-
Notifications
You must be signed in to change notification settings - Fork 18
/
lstm_prediction_evaluation.py
166 lines (133 loc) · 5.3 KB
/
lstm_prediction_evaluation.py
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
# -*- coding: utf-8 -*-
"""
@author: Ye Xia
"""
import numpy as np
import data_point_collector
import BatchDatasetReader
import scipy.misc as misc
import tensorflow as tf
import pickle
import re
import os
from keras import backend as K
import networks
import argparse
import ut
import pandas as pd
import feather
#set flags--------------------------
parser = argparse.ArgumentParser()
ut.add_args_for_general(parser)
ut.add_args_for_inference(parser)
ut.add_args_for_evaluation(parser)
ut.add_args_for_feature(parser)
ut.add_args_for_lstm(parser)
args = parser.parse_args()
ut.parse_for_general(args)
ut.parse_for_feature(args)
#set parameters-------------------
args.epsilon = 1e-12
args.gaze_map_size = (36, 64)
#set up session------------------
if args.gpu_memory_fraction is not None:
config = tf.ConfigProto()
config.gpu_options.per_process_gpu_memory_fraction = args.gpu_memory_fraction
sess = tf.Session(config=config)
else:
sess = tf.Session()
#assign session for Keras
K.set_session(sess)
#set up placeholders---------------------------
feature_map_in_seqs = tf.placeholder(tf.float32, shape=(None, None) + args.feature_map_size + (args.feature_map_channels,),
name="feature_map_in_seqs")
annotation_in_seqs = tf.placeholder(tf.float32, shape=(None, None) + args.gaze_map_size + (1,),
name="annotation")
#set up readout net-----------------
if args.readout=='default':
readout_net = networks.lstm_readout_net
elif args.readout=='conv_lstm':
readout_net = networks.conv_lstm_readout_net
elif args.readout=='big_conv_lstm':
readout_net = networks.big_conv_lstm_readout_net
if args.use_prior is True:
#load prior map
with open(args.data_dir + 'gaze_prior.pickle', 'rb') as f:
gaze_prior = pickle.load(f)
if gaze_prior.shape != args.gaze_map_size:
gaze_prior = ut.resize_distribution(gaze_prior, args.gaze_map_size)
gaze_prior = gaze_prior.astype(np.float32)
gaze_prior /= np.sum(gaze_prior)
logits, pre_prior_logits = \
readout_net(feature_map_in_seqs, args.feature_map_size, args.drop_rate, gaze_prior)
pre_prior_annotation = tf.nn.softmax(pre_prior_logits)
else:
logits = readout_net(feature_map_in_seqs, args.feature_map_size, args.drop_rate)
#predicted annotation
pred_annotation = tf.nn.softmax(logits)
#set up loss---------------------------------------
loss, accuracy_loss, reg_loss, spread, kls = \
ut.set_losses(logits,
pred_annotation,
annotation_in_seqs,
args)
#set up data readers-------------------------------
_, valid_data_points = \
data_point_collector.read_datasets(args.data_dir, in_sequences=True)
validation_dataset_reader = \
BatchDatasetReader.BatchDataset(args.data_dir+'validation/',
valid_data_points,
args.image_size,
feature_name=args.feature_name)
#set up savers------------
saver = tf.train.Saver()
#try to reload weights--------------------
ckpt = tf.train.get_checkpoint_state(args.model_dir)
#pdb.set_trace()
if ckpt and ckpt.model_checkpoint_path:
if args.model_iteration is not None:
ckpt_path = re.sub('(ckpt-)[0-9]+', r'\g<1>'+args.model_iteration, ckpt.model_checkpoint_path)
else:
ckpt_path = ckpt.model_checkpoint_path
args.model_iteration = re.search('ckpt-([0-9]+)', ckpt_path).group(1)
saver.restore(sess, ckpt_path)
print("Model restored...")
#start predicting-------------------------
validation_losses = []
n_iteration = np.ceil(len(
validation_dataset_reader.data_point_names)/args.batch_size).astype(np.int)
dir_name = args.model_dir+'prediction_iter_'+args.model_iteration+'/'
if not os.path.isdir(dir_name):
os.makedirs(dir_name)
dfs = []
for itr in range(n_iteration):
print('Doing iteration %d/%d' % (itr, n_iteration))
batch = validation_dataset_reader.next_batch_in_seqs(batch_size=args.batch_size)
valid_feature_maps = validation_dataset_reader.get_feature_maps_in_seqs(batch)
valida_annotations = validation_dataset_reader.\
get_annotations_in_seqs(batch, desired_size=args.gaze_map_size)
feed_dict = {feature_map_in_seqs: valid_feature_maps,
annotation_in_seqs: valida_annotations,
K.learning_phase(): 0}
[prediction, valid_loss, valid_kls] = sess.run([pred_annotation, loss, kls],
feed_dict=feed_dict)
#flatten batch
flat_batch = [data_point for video in batch for data_point in video]
for i in range(len(prediction)):
#save predicted map
prediction_map = prediction[i].reshape(args.gaze_map_size)
fpath = dir_name + flat_batch[i] + '.jpg'
misc.imsave(fpath, prediction_map)
#save kl-divergences
df = pd.DataFrame.from_dict({
'fileName': flat_batch,
'kl': valid_kls})
dfs.append(df)
#save loss
validation_losses.append(valid_loss)
kl_df = pd.concat(dfs)
feather.write_dataframe(kl_df, dir_name+'kls.feather')
mean_valid_loss = np.mean(validation_losses)
with open(dir_name+"mean_validation_loss_%f.txt" % mean_valid_loss, "w") as text_file:
text_file.write("Mean validation loss: %f" % mean_valid_loss)
print('Mean validation loss is %f' % mean_valid_loss)