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import sys
from models.genforce.models import MODEL_ZOO
from models.genforce.models import build_generator
import os
import os.path as osp
import subprocess
import torch
def load_generator(model_name, latent_is_w=False, verbose=False, CHECKPOINT_DIR='models/pretrained/genforce/'):
if verbose:
print(" \\__Building generator for model {}...".format(model_name), end="")
model_config = MODEL_ZOO[model_name].copy()
url = model_config.pop('url') # URL to download model if needed.
model_config.update({'latent_is_w': latent_is_w})
# Build generator
generator = build_generator(**model_config)
if verbose:
print("Done!")
# Load pre-trained weights.
os.makedirs(CHECKPOINT_DIR, exist_ok=True)
checkpoint_path = osp.join(CHECKPOINT_DIR, model_name + '.pth')
if verbose:
print(" \\__Loading checkpoint from {}...".format(checkpoint_path), end="")
if not osp.exists(checkpoint_path):
subprocess.call(['wget', '--quiet', '-O', checkpoint_path, url])
# checkpoint = torch.load(checkpoint_path, map_location='cpu')
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
checkpoint = torch.load(checkpoint_path, map_location=device)
if 'generator_smooth' in checkpoint:
generator.load_state_dict(checkpoint['generator_smooth'])
else:
generator.load_state_dict(checkpoint['generator'])
if verbose:
print("Done!")
generator.dim_z = generator.z_space_dim
return generator
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