本文整理匯總了Python中misc.save_image_grid方法的典型用法代碼示例。如果您正苦於以下問題：Python misc.save_image_grid方法的具體用法？Python misc.save_image_grid怎麽用？Python misc.save_image_grid使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類misc的用法示例。
在下文中一共展示了misc.save_image_grid方法的2個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: generate_fake_images
# 需要導入模塊: import misc [as 別名]
# 或者: from misc import save_image_grid [as 別名]
def generate_fake_images(run_id, snapshot=None, grid_size=[1,1], num_pngs=1, image_shrink=1, png_prefix=None, random_seed=1000, minibatch_size=8):
network_pkl = misc.locate_network_pkl(run_id, snapshot)
if png_prefix is None:
png_prefix = misc.get_id_string_for_network_pkl(network_pkl) + '-'
random_state = np.random.RandomState(random_seed)
print('Loading network from "%s"...' % network_pkl)
G, D, Gs = misc.load_network_pkl(run_id, snapshot)
result_subdir = misc.create_result_subdir(config.result_dir, config.desc)
for png_idx in range(num_pngs):
print('Generating png %d / %d...' % (png_idx, num_pngs))
latents = misc.random_latents(np.prod(grid_size), Gs, random_state=random_state)
labels = np.zeros([latents.shape[0], 0], np.float32)
images = Gs.run(latents, labels, minibatch_size=minibatch_size, num_gpus=config.num_gpus, out_mul=127.5, out_add=127.5, out_shrink=image_shrink, out_dtype=np.uint8)
misc.save_image_grid(images, os.path.join(result_subdir, '%s%06d.png' % (png_prefix, png_idx)), [0,255], grid_size)
open(os.path.join(result_subdir, '_done.txt'), 'wt').close()
#----------------------------------------------------------------------------
# Generate MP4 video of random interpolations using a previously trained network.
# To run, uncomment the appropriate line in config.py and launch train.py. 示例2: predict_gan
# 需要導入模塊: import misc [as 別名]
# 或者: from misc import save_image_grid [as 別名]
def predict_gan():
separate_funcs = False
drange_net = [-1,1]
drange_viz = [-1,1]
image_grid_size = None
image_grid_type = 'default'
resume_network = 'pre-trained_weight'
np.random.seed(config.random_seed)
if resume_network:
print("Resuming weight from:"+resume_network)
G = Generator(num_channels=3, resolution=128, label_size=0, **config.G)
G = load_G_weights(G,resume_network,True)
print(G.summary())
# Misc init.
if image_grid_type == 'default':
if image_grid_size is None:
w, h = G.output_shape[1], G.output_shape[2]
print("w:%d,h:%d"%(w,h))
image_grid_size = np.clip(int(1920 // w), 3, 16).astype('int'), np.clip(1080 / h, 2, 16).astype('int')
print("image_grid_size:",image_grid_size)
else:
raise ValueError('Invalid image_grid_type', image_grid_type)
result_subdir = misc.create_result_subdir('pre-trained_result', config.run_desc)
for i in range(1,6):
snapshot_fake_latents = random_latents(np.prod(image_grid_size), G.input_shape)
snapshot_fake_images = G.predict_on_batch(snapshot_fake_latents)
misc.save_image_grid(snapshot_fake_images, os.path.join(result_subdir, 'pre-trained_%03d.png'%i), drange=drange_viz, grid_size=image_grid_size)