def main(pcorr_path, mig_path=None, figure_dir=None):
pcorr_filename = os.path.split(pcorr_path)[-1]
spec = pcorr_filename.split("_pcorr")[0]
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
fig_size = (8, 3)
fig_kwargs = dict(dpi=300, bbox_inches='tight', pad_inches=0)
with open(pcorr_path, 'rb') as f:
payload = pickle.load(f)
plt.figure(figsize=fig_size)
mig_per_factor = load_mig_per_factor(mig_path) if mig_path else None
plot_partial_correlation(latent_dims,
payload['pcorr'],
payload['cols'],
payload['hrule'],
mig=mig_per_factor)
if figure_dir is not None:
os.makedirs(figure_dir, exist_ok=True)
filename = pcorr_filename.split('.')[0] + ("+mig" if mig_path else
"") + ".pdf"
shape = np.array(payload['pcorr'].shape)
scale = .25 if mig_path else .3
set_size(*(scale * shape)[::-1])
fig_path = os.path.join(figure_dir, filename)
print("Saving figure to", fig_path)
plt.savefig(fig_path, **fig_kwargs)
plt.show()
def main(spec, num_samples, pool):
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
model_type, model_args, dataset_names = spec_util.parse_setup_spec(spec)
if model_type == 'VAE':
model = vae.VAE(model_args)
trainer = vae.Trainer(model, beta=4.)
trainer.cuda()
models.load_checkpoint(trainer, checkpoint_dir)
model.eval()
sample_latent = model.sample_latent(num_samples)
sample_imgs = model.dec(sample_latent)
elif model_type in ['GAN', 'GANmc']:
model = gan.GAN(model_args)
trainer = gan.Trainer(model)
trainer.cuda()
models.load_checkpoint(trainer, checkpoint_dir)
model.eval()
sample_imgs = model(num_samples)
else:
raise ValueError(f"Invalid model type: {model_type}")
print(f"Loaded model {checkpoint_dir}. Measuring samples...")
sample_imgs_np = sample_imgs.detach().cpu().squeeze().numpy()
sample_metrics = measure.measure_batch(sample_imgs_np, pool=pool)
os.makedirs(METRICS_ROOT, exist_ok=True)
metrics_path = os.path.join(METRICS_ROOT, f"{spec}_metrics.csv")
sample_metrics.to_csv(metrics_path, index_label='index')
print(f"Morphometrics saved to {metrics_path}")
def load_gan(spec):
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
device = torch.device('cuda')
gan = InfoGAN(*latent_dims)
trainer = Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
return gan
def load_gan(spec):
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
checkpoint_dir = os.path.join(CHECKPOINT_ROOT, spec)
device = torch.device('cuda')
gan = InfoGAN(*latent_dims)
trainer = Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
return gan
def main(checkpoint_dir, pcorr_dir=None):
spec = os.path.split(checkpoint_dir)[-1]
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
device = torch.device('cuda')
gan = infogan.InfoGAN(*latent_dims)
trainer = infogan.Trainer(gan).to(device)
load_checkpoint(trainer, checkpoint_dir)
gan.eval()
dataset_name = SPEC_TO_DATASET['+'.join(dataset_names)]
data_dirs = [os.path.join(DATA_ROOT, dataset_name)]
test_metrics, test_images, test_labels, test_which = load_test_data(
data_dirs)
print(test_metrics.head())
idx = np.random.permutation(10000) #[:1000]
X = torch.from_numpy(
test_images[idx]).float().unsqueeze(1).to(device) / 255.
cols = ['length', 'thickness', 'slant', 'width', 'height']
test_cols = cols[:]
test_hrule = None
if 'swel+frac' in spec:
add_swel_frac(data_dirs[0], test_metrics)
test_cols += ['swel', 'frac']
test_hrule = len(cols)
if pcorr_dir is None:
pcorr_dir = checkpoint_dir
os.makedirs(pcorr_dir, exist_ok=True)
process(gan, X, test_metrics.loc[idx], test_cols, pcorr_dir, spec, 'test',
test_hrule)
X_ = gan(1000).detach()
with multiprocessing.Pool() as pool:
sample_metrics = measure.measure_batch(X_.cpu().squeeze().numpy(),
pool=pool)
sample_hrule = None
process(gan, X_, sample_metrics, cols, pcorr_dir, spec, 'sample',
sample_hrule)
def main(pcorr_path, figure_dir=None):
pcorr_filename = os.path.split(pcorr_path)[-1]
spec = pcorr_filename.split("_pcorr")[0]
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
fig_size = (8, 3)
fig_kwargs = dict(dpi=300, bbox_inches='tight', pad_inches=0)
with open(pcorr_path, 'rb') as f:
payload = pickle.load(f)
plt.figure(figsize=fig_size)
plot_partial_correlation(latent_dims, payload['pcorr'], payload['cols'], payload['hrule'])
os.makedirs(figure_dir, exist_ok=True)
if figure_dir is not None:
filename = pcorr_filename.split('.')[0] + ".pdf"
shape = np.array(payload['pcorr'].shape)
set_size(*(.3 * shape)[::-1])
plt.savefig(os.path.join(figure_dir, filename), **fig_kwargs)
plt.show()
def main(pcorr_path, figure_dir=None):
pcorr_filename = os.path.split(pcorr_path)[-1]
spec = pcorr_filename.split("_pcorr")[0]
_, latent_dims, dataset_names = spec_util.parse_setup_spec(spec)
fig_size = (8, 3)
fig_kwargs = dict(dpi=300, bbox_inches='tight', pad_inches=0)
with open(pcorr_path, 'rb') as f:
payload = pickle.load(f)
plt.figure(figsize=fig_size)
plot_partial_correlation(latent_dims, payload['pcorr'], payload['cols'],
payload['hrule'])
os.makedirs(figure_dir, exist_ok=True)
if figure_dir is not None:
filename = pcorr_filename.split('.')[0] + ".pdf"
shape = np.array(payload['pcorr'].shape)
set_size(*(.3 * shape)[::-1])
plt.savefig(os.path.join(figure_dir, filename), **fig_kwargs)
plt.show()