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Added image summaries

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Signed-off-by: Jim Martens <github@2martens.de>
This commit is contained in:
Jim Martens
2019-02-08 12:24:07 +01:00
parent 7adb703292
commit 01db450032
1 changed files with 16 additions and 5 deletions
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src/twomartens/masterthesis/aae/train.py
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@ -26,7 +26,7 @@ import tensorflow as tf
from tensorflow.python.ops import summary_ops_v2 from tensorflow.python.ops import summary_ops_v2
from .model import Decoder, Encoder, XDiscriminator, ZDiscriminator from .model import Decoder, Encoder, XDiscriminator, ZDiscriminator
from .util import save_image from .util import prepare_image
# shortcuts for tensorflow sub packages and classes # shortcuts for tensorflow sub packages and classes
k = tf.keras.backend k = tf.keras.backend
@ -191,8 +191,13 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
if not os.path.exists(directory): if not os.path.exists(directory):
os.makedirs(directory) os.makedirs(directory)
comparison = k.concatenate([x[:64], x_decoded[:64]], axis=0) comparison = k.concatenate([x[:64], x_decoded[:64]], axis=0)
save_image(comparison.cpu(), grid = prepare_image(comparison.cpu(), nrow=64)
'results' + str(inlier_classes[0]) + '/reconstruction_' + str(epoch) + '.png', nrow=64) summary_ops_v2.image(name='reconstruction_' + str(epoch), tensor=grid, max_images=1)
from PIL import Image
filename = 'results' + str(inlier_classes[0]) + '/reconstruction_' + str(epoch) + '.png'
ndarr = grid.cpu().numpy()
im = Image.fromarray(ndarr)
im.save(filename)
batch_iteration.assign_add(1) batch_iteration.assign_add(1)
@ -214,8 +219,14 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
resultsample = decoder(sample).cpu() resultsample = decoder(sample).cpu()
directory = 'results' + str(inlier_classes[0]) directory = 'results' + str(inlier_classes[0])
os.makedirs(directory, exist_ok=True) os.makedirs(directory, exist_ok=True)
save_image(resultsample, grid = prepare_image(resultsample)
'results' + str(inlier_classes[0]) + '/sample_' + str(epoch) + '.png') summary_ops_v2.image(name='sample_' + str(epoch), tensor=grid, max_images=1)
from PIL import Image
filename = 'results' + str(inlier_classes[0]) + '/sample_' + str(epoch) + '.png'
ndarr = grid.cpu().numpy()
im = Image.fromarray(ndarr)
im.save(filename)
if verbose: if verbose:
print("Training finish!... save training results") print("Training finish!... save training results")