Exception: Connect timeout on endpoint URL

[GevorgAghekyan]

[GevorgAghekyan]

Also:
distributed.utils_perf - WARNING - full garbage collections took 10% CPU time recently (threshold: 10%)

[davidbuniat]

Looks to me in an internet connection issue with uploading large amounts of data. If you share the minimal reproducible code, we will try to replicate it.

[GevorgAghekyan]

The folder for annotations is here: annotations
The folder for images is here: images

The reproducible script is below:

import os
import cv2
import hub
import psutil
import numpy as np
from hub import Transform
from hub import tensor, dataset

# read file
with open('annot/train.json', 'r') as myfile:
    data=myfile.read()
#     myfile.close()

# parse file
train_annotations = json.loads(data)

train_set_dict = dict()
for annot in train_annotations:
    image_name = annot['image']
    del annot['image']
    train_set_dict[image_name] = annot
    
myfile.close()


# read file
with open('annot/valid.json', 'r') as myfile:
    data=myfile.read()

# parse file
valid_annotations = json.loads(data)

valid_set_dict = dict()
for annot in valid_annotations:
    image_name = annot['image']
    del annot['image']
    valid_set_dict[image_name] = annot
    
myfile.close()
    
# read file
with open('annot/test.json', 'r') as myfile:
    data=myfile.read()

# parse file
test_annotations = json.loads(data)

test_set_dict = dict()
for annot in test_annotations:
    image_name = annot['image']
    del annot['image']
    test_set_dict[image_name] = annot

myfile.close()

# read file
with open('annot/trainval.json', 'r') as myfile:
    data=myfile.read()

# parse file
trainval_annotations = json.loads(data)

trainval_set_dict = dict()
for annot in trainval_annotations:
    image_name = annot['image']
    del annot['image']
    trainval_set_dict[image_name] = annot

myfile.close()

# merging dicts
overall_dict = {**train_set_dict, **test_set_dict, **valid_set_dict, **trainval_set_dict}

directoryName = 'annotated_images'
imageNames = os.listdir(directoryName)

class Convert(Transform):

    def forward(self, elem):
        ds = dict()
        ds['image'] = np.empty(1, object)
        ds['image'][0] = cv2.imread(os.path.join(directoryName, elem))
        ds['annotation'] = np.empty(1, object)
        ds['annotation'][0] = overall_dict[elem]
        return ds

    def meta(self):
        return {'image': {"shape": (1,), "dtype": "object"}, 'annotation': {"shape": (1,), "dtype": "object"}}

hub.init(processes=True, n_workers=psutil.cpu_count(), memory_limit=55e13)

ds = dataset.generate(Convert(), imageNames)

ds.store("gevorg/MPII_Human_Pose_Dataset")`

Note: The upload of the images into the provided "images" folder is still in progress. Will be complete approximately in an hour.

[mynameisvinn]

closed due to inactivity