Repository: hellochick/Indoor-segmentation
Branch: master
Commit: 8f204708e8e7
Files: 7
Total size: 44.9 KB
Directory structure:
gitextract_ubdnr53j/
├── README.md
├── color150.mat
├── download_models.sh
├── inference.py
├── model.py
├── network.py
└── requirements.txt
================================================
FILE CONTENTS
================================================
================================================
FILE: README.md
================================================
# Indoor-segmentation
## Introduction
This is an implementation of TensorFlow-based (TF1) DeepLab-ResNet for Indoor-scene segmentation. The provided model is trained on the [ade20k](http://sceneparsing.csail.mit.edu/) dataset. The code is inherited from [tensorflow-deeplab-resnet](https://github.com/DrSleep/tensorflow-deeplab-resnet) by [Drsleep](https://drsleep.github.io/). Since this model is for `robot navigating`, we `re-label 150 classes into 27 classes` in order to easily classify obstacles and road.
### Re-label list:
```
1 (wall) <- 9(window), 15(door), 33(fence), 43(pillar), 44(sign board), 145(bullertin board)
4 (floor) <- 7(road), 14(ground, 30(field), 53(path), 55(runway)
5 (tree) <- 18(plant)
8 (furniture) <- 8(bed), 11(cabinet), 14(sofa), 16(table), 19(curtain), 20(chair), 25(shelf), 34(desk)
7 (stairs) <- 54(stairs)
26(others) <- class number larger than 26
```
## Quick Start
### Install dependency
The codes are test on `Python 3.7`. Please run the following script to install the packages.
```bash
pip install -r requirements.txt
```
### Download pretrained model
Run the following script to download the provided pretrained model from Google Drive.
```bash
./download_models.sh
```
Or directly get the pretrained model from [Google Drive](https://drive.google.com/file/d/1o7QrlNxH6BX6uYatlR06-A_cutWD9sNg/view?usp=sharing).
### Demo
Run the following sample command for inference
```
python inference.py --img_path input/IMG_0416_640x480.png --restore_from=pretrained_models/ResNet101/
```
## Result
### Video
[](https://youtu.be/4OqW3M-eqaQ)
### Image
Input image | Output image
:-------------------------:|:-------------------------:
 | 
 | 
 | 
================================================
FILE: download_models.sh
================================================
gdown --fuzzy 1o7QrlNxH6BX6uYatlR06-A_cutWD9sNg
unzip pretrained_models.zip
================================================
FILE: inference.py
================================================
from __future__ import print_function
import argparse
import os
import sys
import time
import scipy.io as sio
from PIL import Image
import tensorflow as tf
import numpy as np
from model import DeepLabResNetModel
IMG_MEAN = np.array((104.00698793,116.66876762,122.67891434), dtype=np.float32)
NUM_CLASSES = 27
SAVE_DIR = './output/'
RESTORE_PATH = './restore_weights/'
matfn = 'color150.mat'
def get_arguments():
parser = argparse.ArgumentParser(description="Indoor segmentation parser.")
parser.add_argument("--img_path", type=str, default='',
help="Path to the RGB image file.")
parser.add_argument("--restore_from", type=str, default=RESTORE_PATH,
help="checkpoint location")
return parser.parse_args()
def read_labelcolours(matfn):
mat = sio.loadmat(matfn)
color_table = mat['colors']
shape = color_table.shape
color_list = [tuple(color_table[i]) for i in range(shape[0])]
return color_list
def decode_labels(mask, num_images=1, num_classes=150):
label_colours = read_labelcolours(matfn)
n, h, w, c = mask.shape
assert(n >= num_images), 'Batch size %d should be greater or equal than number of images to save %d.' % (n, num_images)
outputs = np.zeros((num_images, h, w, 3), dtype=np.uint8)
for i in range(num_images):
img = Image.new('RGB', (len(mask[i, 0]), len(mask[i])))
pixels = img.load()
for j_, j in enumerate(mask[i, :, :, 0]):
for k_, k in enumerate(j):
if k < num_classes:
pixels[k_,j_] = label_colours[k]
outputs[i] = np.array(img)
return outputs
def load(saver, sess, ckpt_path):
saver.restore(sess, ckpt_path)
print("Restored model parameters from {}".format(ckpt_path))
def main():
args = get_arguments()
filename = args.img_path.split('/')[-1]
file_type = filename.split('.')[-1]
if os.path.isfile(args.img_path):
print('successful load img: {0}'.format(args.img_path))
else:
print('not found file: {0}'.format(args.img_path))
sys.exit(0)
# Prepare image.
if file_type.lower() == 'png':
img = tf.image.decode_png(tf.read_file(args.img_path), channels=3)
elif file_type.lower() == 'jpg':
img = tf.image.decode_jpeg(tf.read_file(args.img_path), channels=3)
else:
print('cannot process {0} file.'.format(file_type))
# Convert RGB to BGR.
img_r, img_g, img_b = tf.split(axis=2, num_or_size_splits=3, value=img)
img = tf.cast(tf.concat(axis=2, values=[img_b, img_g, img_r]), dtype=tf.float32)
# Extract mean.
img -= IMG_MEAN
# Create network.
net = DeepLabResNetModel({'data': tf.expand_dims(img, dim=0)}, is_training=False, num_classes=NUM_CLASSES)
# Which variables to load.
restore_var = tf.global_variables()
# Predictions.
raw_output = net.layers['fc_out']
raw_output_up = tf.image.resize_bilinear(raw_output, tf.shape(img)[0:2,])
raw_output_up = tf.argmax(raw_output_up, dimension=3)
pred = tf.expand_dims(raw_output_up, dim=3)
# Set up TF session and initialize variables.
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
init = tf.global_variables_initializer()
sess.run(init)
# Load weights.
ckpt = tf.train.get_checkpoint_state(args.restore_from)
if ckpt and ckpt.model_checkpoint_path:
loader = tf.train.Saver(var_list=restore_var)
load_step = int(os.path.basename(ckpt.model_checkpoint_path).split('-')[1])
load(loader, sess, ckpt.model_checkpoint_path)
else:
print('No checkpoint file found.')
load_step = 0
# Perform inference.
preds = sess.run(pred)
msk = decode_labels(preds, num_classes=NUM_CLASSES)
im = Image.fromarray(msk[0])
if not os.path.exists(SAVE_DIR):
os.makedirs(SAVE_DIR)
im.save(SAVE_DIR + filename)
print('The output file has been saved to {0}'.format(SAVE_DIR + filename))
if __name__ == '__main__':
main()
================================================
FILE: model.py
================================================
# Converted to TensorFlow .caffemodel
# with the DeepLab-ResNet configuration.
# The batch normalisation layer is provided by
# the slim library (https://github.com/tensorflow/tensorflow/tree/master/tensorflow/contrib/slim).
from network import Network
import tensorflow as tf
class DeepLabResNetModel(Network):
def setup(self, is_training, num_classes):
'''Network definition.
Args:
is_training: whether to update the running mean and variance of the batch normalisation layer.
If the batch size is small, it is better to keep the running mean and variance of
the-pretrained model frozen.
num_classes: number of classes to predict (including background).
'''
(self.feed('data')
.conv(7, 7, 64, 2, 2, biased=False, relu=False, name='conv1')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn_conv1')
.max_pool(3, 3, 2, 2, name='pool1')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch1')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch1'))
(self.feed('pool1')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2a_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2a')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2a_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2a_branch2b')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2a_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn2a_branch2c'))
(self.feed('bn2a_branch1',
'bn2a_branch2c')
.add(name='res2a')
.relu(name='res2a_relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2b_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2a')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2b_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2b_branch2b')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2b_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn2b_branch2c'))
(self.feed('res2a_relu',
'bn2b_branch2c')
.add(name='res2b')
.relu(name='res2b_relu')
.conv(1, 1, 64, 1, 1, biased=False, relu=False, name='res2c_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2a')
.conv(3, 3, 64, 1, 1, biased=False, relu=False, name='res2c_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn2c_branch2b')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res2c_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn2c_branch2c'))
(self.feed('res2b_relu',
'bn2c_branch2c')
.add(name='res2c')
.relu(name='res2c_relu')
.conv(1, 1, 512, 2, 2, biased=False, relu=False, name='res3a_branch1')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch1'))
(self.feed('res2c_relu')
.conv(1, 1, 128, 2, 2, biased=False, relu=False, name='res3a_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2a')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3a_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3a_branch2b')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3a_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn3a_branch2c'))
(self.feed('bn3a_branch1',
'bn3a_branch2c')
.add(name='res3a')
.relu(name='res3a_relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2a')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b1_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b1_branch2b')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b1_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn3b1_branch2c'))
(self.feed('res3a_relu',
'bn3b1_branch2c')
.add(name='res3b1')
.relu(name='res3b1_relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2a')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b2_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b2_branch2b')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b2_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn3b2_branch2c'))
(self.feed('res3b1_relu',
'bn3b2_branch2c')
.add(name='res3b2')
.relu(name='res3b2_relu')
.conv(1, 1, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2a')
.conv(3, 3, 128, 1, 1, biased=False, relu=False, name='res3b3_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn3b3_branch2b')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res3b3_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn3b3_branch2c'))
(self.feed('res3b2_relu',
'bn3b3_branch2c')
.add(name='res3b3')
.relu(name='res3b3_relu')
.conv(1, 1, 1024, 2, 2, biased=False, relu=False, name='res4a_branch1')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch1'))
### block 4
(self.feed('res3b3_relu')
.conv(1, 1, 256, 2, 2, biased=False, relu=False, name='res4a_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2a')
.atrous_conv(3, 3, 256, 1, padding='SAME', biased=False, relu=False, name='res4a_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4a_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4a_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4a_branch2c'))
(self.feed('bn4a_branch1',
'bn4a_branch2c')
.add(name='res4a')
.relu(name='res4a_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b1_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b1_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b1_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b1_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b1_branch2c'))
(self.feed('res4a_relu',
'bn4b1_branch2c')
.add(name='res4b1')
.relu(name='res4b1_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b2_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b2_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b2_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b2_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b2_branch2c'))
### block 4
### block 5
(self.feed('res4b1_relu',
'bn4b2_branch2c')
.add(name='res4b2')
.relu(name='res4b2_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b3_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b3_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b3_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b3_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b3_branch2c'))
(self.feed('res4b2_relu',
'bn4b3_branch2c')
.add(name='res4b3')
.relu(name='res4b3_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b4_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b4_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b4_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b4_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b4_branch2c'))
(self.feed('res4b3_relu',
'bn4b4_branch2c')
.add(name='res4b4')
.relu(name='res4b4_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b5_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b5_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b5_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b5_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b5_branch2c'))
### block 5
### block 6
(self.feed('res4b4_relu',
'bn4b5_branch2c')
.add(name='res4b5')
.relu(name='res4b5_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b6_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b6_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b6_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b6_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b6_branch2c'))
(self.feed('res4b5_relu',
'bn4b6_branch2c')
.add(name='res4b6')
.relu(name='res4b6_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b7_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b7_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b7_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b7_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b7_branch2c'))
(self.feed('res4b6_relu',
'bn4b7_branch2c')
.add(name='res4b7')
.relu(name='res4b7_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b8_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b8_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b8_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b8_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b8_branch2c'))
### block 6
### block 7
(self.feed('res4b7_relu',
'bn4b8_branch2c')
.add(name='res4b8')
.relu(name='res4b8_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b9_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b9_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b9_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b9_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b9_branch2c'))
(self.feed('res4b8_relu',
'bn4b9_branch2c')
.add(name='res4b9')
.relu(name='res4b9_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b10_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b10_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b10_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b10_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b10_branch2c'))
(self.feed('res4b9_relu',
'bn4b10_branch2c')
.add(name='res4b10')
.relu(name='res4b10_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b11_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b11_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b11_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b11_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b11_branch2c'))
### block 7
(self.feed('res4b10_relu',
'bn4b11_branch2c')
.add(name='res4b11')
.relu(name='res4b11_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b12_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b12_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b12_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b12_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b12_branch2c'))
(self.feed('res4b11_relu',
'bn4b12_branch2c')
.add(name='res4b12')
.relu(name='res4b12_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b13_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b13_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b13_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b13_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b13_branch2c'))
(self.feed('res4b12_relu',
'bn4b13_branch2c')
.add(name='res4b13')
.relu(name='res4b13_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b14_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b14_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b14_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b14_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b14_branch2c'))
(self.feed('res4b13_relu',
'bn4b14_branch2c')
.add(name='res4b14')
.relu(name='res4b14_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b15_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b15_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b15_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b15_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b15_branch2c'))
(self.feed('res4b14_relu',
'bn4b15_branch2c')
.add(name='res4b15')
.relu(name='res4b15_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b16_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b16_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b16_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b16_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b16_branch2c'))
(self.feed('res4b15_relu',
'bn4b16_branch2c')
.add(name='res4b16')
.relu(name='res4b16_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b17_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b17_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b17_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b17_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b17_branch2c'))
(self.feed('res4b16_relu',
'bn4b17_branch2c')
.add(name='res4b17')
.relu(name='res4b17_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b18_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b18_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b18_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b18_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b18_branch2c'))
(self.feed('res4b17_relu',
'bn4b18_branch2c')
.add(name='res4b18')
.relu(name='res4b18_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b19_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b19_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b19_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b19_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b19_branch2c'))
(self.feed('res4b18_relu',
'bn4b19_branch2c')
.add(name='res4b19')
.relu(name='res4b19_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b20_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b20_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b20_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b20_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b20_branch2c'))
(self.feed('res4b19_relu',
'bn4b20_branch2c')
.add(name='res4b20')
.relu(name='res4b20_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b21_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2a')
.atrous_conv(3, 3, 256, 2, padding='SAME', biased=False, relu=False, name='res4b21_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b21_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b21_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b21_branch2c'))
(self.feed('res4b20_relu',
'bn4b21_branch2c')
.add(name='res4b21')
.relu(name='res4b21_relu')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='res4b22_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2a')
.atrous_conv(3, 3, 256, 4, padding='SAME', biased=False, relu=False, name='res4b22_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn4b22_branch2b')
.conv(1, 1, 1024, 1, 1, biased=False, relu=False, name='res4b22_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn4b22_branch2c'))
(self.feed('res4b21_relu',
'bn4b22_branch2c')
.add(name='res4b22')
.relu(name='res4b22_relu')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch1')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch1'))
(self.feed('res4b22_relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5a_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2a')
.atrous_conv(3, 3, 512, 4, padding='SAME', biased=False, relu=False, name='res5a_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5a_branch2b')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5a_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn5a_branch2c'))
(self.feed('bn5a_branch1',
'bn5a_branch2c')
.add(name='res5a')
.relu(name='res5a_relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5b_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2a')
.atrous_conv(3, 3, 512, 8, padding='SAME', biased=False, relu=False, name='res5b_branch2b')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5b_branch2b')
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5b_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn5b_branch2c'))
(self.feed('res5a_relu',
'bn5b_branch2c')
.add(name='res5b')
.relu(name='res5b_relu')
.conv(1, 1, 512, 1, 1, biased=False, relu=False, name='res5c_branch2a')
.batch_normalization(is_training=is_training, activation_fn=tf.nn.relu, name='bn5c_branch2a')
.atrous_conv(3, 3, 512, 16, padding='SAME', biased=False, relu=False, name='res5c_branch2b')
.batch_normalization(activation_fn=tf.nn.relu, name='bn5c_branch2b', is_training=is_training)
.conv(1, 1, 2048, 1, 1, biased=False, relu=False, name='res5c_branch2c')
.batch_normalization(is_training=is_training, activation_fn=None, name='bn5c_branch2c'))
(self.feed('res5b_relu',
'bn5c_branch2c')
.add(name='res5c')
.relu(name='res5c_relu')
.atrous_conv(3, 3, 256, 6, padding='SAME', relu=False, name='fc1_voc12_c0')
.batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c0_bn'))
(self.feed('res5c_relu')
.atrous_conv(3, 3, 256, 12, padding='SAME', relu=False, name='fc1_voc12_c1')
.batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c1_bn'))
(self.feed('res5c_relu')
.atrous_conv(3, 3, 256, 18, padding='SAME', relu=False, name='fc1_voc12_c2')
.batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c2_bn'))
(self.feed('res5c_relu')
.atrous_conv(1, 1, 256, 1, padding='SAME', relu=False, name='fc1_voc12_c3')
.batch_normalization(is_training=is_training, activation_fn=None, name='fc1_voc12_c3_bn'))
(self.feed('fc1_voc12_c0_bn',
'fc1_voc12_c1_bn',
'fc1_voc12_c2_bn',
'fc1_voc12_c3_bn')
.add(name='fc1_voc12')
.conv(1, 1, 256, 1, 1, biased=False, relu=False, name='fc_oooo')
.batch_normalization(is_training=is_training, activation_fn=None, name='fc_oooo_bn')
.conv(1, 1, num_classes, 1, 1, biased=False, relu=False, name='fc_out'))
================================================
FILE: network.py
================================================
import numpy as np
import tensorflow as tf
slim = tf.contrib.slim
DEFAULT_PADDING = 'SAME'
def layer(op):
'''Decorator for composable network layers.'''
def layer_decorated(self, *args, **kwargs):
# Automatically set a name if not provided.
name = kwargs.setdefault('name', self.get_unique_name(op.__name__))
# Figure out the layer inputs.
if len(self.terminals) == 0:
raise RuntimeError('No input variables found for layer %s.' % name)
elif len(self.terminals) == 1:
layer_input = self.terminals[0]
else:
layer_input = list(self.terminals)
# Perform the operation and get the output.
layer_output = op(self, layer_input, *args, **kwargs)
# Add to layer LUT.
self.layers[name] = layer_output
# This output is now the input for the next layer.
self.feed(layer_output)
# Return self for chained calls.
return self
return layer_decorated
class Network(object):
def __init__(self, inputs, trainable=True, is_training=False, num_classes=21):
# The input nodes for this network
self.inputs = inputs
# The current list of terminal nodes
self.terminals = []
# Mapping from layer names to layers
self.layers = dict(inputs)
# If true, the resulting variables are set as trainable
self.trainable = trainable
# Switch variable for dropout
self.use_dropout = tf.placeholder_with_default(tf.constant(1.0),
shape=[],
name='use_dropout')
self.setup(is_training, num_classes)
def setup(self, is_training):
'''Construct the network. '''
raise NotImplementedError('Must be implemented by the subclass.')
def load(self, data_path, session, ignore_missing=False):
'''Load network weights.
data_path: The path to the numpy-serialized network weights
session: The current TensorFlow session
ignore_missing: If true, serialized weights for missing layers are ignored.
'''
data_dict = np.load(data_path).item()
for op_name in data_dict:
with tf.variable_scope(op_name, reuse=True):
for param_name, data in data_dict[op_name].iteritems():
try:
var = tf.get_variable(param_name)
session.run(var.assign(data))
except ValueError:
if not ignore_missing:
raise
def feed(self, *args):
'''Set the input(s) for the next operation by replacing the terminal nodes.
The arguments can be either layer names or the actual layers.
'''
assert len(args) != 0
self.terminals = []
for fed_layer in args:
if isinstance(fed_layer, str):
try:
fed_layer = self.layers[fed_layer]
except KeyError:
raise KeyError('Unknown layer name fed: %s' % fed_layer)
self.terminals.append(fed_layer)
return self
def get_output(self):
'''Returns the current network output.'''
return self.terminals[-1]
def get_unique_name(self, prefix):
'''Returns an index-suffixed unique name for the given prefix.
This is used for auto-generating layer names based on the type-prefix.
'''
ident = sum(t.startswith(prefix) for t, _ in self.layers.items()) + 1
return '%s_%d' % (prefix, ident)
def make_var(self, name, shape):
'''Creates a new TensorFlow variable.'''
return tf.get_variable(name, shape, trainable=self.trainable)
def validate_padding(self, padding):
'''Verifies that the padding is one of the supported ones.'''
assert padding in ('SAME', 'VALID')
@layer
def conv(self,
input,
k_h,
k_w,
c_o,
s_h,
s_w,
name,
relu=True,
padding=DEFAULT_PADDING,
group=1,
biased=True):
# Verify that the padding is acceptable
self.validate_padding(padding)
# Get the number of channels in the input
c_i = input.get_shape()[-1]
# Verify that the grouping parameter is valid
assert c_i % group == 0
assert c_o % group == 0
# Convolution for a given input and kernel
convolve = lambda i, k: tf.nn.conv2d(i, k, [1, s_h, s_w, 1], padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
if group == 1:
# This is the common-case. Convolve the input without any further complications.
output = convolve(input, kernel)
else:
# Split the input into groups and then convolve each of them independently
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
# Concatenate the groups
output = tf.concat(3, output_groups)
# Add the biases
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
# ReLU non-linearity
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def atrous_conv(self,
input,
k_h,
k_w,
c_o,
dilation,
name,
relu=True,
padding=DEFAULT_PADDING,
group=1,
biased=True):
# Verify that the padding is acceptable
self.validate_padding(padding)
# Get the number of channels in the input
c_i = input.get_shape()[-1]
# Verify that the grouping parameter is valid
assert c_i % group == 0
assert c_o % group == 0
# Convolution for a given input and kernel
convolve = lambda i, k: tf.nn.atrous_conv2d(i, k, dilation, padding=padding)
with tf.variable_scope(name) as scope:
kernel = self.make_var('weights', shape=[k_h, k_w, c_i // group, c_o])
if group == 1:
# This is the common-case. Convolve the input without any further complications.
output = convolve(input, kernel)
else:
# Split the input into groups and then convolve each of them independently
input_groups = tf.split(3, group, input)
kernel_groups = tf.split(3, group, kernel)
output_groups = [convolve(i, k) for i, k in zip(input_groups, kernel_groups)]
# Concatenate the groups
output = tf.concat(3, output_groups)
# Add the biases
if biased:
biases = self.make_var('biases', [c_o])
output = tf.nn.bias_add(output, biases)
if relu:
# ReLU non-linearity
output = tf.nn.relu(output, name=scope.name)
return output
@layer
def relu(self, input, name):
return tf.nn.relu(input, name=name)
@layer
def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.max_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PADDING):
self.validate_padding(padding)
return tf.nn.avg_pool(input,
ksize=[1, k_h, k_w, 1],
strides=[1, s_h, s_w, 1],
padding=padding,
name=name)
@layer
def lrn(self, input, radius, alpha, beta, name, bias=1.0):
return tf.nn.local_response_normalization(input,
depth_radius=radius,
alpha=alpha,
beta=beta,
bias=bias,
name=name)
@layer
def concat(self, inputs, axis, name):
return tf.concat(axis=axis, values=inputs, name=name)
@layer
def add(self, inputs, name):
return tf.add_n(inputs, name=name)
@layer
def fc(self, input, num_out, name, relu=True):
with tf.variable_scope(name) as scope:
input_shape = input.get_shape()
if input_shape.ndims == 4:
# The input is spatial. Vectorize it first.
dim = 1
for d in input_shape[1:].as_list():
dim *= d
feed_in = tf.reshape(input, [-1, dim])
else:
feed_in, dim = (input, input_shape[-1].value)
weights = self.make_var('weights', shape=[dim, num_out])
biases = self.make_var('biases', [num_out])
op = tf.nn.relu_layer if relu else tf.nn.xw_plus_b
fc = op(feed_in, weights, biases, name=scope.name)
return fc
@layer
def softmax(self, input, name):
input_shape = map(lambda v: v.value, input.get_shape())
if len(input_shape) > 2:
# For certain models (like NiN), the singleton spatial dimensions
# need to be explicitly squeezed, since they're not broadcast-able
# in TensorFlow's NHWC ordering (unlike Caffe's NCHW).
if input_shape[1] == 1 and input_shape[2] == 1:
input = tf.squeeze(input, squeeze_dims=[1, 2])
else:
raise ValueError('Rank 2 tensor input expected for softmax!')
return tf.nn.softmax(input, name)
@layer
def batch_normalization(self, input, name, is_training, activation_fn=None, scale=True):
with tf.variable_scope(name) as scope:
output = slim.batch_norm(
input,
activation_fn=activation_fn,
is_training=is_training,
updates_collections=None,
scale=scale,
scope=scope)
return output
@layer
def dropout(self, input, keep_prob, name):
keep = 1 - self.use_dropout + (self.use_dropout * keep_prob)
return tf.nn.dropout(input, keep, name=name)
@layer
def resize_bilinear(self, input, size, name):
return tf.image.resize_bilinear(input, size=size, name=name)
@layer
def global_average_pooling(self, input, name):
"""
shape = input.get_shape().as_list()
_mean = tf.reduce_mean(input, [1, 2], name='GAP', keep_dims=True)
_conv = self.conv(_mean, 1, 1, 256, 1, 1, biased=False, relu=False, name='GAP_conv')
_bn = self.batch_normalization(_conv, is_training=is_training, activation_fn=None, name='GAP_conv_bn')
_resize = tf.image.resize_bilinear(_bn, size=shape, name=name)
"""
return tf.reduce_mean(input, [1, 2], name=name, keep_dims=True)
================================================
FILE: requirements.txt
================================================
tensorflow-gpu==1.15.0
scipy==1.7.3
pillow==9.1.0
gdown
gitextract_ubdnr53j/ ├── README.md ├── color150.mat ├── download_models.sh ├── inference.py ├── model.py ├── network.py └── requirements.txt
SYMBOL INDEX (31 symbols across 3 files)
FILE: inference.py
function get_arguments (line 22) | def get_arguments():
function read_labelcolours (line 31) | def read_labelcolours(matfn):
function decode_labels (line 39) | def decode_labels(mask, num_images=1, num_classes=150):
function load (line 55) | def load(saver, sess, ckpt_path):
function main (line 59) | def main():
FILE: model.py
class DeepLabResNetModel (line 9) | class DeepLabResNetModel(Network):
method setup (line 10) | def setup(self, is_training, num_classes):
FILE: network.py
function layer (line 8) | def layer(op):
class Network (line 33) | class Network(object):
method __init__ (line 35) | def __init__(self, inputs, trainable=True, is_training=False, num_clas...
method setup (line 50) | def setup(self, is_training):
method load (line 54) | def load(self, data_path, session, ignore_missing=False):
method feed (line 71) | def feed(self, *args):
method get_output (line 86) | def get_output(self):
method get_unique_name (line 90) | def get_unique_name(self, prefix):
method make_var (line 97) | def make_var(self, name, shape):
method validate_padding (line 101) | def validate_padding(self, padding):
method conv (line 106) | def conv(self,
method atrous_conv (line 149) | def atrous_conv(self,
method relu (line 191) | def relu(self, input, name):
method max_pool (line 195) | def max_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PA...
method avg_pool (line 204) | def avg_pool(self, input, k_h, k_w, s_h, s_w, name, padding=DEFAULT_PA...
method lrn (line 213) | def lrn(self, input, radius, alpha, beta, name, bias=1.0):
method concat (line 222) | def concat(self, inputs, axis, name):
method add (line 226) | def add(self, inputs, name):
method fc (line 230) | def fc(self, input, num_out, name, relu=True):
method softmax (line 248) | def softmax(self, input, name):
method batch_normalization (line 261) | def batch_normalization(self, input, name, is_training, activation_fn=...
method dropout (line 273) | def dropout(self, input, keep_prob, name):
method resize_bilinear (line 278) | def resize_bilinear(self, input, size, name):
method global_average_pooling (line 282) | def global_average_pooling(self, input, name):
Condensed preview — 7 files, each showing path, character count, and a content snippet. Download the .json file or copy for the full structured content (47K chars).
[
{
"path": "README.md",
"chars": 2357,
"preview": "# Indoor-segmentation\n## Introduction\n This is an implementation of TensorFlow-based (TF1) DeepLab-ResNet for Indoor-sc"
},
{
"path": "download_models.sh",
"chars": 77,
"preview": "gdown --fuzzy 1o7QrlNxH6BX6uYatlR06-A_cutWD9sNg\nunzip pretrained_models.zip\n\n"
},
{
"path": "inference.py",
"chars": 4071,
"preview": "from __future__ import print_function\n\nimport argparse\nimport os\nimport sys\nimport time\nimport scipy.io as sio\nfrom PIL "
},
{
"path": "model.py",
"chars": 27765,
"preview": "# Converted to TensorFlow .caffemodel\n# with the DeepLab-ResNet configuration.\n# The batch normalisation layer is provid"
},
{
"path": "network.py",
"chars": 11663,
"preview": "import numpy as np\nimport tensorflow as tf\nslim = tf.contrib.slim\n\nDEFAULT_PADDING = 'SAME'\n\n\ndef layer(op):\n '''Deco"
},
{
"path": "requirements.txt",
"chars": 56,
"preview": "tensorflow-gpu==1.15.0\nscipy==1.7.3\npillow==9.1.0\ngdown\n"
}
]
// ... and 1 more files (download for full content)
About this extraction
This page contains the full source code of the hellochick/Indoor-segmentation GitHub repository, extracted and formatted as plain text for AI agents and large language models (LLMs). The extraction includes 7 files (44.9 KB), approximately 12.9k tokens, and a symbol index with 31 extracted functions, classes, methods, constants, and types. Use this with OpenClaw, Claude, ChatGPT, Cursor, Windsurf, or any other AI tool that accepts text input. You can copy the full output to your clipboard or download it as a .txt file.
Extracted by GitExtract — free GitHub repo to text converter for AI. Built by Nikandr Surkov.