'latent':
128,
'encode': [
(32, 3, (2, 2)), # out: 64, 64 32
(64, 3, (2, 2)), # out: 32, 32, 64
(128, 3, (2, 2)), # out: 16, 16, 128
(256, 3, (2, 2)), # out: 8, 8, 256
],
'decode':
None, # Mirror enconding for reconstruction
'name':
'face_cvae_3'
}
model = CVAE(arch_def)
model.load_weights('models/face_cvae_3/face_cvae_3.weights')
test_root = '/home/martin/dataset/face_test'
test_imgs = [str(p) for p in Path(test_root).glob('*.jpg')]
batch_size = 8
epochs = 16
loss = []
tb = CVAEToolBox(model)
for i in range(len(test_imgs[:5])):
inp, outp = tb.load_and_reconstruct_image(test_imgs[i])
plt.subplot(2, 5, 1 + i)
plt.imshow(inp)
plt.title('Input')
plt.axis('off')
def load_model_weights(latent_dim, dimension, filename):
model = CVAE(latent_dim, dimension)
model.load_weights(filename)
return model
'encode': [
(32, 3, (2, 2)), # out: 256, 256 32
(64, 3, (2, 2)), # out: 128, 128, 64
(128, 3, (2, 2)), # out: 64, 64, 128
(256, 3, (2, 2)), # out: 32, 32, 256
(512, 3, (2, 2)), # out: 16, 3162, 512
(1024, 3, (2, 2)), # out: 8, 8, 1024
(1024, 3, (2, 2)), # out: 4, 4, 1024
],
'decode':
None, # Mirror enconding for reconstruction
'name':
'face_cvae_1'
}
model = CVAE(arch_def)
model.load_weights(weights_path)
else:
model = CVAE.from_file(arch_def_path, weights_path)
load_and_preprocess = get_load_and_preprocess_func(model.arch_def['input'])
image_paths = [
'/home/martin/Desktop/data/validation_set/Bakeriet_4-Action_sequence_00336.jpg',
'/home/martin/Desktop/data/validation_set/Stand_up_small_group_2.jpg'
]
toolbox = CVAEToolBox(model)
a = toolbox.to_tensor(image_paths[0], with_batch_dim=True, preprocess=True)
b = toolbox.to_tensor(image_paths[1], with_batch_dim=True, preprocess=True)
