stat_name}',

reduce_fn(np.array(stat)),

step=step,

)

# pylint: disable=cell-var-from-loop

return [

jax.tree_util.tree_map(lambda x: float(x[i]), diffs)

for i in range(len(train_cameras))

]

# pylint: enable=cell-var-from-loop

def main(unused_argv):

config = configs.load_config(save_config=False)

train_dataset = datasets.load_dataset('train', config.data_dir, config)

test_dataset = datasets.load_dataset('test', config.data_dir, config)

key = random.PRNGKey(20200823)

model, state, render_eval_pfn, _, _ = train_utils.setup_model(

config, key, dataset=train_dataset

)

if config.rawnerf_mode:

postprocess_fn = test_dataset.metadata['postprocess_fn']

else:

postprocess_fn = lambda z: z

metric_harness = image_utils.MetricHarness(

**config.metric_harness_eval_config

)

last_step = 0

out_dir = path.join(

config.checkpoint_dir,

'path_renders' if config.render_path else 'test_preds',

)

path_fn = lambda x: path.join(out_dir, x)

if not config.eval_only_once:

summary_writer = tensorboard.SummaryWriter(

path.join(config.checkpoint_dir, 'eval')

)

jnp_cameras = None

if config.cast_rays_in_eval_step:

np_to_jax = lambda x: jnp.array(x) if isinstance(x, np.ndarray) else x

jnp_cameras = jax.tree_util.tree_map(np_to_jax, test_dataset.cameras)

jnp_cameras_replicated = flax.jax_utils.replicate(jnp_cameras)

last_eval_time = time.time()

while True:

state = checkpoints.restore_checkpoint(config.checkpoint_dir, state)

step = int(state.step)

state_params_replicated = flax.jax_utils.replicate(state.params)

if step <= last_step:

if time.time() - last_eval_time > config.eval_checkpoint_wait_timeout_sec:

raise RuntimeError(

'Waited for a new checkpoint for'

f' {config.eval_checkpoint_wait_timeout_sec} seconds, got no new'

' checkpoint. This likely means that the training script has died.'

' Exiting. If this is expected, increase'

' config.eval_checkpoint_wait_timeout_sec.'

)

logging.info(

'Checkpoint step %d <= last step %d, sleeping.', step, last_step

)

time.sleep(10)

continue

last_eval_time = time.time()

logging.info('Evaluating checkpoint at step %d.', step)

if config.eval_save_output and (not utils.isdir(out_dir)):

utils.makedirs(out_dir)

num_eval = min(test_dataset.size, config.eval_dataset_limit)

key = random.PRNGKey(0 if config.deterministic_showcase else step)

perm = random.permutation(key, num_eval)

showcase_indices = np.sort(perm[: config.num_showcase_images])

metrics = []

metrics_aligned_optimized = []

metrics_aligned_procrustes = []

metrics_cameras = []

metrics_cameras_procrustes = []

showcases = []

render_times = []

state_params_replicated = flax.jax_utils.replicate(state.params)

compute_aligned_metric = (

config.optimize_test_cameras and step == config.max_steps

)

procrustes_cameras = None

if config.compute_procrustes_metric and config.optimize_cameras:

test_image_sizes = np.array(

[(x.shape[1], x.shape[0]) for x in test_dataset.images]

)

test_jax_cameras = jax.vmap(test_dataset.jax_camera_from_tuple_fn)(

test_dataset.cameras, test_image_sizes

)

train_jax_cameras = train_dataset.get_train_cameras(

config, return_jax_cameras=True

)

train_jax_cameras_gt = train_dataset.jax_cameras

camera_params = state.params['camera_params']

camera_delta = config.camera_delta_cls()

train_jax_cameras_opt = camera_delta.apply(

camera_params, train_jax_cameras

)

train_jax_cameras_procrustes, test_jax_cameras_procrustes = (

alignment.compute_procrusted_aligned_cameras(

train_jax_cameras_gt=train_jax_cameras_gt,

train_jax_cameras_opt=train_jax_cameras_opt,

test_jax_cameras=test_jax_cameras,

)

metrics_cameras = plot_camera_metrics(

summary_writer=summary_writer,

camera_params=camera_params,

train_cameras=train_jax_cameras,

train_cameras_gt=train_jax_cameras_gt,

config=config,

step=step,

tag='error',

)

metrics_cameras_procrustes = plot_camera_metrics(

summary_writer=summary_writer,

camera_params=camera_params,

train_cameras=train_jax_cameras,

train_cameras_gt=train_jax_cameras_procrustes,

config=config,

step=step,

tag='error_procrustes',

)

# Convert to tuples.

procrustes_cameras = jax.vmap(camera_utils.tuple_from_jax_camera)(

test_jax_cameras_procrustes

)

procrustes_cameras = (*procrustes_cameras, *test_dataset.cameras[3:])

procrustes_cameras_replicated = flax.jax_utils.replicate(

procrustes_cameras

)

raybatcher = datasets.RayBatcher(test_dataset)

for idx in range(test_dataset.size):

gc.collect()

with jax.profiler.StepTraceAnnotation('eval', step_num=idx):

eval_start_time = time.time()

batch = next(raybatcher)

if idx >= num_eval:

logging.info('Skipping image %d/%d', idx + 1, test_dataset.size)

continue

logging.info('Evaluating image %d/%d', idx + 1, test_dataset.size)

rays = batch.rays

train_frac = state.step / config.max_steps

def _render_image(cameras, rays, train_frac):

return models.render_image( # pytype: disable=wrong-arg-types # jnp-array

functools.partial(

render_eval_pfn,

state_params_replicated,

train_frac,

cameras,

rays=rays,

rng=None,

config=config,

return_all_levels=True,

)

if compute_aligned_metric:

jnp_camera_optimized = alignment.align_test_camera(

model, state, idx, test_dataset, config

)

jnp_camera_optimized_replicated = flax.jax_utils.replicate(

jnp_camera_optimized

)

rendering_aligned_optimized = _render_image(

jnp_camera_optimized_replicated, rays, train_frac

)

rendering_aligned_optimized = jax.tree_util.tree_map(

np.asarray, rendering_aligned_optimized

)

if procrustes_cameras is not None:

rendering_aligned_procrustes = _render_image(

procrustes_cameras_replicated, rays, train_frac

)

rendering_aligned_procrustes = jax.tree_util.tree_map(

np.asarray, rendering_aligned_procrustes

)

rendering = _render_image(jnp_cameras_replicated, rays, train_frac)

rendering = jax.tree_util.tree_map(np.asarray, rendering)

rays = jax.tree_util.tree_map(np.asarray, rays)

if jax.host_id() != 0: # Only record via host 0.

continue

render_times.append((time.time() - eval_start_time))

logging.info('Rendered in %0.3fs', render_times[-1])

# Cast to 64-bit to ensure high precision for color correction function.

gt_rgb = np.array(batch.rgb, dtype=np.float64)

rendering['rgb'] = np.array(rendering['rgb'], dtype=np.float64)

if compute_aligned_metric:

rendering['rgb_aligned_optimized'] = np.array(

rendering_aligned_optimized['rgb'], dtype=np.float64

)

if procrustes_cameras is not None:

rendering['rgb_aligned_procrustes'] = np.array(

rendering_aligned_procrustes['rgb'], dtype=np.float64

)

if not config.eval_only_once and idx in showcase_indices:

showcase_idx = (

idx if config.deterministic_showcase else len(showcases)

)

showcases.append((showcase_idx, rendering, batch))

if not config.render_path:

rgb = postprocess_fn(rendering['rgb'])

if compute_aligned_metric:

rgb_aligned_optimized = postprocess_fn(

rendering['rgb_aligned_optimized']

)

if procrustes_cameras is not None:

rgb_aligned_procrustes = postprocess_fn(

rendering['rgb_aligned_procrustes']

)

rgb_gt = postprocess_fn(gt_rgb)

if config.eval_quantize_metrics:

# Ensures that the images written to disk reproduce the metrics.

rgb = np.round(rgb * 255) / 255

if config.eval_crop_borders > 0:

crop_fn = lambda x, c=config.eval_crop_borders: x[c:-c, c:-c]

rgb = crop_fn(rgb)

rgb_gt = crop_fn(rgb_gt)

if compute_aligned_metric:

rgb_aligned_optimized = crop_fn(rgb_aligned_optimized)

if 'rgb_aligned_procrustes' in rendering:

rgb_aligned_procrustes = crop_fn(rgb_aligned_procrustes)

metric = metric_harness(rgb, rgb_gt)

if compute_aligned_metric:

metric_aligned_optimized = metric_harness(

rgb_aligned_optimized, rgb_gt

)

if procrustes_cameras is not None:

metric_aligned_procrustes = metric_harness(

rgb_aligned_procrustes, rgb_gt

)

if config.compute_disp_metrics:

for tag in ['mean', 'median']:

key = f'distance_{tag}'

if key in rendering:

disparity = 1 / (1 + rendering[key][-1])

metric[f'disparity_{tag}_mse'] = float(

((disparity - batch.disps) ** 2).mean()

)

if config.compute_normal_metrics:

weights = rendering['acc'][-1] * batch.alphas

normalized_normals_gt = ref_utils.l2_normalize(batch.normals)

for key, val in rendering.items():

if key.startswith('normals') and val is not None:

normalized_normals = ref_utils.l2_normalize(val[-1])

metric[key + '_mae'] = ref_utils.compute_weighted_mae(

weights, normalized_normals, normalized_normals_gt

)

for m, v in metric.items():

logging.info('%s = %0.4f', m, v)

metrics.append(metric)

if compute_aligned_metric:

metrics_aligned_optimized.append(metric_aligned_optimized)

if procrustes_cameras is not None:

metrics_aligned_procrustes.append(metric_aligned_procrustes)

if config.eval_save_output and (config.eval_render_interval > 0):

if (idx % config.eval_render_interval) == 0:

image_io.save_img_u8(

postprocess_fn(rendering['rgb']),

path_fn(f'color_{idx:03d}.png'),

)

if compute_aligned_metric:

image_io.save_img_u8(

postprocess_fn(rendering['rgb_aligned_optimized']),

path_fn(f'color_aligned_optimized_{idx:03d}.png'),

)

if procrustes_cameras is not None:

image_io.save_img_u8(

postprocess_fn(rendering['rgb_aligned_procrustes']),

path_fn(f'color_aligned_procrustes_{idx:03d}.png'),

)

for key in ['distance_mean', 'distance_median']:

if key in rendering:

image_io.save_img_f32(

rendering[key][-1], path_fn(f'{key}_{idx:03d}.tiff')

)

for key in ['normals']:

if key in rendering:

image_io.save_img_u8(

rendering[key][-1] / 2.0 + 0.5,

path_fn(f'{key}_{idx:03d}.png'),

)

if 'acc' in rendering:

image_io.save_img_f32(

rendering['acc'][-1], path_fn(f'acc_{idx:03d}.tiff')

)

if batch.masks is not None:

image_io.save_img_u8(

batch.rgb * batch.masks,

path_fn(f'masked_input_{idx:03d}.png'),

)

if (not config.eval_only_once) and (jax.host_id() == 0):

summary_writer.scalar(

'eval_median_render_time', np.median(render_times), step

)

def summarize_metrics(metrics, metrics_suffix):

for name in metrics[0]:

scores = [m[name] for m in metrics]

prefix = f'eval_metrics{metrics_suffix}/'

summary_writer.scalar(prefix + name, np.mean(scores), step)

summary_writer.histogram(prefix + 'perimage_' + name, scores, step)

summarize_metrics(metrics, '')

if compute_aligned_metric:

summarize_metrics(metrics_aligned_optimized, '_aligned_optimized')

if procrustes_cameras is not None:

summarize_metrics(metrics_aligned_procrustes, '_aligned_procrustes')

if config.multiscale_train_factors is not None:

factors = [1] + list(config.multiscale_train_factors)

n_images = len(metrics) // len(factors)

# Split metrics into chunks of n_images (each downsampling level).

for i, f in enumerate(factors):

i0 = i * n_images

i1 = (i + 1) * n_images

image_shapes = np.array([z.shape for z in test_dataset.images[i0:i1]])

if not np.all(image_shapes == image_shapes[0]):

raise ValueError(

'Not all image shapes match for downsampling '

f'factor {f}x in evaluation'

)

summarize_metrics(metrics[i0:i1], f'_{f}x')

if compute_aligned_metric:

summarize_metrics(

metrics_aligned_optimized[i0:i1], f'_{f}x_aligned_optimized'

)

if procrustes_cameras is not None:

summarize_metrics(

metrics_aligned_procrustes[i0:i1], f'_{f}x_aligned_procrustes'

)

for i, r, b in showcases:

if config.vis_decimate > 1:

d = config.vis_decimate

decimate_fn = lambda x, d=d: None if x is None else x[::d, ::d]

else:

decimate_fn = lambda x: x

r = jax.tree_util.tree_map(decimate_fn, r)

b = jax.tree_util.tree_map(decimate_fn, b)

for k, v in vis.visualize_suite(r).items():

if isinstance(v, list):

for ii, vv in enumerate(v):

summary_writer.image(f'output_{k}_{i}/{ii}', vv, step)

else:

summary_writer.image(f'output_{k}_{i}', v, step)

if b.masks is not None:

mask_float_array = jax.numpy.asarray(b.masks, dtype=jax.numpy.float32)

summary_writer.image(f'mask_{i}', mask_float_array, step)

summary_writer.image(

f'masked_image_{i}', b.rgb * mask_float_array, step

)

if not config.render_path:

target = postprocess_fn(b.rgb)

pred = postprocess_fn(r['rgb'])

if compute_aligned_metric:

pred_aligned_optimized = postprocess_fn(r['rgb_aligned_optimized'])

if procrustes_cameras is not None:

pred_aligned_procrustes = postprocess_fn(

r['rgb_aligned_procrustes']

)

summary_writer.image(f'output_color_{i}', pred, step)

if compute_aligned_metric:

summary_writer.image(

f'output_color_aligned_optimized_{i}',

pred_aligned_optimized,

step,

)

if procrustes_cameras is not None:

summary_writer.image(

f'output_color_aligned_procrustes_{i}',

pred_aligned_procrustes,

step,

)

summary_writer.image(f'true_color_{i}', target, step)

residual = pred - target

summary_writer.image(

f'output_residual_{i}', np.clip(residual + 0.5, 0, 1), step

)

if compute_aligned_metric:

residual_aligned_optimized = pred_aligned_optimized - target

summary_writer.image(

f'output_residual_aligned_{i}',

np.clip(residual_aligned_optimized + 0.5, 0, 1),

step,

)

if procrustes_cameras is not None:

residual_aligned_procrustes = pred_aligned_procrustes - target

summary_writer.image(

f'output_residual_aligned_{i}',

np.clip(residual_aligned_procrustes + 0.5, 0, 1),

step,

)

residual_hist = image_utils.render_histogram(

np.array(residual).reshape([-1, 3]),

bins=32,

range=(-1, 1),

log=True,

color=('r', 'g', 'b'),

)

summary_writer.image(f'output_residual_hist_{i}', residual_hist, step)

if config.compute_normal_metrics:

summary_writer.image(

f'true_normals_{i}', b.normals / 2.0 + 0.5, step

)

if (

config.eval_save_output

and (not config.render_path)

and (jax.host_id() == 0)

with utils.open_file(path_fn(f'render_times_{step}.txt'), 'w') as f:

f.write(' '.join([str(r) for r in render_times]))

for name in metrics[0]:

with utils.open_file(path_fn(f'metric_{name}_{step}.txt'), 'w') as f:

f.write(' '.join([str(m[name]) for m in metrics]))

if compute_aligned_metric:

for name in metrics_aligned_optimized[0]:

with utils.open_file(

path_fn(f'metric_aligned_optimized_{name}_{step}.txt'), 'w'

) as f:

f.write(' '.join([str(m[name]) for m in metrics_aligned_optimized]))

if procrustes_cameras is not None:

for name in metrics_aligned_procrustes[0]:

with utils.open_file(

path_fn(f'metric_aligned_procrustes_{name}_{step}.txt'), 'w'

) as f:

f.write(

' '.join([str(m[name]) for m in metrics_aligned_procrustes])

)

if metrics_cameras:

for name in metrics_cameras[0]:

with utils.open_file(

path_fn(f'metric_cameras_{name}_{step}.txt'), 'w'

) as f:

f.write(' '.join([str(m[name]) for m in metrics_cameras]))

if metrics_cameras_procrustes:

for name in metrics_cameras_procrustes[0]:

with utils.open_file(

path_fn(f'metric_cameras_procrustes_{name}_{step}.txt'), 'w'

) as f:

f.write(

' '.join([str(m[name]) for m in metrics_cameras_procrustes])

)

if config.eval_save_ray_data:

for i, r, b in showcases:

rays = {k: v for k, v in r.items() if 'ray_' in k}

np.set_printoptions(threshold=sys.maxsize)

with utils.open_file(path_fn(f'ray_data_{step}_{i}.txt'), 'w') as f:

f.write(repr(rays))

# A hack that forces Jax to keep all TPUs alive until every TPU is finished.

x = jnp.ones([jax.local_device_count()])

x = jax.device_get(jax.pmap(lambda x: jax.lax.psum(x, 'i'), 'i')(x))

print(x)

if config.eval_only_once:

logging.info('Eval only once enabled, shutting down.')

break

if config.early_exit_steps is not None:

num_steps = config.early_exit_steps

else:

num_steps = config.max_steps

if int(step) >= num_steps:

logging.info('Termination num steps reached (%d).', num_steps)

break

last_step = step

if __name__ == '__main__':

with gin.config_scope('eval'):

app.run(main)

zipnerf/eval.py at main · Squadrones/zipnerf · GitHub

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