I'm trying to import the package model_main from object_detection but it Is throwing an error like
Traceback (most recent call last): File "../LearningSpace/ObJTEST01/main.py", line 9, in <module> from object_detection import model_main File "../anaconda3/envs/ObJTESTV01/lib/python3.10/site-packages/object_detection/model_main.py", line 26, in <module> from object_detection import model_lib File "../anaconda3/envs/ObJTESTV01/lib/python3.10/site-packages/object_detection/model_lib.py", line 27, in <module> from object_detection import eval_util File "../anaconda3/envs/ObJTESTV01/lib/python3.10/site-packages/object_detection/eval_util.py", line 35, in <module> slim = tf.contrib.slim AttributeError: module 'tensorflow' has no attribute 'contrib'
My Code:
import tensorflow as tf
from object_detection.utils import config_util
from object_detection.protos import pipeline_pb2
from google.protobuf import text_format
import os
import requests
import shutil
import tarfile
from object_detection import model_main
CUSTOM_MODEL_NAME = 'my_ssd_mobnet'
PRETRAINED_MODEL_NAME = 'ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8'
PRETRAINED_MODEL_URL = 'http://download.tensorflow.org/models/object_detection/tf2/20200711/ssd_mobilenet_v2_fpnlite_320x320_coco17_tpu-8.tar.gz'
TF_RECORD_SCRIPT_NAME = 'generate_tfrecord.py'
LABEL_MAP_NAME = 'label_map.pbtxt'
paths = {
'WORKSPACE_PATH': os.path.join('Tensorflow', 'workspace'),
'SCRIPTS_PATH': os.path.join('Tensorflow','scripts'),
'APIMODEL_PATH': os.path.join('Tensorflow','models'),
'ANNOTATION_PATH': os.path.join('Tensorflow', 'workspace','annotations'),
'IMAGE_PATH': os.path.join('Tensorflow', 'workspace','images'),
'MODEL_PATH': os.path.join('Tensorflow', 'workspace','models'),
'PRETRAINED_MODEL_PATH': os.path.join('Tensorflow', 'workspace','pre-trained-models'),
'CHECKPOINT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME),
'OUTPUT_PATH': os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'export'),
'TFJS_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfjsexport'),
'TFLITE_PATH':os.path.join('Tensorflow', 'workspace','models',CUSTOM_MODEL_NAME, 'tfliteexport'),
'PROTOC_PATH':os.path.join('Tensorflow','protoc')
}
files = {
'PIPELINE_CONFIG':os.path.join('Tensorflow', 'workspace','models', CUSTOM_MODEL_NAME, 'pipeline.config'),
'TF_RECORD_SCRIPT': os.path.join(paths['SCRIPTS_PATH'], TF_RECORD_SCRIPT_NAME),
'LABELMAP': os.path.join(paths['ANNOTATION_PATH'], LABEL_MAP_NAME)
}
for path in paths.values():
if not os.path.exists(path):
if os.name == 'posix' or os.name == 'nt': # Checks if the OS is POSIX-compliant (Unix-like) or Windows (nt)
os.makedirs(path, exist_ok=True) # Creates the directory/directories if they do not exist
# def download_file(url, local_filename):
# with requests.get(url, stream=True) as r:
# r.raise_for_status()
# with open(local_filename, 'wb') as f:
# for chunk in r.iter_content(chunk_size=8192):
# f.write(chunk)
# return local_filename
#
#
# if os.name == 'posix' or os.name == 'nt':
# local_filename = PRETRAINED_MODEL_NAME + '.tar.gz'
# download_file(PRETRAINED_MODEL_URL, local_filename)
# target_path = os.path.join(paths['PRETRAINED_MODEL_PATH'], local_filename)
# shutil.move(local_filename, target_path)
# with tarfile.open(target_path) as tar:
# tar.extractall(path=paths['PRETRAINED_MODEL_PATH'])
# os.remove(target_path)
labels = [{'name':'licence', 'id':1}]
with open(files['LABELMAP'], 'w') as f:
for label in labels:
f.write('item { \n')
f.write('\tname:\'{}\'\n'.format(label['name']))
f.write('\tid:{}\n'.format(label['id']))
f.write('}\n')
#it is done so no needed
# import os
# import subprocess
#
# # Assuming paths and files dictionaries are defined as before
#
# # Check if the TF_RECORD_SCRIPT does not exist
# if not os.path.exists(files['TF_RECORD_SCRIPT']):
# # Define the command to clone the GitHub repository
# clone_command = f"git clone https://github.com/nicknochnack/GenerateTFRecord {paths['SCRIPTS_PATH']}"
#
# # Execute the command
# subprocess.run(clone_command, shell=True, check=True)
# import shutil
# import os
#
# # Assuming paths and PRETRAINED_MODEL_NAME are defined as before
#
# # Construct the source path of the pipeline.config file
# source_path = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'pipeline.config')
#
# # Construct the destination path for the pipeline.config file
# destination_path = os.path.join(paths['CHECKPOINT_PATH'])
#
# # Copy the file from source to destination
# shutil.copy(source_path, destination_path)
#
# print(f"Copied pipeline.config to {destination_path}")
# def read_file_example(file_path):
# with tf.io.gfile.GFile(file_path, "r") as f:
# contents = f.read()
# return contents
#
# config = read_file_example(files['PIPELINE_CONFIG'])
#
# pipeline_config = pipeline_pb2.TrainEvalPipelineConfig()
# with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], "r") as f:
# proto_str = f.read()
# text_format.Merge(proto_str, pipeline_config)
# pipeline_config.model.ssd.num_classes = len(labels)
# pipeline_config.train_config.batch_size = 4
# pipeline_config.train_config.fine_tune_checkpoint = os.path.join(paths['PRETRAINED_MODEL_PATH'], PRETRAINED_MODEL_NAME, 'checkpoint', 'ckpt-0')
# pipeline_config.train_config.fine_tune_checkpoint_type = "detection"
# pipeline_config.train_input_reader.label_map_path= files['LABELMAP']
# pipeline_config.train_input_reader.tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'train.record')]
# pipeline_config.eval_input_reader[0].label_map_path = files['LABELMAP']
# pipeline_config.eval_input_reader[0].tf_record_input_reader.input_path[:] = [os.path.join(paths['ANNOTATION_PATH'], 'test.record')]
#
# config_text = text_format.MessageToString(pipeline_config)
# with tf.io.gfile.GFile(files['PIPELINE_CONFIG'], "wb") as f:
# f.write(config_text)
# TRAINING_SCRIPT = os.path.join(paths['APIMODEL_PATH'], 'research', 'object_detection', 'model_main_tf2.py')
# command = "python {} --model_dir={} --pipeline_config_path={} --num_train_steps=10000".format(TRAINING_SCRIPT, paths['CHECKPOINT_PATH'],files['PIPELINE_CONFIG'])
import subprocess
# Define the command as a string
command = (
"--model_dir=Tensorflow/workspace/models/my_ssd_mobnet "
"--pipeline_config_path=Tensorflow/workspace/models/my_ssd_mobnet/pipeline.config "
"--num_train_steps=10000"
)
Packages:
pip install tensorflow-macos
pip install tensorflow-object-detection-api
I need to train the model using the command => command = ( "--model_dir=Tensorflow/workspace/models/my_ssd_mobnet " "--pipeline_config_path=Tensorflow/workspace/models/my_ssd_mobnet/pipeline.config " "--num_train_steps=10000" ) which i need to train the model but I'm unable to use the packages what should i do
I tried everything nothing is working so can anyone help me
tf.contrib.slimis not a part of TensorFlow 2, as stated in TF documentation: https://www.tensorflow.org/guide/migrate.If you want to use this particular code, find out which version of TensorFlow it supports. Remove the current version, and then install the correct one (1.x).
You can also stick with TF 2.x and install tf-slim from here: https://github.com/google-research/tf-slim for example with:
pip install git+https://github.com/google-research/tf-slim.git