Coverage for /pythoncovmergedfiles/medio/medio/usr/local/lib/python3.8/site-packages/keras/src/applications/resnet_v2.py: 50%

1# Copyright 2019 The TensorFlow Authors. All Rights Reserved. 

2# 

3# Licensed under the Apache License, Version 2.0 (the "License"); 

4# you may not use this file except in compliance with the License. 

5# You may obtain a copy of the License at 

6# 

7# http://www.apache.org/licenses/LICENSE-2.0 

8# 

9# Unless required by applicable law or agreed to in writing, software 

10# distributed under the License is distributed on an "AS IS" BASIS, 

11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. 

12# See the License for the specific language governing permissions and 

13# limitations under the License. 

14# ============================================================================== 

15 

16"""ResNet v2 models for Keras. 

17 

18Reference: 

19 - [Identity Mappings in Deep Residual Networks]( 

20 https://arxiv.org/abs/1603.05027) (CVPR 2016) 

21""" 

22 

23from keras.src.applications import imagenet_utils 

24from keras.src.applications import resnet 

25 

26# isort: off 

27from tensorflow.python.util.tf_export import keras_export 

28 

29 

30@keras_export( 

31 "keras.applications.resnet_v2.ResNet50V2", "keras.applications.ResNet50V2" 

32) 

33def ResNet50V2( 

34 include_top=True, 

35 weights="imagenet", 

36 input_tensor=None, 

37 input_shape=None, 

38 pooling=None, 

39 classes=1000, 

40 classifier_activation="softmax", 

41): 

42 """Instantiates the ResNet50V2 architecture.""" 

43 

44 def stack_fn(x): 

45 x = resnet.stack2(x, 64, 3, name="conv2") 

46 x = resnet.stack2(x, 128, 4, name="conv3") 

47 x = resnet.stack2(x, 256, 6, name="conv4") 

48 return resnet.stack2(x, 512, 3, stride1=1, name="conv5") 

49 

50 return resnet.ResNet( 

51 stack_fn, 

52 True, 

53 True, 

54 "resnet50v2", 

55 include_top, 

56 weights, 

57 input_tensor, 

58 input_shape, 

59 pooling, 

60 classes, 

61 classifier_activation=classifier_activation, 

62 ) 

63 

64 

65@keras_export( 

66 "keras.applications.resnet_v2.ResNet101V2", "keras.applications.ResNet101V2" 

67) 

68def ResNet101V2( 

69 include_top=True, 

70 weights="imagenet", 

71 input_tensor=None, 

72 input_shape=None, 

73 pooling=None, 

74 classes=1000, 

75 classifier_activation="softmax", 

76): 

77 """Instantiates the ResNet101V2 architecture.""" 

78 

79 def stack_fn(x): 

80 x = resnet.stack2(x, 64, 3, name="conv2") 

81 x = resnet.stack2(x, 128, 4, name="conv3") 

82 x = resnet.stack2(x, 256, 23, name="conv4") 

83 return resnet.stack2(x, 512, 3, stride1=1, name="conv5") 

84 

85 return resnet.ResNet( 

86 stack_fn, 

87 True, 

88 True, 

89 "resnet101v2", 

90 include_top, 

91 weights, 

92 input_tensor, 

93 input_shape, 

94 pooling, 

95 classes, 

96 classifier_activation=classifier_activation, 

97 ) 

98 

99 

100@keras_export( 

101 "keras.applications.resnet_v2.ResNet152V2", "keras.applications.ResNet152V2" 

102) 

103def ResNet152V2( 

104 include_top=True, 

105 weights="imagenet", 

106 input_tensor=None, 

107 input_shape=None, 

108 pooling=None, 

109 classes=1000, 

110 classifier_activation="softmax", 

111): 

112 """Instantiates the ResNet152V2 architecture.""" 

113 

114 def stack_fn(x): 

115 x = resnet.stack2(x, 64, 3, name="conv2") 

116 x = resnet.stack2(x, 128, 8, name="conv3") 

117 x = resnet.stack2(x, 256, 36, name="conv4") 

118 return resnet.stack2(x, 512, 3, stride1=1, name="conv5") 

119 

120 return resnet.ResNet( 

121 stack_fn, 

122 True, 

123 True, 

124 "resnet152v2", 

125 include_top, 

126 weights, 

127 input_tensor, 

128 input_shape, 

129 pooling, 

130 classes, 

131 classifier_activation=classifier_activation, 

132 ) 

133 

134 

135@keras_export("keras.applications.resnet_v2.preprocess_input") 

136def preprocess_input(x, data_format=None): 

137 return imagenet_utils.preprocess_input( 

138 x, data_format=data_format, mode="tf" 

139 ) 

140 

141 

142@keras_export("keras.applications.resnet_v2.decode_predictions") 

143def decode_predictions(preds, top=5): 

144 return imagenet_utils.decode_predictions(preds, top=top) 

145 

146 

147preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format( 

148 mode="", 

149 ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF, 

150 error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC, 

151) 

152decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__ 

153 

154DOC = """ 

155 

156 Reference: 

157 - [Identity Mappings in Deep Residual Networks]( 

158 https://arxiv.org/abs/1603.05027) (CVPR 2016) 

159 

160 For image classification use cases, see 

161 [this page for detailed examples]( 

162 https://keras.io/api/applications/#usage-examples-for-image-classification-models). 

163 

164 For transfer learning use cases, make sure to read the 

165 [guide to transfer learning & fine-tuning]( 

166 https://keras.io/guides/transfer_learning/). 

167 

168 Note: each Keras Application expects a specific kind of input preprocessing. 

169 For ResNetV2, call `tf.keras.applications.resnet_v2.preprocess_input` on your 

170 inputs before passing them to the model. 

171 `resnet_v2.preprocess_input` will scale input pixels between -1 and 1. 

172 

173 Args: 

174 include_top: whether to include the fully-connected 

175 layer at the top of the network. 

176 weights: one of `None` (random initialization), 

177 'imagenet' (pre-training on ImageNet), 

178 or the path to the weights file to be loaded. 

179 input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) 

180 to use as image input for the model. 

181 input_shape: optional shape tuple, only to be specified 

182 if `include_top` is False (otherwise the input shape 

183 has to be `(224, 224, 3)` (with `'channels_last'` data format) 

184 or `(3, 224, 224)` (with `'channels_first'` data format). 

185 It should have exactly 3 inputs channels, 

186 and width and height should be no smaller than 32. 

187 E.g. `(200, 200, 3)` would be one valid value. 

188 pooling: Optional pooling mode for feature extraction 

189 when `include_top` is `False`. 

190 - `None` means that the output of the model will be 

191 the 4D tensor output of the 

192 last convolutional block. 

193 - `avg` means that global average pooling 

194 will be applied to the output of the 

195 last convolutional block, and thus 

196 the output of the model will be a 2D tensor. 

197 - `max` means that global max pooling will 

198 be applied. 

199 classes: optional number of classes to classify images 

200 into, only to be specified if `include_top` is True, and 

201 if no `weights` argument is specified. 

202 classifier_activation: A `str` or callable. The activation function to use 

203 on the "top" layer. Ignored unless `include_top=True`. Set 

204 `classifier_activation=None` to return the logits of the "top" layer. 

205 When loading pretrained weights, `classifier_activation` can only 

206 be `None` or `"softmax"`. 

207 

208 Returns: 

209 A `keras.Model` instance. 

210""" 

211 

212setattr(ResNet50V2, "__doc__", ResNet50V2.__doc__ + DOC) 

213setattr(ResNet101V2, "__doc__", ResNet101V2.__doc__ + DOC) 

214setattr(ResNet152V2, "__doc__", ResNet152V2.__doc__ + DOC) 

215