pcb defect detetcion application

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / networks / slim_nets / nets_factory.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory.py b/example-apps/PDD/pcb-defect-detection/libs/networks/slim_nets/nets_factory.py
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+# Copyright 2016 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.
+# ==============================================================================
+"""Contains a factory for building various models."""
+
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+import functools
+
+import tensorflow as tf
+
+from nets import alexnet
+from nets import cifarnet
+from nets import inception
+from nets import lenet
+from nets import mobilenet_v1
+from nets import overfeat
+from nets import resnet_v1
+from nets import resnet_v2
+from nets import vgg
+
+slim = tf.contrib.slim
+
+networks_map = {'alexnet_v2': alexnet.alexnet_v2,
+                'cifarnet': cifarnet.cifarnet,
+                'overfeat': overfeat.overfeat,
+                'vgg_a': vgg.vgg_a,
+                'vgg_16': vgg.vgg_16,
+                'vgg_19': vgg.vgg_19,
+                'inception_v1': inception.inception_v1,
+                'inception_v2': inception.inception_v2,
+                'inception_v3': inception.inception_v3,
+                'inception_v4': inception.inception_v4,
+                'inception_resnet_v2': inception.inception_resnet_v2,
+                'lenet': lenet.lenet,
+                'resnet_v1_50': resnet_v1.resnet_v1_50,
+                'resnet_v1_101': resnet_v1.resnet_v1_101,
+                'resnet_v1_152': resnet_v1.resnet_v1_152,
+                'resnet_v1_200': resnet_v1.resnet_v1_200,
+                'resnet_v2_50': resnet_v2.resnet_v2_50,
+                'resnet_v2_101': resnet_v2.resnet_v2_101,
+                'resnet_v2_152': resnet_v2.resnet_v2_152,
+                'resnet_v2_200': resnet_v2.resnet_v2_200,
+                'mobilenet_v1': mobilenet_v1.mobilenet_v1,
+               }
+
+arg_scopes_map = {'alexnet_v2': alexnet.alexnet_v2_arg_scope,
+                  'cifarnet': cifarnet.cifarnet_arg_scope,
+                  'overfeat': overfeat.overfeat_arg_scope,
+                  'vgg_a': vgg.vgg_arg_scope,
+                  'vgg_16': vgg.vgg_arg_scope,
+                  'vgg_19': vgg.vgg_arg_scope,
+                  'inception_v1': inception.inception_v3_arg_scope,
+                  'inception_v2': inception.inception_v3_arg_scope,
+                  'inception_v3': inception.inception_v3_arg_scope,
+                  'inception_v4': inception.inception_v4_arg_scope,
+                  'inception_resnet_v2':
+                  inception.inception_resnet_v2_arg_scope,
+                  'lenet': lenet.lenet_arg_scope,
+                  'resnet_v1_50': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_101': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_152': resnet_v1.resnet_arg_scope,
+                  'resnet_v1_200': resnet_v1.resnet_arg_scope,
+                  'resnet_v2_50': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_101': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_152': resnet_v2.resnet_arg_scope,
+                  'resnet_v2_200': resnet_v2.resnet_arg_scope,
+                  'mobilenet_v1': mobilenet_v1.mobilenet_v1_arg_scope,
+                 }
+
+
+def get_network_fn(name, num_classes, weight_decay=0.0, is_training=False):
+  """Returns a network_fn such as `logits, end_points = network_fn(images)`.
+
+  Args:
+    name: The name of the network.
+    num_classes: The number of classes to use for classification.
+    weight_decay: The l2 coefficient for the model weights.
+    is_training: `True` if the model is being used for training and `False`
+      otherwise.
+
+  Returns:
+    network_fn: A function that applies the model to a batch of images. It has
+      the following signature:
+        logits, end_points = network_fn(images)
+  Raises:
+    ValueError: If network `name` is not recognized.
+  """
+  if name not in networks_map:
+    raise ValueError('Name of network unknown %s' % name)
+  arg_scope = arg_scopes_map[name](weight_decay=weight_decay)
+  func = networks_map[name]
+  @functools.wraps(func)
+  def network_fn(images):
+    with slim.arg_scope(arg_scope):
+      return func(images, num_classes, is_training=is_training)
+  if hasattr(func, 'default_image_size'):
+    network_fn.default_image_size = func.default_image_size
+
+  return network_fn