SSD Network Architecture Special Lyaers--keras version

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"""Some special pupropse layers for SSD."""import keras.backend as Kfrom keras.engine.topology import InputSpecfrom keras.engine.topology import Layerimport numpy as npimport tensorflow as tfclass Normalize(Layer):    """Normalization layer as described in ParseNet paper.    # Arguments        scale: Default feature scale.    # Input shape        4D tensor with shape:        `(samples, channels, rows, cols)` if dim_ordering=‘th‘        or 4D tensor with shape:        `(samples, rows, cols, channels)` if dim_ordering=‘tf‘.    # Output shape        Same as input
    """    def __init__(self, scale, **kwargs):        if K.image_dim_ordering() == ‘tf‘:            self.axis = 3        else:            self.axis = 1        self.scale = scale        super(Normalize, self).__init__(**kwargs)    def build(self, input_shape):        self.input_spec = [InputSpec(shape=input_shape)]        shape = (input_shape[self.axis],)        init_gamma = self.scale * np.ones(shape)        self.gamma = K.variable(init_gamma, name=‘{}_gamma‘.format(self.name))        self.trainable_weights = [self.gamma]    def call(self, x, mask=None):        output = K.l2_normalize(x, self.axis)        output *= self.gamma        return output
#上面這個層就不多說了，在keras中自訂Layer中以及詳細介紹了，下面還是說說那個產生default box的層吧class PriorBox(Layer):    """Generate the prior boxes of designated sizes and aspect ratios.    # Arguments        img_size: Size of the input image as tuple (w, h).        min_size: Minimum box size in pixels.        max_size: Maximum box size in pixels.        aspect_ratios: List of aspect ratios of boxes.        flip: Whether to consider reverse aspect ratios.        variances: List of variances for x, y, w, h.        clip: Whether to clip the prior‘s coordinates            such that they are within [0, 1].    # Input shape        4D tensor with shape:        `(samples, channels, rows, cols)` if dim_ordering=‘th‘        or 4D tensor with shape:        `(samples, rows, cols, channels)` if dim_ordering=‘tf‘.    # Output shape        3D tensor with shape:        (samples, num_boxes, 8)
    """    def __init__(self, img_size, min_size, max_size=None, aspect_ratios=None,                 flip=True, variances=[0.1], clip=True, **kwargs):        if K.image_dim_ordering() == ‘tf‘:            self.waxis = 2            self.haxis = 1        else:            self.waxis = 3            self.haxis = 2        self.img_size = img_size#         print "self.img_size"#         print self.img_size#         print "self.img_siez"        if min_size <= 0:            raise Exception(‘min_size must be positive.‘)        self.min_size = min_size        self.max_size = max_size        self.aspect_ratios = [1.0]        if max_size:            if max_size < min_size:                raise Exception(‘max_size must be greater than min_size.‘)            self.aspect_ratios.append(1.0)        if aspect_ratios:            for ar in aspect_ratios:                if ar in self.aspect_ratios:                    continue                self.aspect_ratios.append(ar)                if flip:                    self.aspect_ratios.append(1.0 / ar)        self.variances = np.array(variances)        self.clip = True        super(PriorBox, self).__init__(**kwargs)    def get_output_shape_for(self, input_shape):#         print "input_shape start"#         print input_shape#         print "input_shape end"        num_priors_ = len(self.aspect_ratios)#         print "-----------------------------------"#         print num_priors        layer_width = input_shape[self.waxis]#         print layer_width        layer_height = input_shape[self.haxis]#         print layer_height#         print "----------------------------------"        num_boxes = num_priors_ * layer_width * layer_height#         print (input_shape[0], num_boxes, 8)        return (input_shape[0], num_boxes, 8)    def call(self, x, mask=None):#         print dir(x)        if hasattr(x, ‘_keras_shape‘):            input_shape = x._keras_shape#             print "1"        elif hasattr(K, ‘int_shape‘):            input_shape = K.int_shape(x)#             print "2"#         print input_shape        layer_width = input_shape[self.waxis]        layer_height = input_shape[self.haxis]        img_width = self.img_size[0]        img_height = self.img_size[1]#         print img_width,img_height,layer_width,layer_height        # define prior boxes shapes        box_widths = []        box_heights = []#         print self.min_size#         print self.aspect_ratios        for ar in self.aspect_ratios:            if ar == 1 and len(box_widths) == 0:                box_widths.append(self.min_size)                box_heights.append(self.min_size)            elif ar == 1 and len(box_widths) > 0:                box_widths.append(np.sqrt(self.min_size * self.max_size))                box_heights.append(np.sqrt(self.min_size * self.max_size))            elif ar != 1:                box_widths.append(self.min_size * np.sqrt(ar))                box_heights.append(self.min_size / np.sqrt(ar))        box_widths = 0.5 * np.array(box_widths)        box_heights = 0.5 * np.array(box_heights)#         print len(box_widths)#         print len(box_heights)        # define centers of prior boxes        step_x = img_width / layer_width        step_y = img_height / layer_height#         print step_x,step_y#         print img_width,img_height        linx = np.linspace(0.5 * step_x, img_width - 0.5 * step_x,                           layer_width)        liny = np.linspace(0.5 * step_y, img_height - 0.5 * step_y,                           layer_height)#         print linx.shape,liny.shape        centers_x, centers_y = np.meshgrid(linx, liny)        centers_x = centers_x.reshape(-1, 1)        centers_y = centers_y.reshape(-1, 1)#         print centers_x.shape,centers_y.shape        # define xmin, ymin, xmax, ymax of prior boxes        num_priors_ = len(self.aspect_ratios)#         print num_priors        prior_boxes = np.concatenate((centers_x, centers_y), axis=1)#         print prior_boxes.shape        prior_boxes = np.tile(prior_boxes, (1, 2 * num_priors_))#         print prior_boxes.shape        prior_boxes[:, ::4] -= box_widths        prior_boxes[:, 1::4] -= box_heights        prior_boxes[:, 2::4] += box_widths        prior_boxes[:, 3::4] += box_heights        prior_boxes[:, ::2] /= img_width        prior_boxes[:, 1::2] /= img_height        prior_boxes = prior_boxes.reshape(-1, 4)        if self.clip:            prior_boxes = np.minimum(np.maximum(prior_boxes, 0.0), 1.0)        # define variances        num_boxes = len(prior_boxes)        if len(self.variances) == 1:            variances = np.ones((num_boxes, 4)) * self.variances[0]        elif len(self.variances) == 4:            variances = np.tile(self.variances, (num_boxes, 1))        else:            raise Exception(‘Must provide one or four variances.‘)        prior_boxes = np.concatenate((prior_boxes, variances), axis=1)        prior_boxes_tensor = K.expand_dims(K.variable(prior_boxes), 0)        if K.backend() == ‘tensorflow‘:            pattern = [tf.shape(x)[0], 1, 1]            prior_boxes_tensor = tf.tile(prior_boxes_tensor, pattern)        elif K.backend() == ‘theano‘:            #TODO            pass#         print prior_boxes_tensor.shape        return prior_boxes_tensor# print dir(PriorBox((300,300),100))

 

SSD Network Architecture Special Lyaers--keras version