+++ /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.
-# ==============================================================================
-"""Convolution blocks for mobilenet."""
-import contextlib
-import functools
-
-import tensorflow as tf
-
-slim = tf.contrib.slim
-
-
-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
-
-
-def _split_divisible(num, num_ways, divisible_by=8):
- """Evenly splits num, num_ways so each piece is a multiple of divisible_by."""
- assert num % divisible_by == 0
- assert num / num_ways >= divisible_by
- # Note: want to round down, we adjust each split to match the total.
- base = num // num_ways // divisible_by * divisible_by
- result = []
- accumulated = 0
- for i in range(num_ways):
- r = base
- while accumulated + r < num * (i + 1) / num_ways:
- r += divisible_by
- result.append(r)
- accumulated += r
- assert accumulated == num
- return result
-
-
-@contextlib.contextmanager
-def _v1_compatible_scope_naming(scope):
- if scope is None: # Create uniqified separable blocks.
- with tf.variable_scope(None, default_name='separable') as s, \
- tf.name_scope(s.original_name_scope):
- yield ''
- else:
- # We use scope_depthwise, scope_pointwise for compatibility with V1 ckpts.
- # which provide numbered scopes.
- scope += '_'
- yield scope
-
-
-@slim.add_arg_scope
-def split_separable_conv2d(input_tensor,
- num_outputs,
- scope=None,
- normalizer_fn=None,
- stride=1,
- rate=1,
- endpoints=None,
- use_explicit_padding=False):
- """Separable mobilenet V1 style convolution.
-
- Depthwise convolution, with default non-linearity,
- followed by 1x1 depthwise convolution. This is similar to
- slim.separable_conv2d, but differs in tha it applies batch
- normalization and non-linearity to depthwise. This matches
- the basic building of Mobilenet Paper
- (https://arxiv.org/abs/1704.04861)
-
- Args:
- input_tensor: input
- num_outputs: number of outputs
- scope: optional name of the scope. Note if provided it will use
- scope_depthwise for deptwhise, and scope_pointwise for pointwise.
- normalizer_fn: which normalizer function to use for depthwise/pointwise
- stride: stride
- rate: output rate (also known as dilation rate)
- endpoints: optional, if provided, will export additional tensors to it.
- 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.
-
- Returns:
- output tesnor
- """
-
- with _v1_compatible_scope_naming(scope) as scope:
- dw_scope = scope + 'depthwise'
- endpoints = endpoints if endpoints is not None else {}
- kernel_size = [3, 3]
- padding = 'SAME'
- if use_explicit_padding:
- padding = 'VALID'
- input_tensor = _fixed_padding(input_tensor, kernel_size, rate)
- net = slim.separable_conv2d(
- input_tensor,
- None,
- kernel_size,
- depth_multiplier=1,
- stride=stride,
- rate=rate,
- normalizer_fn=normalizer_fn,
- padding=padding,
- scope=dw_scope)
-
- endpoints[dw_scope] = net
-
- pw_scope = scope + 'pointwise'
- net = slim.conv2d(
- net,
- num_outputs, [1, 1],
- stride=1,
- normalizer_fn=normalizer_fn,
- scope=pw_scope)
- endpoints[pw_scope] = net
- return net
-
-
-def expand_input_by_factor(n, divisible_by=8):
- return lambda num_inputs, **_: _make_divisible(num_inputs * n, divisible_by)
-
-
-@slim.add_arg_scope
-def expanded_conv(input_tensor,
- num_outputs,
- expansion_size=expand_input_by_factor(6),
- stride=1,
- rate=1,
- kernel_size=(3, 3),
- residual=True,
- normalizer_fn=None,
- split_projection=1,
- split_expansion=1,
- expansion_transform=None,
- depthwise_location='expansion',
- depthwise_channel_multiplier=1,
- endpoints=None,
- use_explicit_padding=False,
- scope=None):
- """Depthwise Convolution Block with expansion.
-
- Builds a composite convolution that has the following structure
- expansion (1x1) -> depthwise (kernel_size) -> projection (1x1)
-
- Args:
- input_tensor: input
- num_outputs: number of outputs in the final layer.
- expansion_size: the size of expansion, could be a constant or a callable.
- If latter it will be provided 'num_inputs' as an input. For forward
- compatibility it should accept arbitrary keyword arguments.
- Default will expand the input by factor of 6.
- stride: depthwise stride
- rate: depthwise rate
- kernel_size: depthwise kernel
- residual: whether to include residual connection between input
- and output.
- normalizer_fn: batchnorm or otherwise
- split_projection: how many ways to split projection operator
- (that is conv expansion->bottleneck)
- split_expansion: how many ways to split expansion op
- (that is conv bottleneck->expansion) ops will keep depth divisible
- by this value.
- expansion_transform: Optional function that takes expansion
- as a single input and returns output.
- depthwise_location: where to put depthwise covnvolutions supported
- values None, 'input', 'output', 'expansion'
- depthwise_channel_multiplier: depthwise channel multiplier:
- each input will replicated (with different filters)
- that many times. So if input had c channels,
- output will have c x depthwise_channel_multpilier.
- endpoints: An optional dictionary into which intermediate endpoints are
- placed. The keys "expansion_output", "depthwise_output",
- "projection_output" and "expansion_transform" are always populated, even
- if the corresponding functions are not invoked.
- 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 scope.
-
- Returns:
- Tensor of depth num_outputs
-
- Raises:
- TypeError: on inval
- """
- with tf.variable_scope(scope, default_name='expanded_conv') as s, \
- tf.name_scope(s.original_name_scope):
- prev_depth = input_tensor.get_shape().as_list()[3]
- if depthwise_location not in [None, 'input', 'output', 'expansion']:
- raise TypeError('%r is unknown value for depthwise_location' %
- depthwise_location)
- padding = 'SAME'
- if use_explicit_padding:
- padding = 'VALID'
- depthwise_func = functools.partial(
- slim.separable_conv2d,
- num_outputs=None,
- kernel_size=kernel_size,
- depth_multiplier=depthwise_channel_multiplier,
- stride=stride,
- rate=rate,
- normalizer_fn=normalizer_fn,
- padding=padding,
- scope='depthwise')
- # b1 -> b2 * r -> b2
- # i -> (o * r) (bottleneck) -> o
- input_tensor = tf.identity(input_tensor, 'input')
- net = input_tensor
-
- if depthwise_location == 'input':
- if use_explicit_padding:
- net = _fixed_padding(net, kernel_size, rate)
- net = depthwise_func(net, activation_fn=None)
-
- if callable(expansion_size):
- inner_size = expansion_size(num_inputs=prev_depth)
- else:
- inner_size = expansion_size
-
- if inner_size > net.shape[3]:
- net = split_conv(
- net,
- inner_size,
- num_ways=split_expansion,
- scope='expand',
- stride=1,
- normalizer_fn=normalizer_fn)
- net = tf.identity(net, 'expansion_output')
- if endpoints is not None:
- endpoints['expansion_output'] = net
-
- if depthwise_location == 'expansion':
- if use_explicit_padding:
- net = _fixed_padding(net, kernel_size, rate)
- net = depthwise_func(net)
-
- net = tf.identity(net, name='depthwise_output')
- if endpoints is not None:
- endpoints['depthwise_output'] = net
- if expansion_transform:
- net = expansion_transform(expansion_tensor=net, input_tensor=input_tensor)
- # Note in contrast with expansion, we always have
- # projection to produce the desired output size.
- net = split_conv(
- net,
- num_outputs,
- num_ways=split_projection,
- stride=1,
- scope='project',
- normalizer_fn=normalizer_fn,
- activation_fn=tf.identity)
- if endpoints is not None:
- endpoints['projection_output'] = net
- if depthwise_location == 'output':
- if use_explicit_padding:
- net = _fixed_padding(net, kernel_size, rate)
- net = depthwise_func(net, activation_fn=None)
-
- if callable(residual): # custom residual
- net = residual(input_tensor=input_tensor, output_tensor=net)
- elif (residual and
- # stride check enforces that we don't add residuals when spatial
- # dimensions are None
- stride == 1 and
- # Depth matches
- net.get_shape().as_list()[3] ==
- input_tensor.get_shape().as_list()[3]):
- net += input_tensor
- return tf.identity(net, name='output')
-
-
-def split_conv(input_tensor,
- num_outputs,
- num_ways,
- scope,
- divisible_by=8,
- **kwargs):
- """Creates a split convolution.
-
- Split convolution splits the input and output into
- 'num_blocks' blocks of approximately the same size each,
- and only connects $i$-th input to $i$ output.
-
- Args:
- input_tensor: input tensor
- num_outputs: number of output filters
- num_ways: num blocks to split by.
- scope: scope for all the operators.
- divisible_by: make sure that every part is divisiable by this.
- **kwargs: will be passed directly into conv2d operator
- Returns:
- tensor
- """
- b = input_tensor.get_shape().as_list()[3]
-
- if num_ways == 1 or min(b // num_ways,
- num_outputs // num_ways) < divisible_by:
- # Don't do any splitting if we end up with less than 8 filters
- # on either side.
- return slim.conv2d(input_tensor, num_outputs, [1, 1], scope=scope, **kwargs)
-
- outs = []
- input_splits = _split_divisible(b, num_ways, divisible_by=divisible_by)
- output_splits = _split_divisible(
- num_outputs, num_ways, divisible_by=divisible_by)
- inputs = tf.split(input_tensor, input_splits, axis=3, name='split_' + scope)
- base = scope
- for i, (input_tensor, out_size) in enumerate(zip(inputs, output_splits)):
- scope = base + '_part_%d' % (i,)
- n = slim.conv2d(input_tensor, out_size, [1, 1], scope=scope, **kwargs)
- n = tf.identity(n, scope + '_output')
- outs.append(n)
- return tf.concat(outs, 3, name=scope + '_concat')
Code Review / ealt-edge.git / blobdiff