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make_feature_maps.py
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import argparse
import sys
import os
import tensorflow as tf
import networks
import add_args
from keras import backend as K
import shutil
import numpy as np
import scipy.misc as misc
def model_fn(features, labels, mode, params):
"""The model_fn argument for creating an Estimator."""
# input
cameras = features['cameras']
video_id = features['video_id']
predicted_time_points = features['predicted_time_points']
# build up model
#set up encoder net-----------------
input_tensor = tf.reshape(tf.cast(cameras, tf.float32),
[-1, params['image_size'][0], params['image_size'][1], 3])
input_tensor = input_tensor - [123.68, 116.79, 103.939]
with tf.variable_scope("encoder"):
feature_net = networks.alex_encoder(params)
feature_maps = feature_net(input_tensor)
predictions = {
'feature_maps': feature_maps
}
if mode == tf.estimator.ModeKeys.PREDICT:
predictions['video_id'] = tf.tile(video_id, tf.shape(feature_maps)[0:1])
predictions['predicted_time_points'] = tf.reshape(predicted_time_points, shape=[-1, 1])
return tf.estimator.EstimatorSpec(mode=mode, predictions=predictions)
def input_fn(dataset, batch_size, n_steps, shuffle, n_epochs, args):
"""Prepare data for training."""
# get and shuffle tfrecords files
if dataset is not None:
file_path = os.path.join(args.data_dir, dataset, 'tfrecords', 'cameras_*.tfrecords')
else:
file_path = os.path.join(args.data_dir, 'tfrecords', 'cameras_*.tfrecords')
files = tf.data.Dataset.list_files(file_path)
if shuffle:
files = files.shuffle(buffer_size=10)
# parellel interleave to get raw bytes
dataset = files.apply(tf.contrib.data.parallel_interleave(
tf.data.TFRecordDataset, cycle_length=5, block_length=batch_size))
# shuffle before parsing
if shuffle:
dataset = dataset.shuffle(buffer_size=5*batch_size)
# parse data
def _parse_function(example):
# parsing
context_feature_info = {
'video_id': tf.FixedLenFeature(shape=[], dtype=tf.int64)
}
sequence_feature_info = {
'cameras': tf.FixedLenSequenceFeature(shape=[], dtype=tf.string),
'predicted_time_points': tf.FixedLenSequenceFeature(shape=[], dtype=tf.int64)
}
context_features, sequence_features = tf.parse_single_sequence_example(example,
context_features=context_feature_info,
sequence_features=sequence_feature_info)
video_id = context_features['video_id']
cameras = tf.reshape(tf.decode_raw(sequence_features["cameras"], tf.uint8),
[-1,]+args.image_size+[3])
predicted_time_points = sequence_features["predicted_time_points"]
if n_steps is not None:
#select a subsequence
length = tf.shape(cameras)[0]
offset = tf.random_uniform(shape=[], minval=0, maxval=tf.maximum(length-n_steps+1, 1), dtype=tf.int32)
end = tf.minimum(offset+n_steps, length)
cameras = cameras[offset:end]
feature_maps = feature_maps[offset:end]
gazemaps = gazemaps[offset:end]
predicted_time_points = predicted_time_points[offset:end]
# return features and labels
features = {}
features['cameras'] = cameras
features['video_id'] = video_id
features['predicted_time_points'] = predicted_time_points
return features
dataset = dataset.map(_parse_function, num_parallel_calls=10)
padded_shapes = {'cameras': [None,]+args.image_size+[3],
'video_id': [],
'predicted_time_points': [None,]}
dataset = dataset.padded_batch(batch_size, padded_shapes=padded_shapes)
dataset = dataset.prefetch(buffer_size=batch_size)
dataset = dataset.repeat(n_epochs)
return dataset
def main(argv):
parser = argparse.ArgumentParser()
add_args.for_general(parser)
add_args.for_inference(parser)
add_args.for_evaluation(parser)
add_args.for_feature(parser)
add_args.for_lstm(parser)
args = parser.parse_args()
config = tf.estimator.RunConfig(save_summary_steps=float('inf'),
log_step_count_steps=10)
params = {
'image_size': args.image_size,
'gazemap_size': args.gazemap_size,
'model_dir': args.model_dir
}
model = tf.estimator.Estimator(
model_fn=model_fn,
model_dir=args.model_dir,
config=config,
params=params)
#determine which checkpoint to restore
if args.model_iteration is None:
best_ckpt_dir = os.path.join(args.model_dir, 'best_ckpt')
if os.path.isdir(best_ckpt_dir):
ckpt_name = [f.split('.index')[0] for f in os.listdir(best_ckpt_dir) if f.endswith('.index')][0]
ckpt_path = os.path.join(best_ckpt_dir, ckpt_name)
args.model_iteration = ckpt_name.split('-')[1]
else:
ckpt_name = 'model.ckpt-'+args.model_iteration
ckpt_path = os.path.join(args.model_dir, ckpt_name)
K.clear_session()
predict_generator = model.predict(
input_fn = lambda: input_fn(None,
batch_size=1, n_steps=None,
shuffle=False,
n_epochs=1, args=args),
checkpoint_path=ckpt_path)
output_dir = os.path.join(args.data_dir, 'image_features_'+args.feature_name)
if not os.path.isdir(output_dir):
os.makedirs(output_dir)
previous_video_id = None
for res in predict_generator:
if previous_video_id is None:
print('Start inference for video: %s' % res['video_id'])
previous_video_id = res['video_id']
elif res['video_id'] != previous_video_id:
print('Start inference for video: %s' % res['video_id'])
previous_video_id = res['video_id']
output_path = os.path.join(output_dir,
str(res['video_id'])+'_'+str(res['predicted_time_points'][0]).zfill(5)+'.npy')
feature_map = res['feature_maps']
np.save(output_path, feature_map)
if __name__ == '__main__':
tf.logging.set_verbosity(tf.logging.INFO)
main(argv=sys.argv)