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sigma type & doc #72

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merged 6 commits into from
Nov 14, 2023
Merged

sigma type & doc #72

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ed6b607
docstrings OTPlanSampler
guillaumehu Nov 13, 2023
b12f8fa
property for sigma int/float
guillaumehu Nov 13, 2023
25c519e
Merge pull request #1 from guillaumehu/doc_otplan
guillaumehu Nov 13, 2023
01fd2b8
sigma tensor
guillaumehu Nov 13, 2023
5975eac
init from base
guillaumehu Nov 13, 2023
53fbf6d
rm property & fix instance cond
guillaumehu Nov 14, 2023
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24 changes: 9 additions & 15 deletions torchcfm/conditional_flow_matching.py
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Original file line number Diff line number Diff line change
Expand Up @@ -6,6 +6,7 @@
# License: MIT License

import math
from typing import Union

import torch

Expand All @@ -31,7 +32,7 @@ def pad_t_like_x(t, x):
t: Vector (bs)
pad_t_like_x(t, x): Tensor (bs, 1, 1, 1)
"""
if isinstance(t, float):
if isinstance(t, (float, int)):
return t
return t.reshape(-1, *([1] * (x.dim() - 1)))

Expand All @@ -47,21 +48,14 @@ class ConditionalFlowMatcher:
- score function $\nabla log p_t(x|x0, x1)$
"""

def __init__(self, sigma: float = 0.0):
def __init__(self, sigma: Union[float, int] = 0.0):
r"""Initialize the ConditionalFlowMatcher class. It requires the hyper-parameter $\sigma$.

Parameters
----------
sigma : float
sigma : Union[float, int]
"""
self._sigma = sigma

@property
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@josephdviviano josephdviviano Nov 14, 2023 •
edited
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Looks good. One comment - the @property method is useful for allowing one to "get" a variable, without allowing the user to change it. So if someone did

obj = FlowMatcher(sigma=1.0)
obj.sigma = 2

they would get an error. They could be doing something like obj._sigma = 2 to overwrite the property, but they would be very intentionally doing this. So it's mostly a way of telling the user how to not shoot themselves in the foot.

If you think it would be bad practice for people to be overwriting the obj.sigma property, then adding the @property logic back in still makes sense (sans the type casting).

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Thank you @josephdviviano for the clarification! I am not sure what is the best for sigma, I think it is fine for now if we keep it like this, but happy to make the change if someone thinks otherwise.

def sigma(self):
if isinstance(self._sigma, int):
return float(self._sigma)
else:
return self._sigma
self.sigma = sigma

def compute_mu_t(self, x0, x1, t):
"""
Expand Down Expand Up @@ -222,12 +216,12 @@ class ExactOptimalTransportConditionalFlowMatcher(ConditionalFlowMatcher):
It overrides the sample_location_and_conditional_flow.
"""

def __init__(self, sigma: float = 0.0):
def __init__(self, sigma: Union[float, int] = 0.0):
r"""Initialize the ConditionalFlowMatcher class. It requires the hyper-parameter $\sigma$.

Parameters
----------
sigma : float
sigma : Union[float, int]
ot_sampler: exact OT method to draw couplings (x0, x1) (see Eq.(17) [1]).
"""
super().__init__(sigma)
Expand Down Expand Up @@ -389,13 +383,13 @@ class SchrodingerBridgeConditionalFlowMatcher(ConditionalFlowMatcher):
sample_location_and_conditional_flow functions.
"""

def __init__(self, sigma: float = 1.0, ot_method="exact"):
def __init__(self, sigma: Union[float, int] = 1.0, ot_method="exact"):
r"""Initialize the SchrodingerBridgeConditionalFlowMatcher class. It requires the hyper-
parameter $\sigma$ and the entropic OT map.

Parameters
----------
sigma : float
sigma : Union[float, int]
ot_sampler: exact OT method to draw couplings (x0, x1) (see Eq.(17) [1]).
"""
super().__init__(sigma)
Expand Down
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