def _col_kernel(upsampled_region_size, upsample_factor, axis_offsets,
data_shape):
data_shape_float = tf.cast(data_shape, tf.float32)
col_constant = tf.cast(data_shape_float[2] * upsample_factor, tf.complex64)
col_constant = (-1j * 2 * np.pi / col_constant)
col_kernel_a = tf.range(0, data_shape_float[2], dtype=tf.float32)
col_kernel_a = fftshift1d(col_kernel_a)
col_kernel_a = tf.reshape(col_kernel_a, (-1, 1))
col_kernel_a -= tf.floor(data_shape_float[2] / 2.)
col_kernel_a = tf.reshape(col_kernel_a, (1, -1))
col_kernel_a = tf.tile(col_kernel_a, (data_shape[0], 1))
col_kernel_b = tf.range(0, upsampled_region_size, dtype=tf.float32)
col_kernel_b = tf.reshape(col_kernel_b, (1, -1))
col_kernel_b = tf.tile(col_kernel_b, (data_shape[0], 1))
col_kernel_b = tf.transpose(col_kernel_b)
col_kernel_b -= tf.transpose(axis_offsets[:, 1])
col_kernel_b = tf.transpose(col_kernel_b)
col_kernel_a = tf.expand_dims(col_kernel_a, 1)
col_kernel_b = tf.expand_dims(col_kernel_b, -1)
col_kernel = col_kernel_a * col_kernel_b
col_kernel = tf.transpose(col_kernel, perm=(0, 2, 1))
col_kernel = col_constant * tf.cast(col_kernel, tf.complex64)
col_kernel = tf.exp(col_kernel)
return col_kernel
def sparse_bool_mask(x, mask, axis=0):
# Only necessary if indices may have non-unique elements
indices = tf.boolean_mask(tf.range(tf.shape(x)[axis]), mask)
n_indices = tf.size(indices)
# Get indices for the axis
idx = x.indices[:, axis]
# Find where indices match the selection
eq = tf.equal(tf.expand_dims(idx, 1),
tf.cast(indices, tf.int64)) # TODO this has quadratic cost
# Mask for selected values
sel = tf.reduce_any(eq, axis=1)
# Selected values
values_new = tf.boolean_mask(x.values, sel, axis=0)
# New index value for selected elements
n_indices = tf.cast(n_indices, tf.int64)
idx_new = tf.reduce_sum(tf.cast(eq, tf.int64) * tf.range(n_indices),
axis=1)
idx_new = tf.boolean_mask(idx_new, sel, axis=0)
# New full indices tensor
indices_new = tf.boolean_mask(x.indices, sel, axis=0)
indices_new = tf.concat([
indices_new[:, :axis],
tf.expand_dims(idx_new, 1), indices_new[:, axis + 1:]
],
axis=1)
# New shape
shape_new = tf.concat(
[x.dense_shape[:axis], [n_indices], x.dense_shape[axis + 1:]], axis=0)
return tf.SparseTensor(indices_new, values_new, shape_new)
def tf_map_coordinates(input, coords, order=1):
"""Tensorflow verion of scipy.ndimage.map_coordinates"""
assert order == 1
coords_tl = tf.cast(tf.floor(coords), 'int32')
coords_br = tf.cast(tf.ceil(coords), 'int32')
coords_bl = tf.stack([coords_tl[:, 0], coords_br[:, 1]], axis=1)
coords_tr = tf.stack([coords_br[:, 0], coords_tl[:, 1]], axis=1)
vals_tl = tf.gather_nd(input, coords_tl)
vals_br = tf.gather_nd(input, coords_br)
vals_bl = tf.gather_nd(input, coords_bl)
vals_tr = tf.gather_nd(input, coords_tr)
h_offset = coords[:, 0] - tf.cast(coords_tl[:, 0], tf.float32)
h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
v_offset = coords[:, 1] - tf.cast(coords_tl[:, 1], tf.float32)
int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
return int_vals
def read_and_decode(filename, one_hot=True, n_class=None, is_train=None):
""" Return tensor to read from TFRecord """
filename_queue = tf.train.string_input_producer([filename])
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = tf.parse_single_example(serialized_example,
features={
'label':
tf.FixedLenFeature([], tf.int64),
'image_raw':
tf.FixedLenFeature([], tf.string),
})
# You can do more image distortion here for training data
img = tf.decode_raw(features['image_raw'], tf.uint8)
img.set_shape([28 * 28])
img = tf.reshape(img, [28, 28, 1])
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
# img = tf.cast(img, tf.float32) * (1. / 255)
label = tf.cast(features['label'], tf.int32)
if one_hot and n_class:
label = tf.one_hot(label, n_class)
return img, label
def radon_transform(x, theta):
x = tf.cast(x, dtype=tf.float32)
x_shape = tf.shape(x)
n_cols = x_shape[2]
n_rows = x_shape[1]
n_frames = x_shape[0]
n_angles = tf.shape(theta)[0]
x = tf.reshape(x, (-1, 1, n_rows, n_cols, 1))
x = tf.tile(x, (1, n_angles, 1, 1, 1))
x = tf.reshape(x, (-1, n_rows, n_cols, 1))
repeated_theta = repeat_theta(theta, n_angles, n_frames)
x = tf.cast(x, dtype=tf.uint8)
#x = tf.contrib.image.rotate(x, repeated_theta, interpolation='BILINEAR')
x = tf.cast(x, dtype=tf.float32)
x = tf.reshape(x, (-1, n_angles, n_rows, n_cols, 1))
x = tf.cast(x, dtype=tf.float32)
x = tf.reduce_sum(x, 2)
return x
def degree_matrix(A, return_sparse_batch=False):
"""
Computes the degree matrix of A, deals with sparse A and batch mode
automatically.
:param A: Tensor or SparseTensor with rank k = {2, 3}.
:param return_sparse_batch: if operating in batch mode, return a
SparseTensor. Note that the sparse degree tensor returned by this function
cannot be used for sparse matrix multiplication afterwards.
:return: SparseTensor of rank k.
"""
D = degrees(A)
batch_mode = K.ndim(D) == 2
N = tf.shape(D)[-1]
batch_size = tf.shape(D)[0] if batch_mode else 1
inner_index = tf.tile(tf.stack([tf.range(N)] * 2, axis=1), (batch_size, 1))
if batch_mode:
if return_sparse_batch:
outer_index = tf_repeat_1d(
tf.range(batch_size),
tf.ones(batch_size) * tf.cast(N, tf.float32))
indices = tf.concat([outer_index[:, None], inner_index], 1)
dense_shape = (batch_size, N, N)
else:
return tf.linalg.diag(D)
else:
indices = inner_index
dense_shape = (N, N)
indices = tf.cast(indices, tf.int64)
values = tf.reshape(D, (-1, ))
return tf.SparseTensor(indices, values, dense_shape)
def _row_kernel(upsampled_region_size, upsample_factor, axis_offsets,
data_shape):
data_shape_float = tf.cast(data_shape, tf.float32)
row_constant = tf.cast(data_shape_float[1] * upsample_factor, tf.complex64)
row_constant = (-1j * 2 * np.pi / row_constant)
row_kernel_a = tf.range(0, upsampled_region_size, dtype=tf.float32)
row_kernel_a = tf.reshape(row_kernel_a, (1, -1))
row_kernel_a = tf.tile(row_kernel_a, (data_shape[0], 1))
row_kernel_a = tf.transpose(row_kernel_a)
row_kernel_a = row_kernel_a - axis_offsets[:, 0]
row_kernel_b = tf.range(0, data_shape_float[1], dtype=tf.float32)
row_kernel_b = fftshift1d(row_kernel_b)
row_kernel_b = tf.reshape(row_kernel_b, (1, -1))
row_kernel_b = tf.tile(row_kernel_b, (data_shape[0], 1))
row_kernel_b = row_kernel_b - tf.floor(data_shape_float[1] / 2.)
row_kernel_a = tf.expand_dims(row_kernel_a, 1)
row_kernel_b = tf.expand_dims(row_kernel_b, -1)
row_kernel = tf.transpose(row_kernel_a) * row_kernel_b
row_kernel = tf.transpose(row_kernel, perm=(0, 2, 1))
row_kernel = row_constant * tf.cast(row_kernel, tf.complex64)
row_kernel = tf.exp(row_kernel)
return row_kernel
def find_maxima(x):
col_max = tf.reduce_max(x, axis=1)
row_max = tf.reduce_max(x, axis=2)
cols = tf.cast(tf.argmax(col_max, 1), tf.float32)
rows = tf.cast(tf.argmax(row_max, 1), tf.float32)
cols = tf.reshape(cols, (-1, 1))
rows = tf.reshape(rows, (-1, 1))
maxima = tf.concat([rows, cols], -1)
return maxima
def top_k(scores, I, ratio, top_k_var):
"""
Returns indices to get the top K values in `scores` segment-wise, with
segments defined by I. K is not fixed, but it is defined as a ratio of the
number of elements in each segment.
:param scores: a rank 1 tensor with scores;
:param I: a rank 1 tensor with segment IDs;
:param ratio: float, ratio of elements to keep for each segment;
:param top_k_var: a tf.Variable without shape validation (e.g.,
`tf.Variable(0.0, validate_shape=False)`);
:return: a rank 1 tensor containing the indices to get the top K values of
each segment in `scores`.
"""
num_nodes = tf.segment_sum(tf.ones_like(I),
I) # Number of nodes in each graph
cumsum = tf.cumsum(num_nodes) # Cumulative number of nodes (A, A+B, A+B+C)
cumsum_start = cumsum - num_nodes # Start index of each graph
n_graphs = tf.shape(num_nodes)[0] # Number of graphs in batch
max_n_nodes = tf.reduce_max(num_nodes) # Order of biggest graph in batch
batch_n_nodes = tf.shape(I)[0] # Number of overall nodes in batch
to_keep = tf.ceil(ratio * tf.cast(num_nodes, tf.float32))
to_keep = tf.cast(to_keep, tf.int32) # Nodes to keep in each graph
index = tf.range(batch_n_nodes)
index = (index - tf.gather(cumsum_start, I)) + (I * max_n_nodes)
y_min = tf.reduce_min(scores)
dense_y = tf.ones((n_graphs * max_n_nodes, ))
dense_y = dense_y * tf.cast(
y_min - 1, tf.float32
) # subtract 1 to ensure that filler values do not get picked
dense_y = tf.assign(
top_k_var, dense_y, validate_shape=False
) # top_k_var is a variable with unknown shape defined in the elsewhere
dense_y = tf.scatter_update(dense_y, index, scores)
dense_y = tf.reshape(dense_y, (n_graphs, max_n_nodes))
perm = tf.argsort(dense_y, direction='DESCENDING')
perm = perm + cumsum_start[:, None]
perm = tf.reshape(perm, (-1, ))
to_rep = tf.tile(tf.constant([1., 0.]), (n_graphs, ))
rep_times = tf.reshape(
tf.concat((to_keep[:, None], (max_n_nodes - to_keep)[:, None]), -1),
(-1, ))
mask = tf_repeat_1d(to_rep, rep_times)
perm = tf.boolean_mask(perm, mask)
return perm
def _register_rotation(target_image, src_image, rotation_resolution,
rotation_guess, upsample_factor):
n_angles = tf.cast(tf.round(180. / rotation_resolution), tf.int32)
theta = tf.linspace(0., 180. - rotation_resolution, n_angles)
theta = -radians(theta)
target_shape = tf.shape(target_image)
target_image = tf.reshape(target_image, target_shape[:3])
src_shape = tf.shape(src_image)
src_image = tf.reshape(src_image, src_shape[:3])
rotation_guess = tf.constant(rotation_guess, tf.float32)
rotation_resolution = tf.constant(rotation_resolution, tf.float32)
src_image = radon_transform_fft(src_image, theta)
target_image = radon_transform_fft(target_image, theta)
shifts = _upsampled_registration(target_image, src_image, upsample_factor)
angles = shifts[:, 0] * rotation_resolution
angles = tf.reshape(angles, [-1, 1])
angles = check_angles(angles, rotation_guess)
angles = radians(angles)
return angles
def _find_subpixel_maxima(x,
kernel_size,
sigma,
upsample_factor,
coordinate_scale=1,
confidence_scale=255.):
kernel = gaussian_kernel_2d(kernel_size, sigma)
kernel = tf.expand_dims(kernel, 0)
x_shape = tf.shape(x)
rows = x_shape[1]
cols = x_shape[2]
max_vals = tf.reduce_max(tf.reshape(x, [-1, rows * cols]), axis=1)
max_vals = tf.reshape(max_vals, [-1, 1]) / confidence_scale
row_pad = rows // 2 - kernel_size // 2
col_pad = cols // 2 - kernel_size // 2
padding = [[0, 0], [row_pad, row_pad - 1], [col_pad, col_pad - 1]]
kernel = tf.pad(kernel, padding)
row_center = row_pad + (kernel_size // 2)
col_center = col_pad + (kernel_size // 2)
center = tf.stack([row_center, col_center])
center = tf.expand_dims(center, 0)
center = tf.cast(center, dtype=tf.float32)
shifts = _upsampled_registration(x, kernel, upsample_factor)
shifts = center - shifts
shifts *= coordinate_scale
maxima = tf.concat([shifts[:, ::-1], max_vals], -1)
return maxima
def fftshift1d(x, axis=0):
x_shape = tf.shape(x)
x = tf.reshape(x, (-1, 1))
n_samples = tf.cast(tf.shape(x)[0], tf.float32)
even = n_samples / 2.
even = tf.round(even)
even = even * 2.
even = tf.equal(n_samples, even)
def true_fn():
return x
def false_fn():
x_padded = tf.concat([x, tf.zeros((1, 1))], axis=0)
return x_padded
x = tf.cond(even, true_fn, false_fn)
x1, x2 = tf.split(x, 2, axis=axis)
def true_fn():
return x2
def false_fn():
x2_unpadded = x2[:-1]
return x2_unpadded
x2 = tf.cond(even, true_fn, false_fn)
x = tf.concat((x2, x1), axis=axis)
x = tf.reshape(x, x_shape)
return x
def batch_map_coordinates(input, coords, order=1):
"""Batch version of tf_map_coordinates"""
input_shape = tf.shape(input)
batch_size = input_shape[0]
input_size = input_shape[1]
#coords = tf.reshape(coords, (batch_size, -1, 2))
n_coords = tf.shape(coords)[1]
coords = tf.clip_by_value(coords, 0, tf.cast(input_size, 'float32') - 1)
coords_tl = tf.cast(tf.floor(coords), 'int32')
coords_br = tf.cast(tf.ceil(coords), 'int32')
coords_bl = tf.stack([coords_tl[..., 0], coords_br[..., 1]], axis=-1)
coords_tr = tf.stack([coords_br[..., 0], coords_tl[..., 1]], axis=-1)
idx = tf.range(batch_size)
idx = tf.expand_dims(idx, -1)
idx = tf.tile(idx, [1, n_coords])
idx = tf.reshape(idx, [-1])
def _get_vals_by_coords(input, coords):
coords_0_flat = tf.reshape(coords[..., 0], [-1])
coords_1_flat = tf.reshape(coords[..., 1], [-1])
indices = tf.stack([idx, coords_0_flat, coords_1_flat], axis=-1)
vals = tf.gather_nd(input, indices)
vals = tf.reshape(vals, (batch_size, n_coords))
return vals
vals_tl = _get_vals_by_coords(input, coords_tl)
vals_br = _get_vals_by_coords(input, coords_br)
vals_bl = _get_vals_by_coords(input, coords_bl)
vals_tr = _get_vals_by_coords(input, coords_tr)
h_offset = coords[..., 0] - tf.cast(coords_tl[..., 0], tf.float32)
h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
v_offset = coords[..., 1] - tf.cast(coords_tl[..., 1], tf.float32)
int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
return int_vals
def attention_mask_fun(input):
"""Align text representation with neural soft attention"""
mask1 = K.reshape(input[0], (-1, max_sentence_length, 1))
mask2 = K.reshape(input[1], (-1, max_sentence_length, 1))
result = keras.layers.Dot(axes=-1)([mask1, mask2])
result = (1.0 - tf.cast(result, tf.float32)) * -100000.0
return result
def _upsampled_registration(target_image, src_image, upsample_factor):
upsample_factor = tf.constant(upsample_factor, tf.float32)
target_shape = tf.shape(target_image)
target_image = tf.reshape(target_image, target_shape[:3])
src_shape = tf.shape(src_image)
src_image = tf.reshape(src_image, src_shape[:3])
src_freq = fft2d(src_image)
target_freq = fft2d(target_image)
shape = tf.reshape(tf.shape(src_freq)[1:3], (1, 2))
shape = tf.cast(shape, tf.float32)
shape = tf.tile(shape, (tf.shape(target_freq)[0], 1))
image_product = src_freq * tf.conj(target_freq)
cross_correlation = tf.spectral.ifft2d(image_product)
maxima = find_maxima(tf.abs(cross_correlation))
midpoints = fix(tf.cast(shape, tf.float32) / 2.)
shifts = maxima
shifts = tf.where(shifts > midpoints, shifts - shape, shifts)
shifts = tf.round(shifts * upsample_factor) / upsample_factor
upsampled_region_size = tf.ceil(upsample_factor * 1.5)
dftshift = fix(upsampled_region_size / 2.0)
normalization = tf.cast(tf.size(src_freq[0]), tf.float32)
normalization *= upsample_factor**2
sample_region_offset = dftshift - shifts * upsample_factor
data = tf.conj(image_product)
upsampled_dft = _upsampled_dft(data, upsampled_region_size,
upsample_factor, sample_region_offset)
cross_correlation = tf.conj(upsampled_dft)
cross_correlation /= tf.cast(normalization, tf.complex64)
cross_correlation = tf.abs(cross_correlation)
maxima = find_maxima(cross_correlation)
maxima = maxima - dftshift
shifts = shifts + maxima / upsample_factor
return shifts
def radon_fft(x):
x_shape = tf.shape(x)
n_angles = x_shape[1]
n_cols = x_shape[2]
x = tf.reshape(x, (-1, n_cols))
x = tf.cast(x, tf.complex64)
x = tf.spectral.fft(x)
x = tf.abs(x)
x = tf.reshape(x, (-1, n_angles, n_cols, 1))
return x
def binary_focal_loss_fixed(y_true, y_pred):
"""
y_true shape need be (None,1)
y_pred need be compute after sigmoid
"""
y_true = tf.cast(y_true, tf.float32)
alpha_t = y_true * alpha + (K.ones_like(y_true) - y_true) * (1 - alpha)
p_t = y_true * y_pred + (K.ones_like(y_true) - y_true) * (
K.ones_like(y_true) - y_pred) + K.epsilon()
focal_loss = -alpha_t * K.pow(
(K.ones_like(y_true) - p_t), gamma) * K.log(p_t)
return K.mean(focal_loss)
def batch_map_offsets(input, offsets, order=1):
"""Batch map offsets into input
Adds index of every entry to the entry to make it's interpolation
relevant to it's location
"""
offset_shape = offsets.get_shape()
batch_size = tf.shape(offsets)[0]
input_h = offset_shape[1]
input_w = offset_shape[2]
channel_size = int(offset_shape[3].value)
#offsets = tf.reshape(offsets, (batch_size, -1, 2))
#################### DEFAULT COORDINATES FOR EVERY POINT ####################
ind_add = tf.meshgrid(tf.range(1, input_h + 1, delta=1),
tf.range(1, input_w + 1, delta=1),
indexing='ij')
ind_add = tf.stack(ind_add, axis=-1)
ind_add = tf.cast(ind_add, 'float32')
ind_add = tf.reshape(ind_add, (1, input_h, input_w, 2))
ind_add = tf.tile(ind_add, [batch_size, 1, 1, int(channel_size / 2)])
#############################################################################
#################### KERNEL OFFSET FOR EVERY POINT ####################
ind_zero = tf.meshgrid(tf.range(-1, 2, delta=1),
tf.range(-1, 2, delta=1),
indexing='ij')
ind_zero = tf.stack(ind_zero, axis=-1)
ind_zero = tf.cast(ind_zero, 'float32')
ind_zero = tf.reshape(ind_zero, (1, 1, 1, channel_size))
ind_zero = tf.tile(ind_zero, [batch_size, input_h, input_w, 1])
#######################################################################
coords = offsets + ind_add + ind_zero
int_vals = batch_map_coordinates(input, coords, int(channel_size / 2))
return int_vals
def _postprocess_conv2d_output(x, data_format):
"""Transpose and cast the output from conv2d if needed.
# Arguments
x: A tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
"""
if data_format == 'channels_first':
x = tf.transpose(x, (0, 3, 1, 2))
if floatx() == 'float64':
x = tf.cast(x, 'float64')
return x
def _preprocess_conv2d_input(x, data_format):
"""Transpose and cast the input before the conv2d.
# Arguments
x: input tensor.
data_format: string, `"channels_last"` or `"channels_first"`.
# Returns
A tensor.
"""
if dtype(x) == 'float64':
x = tf.cast(x, 'float32')
if data_format == 'channels_first':
# TF uses the last dimension as channel dimension,
# instead of the 2nd one.
# TH input shape: (samples, input_depth, rows, cols)
# TF input shape: (samples, rows, cols, input_depth)
x = tf.transpose(x, (0, 2, 3, 1))
return x
def read_and_decode(filename,
w,
h,
one_hot=True,
n_class=None,
is_train=None,
bResize=False,
origImgW=0,
origImgH=0):
""" Return tensor to read from TFRecord """
# files = tf.train.match_filenames_once(filename)
files = filename
# print(files)
filename_queue = tf.train.string_input_producer(files)
reader = tf.TFRecordReader()
_, serialized_example = reader.read(filename_queue)
features = \
tf.parse_single_example(serialized_example,
features={
'height': tf.FixedLenFeature([], tf.int64),
'width': tf.FixedLenFeature([], tf.int64),
'depth': tf.FixedLenFeature([], tf.int64),
'image_raw': tf.FixedLenFeature([], tf.string),
'label': tf.FixedLenFeature([], tf.int64)
})
# You can do more image distortion here for training data
img = tf.decode_raw(features['image_raw'], tf.uint8)
img = tf.reshape(img, [origImgW, origImgH, 3])
if bResize:
img = tf.image.resize_images(img, (w, h), method=0)
img = tf.cast(img, tf.float32) * (1. / 255) - 0.5
# img = tf.cast(img, tf.float32) * (1. / 255)
label = features['label']
# label = tf.cast(label, tf.float32)
if one_hot and n_class:
label = tf.one_hot(label, n_class)
return img, label
def batch_map_offsets(input, offsets, order=1):
"""Batch map offsets into input
Adds index of every entry to the entry to make it's interpolation
relevant to it's location
"""
input_shape = tf.shape(input)
batch_size = input_shape[0]
input_w = input_shape[1]
input_h = input_shape[2]
offsets = tf.reshape(offsets, (batch_size, -1, 2))
ind_add = tf.meshgrid(tf.range(input_w), tf.range(input_h), indexing='ij')
ind_add = tf.stack(ind_add, axis=-1)
ind_add = tf.cast(ind_add, 'float32')
ind_add = tf.reshape(ind_add, (-1, 2))
ind_add = tf.expand_dims(ind_add, 0)
ind_add = tf.tile(ind_add, [batch_size, 1, 1])
coords = offsets + ind_add
int_vals = batch_map_coordinates(input, coords)
return int_vals
def _get_vals_by_coords(input, coords, n_coords):
coords_shape = tf.shape(coords)
input_shape = input.get_shape()
input_w = input_shape[2].value
input_h = input_shape[1].value
channel_size = input_shape[3].value
batch_size = tf.shape(input)[0]
input = tf.transpose(input, (0, 3, 1, 2))
input = tf.reshape(input, (-1, channel_size, input_h * input_w))
indices = coords[..., 0] * input_w + coords[..., 1]
#indices = tf.expand_dims(indices, axis=1)
#indices = tf.tile(indices, [1, channel_size, 1, 1, 1])
#indices = tf.reshape(indices, (-1, channel_size, input_h * input_w * n_coords))
#indices = tf.transpose(indices, (0, 3, 1, 2))
indices = tf.reshape(indices, (-1, input_h * input_w * n_coords))
indices = tf.cast(indices, 'int32')
#indices = tf.reshape(indices, [-1])
#input = tf.reshape(input, [-1])
vals = tf.gather(input, indices[0], axis=-1)
#vals = tf.map_fn(lambda x: tf.gather(x[0], x[1], axis=-1), (input,indices), dtype=tf.float32)
vals = tf.reshape(vals, (-1, channel_size, input_h, input_w, n_coords))
return vals
def train():
ff_apr = ktf.matmul(f, f, transpose_b=True) / (
ktf.cast(bs * w * h, ktf.float32) - 1.)
if self.decomposition in ['pca-cor', 'zca-cor']:
dinv = ktf.diag(ktf.sqrt(ktf.diag_part(ff_apr)))
ff_apr = ktf.matmul(ktf.matmul(dinv, ff_apr),
ktf.matrix_inverse(dinv),
transpose_b=True)
self.add_update([
K.moving_average_update(self.moving_mean, m, self.momentum),
K.moving_average_update(self.moving_cov, ff_apr, self.momentum)
], inputs)
ff_apr_shrinked = (
1 - self.epsilon) * ff_apr + ktf.eye(c) * self.epsilon
if self.renorm:
l, l_inv = get_inv_sqrt(ff_apr_shrinked)
ff_mov = (1 - self.epsilon
) * self.moving_cov + ktf.eye(c) * self.epsilon
_, l_mov_inverse = get_inv_sqrt(ff_mov)
l_ndiff = K.stop_gradient(l)
return ktf.matmul(ktf.matmul(l_mov_inverse, l_ndiff), l_inv)
return get_inv_sqrt(ff_apr_shrinked)[1]
def fft2d(x):
x = tf.cast(x, tf.complex64)
x = tf.spectral.fft2d(x)
return x
def cast_all(values, reference_type_vals):
return [ktf.cast(alpha, dtype=ref.dtype) for alpha, ref in zip(values, reference_type_vals)]
def batch_map_coordinates(input, coords, n_coords):
"""Batch version of tf_map_coordinates"""
#init_input_shape = input.get_shape()
#input = tf.reshape(input, (-1, init_input_shape[3]))
#coords = tf.reshape(coords, (-1, n_coords * 2))
input_shape = input.get_shape()
input_h = input_shape[1].value
input_w = input_shape[2].value
#batch_size = input_shape[0]
#input_size = input_shape[1]
#coords = tf.reshape(coords, (batch_size, -1, 2))
#n_coords = tf.shape(coords)[1]
coords_h = tf.clip_by_value(coords[..., :n_coords], 0,
tf.cast(input_h, 'float32') - 1)
coords_w = tf.clip_by_value(coords[..., n_coords:], 0,
tf.cast(input_w, 'float32') - 1)
coords = tf.stack([coords_h, coords_w], axis=-1)
coords_tl = tf.cast(tf.floor(coords), 'float32')
coords_br = tf.cast(tf.ceil(coords), 'float32')
coords_bl = tf.stack([coords_tl[..., 0], coords_br[..., 1]], axis=-1)
coords_tr = tf.stack([coords_br[..., 0], coords_tl[..., 1]], axis=-1)
#idx = tf.range(batch_size)
#idx = tf.expand_dims(idx, -1)
#idx = tf.tile(idx, [1, n_coords])
#idx = tf.reshape(idx, [-1])
def _get_vals_by_coords(input, coords, n_coords):
coords_shape = tf.shape(coords)
input_shape = input.get_shape()
input_w = input_shape[2].value
input_h = input_shape[1].value
channel_size = input_shape[3].value
batch_size = tf.shape(input)[0]
input = tf.transpose(input, (0, 3, 1, 2))
input = tf.reshape(input, (-1, channel_size, input_h * input_w))
indices = coords[..., 0] * input_w + coords[..., 1]
#indices = tf.expand_dims(indices, axis=1)
#indices = tf.tile(indices, [1, channel_size, 1, 1, 1])
#indices = tf.reshape(indices, (-1, channel_size, input_h * input_w * n_coords))
#indices = tf.transpose(indices, (0, 3, 1, 2))
indices = tf.reshape(indices, (-1, input_h * input_w * n_coords))
indices = tf.cast(indices, 'int32')
#indices = tf.reshape(indices, [-1])
#input = tf.reshape(input, [-1])
vals = tf.gather(input, indices[0], axis=-1)
#vals = tf.map_fn(lambda x: tf.gather(x[0], x[1], axis=-1), (input,indices), dtype=tf.float32)
vals = tf.reshape(vals, (-1, channel_size, input_h, input_w, n_coords))
return vals
vals_tl = (1 + (coords_tl[..., 0] - coords[..., 0])) * \
(1 + (coords_tl[..., 1] - coords[..., 1]))
vals_br = (1 - (coords_br[..., 0] - coords[..., 0])) * \
(1 - (coords_br[..., 1] - coords[..., 1]))
vals_bl = (1 + (coords_bl[..., 0] - coords[..., 0])) * \
(1 + (coords_bl[..., 1] - coords[..., 1]))
vals_tr = (1 - (coords_tr[..., 0] - coords[..., 0])) * \
(1 - (coords_tr[..., 1] - coords[..., 1]))
x_vals_tl = _get_vals_by_coords(input, coords_tl, n_coords)
x_vals_br = _get_vals_by_coords(input, coords_br, n_coords)
x_vals_bl = _get_vals_by_coords(input, coords_bl, n_coords)
x_vals_tr = _get_vals_by_coords(input, coords_tr, n_coords)
#h_offset = coords[..., 0] - tf.cast(coords_tl[..., 0], tf.float32)
#h_int_t = (((1.0 - h_offset) * vals_tl) + (h_offset * vals_tr))
#h_int_b = (((1.0 - h_offset) * vals_bl) + (h_offset * vals_br))
#v_offset = coords[..., 1] - tf.cast(coords_tl[..., 1], tf.float32)
#int_vals = (((1.0 - v_offset) * h_int_t) + (v_offset * h_int_b))
int_vals = tf.expand_dims(vals_tl, 1) * x_vals_tl + \
tf.expand_dims(vals_br, 1) * x_vals_br + \
tf.expand_dims(vals_bl, 1) * x_vals_bl + \
tf.expand_dims(vals_tr, 1) * x_vals_tr
return int_vals
def build(self, input_shape):
self.xx, self.yy = ktf.meshgrid(ktf.range(self.image_size[1]),
ktf.range(self.image_size[0]))
self.xx = ktf.expand_dims(ktf.cast(self.xx, 'float32'), 2)
self.yy = ktf.expand_dims(ktf.cast(self.yy, 'float32'), 2)
