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file/assignment1/model.py

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+#!/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()