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# Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
import math
from typing import Any, TypeVar
import torch
from mattergen.diffusion.corruption.sde_lib import SDE
from mattergen.diffusion.data.batched_data import BatchedData
from mattergen.diffusion.model_target import ModelTarget
T = TypeVar("T", bound=BatchedData)
def convert_model_out_to_score(
*,
model_target: ModelTarget,
sde: SDE,
model_out: torch.Tensor,
batch_idx: torch.LongTensor,
t: torch.Tensor,
batch: Any
) -> torch.Tensor:
"""
Convert a model output to a score, according to the specified model_target.
model_target: says what the model predicts.
For example, in RFDiffusion the model predicts clean coordinates;
in EDM the model predicts the raw noise.
sde: corruption process
model_out: model output
batch_idx: indicates which sample each row of model_out belongs to
noisy_x: noisy data
t: diffusion timestep
batch: noisy batch, ignored except by strange SDEs
"""
_, std = sde.marginal_prob(
x=torch.ones_like(model_out),
t=t,
batch_idx=batch_idx,
batch=batch,
)
# Note the slack tolerances in test_model_utils.py: the choice of ModelTarget does make a difference.
if model_target == ModelTarget.score_times_std:
return model_out / std
elif model_target == ModelTarget.logits:
# Not really a score, but logits will be handled downstream.
return model_out
else:
raise NotImplementedError
class NoiseLevelEncoding(torch.nn.Module):
"""
From: https://pytorch.org/tutorials/beginner/transformer_tutorial.html
"""
def __init__(self, d_model: int, dropout: float = 0.0):
super().__init__()
self.dropout = torch.nn.Dropout(p=dropout)
self.d_model = d_model
div_term = torch.exp(torch.arange(0, d_model, 2) * (-math.log(10000.0) / d_model))
self.register_buffer("div_term", div_term)
def forward(self, t: torch.Tensor) -> torch.Tensor:
"""
Args:
t: Tensor, shape [batch_size]
"""
x = torch.zeros((t.shape[0], self.d_model), device=self.div_term.device)
x[:, 0::2] = torch.sin(t[:, None] * self.div_term[None])
x[:, 1::2] = torch.cos(t[:, None] * self.div_term[None])
return self.dropout(x)
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