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from models.network import NetWork import tensorflow as tf
class ResNet38(NetWork): def setup(self, is_training, num_classes): pass
class ResNet50(NetWork): pass
class ResNet101(NetWork): '''Network definition.
Args: is_training: whether to update the running mean and variance of the batch normalisation layer. If the batch size is small, it is better to keep the running mean and variance of the-pretrained model frozen. num_classes: number of classes to predict (including background). ''' def setup(self, is_training, num_classes): (self.feed('data') .conv([7, 7], 64, [2, 2], biased=False, relu=False, name='conv1') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn_conv1') .max_pool([3, 3], [2, 2], name='pool1') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res2a_branch1') .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch1')) (self.feed('pool1') .conv([1, 1], 64, [1, 1], biased=False, relu=False, name='res2a_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2a') .conv([3, 3], 64, [1, 1], biased=False, relu=False, name='res2a_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2b') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res2a_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch2c')) (self.feed('bn2a_branch1', 'bn2a_branch2c') # output_stride = 4 .add(name='res2a') .relu(name='res2a_relu') .conv([1, 1], 64, [1, 1], biased=False, relu=False, name='res2b_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2a') .conv([3, 3], 64, [1, 1], biased=False, relu=False, name='res2b_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2b') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res2b_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn2b_branch2c')) (self.feed('res2a_relu', 'bn2b_branch2c') .add(name='res2b') .relu(name='res2b_relu') .conv([1, 1], 64, [1, 1], biased=False, relu=False, name='res2c_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2a') .conv([3, 3], 64, [1, 1], biased=False, relu=False, name='res2c_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2b') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res2c_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn2c_branch2c')) (self.feed('res2b_relu', 'bn2c_branch2c') .add(name='res2c') .relu(name='res2c_relu') .conv([1, 1], 512, [2, 2], biased=False, relu=False, name='res3a_branch1') .batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch1')) (self.feed('res2c_relu') .conv([1, 1], 128, [2, 2], biased=False, relu=False, name='res3a_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2a') .conv([3, 3], 128, [1, 1], biased=False, relu=False, name='res3a_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2b') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res3a_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch2c')) (self.feed('bn3a_branch1', 'bn3a_branch2c') # output_stride = 8 .add(name='res3a') .relu(name='res3a_relu') .conv([1, 1], 128, [1, 1], biased=False, relu=False, name='res3b1_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2a') .conv([3, 3], 128, [1, 1], biased=False, relu=False, name='res3b1_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2b') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res3b1_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b1_branch2c')) (self.feed('res3a_relu', 'bn3b1_branch2c') .add(name='res3b1') .relu(name='res3b1_relu') .conv([1, 1], 128, [1, 1], biased=False, relu=False, name='res3b2_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2a') .conv([3, 3], 128, [1, 1], biased=False, relu=False, name='res3b2_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2b') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res3b2_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b2_branch2c')) (self.feed('res3b1_relu', 'bn3b2_branch2c') .add(name='res3b2') .relu(name='res3b2_relu') .conv([1, 1], 128, [1, 1], biased=False, relu=False, name='res3b3_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2a') .conv([3, 3], 128, [1, 1], biased=False, relu=False, name='res3b3_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2b') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res3b3_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn3b3_branch2c')) (self.feed('res3b2_relu', 'bn3b3_branch2c') .add(name='res3b3') .relu(name='res3b3_relu') .conv([1, 1], 1024, [2, 2], biased=False, relu=False, name='res4a_branch1') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch1')) (self.feed('res3b3_relu') .conv([1, 1], 256, [2, 2], biased=False, relu=False, name='res4a_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4a_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4a_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4a_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch2c')) (self.feed('bn4a_branch1', 'bn4a_branch2c') # output_stride = 16 .add(name='res4a') .relu(name='res4a_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b1_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b1_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b1_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b1_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b1_branch2c')) (self.feed('res4a_relu', 'bn4b1_branch2c') .add(name='res4b1') .relu(name='res4b1_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b2_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b2_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b2_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b2_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b2_branch2c')) (self.feed('res4b1_relu', 'bn4b2_branch2c') .add(name='res4b2') .relu(name='res4b2_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b3_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b3_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b3_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b3_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b3_branch2c')) (self.feed('res4b2_relu', 'bn4b3_branch2c') .add(name='res4b3') .relu(name='res4b3_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b4_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b4_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b4_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b4_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b4_branch2c')) (self.feed('res4b3_relu', 'bn4b4_branch2c') .add(name='res4b4') .relu(name='res4b4_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b5_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b5_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b5_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b5_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b5_branch2c')) (self.feed('res4b4_relu', 'bn4b5_branch2c') .add(name='res4b5') .relu(name='res4b5_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b6_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b6_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b6_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b6_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b6_branch2c')) (self.feed('res4b5_relu', 'bn4b6_branch2c') .add(name='res4b6') .relu(name='res4b6_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b7_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b7_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b7_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b7_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b7_branch2c')) (self.feed('res4b6_relu', 'bn4b7_branch2c') .add(name='res4b7') .relu(name='res4b7_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b8_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b8_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b8_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b8_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b8_branch2c')) (self.feed('res4b7_relu', 'bn4b8_branch2c') .add(name='res4b8') .relu(name='res4b8_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b9_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b9_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b9_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b9_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b9_branch2c')) (self.feed('res4b8_relu', 'bn4b9_branch2c') .add(name='res4b9') .relu(name='res4b9_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b10_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b10_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b10_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b10_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b10_branch2c')) (self.feed('res4b9_relu', 'bn4b10_branch2c') .add(name='res4b10') .relu(name='res4b10_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b11_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b11_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b11_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b11_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b11_branch2c')) (self.feed('res4b10_relu', 'bn4b11_branch2c') .add(name='res4b11') .relu(name='res4b11_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b12_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b12_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b12_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b12_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b12_branch2c')) (self.feed('res4b11_relu', 'bn4b12_branch2c') .add(name='res4b12') .relu(name='res4b12_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b13_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b13_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b13_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b13_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b13_branch2c')) (self.feed('res4b12_relu', 'bn4b13_branch2c') .add(name='res4b13') .relu(name='res4b13_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b14_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b14_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b14_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b14_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b14_branch2c')) (self.feed('res4b13_relu', 'bn4b14_branch2c') .add(name='res4b14') .relu(name='res4b14_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b15_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b15_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b15_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b15_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b15_branch2c')) (self.feed('res4b14_relu', 'bn4b15_branch2c') .add(name='res4b15') .relu(name='res4b15_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b16_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b16_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b16_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b16_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b16_branch2c')) (self.feed('res4b15_relu', 'bn4b16_branch2c') .add(name='res4b16') .relu(name='res4b16_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b17_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b17_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b17_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b17_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b17_branch2c')) (self.feed('res4b16_relu', 'bn4b17_branch2c') .add(name='res4b17') .relu(name='res4b17_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b18_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b18_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b18_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b18_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b18_branch2c')) (self.feed('res4b17_relu', 'bn4b18_branch2c') .add(name='res4b18') .relu(name='res4b18_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b19_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b19_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b19_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b19_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b19_branch2c')) (self.feed('res4b18_relu', 'bn4b19_branch2c') .add(name='res4b19') .relu(name='res4b19_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b20_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b20_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b20_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b20_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b20_branch2c')) (self.feed('res4b19_relu', 'bn4b20_branch2c') .add(name='res4b20') .relu(name='res4b20_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b21_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b21_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b21_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b21_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b21_branch2c')) (self.feed('res4b20_relu', 'bn4b21_branch2c') .add(name='res4b21') .relu(name='res4b21_relu') .conv([1, 1], 256, [1, 1], biased=False, relu=False, name='res4b22_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2a') .conv([3, 3], 256, [1, 1], biased=False, relu=False, name='res4b22_branch2b') #.atrous_conv([3, 3], 256, 2, padding='SAME', biased=False, relu=False, name='res4b22_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2b') .conv([1, 1], 1024, [1, 1], biased=False, relu=False, name='res4b22_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn4b22_branch2c')) (self.feed('res4b21_relu', 'bn4b22_branch2c') .add(name='res4b22') .relu(name='res4b22_relu') .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='res5a_branch1') .batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch1')) (self.feed('res4b22_relu') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res5a_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2a') .atrous_conv([3, 3], 512, 2, padding='SAME', biased=False, relu=False, name='res5a_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2b') .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='res5a_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch2c')) (self.feed('bn5a_branch1', 'bn5a_branch2c') # output_stride = 16 .add(name='res5a') .relu(name='res5a_relu') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res5b_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2a') .atrous_conv([3, 3], 512, 4, padding='SAME', biased=False, relu=False, name='res5b_branch2b') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2b') .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='res5b_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn5b_branch2c')) (self.feed('res5a_relu', 'bn5b_branch2c') .add(name='res5b') .relu(name='res5b_relu') .conv([1, 1], 512, [1, 1], biased=False, relu=False, name='res5c_branch2a') .batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5c_branch2a') .atrous_conv([3, 3], 512, 8, padding='SAME', biased=False, relu=False, name='res5c_branch2b') .batch_normalization(activation_fn=tf.nn.relu, name='bn5c_branch2b', is_training=is_training) .conv([1, 1], 2048, [1, 1], biased=False, relu=False, name='res5c_branch2c') .batch_normalization(is_training=is_training, activation_fn=None, name='bn5c_branch2c')) (self.feed('res5b_relu', 'bn5c_branch2c') .add(name='res5c') .relu(name='res5c_relu'))
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from models.network import NetWork
import tensorflow as tf
class ResNet38(NetWork):
def setup(self, is_training, num_classes):
pass
class ResNet50(NetWork):
pass
class ResNet101(NetWork):
'''Network definition.
The text was updated successfully, but these errors were encountered: