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import tensorflow as tf
from tensorflow.keras.__internal__.layers import BaseRandomLayer
from tensorflow.keras.layers import (
Dense, Flatten, Conv2D, Activation, BatchNormalization,
MaxPooling2D, AveragePooling2D, GlobalAveragePooling2D,
Dropout, Input, concatenate, add, Conv2DTranspose, Lambda,
SpatialDropout2D, Cropping2D, UpSampling2D, LeakyReLU,
ZeroPadding2D, Reshape, Concatenate, Multiply, Permute, Add
)
from keras import backend as K
from .utils import normalize_tuple
class MultipleTrackers():
def __init__(self, callback_lists: list):
self.callbacks_list = callback_lists
def __getattr__(self, attr):
def helper(*arg, **kwarg):
for cb in self.callbacks_list:
getattr(cb, attr)(*arg, **kwarg)
if attr in self.__class__.__dict__:
return getattr(self, attr)
else:
return helper
class DropBlockNoise(BaseRandomLayer):
def __init__(
self,
rate,
block_size,
seed=None,
**kwargs,
):
super().__init__(seed=seed, **kwargs)
if not 0.0 <= rate <= 1.0:
raise ValueError(
f"rate must be a number between 0 and 1. " f"Received: {rate}"
)
self._rate = rate
(
self._dropblock_height,
self._dropblock_width,
) = normalize_tuple(
value=block_size, n=2, name="block_size", allow_zero=False
)
self.seed = seed
def call(self, x, training=None):
if not training or self._rate == 0.0:
return x
_, height, width, _ = tf.split(tf.shape(x), 4)
# Unnest scalar values
height = tf.squeeze(height)
width = tf.squeeze(width)
dropblock_height = tf.math.minimum(self._dropblock_height, height)
dropblock_width = tf.math.minimum(self._dropblock_width, width)
gamma = (
self._rate
* tf.cast(width * height, dtype=tf.float32)
/ tf.cast(dropblock_height * dropblock_width, dtype=tf.float32)
/ tf.cast(
(width - self._dropblock_width + 1)
* (height - self._dropblock_height + 1),
tf.float32,
)
)
# Forces the block to be inside the feature map.
w_i, h_i = tf.meshgrid(tf.range(width), tf.range(height))
valid_block = tf.logical_and(
tf.logical_and(
w_i >= int(dropblock_width // 2),
w_i < width - (dropblock_width - 1) // 2,
),
tf.logical_and(
h_i >= int(dropblock_height // 2),
h_i < width - (dropblock_height - 1) // 2,
),
)
valid_block = tf.reshape(valid_block, [1, height, width, 1])
random_noise = self._random_generator.random_uniform(
tf.shape(x), dtype=tf.float32
)
valid_block = tf.cast(valid_block, dtype=tf.float32)
seed_keep_rate = tf.cast(1 - gamma, dtype=tf.float32)
block_pattern = (1 - valid_block + seed_keep_rate + random_noise) >= 1
block_pattern = tf.cast(block_pattern, dtype=tf.float32)
window_size = [1, self._dropblock_height, self._dropblock_width, 1]
# Double negative and max_pool is essentially min_pooling
block_pattern = -tf.nn.max_pool(
-block_pattern,
ksize=window_size,
strides=[1, 1, 1, 1],
padding="SAME",
)
return (
x * tf.cast(block_pattern, x.dtype)
)
def squeeze_excite_block(input, ratio=16):
''' Create a channel-wise squeeze-excite block
Args:
input: input tensor
filters: number of output filters
Returns: a keras tensor
References
- [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
'''
init = input
channel_axis = 1 if K.image_data_format() == "channels_first" else -1
filters = int(init.shape[channel_axis])
se_shape = (1, 1, filters)
se = GlobalAveragePooling2D()(init)
se = Reshape(se_shape)(se)
se = Dense(filters // ratio, activation='relu', kernel_initializer='he_normal', use_bias=False)(se)
se = Dense(filters, activation='sigmoid', kernel_initializer='he_normal', use_bias=False)(se)
if K.image_data_format() == 'channels_first':
se = Permute((3, 1, 2))(se)
x = Multiply()([init, se])
return x
def spatial_squeeze_excite_block(input):
''' Create a spatial squeeze-excite block
Args:
input: input tensor
Returns: a keras tensor
References
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
'''
se = Conv2D(1, (1, 1), activation='sigmoid', use_bias=False,
kernel_initializer='he_normal')(input)
x = Multiply()([input, se])
return x
def channel_spatial_squeeze_excite(input, ratio=16):
''' Create a spatial squeeze-excite block
Args:
input: input tensor
filters: number of output filters
Returns: a keras tensor
References
- [Squeeze and Excitation Networks](https://arxiv.org/abs/1709.01507)
- [Concurrent Spatial and Channel Squeeze & Excitation in Fully Convolutional Networks](https://arxiv.org/abs/1803.02579)
'''
cse = squeeze_excite_block(input, ratio)
sse = spatial_squeeze_excite_block(input)
x = Add()([cse, sse])
return x
def DoubleConv(filters, kernel_size, initializer='glorot_uniform'):
def layer(x):
x = Conv2D(filters, kernel_size, padding='same', kernel_initializer=initializer)(x)
x = BatchNormalization()(x)
x = Activation('swish')(x)
x = Conv2D(filters, kernel_size, padding='same', kernel_initializer=initializer)(x)
x = BatchNormalization()(x)
x = Activation('swish')(x)
return x
return layer
def UpSampling2D_block(filters, kernel_size=(3, 3), upsample_rate=(2, 2), interpolation='bilinear',
initializer='glorot_uniform', skip=None):
def layer(input_tensor):
x = UpSampling2D(size=upsample_rate, interpolation=interpolation)(input_tensor)
if skip is not None:
x = Concatenate()([x, skip])
x = DoubleConv(filters, kernel_size, initializer=initializer)(x)
x = channel_spatial_squeeze_excite(x)
return x
return layer
def Conv2DTranspose_block(filters, transpose_kernel_size=(3, 3), upsample_rate=(2, 2),
initializer='glorot_uniform', skip=None, met_input=None, sat_input=None):
def layer(input_tensor):
x = Conv2DTranspose(filters, transpose_kernel_size, strides=upsample_rate, padding='same')(input_tensor)
if skip is not None:
x = Concatenate()([x, skip])
x = DoubleConv(filters, transpose_kernel_size, initializer=initializer)(x)
x = channel_spatial_squeeze_excite(x)
return x
return layer
def PixelShuffle_block(filters, kernel_size=(3, 3), upsample_rate=2,
initializer='glorot_uniform', skip=None, met_input=None, sat_input=None):
def layer(input_tensor):
x = Conv2D(filters * (upsample_rate ** 2), kernel_size, padding="same",
activation="swish", kernel_initializer='Orthogonal')(input_tensor)
x = tf.nn.depth_to_space(x, upsample_rate)
if skip is not None:
x = Concatenate()([x, skip])
x = DoubleConv(filters, kernel_size, initializer=initializer)(x)
x = channel_spatial_squeeze_excite(x)
return x
return layer |