本文整理汇总了Python中tensorflow.ifft2d方法的典型用法代码示例。如果您正苦于以下问题：Python tensorflow.ifft2d方法的具体用法？Python tensorflow.ifft2d怎么用？Python tensorflow.ifft2d使用的例子？那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类tensorflow的用法示例。
在下文中一共展示了tensorflow.ifft2d方法的7个代码示例，这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞，您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: call
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def call(self, inputx):
if not inputx.dtype in [tf.complex64, tf.complex128]:
print('Warning: inputx is not complex. Converting.', file=sys.stderr)
# if inputx is float, this will assume 0 imag channel
inputx = tf.cast(inputx, tf.complex64)
# get the right fft
if self.ndims == 1:
ifft = tf.ifft
elif self.ndims == 2:
ifft = tf.ifft2d
else:
ifft = tf.ifft3d
perm_dims = [0, self.ndims + 1] + list(range(1, self.ndims + 1))
invert_perm_ndims = [0] + list(range(2, self.ndims + 2)) + [1]
perm_inputx = K.permute_dimensions(inputx, perm_dims) # [batch_size, nb_features, *vol_size]
ifft_inputx = ifft(perm_inputx)
return K.permute_dimensions(ifft_inputx, invert_perm_ndims) 示例2: compute_fft
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def compute_fft(x, direction="C2C", inverse=False):
if direction == 'C2R':
inverse = True
x_shape = x.get_shape().as_list()
h, w = x_shape[-2], x_shape[-3]
x_complex = tf.complex(x[..., 0], x[..., 1])
if direction == 'C2R':
out = tf.real(tf.ifft2d(x_complex)) * h * w
return out
else:
if inverse:
out = stack_real_imag(tf.ifft2d(x_complex)) * h * w
else:
out = stack_real_imag(tf.fft2d(x_complex))
return out 示例3: test_IFFT2D
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def test_IFFT2D(self):
# only defined for gpu
if DEVICE == GPU:
t = tf.ifft2d(self.random(3, 4, complex=True))
self.check(t) 示例4: _tfIFFTForRank
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def _tfIFFTForRank(self, rank):
if rank == 1:
return tf.ifft
elif rank == 2:
return tf.ifft2d
elif rank == 3:
return tf.ifft3d
else:
raise ValueError("invalid rank") 示例5: fft2c
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def fft2c(im,
data_format='channels_last',
orthonorm=True,
transpose=False,
name='fft2c'):
"""Centered FFT2 on last two non-channel dimensions."""
with tf.name_scope(name):
im_out = im
if data_format == 'channels_last':
permute_orig = np.arange(len(im.shape))
permute = permute_orig.copy()
permute[-3] = permute_orig[-1]
permute[-2:] = permute_orig[-3:-1]
im_out = tf.transpose(im_out, permute)
if orthonorm:
fftscale = tf.sqrt(
tf.cast(im_out.shape[-1], tf.float32) * tf.cast(
im_out.shape[-2], tf.float32))
else:
fftscale = 1.0
fftscale = tf.cast(fftscale, dtype=tf.complex64)
im_out = fftshift(im_out, axis=(-2, -1))
if transpose:
im_out = tf.ifft2d(im_out) * fftscale
else:
im_out = tf.fft2d(im_out) / fftscale
im_out = fftshift(im_out, axis=(-2, -1))
if data_format == 'channels_last':
permute[-3:-1] = permute_orig[-2:]
permute[-1] = permute_orig[-3]
im_out = tf.transpose(im_out, permute)
return im_out 示例6: dc
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def dc(generated, X_k, mask):
gene_complex = real2complex(generated)
gene_complex = tf.transpose(gene_complex,[0, 3, 1, 2])
mask = tf.transpose(mask,[0, 3, 1, 2])
X_k = tf.transpose(X_k,[0, 3, 1, 2])
gene_fft = tf.fft2d(gene_complex)
out_fft = X_k + gene_fft * (1.0 - mask)
output_complex = tf.ifft2d(out_fft)
output_complex = tf.transpose(output_complex, [0, 2, 3, 1])
output_real = tf.cast(tf.real(output_complex), dtype=tf.float32)
output_imag = tf.cast(tf.imag(output_complex), dtype=tf.float32)
output = tf.concat([output_real,output_imag], axis=-1)
return output 示例7: setup_inputs
# 需要导入模块: import tensorflow [as 别名]
# 或者: from tensorflow import ifft2d [as 别名]
def setup_inputs(x, mask, batch_size):
channel = x.shape[-1].value // 2
mask = np.tile(mask, (channel, 1, 1))
mask_tf = tf.cast(tf.constant(mask), tf.float32)
mask_tf_c = tf.cast(mask_tf, tf.complex64)
x_complex = real2complex(x)
x_complex = tf.cast(x_complex, tf.complex64)
x_complex = tf.transpose(x_complex, [2, 0, 1])
kx = tf.fft2d(x_complex)
kx_mask = kx * mask_tf_c
x_u = tf.ifft2d(kx_mask)
x_u = tf.transpose(x_u, [1, 2, 0])
kx_mask = tf.transpose(kx_mask, [1, 2, 0])
x_u_cat = complex2real(x_u)
x_cat = tf.cast(x, tf.float32)
mask_tf_c = tf.transpose(mask_tf_c, [1, 2, 0])
features, labels, kx_mask, masks = tf.train.shuffle_batch([x_u_cat,x_cat, kx_mask, mask_tf_c],
batch_size=batch_size,
num_threads=64,
capacity=50,
min_after_dequeue=10)
return features, labels, kx_mask, masks