@ -26,7 +26,7 @@ import tensorflow as tf
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from tensorflow.python.ops import summary_ops_v2
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from .model import Decoder, Encoder, XDiscriminator, ZDiscriminator
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from .util import save_image
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from .util import prepare_image
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# shortcuts for tensorflow sub packages and classes
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k = tf.keras.backend
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@ -191,8 +191,13 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
|
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if not os.path.exists(directory):
|
||||
os.makedirs(directory)
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||||
comparison = k.concatenate([x[:64], x_decoded[:64]], axis=0)
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save_image(comparison.cpu(),
|
||||
'results' + str(inlier_classes[0]) + '/reconstruction_' + str(epoch) + '.png', nrow=64)
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||||
grid = prepare_image(comparison.cpu(), 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)
|
||||
|
||||
@ -214,8 +219,14 @@ def train_mnist(folding_id: int, inlier_classes: Sequence[int], total_classes: i
|
||||
resultsample = decoder(sample).cpu()
|
||||
directory = 'results' + str(inlier_classes[0])
|
||||
os.makedirs(directory, exist_ok=True)
|
||||
save_image(resultsample,
|
||||
'results' + str(inlier_classes[0]) + '/sample_' + str(epoch) + '.png')
|
||||
grid = prepare_image(resultsample)
|
||||
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:
|
||||
print("Training finish!... save training results")
|
||||
|
||||
|
||||
Reference in New Issue
Block a user