如果我有两个 3-D 张量img和gen。如何将 的img2D 子集分配给 的 2D 子集gen？以下方法不起作用，因为 tensorflow 不允许直接分配张量。
img
gen
img[96:160 , 144:240 , :] = gen[96:160 , 144:240 , :]
编辑：
这是周围的代码。所以我使用自定义 keras 层。此层必须接收输入图像img和生成的图像。它必须用x替换部分，并且必须返回修改后的。img``x``img
x
img``x``img
def patcher(tensors): img = tensor[1] gen = tensor[0] #This is where the slicing must happen img[96:160 , 144:240 , :] = gen[96:160 , 144:240 , :] return [img] img = Input( .. ) x = Conv( .. )(img) out = Lambda(patcher,lambda a : [a[1]] )([x , img]) model = Model(img, out)
使用当前 API，您必须找出构建所需张量的最佳方法。在这种情况下，假设img和gen具有相同的形状，您可以这样做：
import tensorflow as tf import numpy as np # Input img = tf.placeholder(tf.float32, [None, None, None]) gen = tf.placeholder(tf.float32, [None, None, None]) row_start = tf.placeholder(tf.int32, []) row_end = tf.placeholder(tf.int32, []) col_start = tf.placeholder(tf.int32, []) col_end = tf.placeholder(tf.int32, []) # Masks rows and columns to be replaced shape = tf.shape(img) rows = shape[0] cols = shape[1] channels = shape[2] i = tf.range(rows) row_mask = (row_start <= i) & (i < row_end) j = tf.range(cols) col_mask = (col_start <= j) & (j < col_end) # Full mask of replaced elements mask = row_mask[:, tf.newaxis] & col_mask # Select elements from flattened arrays img_flat = tf.reshape(img, [-1, channels]) gen_flat = tf.reshape(gen, [-1, channels]) mask_flat = tf.reshape(mask, [-1]) result_flat = tf.where(mask_flat, gen_flat, img_flat) # Reshape back result = tf.reshape(result_flat, shape)
以下是一个小测试：
with tf.Session() as sess: # img is positive and gen is negative img_val = np.arange(60).reshape((4, 5, 3)) gen_val = -img_val # Do img[2:4, 0:3, :] = gen[2:4, 0:3, :] result_val = sess.run(result, feed_dict={ img: img_val, gen: gen_val, row_start: 2, row_end: 4, col_start: 0, col_end: 3, }) # Print one channel only for clarity print(result_val[:, :, 0])
输出：
[[ 0. 3. 6. 9. 12.] [ 15. 18. 21. 24. 27.] [-30. -33. -36. 39. 42.] [-45. -48. -51. 54. 57.]]
以下是您发布的代码的可能实现。我在这里使用了一种基于乘法的略有不同的方法，我认为当您有许多图像时这种方法会更好。
import tensorflow as tf def replace_slices(img, gen, row_start, row_end, col_start, col_end): # Masks rows and columns to be replaced shape = tf.shape(img) rows = shape[1] cols = shape[2] i = tf.range(rows) row_mask = (row_start <= i) & (i < row_end) j = tf.range(cols) col_mask = (col_start <= j) & (j < col_end) # Full mask of replaced elements mask = row_mask[:, tf.newaxis] & col_mask # Add channel dimension to mask and cast mask = tf.cast(mask[:, :, tf.newaxis], img.dtype) # Compute result result = img * (1 - mask) + gen * mask return result def patcher(tensors): img = tensor[1] gen = tensor[0] img = replace_slices(img, gen, 96, 160, 144, 240) return [img] img = Input( .. ) x = Conv( .. )(img) out = Lambda(patcher, ambda a: [a[1]])([x , img]) model = Model(img, out)
