def _image_to_head(self, is_training, reuse=False):
with tf.variable_scope(self._scope, self._scope, reuse=reuse):
# [VGG16] conv1
# input shape : 224 * 224 * 3
# output shape : 112 * 112 * 64
net = slim.repeat(self._image, 2, slim.conv2d, 64, [3, 3],
trainable=False, scope='conv1')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool1')
# [VGG16] conv2
# input shape : 112 * 112 * 64
# output shape : 56 * 56 * 128
net = slim.repeat(net, 2, slim.conv2d, 128, [3, 3],
trainable=False, scope='conv2')
net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool2')
# [Hand Detection] REMOVE net = slim.max_pool2d(net, [2, 2], padding='SAME', scope='pool3')
# [Hand Detection] conv3
# input shape : 56 * 56 * 128
# output shape : 56 * 56 * 256
net = slim.repeat(net, 3, slim.conv2d, 256, [3, 3],
trainable=is_training, scope='conv3')
to_be_normalized_1 = net
# [Hand Detection] conv4
# input shape : 56 * 56 * 256
# output shape : 56 * 56 * 256
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv4')
to_be_normalized_2 = net
# [Hand Detection] conv5
# input shape : 56 * 56 * 256
# output shape : 56 * 56 * 256
net = slim.repeat(net, 3, slim.conv2d, 512, [3, 3],
trainable=is_training, scope='conv5')
to_be_normalized_3 = net
return to_be_normalized_1, to_be_normalized_2, to_be_normalized_3
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