from numpy import *
import os
import numpy as np
from PIL import Image
from numpy import random
import tensorflow as tf

def conv(input, kernel, biases, k_h, k_w, c_o, s_h, s_w,  padding="VALID", group=1):
    c_i = input.get_shape()[-1]
    assert c_i%group==0
    assert c_o%group==0
    convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
    
    
    if group==1:
        conv = convolve(input, kernel)
    else:
        input_groups = tf.split(input, group, 3)
        kernel_groups = tf.split(kernel, group, 3)
        output_groups = [convolve(i, k) for i,k in zip(input_groups, kernel_groups)]
        conv = tf.concat(output_groups, 3)
    return  tf.reshape(tf.nn.bias_add(conv, biases), [-1]+conv.get_shape().as_list()[1:])

class AlexNet(object):
  def __init__(self):
    self.net_data = load("model/bvlc_alexnet.npy").item()
  def __call__(self, x_input):
    #conv1
    #conv(11, 11, 96, 4, 4, padding='VALID', name='conv1')
    k_h = 11; k_w = 11; c_o = 96; s_h = 4; s_w = 4
    conv1W = tf.Variable(self.net_data["conv1"][0])
    conv1b = tf.Variable(self.net_data["conv1"][1])
    conv1_in = conv(x_input, conv1W, conv1b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=1)
    conv1 = tf.nn.relu(conv1_in)
    
    #lrn1
    #lrn(2, 2e-05, 0.75, name='norm1')
    radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
    lrn1 = tf.nn.local_response_normalization(conv1,
                                                      depth_radius=radius,
                                                      alpha=alpha,
                                                      beta=beta,
                                                      bias=bias)
    
    #maxpool1
    #max_pool(3, 3, 2, 2, padding='VALID', name='pool1')
    k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
    maxpool1 = tf.nn.max_pool(lrn1, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
    
    
    #conv2
    #conv(5, 5, 256, 1, 1, group=2, name='conv2')
    k_h = 5; k_w = 5; c_o = 256; s_h = 1; s_w = 1; group = 2
    conv2W = tf.Variable(self.net_data["conv2"][0])
    conv2b = tf.Variable(self.net_data["conv2"][1])
    conv2_in = conv(maxpool1, conv2W, conv2b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
    conv2 = tf.nn.relu(conv2_in)
    
    
    #lrn2
    #lrn(2, 2e-05, 0.75, name='norm2')
    radius = 2; alpha = 2e-05; beta = 0.75; bias = 1.0
    lrn2 = tf.nn.local_response_normalization(conv2,
                                                      depth_radius=radius,
                                                      alpha=alpha,
                                                      beta=beta,
                                                      bias=bias)
    
    #maxpool2
    #max_pool(3, 3, 2, 2, padding='VALID', name='pool2')                                                  
    k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
    maxpool2 = tf.nn.max_pool(lrn2, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
    
    #conv3
    #conv(3, 3, 384, 1, 1, name='conv3')
    k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 1
    conv3W = tf.Variable(self.net_data["conv3"][0])
    conv3b = tf.Variable(self.net_data["conv3"][1])
    conv3_in = conv(maxpool2, conv3W, conv3b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
    conv3 = tf.nn.relu(conv3_in)
    
    #conv4
    #conv(3, 3, 384, 1, 1, group=2, name='conv4')
    k_h = 3; k_w = 3; c_o = 384; s_h = 1; s_w = 1; group = 2
    conv4W = tf.Variable(self.net_data["conv4"][0])
    conv4b = tf.Variable(self.net_data["conv4"][1])
    conv4_in = conv(conv3, conv4W, conv4b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
    conv4 = tf.nn.relu(conv4_in)
    
    
    #conv5
    #conv(3, 3, 256, 1, 1, group=2, name='conv5')
    k_h = 3; k_w = 3; c_o = 256; s_h = 1; s_w = 1; group = 2
    conv5W = tf.Variable(self.net_data["conv5"][0])
    conv5b = tf.Variable(self.net_data["conv5"][1])
    conv5_in = conv(conv4, conv5W, conv5b, k_h, k_w, c_o, s_h, s_w, padding="SAME", group=group)
    conv5 = tf.nn.relu(conv5_in)
    
    #maxpool5
    #max_pool(3, 3, 2, 2, padding='VALID', name='pool5')
    k_h = 3; k_w = 3; s_h = 2; s_w = 2; padding = 'VALID'
    maxpool5 = tf.nn.max_pool(conv5, ksize=[1, k_h, k_w, 1], strides=[1, s_h, s_w, 1], padding=padding)
    
    #fc6
    #fc(4096, name='fc6')
    fc6W = tf.Variable(self.net_data["fc6"][0])
    fc6b = tf.Variable(self.net_data["fc6"][1])
    fc6 = tf.nn.relu_layer(tf.reshape(maxpool5, [-1, int(prod(maxpool5.get_shape()[1:]))]), fc6W, fc6b)
    
    #fc7
    #fc(4096, name='fc7')
    fc7W = tf.Variable(self.net_data["fc7"][0])
    fc7b = tf.Variable(self.net_data["fc7"][1])
    fc7 = tf.nn.relu_layer(fc6, fc7W, fc7b)
    
    #fc8
    #fc(1000, relu=False, name='fc8')
    fc8W = tf.Variable(self.net_data["fc8"][0])
    fc8b = tf.Variable(self.net_data["fc8"][1])
    fc8 = tf.nn.xw_plus_b(fc7, fc8W, fc8b)
    
    #prob
    prob = tf.nn.softmax(fc8)
    return prob,fc8
