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jblitzar
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github-python

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r = {"video": self.cache["titles"][hl], "segments": self.cache["segments"][id] }
return r
# <FILESEP>
# Copyright 2021 Google LLC
#
# 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
"""Training script for Nerf."""
import functools
import gc
import time
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 jax.numpy as jnp
import numpy as np
from internal import datasets
from internal import math
from internal import models
from internal import utils
from internal import vis
FLAGS = flags.FLAGS
utils.define_common_flags()
flags.DEFINE_integer('render_every', 5000,
'The number of steps between test set image renderings.')
jax.config.parse_flags_with_absl()
def train_step(model, config, rng, state, batch, lr):
"""One optimization step.
Args:
model: The linen model.
config: The configuration.
rng: jnp.ndarray, random number generator.
state: utils.TrainState, state of the model/optimizer.
batch: dict, a mini-batch of data for training.
lr: float, real-time learning rate.
Returns:
new_state: utils.TrainState, new training state.
stats: list. [(loss, psnr), (loss_coarse, psnr_coarse)].
rng: jnp.ndarray, updated random number generator.
"""
rng, key = random.split(rng)
def loss_fn(variables):
def tree_sum_fn(fn):
return jax.tree_util.tree_reduce(
lambda x, y: x + fn(y), variables, initializer=0)
weight_l2 = config.weight_decay_mult * (
tree_sum_fn(lambda z: jnp.sum(z**2)) /
tree_sum_fn(lambda z: jnp.prod(jnp.array(z.shape))))
ret = model.apply(
variables,
key,
batch['rays'],
randomized=config.randomized,
white_bkgd=config.white_bkgd)
mask = batch['rays'].lossmult
if config.disable_multiscale_loss:
mask = jnp.ones_like(mask)
losses = []
for (rgb, _, _) in ret:
losses.append(
(mask * (rgb - batch['pixels'][..., :3])**2).sum() / mask.sum())
losses = jnp.array(losses)
loss = (