import tensorflow as tf
import os
import random
import math
import tensorflow.contrib.slim as slim
import time
import logging
import numpy as np
import pickle
from PIL import Imagelogger = logging.getLogger('Training a chinese write char recognition')
logger.setLevel(logging.INFO)
# formatter = logging.Formatter('%(asctime)s - %(name)s - %(levelname)s - %(message)s')
ch = logging.StreamHandler()
ch.setLevel(logging.INFO)
logger.addHandler(ch)run_mode = "train"
charset_size = 100 # 3755
max_steps = 12002
save_steps = 2000"""
# for online 3755 words training
checkpoint_dir = '/aiml/dfs/checkpoint/'
train_data_dir = '/aiml/data/train/'
test_data_dir = '/aiml/data/test/'
log_dir = '/aiml/dfs/'
"""checkpoint_dir = './checkpoint2/'
train_data_dir = './data/'
test_data_dir = './data/'
log_dir = './'tf.app.flags.DEFINE_string('mode', run_mode, 'Running mode. One of {"train", "valid", "test"}')
tf.app.flags.DEFINE_boolean('random_flip_up_down', True, "Whether to random flip up down")
tf.app.flags.DEFINE_boolean('random_brightness', True, "whether to adjust brightness")
tf.app.flags.DEFINE_boolean('random_contrast', True, "whether to random constrast")tf.app.flags.DEFINE_integer('charset_size', charset_size, "Choose the first `charset_size` character to conduct our experiment.")
tf.app.flags.DEFINE_integer('image_size', 64, "Needs to provide same value as in training.")
tf.app.flags.DEFINE_boolean('gray', True, "whether to change the rbg to gray")
tf.app.flags.DEFINE_integer('max_steps', max_steps, 'the max training steps ')
tf.app.flags.DEFINE_integer('eval_steps', 50, "the step num to eval")
tf.app.flags.DEFINE_integer('save_steps', save_steps, "the steps to save")tf.app.flags.DEFINE_string('checkpoint_dir', checkpoint_dir, 'the checkpoint dir')
tf.app.flags.DEFINE_string('train_data_dir', train_data_dir, 'the train dataset dir')
tf.app.flags.DEFINE_string('test_data_dir', test_data_dir, 'the test dataset dir')
tf.app.flags.DEFINE_string('log_dir', log_dir, 'the logging dir')##############################
# resume training
tf.app.flags.DEFINE_boolean('restore', True, 'whether to restore from checkpoint')
##############################tf.app.flags.DEFINE_boolean('epoch', 10, 'Number of epoches')
tf.app.flags.DEFINE_boolean('batch_size', 128, 'Validation batch size')
FLAGS = tf.app.flags.FLAGSclass DataIterator:def __init__(self, data_dir):# Set FLAGS.charset_size to a small value if available computation power is limited.truncate_path = data_dir + ('%05d' % FLAGS.charset_size)print(truncate_path)self.image_names = []for root, sub_folder, file_list in os.walk(data_dir):if root < truncate_path:self.image_names += [os.path.join(root, file_path) for file_path in file_list]random.shuffle(self.image_names)self.labels = [int(file_name[len(data_dir):].split(os.sep)[0]) for file_name in self.image_names]@propertydef size(self):return len(self.labels)@staticmethoddef data_augmentation(images):if FLAGS.random_flip_up_down:# images = tf.image.random_flip_up_down(images)images = tf.contrib.image.rotate(images, random.randint(0, 30) * math.pi / 180, interpolation='BILINEAR')if FLAGS.random_brightness:images = tf.image.random_brightness(images, max_delta=0.3)if FLAGS.random_contrast:images = tf.image.random_contrast(images, 0.8, 1.2)return imagesdef input_pipeline(self, batch_size, num_epochs=None, aug=False):images_tensor = tf.convert_to_tensor(self.image_names, dtype=tf.string)labels_tensor = tf.convert_to_tensor(self.labels, dtype=tf.int64)input_queue = tf.train.slice_input_producer([images_tensor, labels_tensor], num_epochs=num_epochs)labels = input_queue[1]images_content = tf.read_file(input_queue[0])images = tf.image.convert_image_dtype(tf.image.decode_png(images_content, channels=1), tf.float32)if aug:images = self.data_augmentation(images)new_size = tf.constant([FLAGS.image_size, FLAGS.image_size], dtype=tf.int32)images = tf.image.resize_images(images, new_size)image_batch, label_batch = tf.train.shuffle_batch([images, labels], batch_size=batch_size, capacity=50000,min_after_dequeue=10000)return image_batch, label_batchdef build_graph(top_k):# with tf.device('/cpu:0'):keep_prob = tf.placeholder(dtype=tf.float32, shape=[], name='keep_prob')images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1], name='image_batch')labels = tf.placeholder(dtype=tf.int64, shape=[None], name='label_batch')conv_1 = slim.conv2d(images, 64, [3, 3], 1, padding='SAME', scope='conv1')max_pool_1 = slim.max_pool2d(conv_1, [2, 2], [2, 2], padding='SAME')conv_2 = slim.conv2d(max_pool_1, 128, [3, 3], padding='SAME', scope='conv2')max_pool_2 = slim.max_pool2d(conv_2, [2, 2], [2, 2], padding='SAME')conv_3 = slim.conv2d(max_pool_2, 256, [3, 3], padding='SAME', scope='conv3')max_pool_3 = slim.max_pool2d(conv_3, [2, 2], [2, 2], padding='SAME')conv_4 = slim.conv2d(max_pool_3, 512, [3, 3], [2, 2], scope="conv4", padding="SAME")max_pool_4 = slim.max_pool2d(conv_4, [2, 2], [2, 2], padding="SAME")flatten = slim.flatten(max_pool_4)fc1 = slim.fully_connected(slim.dropout(flatten, keep_prob), 1024, activation_fn=tf.nn.tanh, scope='fc1')logits = slim.fully_connected(slim.dropout(fc1, keep_prob), FLAGS.charset_size, activation_fn=None, scope='fc2')# logits = slim.fully_connected(flatten, FLAGS.charset_size, activation_fn=None, reuse=reuse, scope='fc')loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels))accuracy = tf.reduce_mean(tf.cast(tf.equal(tf.argmax(logits, 1), labels), tf.float32))global_step = tf.get_variable("step", [], initializer=tf.constant_initializer(0.0), trainable=False)rate = tf.train.exponential_decay(2e-4, global_step, decay_steps=2000, decay_rate=0.97, staircase=True)train_op = tf.train.AdamOptimizer(learning_rate=rate).minimize(loss, global_step=global_step)probabilities = tf.nn.softmax(logits)tf.summary.scalar('loss', loss)tf.summary.scalar('accuracy', accuracy)merged_summary_op = tf.summary.merge_all()predicted_val_top_k, predicted_index_top_k = tf.nn.top_k(probabilities, k=top_k)accuracy_in_top_k = tf.reduce_mean(tf.cast(tf.nn.in_top_k(probabilities, labels, top_k), tf.float32))return {'images': images,'labels': labels,'keep_prob': keep_prob,'top_k': top_k,'global_step': global_step,'train_op': train_op,'loss': loss,'accuracy': accuracy,'accuracy_top_k': accuracy_in_top_k,'merged_summary_op': merged_summary_op,'predicted_distribution': probabilities,'predicted_index_top_k': predicted_index_top_k,'predicted_val_top_k': predicted_val_top_k}def train():print('Begin training')train_feeder = DataIterator(FLAGS.train_data_dir)test_feeder = DataIterator(FLAGS.test_data_dir)with tf.Session() as sess:train_images, train_labels = train_feeder.input_pipeline(batch_size=FLAGS.batch_size, aug=True)test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size)graph = build_graph(top_k=1)sess.run(tf.global_variables_initializer())coord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)saver = tf.train.Saver()train_writer = tf.summary.FileWriter(FLAGS.log_dir + '/train', sess.graph)test_writer = tf.summary.FileWriter(FLAGS.log_dir + '/val')start_step = 0if FLAGS.restore:ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)if ckpt:saver.restore(sess, ckpt)print("restore from the checkpoint {0}".format(ckpt))start_step += int(ckpt.split('-')[-1])logger.info(':::Training Start:::')try:while not coord.should_stop():start_time = time.time()train_images_batch, train_labels_batch = sess.run([train_images, train_labels])feed_dict = {graph['images']: train_images_batch,graph['labels']: train_labels_batch,graph['keep_prob']: 0.8}_, loss_val, train_summary, step = sess.run([graph['train_op'], graph['loss'], graph['merged_summary_op'], graph['global_step']],feed_dict=feed_dict)train_writer.add_summary(train_summary, step)end_time = time.time()logger.info("the step {0} takes {1} loss {2}".format(step, end_time - start_time, loss_val))if step > FLAGS.max_steps:breakif step % FLAGS.eval_steps == 1:test_images_batch, test_labels_batch = sess.run([test_images, test_labels])feed_dict = {graph['images']: test_images_batch,graph['labels']: test_labels_batch,graph['keep_prob']: 1.0}accuracy_test, test_summary = sess.run([graph['accuracy'], graph['merged_summary_op']],feed_dict=feed_dict)test_writer.add_summary(test_summary, step)logger.info('===============Eval a batch=======================')logger.info('the step {0} test accuracy: {1}'.format(step, accuracy_test))logger.info('===============Eval a batch=======================')if step % FLAGS.save_steps == 1:logger.info('Save the ckpt of {0}'.format(step))saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'),global_step=graph['global_step'])except tf.errors.OutOfRangeError:logger.info('==================Train Finished================')saver.save(sess, os.path.join(FLAGS.checkpoint_dir, 'my-model'), global_step=graph['global_step'])finally:coord.request_stop()coord.join(threads)def validation():print('validation')test_feeder = DataIterator(FLAGS.test_data_dir)final_predict_val = []final_predict_index = []groundtruth = []with tf.Session() as sess:test_images, test_labels = test_feeder.input_pipeline(batch_size=FLAGS.batch_size, num_epochs=1)graph = build_graph(top_k=3)sess.run(tf.global_variables_initializer())sess.run(tf.local_variables_initializer())  # initialize test_feeder's inside statecoord = tf.train.Coordinator()threads = tf.train.start_queue_runners(sess=sess, coord=coord)saver = tf.train.Saver()ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)if ckpt:saver.restore(sess, ckpt)print("restore from the checkpoint {0}".format(ckpt))print(':::Start validation:::')try:i = 0acc_top_1, acc_top_k = 0.0, 0.0while not coord.should_stop():i += 1start_time = time.time()test_images_batch, test_labels_batch = sess.run([test_images, test_labels])feed_dict = {graph['images']: test_images_batch,graph['labels']: test_labels_batch,graph['keep_prob']: 1.0}batch_labels, probs, indices, acc_1, acc_k = sess.run([graph['labels'],graph['predicted_val_top_k'],graph['predicted_index_top_k'],graph['accuracy'],graph['accuracy_top_k']], feed_dict=feed_dict)final_predict_val += probs.tolist()final_predict_index += indices.tolist()groundtruth += batch_labels.tolist()acc_top_1 += acc_1acc_top_k += acc_kend_time = time.time()logger.info("the batch {0} takes {1} seconds, accuracy = {2}(top_1) {3}(top_k)".format(i, end_time - start_time, acc_1, acc_k))except tf.errors.OutOfRangeError:logger.info('==================Validation Finished================')acc_top_1 = acc_top_1 * FLAGS.batch_size / test_feeder.sizeacc_top_k = acc_top_k * FLAGS.batch_size / test_feeder.sizelogger.info('top 1 accuracy {0} top k accuracy {1}'.format(acc_top_1, acc_top_k))finally:coord.request_stop()coord.join(threads)return {'prob': final_predict_val, 'indices': final_predict_index, 'groundtruth': groundtruth}def inference(image):print('inference')temp_image = Image.open(image).convert('L')temp_image = temp_image.resize((FLAGS.image_size, FLAGS.image_size), Image.ANTIALIAS)temp_image = np.asarray(temp_image) / 255.0temp_image = temp_image.reshape([-1, 64, 64, 1])with tf.Session() as sess:logger.info('========start inference============')# images = tf.placeholder(dtype=tf.float32, shape=[None, 64, 64, 1])# Pass a shadow label 0. This label will not affect the computation graph.graph = build_graph(top_k=3)saver = tf.train.Saver()ckpt = tf.train.latest_checkpoint(FLAGS.checkpoint_dir)if ckpt:saver.restore(sess, ckpt)predict_val, predict_index = sess.run([graph['predicted_val_top_k'], graph['predicted_index_top_k']],feed_dict={graph['images']: temp_image, graph['keep_prob']: 1.0})return predict_val, predict_indexdef main(_):print(FLAGS.mode)if FLAGS.mode == "train":train()elif FLAGS.mode == 'validation':dct = validation()result_file = 'result.dict'logger.info('Write result into {0}'.format(result_file))with open(result_file, 'wb') as f:pickle.dump(dct, f)logger.info('Write file ends')elif FLAGS.mode == 'inference':image_path = './data/00098/102544.png'final_predict_val, final_predict_index = inference(image_path)logger.info('the result info label {0} predict index {1} predict_val {2}'.format(190, final_predict_index,final_predict_val))if __name__ == "__main__":tf.app.run()