X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v2.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fslim_nets%2Finception_v2.py;h=0000000000000000000000000000000000000000;hb=3ed2c61d9d7e7916481650c41bfe5604f7db22e9;hp=2651f71f9ab60484d183338616c2cedd0a1fd5a5;hpb=e6d40ddb2640f434a9d7d7ed99566e5e8fa60cc1;p=ealt-edge.git diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py deleted file mode 100755 index 2651f71..0000000 --- a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/inception_v2.py +++ /dev/null @@ -1,520 +0,0 @@ -# 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 for inception v2 classification network.""" - -from __future__ import absolute_import -from __future__ import division -from __future__ import print_function - -import tensorflow as tf - -from nets import inception_utils - -slim = tf.contrib.slim -trunc_normal = lambda stddev: tf.truncated_normal_initializer(0.0, stddev) - - -def inception_v2_base(inputs, - final_endpoint='Mixed_5c', - min_depth=16, - depth_multiplier=1.0, - scope=None): - """Inception v2 (6a2). - - Constructs an Inception v2 network from inputs to the given final endpoint. - This method can construct the network up to the layer inception(5b) as - described in http://arxiv.org/abs/1502.03167. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - final_endpoint: specifies the endpoint to construct the network up to. It - can be one of ['Conv2d_1a_7x7', 'MaxPool_2a_3x3', 'Conv2d_2b_1x1', - 'Conv2d_2c_3x3', 'MaxPool_3a_3x3', 'Mixed_3b', 'Mixed_3c', 'Mixed_4a', - 'Mixed_4b', 'Mixed_4c', 'Mixed_4d', 'Mixed_4e', 'Mixed_5a', 'Mixed_5b', - 'Mixed_5c']. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - 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 depth_multiplier <= 0 - """ - - # end_points will collect relevant activations for external use, for example - # summaries or losses. - end_points = {} - - # Used to find thinned depths for each layer. - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - depth = lambda d: max(int(d * depth_multiplier), min_depth) - - with tf.variable_scope(scope, 'InceptionV2', [inputs]): - with slim.arg_scope( - [slim.conv2d, slim.max_pool2d, slim.avg_pool2d, slim.separable_conv2d], - stride=1, padding='SAME'): - - # Note that sizes in the comments below assume an input spatial size of - # 224x224, however, the inputs can be of any size greater 32x32. - - # 224 x 224 x 3 - end_point = 'Conv2d_1a_7x7' - # depthwise_multiplier here is different from depth_multiplier. - # depthwise_multiplier determines the output channels of the initial - # depthwise conv (see docs for tf.nn.separable_conv2d), while - # depth_multiplier controls the # channels of the subsequent 1x1 - # convolution. Must have - # in_channels * depthwise_multipler <= out_channels - # so that the separable convolution is not overparameterized. - depthwise_multiplier = min(int(depth(64) / 3), 8) - net = slim.separable_conv2d( - inputs, depth(64), [7, 7], depth_multiplier=depthwise_multiplier, - stride=2, weights_initializer=trunc_normal(1.0), - scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 112 x 112 x 64 - end_point = 'MaxPool_2a_3x3' - net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 64 - end_point = 'Conv2d_2b_1x1' - net = slim.conv2d(net, depth(64), [1, 1], scope=end_point, - weights_initializer=trunc_normal(0.1)) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 64 - end_point = 'Conv2d_2c_3x3' - net = slim.conv2d(net, depth(192), [3, 3], scope=end_point) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 56 x 56 x 192 - end_point = 'MaxPool_3a_3x3' - net = slim.max_pool2d(net, [3, 3], scope=end_point, stride=2) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 192 - # Inception module. - end_point = 'Mixed_3b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(64), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(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, depth(32), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 256 - end_point = 'Mixed_3c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(64), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(96), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(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, depth(64), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 28 x 28 x 320 - end_point = 'Mixed_4a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, depth(160), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], stride=2, scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d( - net, [3, 3], stride=2, scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(224), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(64), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d( - branch_1, depth(96), [3, 3], scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(128), [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, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(192), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(96), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(128), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(128), [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, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_4d' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(160), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(160), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(160), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(160), [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, depth(96), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # 14 x 14 x 576 - end_point = 'Mixed_4e' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(96), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(192), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(160), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(192), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(192), [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, depth(96), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 14 x 14 x 576 - end_point = 'Mixed_5a' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d( - net, depth(128), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_0 = slim.conv2d(branch_0, depth(192), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], - scope='Conv2d_0b_3x3') - branch_1 = slim.conv2d(branch_1, depth(256), [3, 3], stride=2, - scope='Conv2d_1a_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.max_pool2d(net, [3, 3], stride=2, - scope='MaxPool_1a_3x3') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - # 7 x 7 x 1024 - end_point = 'Mixed_5b' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(160), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(224), [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, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - - # 7 x 7 x 1024 - end_point = 'Mixed_5c' - with tf.variable_scope(end_point): - with tf.variable_scope('Branch_0'): - branch_0 = slim.conv2d(net, depth(352), [1, 1], scope='Conv2d_0a_1x1') - with tf.variable_scope('Branch_1'): - branch_1 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_1 = slim.conv2d(branch_1, depth(320), [3, 3], - scope='Conv2d_0b_3x3') - with tf.variable_scope('Branch_2'): - branch_2 = slim.conv2d( - net, depth(192), [1, 1], - weights_initializer=trunc_normal(0.09), - scope='Conv2d_0a_1x1') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0b_3x3') - branch_2 = slim.conv2d(branch_2, depth(224), [3, 3], - scope='Conv2d_0c_3x3') - with tf.variable_scope('Branch_3'): - branch_3 = slim.max_pool2d(net, [3, 3], scope='MaxPool_0a_3x3') - branch_3 = slim.conv2d( - branch_3, depth(128), [1, 1], - weights_initializer=trunc_normal(0.1), - scope='Conv2d_0b_1x1') - net = tf.concat(axis=3, values=[branch_0, branch_1, branch_2, branch_3]) - end_points[end_point] = net - if end_point == final_endpoint: return net, end_points - raise ValueError('Unknown final endpoint %s' % final_endpoint) - - -def inception_v2(inputs, - num_classes=1000, - is_training=True, - dropout_keep_prob=0.8, - min_depth=16, - depth_multiplier=1.0, - prediction_fn=slim.softmax, - spatial_squeeze=True, - reuse=None, - scope='InceptionV2'): - """Inception v2 model for classification. - - Constructs an Inception v2 network for classification as described in - http://arxiv.org/abs/1502.03167. - - The default image size used to train this network is 224x224. - - Args: - inputs: a tensor of shape [batch_size, height, width, channels]. - num_classes: number of predicted classes. - is_training: whether is training or not. - dropout_keep_prob: the percentage of activation values that are retained. - min_depth: Minimum depth value (number of channels) for all convolution ops. - Enforced when depth_multiplier < 1, and not an active constraint when - depth_multiplier >= 1. - depth_multiplier: Float multiplier for the depth (number of channels) - for all convolution ops. The value must be greater than zero. Typical - usage will be to set this value in (0, 1) to reduce the number of - parameters or computation cost of the model. - prediction_fn: a function to get predictions out of logits. - spatial_squeeze: if True, logits is of shape [B, C], if false logits is - of shape [B, 1, 1, C], where B is batch_size and C is number of classes. - 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. - - Returns: - logits: the pre-softmax activations, a tensor of size - [batch_size, num_classes] - end_points: a dictionary from components of the network to the corresponding - activation. - - Raises: - ValueError: if final_endpoint is not set to one of the predefined values, - or depth_multiplier <= 0 - """ - if depth_multiplier <= 0: - raise ValueError('depth_multiplier is not greater than zero.') - - # Final pooling and prediction - with tf.variable_scope(scope, 'InceptionV2', [inputs, num_classes], - reuse=reuse) as scope: - with slim.arg_scope([slim.batch_norm, slim.dropout], - is_training=is_training): - net, end_points = inception_v2_base( - inputs, scope=scope, min_depth=min_depth, - depth_multiplier=depth_multiplier) - with tf.variable_scope('Logits'): - kernel_size = _reduced_kernel_size_for_small_input(net, [7, 7]) - net = slim.avg_pool2d(net, kernel_size, padding='VALID', - scope='AvgPool_1a_{}x{}'.format(*kernel_size)) - # 1 x 1 x 1024 - net = slim.dropout(net, keep_prob=dropout_keep_prob, scope='Dropout_1b') - 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') - end_points['Logits'] = logits - end_points['Predictions'] = prediction_fn(logits, scope='Predictions') - return logits, end_points -inception_v2.default_image_size = 224 - - -def _reduced_kernel_size_for_small_input(input_tensor, kernel_size): - """Define kernel size which is automatically reduced for small input. - - If the shape of the input images is unknown at graph construction time this - function assumes that the input images are is large enough. - - Args: - input_tensor: input tensor of size [batch_size, height, width, channels]. - kernel_size: desired kernel size of length 2: [kernel_height, kernel_width] - - Returns: - a tensor with the kernel size. - - TODO(jrru): Make this function work with unknown shapes. Theoretically, this - can be done with the code below. Problems are two-fold: (1) If the shape was - known, it will be lost. (2) inception.slim.ops._two_element_tuple cannot - handle tensors that define the kernel size. - shape = tf.shape(input_tensor) - return = tf.pack([tf.minimum(shape[1], kernel_size[0]), - tf.minimum(shape[2], kernel_size[1])]) - - """ - shape = input_tensor.get_shape().as_list() - if shape[1] is None or shape[2] is None: - kernel_size_out = kernel_size - else: - kernel_size_out = [min(shape[1], kernel_size[0]), - min(shape[2], kernel_size[1])] - return kernel_size_out - - -inception_v2_arg_scope = inception_utils.inception_arg_scope