Coverage for /home/ubuntu/flatiron/python/flatiron/tf/metric.py: 30%

1from typing import Any, Union # noqa F401

2import numpy

4import tensorflow as tf

6import flatiron.tf.tools as fi_tftools

8Arraylike = Union[numpy.ndarray, tf.Tensor]

9# ------------------------------------------------------------------------------

12def get(config):

13 # type: (dict) -> Any

14 '''

15 Get function from this module.

17 Args:

18 config (dict): Optimizer config.

20 Returns:

21 function: Module function.

22 '''

23 return fi_tftools.get(config, __name__, 'keras.api.metrics')

24# ------------------------------------------------------------------------------

27def intersection_over_union(y_true, y_pred, smooth=1.0):

28 # type: (Arraylike, Arraylike, float) -> tf.Tensor

29 r'''

30 Intersection over union metric.

32 See: https://medium.com/analytics-vidhya/iou-intersection-over-union-705a39e7acef

34 Equation:

36 .. math::

37 :nowrap:

39 \begin{alignat*}{3}

40 \definecolor{blue2}{rgb}{0.58, 0.71, 0.9}

41 \definecolor{cyan2}{rgb}{0.71, 0.93, 0.95}

42 \definecolor{green2}{rgb}{0.63, 0.82, 0.48}

43 \definecolor{light1}{rgb}{0.64, 0.64, 0.64}

44 \definecolor{red2}{rgb}{0.87, 0.58, 0.56}

46 \color{cyan2} IOU (y, \hat{y}, S) && = \frac{I + S}{U + S}

47 \end{alignat*}

49 Terms:

51 .. math::

52 :nowrap:

54 \begin{alignat*}{3}

55 intersection & \rightarrow \color{red2}

56 I(y, \hat{y}) && = \sum{(y_i * \hat{y_i})}

57 \\

58 union & \rightarrow \color{green2}

59 U(y, \hat{y}) && = \sum{(y_i + \hat{y_i})} - I(y_i, \hat{y_i})

60 \\

61 \text{smoothing factor} & \rightarrow \color{blue2} S

62 \\

63 \text{expansion} & \rightarrow

64 \color{cyan2} IOU(y, \hat{y}, S) && =

65 \frac{

66 \color{red2} \sum{(y_i * \hat{y_i})}

67 \color{white} + \color{blue2} S

68 }{

69 \color{green2} \sum{(y_i + \hat{y_i})} - \sum{(y_i * \hat{y_i})}

70 \color{white} + \color{blue2} S

71 }

72 \end{alignat*}

74 Args:

75 y_true (NDArray or Tensor): True labels.

76 y_pred (NDArray or Tensor): Predicted labels.

77 smooth (float, optional): Smoothing factor. Default: 1.0

79 Returns:

80 tf.Tensor: IOU metric.

81 '''

82 y_true = tf.cast(y_true, dtype='float16')

83 y_pred = tf.cast(y_pred, dtype='float16')

84 yt = tf.reshape(y_true, [-1])

85 yp = tf.reshape(y_pred, [-1])

86 i = tf.reduce_sum(yt * yp)

87 u = tf.reduce_sum(yt) + tf.reduce_sum(yp) - i

88 iou = (i + smooth) / (u + smooth)

89 return iou

92def jaccard(y_true, y_pred):

93 # type: (Arraylike, Arraylike) -> tf.Tensor

94 r'''

95 Jaccard metric.

97 See: https://en.wikipedia.org/wiki/Jaccard_index

99 Equation:

100

101 .. math::

102 :nowrap:

103

104 \begin{alignat*}{3}

105 \definecolor{blue2}{rgb}{0.58, 0.71, 0.9}

106 \definecolor{cyan2}{rgb}{0.71, 0.93, 0.95}

107 \definecolor{green2}{rgb}{0.63, 0.82, 0.48}

108 \definecolor{light1}{rgb}{0.64, 0.64, 0.64}

109 \definecolor{red2}{rgb}{0.87, 0.58, 0.56}

110

111 \color{cyan2} Jacc(y, \hat{y}) && =

112 \frac{1}{N} \sum_{i=0}^{N} \frac{I + 1}{U + 1}

113 \end{alignat*}

114

115 Terms:

116

117 .. math::

118 :nowrap:

119

120 \begin{alignat*}{3}

121 intersection & \rightarrow \color{red2}

122 I(y, \hat{y}) && = \sum{(y_i * \hat{y_i})}

123 \\

124 union & \rightarrow \color{green2}

125 U(y, \hat{y}) && = \sum{(y_i + \hat{y_i})} - I(y_i, \hat{y_i})

126 \\

127 \text{expansion} & \rightarrow

128 \color{cyan2} Jacc(y, \hat{y}) && =

129 \frac{1}{N} \sum_{i=0}^{N}

130 \frac{

131 \color{red2} \sum{(y_i * \hat{y_i})}

132 \color{white} + 1

133 }{

134 \color{green2} \sum{(y_i + \hat{y_i})} - \sum{(y_i * \hat{y_i})}

135 \color{white} + 1

136 }

137 \end{alignat*}

138

139 Args:

140 y_true (NDArray or Tensor): True labels.

141 y_pred (NDArray or Tensor): Predicted labels.

142

143 Returns:

144 tf.Tensor: Jaccard metric.

145 '''

146 y_true = tf.cast(y_true, dtype='float16')

147 y_pred = tf.cast(y_pred, dtype='float16')

148 i = tf.reduce_sum(y_true * y_pred)

149 u = tf.reduce_sum(y_true + y_pred) - i

150 jacc = (i + 1.0) / (u + 1.0)

151 jacc = tf.reduce_mean(jacc)

152 return jacc

153

154

155def dice(y_true, y_pred, smooth=1.0):

156 # type: (Arraylike, Arraylike, float) -> tf.Tensor

157 r'''

158 Dice metric.

159

160 See: https://en.wikipedia.org/wiki/S%C3%B8rensen%E2%80%93Dice_coefficient

161

162 Equation:

163

164 .. math::

165 :nowrap:

166

167 \begin{alignat*}{3}

168 \definecolor{blue2}{rgb}{0.58, 0.71, 0.9}

169 \definecolor{cyan2}{rgb}{0.71, 0.93, 0.95}

170 \definecolor{green2}{rgb}{0.63, 0.82, 0.48}

171 \definecolor{light1}{rgb}{0.64, 0.64, 0.64}

172 \definecolor{red2}{rgb}{0.87, 0.58, 0.56}

173

174 \color{cyan2} Dice(y, \hat{y}) && = \frac{2 * I + S}{U + S}

175 \end{alignat*}

176

177 Terms:

178

179 .. math::

180 :nowrap:

181

182 \begin{alignat*}{3}

183 intersection & \rightarrow \color{red2}

184 I(y, \hat{y}) && = \sum{(y_i * \hat{y_i})}

185 \\

186 \text{union} & \rightarrow \color{green2}

187 U(y, \hat{y}) && = \sum{(y_i + \hat{y_i})}

188 \\

189 \text{smoothing factor} & \rightarrow \color{blue2} S

190 \\

191 \text{expansion} & \rightarrow

192 \color{cyan2} Dice(y, \hat{y}, S) && =

193 \frac{

194 \color{white} 2 * \color{red2} \sum{(y_i * \hat{y_i})}

195 \color{white} + \color{blue2} S

196 }{

197 \color{green2} \sum{(y_i + \hat{y_i})}

198 \color{white} + \color{blue2} S

199 }

200 \end{alignat*}

201

202 Args:

203 y_true (NDArray or Tensor): True labels.

204 y_pred (NDArray or Tensor): Predicted labels.

205 smooth (float, optional): Smoothing factor. Default: 1.0

206

207 Returns:

208 tf.Tensor: Dice metric.

209 '''

210 y_true = tf.cast(y_true, dtype='float16')

211 y_pred = tf.cast(y_pred, dtype='float16')

212 yt = tf.reshape(y_true, [-1])

213 yp = tf.reshape(y_pred, [-1])

214 i = tf.reduce_sum(yt * yp)

215 u = tf.reduce_sum(yt) + tf.reduce_sum(yp)

216 dice = (2.0 * i + smooth) / (u + smooth)

217 return dice