本文整理匯總了Python中config.grid方法的典型用法代碼示例。如果您正苦於以下問題：Python config.grid方法的具體用法？Python config.grid怎麽用？Python config.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config的用法示例。
在下文中一共展示了config.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: setup_snapshot_image_grid
# 需要導入模塊: import config [as 別名]
# 或者: from config import grid [as 別名]
def setup_snapshot_image_grid(G, training_set,
size = '1080p', # '1080p' = to be viewed on 1080p display, '4k' = to be viewed on 4k display.
layout = 'random'): # 'random' = grid contents are selected randomly, 'row_per_class' = each row corresponds to one class label.
# Select size.
gw = 1; gh = 1
if size == '1080p':
gw = np.clip(1920 // G.output_shape[3], 3, 32)
gh = np.clip(1080 // G.output_shape[2], 2, 32)
if size == '4k':
gw = np.clip(3840 // G.output_shape[3], 7, 32)
gh = np.clip(2160 // G.output_shape[2], 4, 32)
# Fill in reals and labels.
reals = np.zeros([gw * gh] + training_set.shape, dtype=training_set.dtype)
labels = np.zeros([gw * gh, training_set.label_size], dtype=training_set.label_dtype)
masks = np.zeros([gw * gh] + [1, training_set.shape[-1], training_set.shape[-1]], dtype=training_set.dtype)
for idx in range(gw * gh):
x = idx % gw; y = idx // gw
while True:
real, label, mask = training_set.get_minibatch_np(1)
if layout == 'row_per_class' and training_set.label_size > 0:
if label[0, y % training_set.label_size] == 0.0:
continue
reals[idx] = real[0]
labels[idx] = label[0]
masks[idx] = mask[0]
break
# Generate latents.
latents = misc.random_latents(gw * gh, G)
return (gw, gh), reals, labels, latents, masks
#----------------------------------------------------------------------------
# Just-in-time processing of training images before feeding them to the networks. 示例2: setup_snapshot_image_grid
# 需要導入模塊: import config [as 別名]
# 或者: from config import grid [as 別名]
def setup_snapshot_image_grid(G, training_set,
size = '1080p', # '1080p' = to be viewed on 1080p display, '4k' = to be viewed on 4k display.
layout = 'random'): # 'random' = grid contents are selected randomly, 'row_per_class' = each row corresponds to one class label.
# Select size.
gw = 1; gh = 1
if size == '1080p':
gw = np.clip(1920 // G.output_shape[3], 3, 32)
gh = np.clip(1080 // G.output_shape[2], 2, 32)
if size == '4k':
gw = np.clip(3840 // G.output_shape[3], 7, 32)
gh = np.clip(2160 // G.output_shape[2], 4, 32)
# Fill in reals and labels.
reals = np.zeros([gw * gh] + training_set.shape, dtype=training_set.dtype)
labels = np.zeros([gw * gh, training_set.label_size], dtype=training_set.label_dtype)
for idx in range(gw * gh):
x = idx % gw; y = idx // gw
while True:
real, label = training_set.get_minibatch_np(1)
if layout == 'row_per_class' and training_set.label_size > 0:
if label[0, y % training_set.label_size] == 0.0:
continue
reals[idx] = real[0]
labels[idx] = label[0]
break
# Generate latents.
latents = misc.random_latents(gw * gh, G)
return (gw, gh), reals, labels, latents
#----------------------------------------------------------------------------
# Just-in-time processing of training images before feeding them to the networks.