+++ /dev/null
-# 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
Code Review / ealt-edge.git / blobdiff