| #!/usr/bin/env python |
| # Licensed to the Apache Software Foundation (ASF) under one |
| # or more contributor license agreements. See the NOTICE file |
| # distributed with this work for additional information |
| # regarding copyright ownership. The ASF licenses this file |
| # to you 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. |
| |
| """Contains the definition of the Inception V4 architecture. |
| |
| As described in http://arxiv.org/abs/1602.07261. |
| |
| Inception-v4, Inception-ResNet and the Impact of Residual Connections |
| on Learning |
| Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi |
| """ |
| from __future__ import absolute_import |
| from __future__ import division |
| from __future__ import print_function |
| |
| import tensorflow as tf |
| |
| slim = tf.contrib.slim |
| |
| |
| def block_inception_a(inputs, scope=None, reuse=None): |
| """Builds Inception-A block for Inception v4 network.""" |
| # By default use stride=1 and SAME padding |
| with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], |
| stride=1, padding='SAME'): |
| with tf.variable_scope(scope, 'BlockInceptionA', [inputs], reuse=reuse): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(inputs, 96, [1, 1], scope='Conv2d_0a_1x1') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(inputs, 64, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = slim.conv2d(branch_1, 96, [3, 3], scope='Conv2d_0b_3x3') |
| with tf.variable_scope('Branch_2'): |
| branch_2 = slim.conv2d(inputs, 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(inputs, [3, 3], scope='AvgPool_0a_3x3') |
| branch_3 = slim.conv2d(branch_3, 96, [1, 1], scope='Conv2d_0b_1x1') |
| return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) |
| |
| |
| def block_reduction_a(inputs, scope=None, reuse=None): |
| """Builds Reduction-A block for Inception v4 network.""" |
| # By default use stride=1 and SAME padding |
| with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], |
| stride=1, padding='SAME'): |
| with tf.variable_scope(scope, 'BlockReductionA', [inputs], reuse=reuse): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(inputs, 384, [3, 3], stride=2, padding='VALID', |
| scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = slim.conv2d(branch_1, 224, [3, 3], scope='Conv2d_0b_3x3') |
| branch_1 = slim.conv2d(branch_1, 256, [3, 3], stride=2, |
| padding='VALID', scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_2'): |
| branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', |
| scope='MaxPool_1a_3x3') |
| return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) |
| |
| |
| def block_inception_b(inputs, scope=None, reuse=None): |
| """Builds Inception-B block for Inception v4 network.""" |
| # By default use stride=1 and SAME padding |
| with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], |
| stride=1, padding='SAME'): |
| with tf.variable_scope(scope, 'BlockInceptionB', [inputs], reuse=reuse): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = slim.conv2d(branch_1, 224, [1, 7], scope='Conv2d_0b_1x7') |
| branch_1 = slim.conv2d(branch_1, 256, [7, 1], scope='Conv2d_0c_7x1') |
| with tf.variable_scope('Branch_2'): |
| branch_2 = slim.conv2d(inputs, 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, 224, [1, 7], scope='Conv2d_0c_1x7') |
| branch_2 = slim.conv2d(branch_2, 224, [7, 1], scope='Conv2d_0d_7x1') |
| branch_2 = slim.conv2d(branch_2, 256, [1, 7], scope='Conv2d_0e_1x7') |
| with tf.variable_scope('Branch_3'): |
| branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') |
| branch_3 = slim.conv2d(branch_3, 128, [1, 1], scope='Conv2d_0b_1x1') |
| return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) |
| |
| |
| def block_reduction_b(inputs, scope=None, reuse=None): |
| """Builds Reduction-B block for Inception v4 network.""" |
| # By default use stride=1 and SAME padding |
| with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], |
| stride=1, padding='SAME'): |
| with tf.variable_scope(scope, 'BlockReductionB', [inputs], reuse=reuse): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(inputs, 192, [1, 1], scope='Conv2d_0a_1x1') |
| branch_0 = slim.conv2d(branch_0, 192, [3, 3], stride=2, |
| padding='VALID', scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = slim.conv2d(branch_1, 256, [1, 7], scope='Conv2d_0b_1x7') |
| branch_1 = slim.conv2d(branch_1, 320, [7, 1], scope='Conv2d_0c_7x1') |
| branch_1 = slim.conv2d(branch_1, 320, [3, 3], stride=2, |
| padding='VALID', scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_2'): |
| branch_2 = slim.max_pool2d(inputs, [3, 3], stride=2, padding='VALID', |
| scope='MaxPool_1a_3x3') |
| return tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) |
| |
| |
| def block_inception_c(inputs, scope=None, reuse=None): |
| """Builds Inception-C block for Inception v4 network.""" |
| # By default use stride=1 and SAME padding |
| with slim.arg_scope([slim.conv2d, slim.avg_pool2d, slim.max_pool2d], |
| stride=1, padding='SAME'): |
| with tf.variable_scope(scope, 'BlockInceptionC', [inputs], reuse=reuse): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(inputs, 256, [1, 1], scope='Conv2d_0a_1x1') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = tf.concat(axis=3, values=[ |
| slim.conv2d(branch_1, 256, [1, 3], scope='Conv2d_0b_1x3'), |
| slim.conv2d(branch_1, 256, [3, 1], scope='Conv2d_0c_3x1')]) |
| with tf.variable_scope('Branch_2'): |
| branch_2 = slim.conv2d(inputs, 384, [1, 1], scope='Conv2d_0a_1x1') |
| branch_2 = slim.conv2d(branch_2, 448, [3, 1], scope='Conv2d_0b_3x1') |
| branch_2 = slim.conv2d(branch_2, 512, [1, 3], scope='Conv2d_0c_1x3') |
| branch_2 = tf.concat(axis=3, values=[ |
| slim.conv2d(branch_2, 256, [1, 3], scope='Conv2d_0d_1x3'), |
| slim.conv2d(branch_2, 256, [3, 1], scope='Conv2d_0e_3x1')]) |
| with tf.variable_scope('Branch_3'): |
| branch_3 = slim.avg_pool2d(inputs, [3, 3], scope='AvgPool_0a_3x3') |
| branch_3 = slim.conv2d(branch_3, 256, [1, 1], scope='Conv2d_0b_1x1') |
| return tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) |
| |
| |
| def inception_v4_base(inputs, final_endpoint='Mixed_7d', scope=None): |
| """Creates the Inception V4 network up to the given final endpoint. |
| |
| Args: |
| inputs: a 4-D tensor of size [batch_size, height, width, 3]. |
| final_endpoint: specifies the endpoint to construct the network up to. |
| It can be one of [ 'Conv2d_1a_3x3', 'Conv2d_2a_3x3', 'Conv2d_2b_3x3', |
| 'Mixed_3a', 'Mixed_4a', 'Mixed_5a', 'Mixed_5b', 'Mixed_5c', 'Mixed_5d', |
| 'Mixed_5e', 'Mixed_6a', 'Mixed_6b', 'Mixed_6c', 'Mixed_6d', 'Mixed_6e', |
| 'Mixed_6f', 'Mixed_6g', 'Mixed_6h', 'Mixed_7a', 'Mixed_7b', 'Mixed_7c', |
| 'Mixed_7d'] |
| scope: Optional variable_scope. |
| |
| Returns: |
| logits: the logits outputs of the model. |
| end_points: the set of end_points from the inception model. |
| |
| Raises: |
| ValueError: if final_endpoint is not set to one of the predefined values, |
| """ |
| end_points = {} |
| |
| def add_and_check_final(name, net): |
| end_points[name] = net |
| return name == final_endpoint |
| |
| with tf.variable_scope(scope, 'InceptionV4', [inputs]): |
| with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], |
| stride=1, padding='SAME'): |
| # 299 x 299 x 3 |
| net = slim.conv2d(inputs, 32, [3, 3], stride=2, |
| padding='VALID', scope='Conv2d_1a_3x3') |
| if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points |
| # 149 x 149 x 32 |
| net = slim.conv2d(net, 32, [3, 3], padding='VALID', |
| scope='Conv2d_2a_3x3') |
| if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points |
| # 147 x 147 x 32 |
| net = slim.conv2d(net, 64, [3, 3], scope='Conv2d_2b_3x3') |
| if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points |
| # 147 x 147 x 64 |
| with tf.variable_scope('Mixed_3a'): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', |
| scope='MaxPool_0a_3x3') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(net, 96, [3, 3], stride=2, padding='VALID', |
| scope='Conv2d_0a_3x3') |
| net = tf.concat(axis=3, values=[branch_0, branch_1]) |
| if add_and_check_final('Mixed_3a', net): return net, end_points |
| |
| # 73 x 73 x 160 |
| with tf.variable_scope('Mixed_4a'): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') |
| branch_0 = slim.conv2d(branch_0, 96, [3, 3], padding='VALID', |
| scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.conv2d(net, 64, [1, 1], scope='Conv2d_0a_1x1') |
| branch_1 = slim.conv2d(branch_1, 64, [1, 7], scope='Conv2d_0b_1x7') |
| branch_1 = slim.conv2d(branch_1, 64, [7, 1], scope='Conv2d_0c_7x1') |
| branch_1 = slim.conv2d(branch_1, 96, [3, 3], padding='VALID', |
| scope='Conv2d_1a_3x3') |
| net = tf.concat(axis=3, values=[branch_0, branch_1]) |
| if add_and_check_final('Mixed_4a', net): return net, end_points |
| |
| # 71 x 71 x 192 |
| with tf.variable_scope('Mixed_5a'): |
| with tf.variable_scope('Branch_0'): |
| branch_0 = slim.conv2d(net, 192, [3, 3], stride=2, padding='VALID', |
| scope='Conv2d_1a_3x3') |
| with tf.variable_scope('Branch_1'): |
| branch_1 = slim.max_pool2d(net, [3, 3], stride=2, padding='VALID', |
| scope='MaxPool_1a_3x3') |
| net = tf.concat(axis=3, values=[branch_0, branch_1]) |
| if add_and_check_final('Mixed_5a', net): return net, end_points |
| |
| # 35 x 35 x 384 |
| # 4 x Inception-A blocks |
| for idx in range(4): |
| block_scope = 'Mixed_5' + chr(ord('b') + idx) |
| net = block_inception_a(net, block_scope) |
| if add_and_check_final(block_scope, net): return net, end_points |
| |
| # 35 x 35 x 384 |
| # Reduction-A block |
| net = block_reduction_a(net, 'Mixed_6a') |
| if add_and_check_final('Mixed_6a', net): return net, end_points |
| |
| # 17 x 17 x 1024 |
| # 7 x Inception-B blocks |
| for idx in range(7): |
| block_scope = 'Mixed_6' + chr(ord('b') + idx) |
| net = block_inception_b(net, block_scope) |
| if add_and_check_final(block_scope, net): return net, end_points |
| |
| # 17 x 17 x 1024 |
| # Reduction-B block |
| net = block_reduction_b(net, 'Mixed_7a') |
| if add_and_check_final('Mixed_7a', net): return net, end_points |
| |
| # 8 x 8 x 1536 |
| # 3 x Inception-C blocks |
| for idx in range(3): |
| block_scope = 'Mixed_7' + chr(ord('b') + idx) |
| net = block_inception_c(net, block_scope) |
| if add_and_check_final(block_scope, net): return net, end_points |
| raise ValueError('Unknown final endpoint %s' % final_endpoint) |
| |
| |
| def inception_v4(inputs, num_classes=1001, is_training=True, |
| dropout_keep_prob=0.8, |
| reuse=None, |
| scope='InceptionV4', |
| create_aux_logits=True): |
| """Creates the Inception V4 model. |
| |
| Args: |
| inputs: a 4-D tensor of size [batch_size, height, width, 3]. |
| num_classes: number of predicted classes. |
| is_training: whether is training or not. |
| dropout_keep_prob: float, the fraction to keep before final layer. |
| reuse: whether or not the network and its variables should be reused. To be |
| able to reuse 'scope' must be given. |
| scope: Optional variable_scope. |
| create_aux_logits: Whether to include the auxiliary logits. |
| |
| Returns: |
| logits: the logits outputs of the model. |
| end_points: the set of end_points from the inception model. |
| """ |
| end_points = {} |
| with tf.variable_scope(scope, 'InceptionV4', [inputs], reuse=reuse) as scope: |
| with slim.arg_scope([slim.batch_norm, slim.dropout], |
| is_training=is_training): |
| net, end_points = inception_v4_base(inputs, scope=scope) |
| |
| with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], |
| stride=1, padding='SAME'): |
| # Auxiliary Head logits |
| if create_aux_logits: |
| with tf.variable_scope('AuxLogits'): |
| # 17 x 17 x 1024 |
| aux_logits = end_points['Mixed_6h'] |
| 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') |
| aux_logits = slim.conv2d(aux_logits, 768, |
| aux_logits.get_shape()[1:3], |
| padding='VALID', scope='Conv2d_2a') |
| aux_logits = slim.flatten(aux_logits) |
| aux_logits = slim.fully_connected(aux_logits, num_classes, |
| activation_fn=None, |
| scope='Aux_logits') |
| end_points['AuxLogits'] = aux_logits |
| |
| # Final pooling and prediction |
| with tf.variable_scope('Logits'): |
| # 8 x 8 x 1536 |
| net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', |
| scope='AvgPool_1a') |
| # 1 x 1 x 1536 |
| net = slim.dropout(net, dropout_keep_prob, scope='Dropout_1b') |
| net = slim.flatten(net, scope='PreLogitsFlatten') |
| end_points['PreLogitsFlatten'] = net |
| # 1536 |
| logits = slim.fully_connected(net, num_classes, activation_fn=None, |
| scope='Logits') |
| end_points['Logits'] = logits |
| end_points['Predictions'] = tf.nn.softmax(logits, name='Predictions') |
| return logits, end_points |
| |
| |
| def inception_v4_arg_scope(weight_decay=0.00004, |
| use_batch_norm=True, |
| batch_norm_decay=0.9997, |
| batch_norm_epsilon=0.001): |
| """Defines the default arg scope for inception models. |
| Args: |
| weight_decay: The weight decay to use for regularizing the model. |
| use_batch_norm: "If `True`, batch_norm is applied after each convolution. |
| batch_norm_decay: Decay for batch norm moving average. |
| batch_norm_epsilon: Small float added to variance to avoid dividing by zero |
| in batch norm. |
| Returns: |
| An `arg_scope` to use for the inception models. |
| """ |
| batch_norm_params = { |
| # Decay for the moving averages. |
| 'decay': batch_norm_decay, |
| # epsilon to prevent 0s in variance. |
| 'epsilon': batch_norm_epsilon, |
| # collection containing update_ops. |
| 'updates_collections': tf.GraphKeys.UPDATE_OPS, |
| } |
| if use_batch_norm: |
| normalizer_fn = slim.batch_norm |
| normalizer_params = batch_norm_params |
| else: |
| normalizer_fn = None |
| normalizer_params = {} |
| # Set weight_decay for weights in Conv and FC layers. |
| with slim.arg_scope([slim.conv2d, slim.fully_connected], |
| weights_regularizer=slim.l2_regularizer(weight_decay)): |
| with slim.arg_scope( |
| [slim.conv2d], |
| weights_initializer=slim.variance_scaling_initializer(), |
| activation_fn=tf.nn.relu, |
| normalizer_fn=normalizer_fn, |
| normalizer_params=normalizer_params) as sc: |
| return sc |
| |
| |
| default_image_size = 299 |
