[英]Failed to convert tensorflow frozen graph to pbtxt file
我想提取 tensorflow 凍結推理圖輸入的 pbtxt 文件。 為了做到這一點,我使用以下腳本:
import tensorflow as tf
#from google.protobuf import text_format
from tensorflow.python.platform import gfile
def converter(filename):
with gfile.FastGFile(filename,'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
tf.import_graph_def(graph_def, name='')
tf.train.write_graph(graph_def, 'pbtxt/', 'protobuf.pbtxt', as_text=True)
print(graph_def)
return
#converter('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb') # here you can write the name of the file to be converted
# and then a new file will be made in pbtxt directory.
converter('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb')
例如,我使用的是 ssd mobilenet 架構。 使用上面的代碼我得到輸出為 pbtxt 但我不能使用它。 參考下圖
右:移動網絡架構的原始 pbtxt 文件圖像
左:使用上述腳本獲得的 pbtxt 文件的圖像。
當我使用右側的官方 pbtxt 時,我得到了正確的結果。 但是,當我使用我使用上述腳本生成的 LEFT pbtxt 時,我沒有得到任何預測
我在 open cv DNN 模塊上使用這些預測
tensorflowNet = cv2.dnn.readNetFromTensorflow('ssd_mobilenet_v1_coco_2017_11_17/frozen_inference_graph.pb', 'pbtxt/protobuf.pbtxt')
如何將 mobilenet 凍結推理圖轉換為正確的 pbtxt 格式,以便進行推理?
參考資料: https : //gist.github.com/Arafatk/c063bddb9b8d17a037695d748db4f592
這對我有用
並運行這個腳本:
python3 tf_text_graph_ssd.py --input frozen_inference_graph.pb --output exported_pbtxt/output.pbtxt --config pipeline.config
這就是你所需要的,現在復制凍結的推理圖和新生成的 pbtxt 文件。 並且,使用以下腳本使用 OpenCV 運行您的模型:
import cv2
# Load a model imported from Tensorflow
tensorflowNet = cv2.dnn.readNetFromTensorflow('card_graph/frozen_inference_graph.pb', 'exported_pbtxt/output.pbtxt')
# Input image
img = cv2.imread('image.jpg')
rows, cols, channels = img.shape
# Use the given image as input, which needs to be blob(s).
tensorflowNet.setInput(cv2.dnn.blobFromImage(img, size=(300, 300), swapRB=True, crop=False))
# Runs a forward pass to compute the net output
networkOutput = tensorflowNet.forward()
# Loop on the outputs
for detection in networkOutput[0,0]:
score = float(detection[2])
if score > 0.9:
left = detection[3] * cols
top = detection[4] * rows
right = detection[5] * cols
bottom = detection[6] * rows
#draw a red rectangle around detected objects
cv2.rectangle(img, (int(left), int(top)), (int(right), int(bottom)), (0, 0, 255), thickness=2)
# Show the image with a rectagle surrounding the detected objects
cv2.imshow('Image', img)
cv2.waitKey()
cv2.destroyAllWindows()
請遵循本指南: https : //github.com/opencv/opencv/wiki/TensorFlow-Object-Detection-API 。 創建 .pbtxt 而不修改它是沒有意義的。 指南中的腳本創建了一個額外的文本圖,用於導入到 OpenCV。
可能會幫助某人。 從 master 中提取的 OpenCV 4.3.0 的 mars-small128.pb 遇到了同樣的問題
import argparse
import tensorflow as tf
from tensorflow.python.saved_model import signature_constants
def save(graph_pb, export_dir):
builder = tf.saved_model.builder.SavedModelBuilder(export_dir)
with tf.gfile.GFile(graph_pb, "rb") as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
sigs = {}
with tf.Session(graph=tf.Graph()) as sess:
# INFO: name="" is important to ensure we don't get spurious prefixing
tf.import_graph_def(graph_def, name='')
g = tf.get_default_graph()
# INFO: if name is added the input/output should be prefixed like:
# name=net => net/images:0 & net/features:0
inp = tf.get_default_graph().get_tensor_by_name("images:0")
out = tf.get_default_graph().get_tensor_by_name("features:0")
sigs[signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY] = \
tf.saved_model.signature_def_utils.predict_signature_def(
{"in": inp}, {"out": out})
builder.add_meta_graph_and_variables(sess,
[tag_constants.SERVING],
signature_def_map=sigs)
builder.save(as_text=True)
if __name__ == '__main__':
# export_dir = './saved'
# graph_pb = '../models/deep_sort/mars-small128.pb'
parser = argparse.ArgumentParser()
parser.add_argument('--input', help="path to frozen pb file")
parser.add_argument('--output', help="Folder to save")
args = parser.parse_args()
if args.input is not None and args.output:
save(args.input, args.output)
else:
print(f"Usage adapt_opencv.py.py --input 'path_to_bp' --output './saved'")
將 TF 2.xxx 的 pb 轉換為 pbtxt:
import tensorflow as tf
from google.protobuf import text_format
from tensorflow.python.platform import gfile
def graphdef_to_pbtxt(filename):
with open(filename,'rb') as f:
graph_def = tf.compat.v1.GraphDef()
graph_def.ParseFromString(f.read())
with open('protobuf.txt', 'w') as fp:
fp.write(str(graph_def))
graphdef_to_pbtxt('saved_model.pb')
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