我试图在本地机器上运行保存的服务模型。然而，它需要字符串张量作为输入，我有麻烦将图像转换为正确的字符串格式。
加载模型，我使用:
saved_model = tf.saved_model.load('model/1/')
inf_model = saved_model.signatures['serving_default']
模型的输入输出结构如下:
inputs {
key: "encoded"
value {
name: "serving_default_encoded:0"
dtype: DT_STRING
tensor_shape {
}
}
}
outputs {
key: "output_0"
value {
name: "StatefulPartitionedCall:0"
dtype: DT_FLOAT
tensor_shape {
dim {
size: 19451
}
}
}
}
method_name:"tensorflow/服务/predict"为了处理图像，我使用以下命令:
img = tf.io.read_file(path)
# Decodes the image to W x H x 3 shape tensor with type of uint8
img = tf.io.decode_image(img, channels=3)
img = tf.image.resize_with_pad(img, 224, 224)
img = tf.image.convert_image_dtype(img, tf.float32)
我试着把它转换成这样的字符串张量格式
img_encoded = base64.urlsafe_b64encode(img).decode("utf-8")
img_encoded = tf.constant(img_encoded)
预测:
pred = inf_model(encoded=enc)['sequential_1'][0]
但是，我得到以下错误:
Traceback (most recent call last):
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/spyder_kernels/py3compat.py", line 356, in compat_exec
exec(code, globals, locals)
File "/home/james/Desktop/Project/dev_test/inference.py", line 79, in <module>
res = inf_model(encoded=enc)['sequential_1'][0]
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1669, in __call__
return self._call_impl(args, kwargs)
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1678, in _call_impl
return self._call_with_structured_signature(args, kwargs,
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1759, in _call_with_structured_signature
return self._call_flat(
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/saved_model/load.py", line 115, in _call_flat
return super(_WrapperFunction, self)._call_flat(args, captured_inputs,
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 1918, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/function.py", line 555, in call
outputs = execute.execute(
File "/home/james/anaconda3/envs/james/lib/python3.8/site-packages/tensorflow/python/eager/execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
InvalidArgumentError: Unknown image file format. One of JPEG, PNG, GIF, BMP required.
[[{{node StatefulPartitionedCall/decode_image/DecodeImage}}]] [Op:__inference_signature_wrapper_129216]
Function call stack:
signature_wrapper
该错误是由于图像格式，这不是JPEG, PNG, GIF, BMP格式。这些图像的扩展名可能是jpg，但格式可能是tiff。由于错误提示图像文件格式未知，请检查图像类型，并使用以下代码从数据集中删除非JPEG, PNG, GIF, BMP类型的图像;
import os
import cv2
import imghdr
def check_images( s_dir, ext_list):
bad_images=[]
bad_ext=[]
s_list= os.listdir(s_dir)
for klass in s_list:
klass_path=os.path.join (s_dir, klass)
print ('processing class directory ', klass)
if os.path.isdir(klass_path):
file_list=os.listdir(klass_path)
for f in file_list:
f_path=os.path.join (klass_path,f)
tip = imghdr.what(f_path)
if ext_list.count(tip) == 0:
bad_images.append(f_path)
if os.path.isfile(f_path):
try:
img=cv2.imread(f_path)
shape=img.shape
except:
print('file ', f_path, ' is not a valid image file')
bad_images.append(f_path)
else:
print('*** fatal error, you a sub directory ', f, ' in class directory ', klass)
else:
print ('*** WARNING*** you have files in ', s_dir, ' it should only contain sub directories')
return bad_images, bad_ext
source_dir =r'c:temppeoplestorage'
good_exts=['jpg', 'png', 'jpeg', 'gif', 'bmp' ] # list of acceptable extensions
bad_file_list, bad_ext_list=check_images(source_dir, good_exts)
if len(bad_file_list) !=0:
print('improper image files are listed below')
for i in range (len(bad_file_list)):
print (bad_file_list[i])
else:
print(' no improper image files were found')
从数据集中删除这些图像将有所帮助。