def main(hparams):
# Set up some stuff accoring to hparams
hparams.n_input = np.prod(hparams.image_shape)
utils.set_num_measurements(hparams)
utils.print_hparams(hparams)
# get inputs
xs_dict = model_input(hparams)
estimators = utils.get_estimators(hparams)
utils.setup_checkpointing(hparams)
measurement_losses, l2_losses = utils.load_checkpoints(hparams)
x_hats_dict = {model_type: {} for model_type in hparams.model_types}
x_batch_dict = {}
for key, x in xs_dict.iteritems():
if not hparams.not_lazy:
# If lazy, first check if the image has already been
# saved before by *all* estimators. If yes, then skip this image.
save_paths = utils.get_save_paths(hparams, key)
is_saved = all([
os.path.isfile(save_path) for save_path in save_paths.values()
])
if is_saved:
continue
x_batch_dict[key] = x
if len(x_batch_dict) < hparams.batch_size:
continue
# Reshape input
x_batch_list = [
x.reshape(1, hparams.n_input) for _, x in x_batch_dict.iteritems()
]
x_batch = np.concatenate(x_batch_list)
# Construct noise and measurements
A = utils.get_A(hparams)
noise_batch = hparams.noise_std * np.random.randn(
hparams.batch_size, hparams.num_measurements)
if hparams.measurement_type == 'project':
y_batch = x_batch + noise_batch
else:
y_batch = np.matmul(x_batch, A) + noise_batch
# Construct estimates using each estimator
for model_type in hparams.model_types:
estimator = estimators[model_type]
x_hat_batch = estimator(A, y_batch, hparams)
for i, key in enumerate(x_batch_dict.keys()):
x = xs_dict[key]
y = y_batch[i]
x_hat = x_hat_batch[i]
# Save the estimate
x_hats_dict[model_type][key] = x_hat
# Compute and store measurement and l2 loss
measurement_losses[model_type][
key] = utils.get_measurement_loss(x_hat, A, y)
l2_losses[model_type][key] = utils.get_l2_loss(x_hat, x)
print('Processed upto image {0} / {1}'.format(key + 1, len(xs_dict)))
# Checkpointing
if (hparams.save_images) and ((key + 1) % hparams.checkpoint_iter
== 0):
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses,
save_image, hparams)
x_hats_dict = {
model_type: {}
for model_type in hparams.model_types
}
print('\nProcessed and saved first ', key + 1, 'images\n')
x_batch_dict = {}
# Final checkpoint
if hparams.save_images:
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses,
save_image, hparams)
print('\nProcessed and saved all {0} image(s)\n'.format(len(xs_dict)))
if hparams.print_stats:
for model_type in hparams.model_types:
print(model_type)
mean_m_loss = np.mean(measurement_losses[model_type].values())
mean_l2_loss = np.mean(l2_losses[model_type].values())
print('mean measurement loss = {0}'.format(mean_m_loss))
print('mean l2 loss = {0}'.format(mean_l2_loss))
if hparams.image_matrix > 0:
utils.image_matrix(xs_dict, x_hats_dict, view_image, hparams)
# Warn the user that some things were not processsed
if len(x_batch_dict) > 0:
print(
'\nDid NOT process last {} images because they did not fill up the last batch.'
.format(len(x_batch_dict)))
print('Consider rerunning lazily with a smaller batch size.')
def main(hparams):
# set up perceptual loss
device = 'cuda:0'
percept = PerceptualLoss(
model="net-lin", net="vgg", use_gpu=device.startswith("cuda")
)
utils.print_hparams(hparams)
# get inputs
xs_dict = model_input(hparams)
estimators = utils.get_estimators(hparams)
utils.setup_checkpointing(hparams)
measurement_losses, l2_losses, lpips_scores, z_hats = utils.load_checkpoints(hparams)
x_hats_dict = {model_type : {} for model_type in hparams.model_types}
x_batch_dict = {}
A = utils.get_A(hparams)
noise_batch = hparams.noise_std * np.random.standard_t(2, size=(hparams.batch_size, hparams.num_measurements))
for key, x in xs_dict.items():
if not hparams.not_lazy:
# If lazy, first check if the image has already been
# saved before by *all* estimators. If yes, then skip this image.
save_paths = utils.get_save_paths(hparams, key)
is_saved = all([os.path.isfile(save_path) for save_path in save_paths.values()])
if is_saved:
continue
x_batch_dict[key] = x
if len(x_batch_dict) < hparams.batch_size:
continue
# Reshape input
x_batch_list = [x.reshape(1, hparams.n_input) for _, x in x_batch_dict.items()]
x_batch = np.concatenate(x_batch_list)
# Construct noise and measurements
y_batch = utils.get_measurements(x_batch, A, noise_batch, hparams)
# Construct estimates using each estimator
for model_type in hparams.model_types:
estimator = estimators[model_type]
x_hat_batch, z_hat_batch, m_loss_batch = estimator(A, y_batch, hparams)
for i, key in enumerate(x_batch_dict.keys()):
x = xs_dict[key]
y_train = y_batch[i]
x_hat = x_hat_batch[i]
# Save the estimate
x_hats_dict[model_type][key] = x_hat
# Compute and store measurement and l2 loss
measurement_losses[model_type][key] = m_loss_batch[key]
l2_losses[model_type][key] = utils.get_l2_loss(x_hat, x)
lpips_scores[model_type][key] = utils.get_lpips_score(percept, x_hat, x, hparams.image_shape)
z_hats[model_type][key] = z_hat_batch[i]
print('Processed upto image {0} / {1}'.format(key+1, len(xs_dict)))
# Checkpointing
if (hparams.save_images) and ((key+1) % hparams.checkpoint_iter == 0):
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, lpips_scores, z_hats, save_image, hparams)
x_hats_dict = {model_type : {} for model_type in hparams.model_types}
print('\nProcessed and saved first ', key+1, 'images\n')
x_batch_dict = {}
# Final checkpoint
if hparams.save_images:
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, lpips_scores, z_hats, save_image, hparams)
print('\nProcessed and saved all {0} image(s)\n'.format(len(xs_dict)))
if hparams.print_stats:
for model_type in hparams.model_types:
print(model_type)
measurement_loss_list = list(measurement_losses[model_type].values())
l2_loss_list = list(l2_losses[model_type].values())
mean_m_loss = np.mean(measurement_loss_list)
mean_l2_loss = np.mean(l2_loss_list)
print('mean measurement loss = {0}'.format(mean_m_loss))
print('mean l2 loss = {0}'.format(mean_l2_loss))
if hparams.image_matrix > 0:
utils.image_matrix(xs_dict, x_hats_dict, view_image, hparams)
# Warn the user that some things were not processsed
if len(x_batch_dict) > 0:
print('\nDid NOT process last {} images because they did not fill up the last batch.'.format(len(x_batch_dict)))
print('Consider rerunning lazily with a smaller batch size.')
def main(hparams):
# Set up some stuff accoring to hparams
hparams.n_input = np.prod(hparams.image_shape)
utils.set_num_measurements(hparams)
utils.print_hparams(hparams)
if hparams.dataset == 'mnist':
hparams.n_z = latent_dim
elif hparams.dataset == 'celebA':
hparams.z_dim = latent_dim
# get inputs
xs_dict = model_input(hparams)
estimators = utils.get_estimators(hparams)
utils.setup_checkpointing(hparams)
measurement_losses, l2_losses = utils.load_checkpoints(hparams)
image_loss_mnist = []
meas_loss_mnist = []
x_hat_mnist = []
x_hats_dict = {model_type : {} for model_type in hparams.model_types}
x_batch_dict = {}
for key, x in xs_dict.iteritems():
if not hparams.not_lazy:
# If lazy, first check if the image has already been
# saved before by *all* estimators. If yes, then skip this image.
save_paths = utils.get_save_paths(hparams, key)
is_saved = all([os.path.isfile(save_path) for save_path in save_paths.values()])
if is_saved:
continue
x_batch_dict[key] = x
if len(x_batch_dict) < hparams.batch_size:
continue
# Reshape input
x_batch_list = [x.reshape(1, hparams.n_input) for _, x in x_batch_dict.iteritems()]
x_batch = np.concatenate(x_batch_list)
# Construct noise and measurements
A = utils.get_A(hparams)
noise_batch = hparams.noise_std * np.random.randn(hparams.batch_size, hparams.num_measurements)
if hparams.measurement_type == 'project':
y_batch = x_batch + noise_batch
else:
measure = np.matmul(x_batch, A)
y_batch = np.absolute(measure) + noise_batch
# Construct estimates using each estimator
for model_type in hparams.model_types:
x_main_batch = 10000*np.ones_like(x_batch)
for k in range(num_restarts):
print "Restart #", str(k+1)
# Solve deep pr problem with random initial iterate
init_iter = np.random.randn(hparams.batch_size, latent_dim)
# First gradient descent
z_opt_batch = init_iter
estimator = estimators[model_type]
items = estimator(A, y_batch, z_opt_batch, hparams)
x_hat_batch1 = items[0]
z_opt_batch1 = items[1]
losses_val1 = items[2]
x_hat_batch = x_hat_batch1
x_hat_batch = utils.resolve_ambiguity(x_hat_batch, x_batch, hparams.batch_size)
# Use reflection of initial iterate
z_opt_batch2 = -1*init_iter
items = estimator(A, y_batch, z_opt_batch2, hparams)
x_hat_batch2 = items[0]
z_opt_batch2 = items[1]
losses_val2 = items[2]
x_hat_batch2 = utils.resolve_ambiguity(x_hat_batch2, x_batch, hparams.batch_size)
x_hat_batchnew = utils.get_optimal_x_batch(x_hat_batch, x_hat_batch2, x_batch, hparams.batch_size)
x_main_batch = utils.get_optimal_x_batch(x_hat_batchnew, x_main_batch, x_batch, hparams.batch_size)
x_hat_batch = x_main_batch
if hparams.dataset == 'mnist':
utils.print_stats(x_hat_batch, x_batch, hparams.batch_size)
for i, key in enumerate(x_batch_dict.keys()):
x = xs_dict[key]
y = y_batch[i]
x_hat = x_hat_batch[i]
# Save the estimate
x_hats_dict[model_type][key] = x_hat
# Compute and store measurement and l2 loss
measurement_losses[model_type][key] = utils.get_measurement_loss(x_hat, A, y)
meas_loss_mnist.append(utils.get_measurement_loss(x_hat, A, y))
l2_losses[model_type][key] = utils.get_l2_loss(x_hat, x)
image_loss_mnist.append(utils.get_l2_loss(x_hat,x))
print 'Processed upto image {0} / {1}'.format(key+1, len(xs_dict))
# Checkpointing
if (hparams.save_images) and ((key+1) % hparams.checkpoint_iter == 0):
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, save_image, hparams)
x_hats_dict = {model_type : {} for model_type in hparams.model_types}
print '\nProcessed and saved first ', key+1, 'images\n'
x_batch_dict = {}
# Final checkpoint
if hparams.save_images:
utils.checkpoint(x_hats_dict, measurement_losses, l2_losses, save_image, hparams)
print '\nProcessed and saved all {0} image(s)\n'.format(len(xs_dict))
if hparams.print_stats:
for model_type in hparams.model_types:
mean_m_loss = np.mean(measurement_losses[model_type].values())
mean_l2_loss = np.mean(l2_losses[model_type].values())
print 'mean measurement loss = {0}'.format(mean_m_loss)
print 'mean l2 loss = {0}'.format(mean_l2_loss)
if hparams.image_matrix > 0:
utils.image_matrix(xs_dict, x_hats_dict, view_image, hparams)
# Warn the user that some things were not processsed
if len(x_batch_dict) > 0:
print '\nDid NOT process last {} images because they did not fill up the last batch.'.format(len(x_batch_dict))
print 'Consider rerunning lazily with a smaller batch size.'