def train(epoch):
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
if epoch < 10: critic_rate, generator_rate = 1, 4
else: critic_rate, generator_rate = 1, 4
else:
if epoch < 10: critic_rate, generator_rate = 2, 2
else: critic_rate, generator_rate = 2, 2
trans_steps, disc_steps, gen_steps = 0, 0, 0
turn = 'gen'
in_between_vis = 3
report_count = 0
data_loader.train()
train_gen_loss_accum, train_dis_loss_accum, train_likelihood_accum, train_kl_accum, batch_size_accum = 0, 0, 0, 0, 0
start = time.time();
for batch_idx, curr_batch_size, batch in data_loader:
if turn == 'trans' or turn == 'disc' or turn == 'gen':
# trans_train_step_np, generator_cost_np, discriminator_cost_np, transporter_cost_np, mean_transport_cost_np = \
# sess.run([train_transport_step_tf, train_outs_dict['generator_cost'], train_outs_dict['discriminator_cost'], train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost']],
# feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
trans_train_step_np, transporter_cost_np, mean_transport_cost_np = \
sess.run([train_transport_step_tf, train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost']],
feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
trans_steps = trans_steps+1
if turn == 'disc' or turn == 'gen':
disc_train_step_np, transporter_cost_np, mean_transport_cost_np = \
sess.run([train_discriminator_step_tf, train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost']],
feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
disc_steps = disc_steps+1
if turn == 'gen':
gen_train_step_np, generator_cost_np, discriminator_cost_np, transporter_cost_np, mean_transport_cost_np, convex_mask_np = \
sess.run([train_generator_step_tf, train_outs_dict['generator_cost'], train_outs_dict['discriminator_cost'],
train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost'], train_outs_dict['convex_mask']],
feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
gen_steps = gen_steps+1
if turn == 'trans':
# if discriminator_cost_np/np.abs(generator_cost_np) < 0.5: turn = 'disc'
if trans_steps % (critic_rate*generator_rate) == 0: turn = 'disc'
elif turn == 'disc':
if disc_steps % generator_rate == 0: turn = 'gen'
elif turn == 'gen':
if gen_steps % 1 == 0: turn = 'trans'
max_discriminator_weight = sess.run(max_abs_discriminator_vars)
train_gen_loss_accum += curr_batch_size*generator_cost_np
train_dis_loss_accum += curr_batch_size*discriminator_cost_np
batch_size_accum += curr_batch_size
if batch_idx % global_args.log_interval == 0:
report_count = report_count+1
end = time.time();
print('Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.3f}\tDiscriminator Cost: {:.3f}\tTime: {:.3f}, variational cost {:.3f}, transport cost {:.3f}, t {:2d} d {:2d} g {:2d}, t/d {:.1f}, t/g {:.1f}, mask {:.3f}'.format(
epoch, batch_idx * curr_batch_size, generator_cost_np, discriminator_cost_np, (end - start), transporter_cost_np, mean_transport_cost_np, trans_steps, disc_steps, gen_steps, trans_steps/disc_steps, trans_steps/gen_steps, np.mean(convex_mask_np)))
with open(global_args.exp_dir+"training_traces.txt", "a") as text_file:
text_file.write(str(generator_cost_np) + ', ' + str(discriminator_cost_np) + '\n')
start = time.time()
if in_between_vis>0 and report_count % in_between_vis:
distributions.visualizeProductDistribution3(sess, input_dict_func(batch, np.asarray([epoch, float(epoch>2)])), batch, inference_obs_dist, transport_dist, rec_dist, generative_dict['obs_dist'],
save_dir=global_args.exp_dir+'Visualization/Train/Random/', postfix='train_'+str(epoch))
batch['observed']['data']['image'] = fixed_batch_data
distributions.visualizeProductDistribution3(sess, input_dict_func(batch, np.asarray([epoch, float(epoch>2)])), batch, inference_obs_dist, transport_dist, rec_dist, generative_dict['obs_dist'],
save_dir=global_args.exp_dir+'Visualization/Train/Fixed/', postfix='train_fixed_'+str(epoch))
# prev_mean_transport_cost_np = mean_transport_cost_np
# prev_transporter_cost_np = transporter_cost_np
summary_str = sess.run(merged_summaries, feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
summary_writer.add_summary(summary_str, (tf.train.global_step(sess, global_step)))
checkpoint_time = 1
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
checkpoint_time = 20
if epoch % checkpoint_time == 0:
print('====> Average Train: Epoch: {}\tGenerator Cost: {:.3f}\tDiscriminator Cost: {:.3f}'.format(
epoch, train_gen_loss_accum/batch_size_accum, train_dis_loss_accum/batch_size_accum))
# helper.draw_bar_plot(effective_z_cost_np[:,0,0], thres = [np.mean(effective_z_cost_np), np.max(effective_z_cost_np)], save_dir=global_args.exp_dir+'Visualization/inversion_cost/', postfix='inversion_cost'+str(epoch))
# helper.draw_bar_plot(disc_cost_gen_np[:,0,0], thres = [0, 0], save_dir=global_args.exp_dir+'Visualization/disc_cost/', postfix='disc_cost'+str(epoch))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
helper.visualize_datasets(sess, input_dict_func(batch), data_loader.dataset, generative_dict['obs_sample_out'],
generative_dict['latent_sample_out'], train_outs_dict['transport_sample'], train_outs_dict['input_sample'],
save_dir=global_args.exp_dir+'Visualization/', postfix=str(epoch))
xmin, xmax, ymin, ymax, X_dense, Y_dense = -3.5, 3.5, -3.5, 3.5, 250, 250
xlist = np.linspace(xmin, xmax, X_dense)
ylist = np.linspace(ymin, ymax, Y_dense)
X, Y = np.meshgrid(xlist, ylist)
XY = np.concatenate([X.reshape(-1,1), Y.reshape(-1,1)], axis=1)
batch['observed']['data']['flat'] = XY[:, np.newaxis, :]
disc_cost_real_np = sess.run(train_outs_dict['critic_real'], feed_dict = input_dict_func(batch, np.asarray([epoch, float(epoch>2)])))
f = np.reshape(disc_cost_real_np[:,0,0], [Y_dense, X_dense])
helper.plot_ffs(X, Y, f, save_dir=global_args.exp_dir+'Visualization/discriminator_function/', postfix='discriminator_function'+str(epoch))
else:
# helper.draw_bar_plot(convex_mask_np, y_min_max = [0,1], save_dir=global_args.exp_dir+'Visualization/convex_mask/', postfix='convex_mask'+str(epoch))
distributions.visualizeProductDistribution3(sess, input_dict_func(batch, np.asarray([epoch, float(epoch>2)])), batch, inference_obs_dist, transport_dist, rec_dist, generative_dict['obs_dist'],
save_dir=global_args.exp_dir+'Visualization/Train/Random/', postfix='train_'+str(epoch))
batch['observed']['data']['image'] = fixed_batch_data
distributions.visualizeProductDistribution3(sess, input_dict_func(batch, np.asarray([epoch, float(epoch>2)])), batch, inference_obs_dist, transport_dist, rec_dist, generative_dict['obs_dist'],
save_dir=global_args.exp_dir+'Visualization/Train/Fixed/', postfix='train_fixed_'+str(epoch))
checkpoint_path1 = global_args.exp_dir+'checkpoint/'
checkpoint_path2 = global_args.exp_dir+'checkpoint2/'
print('====> Saving checkpoint. Epoch: ', epoch); start_tmp = time.time()
# helper.save_checkpoint(saver, sess, global_step, checkpoint_path1)
end_tmp = time.time(); print('Checkpoint path: '+checkpoint_path1+' ====> It took: ', end_tmp - start_tmp)
if epoch % 60 == 0:
print('====> Saving checkpoint backup. Epoch: ', epoch); start_tmp = time.time()
# helper.save_checkpoint(saver, sess, global_step, checkpoint_path2)
end_tmp = time.time(); print('Checkpoint path: '+checkpoint_path2+' ====> It took: ', end_tmp - start_tmp)
def train(epoch):
data_loader.train()
train_gen_loss_accum, train_dis_loss_accum, train_likelihood_accum, train_kl_accum, batch_size_accum = 0, 0, 0, 0, 0
start = time.time();
for batch_idx, curr_batch_size, batch in data_loader:
disc_train_step_np = sess.run([train_discriminator_step_tf], feed_dict = input_dict_func(batch, np.asarray([0,])))
if batch_idx % 5 !=0: continue
gen_train_step_np, generator_cost_np, discriminator_cost_np = \
sess.run([train_generator_step_tf, train_outs_dict['generator_cost'], train_outs_dict['discriminator_cost']],
feed_dict = input_dict_func(batch, np.asarray([0,])))
max_discriminator_weight = sess.run(max_abs_discriminator_vars)
train_gen_loss_accum += curr_batch_size*generator_cost_np
train_dis_loss_accum += curr_batch_size*discriminator_cost_np
batch_size_accum += curr_batch_size
if batch_idx % global_args.log_interval == 0:
end = time.time();
print('Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}\tTime: {:.3f}, Max disc weight {:.6f}'.format(
epoch, batch_idx * curr_batch_size, generator_cost_np, discriminator_cost_np, (end - start), max_discriminator_weight))
with open(global_args.exp_dir+"training_traces.txt", "a") as text_file:
text_file.write(str(generator_cost_np) + ', ' + str(discriminator_cost_np) + '\n')
start = time.time()
summary_str = sess.run(merged_summaries, feed_dict = input_dict_func(batch, np.asarray([0,])))
summary_writer.add_summary(summary_str, (tf.train.global_step(sess, global_step)))
checkpoint_time = 1
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
checkpoint_time = 20
if epoch % checkpoint_time == 0:
print('====> Average Train: Epoch: {}\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}'.format(
epoch, train_gen_loss_accum/batch_size_accum, train_dis_loss_accum/batch_size_accum))
# helper.draw_bar_plot(rate_similarity_gen_np[:,0,0], y_min_max = [0,1], save_dir=global_args.exp_dir+'Visualization/inversion_weight/', postfix='inversion_weight'+str(epoch))
# helper.draw_bar_plot(effective_z_cost_np[:,0,0], thres = [np.mean(effective_z_cost_np), np.max(effective_z_cost_np)], save_dir=global_args.exp_dir+'Visualization/inversion_cost/', postfix='inversion_cost'+str(epoch))
# helper.draw_bar_plot(disc_cost_gen_np[:,0,0], thres = [0, 0], save_dir=global_args.exp_dir+'Visualization/disc_cost/', postfix='disc_cost'+str(epoch))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
helper.visualize_datasets(sess, input_dict_func(batch), data_loader.dataset, generative_dict['obs_sample_out'], generative_dict['latent_sample_out'],
save_dir=global_args.exp_dir+'Visualization/', postfix=str(epoch))
xmin, xmax, ymin, ymax, X_dense, Y_dense = -3.5, 3.5, -3.5, 3.5, 250, 250
xlist = np.linspace(xmin, xmax, X_dense)
ylist = np.linspace(ymin, ymax, Y_dense)
X, Y = np.meshgrid(xlist, ylist)
XY = np.concatenate([X.reshape(-1,1), Y.reshape(-1,1)], axis=1)
batch['observed']['data']['flat'] = XY[:, np.newaxis, :]
disc_cost_real_np = sess.run(train_outs_dict['critic_real'], feed_dict = input_dict_func(batch, np.asarray([0,])))
f = np.reshape(disc_cost_real_np[:,0,0], [Y_dense, X_dense])
helper.plot_ffs(X, Y, f, save_dir=global_args.exp_dir+'Visualization/discriminator_function/', postfix='discriminator_function'+str(epoch))
else:
distributions.visualizeProductDistribution(sess, input_dict_func(batch), batch, inference_obs_dist, generative_dict['obs_dist'],
save_dir=global_args.exp_dir+'Visualization/Train/', postfix='train_'+str(epoch))
checkpoint_path1 = global_args.exp_dir+'checkpoint/'
checkpoint_path2 = global_args.exp_dir+'checkpoint2/'
print('====> Saving checkpoint. Epoch: ', epoch); start_tmp = time.time()
helper.save_checkpoint(saver, sess, global_step, checkpoint_path1)
end_tmp = time.time(); print('Checkpoint path: '+checkpoint_path1+' ====> It took: ', end_tmp - start_tmp)
if epoch % 60 == 0:
print('====> Saving checkpoint backup. Epoch: ', epoch); start_tmp = time.time()
helper.save_checkpoint(saver, sess, global_step, checkpoint_path2)
end_tmp = time.time(); print('Checkpoint path: '+checkpoint_path2+' ====> It took: ', end_tmp - start_tmp)
def train(epoch):
global curr_meanp, curr_stdp, p_real
trans_steps, disc_steps, gen_steps = 0, 0, 0
turn = 'gen'
in_between_vis = 5
report_count = 0
data_loader.train()
train_gen_loss_accum, train_dis_loss_accum, train_likelihood_accum, train_kl_accum, batch_size_accum = 0, 0, 0, 0, 0
start = time.time()
hyperparam_dict = {'b_identity': 0.}
helper.update_dict_from_file(hyperparam_dict, './hyperparam_file.py')
for batch_idx, curr_batch_size, batch in data_loader:
gen_bool, disc_bool, trans_bool = scheduler(epoch, batch_idx)
hyper_param = np.asarray(
[epoch, hyperparam_dict['b_identity'], curr_meanp, p_real])
curr_feed_dict = input_dict_func(batch, hyper_param)
if trans_bool:
trans_train_step_np, transporter_cost_np, mean_transport_cost_np = \
sess.run([train_transport_step_tf, train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost']],
feed_dict = curr_feed_dict)
trans_steps = trans_steps + 1
# p_real = np.exp(-mean_transport_cost_np)
if disc_bool:
disc_train_step_np, transporter_cost_np, mean_transport_cost_np = \
sess.run([train_discriminator_step_tf, train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost']],
feed_dict = curr_feed_dict)
disc_steps = disc_steps + 1
if gen_bool:
gen_train_step_np, generator_cost_np, discriminator_cost_np, transporter_cost_np, mean_transport_cost_np, convex_mask_np, critic_real_np, \
critic_gen_np, expected_log_pdf_prior_np, expected_log_pdf_agg_post_np, aaa = \
sess.run([train_generator_step_tf, train_outs_dict['generator_cost'], train_outs_dict['discriminator_cost'],
train_outs_dict['transporter_cost'], train_outs_dict['mean_transport_cost'], train_outs_dict['convex_mask'], train_outs_dict['critic_real'],
train_outs_dict['critic_gen'], train_outs_dict['expected_log_pdf_prior'], train_outs_dict['expected_log_pdf_agg_post'], model.AAA],
feed_dict = curr_feed_dict)
gen_steps = gen_steps + 1
max_discriminator_weight = sess.run(max_abs_discriminator_vars)
train_gen_loss_accum += curr_batch_size * generator_cost_np
train_dis_loss_accum += curr_batch_size * discriminator_cost_np
batch_size_accum += curr_batch_size
curr_meanp_delta, curr_stdp_delta = np.mean(critic_gen_np), np.std(
critic_gen_np)
curr_meanp = 0.9 * curr_meanp + 0.1 * curr_meanp_delta
curr_stdp = 0.9 * curr_stdp + 0.1 * curr_stdp_delta
if batch_idx % global_args.log_interval == 0:
report_count = report_count + 1
end = time.time()
# print('Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.3f}\tDiscriminator Cost: {:.3f}\tTime: {:.3f}, variational cost {:.3f}, transport cost {:.3f}, t {:2d} d {:2d} g {:2d}, t/d {:.1f}, t/g {:.1f}, mask {:.3f}, m {:.2f}, std {:.2f}'.format(
# epoch, batch_idx * curr_batch_size, generator_cost_np, discriminator_cost_np, (end - start), transporter_cost_np, mean_transport_cost_np, trans_steps, disc_steps, gen_steps, trans_steps/disc_steps, trans_steps/gen_steps, np.mean(convex_mask_np[:,0]), curr_meanp, curr_stdp))
print(
'Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.3f}\tDiscriminator Cost: {:.3f}\tTime: {:.3f}, variational cost {:.3f}, transport cost {:.3f}, t {:2d} d {:2d} g {:2d}, t/d {:.1f}, t/g {:.1f}, pri {:.1f}, agg {:.1f}, mask {:.2f}, p_real {:.2f}'
.format(epoch, batch_idx * curr_batch_size,
generator_cost_np, discriminator_cost_np,
(end - start), transporter_cost_np,
mean_transport_cost_np, trans_steps, disc_steps,
gen_steps, trans_steps / disc_steps,
trans_steps / gen_steps, expected_log_pdf_prior_np,
expected_log_pdf_agg_post_np,
np.mean(convex_mask_np[:, 0]), p_real))
with open(global_args.exp_dir + "training_traces.txt",
"a") as text_file:
text_file.write(
str(generator_cost_np) + ', ' +
str(discriminator_cost_np) + '\n')
start = time.time()
if data_loader.__module__ == 'datasetLoaders.CelebA1QueueLoader' and in_between_vis > 0 and report_count % in_between_vis:
distributions.visualizeProductDistribution3(
sess,
curr_feed_dict,
batch,
inference_obs_dist,
transport_dist,
rec_dist,
generative_dict['obs_dist'],
save_dir=global_args.exp_dir +
'Visualization/Train/Random/',
postfix='train_' + str(epoch))
batch['observed']['data']['image'] = fixed_batch_data
curr_feed_dict = input_dict_func(batch, hyper_param)
distributions.visualizeProductDistribution3(
sess,
curr_feed_dict,
batch,
inference_obs_dist,
transport_dist,
rec_dist,
generative_dict['obs_dist'],
save_dir=global_args.exp_dir +
'Visualization/Train/Fixed/',
postfix='train_fixed_' + str(epoch))
summary_str = sess.run(merged_summaries, feed_dict=curr_feed_dict)
summary_writer.add_summary(summary_str,
(tf.train.global_step(sess, global_step)))
checkpoint_time = 1
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
checkpoint_time = 20
if epoch % checkpoint_time == 0:
print(
'====> Average Train: Epoch: {}\tGenerator Cost: {:.3f}\tDiscriminator Cost: {:.3f}'
.format(epoch, train_gen_loss_accum / batch_size_accum,
train_dis_loss_accum / batch_size_accum))
# helper.draw_bar_plot(effective_z_cost_np[:,0,0], thres = [np.mean(effective_z_cost_np), np.max(effective_z_cost_np)], save_dir=global_args.exp_dir+'Visualization/inversion_cost/', postfix='inversion_cost'+str(epoch))
# helper.draw_bar_plot(disc_cost_gen_np[:,0,0], thres = [0, 0], save_dir=global_args.exp_dir+'Visualization/disc_cost/', postfix='disc_cost'+str(epoch))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
helper.visualize_datasets(sess,
input_dict_func(batch),
data_loader.dataset,
generative_dict['obs_sample_out'],
generative_dict['latent_sample_out'],
train_outs_dict['transport_sample'],
train_outs_dict['input_sample'],
save_dir=global_args.exp_dir +
'Visualization/',
postfix=str(epoch))
xmin, xmax, ymin, ymax, X_dense, Y_dense = -2.5, 2.5, -2.5, 2.5, 250, 250
xlist = np.linspace(xmin, xmax, X_dense)
ylist = np.linspace(ymin, ymax, Y_dense)
X, Y = np.meshgrid(xlist, ylist)
XY = np.concatenate(
[X.reshape(-1, 1), Y.reshape(-1, 1)], axis=1)
batch['observed']['data']['flat'] = XY[:, np.newaxis, :]
disc_cost_real_np = sess.run(train_outs_dict['critic_real'],
feed_dict=input_dict_func(
batch, hyper_param))
batch['observed']['data'][
'flat'] = data_loader.dataset[:, np.newaxis, :]
disc_cost_real_real_np = sess.run(
train_outs_dict['critic_real'],
feed_dict=input_dict_func(batch, hyper_param))
# disc_mean = disc_cost_real_real_np.max()
# disc_max = disc_cost_real_real_np.max()
# disc_min = disc_cost_real_real_np.min()
disc_mean = disc_cost_real_real_np.mean()
disc_std = disc_cost_real_real_np.std()
disc_max = disc_mean + 2 * disc_std
disc_min = disc_mean - 2 * disc_std
np.clip(disc_cost_real_np,
disc_min,
disc_max,
out=disc_cost_real_np)
f = np.reshape(disc_cost_real_np[:, 0, 0], [Y_dense, X_dense])
helper.plot_ffs(X,
Y,
f,
save_dir=global_args.exp_dir +
'Visualization/discriminator_function/',
postfix='discriminator_function' + str(epoch))
else:
# helper.draw_bar_plot(convex_mask_np, y_min_max = [0,1], save_dir=global_args.exp_dir+'Visualization/convex_mask/', postfix='convex_mask'+str(epoch))
distributions.visualizeProductDistribution3(
sess,
curr_feed_dict,
batch,
inference_obs_dist,
transport_dist,
rec_dist,
generative_dict['obs_dist'],
save_dir=global_args.exp_dir +
'Visualization/Train/Random/',
postfix='train_' + str(epoch))
batch['observed']['data']['image'] = fixed_batch_data
curr_feed_dict = input_dict_func(batch, hyper_param)
distributions.visualizeProductDistribution3(
sess,
curr_feed_dict,
batch,
inference_obs_dist,
transport_dist,
rec_dist,
generative_dict['obs_dist'],
save_dir=global_args.exp_dir +
'Visualization/Train/Fixed/',
postfix='train_fixed_' + str(epoch))
checkpoint_path1 = global_args.exp_dir + 'checkpoint/'
checkpoint_path2 = global_args.exp_dir + 'checkpoint2/'
print('====> Saving checkpoint. Epoch: ', epoch)
start_tmp = time.time()
# helper.save_checkpoint(saver, sess, global_step, checkpoint_path1)
end_tmp = time.time()
print(
'Checkpoint path: ' + checkpoint_path1 + ' ====> It took: ',
end_tmp - start_tmp)
if epoch % 60 == 0:
print('====> Saving checkpoint backup. Epoch: ', epoch)
start_tmp = time.time()
# helper.save_checkpoint(saver, sess, global_step, checkpoint_path2)
end_tmp = time.time()
print(
'Checkpoint path: ' + checkpoint_path2 +
' ====> It took: ', end_tmp - start_tmp)
def train(epoch):
data_loader.train()
train_gen_loss_accum, train_dis_loss_accum, train_likelihood_accum, train_kl_accum, batch_size_accum = 0, 0, 0, 0, 0
start = time.time()
for batch_idx, curr_batch_size, batch in data_loader:
disc_train_step_np = sess.run([train_discriminator_step_tf],
feed_dict=input_dict_func(
batch, np.asarray([
0,
])))
if batch_idx % 5 != 0: continue
# gen_train_step_np, generator_cost_np, discriminator_cost_np, mean_geo_constraint_1_np, mean_geo_constraint_2_np, mean_geo_constraint_3_np = \
# sess.run([train_geo_generator_step_tf, train_outs_dict['geo_generator_cost'], train_outs_dict['discriminator_cost'],
# train_outs_dict['mean_geo_constraint_1'], train_outs_dict['mean_geo_constraint_2'], train_outs_dict['mean_geo_constraint_3']],
# feed_dict = input_dict_func(batch, np.asarray([0,])))
# gen_train_step_np, generator_cost_np, discriminator_cost_np, mean_geo_constraint_1_np, mean_geo_constraint_2_np, jacobian_alpha_constraint_np = \
# sess.run([train_geo_generator_step_tf, train_outs_dict['geo_generator_cost'], train_outs_dict['discriminator_cost'],
# train_outs_dict['mean_geo_constraint_1'], train_outs_dict['mean_geo_constraint_2'], train_outs_dict['jacobian_alpha_constraint']],
# feed_dict = input_dict_func(batch, np.asarray([0,])))
mean_geo_constraint_1_np, jacobian_alpha_constraint_np = 0, 0
# mean_geo_constraint_1_np = 0
gen_train_step_np, generator_cost_np, discriminator_cost_np, mean_geo_constraint_2_np = \
sess.run([train_geo_generator_step_tf, train_outs_dict['geo_generator_cost'], train_outs_dict['discriminator_cost'],
train_outs_dict['mean_geo_constraint_2']], #, train_outs_dict['jacobian_alpha_constraint']
feed_dict = input_dict_func(batch, np.asarray([0,])))
# gm_train_step_np = sess.run([train_generator_step_tf], feed_dict = input_dict_func(batch, np.asarray([0,])))
# max_discriminator_weight = sess.run(max_abs_discriminator_vars)
train_gen_loss_accum += curr_batch_size * generator_cost_np
train_dis_loss_accum += curr_batch_size * discriminator_cost_np
batch_size_accum += curr_batch_size
if batch_idx % global_args.log_interval == 0:
end = time.time()
# print('Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}\tTime: {:.3f}, geo_const_1 {:.6f}, geo_const_2 {:.6f}, geo_const_3 {:.6f}'.format(
# epoch, batch_idx * curr_batch_size, generator_cost_np, discriminator_cost_np, (end - start), mean_geo_constraint_1_np, mean_geo_constraint_2_np, mean_geo_constraint_3_np))
print(
'Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}\tTime: {:.3f}, geo_const_1 {:.6f}, geo_const_2 {:.6f}, alpha_const {:.6f}'
.format(epoch, batch_idx * curr_batch_size,
generator_cost_np, discriminator_cost_np,
(end - start), mean_geo_constraint_1_np,
mean_geo_constraint_2_np,
jacobian_alpha_constraint_np))
with open(global_args.exp_dir + "training_traces.txt",
"a") as text_file:
text_file.write(
str(generator_cost_np) + ', ' +
str(discriminator_cost_np) + '\n')
start = time.time()
# summary_str = sess.run(merged_summaries, feed_dict = input_dict_func(batch, np.asarray([0,])))
# summary_writer.add_summary(summary_str, (tf.train.global_step(sess, global_step)))
checkpoint_time = 1
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
checkpoint_time = 20
if epoch % checkpoint_time == 0:
print(
'====> Average Train: Epoch: {}\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}'
.format(epoch, train_gen_loss_accum / batch_size_accum,
train_dis_loss_accum / batch_size_accum))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
helper.visualize_datasets(sess,
input_dict_func(batch),
data_loader.dataset,
generative_dict['obs_sample_out'],
generative_dict['latent_sample_out'],
save_dir=global_args.exp_dir +
'Visualization/',
postfix=str(epoch))
helper.visualize_datasets2(
sess,
input_dict_func,
data_loader,
data_loader.dataset,
generative_dict['interpolated_sample'],
save_dir=global_args.exp_dir + 'Visualization/',
postfix=str(epoch))
helper.visualize_datasets3(
sess,
input_dict_func,
data_loader,
data_loader.dataset,
generative_dict['doubly_interpolated_sample'],
save_dir=global_args.exp_dir + 'Visualization/',
postfix=str(epoch))
helper.visualize_datasets4(
sess,
input_dict_func,
data_loader,
generative_dict['interpolated_sample_begin'],
generative_dict['interpolated_sample_end'],
generative_dict['interpolated_sample'],
save_dir=global_args.exp_dir + 'Visualization/',
postfix=str(epoch))
xmin, xmax, ymin, ymax, X_dense, Y_dense = -3.5, 3.5, -3.5, 3.5, 250, 250
xlist = np.linspace(xmin, xmax, X_dense)
ylist = np.linspace(ymin, ymax, Y_dense)
X, Y = np.meshgrid(xlist, ylist)
XY = np.concatenate(
[X.reshape(-1, 1), Y.reshape(-1, 1)], axis=1)
batch['observed']['data']['flat'] = np.concatenate([
XY[:int(XY.shape[0] / 2), np.newaxis, :], XY[:,
np.newaxis, :]
],
axis=0)
# batch['observed']['data']['flat'] = XY[:, np.newaxis, :]
disc_cost_real_np = sess.run(train_outs_dict['critic_real'],
feed_dict=input_dict_func(
batch, np.asarray([
0,
])))
f = np.reshape(disc_cost_real_np[:, 0, 0], [Y_dense, X_dense])
helper.plot_ffs(X,
Y,
f,
save_dir=global_args.exp_dir +
'Visualization/discriminator_function/',
postfix='discriminator_function' + str(epoch))
else:
# distributions.visualizeProductDistribution(sess, input_dict_func(batch), batch, model.obs_dist, generative_dict['obs_dist'],
# save_dir=global_args.exp_dir+'Visualization/Train/', postfix='train_'+str(epoch))
helper.visualizeTransitions(sess,
input_dict_func(batch),
generative_dict,
save_dir=global_args.exp_dir +
'Visualization/Train/',
postfix='train_' + str(epoch))
checkpoint_path1 = global_args.exp_dir + 'checkpoint/'
checkpoint_path2 = global_args.exp_dir + 'checkpoint2/'
print('====> Saving checkpoint. Epoch: ', epoch)
start_tmp = time.time()
helper.save_checkpoint(saver, sess, global_step, checkpoint_path1)
end_tmp = time.time()
print(
'Checkpoint path: ' + checkpoint_path1 + ' ====> It took: ',
end_tmp - start_tmp)
if epoch % 60 == 0:
print('====> Saving checkpoint backup. Epoch: ', epoch)
start_tmp = time.time()
helper.save_checkpoint(saver, sess, global_step,
checkpoint_path2)
end_tmp = time.time()
print(
'Checkpoint path: ' + checkpoint_path2 +
' ====> It took: ', end_tmp - start_tmp)
def train(epoch):
global k_t_np, average_real, average_gen, average_set, constant
data_loader.train()
train_gen_loss_accum, train_dis_loss_accum, train_likelihood_accum, train_kl_accum, batch_size_accum = 0, 0, 0, 0, 0
start = time.time()
for batch_idx, curr_batch_size, batch in data_loader:
# # # Single loop
# disc_train_step, gen_train_step, batch_gen_loss, batch_dis_loss =\
# sess.run([train_discriminator_step_tf, train_generator_step_tf, *train_out_list[:2]], feed_dict = input_dict_func(batch))
# disc_train_step = sess.run(train_discriminator_step_tf, feed_dict = input_dict_func(batch))
# gen_train_step, batch_gen_loss, batch_dis_loss = sess.run([train_generator_step_tf, *train_out_list[:2]], feed_dict = input_dict_func(batch))
disc_train_step, train_dis_gen_mean_np, train_dis_real_mean_np = \
sess.run([train_discriminator_step_tf, train_dis_gen_mean, train_dis_real_mean], feed_dict = {additional_inputs_tf: np.asarray([k_t_np, average_real, average_gen, constant]), **input_dict_func(batch)})
# inverter_train_step = sess.run(train_inverter_step_tf, feed_dict = {additional_inputs_tf: np.asarray([k_t_np, average_real, average_gen, constant]), **input_dict_func(batch)})
if not average_set:
average_real = train_dis_real_mean_np
average_gen = train_dis_real_mean_np
constant = 1 if average_real < average_gen else 0
average_set = True
gen_train_step, batch_gen_loss, batch_dis_loss, train_dis_gen_mean_np, train_dis_real_mean_np, z_cost_list_np, disc_cost_list_np, rate_similarity_gen_np = \
sess.run([train_generator_step_tf, *train_out_list[:2], train_dis_gen_mean, train_dis_real_mean, z_cost_list, disc_cost_list, rate_similarity_gen],
feed_dict = {additional_inputs_tf: np.asarray([k_t_np, average_real, average_gen, constant]), **input_dict_func(batch)})
average_real = (1 - average_lr) * average_real + average_lr * (
train_dis_real_mean_np)
average_gen = (1 - average_lr) * average_gen + average_lr * (
train_dis_gen_mean_np)
constant = 1 if (average_real - average_gen) < 0 else 0
# k_t_np = k_t_np+lambd*(gamma*average_real-average_gen)
k_t_np = k_t_np + lambd * (gamma * train_dis_real_mean_np -
train_dis_gen_mean_np)
k_t_np = max(min(1., k_t_np), 0.)
max_discriminator_weight = sess.run(max_abs_discriminator_vars)
train_gen_loss_accum += curr_batch_size * batch_gen_loss
train_dis_loss_accum += curr_batch_size * batch_dis_loss
batch_size_accum += curr_batch_size
if batch_idx % global_args.log_interval == 0:
end = time.time()
print(
'Train: Epoch {} [{:7d} ()]\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}\tTime: {:.3f}, Max disc weight {:.6f}'
.format(epoch, batch_idx * curr_batch_size, batch_gen_loss,
batch_dis_loss, (end - start),
max_discriminator_weight))
with open(global_args.exp_dir + "training_traces.txt",
"a") as text_file:
text_file.write(
str(batch_gen_loss) + ', ' + str(batch_dis_loss) +
'\n')
start = time.time()
# summary_str = sess.run(merged_summaries, feed_dict=input_dict_func(batch))
summary_str = sess.run(
merged_summaries,
feed_dict={
additional_inputs_tf:
np.asarray([k_t_np, average_real, average_gen, constant]),
**input_dict_func(batch)
})
summary_writer.add_summary(summary_str,
(tf.train.global_step(sess, global_step)))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
checkpoint_time = 20
else:
checkpoint_time = 1
if epoch % checkpoint_time == 0:
print(
'====> Average Train: Epoch: {}\tGenerator Cost: {:.6f}\tDiscriminator Cost: {:.6f}'
.format(epoch, train_gen_loss_accum / batch_size_accum,
train_dis_loss_accum / batch_size_accum))
helper.draw_bar_plot(rate_similarity_gen_np[:, 0, 0],
y_min_max=[0, 1],
save_dir=global_args.exp_dir +
'Visualization/inversion_weight/',
postfix='inversion_weight' + str(epoch))
helper.draw_bar_plot(
z_cost_list_np[:, 0, 0],
thres=[np.mean(z_cost_list_np),
np.max(z_cost_list_np)],
save_dir=global_args.exp_dir + 'Visualization/inversion_cost/',
postfix='inversion_cost' + str(epoch))
helper.draw_bar_plot(disc_cost_list_np[:, 0, 0],
thres=[average_real, average_gen],
save_dir=global_args.exp_dir +
'Visualization/disc_cost/',
postfix='disc_cost' + str(epoch))
if data_loader.__module__ == 'datasetLoaders.RandomManifoldDataLoader' or data_loader.__module__ == 'datasetLoaders.ToyDataLoader':
helper.visualize_datasets(sess,
input_dict_func(batch),
data_loader.dataset,
obs_sample_out_tf,
latent_sample_out_tf,
save_dir=global_args.exp_dir +
'Visualization/',
postfix=str(epoch))
xmin, xmax = -3.5, 3.5
ymin, ymax = -3.5, 3.5
X_dense = 250
Y_dense = 250
xlist = np.linspace(xmin, xmax, X_dense)
ylist = np.linspace(ymin, ymax, Y_dense)
X, Y = np.meshgrid(xlist, ylist)
XY = np.concatenate(
[X.reshape(-1, 1), Y.reshape(-1, 1)], axis=1)
batch['observed']['data']['flat'] = XY[:, np.newaxis, :]
disc_cost_from_input_np = sess.run(disc_cost_from_input,
feed_dict={
additional_inputs_tf:
np.asarray([
k_t_np,
average_real,
average_gen,
constant
]),
**input_dict_func(batch)
})
disc_cost_from_input_np = disc_cost_from_input_np[:, 0, 0]
f = np.reshape(disc_cost_from_input_np, [Y_dense, X_dense])
helper.plot_ffs(X,
Y,
f,
save_dir=global_args.exp_dir +
'Visualization/discriminator_function/',
postfix='discriminator_function' + str(epoch))
else:
distributions.visualizeProductDistribution(
sess,
input_dict_func(batch),
batch,
obs_dist,
sample_obs_dist,
save_dir=global_args.exp_dir + 'Visualization/Train/',
postfix='train_' + str(epoch))
checkpoint_path1 = global_args.exp_dir + 'checkpoint/'
checkpoint_path2 = global_args.exp_dir + 'checkpoint2/'
print('====> Saving checkpoint. Epoch: ', epoch)
start_tmp = time.time()
# helper.save_checkpoint(saver, sess, global_step, checkpoint_path1)
end_tmp = time.time()
print(
'Checkpoint path: ' + checkpoint_path1 + ' ====> It took: ',
end_tmp - start_tmp)