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mean.py
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mean.py
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from datetime import datetime
from dataloader.outdoor_data import ActionsDataLoader
from models.multimodal import FuseDecoder
from models.unet_sound2 import UNetSound
from models.unet_architecture_energy import UNetE
from models.unet_noconc import UNetAc
from models.unet_architecture_noconc import UNet
import numpy as np
import tensorflow as tf
import os
import cv2
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', 9, '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('probability', 1, 'Use vae')
flags.DEFINE_string('datatype', 'outdoor', 'music or outdoor or old')
FLAGS = flags.FLAGS
'''Save old features'''
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]
nameac = '{}_{}_{}'.format(dataset, 'meanAc', name)
nameaudio = '{}_{}_{}'.format(dataset, 'meanAudio', name)
nameimages = '{}_{}_{}'.format(dataset, 'meanVideo', name)
nameimagesaudio = '{}_{}_{}'.format(dataset, 'meanVideoAudio', name)
data_dirac = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [nameac])
data_diraudio = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [nameaudio])
data_dirimages = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [nameimages])
data_dirimagesaudio = str.join('/', FLAGS.init_checkpoint.split('/')[:-1] + [nameimagesaudio])
num_classes = FLAGS.num_classes
temporal_pooling = FLAGS.temporal_pooling
nr_frames = FLAGS.nr_frames
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 = UNetE(input_shape=[36, 48, 1])
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()
logenergy = tf.slice(next_batch[0], [0, 0, 0, 0, 0], [-1, 1, 36, 48, 1])
logenergy = tf.reshape(logenergy, shape=[-1, 36, 48, 1])
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])
logenergy = logenergy - tf.reduce_min(logenergy, axis=[1, 2], keep_dims=True)
logenergy = logenergy / tf.reduce_max(logenergy, axis=[1, 2], keep_dims=True)
# 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])
modelac._build_model(acoustic)
modelaudio._build_model(mfcc)
modelimages._build_model(images)
samples = tf.random_normal([tf.shape(modelimages.std)[0], tf.shape(modelimages.std)[1]], 0, 1,
dtype=tf.float32)
extractedac = modelac.mean + (modelac.std * samples)
extractedaudio = modelaudio.mean + (modelaudio.std * samples)
extractedvideo = modelimages.mean + (modelimages.std * samples)
extractedvideoaudio = modelaudio.mean + modelimages.mean + ((modelimages.std + modelaudio.std) * samples)
#FLAGS.model == 'UNet'
var_listac = slim.get_variables(modelac.scope + '/')
var_listaudio = slim.get_variables(modelaudio.scope + '/')
var_listimages = slim.get_variables(modelimages.scope + '/')
if os.path.exists(data_dirac):
print("Features already computed!")
else:
os.makedirs(data_dirac) # mkdir creates one directory, makedirs all intermediate directories
if os.path.exists(data_diraudio):
print("Features already computed!")
else:
os.makedirs(data_diraudio) # mkdir creates one directory, makedirs all intermediate directories
if os.path.exists(data_dirimages):
print("Features already computed!")
else:
os.makedirs(data_dirimages) # mkdir creates one directory, makedirs all intermediate directories
if os.path.exists(data_dirimagesaudio):
print("Features already computed!")
else:
os.makedirs(data_dirimagesaudio) # mkdir creates one directory, makedirs all intermediate directories
total_size = 0
batch_count = 0
dataset_list_featuresimagesaudio = np.zeros([data_size, num_embedding], dtype=float)
dataset_list_featuresac = np.zeros([data_size, num_embedding], dtype=float)
dataset_list_featuresaudio = np.zeros([data_size, num_embedding], dtype=float)
dataset_list_featuresimages = np.zeros([data_size, num_embedding], dtype=float)
dataset_labels = np.zeros([data_size, num_actions], dtype=int)
dataset_scenario = np.zeros([data_size, num_locations], dtype=int)
print('{} - Starting'.format(datetime.now()))
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_listimages)
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)
while True:
try:
labels_data, scenario_data, featuresac, featuresaudio, featuresimages, featuresimagesaudio = session.run(
[labels, scenario, extractedac, extractedaudio, extractedvideo, extractedvideoaudio],
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,
modelimages.network['keep_prob']: 1.0,
modelimages.network['is_training']: 0})
batchnum = labels_data.shape[0]
# copy block of data
dataset_list_featuresimages[total_size:total_size + batchnum, :] = featuresimages
dataset_list_featuresaudio[total_size:total_size + batchnum, :] = featuresaudio
dataset_list_featuresimagesaudio[total_size:total_size + batchnum, :] = featuresimagesaudio
dataset_list_featuresac[total_size:total_size + batchnum, :] = featuresac
dataset_labels[total_size:total_size + batchnum, :] = labels_data
dataset_scenario[total_size:total_size + batchnum, :] = scenario_data
# increase number of data
total_size += batchnum
end_time = datetime.now()
print('{} samples'.format(total_size))
except tf.errors.OutOfRangeError:
break
batch_count += 1
print('{}'.format(data_size))
print('{} - Completed, got {} samples'.format(datetime.now(), total_size))
np.save('{}/{}_data.npy'.format(data_dirac, dataset), dataset_list_featuresac)
np.save('{}/{}_labels.npy'.format(data_dirac, dataset), dataset_labels)
np.save('{}/{}_scenario.npy'.format(data_dirac, dataset), dataset_scenario)
np.save('{}/{}_data.npy'.format(data_diraudio, dataset), dataset_list_featuresaudio)
np.save('{}/{}_labels.npy'.format(data_diraudio, dataset), dataset_labels)
np.save('{}/{}_scenario.npy'.format(data_diraudio, dataset), dataset_scenario)
np.save('{}/{}_data.npy'.format(data_dirimages, dataset), dataset_list_featuresimages)
np.save('{}/{}_labels.npy'.format(data_dirimages, dataset), dataset_labels)
np.save('{}/{}_scenario.npy'.format(data_dirimages, dataset), dataset_scenario)
np.save('{}/{}_data.npy'.format(data_dirimagesaudio, dataset), dataset_list_featuresimagesaudio)
np.save('{}/{}_labels.npy'.format(data_dirimagesaudio, dataset), dataset_labels)
np.save('{}/{}_scenario.npy'.format(data_dirimagesaudio, dataset), dataset_scenario)
if __name__ == '__main__':
flags.mark_flags_as_required(['train_file'])
tf.app.run()