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e12111a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 | """Diffusion model for segmentation."""
from __future__ import annotations
from dataclasses import dataclass
import jax.numpy as jnp
import jax.random
from imgx import EPS
from imgx.diffusion.gaussian.gaussian_diffusion import (
GaussianDiffusion,
get_gaussian_diffusion_attributes,
)
from imgx.diffusion.gaussian.sampler import DDIMSampler, DDPMSampler
from imgx.task.diffusion_segmentation.diffusion import DiffusionSegmentation
@dataclass
class GaussianDiffusionSegmentation(GaussianDiffusion, DiffusionSegmentation):
# pylint: disable=abstract-method
"""Class for segmentation diffusion sampling.
x is probabilities scaled in [-1, 1].
model_out is logits.
"""
classes_are_exclusive: bool
@classmethod
def create( # type: ignore[no-untyped-def]
cls: type[GaussianDiffusionSegmentation],
num_timesteps: int, # T
num_timesteps_beta: int,
beta_schedule: str,
beta_start: float,
beta_end: float,
model_out_type: str,
model_var_type: str,
**kwargs,
) -> GaussianDiffusionSegmentation:
"""Create a new instance.
Args:
num_timesteps: number of diffusion steps.
num_timesteps_beta: number of steps when defining beta schedule.
beta_schedule: schedule for betas.
beta_start: beta for t=0.
beta_end: beta for t=T.
model_out_type: type of model output.
model_var_type: type of variance for p(x_{t-1} | x_t).
kwargs: arguments, including classes_are_exclusive.
Returns:
Instance of GaussianDiffusionSegmentation.
"""
# sanity check for string variables
if model_out_type not in ["x_start", "noise"]:
raise ValueError(
f"Unknown DiffusionModelOutputType {model_out_type}, should be x_start or noise."
)
if model_var_type not in [
"fixed_small",
"fixed_large",
"learned",
"learned_range",
]:
raise ValueError(
f"Unknown DiffusionModelVarianceType {model_var_type},"
f"should be fixed_small, fixed_large, learned or learned_range."
)
# set variance schedule
attr_dict = get_gaussian_diffusion_attributes(
num_timesteps=num_timesteps,
num_timesteps_beta=num_timesteps_beta,
beta_schedule=beta_schedule,
beta_start=beta_start,
beta_end=beta_end,
)
return cls(
num_timesteps=num_timesteps,
noise_fn=jax.random.normal,
num_timesteps_beta=num_timesteps_beta,
beta_schedule=beta_schedule,
beta_start=beta_start,
beta_end=beta_end,
model_out_type=model_out_type,
model_var_type=model_var_type,
**attr_dict,
**kwargs,
)
def model_out_to_x(self, model_out: jnp.ndarray) -> jnp.ndarray:
"""Convert model outputs to x space.
Args:
model_out: unnormalised values,
classes are assumed to be in the last axis.
shape = (..., num_classes).
Returns:
Probabilities scaled to [-1, 1].
"""
fn = jax.nn.softmax if self.classes_are_exclusive else jax.nn.sigmoid
x = fn(model_out)
x = x * 2.0 - 1.0
return x
def mask_to_x(self, mask: jnp.ndarray) -> jnp.ndarray:
"""Convert mask to x.
Args:
mask: boolean segmentation mask, shape = (batch, ..., num_classes).
Returns:
x, shape = (batch, ..., num_classes), of values in [-1, 1].
"""
return mask * 2 - 1
def x_to_mask(self, x: jnp.ndarray) -> jnp.ndarray:
"""Convert x to mask.
Args:
x: shape = (batch, ..., num_classes), of values in [-1, 1].
Returns:
boolean segmentation mask, shape = (batch, ..., num_classes).
"""
x = jnp.clip(x, -1.0, 1.0)
return (x + 1) / 2
def x_to_logits(self, x: jnp.ndarray) -> jnp.ndarray:
"""Convert x into model output space, which is logits.
Args:
x: probabilities scaled to [-1, 1].
Returns:
unnormalised logits.
"""
probs = (x + 1) / 2
probs = jnp.clip(probs, EPS, 1.0)
return jnp.log(probs)
def model_out_to_logits_start(
self, model_out: jnp.ndarray, x_t: jnp.ndarray, t_index: jnp.ndarray
) -> jnp.ndarray:
"""Convert model outputs to logits at time 0, noiseless.
Args:
model_out: unnormalised values,
classes are assumed to be in the last axis.
shape = (..., num_classes).
x_t: label at time t of shape (..., num_classes).
t_index: time of shape (...,).
Returns:
logits, shape = (..., num_classes).
"""
if self.model_out_type == "x_start":
# model output is logits
return model_out
if self.model_out_type == "noise":
x_start = self.predict_xstart_from_noise_xt(x_t=x_t, noise=model_out, t_index=t_index)
return self.x_to_logits(x_start)
raise ValueError(f"Unknown DiffusionModelOutputType {self.model_out_type}.")
@dataclass
class DDPMSegmentationSampler(GaussianDiffusionSegmentation, DDPMSampler):
"""DDPM for segmentation."""
@dataclass
class DDIMSegmentationSampler(GaussianDiffusionSegmentation, DDIMSampler):
"""DDIM for segmentation."""
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