打开拂锡调用操作错误： "error" ： "The action did not return a dictionary."

Question

我有一个使用Tensorflow库进行图像识别的python函数。我通过Dockerfile将其部署在IBM Cloud Functions(Openwhisk(上，并从wsk CLI调用它。但是当我调用该函数时，显示以下错误：

{
"error": "The action did not return a dictionary."
}

如何更改输出类型？以下是函数代码：

import boto3
import numpy as np
import os.path
import re
from urllib.request import urlretrieve
import json
from botocore.client import Config
import mimetypes
import os
import requests
SESSION = None
bucket = '---'
def main(event, context):
global bucket
global SESSION
if not os.path.exists('/tmp/imagenet/'):
os.makedirs('/tmp/imagenet/')
if SESSION is None:
downloadFromS3(bucket,'Imagenet/imagenet_2012_challenge_label_map_proto.pbtxt','/tmp/imagenet/imagenet_2012_challenge_label_map_proto.pbtxt')
downloadFromS3(bucket,'Imagenet/imagenet_synset_to_human_label_map.txt','/tmp/imagenet/imagenet_synset_to_human_label_map.txt')
strFile = '/tmp/imagenet/inputimage.png'
if ('queryStringParameters' in event):
if (event['queryStringParameters'] is not None):
if ('url' in event['queryStringParameters']):
urlretrieve(event['queryStringParameters']['url'], strFile)
else:
downloadFromS3(bucket,'Imagenet/inputimage.png',strFile)
else:
downloadFromS3(bucket,'Imagenet/inputimage.png',strFile)
else:
downloadFromS3(bucket,'Imagenet/inputimage.png',strFile)
strResult = run_inference_on_image(strFile)
return {
'statusCode': 200,
'body': json.dumps(strResult)
}
def run_inference_on_image(image):
image_data = tf.gfile.FastGFile(image, 'rb').read()
global SESSION
if SESSION is None:
SESSION = tf.InteractiveSession()
create_graph()
softmax_tensor = tf.get_default_graph().get_tensor_by_name('softmax:0')
predictions = SESSION.run(softmax_tensor, {'DecodeJpeg/contents:0': image_data})
predictions = np.squeeze(predictions)
top_k = predictions.argsort()[-5:][::-1]
node_lookup = NodeLookup()
strResult = '%s (score = %.5f)' % (node_lookup.id_to_string(top_k[0]), predictions[top_k[0]])
vecStr = []
for node_id in top_k:
human_string = node_lookup.id_to_string(node_id)
score = predictions[node_id]
vecStr.append('%s (score = %.5f)' % (human_string, score))
return vecStr
def downloadFromS3(bucket,strKey,strFile):
s3_client = boto3.client('s3')
s3_client.download_file(bucket, strKey, strFile)
def getObject(bucket,strKey):
s3_client = boto3.client('s3')
s3_response_object = s3_client.get_object(Bucket=bucket, Key=strKey)
return s3_response_object['Body'].read()  
def create_graph():
global bucket
graph_def = tf.GraphDef()
graph_def.ParseFromString(getObject(bucket,'Imagenet/classify_image_graph_def.pb'))
_ = tf.import_graph_def(graph_def, name='')
class NodeLookup(object):
"""Converts integer node ID's to human readable labels."""
def __init__(self,
label_lookup_path=None,
uid_lookup_path=None):
if not label_lookup_path:
label_lookup_path = os.path.join(
'/tmp/imagenet/', 'imagenet_2012_challenge_label_map_proto.pbtxt')
if not uid_lookup_path:
uid_lookup_path = os.path.join(
'/tmp/imagenet/', 'imagenet_synset_to_human_label_map.txt')
self.node_lookup = self.load(label_lookup_path, uid_lookup_path)
def load(self, label_lookup_path, uid_lookup_path):
if not tf.gfile.Exists(uid_lookup_path):
tf.logging.fatal('File does not exist %s', uid_lookup_path)
if not tf.gfile.Exists(label_lookup_path):
tf.logging.fatal('File does not exist %s', label_lookup_path)
# Loads mapping from string UID to human-readable string
proto_as_ascii_lines = tf.gfile.GFile(uid_lookup_path).readlines()
uid_to_human = {}
p = re.compile(r'[nd]*[ S,]*')
for line in proto_as_ascii_lines:
parsed_items = p.findall(line)
uid = parsed_items[0]
human_string = parsed_items[2]
uid_to_human[uid] = human_string
# Loads mapping from string UID to integer node ID.
node_id_to_uid = {}
proto_as_ascii = tf.gfile.GFile(label_lookup_path).readlines()
for line in proto_as_ascii:
if line.startswith('  target_class:'):
target_class = int(line.split(': ')[1])
if line.startswith('  target_class_string:'):
target_class_string = line.split(': ')[1]
node_id_to_uid[target_class] = target_class_string[1:-2]
# Loads the final mapping of integer node ID to human-readable string
node_id_to_name = {}
for key, val in node_id_to_uid.items():
if val not in uid_to_human:
tf.logging.fatal('Failed to locate: %s', val)
name = uid_to_human[val]
node_id_to_name[key] = name
return node_id_to_name
def id_to_string(self, node_id):
if node_id not in self.node_lookup:
return ''
return self.node_lookup[node_id]

Answer 1

正如错误所述，您编写的任何代码都不会返回字典。 根据我迄今为止使用Openwhisk的经验，为了理解它必须显示/计算的内容，O/P应该采用字典的形式。

所以把它改成那个，你就可以走了