--- /dev/null
+# Copyright 2018 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.
+# ==============================================================================
+"""Mobilenet Base Class."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import collections
+import contextlib
+import copy
+import os
+
+import tensorflow as tf
+
+
+slim = tf.contrib.slim
+
+
+@slim.add_arg_scope
+def apply_activation(x, name=None, activation_fn=None):
+ return activation_fn(x, name=name) if activation_fn else x
+
+
+def _fixed_padding(inputs, kernel_size, rate=1):
+ """Pads the input along the spatial dimensions independently of input size.
+
+ Pads the input such that if it was used in a convolution with 'VALID' padding,
+ the output would have the same dimensions as if the unpadded input was used
+ in a convolution with 'SAME' padding.
+
+ Args:
+ inputs: A tensor of size [batch, height_in, width_in, channels].
+ kernel_size: The kernel to be used in the conv2d or max_pool2d operation.
+ rate: An integer, rate for atrous convolution.
+
+ Returns:
+ output: A tensor of size [batch, height_out, width_out, channels] with the
+ input, either intact (if kernel_size == 1) or padded (if kernel_size > 1).
+ """
+ kernel_size_effective = [kernel_size[0] + (kernel_size[0] - 1) * (rate - 1),
+ kernel_size[0] + (kernel_size[0] - 1) * (rate - 1)]
+ pad_total = [kernel_size_effective[0] - 1, kernel_size_effective[1] - 1]
+ pad_beg = [pad_total[0] // 2, pad_total[1] // 2]
+ pad_end = [pad_total[0] - pad_beg[0], pad_total[1] - pad_beg[1]]
+ padded_inputs = tf.pad(inputs, [[0, 0], [pad_beg[0], pad_end[0]],
+ [pad_beg[1], pad_end[1]], [0, 0]])
+ return padded_inputs
+
+
+def _make_divisible(v, divisor, min_value=None):
+ if min_value is None:
+ min_value = divisor
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+ # Make sure that round down does not go down by more than 10%.
+ if new_v < 0.9 * v:
+ new_v += divisor
+ return new_v
+
+
+@contextlib.contextmanager
+def _set_arg_scope_defaults(defaults):
+ """Sets arg scope defaults for all items present in defaults.
+
+ Args:
+ defaults: dictionary/list of pairs, containing a mapping from
+ function to a dictionary of default args.
+
+ Yields:
+ context manager where all defaults are set.
+ """
+ if hasattr(defaults, 'items'):
+ items = defaults.items()
+ else:
+ items = defaults
+ if not items:
+ yield
+ else:
+ func, default_arg = items[0]
+ with slim.arg_scope(func, **default_arg):
+ with _set_arg_scope_defaults(items[1:]):
+ yield
+
+
+@slim.add_arg_scope
+def depth_multiplier(output_params,
+ multiplier,
+ divisible_by=8,
+ min_depth=8,
+ **unused_kwargs):
+ if 'num_outputs' not in output_params:
+ return
+ d = output_params['num_outputs']
+ output_params['num_outputs'] = _make_divisible(d * multiplier, divisible_by,
+ min_depth)
+
+
+_Op = collections.namedtuple('Op', ['op', 'params', 'multiplier_func'])
+
+
+def op(opfunc, **params):
+ multiplier = params.pop('multiplier_transorm', depth_multiplier)
+ return _Op(opfunc, params=params, multiplier_func=multiplier)
+
+
+@slim.add_arg_scope
+def mobilenet_base( # pylint: disable=invalid-name
+ inputs,
+ conv_defs,
+ multiplier=1.0,
+ final_endpoint=None,
+ output_stride=None,
+ use_explicit_padding=False,
+ scope=None,
+ is_training=False):
+ """Mobilenet base network.
+
+ Constructs a network from inputs to the given final endpoint. By default
+ the network is constructed in inference mode. To create network
+ in training mode use:
+
+ with slim.arg_scope(mobilenet.training_scope()):
+ logits, endpoints = mobilenet_base(...)
+
+ Args:
+ inputs: a tensor of shape [batch_size, height, width, channels].
+ conv_defs: A list of op(...) layers specifying the net architecture.
+ 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.
+ final_endpoint: The name of last layer, for early termination for
+ for V1-based networks: last layer is "layer_14", for V2: "layer_20"
+ output_stride: An integer that specifies the requested ratio of input to
+ output spatial resolution. If not None, then we invoke atrous convolution
+ if necessary to prevent the network from reducing the spatial resolution
+ of the activation maps. Allowed values are 1 or any even number, excluding
+ zero. Typical values are 8 (accurate fully convolutional mode), 16
+ (fast fully convolutional mode), and 32 (classification mode).
+
+ NOTE- output_stride relies on all consequent operators to support dilated
+ operators via "rate" parameter. This might require wrapping non-conv
+ operators to operate properly.
+
+ use_explicit_padding: Use 'VALID' padding for convolutions, but prepad
+ inputs so that the output dimensions are the same as if 'SAME' padding
+ were used.
+ scope: optional variable scope.
+ is_training: How to setup batch_norm and other ops. Note: most of the time
+ this does not need be set directly. Use mobilenet.training_scope() to set
+ up training instead. This parameter is here for backward compatibility
+ only. It is safe to set it to the value matching
+ training_scope(is_training=...). It is also safe to explicitly set
+ it to False, even if there is outer training_scope set to to training.
+ (The network will be built in inference mode).
+ Returns:
+ tensor_out: output tensor.
+ end_points: a set of activations for external use, for example summaries or
+ losses.
+
+ Raises:
+ ValueError: depth_multiplier <= 0, or the target output_stride is not
+ allowed.
+ """
+ if multiplier <= 0:
+ raise ValueError('multiplier is not greater than zero.')
+
+ # Set conv defs defaults and overrides.
+ conv_defs_defaults = conv_defs.get('defaults', {})
+ conv_defs_overrides = conv_defs.get('overrides', {})
+ if use_explicit_padding:
+ conv_defs_overrides = copy.deepcopy(conv_defs_overrides)
+ conv_defs_overrides[
+ (slim.conv2d, slim.separable_conv2d)] = {'padding': 'VALID'}
+
+ if output_stride is not None:
+ if output_stride == 0 or (output_stride > 1 and output_stride % 2):
+ raise ValueError('Output stride must be None, 1 or a multiple of 2.')
+
+ # a) Set the tensorflow scope
+ # b) set padding to default: note we might consider removing this
+ # since it is also set by mobilenet_scope
+ # c) set all defaults
+ # d) set all extra overrides.
+ with _scope_all(scope, default_scope='Mobilenet'), \
+ slim.arg_scope([slim.batch_norm], is_training=is_training), \
+ _set_arg_scope_defaults(conv_defs_defaults), \
+ _set_arg_scope_defaults(conv_defs_overrides):
+ # The current_stride variable keeps track of the output stride of the
+ # activations, i.e., the running product of convolution strides up to the
+ # current network layer. This allows us to invoke atrous convolution
+ # whenever applying the next convolution would result in the activations
+ # having output stride larger than the target output_stride.
+ current_stride = 1
+
+ # The atrous convolution rate parameter.
+ rate = 1
+
+ net = inputs
+ # Insert default parameters before the base scope which includes
+ # any custom overrides set in mobilenet.
+ end_points = {}
+ scopes = {}
+ for i, opdef in enumerate(conv_defs['spec']):
+ params = dict(opdef.params)
+ opdef.multiplier_func(params, multiplier)
+ stride = params.get('stride', 1)
+ if output_stride is not None and current_stride == output_stride:
+ # If we have reached the target output_stride, then we need to employ
+ # atrous convolution with stride=1 and multiply the atrous rate by the
+ # current unit's stride for use in subsequent layers.
+ layer_stride = 1
+ layer_rate = rate
+ rate *= stride
+ else:
+ layer_stride = stride
+ layer_rate = 1
+ current_stride *= stride
+ # Update params.
+ params['stride'] = layer_stride
+ # Only insert rate to params if rate > 1.
+ if layer_rate > 1:
+ params['rate'] = layer_rate
+ # Set padding
+ if use_explicit_padding:
+ if 'kernel_size' in params:
+ net = _fixed_padding(net, params['kernel_size'], layer_rate)
+ else:
+ params['use_explicit_padding'] = True
+
+ end_point = 'layer_%d' % (i + 1)
+ try:
+ net = opdef.op(net, **params)
+ except Exception:
+ print('Failed to create op %i: %r params: %r' % (i, opdef, params))
+ raise
+ end_points[end_point] = net
+ scope = os.path.dirname(net.name)
+ scopes[scope] = end_point
+ if final_endpoint is not None and end_point == final_endpoint:
+ break
+
+ # Add all tensors that end with 'output' to
+ # endpoints
+ for t in net.graph.get_operations():
+ scope = os.path.dirname(t.name)
+ bn = os.path.basename(t.name)
+ if scope in scopes and t.name.endswith('output'):
+ end_points[scopes[scope] + '/' + bn] = t.outputs[0]
+ return net, end_points
+
+
+@contextlib.contextmanager
+def _scope_all(scope, default_scope=None):
+ with tf.variable_scope(scope, default_name=default_scope) as s,\
+ tf.name_scope(s.original_name_scope):
+ yield s
+
+
+@slim.add_arg_scope
+def mobilenet(inputs,
+ num_classes=1001,
+ prediction_fn=slim.softmax,
+ reuse=None,
+ scope='Mobilenet',
+ base_only=False,
+ **mobilenet_args):
+ """Mobilenet model for classification, supports both V1 and V2.
+
+ Note: default mode is inference, use mobilenet.training_scope to create
+ training network.
+
+
+ Args:
+ inputs: a tensor of shape [batch_size, height, width, channels].
+ 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.
+ prediction_fn: a function to get predictions out of logits
+ (default softmax).
+ 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.
+ base_only: if True will only create the base of the network (no pooling
+ and no logits).
+ **mobilenet_args: passed to mobilenet_base verbatim.
+ - conv_defs: list of conv defs
+ - 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.
+ - output_stride: will ensure that the last layer has at most total stride.
+ If the architecture calls for more stride than that provided
+ (e.g. output_stride=16, but the architecture has 5 stride=2 operators),
+ it will replace output_stride with fractional convolutions using Atrous
+ Convolutions.
+
+ 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 tensor.
+
+ Raises:
+ ValueError: Input rank is invalid.
+ """
+ is_training = mobilenet_args.get('is_training', False)
+ input_shape = inputs.get_shape().as_list()
+ if len(input_shape) != 4:
+ raise ValueError('Expected rank 4 input, was: %d' % len(input_shape))
+
+ with tf.variable_scope(scope, 'Mobilenet', reuse=reuse) as scope:
+ inputs = tf.identity(inputs, 'input')
+ net, end_points = mobilenet_base(inputs, scope=scope, **mobilenet_args)
+ if base_only:
+ return net, end_points
+
+ net = tf.identity(net, name='embedding')
+
+ with tf.variable_scope('Logits'):
+ net = global_pool(net)
+ end_points['global_pool'] = net
+ if not num_classes:
+ return net, end_points
+ net = slim.dropout(net, scope='Dropout', is_training=is_training)
+ # 1 x 1 x num_classes
+ # Note: legacy scope name.
+ logits = slim.conv2d(
+ net,
+ num_classes, [1, 1],
+ activation_fn=None,
+ normalizer_fn=None,
+ biases_initializer=tf.zeros_initializer(),
+ scope='Conv2d_1c_1x1')
+
+ logits = tf.squeeze(logits, [1, 2])
+
+ logits = tf.identity(logits, name='output')
+ end_points['Logits'] = logits
+ if prediction_fn:
+ end_points['Predictions'] = prediction_fn(logits, 'Predictions')
+ return logits, end_points
+
+
+def global_pool(input_tensor, pool_op=tf.nn.avg_pool):
+ """Applies avg pool to produce 1x1 output.
+
+ NOTE: This function is funcitonally equivalenet to reduce_mean, but it has
+ baked in average pool which has better support across hardware.
+
+ Args:
+ input_tensor: input tensor
+ pool_op: pooling op (avg pool is default)
+ Returns:
+ a tensor batch_size x 1 x 1 x depth.
+ """
+ shape = input_tensor.get_shape().as_list()
+ if shape[1] is None or shape[2] is None:
+ kernel_size = tf.convert_to_tensor(
+ [1, tf.shape(input_tensor)[1],
+ tf.shape(input_tensor)[2], 1])
+ else:
+ kernel_size = [1, shape[1], shape[2], 1]
+ output = pool_op(
+ input_tensor, ksize=kernel_size, strides=[1, 1, 1, 1], padding='VALID')
+ # Recover output shape, for unknown shape.
+ output.set_shape([None, 1, 1, None])
+ return output
+
+
+def training_scope(is_training=True,
+ weight_decay=0.00004,
+ stddev=0.09,
+ dropout_keep_prob=0.8,
+ bn_decay=0.997):
+ """Defines Mobilenet training scope.
+
+ Usage:
+ with tf.contrib.slim.arg_scope(mobilenet.training_scope()):
+ logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+ # the network created will be trainble with dropout/batch norm
+ # initialized appropriately.
+ Args:
+ is_training: if set to False this will ensure that all customizations are
+ set to non-training mode. This might be helpful for code that is reused
+ across both training/evaluation, but most of the time training_scope with
+ value False is not needed.
+
+ weight_decay: The weight decay to use for regularizing the model.
+ stddev: Standard deviation for initialization, if negative uses xavier.
+ dropout_keep_prob: dropout keep probability
+ bn_decay: decay for the batch norm moving averages.
+
+ Returns:
+ An argument scope to use via arg_scope.
+ """
+ # Note: do not introduce parameters that would change the inference
+ # model here (for example whether to use bias), modify conv_def instead.
+ batch_norm_params = {
+ 'is_training': is_training,
+ 'decay': bn_decay,
+ }
+
+ if stddev < 0:
+ weight_intitializer = slim.initializers.xavier_initializer()
+ else:
+ weight_intitializer = tf.truncated_normal_initializer(stddev=stddev)
+
+ # Set weight_decay for weights in Conv and FC layers.
+ with slim.arg_scope(
+ [slim.conv2d, slim.fully_connected, slim.separable_conv2d],
+ weights_initializer=weight_intitializer,
+ normalizer_fn=slim.batch_norm), \
+ slim.arg_scope([mobilenet_base, mobilenet], is_training=is_training),\
+ slim.arg_scope([slim.batch_norm], **batch_norm_params), \
+ slim.arg_scope([slim.dropout], is_training=is_training,
+ keep_prob=dropout_keep_prob), \
+ slim.arg_scope([slim.conv2d], \
+ weights_regularizer=slim.l2_regularizer(weight_decay)), \
+ slim.arg_scope([slim.separable_conv2d], weights_regularizer=None) as s:
+ return s
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