deep_isp/modules.py at master · puneesh00/deep_isp · GitHub

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
from keras.layers import Conv2D, Conv2DTranspose, DepthwiseConv2D, Dense, GlobalAveragePooling2D, Reshape, Concatenate, Multiply
from keras.layers import Layer, LeakyReLU, PReLU, BatchNormalization, Add, Lambda
import tensorflow as tf

def SubpixelConv2D(scale = 2):
    def subpixel(x):
        import tensorflow as tf
        return tf.depth_to_space(x, scale)
    return Lambda(subpixel)


def crop(start, end):
    # Crops (or slices) a Tensor on a given dimension from start to end
    # example : to crop tensor x[:, :, 5:10]
    # call slice(2, 5, 10) as you want to crop on the second dimension
    def func(x):
       return x[:, :, :, start: end]
    return Lambda(func)


class adapwt(Layer):
    def __init__(self, **kwargs):
        #self.output_dim = output_dim
        super(adapwt, self).__init__(**kwargs)

    def build(self, input_shape):
        # Create a trainable weight variable for this layer.
        self.W = self.add_weight(name='wt',shape=[1,1],initializer='uniform',trainable=True)
        super(adapwt, self).build(input_shape)  # Be sure to call this at the end

    def call(self, x):
        return x*self.W

    def compute_output_shape(self, input_shape):
        return input_shape


def squeeze_excite(inp_layer, ratio):
    '''
    inp_layer : input tensor
    ratio : speaks for itself
    '''

    filters = inp_layer._keras_shape[-1]


    se = GlobalAveragePooling2D()(inp_layer)
    se = Reshape((1, 1, filters))(se)
    se = Dense(filters // ratio, activation = 'relu', kernel_initializer = 'he_normal', use_bias = True, bias_initializer = 'zeros')(se)
    se = Dense(filters, activation = 'sigmoid', kernel_initializer = 'he_normal', use_bias = True, bias_initializer = 'zeros')(se)

    se = Multiply()([inp_layer, se])

    return se


def inception(inp, f1, f13, f3, f133, f33, f5, gamma_init, trainable):

    '''
    f1 : no. of feature maps(channels) from receptive field 1x1

    f13: no. of features maps(channels) from 1x1 input to 3x3
    f3: no. of feature maps(channels) out from receptive field 3x3

    f133: no. of features maps(channels) from 1x1 input to next two 3x3s
    f33: no. of feature maps(channels) from 3x3 to 3x3
    f5: no. of feature maps(channels) out from receptive field 5x5

    inp: input tensor
    '''

    x1 = Conv2D(f1, (1,1), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(inp)
    x1 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x1)
    x1 = PReLU(shared_axes=[1,2])(x1)

    x3 = Conv2D(f13, (1,1), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(inp)
    x3 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x3)
    x3 = PReLU(shared_axes=[1,2])(x3)
    x3 = Conv2D(f3, (3,3), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x3)
    x3 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x3)
    x3 = PReLU(shared_axes=[1,2])(x3)

    x5 = Conv2D(f133, (1,1), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(inp)
    x5 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x5)
    x5 = PReLU(shared_axes=[1,2])(x5)
    x5 = Conv2D(f33, (3,3), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x5)
    x5 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x5)
    x5 = PReLU(shared_axes=[1,2])(x5)
    x5 = Conv2D(f5, (3,3), strides = (1,1), padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x5)
    x5 = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x5)
    x5 = PReLU(shared_axes=[1,2])(x5)

    x5 = Concatenate(axis = -1)([x1, x3, x5])

    return x5


def rise(x, f1, f13, f3, f133, f33, f5, ratio, gamma_init, trainable, beta_tr=False):
  x1 = inception(x, f1, f13, f3, f133, f33, f5, gamma_init, trainable)
  x1 = squeeze_excite(x1, ratio)
  if beta_tr:
    x=adapwt()(x)
  x1 = Add()([x1, x])
  return x1


def dil_out(x, d, m, gamma_init, trainable):

  '''
  d : no. of feature maps
  m : dilation rate
  x: input tensor
  '''

  #x=ZeroPadding2D(padding=2**m)(x)
  x=Conv2D(filters = d, kernel_size = 3, strides = 1, dilation_rate = m, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  x=BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x=PReLU(shared_axes=[1,2])(x)
  return x


def espy(x, d, level, gamma_init, trainable, beta_tr=False):
    '''
    d : no. of feature maps
    level : no. of levels of dilation rates (1,...,level)
    x: input tensor
    '''

    x0=Conv2D(filters = d, kernel_size = 1, strides = 1, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
    x0=BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x0)
    x0=PReLU(shared_axes=[1,2])(x0)

    x1 = dil_out(x0, d, 1, gamma_init, trainable)
    x1c = x1

    for m in range(level-1):
      x2 = dil_out(x0, d, m+2, gamma_init, trainable)
      if beta_tr:
        x1=adapwt()(x1)
      x2 = Add()([x1, x2])
      x1 = x2
      x2 = Concatenate(axis = -1)([x1c, x2])
      x1c = x2

    if beta_tr:
      x=adapwt()(x)
    x_out = Add()([x, x2])

    return x_out

def conv(x, ch, k, s, gamma_init, trainable):
  x = Conv2D(ch, k, strides = s, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  x = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x = PReLU(shared_axes = [1,2])(x)
  return x

def conv_trans(x, ch, k, s, gamma_init, trainable):
  x = Conv2DTranspose(ch, k, strides = s, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  x = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x = PReLU(shared_axes = [1,2])(x)
  return x

def convl(x, ch, k, s, gamma_init, trainable):
  x = Conv2D(ch, k, strides = s, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  #x = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x = LeakyReLU(alpha = 0.2)(x)
  return x

def convl_trans(x, ch, k, s, gamma_init, trainable):
  x = Conv2DTranspose(ch, k, strides = s, padding = 'same', use_bias = True, kernel_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  #x = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x = LeakyReLU(alpha = 0.2)(x)
  return x

def depthwise_conv(x, ch, k, s, gamma_init, trainable):
  x = DepthwiseConv2D(depth_multiplier=ch, kernel_size=k, strides = s, padding = 'same', use_bias = True, depthwise_initializer = 'he_normal', bias_initializer = 'zeros')(x)
  x = BatchNormalization(gamma_initializer = gamma_init, trainable = trainable)(x)
  x = PReLU(shared_axes = [1,2])(x)
  return x