removed exmple apps code

[ealt-edge.git] / example-apps / PDD / pcb-defect-detection / libs / networks / mobilenet_v2.py

diff --git a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py b/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py
deleted file mode 100755 (executable)
index eced27c..0000000
--- a/example-apps/PDD/pcb-defect-detection/libs/networks/mobilenet_v2.py
+++ /dev/null
@@ -1,126 +0,0 @@
-# -*- coding: utf-8 -*-
-
-from __future__ import absolute_import, print_function, division
-import tensorflow.contrib.slim as slim
-import tensorflow as tf
-
-from libs.networks.mobilenet import mobilenet_v2
-from libs.networks.mobilenet.mobilenet import training_scope
-from libs.networks.mobilenet.mobilenet_v2 import op
-from libs.networks.mobilenet.mobilenet_v2 import ops
-expand_input = ops.expand_input_by_factor
-
-V2_BASE_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, scope='expanded_conv'),
-        op(ops.expanded_conv, stride=2, num_outputs=24, scope='expanded_conv_1'),
-        op(ops.expanded_conv, stride=1, num_outputs=24, scope='expanded_conv_2'),
-        op(ops.expanded_conv, stride=2, num_outputs=32, scope='expanded_conv_3'),
-        op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_4'),
-        op(ops.expanded_conv, stride=1, num_outputs=32, scope='expanded_conv_5'),
-        op(ops.expanded_conv, stride=2, num_outputs=64, scope='expanded_conv_6'),
-        op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_7'),
-        op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_8'),
-        op(ops.expanded_conv, stride=1, num_outputs=64, scope='expanded_conv_9'),
-        op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_10'),
-        op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_11'),
-        op(ops.expanded_conv, stride=1, num_outputs=96, scope='expanded_conv_12')
-    ],
-)
-
-
-V2_HEAD_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(ops.expanded_conv, stride=2, num_outputs=160, scope='expanded_conv_13'),
-        op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_14'),
-        op(ops.expanded_conv, stride=1, num_outputs=160, scope='expanded_conv_15'),
-        op(ops.expanded_conv, stride=1, num_outputs=320, scope='expanded_conv_16'),
-        op(slim.conv2d, stride=1, kernel_size=[1, 1], num_outputs=1280, scope='Conv_1')
-    ],
-)
-def mobilenetv2_scope(is_training=True,
-                      trainable=True,
-                      weight_decay=0.00004,
-                      stddev=0.09,
-                      dropout_keep_prob=0.8,
-                      bn_decay=0.997):
-  """Defines Mobilenet training scope.
-  In default. We do not use BN
-
-  ReWrite the scope.
-  """
-  batch_norm_params = {
-      'is_training': False,
-      'trainable': False,
-      'decay': bn_decay,
-  }
-  with slim.arg_scope(training_scope(is_training=is_training, weight_decay=weight_decay)):
-      with slim.arg_scope([slim.conv2d, slim.fully_connected, slim.separable_conv2d],
-                          trainable=trainable):
-          with slim.arg_scope([slim.batch_norm], **batch_norm_params) as sc:
-              return sc
-
-
-
-def mobilenetv2_base(img_batch, is_training=True):
-
-    with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)):
-
-        feature_to_crop, endpoints = mobilenet_v2.mobilenet_base(input_tensor=img_batch,
-                                                      num_classes=None,
-                                                      is_training=False,
-                                                      depth_multiplier=1.0,
-                                                      scope='MobilenetV2',
-                                                      conv_defs=V2_BASE_DEF,
-                                                      finegrain_classification_mode=False)
-
-        # feature_to_crop = tf.Print(feature_to_crop, [tf.shape(feature_to_crop)], summarize=10, message='rpn_shape')
-        return feature_to_crop
-
-
-def mobilenetv2_head(inputs, is_training=True):
-    with slim.arg_scope(mobilenetv2_scope(is_training=is_training, trainable=True)):
-        net, _ = mobilenet_v2.mobilenet(input_tensor=inputs,
-                                        num_classes=None,
-                                        is_training=False,
-                                        depth_multiplier=1.0,
-                                        scope='MobilenetV2',
-                                        conv_defs=V2_HEAD_DEF,
-                                        finegrain_classification_mode=False)
-
-        net = tf.squeeze(net, [1, 2])
-
-        return net
\ No newline at end of file