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 measure_dir(data_dir, pool):
for name in ['t10k', 'train']:
in_path = os.path.join(data_dir, name + "-images-idx3-ubyte.gz")
out_path = os.path.join(data_dir, name + "-morpho.csv")
print(f"Processing MNIST data file {in_path}...")
data = io.load_idx(in_path)
df = measure.measure_batch(data, pool=pool, chunksize=100)
df.to_csv(out_path, index_label='index')
print(f"Morphometrics saved to {out_path}")
def measure_dir(data_dir, pool):
for name in ['t10k', 'train']:
in_path = os.path.join(data_dir, name + "-images-idx3-ubyte.gz")
out_path = os.path.join(data_dir, name + "-morpho.csv")
print(f"Processing MNIST data file {in_path}...")
data = io.load_idx(in_path)
df = measure.measure_batch(data, pool=pool, chunksize=100)
df.to_csv(out_path, index_label='index')
print(f"Morphometrics saved to {out_path}")
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)