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decodeimagesfusion.py
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decodeimagesfusion.py
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from datetime import datetime
from dataloader.outdoor_data import ActionsDataLoader
from models.multimodal import AssociatorVideoAc
from models.multimodal import AssociatorAudioAc
from models.multimodal import AssociatorAudio
from models.unet_sound2 import UNetSound
from models.unet_z import UNetAc
from models.unet_architecture_noconc import UNet
import numpy as np
import tensorflow as tf
import os
import matplotlib.pyplot as plt
flags = tf.app.flags
slim = tf.contrib.slim
flags.DEFINE_string('model', None, 'Model type, it can AudioCoeff')
flags.DEFINE_integer('temporal_pooling', 0, 'Temporal pooling')
flags.DEFINE_string('train_file', None, 'File for training data')
flags.DEFINE_string('init_checkpoint', None, 'Checkpoint file for model initialization')
flags.DEFINE_integer('num_classes', 10, 'Number of classes')
flags.DEFINE_integer('batch_size', 2, 'Batch size choose')
flags.DEFINE_integer('nr_frames', 1, 'Number of frames') # 12*FLAGS.sample_length max
flags.DEFINE_integer('sample_length', 1, 'Length in seconds of a sequence sample')
flags.DEFINE_integer('onlyaudio', 0, 'Using only audio associator no sound net')
flags.DEFINE_integer('probability', 1, 'Use vae')
flags.DEFINE_string('datatype', 'outdoor', 'music or outdoor or old')
FLAGS = flags.FLAGS
'''Plot energy old'''
def main(_):
plotdecodeimages()
def plotdecodeimages():
dataset = FLAGS.train_file.split('/')[-1]
dataset = dataset.split('.')[0]
s = FLAGS.init_checkpoint.split('/')[-1]
name = (s.split('_')[1]).split('.ckpt')[0]
name = '{}_{}_{}_{}'.format(FLAGS.model, dataset, 'Ac', name)
data_dir = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [name])
random_pick = True
build_spectrogram = (FLAGS.model == 'AudioCoefficients' or FLAGS.model == 'ResNet50' or FLAGS.model == 'HearNet'
or FLAGS.model == 'UNet' or FLAGS.model == 'ResNet18_v1')
normalize = FLAGS.model == 'HearNet'
# Create data loaders according to the received program arguments
print('{} - Creating data loaders'.format(datetime.now()))
modalities = []
modalities.append(0)
modalities.append(1)
modalities.append(2)
with tf.device('/cpu:0'):
train_data = ActionsDataLoader(FLAGS.train_file, 'inference', batch_size=FLAGS.batch_size, num_epochs=1, sample_length=1,
datakind='outdoor', buffer_size=10, shuffle=False,
normalize=normalize, build_spectrogram=build_spectrogram, correspondence=0,
random_pick=random_pick, modalities=modalities, nr_frames=FLAGS.nr_frames)
data_size = train_data.num_samples
# Build model
print('{} - Building model'.format(datetime.now()))
with tf.device('/gpu:0'):
# modelimages = UNet(input_shape=[224, 298, 3])
modelaudio = UNetSound(input_shape=[99, 257, 1])
modelac = UNetAc(input_shape=[36, 48, 12])
# model_associator = AssociatorVideoAc(input_shape=1024)
if FLAGS.onlyaudio:
model_associator1 = AssociatorAudio(input_shape=[193, 257, 1])
else:
model_associator1 = AssociatorAudioAc(input_shape=256)
handle = tf.placeholder(tf.string, shape=())
iterator = tf.data.Iterator.from_string_handle(handle, train_data.data.output_types,
train_data.data.output_shapes)
train_iterat = train_data.data.make_initializable_iterator()
next_batch = iterator.get_next()
mfcc = tf.reshape(next_batch[1], shape=[-1, 99, 257, 1])
mfcc = tf.image.resize_bilinear(mfcc, [193, 257], align_corners=False)
# images = tf.reshape(next_batch[2], shape=[-1, 224, 298, 3])
acoustic = tf.reshape(next_batch[0], shape=[-1, 36, 48, 12])
# mfcc = mfcc - tf.reduce_min(mfcc, axis=[1, 2], keep_dims=True)
# mfcc = mfcc / tf.reduce_max(mfcc, axis=[1, 2], keep_dims=True)
if FLAGS.datatype == 'music':
num_actions = 9
num_locations = 11 # maximum number of videos for a class
else: # self.datakind == 'outdoor':
num_actions = 10
num_locations = 61
num_embedding = 128
labels = tf.reshape(next_batch[3], shape=[-1, num_actions])
scenario = tf.reshape(next_batch[4], shape=[-1, num_locations])
# modelimages._build_model(images)
# model_associator._build_model(modelimages.mean, modelimages.std)
if not FLAGS.onlyaudio:
modelaudio._build_model(mfcc)
model_associator1._build_model(modelaudio.mean, modelaudio.std)
else:
model_associator1._build_model(mfcc)
mean = model_associator1.mean
std = model_associator1.std
modelac._build_model(acoustic, mean, std)
#FLAGS.model == 'UNet'
var_listac = slim.get_variables(modelac.scope + '/')
var_listaudio = slim.get_variables(modelaudio.scope + '/')
# var_listimages = slim.get_variables(modelimages.scope + '/')
# var_listassociatorimages = slim.get_variables(model_associator.scope + '/')
var_listassociatoraudio = slim.get_variables(model_associator1.scope + '/')
if os.path.exists(data_dir):
print("Features already computed!")
else:
os.makedirs(data_dir) # mkdir creates one directory, makedirs all intermediate directories
num = 0
total_size = 0
batch_count = 0
print('{} - Starting'.format(datetime.now()))
namesimage = ['Acoustic image', 'Reconstructed']
with tf.Session(
config=tf.ConfigProto(allow_soft_placement=True, gpu_options=tf.GPUOptions(allow_growth=True))) as session:
train_handle = session.run(train_iterat.string_handle())
saver = tf.train.Saver(var_list=var_listac + var_listaudio + var_listassociatoraudio)
saver.restore(session, FLAGS.init_checkpoint)
print('{} - Done'.format(datetime.now()))
#variables_in_checkpoint = tf.train.list_variables('path.ckpt')
session.run(train_iterat.initializer)
if FLAGS.onlyaudio:
while True:
try:
data, reconstructed = session.run(
[acoustic, modelac.output],
feed_dict={handle: train_handle,
modelac.network['keep_prob']: 1.0,
modelac.network['is_training']: 0,
model_associator1.network['keep_prob']: 1.0,
model_associator1.network['is_training']: 0})
batchnum = reconstructed.shape[0]
# copy block of data
# increase number of data
total_size += batchnum
print('{} samples'.format(total_size))
for h in range(np.shape(reconstructed)[0]):
# original and reconstructed
fig, axs = plt.subplots(4, 2, figsize=(6, 2.9 * 4))
plt.tight_layout(pad=1.0)
fig.suptitle('Reconstructed image')
imagesvideo = np.stack((data, reconstructed), 0)
for i in range(2):
for j in range(4):
x = j
y = i
axs[x, y].imshow(imagesvideo[i, h, :, :, j * 3:(j + 1) * 3])
axs[x, y].axis('off')
axs[x, y].set_title('{}'.format(namesimage[i]))
outImage_path = '{}/{}_images_{}.png'.format(data_dir, dataset, num)
plt.savefig(outImage_path)
plt.clf()
num = num + 1
except tf.errors.OutOfRangeError:
break
batch_count += 1
else:
while True:
try:
data, reconstructed = session.run(
[acoustic, modelac.output],
feed_dict={handle: train_handle,
modelac.network['keep_prob']: 1.0,
modelac.network['is_training']: 0,
modelaudio.network['keep_prob']: 1.0,
modelaudio.network['is_training']: 0})
batchnum = reconstructed.shape[0]
# copy block of data
# increase number of data
total_size += batchnum
print('{} samples'.format(total_size))
for h in range(np.shape(reconstructed)[0]):
# original and reconstructed
fig, axs = plt.subplots(4, 2, figsize=(6, 2.9 * 4))
plt.tight_layout(pad=1.0)
fig.suptitle('Reconstructed image')
imagesvideo = np.stack((data, reconstructed), 0)
for i in range(2):
for j in range(4):
x = j
y = i
axs[x, y].imshow(imagesvideo[i, h, :, :, j * 3:(j + 1) * 3])
axs[x, y].axis('off')
axs[x, y].set_title('{}'.format(namesimage[i]))
outImage_path = '{}/{}_images_{}.png'.format(data_dir, dataset, num)
plt.savefig(outImage_path)
plt.clf()
num = num + 1
except tf.errors.OutOfRangeError:
break
batch_count += 1
print('{}'.format(data_size))
print('{} - Completed, got {} samples'.format(datetime.now(), total_size))
if __name__ == '__main__':
flags.mark_flags_as_required(['train_file'])
tf.app.run()