Following this tutorial im stuck on .xml to .record conversion.
In fact, when i use the following query :
C:\XXXX\scripts\processing>python generate_tfrecord.py -x C:/XXXX/workspace/training_demo/images/train -l C:/XXXX/training_demo/annotations/label_map.pbtxt -o C:/XXXX/workspace/training_demo/annotations/train.record
This does return me :
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 107: invalid start byte
the .xml are like this :
<annotation>
<folder>train</folder>
<filename>XXXX.PNG</filename>
<path>C:\XXXX\workspace\training_demo\images\train\XXXX.PNG</path>
<source>
<database>Unknown</database>
</source>
<size>
<width>93</width>
<height>66</height>
<depth>3</depth>
</size>
<segmented>0</segmented>
<object>
<name>XXXX</name>
<pose>Unspecified</pose>
<truncated>1</truncated>
<difficult>0</difficult>
<bndbox>
<xmin>1</xmin>
<ymin>1</ymin>
<xmax>93</xmax>
<ymax>66</ymax>
</bndbox>
</object>
</annotation>
and the code is exactly the same as in the tutorial :
""" Sample TensorFlow XML-to-TFRecord converter
usage: generate_tfrecord.py [-h] [-x XML_DIR] [-l LABELS_PATH] [-o OUTPUT_PATH] [-i IMAGE_DIR] [-c CSV_PATH]
optional arguments:
-h, --help show this help message and exit
-x XML_DIR, --xml_dir XML_DIR
Path to the folder where the input .xml files are stored.
-l LABELS_PATH, --labels_path LABELS_PATH
Path to the labels (.pbtxt) file.
-o OUTPUT_PATH, --output_path OUTPUT_PATH
Path of output TFRecord (.record) file.
-i IMAGE_DIR, --image_dir IMAGE_DIR
Path to the folder where the input image files are stored. Defaults to the same directory as XML_DIR.
-c CSV_PATH, --csv_path CSV_PATH
Path of output .csv file. If none provided, then no file will be written.
"""
import os
import glob
import pandas as pd
import io
import xml.etree.ElementTree as ET
import argparse
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2' # Suppress TensorFlow logging (1)
import tensorflow.compat.v1 as tf
from PIL import Image
from object_detection.utils import dataset_util, label_map_util
from collections import namedtuple
# Initiate argument parser
parser = argparse.ArgumentParser(
description="Sample TensorFlow XML-to-TFRecord converter")
parser.add_argument("-x",
"--xml_dir",
help="Path to the folder where the input .xml files are stored.",
type=str)
parser.add_argument("-l",
"--labels_path",
help="Path to the labels (.pbtxt) file.", type=str)
parser.add_argument("-o",
"--output_path",
help="Path of output TFRecord (.record) file.", type=str)
parser.add_argument("-i",
"--image_dir",
help="Path to the folder where the input image files are stored. "
"Defaults to the same directory as XML_DIR.",
type=str, default=None)
parser.add_argument("-c",
"--csv_path",
help="Path of output .csv file. If none provided, then no file will be "
"written.",
type=str, default=None)
args = parser.parse_args()
if args.image_dir is None:
args.image_dir = args.xml_dir
label_map = label_map_util.load_labelmap(args.labels_path)
label_map_dict = label_map_util.get_label_map_dict(label_map)
def xml_to_csv(path):
"""Iterates through all .xml files (generated by labelImg) in a given directory and combines
them in a single Pandas dataframe.
Parameters:
----------
path : str
The path containing the .xml files
Returns
-------
Pandas DataFrame
The produced dataframe
"""
xml_list = []
for xml_file in glob.glob(path + '/*.xml'):
tree = ET.parse(xml_file)
root = tree.getroot()
for member in root.findall('object'):
value = (root.find('filename').text,
int(root.find('size')[0].text),
int(root.find('size')[1].text),
member[0].text,
int(member[4][0].text),
int(member[4][1].text),
int(member[4][2].text),
int(member[4][3].text)
)
xml_list.append(value)
column_name = ['filename', 'width', 'height',
'class', 'xmin', 'ymin', 'xmax', 'ymax']
xml_df = pd.DataFrame(xml_list, columns=column_name)
return xml_df
def class_text_to_int(row_label):
return label_map_dict[row_label]
def split(df, group):
data = namedtuple('data', ['filename', 'object'])
gb = df.groupby(group)
return [data(filename, gb.get_group(x)) for filename, x in zip(gb.groups.keys(), gb.groups)]
def create_tf_example(group, path):
with tf.gfile.GFile(os.path.join(path, '{}'.format(group.filename)), 'rb') as fid:
encoded_jpg = fid.read()
encoded_jpg_io = io.BytesIO(encoded_jpg)
image = Image.open(encoded_jpg_io)
width, height = image.size
filename = group.filename.encode('utf8')
image_format = b'jpg'
xmins = []
xmaxs = []
ymins = []
ymaxs = []
classes_text = []
classes = []
for index, row in group.object.iterrows():
xmins.append(row['xmin'] / width)
xmaxs.append(row['xmax'] / width)
ymins.append(row['ymin'] / height)
ymaxs.append(row['ymax'] / height)
classes_text.append(row['class'].encode('utf8'))
classes.append(class_text_to_int(row['class']))
tf_example = tf.train.Example(features=tf.train.Features(feature={
'image/height': dataset_util.int64_feature(height),
'image/width': dataset_util.int64_feature(width),
'image/filename': dataset_util.bytes_feature(filename),
'image/source_id': dataset_util.bytes_feature(filename),
'image/encoded': dataset_util.bytes_feature(encoded_jpg),
'image/format': dataset_util.bytes_feature(image_format),
'image/object/bbox/xmin': dataset_util.float_list_feature(xmins),
'image/object/bbox/xmax': dataset_util.float_list_feature(xmaxs),
'image/object/bbox/ymin': dataset_util.float_list_feature(ymins),
'image/object/bbox/ymax': dataset_util.float_list_feature(ymaxs),
'image/object/class/text': dataset_util.bytes_list_feature(classes_text),
'image/object/class/label': dataset_util.int64_list_feature(classes),
}))
return tf_example
def main(_):
writer = tf.python_io.TFRecordWriter(args.output_path)
path = os.path.join(args.image_dir)
examples = xml_to_csv(args.xml_dir)
grouped = split(examples, 'filename')
for group in grouped:
tf_example = create_tf_example(group, path)
writer.write(tf_example.SerializeToString())
writer.close()
print('Successfully created the TFRecord file: {}'.format(args.output_path))
if args.csv_path is not None:
examples.to_csv(args.csv_path, index=None)
print('Successfully created the CSV file: {}'.format(args.csv_path))
if __name__ == '__main__':
tf.app.run()
and there is the label_map.pbtxt file
item {
id: 21
name: 'XXXX'
}
item {
id: 31
name: 'XXXX'
}
item {
id: 41
name: 'XXXX'
}
Full consol returns :
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\_distributor_init.py:30: UserWarning: loaded more than 1 DLL from .libs:
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\.libs\libopenblas.JPIJNSWNNAN3CE6LLI5FWSPHUT2VXMTH.gfortran-win_amd64.dll
C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\numpy\.libs\libopenblas.QVLO2T66WEPI7JZ63PS3HMOHFEY472BC.gfortran-win_amd64.dll
warnings.warn("loaded more than 1 DLL from .libs:"
Traceback (most recent call last):
File "generate_tfrecord.py", line 61, in <module>
label_map = label_map_util.load_labelmap(args.labels_path)
File "C:\Users\Dorian\anaconda3\envs\XXXX\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\utils\label_map_util.py", line 168, in load_labelmap
label_map_string = fid.read()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 107: invalid start byte
EDIT here is label_map
item {
id: 21
name: '2Carreau'
}
item {
id: 31
name: '3Carreau'
}
item {
id: 41
name: '4Carreau'
}
item {
id: 51
name: '5Carreau'
}
item {
id: 61
name: '6Carreau'
}
item {
id: 71
name: '7Carreau'
}
item {
id: 81
name: '8Carreau'
}
item {
id: 91
name: '9Carreau'
}
item {
id: 101
name: '10Carreau'
}
item {
id: 111
name: '11Carreau'
}
item {
id: 121
name: '12Carreau'
}
item {
id: 131
name: '13Carreau'
}
item {
id: 141
name: '14Carreau'
}
item {
id: 22
name: '2Coeur'
}
item {
id: 32
name: '3Coeur'
}
item {
id: 42
name: '4Coeur'
}
item {
id: 52
name: '5Coeur'
}
item {
id: 62
name: '6Coeur'
}
item {
id: 72
name: '7Coeur'
}
item {
id: 82
name: '8Coeur'
}
item {
id: 92
name: '9Coeur'
}
item {
id: 102
name: '10Coeur'
}
item {
id: 112
name: '11Coeur'
}
item {
id: 122
name: '12Coeur'
}
item {
id: 132
name: '13Coeur'
}
item {
id: 142
name: '14Coeur'
}
item {
id: 23
name: '2Trefle'
}
item {
id: 33
name: '3Trefle'
}
item {
id: 43
name: '4Trefle'
}
item {
id: 53
name: '5Trefle'
}
item {
id: 63
name: '6Trefle'
}
item {
id: 73
name: '7Trefle'
}
item {
id: 83
name: '8Trefle'
}
item {
id: 93
name: '9Trefle'
}
item {
id: 103
name: '10Trefle'
}
item {
id: 113
name: '11Trefle'
}
item {
id: 123
name: '12Trefle'
}
item {
id: 133
name: '13Trefle'
}
item {
id: 143
name: '14Trefle'
}
item {
id: 24
name: '2Pic'
}
item {
id: 34
name: '3Pic'
}
item {
id: 44
name: '4Pic'
}
item {
id: 54
name: '5Pic'
}
item {
id: 64
name: '6Pic'
}
item {
id: 74
name: '7Pic'
}
item {
id: 84
name: '8Pic'
}
item {
id: 94
name: '9Pic'
}
item {
id: 104
name: '10Pic'
}
item {
id: 114
name: '11Pic'
}
item {
id: 124
name: '12Pic'
}
item {
id: 134
name: '13Pic'
}
item {
id: 144
name: '14Pic'
}
Now I use this query :
C:\####\workspace\training_demo>python model_main_tf2.py --model_dir=models/my_ssd_resnet50_v1_fpn --pipeline_config_path=models/my_ssd_resnet50_v1_fpn/pipeline.config
It started well but, throw the same issue at the end, I checked pipeline.config and model_main_tf2 but your answer did not correct this one... Do you have any idea ?
2021-03-03 09:53:43.878440: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.745301: I tensorflow/compiler/jit/xla_cpu_device.cc:41] Not creating XLA devices, tf_xla_enable_xla_devices not set
2021-03-03 09:53:48.749824: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library nvcuda.dll
2021-03-03 09:53:48.779768: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1c:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2021-03-03 09:53:48.786205: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.800110: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-03-03 09:53:48.803731: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-03-03 09:53:48.812755: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-03-03 09:53:48.822516: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-03-03 09:53:48.837930: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-03-03 09:53:48.851302: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-03-03 09:53:48.856177: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-03-03 09:53:48.860712: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-03-03 09:53:48.863378: I tensorflow/core/platform/cpu_feature_guard.cc:142] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
2021-03-03 09:53:48.873474: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1720] Found device 0 with properties:
pciBusID: 0000:1c:00.0 name: GeForce GTX 1070 Ti computeCapability: 6.1
coreClock: 1.683GHz coreCount: 19 deviceMemorySize: 8.00GiB deviceMemoryBandwidth: 238.66GiB/s
2021-03-03 09:53:48.881298: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudart64_110.dll
2021-03-03 09:53:48.884006: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublas64_11.dll
2021-03-03 09:53:48.887551: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cublasLt64_11.dll
2021-03-03 09:53:48.891894: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cufft64_10.dll
2021-03-03 09:53:48.895372: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library curand64_10.dll
2021-03-03 09:53:48.898176: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusolver64_10.dll
2021-03-03 09:53:48.903001: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cusparse64_11.dll
2021-03-03 09:53:48.906421: I tensorflow/stream_executor/platform/default/dso_loader.cc:49] Successfully opened dynamic library cudnn64_8.dll
2021-03-03 09:53:48.910388: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1862] Adding visible gpu devices: 0
2021-03-03 09:53:49.506138: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1261] Device interconnect StreamExecutor with strength 1 edge matrix:
2021-03-03 09:53:49.509246: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1267] 0
2021-03-03 09:53:49.511875: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1280] 0: N
2021-03-03 09:53:49.513745: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1406] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 6278 MB memory) -> physical GPU (device: 0, name: GeForce GTX 1070 Ti, pci bus id: 0000:1c:00.0, compute capability: 6.1)
2021-03-03 09:53:49.521636: I tensorflow/compiler/jit/xla_gpu_device.cc:99] Not creating XLA devices, tf_xla_enable_xla_devices not set
INFO:tensorflow:Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
I0303 09:53:49.527721 12968 mirrored_strategy.py:350] Using MirroredStrategy with devices ('/job:localhost/replica:0/task:0/device:GPU:0',)
Traceback (most recent call last):
File "model_main_tf2.py", line 113, in <module>
tf.compat.v1.app.run()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\platform\app.py", line 40, in run
_run(main=main, argv=argv, flags_parser=_parse_flags_tolerate_undef)
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\absl\app.py", line 303, in run
_run_main(main, args)
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\absl\app.py", line 251, in _run_main
sys.exit(main(argv))
File "model_main_tf2.py", line 104, in main
model_lib_v2.train_loop(
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\model_lib_v2.py", line 474, in train_loop
configs = get_configs_from_pipeline_file(
File "C:\Users\Dorian\anaconda3\envs\####\lib\site-packages\object_detection-0.1-py3.8.egg\object_detection\utils\config_util.py", line 138, in get_configs_from_pipeline_file
proto_str = f.read()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 117, in read
self._preread_check()
File "C:\Users\Dorian\AppData\Roaming\Python\Python38\site-packages\tensorflow\python\lib\io\file_io.py", line 79, in _preread_check
self._read_buf = _pywrap_file_io.BufferedInputStream(
UnicodeDecodeError: 'utf-8' codec can't decode byte 0x92 in position 102: invalid start byte
