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resnet-tf-1_3.patch
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resnet-tf-1_3.patch
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commit 89ea3e41bfc7f5fe0fb1fc609e2fcde4d4caa32f
Author: Josh Hejna <josh.hejna@gmail.com>
Date: Thu Aug 2 15:28:14 2018 -0700
TEMP
diff --git b/ros_workspace/src/deep_segmentation/src/network.py a/ros_workspace/src/deep_segmentation/src/network.py
index a947f2a..cd52478 100755
--- b/ros_workspace/src/deep_segmentation/src/network.py
+++ a/ros_workspace/src/deep_segmentation/src/network.py
@@ -1,6 +1,7 @@
+#!/usr/bin/env python
import tensorflow as tf
slim = tf.contrib.slim
-from resnet import resnet_v2, resnet_utils
+import resnet_v2, resnet_utils
# ImageNet mean statistics
_R_MEAN = 123.68
@@ -9,19 +10,12 @@ _B_MEAN = 103.94
@slim.add_arg_scope
def atrous_spatial_pyramid_pooling(net, scope, depth=256, reuse=None):
- """
- ASPP consists of (a) one 1×1 convolution and three 3×3 convolutions with rates = (6, 12, 18) when output stride = 16
- (all with 256 filters and batch normalization), and (b) the image-level features as described in https://arxiv.org/abs/1706.05587
- :param net: tensor of shape [BATCH_SIZE, WIDTH, HEIGHT, DEPTH]
- :param scope: scope name of the aspp layer
- :return: network layer with aspp applyed to it.
- """
with tf.variable_scope(scope, reuse=reuse):
feature_map_size = tf.shape(net)
# apply global average pooling
- image_level_features = tf.reduce_mean(net, [1, 2], name='image_level_global_pool', keepdims=True)
+ image_level_features = tf.reduce_mean(net, [1, 2], name='image_level_global_pool', keep_dims=True)
image_level_features = slim.conv2d(image_level_features, depth, [1, 1], scope="image_level_conv_1x1",
activation_fn=None)
image_level_features = tf.image.resize_bilinear(image_level_features, (feature_map_size[1], feature_map_size[2]))
@@ -65,7 +59,7 @@ def deeplab_v3(inputs, args, is_training, reuse):
net = atrous_spatial_pyramid_pooling(net, "ASPP_layer", depth=256, reuse=reuse)
- net = slim.conv2d(net, args.number_of_classes, [1, 1], activation_fn=None,
+ net = slim.conv2d(net, 5, [1, 1], activation_fn=None,
normalizer_fn=None, scope='logits')
size = tf.shape(inputs)[1:3]
diff --git b/ros_workspace/src/deep_segmentation/src/resnet_utils.py a/ros_workspace/src/deep_segmentation/src/resnet_utils.py
index 59f6ad4..8d657bf 100755
--- b/ros_workspace/src/deep_segmentation/src/resnet_utils.py
+++ a/ros_workspace/src/deep_segmentation/src/resnet_utils.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
diff --git b/ros_workspace/src/deep_segmentation/src/resnet_v2.py a/ros_workspace/src/deep_segmentation/src/resnet_v2.py
index 1ab1835..c8b1871 100755
--- b/ros_workspace/src/deep_segmentation/src/resnet_v2.py
+++ a/ros_workspace/src/deep_segmentation/src/resnet_v2.py
@@ -1,3 +1,4 @@
+#!/usr/bin/env python
# Copyright 2016 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
@@ -52,7 +53,7 @@ from __future__ import print_function
import tensorflow as tf
-from resnet import resnet_utils
+import resnet_utils
slim = tf.contrib.slim
resnet_arg_scope = resnet_utils.resnet_arg_scope
diff --git b/ros_workspace/src/deep_segmentation/src/train.py a/ros_workspace/src/deep_segmentation/src/train.py
index 278fc46..34e3a92 100755
--- b/ros_workspace/src/deep_segmentation/src/train.py
+++ a/ros_workspace/src/deep_segmentation/src/train.py
@@ -6,10 +6,10 @@ slim = tf.contrib.slim
import os
import json
import network
-from preprocessing.read_data import download_resnet_checkpoint_if_necessary, tf_record_parser, \
+from read_data import download_resnet_checkpoint_if_necessary, tf_record_parser, \
rescale_image_and_annotation_by_factor, scale_image_with_crop_padding, \
random_flip_image_and_annotation, distort_randomly_image_color
-from preprocessing import training
+import training
# 0=background
# 1=aeroplane
@@ -39,7 +39,7 @@ parser = argparse.ArgumentParser()
envarg = parser.add_argument_group('Training params')
envarg.add_argument("--batch_norm_epsilon", type=float, default=1e-5, help="batch norm epsilon argument for batch normalization")
envarg.add_argument('--batch_norm_decay', type=float, default=0.9997, help='batch norm decay argument for batch normalization.')
-envarg.add_argument("--number_of_classes", type=int, default=21, help="Number of classes to be predicted.")
+envarg.add_argument("--number_of_classes", type=int, default=5, help="Number of classes to be predicted.")
envarg.add_argument("--l2_regularizer", type=float, default=0.0001, help="l2 regularizer parameter.")
envarg.add_argument('--starting_learning_rate', type=float, default=0.00001, help="initial learning rate.")
envarg.add_argument("--multi_grid", type=list, default=[1,2,4], help="Spatial Pyramid Pooling rates")
@@ -166,7 +166,7 @@ with tf.Session() as sess:
try:
restorer.restore(sess, "./resnet/checkpoints/" + args.resnet_model + ".ckpt")
print("Model checkpoits for " + args.resnet_model + " restored!")
- except FileNotFoundError:
+ except:
print("ResNet checkpoints not found. Please download " + args.resnet_model + " model checkpoints from: https://github.com/tensorflow/models/tree/master/research/slim")
# The `Iterator.string_handle()` method returns a tensor that can be evaluated