def _build_stem(self, inputs):
    'Build SpineNet stem.'
    x = layers.Conv2D(filters=64, kernel_size=7, strides=2, use_bias=False, padding='same', kernel_initializer=self._kernel_initializer, kernel_regularizer=self._kernel_regularizer, bias_regularizer=self._bias_regularizer)(inputs)
    x = self._norm(axis=self._bn_axis, momentum=self._norm_momentum, epsilon=self._norm_epsilon)(x)
    x = tf_utils.get_activation(self._activation_fn)(x)
    x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    net = []
    for i in range(self._num_init_blocks):
        x = self._block_group(inputs=x, filters=int((FILTER_SIZE_MAP[2] * self._filter_size_scale)), strides=1, block_fn_cand=self._init_block_fn, block_repeats=self._block_repeats, name='stem_block_{}'.format((i + 1)))
        net.append(x)
    return net
Build SpineNet stem.
def _build_stem(self, inputs):
    
    x = layers.Conv2D(filters=64, kernel_size=7, strides=2, use_bias=False, padding='same', kernel_initializer=self._kernel_initializer, kernel_regularizer=self._kernel_regularizer, bias_regularizer=self._bias_regularizer)(inputs)
    x = self._norm(axis=self._bn_axis, momentum=self._norm_momentum, epsilon=self._norm_epsilon)(x)
    x = tf_utils.get_activation(self._activation_fn)(x)
    x = layers.MaxPool2D(pool_size=3, strides=2, padding='same')(x)
    net = []
    for i in range(self._num_init_blocks):
        x = self._block_group(inputs=x, filters=int((FILTER_SIZE_MAP[2] * self._filter_size_scale)), strides=1, block_fn_cand=self._init_block_fn, block_repeats=self._block_repeats, name='stem_block_{}'.format((i + 1)))
        net.append(x)
    return net