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stylegan_output.py
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stylegan_output.py
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import torch
from ptutils import MultiResolutionStore
def add_batch_dim(t):
if torch.is_tensor(t) and t.dim() == 1:
return t.unsqueeze(0)
return t
class GANOutputs:
@staticmethod
def from_seed(seed_indices, seed, dlatent=512):
if type(seed_indices) is int:
seed_indices = tuple(range(seed_indices))
else:
seed_indices = seed_indices
if seed is not None:
torch.manual_seed(seed)
assert seed_indices is not None
z = torch.randn(max(seed_indices) + 1, dlatent)[seed_indices, :]
gs = GANOutputs()
gs.seed = seed
gs.seed_indices = seed_indices
gs.z = z
return gs
def __len__(self):
if hasattr(self, 'z'):
return len(self.z)
if hasattr(self, 'ys'):
return len(self.ys[0])
return None
def __getitem__(self, item):
new = GANOutputs()
for k, v in self.__dict__.items():
if torch.is_tensor(v):
new.__dict__[k] = add_batch_dim(v[item])
elif type(v) is list and torch.is_tensor(v[0]):
new.__dict__[k] = [add_batch_dim(vv[item]) for vv in v]
elif type(v) is dict:
new.__dict__[k] = {kk: add_batch_dim(vv[item]) for kk, vv in v.items()}
elif type(v) is MultiResolutionStore:
new.__dict__[k] = MultiResolutionStore(v.get()[item])
return new
def __repr__(self):
ret = '{} GANOutput(s)'.format(len(self))# + str((self.seed, self.seed_indices))
if hasattr(self, 'seed'):
ret += ': (seed: {}, indices: {})'.format(self.seed, self.seed_indices)
return ret