陆雪松
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| @ -0,0 +1,514 @@ | |||
| #!/usr/bin/python3 | |||
| # -*- coding: utf-8 -*- | |||
| # library modules | |||
| from math import ceil | |||
| import json | |||
| import time | |||
| import os | |||
| import threading | |||
| # External library modules | |||
| import tensorflow as tf | |||
| import numpy as np | |||
| # local modules | |||
| from data import LSVRC2010 | |||
| import logs | |||
| class AlexNet: | |||
| """ | |||
| A tensorflow implementation of the paper: | |||
| `AlexNet <https://papers.nips.cc/paper/4824-imagenet-classification-with-deep-convolutional-neural-networks.pdf>`_ | |||
| """ | |||
| def __init__(self, path, batch_size, resume): | |||
| """ | |||
| Build the AlexNet model | |||
| """ | |||
| self.logger = logs.get_logger() | |||
| self.resume = resume | |||
| self.path = path | |||
| self.batch_size = batch_size | |||
| self.lsvrc2010 = LSVRC2010(self.path, batch_size) | |||
| self.num_classes = len(self.lsvrc2010.wnid2label) | |||
| self.lr = 0.001 | |||
| self.momentum = 0.9 | |||
| self.lambd = tf.constant(0.0005, name='lambda') | |||
| self.input_shape = (None, 227, 227, 3) | |||
| self.output_shape = (None, self.num_classes) | |||
| self.logger.info("Creating placeholders for graph...") | |||
| self.create_tf_placeholders() | |||
| self.logger.info("Creating variables for graph...") | |||
| self.create_tf_variables() | |||
| self.logger.info("Initialize hyper parameters...") | |||
| self.hyper_param = {} | |||
| self.init_hyper_param() | |||
| def create_tf_placeholders(self): | |||
| """ | |||
| Create placeholders for the graph. | |||
| The input for these will be given while training or testing. | |||
| """ | |||
| self.input_image = tf.placeholder(tf.float32, shape=self.input_shape, | |||
| name='input_image') | |||
| self.labels = tf.placeholder(tf.float32, shape=self.output_shape, | |||
| name='output') | |||
| self.learning_rate = tf.placeholder(tf.float32, shape=(), | |||
| name='learning_rate') | |||
| self.dropout = tf.placeholder(tf.float32, shape=(), | |||
| name='dropout') | |||
| def create_tf_variables(self): | |||
| """ | |||
| Create variables for epoch, batch and global step | |||
| """ | |||
| self.global_step = tf.Variable(0, name='global_step', trainable=False) | |||
| self.cur_epoch = tf.Variable(0, name='epoch', trainable=False) | |||
| self.cur_batch = tf.Variable(0, name='batch', trainable=False) | |||
| self.increment_epoch_op = tf.assign(self.cur_epoch, self.cur_epoch+1) | |||
| self.increment_batch_op = tf.assign(self.cur_batch, self.cur_batch+1) | |||
| self.init_batch_op = tf.assign(self.cur_batch, 0) | |||
| def init_hyper_param(self): | |||
| """ | |||
| Store the hyper parameters. | |||
| For each layer store number of filters(kernels) | |||
| and filter size. | |||
| If it's a fully connected layer then store the number of neurons. | |||
| """ | |||
| with open('hparam.json') as f: | |||
| self.hyper_param = json.load(f) | |||
| def get_filter(self, layer_num, layer_name): | |||
| """ | |||
| :param layer_num: Indicates the layer number in the graph | |||
| :type layer_num: int | |||
| :param layer_name: Name of the filter | |||
| """ | |||
| layer = 'L' + str(layer_num) | |||
| filter_height, filter_width, in_channels = self.hyper_param[layer]['filter_size'] | |||
| out_channels = self.hyper_param[layer]['filters'] | |||
| return tf.Variable(tf.truncated_normal( | |||
| [filter_height, filter_width, in_channels, out_channels], | |||
| dtype = tf.float32, stddev = 1e-2), name = layer_name) | |||
| def get_strides(self, layer_num): | |||
| """ | |||
| :param layer_num: Indicates the layer number in the graph | |||
| :type layer_num: int | |||
| """ | |||
| layer = 'L' + str(layer_num) | |||
| stride = self.hyper_param[layer]['stride'] | |||
| strides = [1, stride, stride, 1] | |||
| return strides | |||
| def get_bias(self, layer_num, value=0.0): | |||
| """ | |||
| Get the bias variable for current layer | |||
| :param layer_num: Indicates the layer number in the graph | |||
| :type layer_num: int | |||
| """ | |||
| layer = 'L' + str(layer_num) | |||
| initial = tf.constant(value, | |||
| shape=[self.hyper_param[layer]['filters']], | |||
| name='C' + str(layer_num)) | |||
| return tf.Variable(initial, name='B' + str(layer_num)) | |||
| @property | |||
| def l2_loss(self): | |||
| """ | |||
| Compute the l2 loss for all the weights | |||
| """ | |||
| conv_bias_names = ['B' + str(i) for i in range(1, 6)] | |||
| weights = [] | |||
| for v in tf.trainable_variables(): | |||
| if 'biases' in v.name: continue | |||
| if v.name.split(':')[0] in conv_bias_names: continue | |||
| weights.append(v) | |||
| return self.lambd * tf.add_n([tf.nn.l2_loss(weight) for weight in weights]) | |||
| def build_graph(self): | |||
| """ | |||
| Build the tensorflow graph for AlexNet. | |||
| First 5 layers are Convolutional layers. Out of which | |||
| first 2 and last layer will be followed by *max pooling* | |||
| layers. | |||
| Next 2 layers are fully connected layers. | |||
| L1_conv -> L1_MP -> L2_conv -> L2_MP -> L3_conv | |||
| -> L4_conv -> L5_conv -> L5_MP -> L6_FC -> L7_FC | |||
| Where L1_conv -> Convolutional layer 1 | |||
| L5_MP -> Max pooling layer 5 | |||
| L7_FC -> Fully Connected layer 7 | |||
| Use `tf.nn.conv2d` to initialize the filters so | |||
| as to reduce training time and `tf.layers.max_pooling2d` | |||
| as we don't need to initialize in the pooling layer. | |||
| """ | |||
| # Layer 1 Convolutional layer | |||
| filter1 = self.get_filter(1, 'L1_filter') | |||
| l1_conv = tf.nn.conv2d(self.input_image, filter1, | |||
| self.get_strides(1), | |||
| padding = self.hyper_param['L1']['padding'], | |||
| name='L1_conv') | |||
| l1_conv = tf.add(l1_conv, self.get_bias(1)) | |||
| l1_conv = tf.nn.local_response_normalization(l1_conv, | |||
| depth_radius=5, | |||
| bias=2, | |||
| alpha=1e-4, | |||
| beta=.75) | |||
| l1_conv = tf.nn.relu(l1_conv) | |||
| # Layer 1 Max Pooling layer | |||
| l1_MP = tf.layers.max_pooling2d(l1_conv, | |||
| self.hyper_param['L1_MP']['filter_size'], | |||
| self.hyper_param['L1_MP']['stride'], | |||
| name='L1_MP') | |||
| # Layer 2 Convolutional layer | |||
| filter2 = self.get_filter(2, 'L2_filter') | |||
| l2_conv = tf.nn.conv2d(l1_MP, filter2, | |||
| self.get_strides(2), | |||
| padding = self.hyper_param['L2']['padding'], | |||
| name='L2_conv') | |||
| l2_conv = tf.add(l2_conv, self.get_bias(2, 1.0)) | |||
| l2_conv = tf.nn.local_response_normalization(l2_conv, | |||
| depth_radius=5, | |||
| bias=2, | |||
| alpha=1e-4, | |||
| beta=.75) | |||
| l2_conv = tf.nn.relu(l2_conv) | |||
| # Layer 2 Max Pooling layer | |||
| l2_MP = tf.layers.max_pooling2d(l2_conv, | |||
| self.hyper_param['L2_MP']['filter_size'], | |||
| self.hyper_param['L2_MP']['stride'], | |||
| name='L2_MP') | |||
| # Layer 3 Convolutional layer | |||
| filter3 = self.get_filter(3, 'L3_filter') | |||
| l3_conv = tf.nn.conv2d(l2_MP, filter3, | |||
| self.get_strides(3), | |||
| padding = self.hyper_param['L3']['padding'], | |||
| name='L3_conv') | |||
| l3_conv = tf.add(l3_conv, self.get_bias(3)) | |||
| l3_conv = tf.nn.relu(l3_conv) | |||
| # Layer 4 Convolutional layer | |||
| filter4 = self.get_filter(4, 'L4_filter') | |||
| l4_conv = tf.nn.conv2d(l3_conv, filter4, | |||
| self.get_strides(4), | |||
| padding = self.hyper_param['L4']['padding'], | |||
| name='L4_conv') | |||
| l4_conv = tf.add(l4_conv, self.get_bias(4, 1.0)) | |||
| l4_conv = tf.nn.relu(l4_conv) | |||
| # Layer 5 Convolutional layer | |||
| filter5 = self.get_filter(5, 'L5_filter') | |||
| l5_conv = tf.nn.conv2d(l4_conv, filter5, | |||
| self.get_strides(5), | |||
| padding = self.hyper_param['L5']['padding'], | |||
| name='L5_conv') | |||
| l5_conv = tf.add(l5_conv, self.get_bias(5, 1.0)) | |||
| l5_conv = tf.nn.relu(l5_conv) | |||
| # Layer 5 Max Pooling layer | |||
| l5_MP = tf.layers.max_pooling2d(l5_conv, | |||
| self.hyper_param['L5_MP']['filter_size'], | |||
| self.hyper_param['L5_MP']['stride'], | |||
| name='L5_MP') | |||
| flatten = tf.layers.flatten(l5_MP) | |||
| # Layer 6 Fully connected layer | |||
| l6_FC = tf.contrib.layers.fully_connected(flatten, | |||
| self.hyper_param['FC6']) | |||
| # Dropout layer | |||
| l6_dropout = tf.nn.dropout(l6_FC, self.dropout, | |||
| name='l6_dropout') | |||
| # Layer 7 Fully connected layer | |||
| self.l7_FC = tf.contrib.layers.fully_connected(l6_dropout, | |||
| self.hyper_param['FC7']) | |||
| # Dropout layer | |||
| l7_dropout = tf.nn.dropout(self.l7_FC, self.dropout, | |||
| name='l7_dropout') | |||
| # final layer before softmax | |||
| self.logits = tf.contrib.layers.fully_connected(l7_dropout, | |||
| self.num_classes, None) | |||
| # loss function | |||
| loss_function = tf.nn.softmax_cross_entropy_with_logits( | |||
| logits = self.logits, | |||
| labels = self.labels | |||
| ) | |||
| # total loss | |||
| self.loss = tf.reduce_mean(loss_function) + self.l2_loss | |||
| self.optimizer = tf.train.MomentumOptimizer(self.learning_rate, momentum=self.momentum)\ | |||
| .minimize(self.loss, global_step=self.global_step) | |||
| correct = tf.equal(tf.argmax(self.logits, 1), tf.argmax(self.labels, 1)) | |||
| self.accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) | |||
| self.top5_correct = tf.nn.in_top_k(self.logits, tf.argmax(self.labels, 1), 5) | |||
| self.top5_accuracy = tf.reduce_mean(tf.cast(self.top5_correct, tf.float32)) | |||
| self.add_summaries() | |||
| def add_summaries(self): | |||
| """ | |||
| Add summaries for loss, top1 and top5 accuracies | |||
| Add loss, top1 and top5 accuracies to summary files | |||
| in order to visualize in tensorboard | |||
| """ | |||
| tf.summary.scalar('loss', self.loss) | |||
| tf.summary.scalar('Top-1-Acc', self.accuracy) | |||
| tf.summary.scalar('Top-5-Acc', self.top5_accuracy) | |||
| self.merged = tf.summary.merge_all() | |||
| def save_model(self, sess, saver): | |||
| """ | |||
| Save the current model | |||
| :param sess: Session object | |||
| :param saver: Saver object responsible to store | |||
| """ | |||
| model_base_path = os.path.join(os.getcwd(), 'model') | |||
| if not os.path.exists(model_base_path): | |||
| os.mkdir(model_base_path) | |||
| model_save_path = os.path.join(os.getcwd(), 'model', 'model.ckpt') | |||
| save_path = saver.save(sess, model_save_path) | |||
| self.logger.info("Model saved in path: %s", save_path) | |||
| def restore_model(self, sess, saver): | |||
| """ | |||
| Restore previously saved model | |||
| :param sess: Session object | |||
| :param saver: Saver object responsible to store | |||
| """ | |||
| model_base_path = os.path.join(os.getcwd(), 'model') | |||
| model_restore_path = os.path.join(os.getcwd(), 'model', 'model.ckpt') | |||
| saver.restore(sess, model_restore_path) | |||
| self.logger.info("Model Restored from path: %s", | |||
| model_restore_path) | |||
| def get_summary_writer(self, sess): | |||
| """ | |||
| Get summary writer for training and validation | |||
| Responsible for creating summary writer so it can | |||
| write summaries to a file so it can be read by | |||
| tensorboard later. | |||
| """ | |||
| if not os.path.exists(os.path.join('summary', 'train')): | |||
| os.makedirs(os.path.join('summary', 'train')) | |||
| if not os.path.exists(os.path.join('summary', 'val')): | |||
| os.makedirs(os.path.join('summary', 'val')) | |||
| return (tf.summary.FileWriter(os.path.join(os.getcwd(), | |||
| 'summary', 'train'), | |||
| sess.graph), | |||
| tf.summary.FileWriter(os.path.join(os.getcwd(), | |||
| 'summary', 'val'), | |||
| sess.graph)) | |||
| def train(self, epochs, thread='false'): | |||
| """ | |||
| Train AlexNet. | |||
| """ | |||
| batch_step, val_step = 10, 500 | |||
| self.logger.info("Building the graph...") | |||
| self.build_graph() | |||
| init = tf.global_variables_initializer() | |||
| saver = tf.train.Saver() | |||
| with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess: | |||
| (summary_writer_train, | |||
| summary_writer_val) = self.get_summary_writer(sess) | |||
| if self.resume and os.path.exists(os.path.join(os.getcwd(), | |||
| 'model')): | |||
| self.restore_model(sess, saver) | |||
| else: | |||
| sess.run(init) | |||
| resume_batch = True | |||
| best_loss = float('inf') | |||
| while sess.run(self.cur_epoch) < epochs: | |||
| losses = [] | |||
| accuracies = [] | |||
| epoch = sess.run(self.cur_epoch) | |||
| if not self.resume or ( | |||
| self.resume and not resume_batch): | |||
| sess.run(self.init_batch_op) | |||
| resume_batch = False | |||
| start = time.time() | |||
| gen_batch = self.lsvrc2010.gen_batch | |||
| for images, labels in gen_batch: | |||
| batch_i = sess.run(self.cur_batch) | |||
| # If it's resumed from stored model, | |||
| # this will save from messing up the batch number | |||
| # in subsequent epoch | |||
| if batch_i >= ceil(len(self.lsvrc2010.image_names) / self.batch_size): | |||
| break | |||
| (_, global_step, | |||
| _) = sess.run([self.optimizer, | |||
| self.global_step, self.increment_batch_op], | |||
| feed_dict = { | |||
| self.input_image: images, | |||
| self.labels: labels, | |||
| self.learning_rate: self.lr, | |||
| self.dropout: 0.5 | |||
| }) | |||
| if global_step == 150000: | |||
| self.lr = 0.0001 # Halve the learning rate | |||
| if batch_i % batch_step == 0: | |||
| (summary, loss, acc, top5_acc, _top5, | |||
| logits, l7_FC) = sess.run([self.merged, self.loss, | |||
| self.accuracy, self.top5_accuracy, | |||
| self.top5_correct, | |||
| self.logits, self.l7_FC], | |||
| feed_dict = { | |||
| self.input_image: images, | |||
| self.labels: labels, | |||
| self.learning_rate: self.lr, | |||
| self.dropout: 1.0 | |||
| }) | |||
| losses.append(loss) | |||
| accuracies.append(acc) | |||
| summary_writer_train.add_summary(summary, global_step) | |||
| summary_writer_train.flush() | |||
| end = time.time() | |||
| try: | |||
| self.logger.debug("l7 no of non zeros: %d", np.count_nonzero(l7_FC)) | |||
| true_idx = np.where(_top5[0]==True)[0][0] | |||
| self.logger.debug("logit at %d: %s", true_idx, | |||
| str(logits[true_idx])) | |||
| except IndexError as ie: | |||
| self.logger.debug(ie) | |||
| self.logger.info("Time: %f Epoch: %d Batch: %d Loss: %f " | |||
| "Avg loss: %f Accuracy: %f Avg Accuracy: %f " | |||
| "Top 5 Accuracy: %f", | |||
| end - start, epoch, batch_i, | |||
| loss, sum(losses) / len(losses), | |||
| acc, sum(accuracies) / len(accuracies), | |||
| top5_acc) | |||
| start = time.time() | |||
| if batch_i % val_step == 0: | |||
| images_val, labels_val = self.lsvrc2010.get_batch_val | |||
| (summary, acc, top5_acc, | |||
| loss) = sess.run([self.merged, | |||
| self.accuracy, | |||
| self.top5_accuracy, self.loss], | |||
| feed_dict = { | |||
| self.input_image: images_val, | |||
| self.labels: labels_val, | |||
| self.learning_rate: self.lr, | |||
| self.dropout: 1.0 | |||
| }) | |||
| summary_writer_val.add_summary(summary, global_step) | |||
| summary_writer_val.flush() | |||
| self.logger.info("Validation - Accuracy: %f Top 5 Accuracy: %f Loss: %f", | |||
| acc, top5_acc, loss) | |||
| cur_loss = sum(losses) / len(losses) | |||
| if cur_loss < best_loss: | |||
| best_loss = cur_loss | |||
| self.save_model(sess, saver) | |||
| # Increase epoch number | |||
| sess.run(self.increment_epoch_op) | |||
| def test(self): | |||
| step = 10 | |||
| self.logger_test = logs.get_logger('AlexNetTest', file_name='logs_test.log') | |||
| self.logger_test.info("In Test: Building the graph...") | |||
| self.build_graph() | |||
| init = tf.global_variables_initializer() | |||
| saver = tf.train.Saver() | |||
| top1_count, top5_count, count = 0, 0, 0 | |||
| with tf.Session(config=tf.ConfigProto(log_device_placement=True)) as sess: | |||
| self.restore_model(sess, saver) | |||
| start = time.time() | |||
| batch = self.lsvrc2010.gen_batch_test | |||
| for i, (patches, labels) in enumerate(batch): | |||
| count += patches[0].shape[0] | |||
| avg_logits = np.zeros((patches[0].shape[0], self.num_classes)) | |||
| for patch in patches: | |||
| logits = sess.run(self.logits, | |||
| feed_dict = { | |||
| self.input_image: patch, | |||
| self.dropout: 1.0 | |||
| }) | |||
| avg_logits += logits | |||
| avg_logits /= len(patches) | |||
| top1_count += np.sum(np.argmax(avg_logits, 1) == labels) | |||
| top5_count += np.sum(avg_logits.argsort()[:, -5:] == \ | |||
| np.repeat(labels, 5).reshape(patches[0].shape[0], 5)) | |||
| if i % step == 0: | |||
| end = time.time() | |||
| self.logger_test.info("Time: %f Step: %d " | |||
| "Avg Accuracy: %f " | |||
| "Avg Top 5 Accuracy: %f", | |||
| end - start, i, | |||
| top1_count / count, | |||
| top5_count / count) | |||
| start = time.time() | |||
| self.logger_test.info("Final - Avg Accuracy: %f " | |||
| "Avg Top 5 Accuracy: %f", | |||
| top1_count / count, | |||
| top5_count / count) | |||
| if __name__ == '__main__': | |||
| import argparse | |||
| parser = argparse.ArgumentParser() | |||
| parser.add_argument('image_path', metavar = 'image-path', | |||
| help = 'ImageNet dataset path') | |||
| parser.add_argument('--resume', metavar='resume', | |||
| type=lambda x: x != 'False', default=True, | |||
| required=False, | |||
| help='Resume training (True or False)') | |||
| parser.add_argument('--train', help='Train AlexNet') | |||
| parser.add_argument('--test', help='Test AlexNet') | |||
| args = parser.parse_args() | |||
| alexnet = AlexNet(args.image_path, batch_size=128, resume=args.resume) | |||
| if args.train == 'true': | |||
| alexnet.train(50) | |||
| elif args.test == 'true': | |||
| alexnet.test() | |||