+++ /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
Code Review / ealt-edge.git / blobdiff