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import tensorflow as tf
import tensorflow_addons as tfa
H_AXIS = -3
W_AXIS = -2
@tf.keras.utils.register_keras_serializable(package="gcvit")
class Mlp(tf.keras.layers.Layer):
def __init__(self, hidden_features=None, out_features=None, act_layer='gelu', dropout=0., **kwargs):
super().__init__(**kwargs)
self.hidden_features = hidden_features
self.out_features = out_features
self.act_layer = act_layer
self.dropout = dropout
def build(self, input_shape):
self.in_features = input_shape[-1]
self.hidden_features = self.hidden_features or self.in_features
self.out_features = self.out_features or self.in_features
self.fc1 = tf.keras.layers.Dense(self.hidden_features, name="fc1")
self.act = tf.keras.layers.Activation(self.act_layer, name="act")
self.fc2 = tf.keras.layers.Dense(self.out_features, name="fc2")
self.drop1 = tf.keras.layers.Dropout(self.dropout, name="drop1")
self.drop2 = tf.keras.layers.Dropout(self.dropout, name="drop2")
super().build(input_shape)
def call(self, inputs, **kwargs):
x = self.fc1(inputs)
x = self.act(x)
x = self.drop1(x)
x = self.fc2(x)
x = self.drop2(x)
return x
def get_config(self):
config = super().get_config()
config.update({
"hidden_features":self.hidden_features,
"out_features":self.out_features,
"act_layer":self.act_layer,
"dropout":self.dropout
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class SE(tf.keras.layers.Layer):
def __init__(self, oup=None, expansion=0.25, **kwargs):
super().__init__(**kwargs)
self.expansion = expansion
self.oup = oup
def build(self, input_shape):
inp = input_shape[-1]
self.oup = self.oup or inp
self.avg_pool = tfa.layers.AdaptiveAveragePooling2D(1, name="avg_pool")
self.fc = [
tf.keras.layers.Dense(int(inp * self.expansion), use_bias=False, name='fc/0'),
tf.keras.layers.Activation('gelu', name='fc/1'),
tf.keras.layers.Dense(self.oup, use_bias=False, name='fc/2'),
tf.keras.layers.Activation('sigmoid', name='fc/3')
]
super().build(input_shape)
def call(self, inputs, **kwargs):
b, _, _, c = tf.unstack(tf.shape(inputs), num=4)
x = tf.reshape(self.avg_pool(inputs), (b, c))
for layer in self.fc:
x = layer(x)
x = tf.reshape(x, (b, 1, 1, c))
return x*inputs
def get_config(self):
config = super().get_config()
config.update({
'expansion': self.expansion,
'oup': self.oup,
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class ReduceSize(tf.keras.layers.Layer):
def __init__(self, keep_dim=False, **kwargs):
super().__init__(**kwargs)
self.keep_dim = keep_dim
def build(self, input_shape):
dim = input_shape[-1]
dim_out = dim if self.keep_dim else 2*dim
self.pad1 = tf.keras.layers.ZeroPadding2D(1, name='pad1')
self.pad2 = tf.keras.layers.ZeroPadding2D(1, name='pad2')
self.conv = [
tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=1, padding='valid', use_bias=False, name='conv/0'),
tf.keras.layers.Activation('gelu', name='conv/1'),
SE(name='conv/2'),
tf.keras.layers.Conv2D(dim, kernel_size=1, strides=1, padding='valid', use_bias=False, name='conv/3')
]
self.reduction = tf.keras.layers.Conv2D(dim_out, kernel_size=3, strides=2, padding='valid', use_bias=False,
name='reduction')
self.norm1 = tf.keras.layers.LayerNormalization(axis=-1, epsilon=1e-05, name='norm1') # eps like PyTorch
self.norm2 = tf.keras.layers.LayerNormalization(axis=-1, epsilon=1e-05, name='norm2')
super().build(input_shape)
def call(self, inputs, **kwargs):
x = self.norm1(inputs)
xr = self.pad1(x) # if pad had weights it would've thrown error with .save_weights()
for layer in self.conv:
xr = layer(xr)
x = x + xr
x = self.pad2(x)
x = self.reduction(x)
x = self.norm2(x)
return x
def get_config(self):
config = super().get_config()
config.update({
"keep_dim":self.keep_dim,
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class FeatExtract(tf.keras.layers.Layer):
def __init__(self, keep_dim=False, **kwargs):
super().__init__(**kwargs)
self.keep_dim = keep_dim
def build(self, input_shape):
dim = input_shape[-1]
self.pad1 = tf.keras.layers.ZeroPadding2D(1, name='pad1')
self.pad2 = tf.keras.layers.ZeroPadding2D(1, name='pad2')
self.conv = [
tf.keras.layers.DepthwiseConv2D(kernel_size=3, strides=1, padding='valid', use_bias=False, name='conv/0'),
tf.keras.layers.Activation('gelu', name='conv/1'),
SE(name='conv/2'),
tf.keras.layers.Conv2D(dim, kernel_size=1, strides=1, padding='valid', use_bias=False, name='conv/3')
]
if not self.keep_dim:
self.pool = tf.keras.layers.MaxPool2D(pool_size=3, strides=2, padding='valid', name='pool')
# else:
# self.pool = tf.keras.layers.Activation('linear', name='identity') # hack for PyTorch nn.Identity layer ;)
super().build(input_shape)
def call(self, inputs, **kwargs):
x = inputs
xr = self.pad1(x)
for layer in self.conv:
xr = layer(xr)
x = x + xr # if pad had weights it would've thrown error with .save_weights()
if not self.keep_dim:
x = self.pad2(x)
x = self.pool(x)
return x
def get_config(self):
config = super().get_config()
config.update({
"keep_dim":self.keep_dim,
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class GlobalQueryGen(tf.keras.layers.Layer):
"""
Global query generator based on: "Hatamizadeh et al.,
Global Context Vision Transformers <https://arxiv.org/abs/2206.09959>"
"""
def __init__(self, keep_dims=False, **kwargs):
super().__init__(**kwargs)
self.keep_dims = keep_dims
def build(self, input_shape):
self.to_q_global = [FeatExtract(keep_dim, name=f'to_q_global/{i}') \
for i, keep_dim in enumerate(self.keep_dims)]
super().build(input_shape)
def call(self, inputs, **kwargs):
x = inputs
for layer in self.to_q_global:
x = layer(x)
return x
def get_config(self):
config = super().get_config()
config.update({
"keep_dims":self.keep_dims,
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class Resizing(tf.keras.layers.Layer):
def __init__(self,
height,
width,
interpolation='bilinear',
**kwargs):
self.height = height
self.width = width
self.interpolation = interpolation
super().__init__(**kwargs)
def call(self, inputs):
# tf.image.resize will always output float32 and operate more efficiently on
# float32 unless interpolation is nearest, in which case ouput type matches
# input type.
if self.interpolation == 'nearest':
input_dtype = self.compute_dtype
else:
input_dtype = tf.float32
inputs = tf.cast(inputs, dtype=input_dtype)
size = [self.height, self.width]
outputs = tf.image.resize(
inputs,
size=size,
method=self.interpolation)
return tf.cast(outputs, self.compute_dtype)
def compute_output_shape(self, input_shape):
input_shape = tf.TensorShape(input_shape).as_list()
input_shape[H_AXIS] = self.height
input_shape[W_AXIS] = self.width
return tf.TensorShape(input_shape)
def get_config(self):
config = super().get_config()
config.update({
'height': self.height,
'width': self.width,
'interpolation': self.interpolation,
})
return config
@tf.keras.utils.register_keras_serializable(package="gcvit")
class FitWindow(tf.keras.layers.Layer):
"Pad feature to fit window"
def __init__(self, window_size, **kwargs):
super().__init__(**kwargs)
self.window_size = window_size
def call(self, inputs):
B, H, W, C = tf.unstack(tf.shape(inputs), num=4)
# pad to multiple of window_size
h_pad = (self.window_size - H % self.window_size) % self.window_size
w_pad = (self.window_size - W % self.window_size) % self.window_size
x = tf.pad(inputs, [[0, 0],
[h_pad//2, (h_pad//2 + h_pad%2)], # padding in both directions unlike tfgcvit
[w_pad//2, (w_pad//2 + w_pad%2)],
[0, 0]])
return x
def get_config(self):
config = super().get_config()
config.update({
'window_size': self.window_size,
})
return config |