AttributeError: 'DataFrame' object has no attribute '_data' [Not a duplicate] - pandas

Question

I was trying to run the main.py but it threw an error about attribution.
My Python version is Python 3.5. I am using the CNTK Docker release 2.6-cpu-python3.5. I cannot update the Python version because of CNTK. It only supports Python 3.5 and will only run in Ubuntu 16.04.
Pandas version: pandas==0.25.3
The Error
Traceback (most recent call last):
File "/workspace/main.py", line 5, in <module>
from model import extract_patches, score_patch, del_cache
File "/workspace/model.py", line 2, in <module>
from regressionModel import extract_features, predict_label
File "/workspace/regressionModel.py", line 26, in <module>
regression_model = read_model['model'][0]
File "/usr/local/lib/python3.5/dist-packages/pandas/core/frame.py", line 2898, in __getitem__
if self.columns.is_unique and key in self.columns:
File "/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py", line 5063, in __getattr__
return object.__getattribute__(self, name)
File "pandas/_libs/properties.pyx", line 65, in pandas._libs.properties.AxisProperty.__get__
File "/usr/local/lib/python3.5/dist-packages/pandas/core/generic.py", line 5063, in __getattr__
return object.__getattribute__(self, name)
AttributeError: 'DataFrame' object has no attribute '_data'
main.py
import os
import flask
import numpy as np
from flask import jsonify, request
from model import extract_patches, score_patch, del_cache
app = flask.Flask(__name__)
#app.route('/url/<path:argument>')
def url(argument):
# create a patch folder
patch_path = './patches'
if not os.path.exists(patch_path):
os.mkdir(patch_path)
# get image url from the query string
imageURL = request.url.split('=',1)[1]
# extract patches from imageURL
dimension, face_loc, image_dim = extract_patches(imageURL)
# score each patch
patch_score= score_patch(patch_path)
# delete the downloaded image and the patches from local
del_cache(patch_path)
if os.path.exists('temp.jpg'):
os.remove('temp.jpg')
data = dict()
data['patch_score'] = []
for key in dimension:
tmp = []
tmp[:] = dimension[key]
tmp.append(patch_score[key])
data['patch_score'].append(tmp)
data['image_score'] = round(np.mean(list(patch_score.values())), 2)
data['face_loc'] = face_loc['face_loc']
data['img_dim'] = image_dim
return jsonify(patch_score = str(data['patch_score']), image_score = str(data['image_score']), face_loc = str(data['face_loc']), image_dim = str(data['img_dim']))
if __name__ == '__main__':
app.run(host='0.0.0.0', port = 9580) # port number can be changed in your case
model.py
import getPatches
from regressionModel import extract_features, predict_label
import os
import shutil
def extract_patches(imageURL):
patch_path = './patches'
dimension_dict = dict()
face_dict = dict()
image_dim = []
try:
dim, face, img = getPatches.extract_patches(imageURL, dimension_dict,face_dict, image_dim, patch_path)
print ("extract patches pass")
except:
print ('cannot extract patches from the image')
return dim, face, img
def score_patch(patch_path):
patch_score = dict()
for file in next(os.walk(patch_path))[2]:
file_path = os.path.join(patch_path, file)
score_features = extract_features (file_path)[0].flatten()# extract features from CNTK pretrained model
pred_score_label = predict_label(score_features) # score the extracted features using trained regression model
patch_score[file.split('.')[0]] = float("{0:.2f}".format(pred_score_label[0]))
return patch_score
def infer_label(patch_score, label_mapping):
max_score_name, max_score_value = max(patch_score.items(), key=lambda x:x[1])
pred_label = label_mapping[round(max_score_value)-1]
return pred_label
def del_cache(patch_folder):
shutil.rmtree(patch_folder)
return
regressionModel.py
import numpy as np
import pandas as pd
import cntk as C
from PIL import Image
import pickle
from cntk import load_model, combine
import cntk.io.transforms as xforms
from cntk.logging import graph
from cntk.logging.graph import get_node_outputs
pretrained_model = 'ResNet152_ImageNet_Caffe.model'
pretrained_node_name = 'pool5'
regression_model = 'cntk_regression.dat'
image_width = 224
image_height = 224
# load CNTK pretrained model
#model_file = os.path.join(pretrained_model_path, pretrained_model_name)
loaded_model = load_model(pretrained_model) # a full path is required
node_in_graph = loaded_model.find_by_name(pretrained_node_name)
output_nodes = combine([node_in_graph.owner])
# load the stored regression model
read_model = pd.read_pickle(regression_model)
regression_model = read_model['model'][0]
train_regression = pickle.loads(regression_model)
def extract_features(image_path):
img = Image.open(image_path)
resized = img.resize((image_width, image_height), Image.ANTIALIAS)
bgr_image = np.asarray(resized, dtype=np.float32)[..., [2, 1, 0]]
hwc_format = np.ascontiguousarray(np.rollaxis(bgr_image, 2))
arguments = {loaded_model.arguments[0]: [hwc_format]}
output = output_nodes.eval(arguments)
return output
def predict_label(features):
return train_regression.predict(features.reshape(1,-1))

Answer 1

https://pypi.org/project/cntk/#files has CNTK 2.7 for Python 3.6. Still an obsolete version, but not quite as obsolete.