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config.coarse_loss_mult * jnp.sum(losses[:-1]) + losses[-1] + weight_l2) |
stats = utils.Stats( |
loss=loss, |
losses=losses, |
weight_l2=weight_l2, |
psnr=0.0, |
psnrs=0.0, |
grad_norm=0.0, |
grad_abs_max=0.0, |
grad_norm_clipped=0.0, |
) |
return 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') |
def tree_norm(tree): |
return jnp.sqrt( |
jax.tree_util.tree_reduce( |
lambda x, y: x + jnp.sum(y**2), tree, initializer=0)) |
if config.grad_max_val > 0: |
clip_fn = lambda z: jnp.clip(z, -config.grad_max_val, config.grad_max_val) |
grad = jax.tree_util.tree_map(clip_fn, grad) |
grad_abs_max = jax.tree_util.tree_reduce( |
lambda x, y: jnp.maximum(x, jnp.max(jnp.abs(y))), grad, initializer=0) |
grad_norm = tree_norm(grad) |
if config.grad_max_norm > 0: |
mult = jnp.minimum(1, config.grad_max_norm / (1e-7 + grad_norm)) |
grad = jax.tree_util.tree_map(lambda z: mult * z, grad) |
grad_norm_clipped = tree_norm(grad) |
new_optimizer = state.optimizer.apply_gradient(grad, learning_rate=lr) |
new_state = state.replace(optimizer=new_optimizer) |
psnrs = math.mse_to_psnr(stats.losses) |
stats = utils.Stats( |
loss=stats.loss, |
losses=stats.losses, |
weight_l2=stats.weight_l2, |
psnr=psnrs[-1], |
psnrs=psnrs, |
grad_norm=grad_norm, |
grad_abs_max=grad_abs_max, |
grad_norm_clipped=grad_norm_clipped, |
) |
return new_state, stats, rng |
def main(unused_argv): |
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()) |
config = utils.load_config() |
if config.batch_size % jax.device_count() != 0: |
raise ValueError('Batch size must be divisible by the number of devices.') |
dataset = datasets.get_dataset('train', FLAGS.data_dir, config) |
test_dataset = datasets.get_dataset('test', FLAGS.data_dir, config) |
rng, key = random.split(rng) |
model, variables = models.construct_mipnerf(key, dataset.peek()) |
num_params = jax.tree_util.tree_reduce( |
lambda x, y: x + jnp.prod(jnp.array(y.shape)), variables, initializer=0) |
print(f'Number of parameters being optimized: {num_params}') |
optimizer = flax.optim.Adam(config.lr_init).create(variables) |
state = utils.TrainState(optimizer=optimizer) |
del optimizer, variables |
learning_rate_fn = functools.partial( |
math.learning_rate_decay, |
lr_init=config.lr_init, |
lr_final=config.lr_final, |
max_steps=config.max_steps, |
lr_delay_steps=config.lr_delay_steps, |
lr_delay_mult=config.lr_delay_mult) |
train_pstep = jax.pmap( |
functools.partial(train_step, model, config), |
axis_name='batch', |
in_axes=(0, 0, 0, None), |
donate_argnums=(2,)) |
# Because this is only used for test set rendering, we disable randomization. |
def render_eval_fn(variables, _, rays): |
return jax.lax.all_gather( |
model.apply( |
variables, |
random.PRNGKey(0), # Unused. |
rays, |
randomized=False, |
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