def plot_data(self, step, handle=None):
if handle is None:
handle = self.validation_handle
plot_subdir = self.plot_dir + str(step) + "/"
checkFolders([self.plot_dir, plot_subdir])
fd = {
self.dataset_handle: handle,
self.z: np.zeros([self.batch_size, self.z_dim]),
self.training: False,
self.kl_wt: self.kl_weight
}
in_data, msoln, pred = self.sess.run(
[self.data, self.masked_soln, self.masked_pred], feed_dict=fd)
data, mesh, soln = in_data
for n in range(0, self.batch_size):
soln_name = 'soln_' + str(n) + '.npy'
data_name = 'data_' + str(n) + '.npy'
mesh_name = 'mesh_' + str(n) + '.npy'
msoln_name = 'msoln_' + str(n) + '.npy'
pred_name = 'pred_' + str(n) + '.npy'
np.save(os.path.join(plot_subdir, soln_name), soln[n, :, :, 0])
np.save(os.path.join(plot_subdir, data_name), data[n, :, :, 0])
np.save(os.path.join(plot_subdir, mesh_name), mesh[n, :, :, 0])
np.save(os.path.join(plot_subdir, msoln_name), msoln[n, :, :, 0])
np.save(os.path.join(plot_subdir, pred_name), pred[n, :, :, 0])
def plot_predictions(self, step):
plot_subdir = self.plot_dir + str(step) + "/"
checkFolders([self.plot_dir, plot_subdir])
resized_imgs = self.predict(random_samples=True)
for n in range(0, self.batch_size):
plot_name = 'plot_' + str(n) + '.png'
plt.imsave(os.path.join(plot_subdir, plot_name), resized_imgs[n,:,:,0], cmap='gray')
def plot_comparisons(self, step):
plot_subdir = self.plot_dir + str(step) + "/"
checkFolders([self.plot_dir, plot_subdir])
soln, pred = self.predict()
for n in range(0, self.batch_size):
soln_name = 'soln_' + str(n) + '.npy'; pred_name = 'pred_' + str(n) + '.npy'
np.save(os.path.join(plot_subdir, soln_name), soln[n,:,:,0])
np.save(os.path.join(plot_subdir, pred_name), pred[n,:,:,0])
def plot_comparisons(self, step):
plot_subdir = self.plot_dir + str(step) + "/"
checkFolders([self.plot_dir, plot_subdir])
resized_data, resized_pred = self.predict()
for n in range(0, self.batch_size):
data_name = 'data_' + str(n) + '.png'; pred_name = 'pred_' + str(n) + '.png'
plt.imsave(os.path.join(plot_subdir, data_name), resized_data[n,:,:,0], cmap='gray')
plt.imsave(os.path.join(plot_subdir, pred_name), resized_pred[n,:,:,0], cmap='gray')