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import enum
import torch
import math
import fcbh.utils
def lcm(a, b): #TODO: eventually replace by math.lcm (added in python3.9)
return abs(a*b) // math.gcd(a, b)
class CONDRegular:
def __init__(self, cond):
self.cond = cond
def _copy_with(self, cond):
return self.__class__(cond)
def process_cond(self, batch_size, device, **kwargs):
return self._copy_with(fcbh.utils.repeat_to_batch_size(self.cond, batch_size).to(device))
def can_concat(self, other):
if self.cond.shape != other.cond.shape:
return False
return True
def concat(self, others):
conds = [self.cond]
for x in others:
conds.append(x.cond)
return torch.cat(conds)
class CONDNoiseShape(CONDRegular):
def process_cond(self, batch_size, device, area, **kwargs):
data = self.cond[:,:,area[2]:area[0] + area[2],area[3]:area[1] + area[3]]
return self._copy_with(fcbh.utils.repeat_to_batch_size(data, batch_size).to(device))
class CONDCrossAttn(CONDRegular):
def can_concat(self, other):
s1 = self.cond.shape
s2 = other.cond.shape
if s1 != s2:
if s1[0] != s2[0] or s1[2] != s2[2]: #these 2 cases should not happen
return False
mult_min = lcm(s1[1], s2[1])
diff = mult_min // min(s1[1], s2[1])
if diff > 4: #arbitrary limit on the padding because it's probably going to impact performance negatively if it's too much
return False
return True
def concat(self, others):
conds = [self.cond]
crossattn_max_len = self.cond.shape[1]
for x in others:
c = x.cond
crossattn_max_len = lcm(crossattn_max_len, c.shape[1])
conds.append(c)
out = []
for c in conds:
if c.shape[1] < crossattn_max_len:
c = c.repeat(1, crossattn_max_len // c.shape[1], 1) #padding with repeat doesn't change result
out.append(c)
return torch.cat(out)
class CONDConstant(CONDRegular):
def __init__(self, cond):
self.cond = cond
def process_cond(self, batch_size, device, **kwargs):
return self._copy_with(self.cond)
def can_concat(self, other):
if self.cond != other.cond:
return False
return True
def concat(self, others):
return self.cond
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