import tensorflow as tf
import numpy as np
from tensorflow import flags
FLAGS = flags.FLAGS
class EngineerTransformer:
"""feature transform by feature engineering"""
def transform(self, model_input_raw, num_frames, **unused_params):
feature_dim = len(model_input_raw.get_shape()) - 1
engineer_types = map(lambda x: x.strip(), FLAGS.engineer_types.split(","))
feature_list = []
mask = self.mask(model_input_raw, num_frames)
for etype in engineer_types:
if etype == "avg":
feature_list.append(self.avg(model_input_raw, num_frames, mask))
elif etype == "std":
feature_list.append(self.std(model_input_raw, num_frames, mask))
elif etype == "diff":
feature_list.append(self.diff(model_input_raw, num_frames, mask))
else:
feature_list.append(model_input_raw)
model_input = tf.nn.l2_normalize(tf.concat(model_input_raw, axis=feature_dim), feature_dim)
return model_input, num_frames
def mask(self, model_input_raw, num_frames):
max_frames = model_input_raw.get_shape().as_list()[1]
mask_array = []
for i in xrange(max_frames + 1):
tmp = [0.0] * max_frames
for j in xrange(i):
tmp[j] = 1.0
mask_array.append(tmp)
mask_array = np.array(mask_array)
mask_init = tf.constant_initializer(mask_array)
mask_emb = tf.get_variable("mask_emb", shape = [max_frames + 1, max_frames],
dtype = tf.float32, trainable = False, initializer = mask_init)
mask = tf.nn.embedding_lookup(mask_emb, num_frames)
return mask
def avg(self, model_input_raw, num_frames, mask):
max_frames = model_input_raw.get_shape().as_list()[1]
num_frames_matrix = tf.maximum(tf.cast(
tf.expand_dims(num_frames, axis=1),
dtype=tf.float32), 1.0)
mean_matrix = mask / num_frames_matrix
mean_input = tf.einsum("ijk,ij->ik", model_input_raw, mean_matrix)
mean_input_tile = tf.tile(tf.expand_dims(mean_input, axis=1), multiples=[1,max_frames,1])
return mean_input_tile
def std(self, model_input_raw, num_frames, mask):
mean_input = self.avg(model_input_raw, num_frames, mask)
error = tf.einsum("ijk,ij->ijk", model_input_raw - mean_input, mask)
return error
def diff(self, model_input_raw, num_frames, mask):
max_frames = model_input_raw.get_shape().as_list()[1]
shift_input1 = tf.pad(model_input_raw, paddings=[[0,0], [0,1], [0,0]])
shift_input2 = tf.pad(model_input_raw, paddings=[[0,0], [1,0], [0,0]])
diff_input = shift_input1 - shift_input2
difference = tf.einsum("ijk,ij->ijk", diff_input[:,:max_frames,:], mask)
return difference
