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calculate_jung_radii.py
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import argparse
import numpy as np
import os.path as osp
import torch
from lib import GENFORCE_MODELS
from models.load_generator import load_generator
from sklearn import linear_model
from collections import defaultdict
from tqdm import tqdm
import json
def make_dict():
return defaultdict(make_dict)
def main():
"""A script for calculating the radii of minimal enclosing balls for the latent space of a (i.e., in Z/W/W+ space),
given a truncation parameter. When applicable, a linear model is trained in order to predict the radii of the latent
codes, given a truncation parameter.
The parameters of the linear model (i.e., the weight w and the bias b) are stored for each GAN type and each latent
space in a json file (i.e., models/jung_radii.json) as a dictionary with the following format:
{
...
<gan>:
{
'Z': (<w>, <b>),
'W':
{
...
<stylegan-layer>: (<w>, <b>),
...
},
},
...
}
so as, given a truncation parameter t, the radius is given as `w * t + b`.
Options:
-v, --verbose : set verbose mode on
--num-samples : set the number of latent codes to sample for generating images
--cuda : use CUDA (default)
--no-cuda : do not use CUDA
"""
parser = argparse.ArgumentParser(description="Fit a linear model for the jung radius of GAN's latent code given "
"a truncation parameter")
parser.add_argument('-v', '--verbose', action='store_true', help="verbose mode on")
parser.add_argument('--num-samples', type=int, default=1000, help="set number of latent codes to sample")
parser.add_argument('--cuda', dest='cuda', action='store_true', help="use CUDA during training")
parser.add_argument('--no-cuda', dest='cuda', action='store_false', help="do NOT use CUDA during training")
parser.set_defaults(cuda=True)
# ================================================================================================================ #
# Parse given arguments
args = parser.parse_args()
# CUDA
use_cuda = False
if torch.cuda.is_available():
if args.cuda:
use_cuda = True
torch.set_default_tensor_type('torch.cuda.FloatTensor')
else:
print("*** WARNING ***: It looks like you have a CUDA device, but aren't using CUDA.\n"
" Run with --cuda for optimal training speed.")
torch.set_default_tensor_type('torch.FloatTensor')
else:
torch.set_default_tensor_type('torch.FloatTensor')
# Build jung radii dictionary and populate it
nested_dict = lambda: defaultdict(nested_dict)
jung_radii_dict = nested_dict()
for gan in GENFORCE_MODELS.keys():
################################################################################################################
## ##
## [ StyleGANs ] ##
## ##
################################################################################################################
if 'stylegan' in gan:
############################################################################################################
## ##
## [ StyleGAN / Z-space ] ##
## ##
############################################################################################################
# Build GAN generator model and load with pre-trained weights
if args.verbose:
print(" \\__Build GAN generator model G and load with pre-trained weights...")
print(" \\__GAN generator : {} (res: {})".format(gan, GENFORCE_MODELS[gan][1]))
print(" \\__Pre-trained weights: {}".format(GENFORCE_MODELS[gan][0]))
G = load_generator(model_name=gan, latent_is_w=False, verbose=args.verbose).eval()
# Upload GAN generator model to GPU
if use_cuda:
G = G.cuda()
# Latent codes sampling
if args.verbose:
print(" \\__Sample {} {}-dimensional latent codes...".format(args.num_samples, G.dim_z))
zs = torch.randn(args.num_samples, G.dim_z)
if use_cuda:
zs = zs.cuda()
# Calculate expected latent norm
if args.verbose:
print(" \\__Calculate Jung radius...")
jung_radius = torch.cdist(zs, zs).max() * np.sqrt(G.dim_z / (2 * (G.dim_z + 1)))
jung_radii_dict[gan]['Z'] = (0.0, jung_radius.cpu().detach().item())
############################################################################################################
## ##
## [ StyleGAN / W/W+-space ] ##
## ##
############################################################################################################
# Build GAN generator model and load with pre-trained weights
if args.verbose:
print(" \\__Build GAN generator model G and load with pre-trained weights...")
print(" \\__GAN generator : {} (res: {})".format(gan, GENFORCE_MODELS[gan][1]))
print(" \\__Pre-trained weights: {}".format(GENFORCE_MODELS[gan][0]))
G = load_generator(model_name=gan, latent_is_w=True, verbose=args.verbose).eval()
# Upload GAN generator model to GPU
if use_cuda:
G = G.cuda()
# Latent codes sampling
if args.verbose:
print(" \\__Sample {} {}-dimensional latent codes...".format(args.num_samples, G.dim_z))
zs = torch.randn(args.num_samples, G.dim_z)
if use_cuda:
zs = zs.cuda()
# Get number of W layers for the given StyleGAN
stylegan_num_layers = G.get_w(zs, truncation=1.0).shape[1]
# Calculate expected latent norm and fit a linear model for each version of the W+ space
if args.verbose:
print(" \\__Calculate Jung radii and fit linear models...")
data_per_layer = dict()
tmp = []
for truncation in tqdm(np.linspace(0.1, 1.0, 100), desc=" \\__Calculate radii (W space): "):
ws = G.get_w(zs, truncation=truncation)[:, 0, :]
jung_radius = torch.cdist(ws, ws).max() * np.sqrt(ws.shape[1] / (2 * (ws.shape[1] + 1)))
tmp.append([truncation, jung_radius.cpu().detach().item()])
data_per_layer.update({0: tmp})
for ll in tqdm(range(1, stylegan_num_layers), desc=" \\__Calculate radii (W+ space): "):
tmp = []
for truncation in np.linspace(0.1, 1.0, 100):
ws_plus = G.get_w(zs, truncation=truncation)[:, :ll + 1, :]
ws_plus = ws_plus.reshape(ws_plus.shape[0], -1)
jung_radius = torch.cdist(ws_plus, ws_plus).max() * \
np.sqrt(ws_plus.shape[1] / (2 * (ws_plus.shape[1] + 1)))
tmp.append([truncation, jung_radius.cpu().detach().item()])
data_per_layer.update({ll: tmp})
for ll, v in tqdm(data_per_layer.items(), desc=" \\__Fit linear models"):
v = np.array(v)
lm = linear_model.LinearRegression()
lm.fit(v[:, 0].reshape(-1, 1), v[:, 1].reshape(-1, 1))
jung_radii_dict[gan]['W'][ll] = (float(lm.coef_[0, 0]), float(lm.intercept_[0]))
################################################################################################################
## ##
## [ ProgGAN ] ##
## ##
################################################################################################################
else:
# Build GAN generator model and load with pre-trained weights
if args.verbose:
print(" \\__Build GAN generator model G and load with pre-trained weights...")
print(" \\__GAN generator : {} (res: {})".format(gan, GENFORCE_MODELS[gan][1]))
print(" \\__Pre-trained weights: {}".format(GENFORCE_MODELS[gan][0]))
G = load_generator(model_name=gan, latent_is_w=False, verbose=args.verbose).eval()
# Upload GAN generator model to GPU
if use_cuda:
G = G.cuda()
# Latent codes sampling
if args.verbose:
print(" \\__Sample {} {}-dimensional latent codes...".format(args.num_samples, G.dim_z))
zs = torch.randn(args.num_samples, G.dim_z)
if use_cuda:
zs = zs.cuda()
# Calculate expected latent norm
if args.verbose:
print(" \\__Calculate Jung radius...")
jung_radius = torch.cdist(zs, zs).max() * np.sqrt(G.dim_z / (2 * (G.dim_z + 1)))
print("jung_radius")
print(jung_radius)
print(type(jung_radius))
jung_radii_dict[gan]['Z'] = (0.0, jung_radius.cpu().detach().item())
# Save expected latent norms dictionary
with open(osp.join('models', 'jung_radii.json'), 'w') as fp:
json.dump(jung_radii_dict, fp)
if __name__ == '__main__':
main()