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from __future__ import annotations
from collections.abc import Iterator
from functools import partial
from pathlib import Path
from typing import Callable
import chex
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
import pandas as pd
from flax import jax_utils
from hydra.utils import instantiate
from jax import lax
from omegaconf import DictConfig
from tqdm import tqdm
from imgx import REPLICA_AXIS
from imgx.data.augmentation import AugmentationFn, chain_aug_fns
from imgx.data.augmentation.affine import get_random_affine_augmentation_fn
from imgx.data.augmentation.intensity import (
get_random_gamma_augmentation_fn,
get_rescale_intensity_fn,
rescale_intensity,
)
from imgx.data.augmentation.patch import (
batch_patch_grid_mean_aggregate,
batch_patch_grid_sample,
get_patch_grid,
get_random_patch_fn,
)
from imgx.data.util import unpad
from imgx.datasets.constant import IMAGE, LABEL, LABEL_PRED, UID
from imgx.datasets.dataset_info import DatasetInfo
from imgx.device import bind_rng_to_host_or_device, get_first_replica_values, unshard
from imgx.diffusion.time_sampler import TimeSampler
from imgx.experiment import Experiment
from imgx.metric.segmentation import get_segmentation_metrics_per_step
from imgx.metric.util import merge_aggregated_metrics
from imgx.task.diffusion_segmentation.diffusion import DiffusionSegmentation
from imgx.task.diffusion_segmentation.diffusion_step import get_diffusion_loss_step
from imgx.task.diffusion_segmentation.gaussian_diffusion import (
DDIMSegmentationSampler,
DDPMSegmentationSampler,
GaussianDiffusionSegmentation,
)
from imgx.task.diffusion_segmentation.recycling_step import get_recycling_loss_step
from imgx.task.diffusion_segmentation.save import save_diffusion_segmentation_prediction
from imgx.task.diffusion_segmentation.self_conditioning_step import get_self_conditioning_loss_step
from imgx.task.diffusion_segmentation.train_state import TrainState, create_train_state
from imgx.task.util import decode_uids
from imgx.train_state import (
get_gradients,
get_half_precision_dtype,
get_optimization_metrics,
restore_checkpoint,
update_train_state,
)
def initialized(
key: jax.Array,
batch: dict[str, jnp.ndarray],
model: nn.Module,
dataset_info: DatasetInfo,
self_conditioning: bool,
) -> chex.ArrayTree:
"""Initialize model parameters and batch statistics.
Args:
key: random key.
batch: batch data for determining input shapes.
model: model.
dataset_info: dataset info to transform label to mask.
self_conditioning: whether to use self conditioning.
Returns:
model parameters.
"""
def init(*args) -> chex.ArrayTree: # type: ignore[no-untyped-def]
return model.init(*args)
image = batch[IMAGE]
label = batch[LABEL]
batch_size = image.shape[0]
mask = dataset_info.label_to_mask(label, axis=-1, dtype=image.dtype)
if self_conditioning:
mask = jnp.concatenate([mask, jnp.zeros_like(mask)], axis=-1)
t = jnp.zeros((batch_size,), dtype=image.dtype)
variables = jax.jit(init, backend="cpu", static_argnums=(1,))(
{"params": key}, False, image, mask, t # is_train
)
return variables["params"]
def get_importance_sampling_metrics(
loss_count_hist: jnp.ndarray,
loss_sq_hist: jnp.ndarray,
time_sampler: TimeSampler,
) -> dict[str, jnp.ndarray]:
"""Get importance sampling metrics.
Args:
loss_count_hist: count of time steps, shape (num_timesteps, ).
loss_sq_hist: weighted average of squared loss, shape (num_timesteps, ).
time_sampler: time sampler for training.
Returns:
metrics: metrics dict.
"""
metrics = {}
probs = time_sampler.t_probs_from_loss_sq(loss_sq_hist)
entropy = -jnp.sum(probs * jnp.log(probs))
metrics["loss_hist_entropy"] = entropy
metrics["mean_loss_count_hist"] = jnp.mean(loss_count_hist)
metrics["median_loss_count_hist"] = jnp.median(loss_count_hist)
metrics["min_loss_count_hist"] = jnp.min(loss_count_hist)
metrics["max_loss_count_hist"] = jnp.max(loss_count_hist)
metrics["mean_loss_sq_hist"] = jnp.mean(loss_sq_hist)
metrics["min_loss_sq_hist"] = jnp.min(loss_sq_hist)
metrics["max_loss_sq_hist"] = jnp.max(loss_sq_hist)
return metrics
def sample_logits_progressive(
train_state: TrainState,
image: jnp.ndarray,
key: jax.Array,
dataset_info: DatasetInfo,
diffusion_model: DiffusionSegmentation,
self_conditioning: bool,
) -> Iterator[jnp.ndarray]:
"""Generate segmentation mask logits conditioned on image.
The noise here is defined on segmentation mask.
Args:
train_state: training state.
image: image, (batch, ..., in_channels).
key: random key.
dataset_info: dataset info to transform label to mask.
diffusion_model: segmentation diffusion model.
self_conditioning: whether to use self conditioning.
Yields:
logits, (batch, ..., num_classes)
"""
batch_size = image.shape[0]
noise_shape = (*image.shape[:-1], dataset_info.num_classes)
key_noise, key = jax.random.split(key=key)
x_t = diffusion_model.sample_noise(key=key_noise, shape=noise_shape, dtype=image.dtype)
mask_pred = jnp.zeros_like(diffusion_model.x_to_mask(x_t))
for t_index_scalar in reversed(range(diffusion_model.num_timesteps)):
key_t, key = jax.random.split(key=key)
t_index = jnp.full((batch_size,), t_index_scalar, dtype=jnp.int32)
t = diffusion_model.t_index_to_t(t_index)
mask_t = diffusion_model.x_to_mask(x_t)
if self_conditioning:
mask_t = jnp.concatenate([mask_t, mask_pred], axis=-1)
model_out = train_state.apply_fn(
{"params": train_state.params},
False, # is_train
image,
mask_t,
t,
)
x_t, x_start = diffusion_model.sample(
key=key_t,
model_out=model_out,
x_t=x_t,
t_index=t_index,
)
mask_pred = diffusion_model.x_to_mask(x_start)
yield diffusion_model.x_to_logits(x_start)
def train_step(
train_state: TrainState,
batch: dict[str, jnp.ndarray],
key: jax.Array,
aug_fn: Callable[[jax.Array, chex.ArrayTree], chex.ArrayTree],
dataset_info: DatasetInfo,
config: DictConfig,
diffusion_model: DiffusionSegmentation,
time_sampler: TimeSampler,
) -> tuple[TrainState, chex.ArrayTree]:
"""Perform a training step.
Args:
train_state: training state.
batch: training data.
key: random key.
aug_fn: data augmentation function.
dataset_info: dataset info with helper functions.
config: entire config.
diffusion_model: segmentation diffusion model.
time_sampler: time sampler for training.
Returns:
- new training state.
- metric dict.
"""
if config.task.recycling.use and config.task.self_conditioning.use:
raise ValueError("recycling and self-conditioning cannot be used together.")
if config.task.recycling.use:
loss_step = get_recycling_loss_step(
train_state=train_state,
dataset_info=dataset_info,
diffusion_model=diffusion_model,
time_sampler=time_sampler,
loss_config=config.task.loss,
prev_step=config.task.recycling.prev_step,
reverse_step=config.task.recycling.reverse_step,
)
elif config.task.self_conditioning.use:
loss_step = get_self_conditioning_loss_step(
train_state=train_state,
dataset_info=dataset_info,
diffusion_model=diffusion_model,
time_sampler=time_sampler,
loss_config=config.task.loss,
prev_step=config.task.self_conditioning.prev_step,
probability=config.task.self_conditioning.probability,
)
else:
loss_step = get_diffusion_loss_step(
train_state=train_state,
dataset_info=dataset_info,
diffusion_model=diffusion_model,
time_sampler=time_sampler,
loss_config=config.task.loss,
)
# augment, calculate gradients, update train state
key = bind_rng_to_host_or_device(key, bind_to="device", axis_name=REPLICA_AXIS)
key = jax.random.fold_in(key=key, data=train_state.step)
key_aug, key_loss = jax.random.split(key)
batch = aug_fn(key_aug, batch)
dynamic_scale, is_fin, aux, grads = get_gradients(
train_state,
loss_step,
input_dict={"batch": batch, "key": key_loss},
)
new_state = update_train_state(train_state, dynamic_scale, is_fin, grads)
# values in metrics are scalars
_, metrics, loss_batch, t_index = aux[1]
# sync loss_batch, t_index across replicas
# then update loss history
# (num_shards*batch, )
loss_batch = lax.all_gather(loss_batch, axis_name=REPLICA_AXIS).reshape((-1,))
t_index = lax.all_gather(t_index, axis_name=REPLICA_AXIS).reshape((-1,))
loss_count_hist, loss_sq_hist = time_sampler.update_stats(
loss_batch=loss_batch,
t_index=t_index,
loss_count_hist=train_state.loss_count_hist,
loss_sq_hist=train_state.loss_sq_hist,
)
new_state = new_state.replace(
loss_count_hist=loss_count_hist,
loss_sq_hist=loss_sq_hist,
)
metrics_hist = get_importance_sampling_metrics(loss_count_hist, loss_sq_hist, time_sampler)
metrics = {
**metrics,
**metrics_hist,
}
# add optimization metrics
metrics_optim = get_optimization_metrics(
grads=grads,
train_state=new_state,
config=config,
)
metrics = {**metrics, **metrics_optim}
return new_state, metrics
def eval_step(
train_state: TrainState,
batch: dict[str, jnp.ndarray],
key: jax.Array,
dataset_info: DatasetInfo,
config: DictConfig,
diffusion_model: DiffusionSegmentation,
) -> jnp.ndarray:
"""Perform an evaluation step.
Args:
train_state: training state.
batch: training data without shard axis.
key: random key.
dataset_info: dataset info with helper functions.
config: entire config.
diffusion_model: segmentation diffusion model.
Returns:
logits, shape starts with (batch, ..., num_timesteps).
"""
key = bind_rng_to_host_or_device(key, bind_to="device", axis_name=REPLICA_AXIS)
patch_shape = tuple(config.data.loader.patch_shape)
patch_overlap = tuple(config.data.loader.patch_overlap)
image_shape = batch[IMAGE].shape[1:-1]
patch_start_indices = get_patch_grid(
image_shape=image_shape,
patch_shape=patch_shape,
patch_overlap=patch_overlap,
)
num_patches = patch_start_indices.shape[0]
# (batch, num_patches, *patch_shape, num_channels)
image_patches = batch_patch_grid_sample(
x=batch[IMAGE],
start_indices=patch_start_indices,
patch_shape=patch_shape,
)
# inference per patch
logits_patches = []
for i in range(num_patches):
# (batch, *spatial_shape, num_classes, num_timesteps)
key_i, key = jax.random.split(key)
image_i = rescale_intensity(
image_patches[:, i, ...],
v_min=config.data.loader.data_augmentation.v_min,
v_max=config.data.loader.data_augmentation.v_max,
)
lst_logits_i = list(
sample_logits_progressive(
train_state=train_state,
image=image_i,
key=key_i,
dataset_info=dataset_info,
diffusion_model=diffusion_model,
self_conditioning=config.task.self_conditioning.use,
)
)
logits_i = jnp.stack(lst_logits_i, axis=-1)
logits_patches.append(logits_i)
# (batch, num_patches, *patch_shape, num_classes, num_timesteps)
logits = jnp.stack(logits_patches, axis=1)
# aggregate patch logits
# (batch, *image_shape, num_classes, num_timesteps)
logits = batch_patch_grid_mean_aggregate(
x_patch=logits,
start_indices=patch_start_indices,
image_shape=image_shape,
)
return logits
class DiffusionSegmentationExperiment(Experiment):
"""Experiment for supervised training."""
def train_init(
self, batch: dict[str, jnp.ndarray], ckpt_dir: Path | None = None, step: int | None = None
) -> tuple[TrainState, int]:
"""Initialize data loader, loss, networks for training.
Args:
batch: training data for multi-devices.
ckpt_dir: checkpoint directory to restore from.
step: checkpoint step to restore from, if None use the latest one.
Returns:
initialized training state.
"""
# the batch is for multi-devices
# (num_models, ...)
# num_models is not the same as num_devices_per_replica
batch = get_first_replica_values(batch)
# data augmentation
aug_fns: list[AugmentationFn] = []
aug_fns += [
get_random_affine_augmentation_fn(self.config),
get_random_patch_fn(self.config),
get_random_gamma_augmentation_fn(self.config),
get_rescale_intensity_fn(self.config),
]
aug_fn = chain_aug_fns(aug_fns)
aug_rng = jax.random.PRNGKey(self.config["seed"])
batch = aug_fn(aug_rng, batch)
# init train state on cpu first
dtype = get_half_precision_dtype(self.config.half_precision)
model = instantiate(self.config.task.model, dtype=dtype)
with jax.default_device(jax.devices("cpu")[0]):
train_state = create_train_state(
key=jax.random.PRNGKey(self.config.seed),
batch=batch,
model=model,
config=self.config,
initialized=partial(
initialized,
dataset_info=self.dataset_info,
self_conditioning=self.config.task.self_conditioning.use,
),
)
# resume training
if ckpt_dir is not None:
train_state = restore_checkpoint(state=train_state, ckpt_dir=ckpt_dir, step=step)
# step_offset > 0 if restarting from checkpoint
step_offset = int(train_state.step)
train_state = jax_utils.replicate(train_state)
# training only
train_diffusion_model = GaussianDiffusionSegmentation.create(
classes_are_exclusive=self.dataset_info.classes_are_exclusive,
num_timesteps=self.config.task.diffusion.num_timesteps,
num_timesteps_beta=self.config.task.diffusion.num_timesteps_beta,
beta_schedule=self.config.task.diffusion.beta_schedule,
beta_start=self.config.task.diffusion.beta_start,
beta_end=self.config.task.diffusion.beta_end,
model_out_type=self.config.task.diffusion.model_out_type,
model_var_type=self.config.task.diffusion.model_var_type,
)
time_sampler = TimeSampler(
num_timesteps=self.config.task.diffusion.num_timesteps,
uniform_time_sampling=self.config.task.uniform_time_sampling,
)
# evaluation only
if self.config.task.sampler.name == "DDPM":
eval_diffusion_model_cls = DDPMSegmentationSampler
elif self.config.task.sampler.name == "DDIM":
eval_diffusion_model_cls = DDIMSegmentationSampler # type: ignore[assignment]
else:
raise ValueError(f"Sampler {self.config.task.diffusion.sampler.name} not supported.")
eval_diffusion_model = eval_diffusion_model_cls.create(
classes_are_exclusive=self.dataset_info.classes_are_exclusive,
num_timesteps=self.config.task.sampler.num_inference_timesteps, # different from train
num_timesteps_beta=self.config.task.diffusion.num_timesteps_beta,
beta_schedule=self.config.task.diffusion.beta_schedule,
beta_start=self.config.task.diffusion.beta_start,
beta_end=self.config.task.diffusion.beta_end,
model_out_type=self.config.task.diffusion.model_out_type,
model_var_type=self.config.task.diffusion.model_var_type,
)
self.p_train_step = jax.pmap(
partial(
train_step,
aug_fn=aug_fn,
dataset_info=self.dataset_info,
config=self.config,
diffusion_model=train_diffusion_model,
time_sampler=time_sampler,
),
axis_name=REPLICA_AXIS,
donate_argnums=(0,),
)
self.p_eval_step = jax.pmap(
partial(
eval_step,
dataset_info=self.dataset_info,
config=self.config,
diffusion_model=eval_diffusion_model,
),
axis_name=REPLICA_AXIS,
)
return train_state, step_offset
def eval_step( # pylint:disable=too-many-statements
self,
train_state: TrainState,
iterator: Iterator[dict[str, jnp.ndarray]],
num_steps: int,
key: jax.Array,
out_dir: Path | None = None,
) -> dict[str, jnp.ndarray]:
"""Evaluation on entire validation data set.
Args:
train_state: training state.
iterator: data iterator.
num_steps: number of steps for evaluation.
key: random key.
out_dir: output directory, if not None, predictions will be saved.
Returns:
metric dict.
"""
device_cpu = jax.devices("cpu")[0]
lst_metrics = []
lst_uids = []
for i in tqdm(range(num_steps), total=num_steps):
key_batch = jax.vmap(jax.random.fold_in, in_axes=(0, None))(key, i)
batch = next(iterator)
# get uids
uids = batch.pop(UID)
uids = uids.reshape(-1) # remove shard axis
uids = decode_uids(uids)
# evaluate the batch
uids, metrics, preds = self.eval_batch(
train_state=train_state,
key=key_batch,
batch=batch,
uids=uids,
device_cpu=device_cpu,
)
save_diffusion_segmentation_prediction(
label_pred=preds[LABEL_PRED],
uids=uids,
out_dir=out_dir,
tfds_dir=self.dataset_info.tfds_preprocessed_dir,
)
lst_uids += uids
lst_metrics.append(metrics)
# https://github.com/google/jax/issues/10828
jax.clear_caches()
# concatenate metrics across all samples
# metrics, values of shape (num_samples,)
metrics = jax.tree_map(lambda *args: np.concatenate(args), *lst_metrics)
# aggregate metrics
agg_metrics = merge_aggregated_metrics(metrics)
agg_metrics = jax.tree_map(lambda x: x.item(), agg_metrics)
agg_metrics["num_samples"] = len(lst_uids)
# save a csv file with per-sample metrics
if out_dir is not None:
metrics = jax.tree_map(lambda x: x.tolist(), metrics)
df_metric = pd.DataFrame(metrics)
df_metric[UID] = lst_uids
df_metric.to_csv(out_dir / "metrics_per_sample.csv", index=False)
return agg_metrics
def eval_batch(
self,
train_state: TrainState,
key: jax.Array,
batch: dict[str, jnp.ndarray],
uids: list[str],
device_cpu: jax.Device,
) -> tuple[list[str], dict[str, np.ndarray], dict[str, np.ndarray]]:
"""Evaluate a batch.
Args:
train_state: training state.
key: random key.
batch: batch data without uid.
uids: uids in the batch.
device_cpu: cpu device.
Returns:
uids: uids in the batch, excluding padded samples.
metrics: each item has shape (num_samples,).
prediction dict: each item has shape (num_samples, ...).
"""
# logits (num_shards*batch, *spatial_shape, num_classes)
# label (num_shards*batch, *spatial_shape)
logits = self.p_eval_step(train_state, batch, key)
logits = unshard(logits, device=device_cpu)
label = unshard(batch[LABEL], device=device_cpu)
# remove padded examples
num_samples_in_batch = len(uids)
if "" in uids:
num_samples_in_batch = uids.index("")
uids = uids[:num_samples_in_batch]
logits = unpad(logits, num_samples_in_batch)
label = unpad(label, num_samples_in_batch)
# (batch,) per metric
metrics, label_pred = get_segmentation_metrics_per_step(
logits=logits,
label=label,
dataset_info=self.dataset_info,
)
# change to numpy array
metrics = jax.tree_map(np.asarray, metrics)
label_pred = np.asarray(label_pred, dtype=np.int8)
preds = {LABEL_PRED: label_pred}
return uids, metrics, preds
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