def _save_image(train_data, feature, gene_output, batch, suffix, max_samples=None):
"""Saves a picture showing the current progress of the model"""
if max_samples is None:
max_samples = int(feature.shape[0])
td = train_data
clipped = np.clip(gene_output, 0, 1)
image = np.concatenate([feature, clipped], 2)
image = image[:max_samples,:,:,:]
cols = []
num_cols = 4
samples_per_col = max_samples//num_cols
for c in range(num_cols):
col = np.concatenate([image[samples_per_col*c + i,:,:,:] for i in range(samples_per_col)], 0)
cols.append(col)
image = np.concatenate(cols, 1)
filename = 'batch%06d_%s.png' % (batch, suffix)
filename = os.path.join(FLAGS.train_dir, filename)
dm_utils.save_image(image, filename)
def inference(infer_data):
sess = infer_data.sess
idm = infer_data.infer_model
image = sess.run(idm.gene_out)
image = np.squeeze(image, axis=0)
dm_utils.save_image(image, FLAGS.outfile)