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from typing import Protocol |
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import torch |
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from mattergen.diffusion.corruption.sde_lib import SDE |
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class TimestepSampler(Protocol): |
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min_t: float |
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max_t: float |
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def __call__(self, batch_size: int, device: torch.device) -> torch.FloatTensor: |
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raise NotImplementedError |
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class UniformTimestepSampler: |
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"""Samples diffusion timesteps uniformly over the training time.""" |
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def __init__( |
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self, |
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*, |
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min_t: float, |
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max_t: float, |
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): |
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"""Initializes the sampler. |
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Args: |
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min_t (float): Smallest timestep that will be seen during training. |
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max_t (float): Largest timestep that will be seen during training. |
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""" |
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super().__init__() |
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self.min_t = min_t |
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self.max_t = max_t |
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def __call__(self, batch_size: int, device: torch.device) -> torch.FloatTensor: |
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return torch.rand(batch_size, device=device) * (self.max_t - self.min_t) + self.min_t |
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