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Commit e69e6bba authored by Guillaume HEUSCH's avatar Guillaume HEUSCH
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[script] fixed the default value for the conditional dim, print out the total number of IDs

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...@@ -14,7 +14,7 @@ Options: ...@@ -14,7 +14,7 @@ Options:
-V, --version Show version. -V, --version Show version.
-l, --latent-dim=<int> the dimension of the encoded ID [default: 320] -l, --latent-dim=<int> the dimension of the encoded ID [default: 320]
-n, --noise-dim=<int> the dimension of the noise [default: 100] -n, --noise-dim=<int> the dimension of the noise [default: 100]
-c, --conditional-dim=<int> the dimension of the noise [default: 100] -c, --conditional-dim=<int> the dimension of the conditioning variable [default: 13]
-b, --batch-size=<int> The size of your mini-batch [default: 64] -b, --batch-size=<int> The size of your mini-batch [default: 64]
-e, --epochs=<int> The number of training epochs [default: 100] -e, --epochs=<int> The number of training epochs [default: 100]
-s, --sample=<int> Save generated images at every 'sample' batch iteration [default: 100000000000] -s, --sample=<int> Save generated images at every 'sample' batch iteration [default: 100000000000]
...@@ -124,13 +124,15 @@ def main(user_input=None): ...@@ -124,13 +124,15 @@ def main(user_input=None):
]) ])
) )
dataloader = torch.utils.data.DataLoader(face_dataset, batch_size=batch_size, shuffle=True) dataloader = torch.utils.data.DataLoader(face_dataset, batch_size=batch_size, shuffle=True)
logger.info("There are {} training images".format(len(face_dataset))) logger.info("There are {} training images".format(len(face_dataset)))
# get the number of ids
number_of_ids = numpy.max(face_dataset.id_labels) + 1
logger.info("There are {} different identities".format(number_of_ids))
# get the image size # get the image size
image_size = face_dataset[0]['image'].numpy().shape image_size = face_dataset[0]['image'].numpy().shape
# get the number of ids
number_of_ids = numpy.max(face_dataset.id_labels)
# =============== # ===============
# === NETWORK === # === NETWORK ===
......
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