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,