--- /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.
+# ==============================================================================
+"""Implementation of Mobilenet V2.
+
+Architecture: https://arxiv.org/abs/1801.04381
+
+The base model gives 72.2% accuracy on ImageNet, with 300MMadds,
+3.4 M parameters.
+"""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import copy
+
+import tensorflow as tf
+
+from libs.networks.mobilenet import conv_blocks as ops
+from libs.networks.mobilenet import mobilenet as lib
+
+slim = tf.contrib.slim
+op = lib.op
+
+expand_input = ops.expand_input_by_factor
+
+# pyformat: disable
+# Architecture: https://arxiv.org/abs/1801.04381
+V2_DEF = dict(
+ defaults={
+ # Note: these parameters of batch norm affect the architecture
+ # that's why they are here and not in training_scope.
+ (slim.batch_norm,): {'center': True, 'scale': True},
+ (slim.conv2d, slim.fully_connected, slim.separable_conv2d): {
+ 'normalizer_fn': slim.batch_norm, 'activation_fn': tf.nn.relu6
+ },
+ (ops.expanded_conv,): {
+ 'expansion_size': expand_input(6),
+ 'split_expansion': 1,
+ 'normalizer_fn': slim.batch_norm,
+ 'residual': True
+ },
+ (slim.conv2d, slim.separable_conv2d): {'padding': 'SAME'}
+ },
+ spec=[
+ op(slim.conv2d, stride=2, num_outputs=32, kernel_size=[3, 3]),
+ op(ops.expanded_conv,
+ expansion_size=expand_input(1, divisible_by=1),
+ num_outputs=16),
+ op(ops.expanded_conv, stride=2, num_outputs=24),
+ op(ops.expanded_conv, stride=1, num_outputs=24),
+ op(ops.expanded_conv, stride=2, num_outputs=32),
+ op(ops.expanded_conv, stride=1, num_outputs=32),
+ op(ops.expanded_conv, stride=1, num_outputs=32),
+ op(ops.expanded_conv, stride=2, num_outputs=64),
+ op(ops.expanded_conv, stride=1, num_outputs=64),
+ op(ops.expanded_conv, stride=1, num_outputs=64),
+ op(ops.expanded_conv, stride=1, num_outputs=64),
+ op(ops.expanded_conv, stride=1, num_outputs=96),
+ op(ops.expanded_conv, stride=1, num_outputs=96),
+ op(ops.expanded_conv, stride=1, num_outputs=96),
+ op(ops.expanded_conv, stride=2, num_outputs=160),
+ op(ops.expanded_conv, stride=1, num_outputs=160),
+ op(ops.expanded_conv, stride=1, num_outputs=160),
+ op(ops.expanded_conv, stride=1, num_outputs=320),
+ op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280)
+ ],
+)
+# pyformat: enable
+
+
+@slim.add_arg_scope
+def mobilenet(input_tensor,
+ num_classes=1001,
+ depth_multiplier=1.0,
+ scope='MobilenetV2',
+ conv_defs=None,
+ finegrain_classification_mode=False,
+ min_depth=None,
+ divisible_by=None,
+ **kwargs):
+ """Creates mobilenet V2 network.
+
+ Inference mode is created by default. To create training use training_scope
+ below.
+
+ with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+ logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+ Args:
+ input_tensor: The input tensor
+ num_classes: number of classes
+ depth_multiplier: The multiplier applied to scale number of
+ channels in each layer. Note: this is called depth multiplier in the
+ paper but the name is kept for consistency with slim's model builder.
+ scope: Scope of the operator
+ conv_defs: Allows to override default conv def.
+ finegrain_classification_mode: When set to True, the model
+ will keep the last layer large even for small multipliers. Following
+ https://arxiv.org/abs/1801.04381
+ suggests that it improves performance for ImageNet-type of problems.
+ *Note* ignored if final_endpoint makes the builder exit earlier.
+ min_depth: If provided, will ensure that all layers will have that
+ many channels after application of depth multiplier.
+ divisible_by: If provided will ensure that all layers # channels
+ will be divisible by this number.
+ **kwargs: passed directly to mobilenet.mobilenet:
+ prediciton_fn- what prediction function to use.
+ reuse-: whether to reuse variables (if reuse set to true, scope
+ must be given).
+ Returns:
+ logits/endpoints pair
+
+ Raises:
+ ValueError: On invalid arguments
+ """
+ if conv_defs is None:
+ conv_defs = V2_DEF
+ if 'multiplier' in kwargs:
+ raise ValueError('mobilenetv2 doesn\'t support generic '
+ 'multiplier parameter use "depth_multiplier" instead.')
+ if finegrain_classification_mode:
+ conv_defs = copy.deepcopy(conv_defs)
+ if depth_multiplier < 1:
+ conv_defs['spec'][-1].params['num_outputs'] /= depth_multiplier
+
+ depth_args = {}
+ # NB: do not set depth_args unless they are provided to avoid overriding
+ # whatever default depth_multiplier might have thanks to arg_scope.
+ if min_depth is not None:
+ depth_args['min_depth'] = min_depth
+ if divisible_by is not None:
+ depth_args['divisible_by'] = divisible_by
+
+ with slim.arg_scope((lib.depth_multiplier,), **depth_args):
+ return lib.mobilenet(
+ input_tensor,
+ num_classes=num_classes,
+ conv_defs=conv_defs,
+ scope=scope,
+ multiplier=depth_multiplier,
+ **kwargs)
+
+
+@slim.add_arg_scope
+def mobilenet_base(input_tensor, depth_multiplier=1.0, **kwargs):
+ """Creates base of the mobilenet (no pooling and no logits) ."""
+ return mobilenet(input_tensor,
+ depth_multiplier=depth_multiplier,
+ base_only=True, **kwargs)
+
+
+def training_scope(**kwargs):
+ """Defines MobilenetV2 training scope.
+
+ Usage:
+ with tf.contrib.slim.arg_scope(mobilenet_v2.training_scope()):
+ logits, endpoints = mobilenet_v2.mobilenet(input_tensor)
+
+ with slim.
+
+ Args:
+ **kwargs: Passed to mobilenet.training_scope. The following parameters
+ are supported:
+ 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 `arg_scope` to use for the mobilenet v2 model.
+ """
+ return lib.training_scope(**kwargs)
+
+
+__all__ = ['training_scope', 'mobilenet_base', 'mobilenet', 'V2_DEF']
Code Review / ealt-edge.git / blobdiff