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import functools
import os
from collections.abc import Iterable
from datetime import datetime
import jax
import jax.numpy as jnp
import numpy as np
import optax
import pandas as pd
import tqdm
from flax.training import train_state # Useful dataclass to keep train state
from tensorboardX import SummaryWriter
from jax_nca.utils import make_circle_masks
def get_tensorboard_logger(
experiment_name: str, base_log_path: str = "tensorboard_logs"
):
log_path = "{}/{}_{}".format(base_log_path, experiment_name, datetime.now())
train_writer = SummaryWriter(log_path, flush_secs=10)
full_log_path = os.path.join(os.getcwd(), log_path)
print(
"Follow tensorboard logs with: python -m tensorboard.main --logdir '{}'".format(
full_log_path
)
)
return train_writer
def create_train_state(rng, nca, learning_rate, shape):
nca_seed = nca.create_seed(
nca.num_hidden_channels, nca.num_target_channels, shape=shape[:-1], batch_size=1
)
"""Creates initial `TrainState`."""
params = nca.init(rng, nca_seed, rng)["params"]
tx = optax.chain(
# optax.clip_by_global_norm(10.0),
optax.adam(learning_rate),
)
return train_state.TrainState.create(apply_fn=nca.apply, params=params, tx=tx)
def clip_grad_norm(grad):
factor = 1.0 / (
jnp.linalg.norm(jax.tree_util.tree_leaves(jax.tree_map(jnp.linalg.norm, grad)))
+ 1e-8
)
return jax.tree_map((lambda x: x * factor), grad)
@functools.partial(jax.jit, static_argnames=("apply_fn", "num_steps"))
def train_step(
apply_fn, state, seeds: jnp.array, targets: jnp.array, num_steps: int, rng
):
def mse_loss(pred, y):
squared_diff = jnp.square(pred - y)
return jnp.mean(squared_diff, axis=[-3, -2, -1])
def loss_fn(params):
def forward(carry, inp):
carry = apply_fn({"params": params}, carry, rng)
return carry, carry
x, outs = jax.lax.scan(forward, seeds, None, length=num_steps)
rgb, a = x[..., :3], jnp.clip(x[..., 3:4], 0.0, 1.0)
rgb = jnp.clip(1.0 - a + rgb, 0.0, 1.0)
outs = jnp.transpose(outs, [1, 0, 2, 3, 4])
subset = outs[:, -8:] # B 12 H W C
return jnp.mean(
jax.vmap(mse_loss)(subset[..., :4], jnp.expand_dims(targets, 1))
), (x, rgb)
grad_fn = jax.value_and_grad(loss_fn, has_aux=True)
(loss, aux), grads = grad_fn(state.params)
grads = clip_grad_norm(grads)
updated, rgb = aux
return state.apply_gradients(grads=grads), loss, grads, updated, rgb
class SamplePool:
def __init__(self, max_size: int = 1000):
self.max_size = max_size
self.pool = [None] * max_size
def __getitem__(self, idx):
if isinstance(idx, Iterable):
return [self.pool[i] for i in idx]
return idx
def __setitem__(self, idx, v):
if isinstance(idx, Iterable):
for i in range(len(idx)):
index = idx[i]
self.pool[index] = v[i]
else:
self.pool[idx] = v
def sample(self, num_samples: int):
indices = np.random.randint(0, self.max_size, num_samples)
return self.__getitem__(indices), indices
def flatten(d):
df = pd.json_normalize(d, sep="_")
return df.to_dict(orient="records")[0]
class EmojiTrainer:
def __init__(self, dataset, nca, pool_size: int = 1024, n_damage: int = 0):
self.dataset = dataset
self.img_shape = self.dataset.img_shape
self.nca = nca
self.pool_size = pool_size
self.n_damage = n_damage
self.state = None
def train(
self,
num_epochs,
batch_size: int = 8,
seed: int = 10,
lr: float = 0.001,
min_steps: int = 64,
max_steps: int = 96,
):
pool = SamplePool(self.pool_size)
writer = get_tensorboard_logger("EMOJITrainer")
rng = jax.random.PRNGKey(seed)
rng, init_rng = jax.random.split(rng)
self.state = create_train_state(init_rng, self.nca, lr, self.dataset.img_shape)
bar = tqdm.tqdm(np.arange(num_epochs))
try:
for i in bar:
num_steps = int(np.random.randint(min_steps, max_steps))
samples, indices = pool.sample(batch_size)
for j in range(len(samples)):
if samples[j] is None:
samples[j] = self.nca.create_seed(
self.nca.num_hidden_channels,
self.nca.num_target_channels,
shape=self.img_shape[:-1],
batch_size=1,
)[0]
samples[0] = self.nca.create_seed(
self.nca.num_hidden_channels,
self.nca.num_target_channels,
shape=self.img_shape[:-1],
batch_size=1,
)[0]
batch = np.stack(samples)
if self.n_damage > 0:
damage = (
1.0
- make_circle_masks(
int(self.n_damage), self.img_shape[0], self.img_shape[1]
)[..., None]
)
batch[-self.n_damage :] *= damage
batch = jnp.array(batch)
targets, rgb_targets = self.dataset.get_batch(batch_size)
targets = jnp.array(targets)
self.state, loss, grads, outputs, rgb_outputs = train_step(
self.nca.apply,
self.state,
batch,
targets,
num_steps=num_steps,
rng=rng,
)
grad_dict = {k: dict(grads[k]) for k in grads.keys()}
grad_dict = flatten(grad_dict)
grad_dict = {
k: {kk: np.sum(vv).item() for kk, vv in v.items()}
for k, v in grad_dict.items()
}
grad_dict = flatten(grad_dict)
pool[indices] = np.array(outputs)
bar.set_description("Loss: {}".format(loss.item()))
self.emit_metrics(
writer,
i,
batch,
rgb_outputs,
rgb_targets,
loss.item(),
metrics=grad_dict,
)
return self.state
except Exception:
return self.state
def emit_metrics(
self, train_writer, i: int, batch, outputs, targets, loss, metrics={}
):
train_writer.add_scalar("loss", loss, i)
# train_writer.add_scalar("log10(loss)", math.log10(loss), i)
train_writer.add_images("batch", self.nca.to_rgb(batch), i, dataformats="NHWC")
train_writer.add_images("outputs", outputs, i, dataformats="NHWC")
train_writer.add_images("targets", targets, i, dataformats="NHWC")
for k in metrics:
train_writer.add_scalar(k, metrics[k], i)
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