def get_BigGAN(version="biggan-deep-256"):
cache_path = "/scratch/binxu/torch/"
cfg = BigGANConfig.from_json_file(
join(cache_path, "%s-config.json" % version))
BGAN = BigGAN(cfg)
BGAN.load_state_dict(
torch.load(join(cache_path, "%s-pytorch_model.bin" % version)))
return BGAN
def loadBigGAN(version="biggan-deep-256"):
from pytorch_pretrained_biggan import BigGAN, truncated_noise_sample, BigGANConfig
if platform == "linux":
cache_path = "/scratch/binxu/torch/"
cfg = BigGANConfig.from_json_file(
join(cache_path, "%s-config.json" % version))
BGAN = BigGAN(cfg)
BGAN.load_state_dict(
torch.load(join(cache_path, "%s-pytorch_model.bin" % version)))
else:
BGAN = BigGAN.from_pretrained(version)
for param in BGAN.parameters():
param.requires_grad_(False)
# embed_mat = BGAN.embeddings.parameters().__next__().data
BGAN.cuda()
return BGAN
