Python backend.not_equal方法代码示例

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def call(self,x,mask=None):
        conv_input,theta = x
        s = theta.shape
        theta = T.reshape(theta,[-1,s[2]])
        m = K.not_equal(conv_input,0.)

        #### For translation
        trans = _trans(theta)
        output = _transform_trans(trans, conv_input)
        output = output * K.cast(m,K.floatx())

        ### For rotation
        M = _fusion(theta)
        output = _transform_rot(M,output)

        return output 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    """RPN anchor classifier loss.
    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
    """
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Cross entropy loss
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    """RPN anchor classifier loss.

    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
    """
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Cross entropy loss
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    """RPN anchor classifier loss.

    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
    """
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Crossentropy loss
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def build_masked_loss(loss_function, mask_value):
    """Builds a loss function that masks based on targets

    Args:
        loss_function: The loss function to mask
        mask_value: The value to mask in the targets

    Returns:
        function: a loss function that acts like loss_function with masked inputs
    """

    def masked_loss_function(y_true, y_pred):
        mask = K.cast(K.not_equal(y_true, mask_value), K.floatx())
        return loss_function(y_true * mask, y_pred * mask)

    return masked_loss_function 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    '''RPN anchor classifier loss.

    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
    '''
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Cross entropy loss
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    """RPN anchor classifier loss.

    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for BG/FG.
    """
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Cross entropy loss
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def mbce(y_true, y_pred):
    """ Balanced sigmoid cross-entropy loss with masking """
    mask = K.not_equal(y_true, -1.0)
    mask = K.cast(mask, dtype=np.float32)
    num_examples = K.sum(mask, axis=1)
    pos = K.cast(K.equal(y_true, 1.0), dtype=np.float32)
    num_pos = K.sum(pos, axis=None)
    neg = K.cast(K.equal(y_true, 0.0), dtype=np.float32)
    num_neg = K.sum(neg, axis=None)
    pos_ratio = 1.0 - num_pos / num_neg
    mbce = mask * tf.nn.weighted_cross_entropy_with_logits(
            targets=y_true,
            logits=y_pred,
            pos_weight=pos_ratio
    )
    mbce = K.sum(mbce, axis=1) / num_examples
    return K.mean(mbce, axis=-1) 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def _drop_path(self, inputs):
        count = len(inputs)
        drops = K.switch(
            self.is_global,
            self._gen_global_path(count),
            self._gen_local_drops(count, self.p)
        )
        ave = K.zeros(shape=self.average_shape)
        for i in range(0, count):
            ave += inputs[i] * drops[i]
        sum = K.sum(drops)
        # Check that the sum is not 0 (global droppath can make it
        # 0) to avoid divByZero
        ave = K.switch(
            K.not_equal(sum, 0.),
            ave/sum,
            ave)
        return ave 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def masked_accuracy(y_true, y_pred):
    a = K.sum(K.cast(K.equal(y_true, K.round(y_pred)), K.floatx()))
    c = K.sum(K.cast(K.not_equal(y_true, 0.5), K.floatx()))
    acc = (a) / c
    return acc 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def call(self, x, mask=None):
        mask = K.not_equal(K.sum(K.abs(x), axis=2, keepdims=True), 0)
        n = K.sum(K.cast(mask, 'float32'), axis=1, keepdims=False)
        x_mean = K.sum(x, axis=1, keepdims=False) / (n + 1)
        return K.cast(x_mean, 'float32') 

# 需要导入模块: from keras import backend [as 别名]
# 或者: from keras.backend import not_equal [as 别名]
def rpn_class_loss_graph(rpn_match, rpn_class_logits):
    """RPN anchor classifier loss.

    rpn_match: [batch, anchors, 1]. Anchor match type. 1=positive,
               -1=negative, 0=neutral anchor.
    rpn_class_logits: [batch, anchors, 2]. RPN classifier logits for FG/BG.
    """
    # Squeeze last dim to simplify
    rpn_match = tf.squeeze(rpn_match, -1)
    # Get anchor classes. Convert the -1/+1 match to 0/1 values.
    anchor_class = K.cast(K.equal(rpn_match, 1), tf.int32)
    # Positive and Negative anchors contribute to the loss,
    # but neutral anchors (match value = 0) don't.
    indices = tf.where(K.not_equal(rpn_match, 0))
    # Pick rows that contribute to the loss and filter out the rest.
    rpn_class_logits = tf.gather_nd(rpn_class_logits, indices)
    anchor_class = tf.gather_nd(anchor_class, indices)
    # Crossentropy loss
    
    loss = K.sparse_categorical_crossentropy(target=anchor_class,
                                             output=rpn_class_logits,
                                             from_logits=True)
    #loss = tf.py_func(np.sort,[loss],tf.float32)
    #keep =tf.cast(tf.divide(tf.size(loss),2),tf.int32)
    #loss = loss[0:keep]
    loss = K.switch(tf.size(loss) > 0, K.mean(loss), tf.constant(0.0))
    return loss