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#! /usr/bin/python
# -*- coding: utf-8 -*-
import tensorflow as tf
import tensorlayer as tl
from tensorlayer import logging
from tensorlayer.decorators import deprecated_alias
from tensorlayer.layers.core import Layer
from tensorlayer.layers.utils import quantize
__all__ = ['BinaryConv2d']
class BinaryConv2d(Layer):
    The :class:`BinaryConv2d` class is a 2D binary CNN layer, which weights are either -1 or 1 while inference.
    Note that, the bias vector would not be binarized.
    Parameters
    ----------
    n_filter : int
        The number of filters.
    filter_size : tuple of int
        The filter size (height, width).
    strides : tuple of int
        The sliding window strides of corresponding input dimensions.
        It must be in the same order as the ``shape`` parameter.
    act : activation function
        The activation function of this layer.
    padding : str
        The padding algorithm type: "SAME" or "VALID".
    use_gemm : boolean
        If True, use gemm instead of ``tf.matmul`` for inference.
        TODO: support gemm
    data_format : str
        "channels_last" (NHWC, default) or "channels_first" (NCHW).
    dilation_rate : tuple of int
        Specifying the dilation rate to use for dilated convolution.
    W_init : initializer
        The initializer for the the weight matrix.
    b_init : initializer or None
        The initializer for the the bias vector. If None, skip biases.
    in_channels : int
        The number of in channels.
    name : None or str
        A unique layer name.
    Examples
    ---------
    With TensorLayer
    >>> net = tl.layers.Input([8, 100, 100, 32], name='input')
    >>> binaryconv2d = tl.layers.QuanConv2d(
    ...     n_filter=64, filter_size=(3, 3), strides=(2, 2), act=tf.nn.relu, in_channels=32, name='binaryconv2d'
    ... )(net)
    >>> print(binaryconv2d)
    >>> output shape : (8, 50, 50, 64)
    def __init__(
            self,
            n_filter=32,
            filter_size=(3, 3),
            strides=(1, 1),
            act=None,
            padding='SAME',
            use_gemm=False,
            data_format="channels_last",
            dilation_rate=(1, 1),
            W_init=tl.initializers.truncated_normal(stddev=0.02),
            b_init=tl.initializers.constant(value=0.0),
            in_channels=None,
            name=None  # 'binary_cnn2d',
        super().__init__(name, act=act)
        self.n_filter = n_filter
        self.filter_size = filter_size
        self.strides = self._strides = strides
        self.padding = padding
        self.use_gemm = use_gemm
        self.data_format = data_format
        self._dilation_rate = self.dilation_rate = dilation_rate
        self.W_init = W_init
        self.b_init = b_init
        self.in_channels = in_channels
        if self.in_channels:
            self.build(None)
            self._built = True
        logging.info(
            "BinaryConv2d %s: n_filter: %d filter_size: %s strides: %s pad: %s act: %s" % (
                self.name, n_filter, str(filter_size), str(strides), padding,
                self.act.__name__ if self.act is not None else 'No Activation'
        if use_gemm:
            raise Exception("TODO. The current version use tf.matmul for inferencing.")
        if len(self.strides) != 2:
            raise ValueError("len(strides) should be 2.")
    def __repr__(self):
        actstr = self.act.__name__ if self.act is not None else 'No Activation'
        s = (
            '{classname}(in_channels={in_channels}, out_channels={n_filter}, kernel_size={filter_size}'
            ', strides={strides}, padding={padding}'
        if self.dilation_rate != (1, ) * len(self.dilation_rate):
            s += ', dilation={dilation_rate}'
        if self.b_init is None:
            s += ', bias=False'
        s += (', ' + actstr)
        if self.name is not None:
            s += ', name=\'{name}\''
        s += ')'
        return s.format(classname=self.__class__.__name__, **self.__dict__)
    def build(self, inputs_shape):
        if self.data_format == 'channels_last':
            self.data_format = 'NHWC'
            if self.in_channels is None:
                self.in_channels = inputs_shape[-1]
            self._strides = [1, self._strides[0], self._strides[1], 1]
            self._dilation_rate = [1, self._dilation_rate[0], self._dilation_rate[1], 1]
        elif self.data_format == 'channels_first':
            self.data_format = 'NCHW'
            if self.in_channels is None:
                self.in_channels = inputs_shape[1]
            self._strides = [1, 1, self._strides[0], self._strides[1]]
            self._dilation_rate = [1, 1, self._dilation_rate[0], self._dilation_rate[1]]
        else:
            raise Exception("data_format should be either channels_last or channels_first")
        self.filter_shape = (self.filter_size[0], self.filter_size[1], self.in_channels, self.n_filter)
        self.W = self._get_weights("filters", shape=self.filter_shape, init=self.W_init)
        if self.b_init:
            self.b = self._get_weights("biases", shape=(self.n_filter, ), init=self.b_init)
    def forward(self, inputs):
        _W = quantize(self.W)
        outputs = tf.nn.conv2d(
            input=inputs, filters=_W, strides=self._strides, padding=self.padding, data_format=self.data_format,
            dilations=self._dilation_rate, name=self.name
        if self.b_init:
            outputs = tf.nn.bias_add(outputs, self.b, data_format=self.data_format, name='bias_add')
        if self.act:
            outputs = self.act(outputs)
        return outputs
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