X-Git-Url: https://gerrit.akraino.org/r/gitweb?a=blobdiff_plain;f=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fmobilenet%2Fmobilenet.py;fp=example-apps%2FPDD%2Fpcb-defect-detection%2Flibs%2Fnetworks%2Fmobilenet%2Fmobilenet.py;h=3c8ca50ef7e810c1b96c54b915324779944f4d73;hb=a785567fb9acfc68536767d20f60ba917ae85aa1;hp=0000000000000000000000000000000000000000;hpb=94a133e696b9b2a7f73544462c2714986fa7ab4a;p=ealt-edge.git

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet/mobilenet.py
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+# 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