Transfer learning on TensorFlow-Slim image classification model library
# Copyright 2016 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.# =============================================================================="""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_importfrom __future__ import divisionfrom __future__ import print_functionimport tensorflow.compat.v1 as tfimport tf_slim as slimfrom nets import inception_utilsdef 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. If 0 or None, the logits layer is omitted and the input features to the logits layer (before dropout) are returned instead. 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: net: a Tensor with the logits (pre-softmax activations) if num_classes is a non-zero integer, or the non-dropped input to the logits layer if num_classes is 0 or None. 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 and num_classes: 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 # TODO(sguada,arnoegw): Consider adding a parameter global_pool which # can be set to False to disable pooling here (as in resnet_*()). with tf.variable_scope('Logits'): # 8 x 8 x 1536 kernel_size = net.get_shape()[1:3] if kernel_size.is_fully_defined(): net = slim.avg_pool2d(net, kernel_size, padding='VALID', scope='AvgPool_1a') else: net = tf.reduce_mean( input_tensor=net, axis=[1, 2], keepdims=True, name='global_pool') end_points['global_pool'] = net if not num_classes: return net, end_points # 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_pointsinception_v4.default_image_size = 299inception_v4_arg_scope = inception_utils.inception_arg_scope