X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_resnet_v2.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_resnet_v2.py;h=ddd9392206858f38e46710b47ea93610b514556b;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py new file mode 100755 index 0000000..ddd9392 --- /dev/null +++ b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_resnet_v2.py @@ -0,0 +1,359 @@ +# 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 Resnet V2 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 block35(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 35x35 resnet block.""" + with tf.variable_scope(scope, 'Block35', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 32, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 32, 3, scope='Conv2d_0b_3x3') + with tf.variable_scope('Branch_2'): + tower_conv2_0 = slim.conv2d(net, 32, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2_0, 48, 3, scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 64, 3, scope='Conv2d_0c_3x3') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_1, tower_conv2_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def block17(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 17x17 resnet block.""" + with tf.variable_scope(scope, 'Block17', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 128, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 160, [1, 7], + scope='Conv2d_0b_1x7') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 192, [7, 1], + scope='Conv2d_0c_7x1') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def block8(net, scale=1.0, activation_fn=tf.nn.relu, scope=None, reuse=None): + """Builds the 8x8 resnet block.""" + with tf.variable_scope(scope, 'Block8', [net], reuse=reuse): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 192, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 192, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 224, [1, 3], + scope='Conv2d_0b_1x3') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 256, [3, 1], + scope='Conv2d_0c_3x1') + mixed = tf.concat(axis=3, values=[tower_conv, tower_conv1_2]) + up = slim.conv2d(mixed, net.get_shape()[3], 1, normalizer_fn=None, + activation_fn=None, scope='Conv2d_1x1') + net += scale * up + if activation_fn: + net = activation_fn(net) + return net + + +def inception_resnet_v2_base(inputs, + final_endpoint='Conv2d_7b_1x1', + output_stride=16, + align_feature_maps=False, + scope=None): + """Inception model from http://arxiv.org/abs/1602.07261. + + Constructs an Inception Resnet v2 network from inputs to the given final + endpoint. This method can construct the network up to the final inception + block Conv2d_7b_1x1. + + Args: + inputs: a tensor of size [batch_size, height, width, channels]. + 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', + 'MaxPool_3a_3x3', 'Conv2d_3b_1x1', 'Conv2d_4a_3x3', 'MaxPool_5a_3x3', + 'Mixed_5b', 'Mixed_6a', 'PreAuxLogits', 'Mixed_7a', 'Conv2d_7b_1x1'] + output_stride: A scalar that specifies the requested ratio of input to + output spatial resolution. Only supports 8 and 16. + align_feature_maps: When true, changes all the VALID paddings in the network + to SAME padding so that the feature maps are aligned. + scope: Optional variable_scope. + + Returns: + tensor_out: output tensor corresponding to the final_endpoint. + end_points: a set of activations for external use, for example summaries or + losses. + + Raises: + ValueError: if final_endpoint is not set to one of the predefined values, + or if the output_stride is not 8 or 16, or if the output_stride is 8 and + we request an end point after 'PreAuxLogits'. + """ + if output_stride != 8 and output_stride != 16: + raise ValueError('output_stride must be 8 or 16.') + + padding = 'SAME' if align_feature_maps else 'VALID' + + end_points = {} + + def add_and_check_final(name, net): + end_points[name] = net + return name == final_endpoint + + with tf.variable_scope(scope, 'InceptionResnetV2', [inputs]): + with slim.arg_scope([slim.conv2d, slim.max_pool2d, slim.avg_pool2d], + stride=1, padding='SAME'): + # 149 x 149 x 32 + net = slim.conv2d(inputs, 32, 3, stride=2, padding=padding, + scope='Conv2d_1a_3x3') + if add_and_check_final('Conv2d_1a_3x3', net): return net, end_points + + # 147 x 147 x 32 + net = slim.conv2d(net, 32, 3, padding=padding, + scope='Conv2d_2a_3x3') + if add_and_check_final('Conv2d_2a_3x3', net): return net, end_points + # 147 x 147 x 64 + net = slim.conv2d(net, 64, 3, scope='Conv2d_2b_3x3') + if add_and_check_final('Conv2d_2b_3x3', net): return net, end_points + # 73 x 73 x 64 + net = slim.max_pool2d(net, 3, stride=2, padding=padding, + scope='MaxPool_3a_3x3') + if add_and_check_final('MaxPool_3a_3x3', net): return net, end_points + # 73 x 73 x 80 + net = slim.conv2d(net, 80, 1, padding=padding, + scope='Conv2d_3b_1x1') + if add_and_check_final('Conv2d_3b_1x1', net): return net, end_points + # 71 x 71 x 192 + net = slim.conv2d(net, 192, 3, padding=padding, + scope='Conv2d_4a_3x3') + if add_and_check_final('Conv2d_4a_3x3', net): return net, end_points + # 35 x 35 x 192 + net = slim.max_pool2d(net, 3, stride=2, padding=padding, + scope='MaxPool_5a_3x3') + if add_and_check_final('MaxPool_5a_3x3', net): return net, end_points + + # 35 x 35 x 320 + with tf.variable_scope('Mixed_5b'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 96, 1, scope='Conv2d_1x1') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 48, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 64, 5, + scope='Conv2d_0b_5x5') + with tf.variable_scope('Branch_2'): + tower_conv2_0 = slim.conv2d(net, 64, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2_0, 96, 3, + scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 96, 3, + scope='Conv2d_0c_3x3') + with tf.variable_scope('Branch_3'): + tower_pool = slim.avg_pool2d(net, 3, stride=1, padding='SAME', + scope='AvgPool_0a_3x3') + tower_pool_1 = slim.conv2d(tower_pool, 64, 1, + scope='Conv2d_0b_1x1') + net = tf.concat( + [tower_conv, tower_conv1_1, tower_conv2_2, tower_pool_1], 3) + + if add_and_check_final('Mixed_5b', net): return net, end_points + # TODO(alemi): Register intermediate endpoints + net = slim.repeat(net, 10, block35, scale=0.17) + + # 17 x 17 x 1088 if output_stride == 8, + # 33 x 33 x 1088 if output_stride == 16 + use_atrous = output_stride == 8 + + with tf.variable_scope('Mixed_6a'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 384, 3, stride=1 if use_atrous else 2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + tower_conv1_0 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1_0, 256, 3, + scope='Conv2d_0b_3x3') + tower_conv1_2 = slim.conv2d(tower_conv1_1, 384, 3, + stride=1 if use_atrous else 2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + tower_pool = slim.max_pool2d(net, 3, stride=1 if use_atrous else 2, + padding=padding, + scope='MaxPool_1a_3x3') + net = tf.concat([tower_conv, tower_conv1_2, tower_pool], 3) + + if add_and_check_final('Mixed_6a', net): return net, end_points + + # TODO(alemi): register intermediate endpoints + with slim.arg_scope([slim.conv2d], rate=2 if use_atrous else 1): + net = slim.repeat(net, 20, block17, scale=0.10) + if add_and_check_final('PreAuxLogits', net): return net, end_points + + if output_stride == 8: + # TODO(gpapan): Properly support output_stride for the rest of the net. + raise ValueError('output_stride==8 is only supported up to the ' + 'PreAuxlogits end_point for now.') + + # 8 x 8 x 2080 + with tf.variable_scope('Mixed_7a'): + with tf.variable_scope('Branch_0'): + tower_conv = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv_1 = slim.conv2d(tower_conv, 384, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_1'): + tower_conv1 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv1_1 = slim.conv2d(tower_conv1, 288, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_2'): + tower_conv2 = slim.conv2d(net, 256, 1, scope='Conv2d_0a_1x1') + tower_conv2_1 = slim.conv2d(tower_conv2, 288, 3, + scope='Conv2d_0b_3x3') + tower_conv2_2 = slim.conv2d(tower_conv2_1, 320, 3, stride=2, + padding=padding, + scope='Conv2d_1a_3x3') + with tf.variable_scope('Branch_3'): + tower_pool = slim.max_pool2d(net, 3, stride=2, + padding=padding, + scope='MaxPool_1a_3x3') + net = tf.concat( + [tower_conv_1, tower_conv1_1, tower_conv2_2, tower_pool], 3) + + if add_and_check_final('Mixed_7a', net): return net, end_points + + # TODO(alemi): register intermediate endpoints + net = slim.repeat(net, 9, block8, scale=0.20) + net = block8(net, activation_fn=None) + + # 8 x 8 x 1536 + net = slim.conv2d(net, 1536, 1, scope='Conv2d_7b_1x1') + if add_and_check_final('Conv2d_7b_1x1', net): return net, end_points + + raise ValueError('final_endpoint (%s) not recognized', final_endpoint) + + +def inception_resnet_v2(inputs, num_classes=1001, is_training=True, + dropout_keep_prob=0.8,#0.8 + reuse=None, + scope='InceptionResnetV2', + create_aux_logits=True): + """Creates the Inception Resnet V2 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 auxilliary 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, 'InceptionResnetV2', [inputs, num_classes], + reuse=reuse) as scope: + with slim.arg_scope([slim.batch_norm, slim.dropout], + is_training=is_training): + + net, end_points = inception_resnet_v2_base(inputs, scope=scope) + + if create_aux_logits: + with tf.variable_scope('AuxLogits'): + aux = end_points['PreAuxLogits'] + aux = slim.avg_pool2d(aux, 5, stride=3, padding='VALID', + scope='Conv2d_1a_3x3') + aux = slim.conv2d(aux, 128, 1, scope='Conv2d_1b_1x1') + aux = slim.conv2d(aux, 768, aux.get_shape()[1:3], + padding='VALID', scope='Conv2d_2a_5x5') + aux = slim.flatten(aux) + aux = slim.fully_connected(aux, num_classes, activation_fn=None, + scope='Logits') + end_points['AuxLogits'] = aux + + with tf.variable_scope('Logits'): + net = slim.avg_pool2d(net, net.get_shape()[1:3], padding='VALID', + scope='AvgPool_1a_8x8') + net = slim.flatten(net) + + net = slim.dropout(net, dropout_keep_prob, is_training=is_training, + scope='Dropout') + + end_points['PreLogitsFlatten'] = net + # end_points['yjr_feature'] = tf.squeeze(net, axis=0) + + 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 +inception_resnet_v2.default_image_size = 299 + + +def inception_resnet_v2_arg_scope(weight_decay=0.00004, + batch_norm_decay=0.9997, + batch_norm_epsilon=0.001): + """Yields the scope with the default parameters for inception_resnet_v2. + + Args: + weight_decay: the weight decay for weights variables. + batch_norm_decay: decay for the moving average of batch_norm momentums. + batch_norm_epsilon: small float added to variance to avoid dividing by zero. + + Returns: + a arg_scope with the parameters needed for inception_resnet_v2. + """ + # Set weight_decay for weights in conv2d and fully_connected layers. + with slim.arg_scope([slim.conv2d, slim.fully_connected], + weights_regularizer=slim.l2_regularizer(weight_decay), + biases_regularizer=slim.l2_regularizer(weight_decay)): + + batch_norm_params = { + 'decay': batch_norm_decay, + 'epsilon': batch_norm_epsilon, + } + # Set activation_fn and parameters for batch_norm. + with slim.arg_scope([slim.conv2d], activation_fn=tf.nn.relu, + normalizer_fn=slim.batch_norm, + normalizer_params=batch_norm_params) as scope: + return scope