utils.py

# -----------------------------------------------------------------------------------------
# Code taken from https://github.com/iwantooxxoox/Keras-OpenFace (with minor modifications)
# -----------------------------------------------------------------------------------------

import tensorflow as tf
import numpy as np
import os

from numpy import genfromtxt
from keras.layers import Conv2D, ZeroPadding2D, Activation
from keras.layers.normalization import BatchNormalization

_FLOATX = 'float32'

def variable(value, dtype=_FLOATX, name=None):
  v = tf.Variable(np.asarray(value, dtype=dtype), name=name)
  _get_session().run(v.initializer)
  return v

def shape(x):
  return x.get_shape()

def square(x):
  return tf.square(x)

def zeros(shape, dtype=_FLOATX, name=None):
  return variable(np.zeros(shape), dtype, name)

def concatenate(tensors, axis=-1):
  if axis < 0:
      axis = axis % len(tensors[0].get_shape())
  return tf.concat(axis, tensors)

def LRN2D(x):
  return tf.nn.lrn(x, alpha=1e-4, beta=0.75)

def conv2d_bn(
  x,
  layer=None,
  cv1_out=None,
  cv1_filter=(1, 1),
  cv1_strides=(1, 1),
  cv2_out=None,
  cv2_filter=(3, 3),
  cv2_strides=(1, 1),
  padding=None,
):
  num = '' if cv2_out == None else '1'
  tensor = Conv2D(cv1_out, cv1_filter, strides=cv1_strides, name=layer+'_conv'+num)(x)
  tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+num)(tensor)
  tensor = Activation('relu')(tensor)
  if padding == None:
    return tensor
  tensor = ZeroPadding2D(padding=padding)(tensor)
  if cv2_out == None:
    return tensor
  tensor = Conv2D(cv2_out, cv2_filter, strides=cv2_strides, name=layer+'_conv'+'2')(tensor)
  tensor = BatchNormalization(axis=3, epsilon=0.00001, name=layer+'_bn'+'2')(tensor)
  tensor = Activation('relu')(tensor)
  return tensor

weights = [
  'conv1', 'bn1', 'conv2', 'bn2', 'conv3', 'bn3',
  'inception_3a_1x1_conv', 'inception_3a_1x1_bn',
  'inception_3a_pool_conv', 'inception_3a_pool_bn',
  'inception_3a_5x5_conv1', 'inception_3a_5x5_conv2', 'inception_3a_5x5_bn1', 'inception_3a_5x5_bn2',
  'inception_3a_3x3_conv1', 'inception_3a_3x3_conv2', 'inception_3a_3x3_bn1', 'inception_3a_3x3_bn2',
  'inception_3b_3x3_conv1', 'inception_3b_3x3_conv2', 'inception_3b_3x3_bn1', 'inception_3b_3x3_bn2',
  'inception_3b_5x5_conv1', 'inception_3b_5x5_conv2', 'inception_3b_5x5_bn1', 'inception_3b_5x5_bn2',
  'inception_3b_pool_conv', 'inception_3b_pool_bn',
  'inception_3b_1x1_conv', 'inception_3b_1x1_bn',
  'inception_3c_3x3_conv1', 'inception_3c_3x3_conv2', 'inception_3c_3x3_bn1', 'inception_3c_3x3_bn2',
  'inception_3c_5x5_conv1', 'inception_3c_5x5_conv2', 'inception_3c_5x5_bn1', 'inception_3c_5x5_bn2',
  'inception_4a_3x3_conv1', 'inception_4a_3x3_conv2', 'inception_4a_3x3_bn1', 'inception_4a_3x3_bn2',
  'inception_4a_5x5_conv1', 'inception_4a_5x5_conv2', 'inception_4a_5x5_bn1', 'inception_4a_5x5_bn2',
  'inception_4a_pool_conv', 'inception_4a_pool_bn',
  'inception_4a_1x1_conv', 'inception_4a_1x1_bn',
  'inception_4e_3x3_conv1', 'inception_4e_3x3_conv2', 'inception_4e_3x3_bn1', 'inception_4e_3x3_bn2',
  'inception_4e_5x5_conv1', 'inception_4e_5x5_conv2', 'inception_4e_5x5_bn1', 'inception_4e_5x5_bn2',
  'inception_5a_3x3_conv1', 'inception_5a_3x3_conv2', 'inception_5a_3x3_bn1', 'inception_5a_3x3_bn2',
  'inception_5a_pool_conv', 'inception_5a_pool_bn',
  'inception_5a_1x1_conv', 'inception_5a_1x1_bn',
  'inception_5b_3x3_conv1', 'inception_5b_3x3_conv2', 'inception_5b_3x3_bn1', 'inception_5b_3x3_bn2',
  'inception_5b_pool_conv', 'inception_5b_pool_bn',
  'inception_5b_1x1_conv', 'inception_5b_1x1_bn',
  'dense_layer'
]

conv_shape = {
  'conv1': [64, 3, 7, 7],
  'conv2': [64, 64, 1, 1],
  'conv3': [192, 64, 3, 3],
  'inception_3a_1x1_conv': [64, 192, 1, 1],
  'inception_3a_pool_conv': [32, 192, 1, 1],
  'inception_3a_5x5_conv1': [16, 192, 1, 1],
  'inception_3a_5x5_conv2': [32, 16, 5, 5],
  'inception_3a_3x3_conv1': [96, 192, 1, 1],
  'inception_3a_3x3_conv2': [128, 96, 3, 3],
  'inception_3b_3x3_conv1': [96, 256, 1, 1],
  'inception_3b_3x3_conv2': [128, 96, 3, 3],
  'inception_3b_5x5_conv1': [32, 256, 1, 1],
  'inception_3b_5x5_conv2': [64, 32, 5, 5],
  'inception_3b_pool_conv': [64, 256, 1, 1],
  'inception_3b_1x1_conv': [64, 256, 1, 1],
  'inception_3c_3x3_conv1': [128, 320, 1, 1],
  'inception_3c_3x3_conv2': [256, 128, 3, 3],
  'inception_3c_5x5_conv1': [32, 320, 1, 1],
  'inception_3c_5x5_conv2': [64, 32, 5, 5],
  'inception_4a_3x3_conv1': [96, 640, 1, 1],
  'inception_4a_3x3_conv2': [192, 96, 3, 3],
  'inception_4a_5x5_conv1': [32, 640, 1, 1,],
  'inception_4a_5x5_conv2': [64, 32, 5, 5],
  'inception_4a_pool_conv': [128, 640, 1, 1],
  'inception_4a_1x1_conv': [256, 640, 1, 1],
  'inception_4e_3x3_conv1': [160, 640, 1, 1],
  'inception_4e_3x3_conv2': [256, 160, 3, 3],
  'inception_4e_5x5_conv1': [64, 640, 1, 1],
  'inception_4e_5x5_conv2': [128, 64, 5, 5],
  'inception_5a_3x3_conv1': [96, 1024, 1, 1],
  'inception_5a_3x3_conv2': [384, 96, 3, 3],
  'inception_5a_pool_conv': [96, 1024, 1, 1],
  'inception_5a_1x1_conv': [256, 1024, 1, 1],
  'inception_5b_3x3_conv1': [96, 736, 1, 1],
  'inception_5b_3x3_conv2': [384, 96, 3, 3],
  'inception_5b_pool_conv': [96, 736, 1, 1],
  'inception_5b_1x1_conv': [256, 736, 1, 1],
}

def load_weights():
  weightsDir = './weights'
  fileNames = filter(lambda f: not f.startswith('.'), os.listdir(weightsDir))
  paths = {}
  weights_dict = {}

  for n in fileNames:
    paths[n.replace('.csv', '')] = weightsDir + '/' + n

  for name in weights:
    if 'conv' in name:
      conv_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
      conv_w = np.reshape(conv_w, conv_shape[name])
      conv_w = np.transpose(conv_w, (2, 3, 1, 0))
      conv_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
      weights_dict[name] = [conv_w, conv_b]
    elif 'bn' in name:
      bn_w = genfromtxt(paths[name + '_w'], delimiter=',', dtype=None)
      bn_b = genfromtxt(paths[name + '_b'], delimiter=',', dtype=None)
      bn_m = genfromtxt(paths[name + '_m'], delimiter=',', dtype=None)
      bn_v = genfromtxt(paths[name + '_v'], delimiter=',', dtype=None)
      weights_dict[name] = [bn_w, bn_b, bn_m, bn_v]
    elif 'dense' in name:
      dense_w = genfromtxt(weightsDir+'/dense_w.csv', delimiter=',', dtype=None)
      dense_w = np.reshape(dense_w, (128, 736))
      dense_w = np.transpose(dense_w, (1, 0))
      dense_b = genfromtxt(weightsDir+'/dense_b.csv', delimiter=',', dtype=None)
      weights_dict[name] = [dense_w, dense_b]

  return weights_dict