#!/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()