6_3_InceptionNet.py

#%%
# Copyright 2015 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
import tensorflow as tf

slim = tf.contrib.slim
trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev)


def inception_v3_base(inputs, scope=None):

  end_points = {}

  with tf.variable_scope(scope, 'InceptionV3', [inputs]):
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='VALID'):
      # 299 x 299 x 3
      net = slim.conv2d(inputs, 32, [3, 3], stride=2, scope='Conv2d_1a_3x3')
      # 149 x 149 x 32
      net = slim.conv2d(net, 32, [3, 3], scope='Conv2d_2a_3x3')
      # 147 x 147 x 32
      net = slim.conv2d(net, 64, [3, 3], padding='SAME', scope='Conv2d_2b_3x3')
      # 147 x 147 x 64
      net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_3a_3x3')
      # 73 x 73 x 64
      net = slim.conv2d(net, 80, [1, 1], scope='Conv2d_3b_1x1')
      # 73 x 73 x 80.
      net = slim.conv2d(net, 192, [3, 3], scope='Conv2d_4a_3x3')
      # 71 x 71 x 192.
      net = slim.max_pool2d(net, [3, 3], stride=2, scope='MaxPool_5a_3x3')
      # 35 x 35 x 192.

    # Inception blocks
    with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                        stride=1, padding='SAME'):
      # mixed: 35 x 35 x 256.
      with tf.variable_scope('Mixed_5b'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 32, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_1: 35 x 35 x 288.
      with tf.variable_scope('Mixed_5c'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0b_1x1')
          branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv_1_0c_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_2: 35 x 35 x 288.
      with tf.variable_scope('Mixed_5d'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 48, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 64, [5, 5], scope='Conv2d_0b_5x5')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = slim.conv2d(branch_2, 96, [3, 3], scope='Conv2d_0c_3x3')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 64, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_3: 17 x 17 x 768.
      with tf.variable_scope('Mixed_6a'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 384, [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3')
          branch_1 = slim.conv2d(branch_1, 96, [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_1x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                     scope='MaxPool_1a_3x3')
        net = tf.concat([branch_0, branch_1, branch_2], 3)

      # mixed4: 17 x 17 x 768.
      with tf.variable_scope('Mixed_6b'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 128, [1, 7], scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 128, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, 128, [1, 7], scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, 128, [7, 1], scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_5: 17 x 17 x 768.
      with tf.variable_scope('Mixed_6c'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
      # mixed_6: 17 x 17 x 768.
      with tf.variable_scope('Mixed_6d'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 160, [1, 7], scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 160, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, 160, [1, 7], scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, 160, [7, 1], scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_7: 17 x 17 x 768.
      with tf.variable_scope('Mixed_6e'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0b_7x1')
          branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0c_1x7')
          branch_2 = slim.conv2d(branch_2, 192, [7, 1], scope='Conv2d_0d_7x1')
          branch_2 = slim.conv2d(branch_2, 192, [1, 7], scope='Conv2d_0e_1x7')
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
      end_points['Mixed_6e'] = net

      # mixed_8: 8 x 8 x 1280.
      with tf.variable_scope('Mixed_7a'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
          branch_0 = slim.conv2d(branch_0, 320, [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 192, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = slim.conv2d(branch_1, 192, [1, 7], scope='Conv2d_0b_1x7')
          branch_1 = slim.conv2d(branch_1, 192, [7, 1], scope='Conv2d_0c_7x1')
          branch_1 = slim.conv2d(branch_1, 192, [3, 3], stride=2,
                                 padding='VALID', scope='Conv2d_1a_3x3')
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID',
                                     scope='MaxPool_1a_3x3')
        net = tf.concat([branch_0, branch_1, branch_2], 3)
      # mixed_9: 8 x 8 x 2048.
      with tf.variable_scope('Mixed_7b'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = tf.concat([
              slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
              slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0b_3x1')], 3)
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(
              branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = tf.concat([
              slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
              slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)

      # mixed_10: 8 x 8 x 2048.
      with tf.variable_scope('Mixed_7c'):
        with tf.variable_scope('Branch_0'):
          branch_0 = slim.conv2d(net, 320, [1, 1], scope='Conv2d_0a_1x1')
        with tf.variable_scope('Branch_1'):
          branch_1 = slim.conv2d(net, 384, [1, 1], scope='Conv2d_0a_1x1')
          branch_1 = tf.concat([
              slim.conv2d(branch_1, 384, [1, 3], scope='Conv2d_0b_1x3'),
              slim.conv2d(branch_1, 384, [3, 1], scope='Conv2d_0c_3x1')], 3)
        with tf.variable_scope('Branch_2'):
          branch_2 = slim.conv2d(net, 448, [1, 1], scope='Conv2d_0a_1x1')
          branch_2 = slim.conv2d(
              branch_2, 384, [3, 3], scope='Conv2d_0b_3x3')
          branch_2 = tf.concat([
              slim.conv2d(branch_2, 384, [1, 3], scope='Conv2d_0c_1x3'),
              slim.conv2d(branch_2, 384, [3, 1], scope='Conv2d_0d_3x1')], 3)
        with tf.variable_scope('Branch_3'):
          branch_3 = slim.avg_pool2d(net, [3, 3], scope='AvgPool_0a_3x3')
          branch_3 = slim.conv2d(
              branch_3, 192, [1, 1], scope='Conv2d_0b_1x1')
        net = tf.concat([branch_0, branch_1, branch_2, branch_3], 3)
      return net, end_points


def inception_v3(inputs,
                 num_classes=1000,
                 is_training=True,
                 dropout_keep_prob=0.8,
                 prediction_fn=slim.softmax,
                 spatial_squeeze=True,
                 reuse=None,
                 scope='InceptionV3'):

  with tf.variable_scope(scope, 'InceptionV3', [inputs, num_classes],
                         reuse=reuse) as scope:
    with slim.arg_scope([slim.batch_norm, slim.dropout],
                        is_training=is_training):
      net, end_points = inception_v3_base(inputs, scope=scope)

      # Auxiliary Head logits
      with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d],
                          stride=1, padding='SAME'):
        aux_logits = end_points['Mixed_6e']
        with tf.variable_scope('AuxLogits'):
          aux_logits = slim.avg_pool2d(
              aux_logits, [5, 5], stride=3, padding='VALID',
              scope='AvgPool_1a_5x5')
          aux_logits = slim.conv2d(aux_logits, 128, [1, 1],
                                   scope='Conv2d_1b_1x1')

          # Shape of feature map before the final layer.
          aux_logits = slim.conv2d(
              aux_logits, 768, [5,5],
              weights_initializer=trunc_normal(0.01),
              padding='VALID', scope='Conv2d_2a_5x5')
          aux_logits = slim.conv2d(
              aux_logits, num_classes, [1, 1], activation_fn=None,
              normalizer_fn=None, weights_initializer=trunc_normal(0.001),
              scope='Conv2d_2b_1x1')
          if spatial_squeeze:
            aux_logits = tf.squeeze(aux_logits, [1, 2], name='SpatialSqueeze')
          end_points['AuxLogits'] = aux_logits

      # Final pooling and prediction
      with tf.variable_scope('Logits'):
        net = slim.avg_pool2d(net, [8, 8], padding='VALID',
                              scope='AvgPool_1a_8x8')
        # 1 x 1 x 2048
        net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b')
        end_points['PreLogits'] = net
        # 2048
        logits = slim.conv2d(net, num_classes, [1, 1], activation_fn=None,
                             normalizer_fn=None, scope='Conv2d_1c_1x1')
        if spatial_squeeze:
          logits = tf.squeeze(logits, [1, 2], name='SpatialSqueeze')
        # 1000
      end_points['Logits'] = logits
      end_points['Predictions'] = prediction_fn(logits, scope='Predictions')
  return logits, end_points


def inception_v3_arg_scope(weight_decay=0.00004,
                           stddev=0.1,
                           batch_norm_var_collection='moving_vars'):

  batch_norm_params = {
      'decay': 0.9997,
      'epsilon': 0.001,
      'updates_collections': tf.GraphKeys.UPDATE_OPS,
      'variables_collections': {
          'beta': None,
          'gamma': None,
          'moving_mean': [batch_norm_var_collection],
          'moving_variance': [batch_norm_var_collection],
      }
  }

  with slim.arg_scope([slim.conv2d, slim.fully_connected],
                      weights_regularizer=slim.l2_regularizer(weight_decay)):
    with slim.arg_scope(
        [slim.conv2d],
        weights_initializer=trunc_normal(stddev),
        activation_fn=tf.nn.relu,
        normalizer_fn=slim.batch_norm,
        normalizer_params=batch_norm_params) as sc:
      return sc

  
from datetime import datetime
import math
import time
def time_tensorflow_run(session, target, info_string):
    num_steps_burn_in = 10
    total_duration = 0.0
    total_duration_squared = 0.0
    for i in range(num_batches + num_steps_burn_in):
        start_time = time.time()
        _ = session.run(target)
        duration = time.time() - start_time
        if i >= num_steps_burn_in:
            if not i % 10:
                print ('%s: step %d, duration = %.3f' %
                       (datetime.now(), i - num_steps_burn_in, duration))
            total_duration += duration
            total_duration_squared += duration * duration
    mn = total_duration / num_batches
    vr = total_duration_squared / num_batches - mn * mn
    sd = math.sqrt(vr)
    print ('%s: %s across %d steps, %.3f +/- %.3f sec / batch' %
           (datetime.now(), info_string, num_batches, mn, sd))
    
batch_size = 32
height, width = 299, 299
inputs = tf.random_uniform((batch_size, height, width, 3))
with slim.arg_scope(inception_v3_arg_scope()):
  logits, end_points = inception_v3(inputs, is_training=False)
  
init = tf.global_variables_initializer()
sess = tf.Session()
sess.run(init)  
num_batches=100
time_tensorflow_run(sess, logits, "Forward")