--- /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