# 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 """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 config from jax import random import jax.numpy as jnp import numpy as np # import wandb from tqdm import tqdm from jaxnerf.nerf import datasets from jaxnerf.nerf import models from jaxnerf.nerf import utils from jaxnerf.nerf import clip_utils FLAGS = flags.FLAGS utils.define_flags() config.parse_flags_with_absl() # set up TPU for colab import os if "COLAB_TPU_ADDR" in os.environ: import jax.tools.colab_tpu jax.tools.colab_tpu.setup_tpu() print(f"detected device: {jax.local_devices()}") def train_step(model, clip_model, rng, state, batch, lr, step, K):#, clip_grad): # TODO make clip_grad input enable """One optimization step. Args: model: The linen model. 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_0, key_1 = random.split(rng, 3) def loss_fn(variables): rays = batch["rays"] ret = model.apply(variables, key_0, key_1, rays, FLAGS.randomized) if len(ret) not in (1, 2): raise ValueError( "ret should contain either 1 set of output (coarse only), or 2 sets" "of output (coarse as ret[0] and fine as ret[1]).") # The main prediction is always at the end of the ret list. rgb, unused_disp, unused_acc = ret[-1] loss = ((rgb - batch["pixels"][Ellipsis, :3]) ** 2).mean() psnr = utils.compute_psnr(loss) if len(ret) > 1: # If there are both coarse and fine predictions, we compute the loss for # the coarse prediction (ret[0]) as well. rgb_c, unused_disp_c, unused_acc_c = ret[0] loss_c = ((rgb_c - batch["pixels"][Ellipsis, :3]) ** 2).mean() psnr_c = utils.compute_psnr(loss_c) else: loss_c = 0. psnr_c = 0. def tree_sum_fn(fn): return jax.tree_util.tree_reduce(lambda x, y: x + fn(y), variables, initializer=0) weight_l2 = (tree_sum_fn(lambda z: jnp.sum(z ** 2)) / tree_sum_fn(lambda z: jnp.prod(jnp.array(z.shape)))) total_loss = loss + loss_c + FLAGS.weight_decay_mult * weight_l2 stats = utils.Stats(loss=loss, psnr=psnr, loss_c=loss_c, psnr_c=psnr_c, weight_l2=weight_l2) return total_loss, stats (_, stats), grad = ( jax.value_and_grad(loss_fn, has_aux=True)(state.optimizer.target)) grad = jax.lax.pmean(grad, axis_name="batch") stats = jax.lax.pmean(stats, axis_name="batch") # Clip the gradient by value. if FLAGS.grad_max_val > 0: clip_fn = lambda z: jnp.clip(z, -FLAGS.grad_max_val, FLAGS.grad_max_val) grad = jax.tree_util.tree_map(clip_fn, grad) # Clip the (possibly value-clipped) gradient by norm. if FLAGS.grad_max_norm > 0: grad_norm = jnp.sqrt( jax.tree_util.tree_reduce( lambda x, y: x + jnp.sum(y ** 2), grad, initializer=0)) mult = jnp.minimum(1, FLAGS.grad_max_norm / (1e-7 + grad_norm)) grad = jax.tree_util.tree_map(lambda z: mult * z, grad) #return grad, state, rng new_optimizer = state.optimizer.apply_gradient(grad, learning_rate =lr) new_state = state.replace(optimizer=new_optimizer) return new_state, stats, rng def update_step(state, grad, lr): new_optimizer = state.optimizer.apply_gradient(grad, learning_rate=lr) new_state = state.replace(optimizer=new_optimizer) return new_state def main(unused_argv): #wandb.init(project="hf-flax-clip-nerf", entity="wandb", sync_tensorboard=True) rng = random.PRNGKey(20200823) # Shift the numpy random seed by host_id() to shuffle data loaded by different # hosts. np.random.seed(20201473 + jax.host_id()) if FLAGS.config is not None: utils.update_flags(FLAGS) if FLAGS.batch_size % jax.device_count() != 0: raise ValueError("Batch size must be divisible by the number of devices.") 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.") # setup CLIP model if FLAGS.use_semantic_loss: clip_model = clip_utils.init_CLIP(FLAGS.clip_output_dtype, FLAGS.clip_model_name) print('semantic loss ACTIVATED, CLIP is set up') else: clip_model = None print('semantic loss DEACTIVATED, CLIP is set to None') dataset = datasets.get_dataset("train", FLAGS, clip_model) test_dataset = datasets.get_dataset("test", FLAGS, clip_model) # setup NeRF model rng, key = random.split(rng) model, variables = models.get_model(key, dataset.peek(), FLAGS) optimizer = flax.optim.Adam(FLAGS.lr_init).create(variables) state = utils.TrainState(optimizer=optimizer) del optimizer, variables learning_rate_fn = functools.partial( utils.learning_rate_decay, lr_init=FLAGS.lr_init, lr_final=FLAGS.lr_final, max_steps=FLAGS.max_steps, lr_delay_steps=FLAGS.lr_delay_steps, lr_delay_mult=FLAGS.lr_delay_mult) train_pstep = jax.pmap( functools.partial(train_step, model, clip_model), axis_name="batch", in_axes=(0, 0, 0, None, None, None), donate_argnums=(2,)) update_pstep = jax.pmap( functools.partial(update_step,), axis_name="batch", in_axes=(0, None, None), donate_argnums=(0,)) def render_fn(variables, key_0, key_1, rays): return jax.lax.all_gather( model.apply(variables, key_0, key_1, rays, FLAGS.randomized), axis_name="batch") render_pfn = jax.pmap( render_fn, in_axes=(None, None, None, 0), # Only distribute the data input. 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") if not utils.isdir(FLAGS.train_dir): utils.makedirs(FLAGS.train_dir) state = checkpoints.restore_checkpoint(FLAGS.train_dir, state) # Resume training a the step of the last checkpoint. init_step = state.optimizer.state.step + 1 # for distributive training state = flax.jax_utils.replicate(state) if jax.host_id() == 0: summary_writer = tensorboard.SummaryWriter(FLAGS.train_dir) # Prefetch_buffer_size = 3 x batch_size pdataset = flax.jax_utils.prefetch_to_device(dataset, 3) n_local_devices = jax.local_device_count() rng = rng + jax.host_id() # Make random seed separate across hosts. keys = random.split(rng, n_local_devices) # For pmapping RNG keys. gc.disable() # Disable automatic garbage collection for efficiency. stats_trace = [] reset_timer = True # for semantic loss update cnter = 1 trigger = int(FLAGS.sc_loss_every / n_local_devices) for step, batch in tqdm(zip(range(init_step, FLAGS.max_steps + 1), pdataset)): if reset_timer: t_loop_start = time.time() reset_timer = False lr = learning_rate_fn(step) if step%FLAGS.sc_loss_every == 0 and FLAGS.use_semantic_loss: # remove dimension for device coz its only run in host core sc_batch = dataset.get_clip_data() sc_loss, sc_grad = clip_utils.update_semantic_loss(model, clip_model, keys[0], state, sc_batch, lr) sc_grad = flax.jax_utils.replicate(sc_grad) sc_grad = jax.tree_map( lambda x: x[0], sc_grad) else: sc_loss = 0. state, stats, keys = train_pstep(keys, state, batch, lr, step, FLAGS.sc_loss_every)#, grad) if step%FLAGS.sc_loss_every == 0 and FLAGS.use_semantic_loss: state = update_pstep(state, sc_grad, lr) if jax.host_id() == 0: stats_trace.append(stats) if step % FLAGS.gc_every == 0: gc.collect() # Log training summaries. This is put behind a host_id check because in # multi-host evaluation, all hosts need to run inference even though we # only use host 0 to record results. if jax.host_id() == 0: if step % FLAGS.print_every == 0: summary_writer.scalar("train_loss", stats.loss[0], step) summary_writer.scalar("train_psnr", stats.psnr[0], step) summary_writer.scalar("train_loss_coarse", stats.loss_c[0], step) summary_writer.scalar("train_psnr_coarse", stats.psnr_c[0], step) summary_writer.scalar("weight_l2", stats.weight_l2[0], step) avg_loss = np.mean(np.concatenate([s.loss for s in stats_trace])) avg_psnr = np.mean(np.concatenate([s.psnr for s in stats_trace])) stats_trace = [] summary_writer.scalar("train_avg_loss", avg_loss, step) summary_writer.scalar("train_avg_psnr", avg_psnr, step) summary_writer.scalar("learning_rate", lr, step) steps_per_sec = FLAGS.print_every / (time.time() - t_loop_start) reset_timer = True rays_per_sec = FLAGS.batch_size * steps_per_sec summary_writer.scalar("train_steps_per_sec", steps_per_sec, step) summary_writer.scalar("train_rays_per_sec", rays_per_sec, step) precision = int(np.ceil(np.log10(FLAGS.max_steps))) + 1 print(("{:" + "{:d}".format(precision) + "d}").format(step) + f"/{FLAGS.max_steps:d}: " + f"i_loss={stats.loss[0]:0.4f}, " + f"avg_loss={avg_loss:0.4f}, " + f"weight_l2={stats.weight_l2[0]:0.2e}, " + # f"sc_loss={sc_loss:0.4f}, " + f"lr={lr:0.2e}, {rays_per_sec:0.0f} rays/sec") if step % FLAGS.save_every == 0: state_to_save = jax.device_get(jax.tree_map(lambda x: x[0], state)) checkpoints.save_checkpoint( FLAGS.train_dir, state_to_save, int(step), keep=100) # Test-set evaluation. if FLAGS.render_every > 0 and step % FLAGS.render_every == 0: # We reuse the same random number generator from the optimization step # here on purpose so that the visualization matches what happened in # training. t_eval_start = time.time() eval_variables = jax.device_get(jax.tree_map(lambda x: x[0], state)).optimizer.target test_case = next(test_dataset) pred_color, pred_disp, pred_acc = utils.render_image( functools.partial(render_pfn, eval_variables), test_case["rays"], keys[0], FLAGS.dataset == "llff", chunk=FLAGS.chunk) # Log eval summaries on host 0. if jax.host_id() == 0: psnr = utils.compute_psnr( ((pred_color - test_case["pixels"]) ** 2).mean()) ssim = ssim_fn(pred_color, test_case["pixels"]) eval_time = time.time() - t_eval_start num_rays = jnp.prod(jnp.array(test_case["rays"].directions.shape[:-1])) rays_per_sec = num_rays / eval_time summary_writer.scalar("test_rays_per_sec", rays_per_sec, step) print(f"Eval {step}: {eval_time:0.3f}s., {rays_per_sec:0.0f} rays/sec") summary_writer.scalar("test_psnr", psnr, step) summary_writer.scalar("test_ssim", ssim, step) summary_writer.image("test_pred_color", pred_color, step) summary_writer.image("test_pred_disp", pred_disp, step) summary_writer.image("test_pred_acc", pred_acc, step) summary_writer.image("test_target", test_case["pixels"], step) if FLAGS.max_steps % FLAGS.save_every != 0: state = jax.device_get(jax.tree_map(lambda x: x[0], state)) checkpoints.save_checkpoint( FLAGS.train_dir, state, int(FLAGS.max_steps), keep=100) if __name__ == "__main__": app.run(main)