I'm working on a waste/garbage detector for a personal project. I rely on Tensorflow (in Python 3) to train my own dataset.
I have a script that creates and trains a model from scratch. Then, I freeze the checkpoints to get a PB file for detection.
The code I have for the detection (found here) requires two files to work: the previous PB file and a labelmap.txt.
# Path to frozen detection graph. This is the actual model that is used for the object detection.
PATH_TO_CKPT = 'frozen_inference_graph.pb'
# List of the strings that is used to add correct label for each box.
PATH_TO_LABELS = 'label_map.pbtxt'
I know how a labelmap.txt looks like and it is quite simple to write it myself actually, but I don't know how to generate it because it links each class to an ID and the ID is unknown to me.
I tried to search on the Internet, when people mention the labelmap.txt, it involves Tfrecords. However, I don't use Tf records for my project, I extract each region of interest and save them in subfolders, one subfolder for a class (can, bottle...).
As I am new to Tensorflow, I may have misunderstood something in the training process. Do you have any lead so I can see if my model is accurate by testing it ? I can provide some codes if you need it.
Thanking you in advance,
答案 0 :(得分：0)
labelmap.pbtxt文件将网络内部使用的ID映射到标签名称。您不能在训练后简单地生成一个。您需要确保在培训期间使用相同的ID标签映射，否则可能会得到错误的结果。
如果您使用tensorflow object_detection模型的训练说明，那么您将在某个时候生成此labelmap文件，并且可以重复使用它。
查看您用来训练网络的步骤或在此处发布。
答案 1 :(得分：0)
在训练之前，我收集并标记了数千张图像，提取了每个标记的区域，调整了每个图像的大小，然后根据它们的类别，将它们分成了不同的文件夹。
培训步骤中涉及多个文件。我最初从此repository中检索了代码，并增加了恢复培训的可能性。
trainer.py
import os
import tensorflow as tf
import model_architecture
from utils import utils
from build_model import model_tools
# Images directory.
data_path = os.path.join('dataset' + os.sep)# contains subfolders, one per item
all_classes = os.listdir(data_path)
number_of_classes = len(all_classes)
# Images dimensions.
height = 64
width = 64
# Checkpoints directory.
output_dir = os.path.join(os.pardir + os.sep, 'checkpoints' + os.sep)
model_pattern = 'model.ckpt'
model_base_path = os.path.join(output_dir, model_pattern)
meta_file_path = model_base_path + '.meta'
# Training params.
color_channels = 3
start = 0
epochs = 5
batch_size = 10
batch_counter = 0
# Create Placeholders for images and labels.
images_ph = tf.placeholder(tf.float32, shape=[None, height, width, color_channels])
labels_ph = tf.placeholder(tf.float32, shape=[None, number_of_classes])
def trainer(network, number_of_images):
cross_entropy = tf.nn.softmax_cross_entropy_with_logits_v2(logits=network, labels=labels_ph)
cost = tf.reduce_mean(cross_entropy)
optimizer = tf.train.AdamOptimizer().minimize(cost)
tf.summary.scalar('cost', cost)
tf.add_to_collection('optimizer', optimizer)
global_step = tf.Variable(0, name='global_step', trainable=False)
saver = tf.train.Saver()
# Launch the graph in a session
with tf.Session() as sess:
# Initialize all variables.
tf.global_variables_initializer().run()
# Read checkpoints directory.
ckpt = tf.train.get_checkpoint_state(output_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print('Reloading existing model.')
else:
init = tf.global_variables_initializer()
sess.run(init)
print('Creating a new model.')
# Get last epoch index.
start = global_step.eval()
writer = tf.summary.FileWriter(output_dir, graph=tf.get_default_graph())
merged = tf.summary.merge_all()
saver = tf.train.Saver(write_version=tf.train.SaverDef.V2, max_to_keep=5)
counter = 0
# Training.
for epoch in range(start, epochs):
tools = utils()
for batch in range(int(number_of_images / batch_size)):
counter += 1
images, labels = tools.batch_dispatch()
if images is None:
break
loss, summary = sess.run([cost, merged], feed_dict={images_ph: images, labels_ph: labels})
sess.run(optimizer, feed_dict={images_ph: images, labels_ph: labels})
print('Epoch number {epoch} batch {batch} complete - loss {loss}'.format(
epoch=epoch, batch=batch, loss=loss))
writer.add_summary(summary, counter)
global_step.assign(epoch).eval()
# Save progression.
saver.save(sess, model_base_path, global_step=epoch)
# Main program.
if __name__ == '__main__':
tools = utils()
model = model_tools()
network = model_architecture.generate_model(images_ph, number_of_classes)
number_of_images = sum([len(files) for r, d, files in os.walk('dataset')])
trainer(network, number_of_images)
model_tools.py
class model_tools:
def add_weights(self, shape):
return tf.Variable(tf.truncated_normal(shape=shape, stddev=0.05))
def add_biases(self, shape):
return tf.Variable(tf.constant(0.05, shape=shape))
def conv_layer(self, layer, kernel, input_shape, output_shape, stride_size):
weights = self.add_weights([kernel, kernel, input_shape, output_shape])
biases = self.add_biases([output_shape])
stride = [1, stride_size, stride_size, 1]
layer = tf.nn.conv2d(layer, weights, strides=stride, padding='SAME') + biases
return layer
def pooling_layer(self, layer, kernel_size, stride_size):
kernel = [1, kernel_size, kernel_size, 1]
stride = [1, stride_size, stride_size, 1]
return tf.nn.max_pool(layer, ksize=kernel, strides=stride, padding='SAME')
def flattening_layer(self, layer):
input_size = layer.get_shape().as_list()
new_size = input_size[-1] * input_size[-2] * input_size[-3]
return tf.reshape(layer, [-1, new_size]), new_size
def fully_connected_layer(self, layer, input_shape, output_shape):
weights = self.add_weights([input_shape, output_shape])
biases = self.add_biases([output_shape])
layer = tf.matmul(layer, weights) + biases
return layer
def activation_layer(self, layer):
return tf.nn.relu(layer)
utils.py
import cv2
import random
class utils:
image_count = []
count_buffer = []
class_buffer = all_classes[:]
def __init__(self):
self.image_count = []
self.count_buffer = []
for i in os.walk(data_path):
if len(i[2]):
self.image_count.append(len(i[2]))
self.count_buffer = self.image_count[:]
def batch_dispatch(self, batch_size=batch_size):
global batch_counter
if sum(self.count_buffer):
class_name = random.choice(self.class_buffer)
choice_index = all_classes.index(class_name)
choice_count = self.count_buffer[choice_index]
if choice_count == 0:
class_name = all_classes[self.count_buffer.index(max(self.count_buffer))]
choice_index = all_classes.index(class_name)
choice_count = self.count_buffer[choice_index]
slicer = batch_size if batch_size < choice_count else choice_count
img_ind = self.image_count[choice_index] - choice_count
indices = [img_ind, img_ind + slicer]
images = self.generate_images(class_name, indices)
labels = self.generate_labels(class_name, slicer)
self.count_buffer[choice_index] = self.count_buffer[choice_index] - slicer
else:
images, labels = (None,) * 2
return images, labels
def generate_labels(self, class_name, number_of_samples):
one_hot_labels = [0] * number_of_classes
one_hot_labels[all_classes.index(class_name)] = 1
one_hot_labels = [one_hot_labels] * number_of_samples
return one_hot_labels
def generate_images(self, class_name, indices):
batch_images = []
choice_folder = os.path.join(data_path, class_name)
selected_images = os.listdir(choice_folder)[indices[0]:indices[1]]
for image in selected_images:
img = cv2.imread(os.path.join(choice_folder, image))
batch_images.append(img)
return batch_images
model_architecture.py包含3层图像分类器的结构。
当我运行trainer.py时，我得到一个充满了元文件和索引文件的checkpoints文件夹。看来是正确的。
关于导出模型，我很尴尬，因为我不知道为管道配置路径提供什么作为参数。
python3 export_inference_graph.py \ --input_type image_tensor \ --trained_checkpoint_prefix“ /home/user/model/model.ckpt-4” \ --pipeline_config_path ???? \ --output_directory / home / user / exports /
要获取PB文件，我使用了以下方法：
checkpoint_location = 'checkpoints/model.ckpt-0'
export_dir = 'frozen/'
loaded_graph = tf.Graph()
with tf.Session(graph=loaded_graph) as sess:
loader = tf.train.import_meta_graph(checkpoint_location+ '.meta')
loader.restore(sess, checkpoint_location)
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
builder.add_meta_graph_and_variables(sess,
[tf.saved_model.tag_constants.TRAINING],
strip_default_attrs=True)
builder.add_meta_graph([tf.saved_model.tag_constants.SERVING], strip_default_attrs=True)
builder.save()
它将创建一个save_model.pb文件，但不会创建labelmap.pbtxt。
我应该完全改变训练模型的方式吗？