from sparnn.iterators import NumpyIterator
import os
import random
import numpy
import logging
mode = "test"
## for test data
if(mode == "test"):
iterator_param = {'path': '../../SPARNN/data/hko-example/hko-test.npz',
'minibatch_size': 8,
'use_input_mask': False,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': 'hko-test-iterator'}
test_iterator = NumpyIterator(iterator_param)
test_iterator.begin(do_shuffle=False)
test_iterator.print_stat()
data = test_iterator.data
# elif(mode == "train"):
# elif(mode == "valid"):
imgs = data['input_raw_data']
index = data['clips']
startingP_input = [i[0] for i in index[0]]
startingP_output = [i[0] for i in index[1]]
print(startingP_input[0:3])
print(startingP_output[0:3])
import sparnn
import sparnn.utils
from sparnn.utils import *
from sparnn.iterators import NumpyIterator
import os
import random
import numpy
import logging
iterator_param = {
'path': '../../SPARNN/data/hko-example/hko-test.npz',
'minibatch_size': 8,
'use_input_mask': False,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': 'hko-test-iterator'
}
test_iterator = NumpyIterator(iterator_param)
test_iterator.begin(do_shuffle=False)
test_iterator.print_stat()
data = test_iterator.data
imgs = data['input_raw_data']
for i in range(23280):
img = imgs[i].reshape(100, 100)
misc.imsave('data/img' + str(i) + '.png', img)
save_path = "./hko-record-" + ACTIVATE_FUNC + "-" + COST_FUNC + "/"
log_path = save_path + "valid-" + ACTIVATE_FUNC + "-" + COST_FUNC + ".log"
print("save path is " + save_path)
if not os.path.exists(save_path):
os.makedirs(save_path)
sparnn.utils.quick_logging_config(log_path)
iterator_param = {'path': 'data/hko-example/hko-train.npz',
'minibatch_size': MINIBATCH_SIZE,
'use_input_mask': False,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': 'hko-train-iterator'}
train_iterator = NumpyIterator(iterator_param)
train_iterator.begin(do_shuffle=True)
train_iterator.print_stat()
iterator_param = {'path': 'data/hko-example/hko-valid.npz',
'minibatch_size': MINIBATCH_SIZE,
'use_input_mask': False,
'input_data_type': 'float32',
'is_output_sequence': True,
'name': 'hko-valid-iterator'}
valid_iterator = NumpyIterator(iterator_param)
valid_iterator.begin(do_shuffle=False)
valid_iterator.print_stat()
iterator_param = {'path': 'data/hko-example/hko-test.npz',
'minibatch_size': MINIBATCH_SIZE,
