# Transfer components to device
tensors = SimpleNamespace(
X_comp=torch.from_numpy(X_comp).to(device).float(), #-1, 1, C, H, W
X_global_mean=torch.from_numpy(X_global_mean).to(
device).float(), # 1, C, H, W
X_stdev=torch.from_numpy(X_stdev).to(device).float(),
Z_comp=torch.from_numpy(Z_comp).to(device).float(),
Z_stdev=torch.from_numpy(Z_stdev).to(device).float(),
Z_global_mean=torch.from_numpy(Z_global_mean).to(device).float(),
)
transformer = get_estimator(args.estimator, n_comp, args.sparsity)
tr_param_str = transformer.get_param_str()
# Compute max batch size given VRAM usage
max_batch = args.batch_size or (get_max_batch_size(inst, device)
if has_gpu else 1)
print('Batch size:', max_batch)
def show():
if args.batch_mode:
plt.close('all')
else:
plt.show()
print(f'[{timestamp()}] Creating visualizations')
# Ensure visualization gets new samples
torch.manual_seed(SEED_VISUALIZATION)
np.random.seed(SEED_VISUALIZATION)
# Transfer components to device
tensors = SimpleNamespace(
X_comp = torch.from_numpy(X_comp).to(device).float(), #-1, 1, C, H, W
X_global_mean = torch.from_numpy(X_global_mean).to(device).float(), # 1, C, H, W
X_stdev = torch.from_numpy(X_stdev).to(device).float(),
Z_comp = torch.from_numpy(Z_comp).to(device).float(),
Z_stdev = torch.from_numpy(Z_stdev).to(device).float(),
Z_global_mean = torch.from_numpy(Z_global_mean).to(device).float(),
)
transformer = get_estimator(args.estimator, n_comp, args.sparsity)
tr_param_str = transformer.get_param_str()
# Compute max batch size given VRAM usage
max_batch = args.batch_size or (get_max_batch_size(inst, device) if has_gpu else 1)
print('Batch size:', max_batch)
def show():
if args.batch_mode:
plt.close('all')
else:
plt.show()
print(f'[{timestamp()}] Creating visualizations')
# Ensure visualization gets new samples
torch.manual_seed(SEED_VISUALIZATION)
np.random.seed(SEED_VISUALIZATION)
# Make output directories