When I am running datasets_utils.py from '/usr/local/lib/python3.7/dist-packages/torchtext/data/datasets_utils.py' in Google Colab, the following error occurs even with the most updated versions of Python packages:
ImportError: cannot import name 'functional_datapipe' from 'torch.utils.data' (/usr/local/lib/python3.7/dist-packages/torch/utils/data/init.py)
Are there any solutions to solve such errors, as I could not find functional_datapipe even in the official torch.utils.data documentation? The following is excerpt from datasets._utils.py in the Google Colab environment
import functools
import inspect
import os
import io
import torch
from torchtext.utils import (
validate_file,
download_from_url,
extract_archive,
)
from torch.utils.data import functional_datapipe, IterDataPipe
from torch.utils.data.datapipes.utils.common import StreamWrapper
import codecs
It might be available only on torchdata.datapipes
Related
It seems that when I call a python function from an xmlhttp request so that its output prints to the browser, the script will lead to an error if any of these import statements (commented) are run:
import traceback
import transformers
"""
from transformers import AutoTokenizer, AutoModel, AutoModelForSequenceClassification, TrainingArguments, Trainer
import evaluate
import torch
"""
import pandas as pd
import numpy as np
import datasets
from datasets import load_dataset, load_metric, load_from_disk, Value
import cgi, cgitb
print("Content-Type: text/html\n")
print("Hello!")
That is, if I keep them commented, my hello statement will show on the browser, otherwise, I get an internal server error 500. Any help would be greatly appreciated!!
I already tried suppressing warnings and logs!
import torch
from torch import nn
from torch.nn import functional as F
from .utils import (
round_filters,
round_repeats,
drop_connect,
get_same_padding_conv2d,
get_model_params,
efficientnet_params,
load_pretrained_weights,
Swish,
MemoryEfficientSwish,
calculate_output_image_size
)
I tried a lot but I could'nt find the solution to fix this error
from .utils import round_filters
It gives error: relative import with no known parent package. See discussion for this in stackoverflow thread.
You need to install EfficientNet-PyTorch package. About this package
!pip install EfficientNet-PyTorch
And add parent package as from efficientnet_pytorch.utils import round_filters
So your code should be
import torch
from torch import nn
from torch.nn import functional as F
from efficientnet_pytorch.utils import (
round_filters,
round_repeats,
drop_connect,
get_same_padding_conv2d,
get_model_params,
efficientnet_params,
load_pretrained_weights,
Swish,
MemoryEfficientSwish,
calculate_output_image_size
)
I am trying to download a pre-trained tensorflow model. I am using the below code
import numpy as np
import time
import PIL.Image
import IPython.display as display
import matplotlib.pylab as plt
import tensorflow as tf
import tensorflow_hub as hub
import datetime
from tensorflow.keras.preprocessing import image
from dateutil import parser
from keras.applications.inception_v3 import InceptionV3
model = InceptionV3()
model.summary()
I am getting the following error
AttributeError: module 'dateutil' has no attribute 'parser'
I am using python -3.7 and TF-2.7, python-dateutil-2.8.1
Please help me fix this. Thank You :)
The correct import syntax is:
import dateutil.parser
and then:
parser.parse(time_string)
or:
from dateutil.parser import parse
parse(time_string)
Documentation: https://dateutil.readthedocs.io/en/stable/parser.html
I've seen countless issues here on stack about this but still can't figure out why I cant get mine to work. I have a 2 .ipynb files and I'm looking to import a class from one file to the other as follows:
CV_Screening_Interface:
from joblib import dump, load
import sys
import pandas as pd
import os
import import_ipynb
import docx
import readDocx ***(This is another ipynb file)***
from docx import Document
import string
model = load('model.joblib')
class CV:
def __init__(self,university,major,masters,company,certification,GPA):
self.university = university
self.major = major
self.masters = masters
self.company = company
self.certification = certification
self.GPA = GPA
#And a bunch of other functions
Now in FirstProgram
from tkinter import *
from tkinter import filedialog
from docx import Document
import io
import import_ipynb
import CV_Screening_Interface
#Till here works fine
When I try to import class CV I get an import error
from CV_Screening_Interface import CV
OR
test = CV_Screening_Interface.CV()
ImportError: cannot import name 'CV' from 'CV_Screening_Interface' (CV_Screening_Interface.ipynb)
I checked PYTHONPATH, I have an empty init.py in the directory already. What's weird is that importing the module works, but importing the class in the module doesn't.
Note in CV_Screening_Interface CV class works perfectly fine so I don't think there is any issue with it specifically. Probably need a if name="main" inside it?
Ok so this solved my problems:
I created a new ipynb called ClassFile.ipynb and it only contains the Class CV without importing any packages
Then i converted ClassFile.ipynb to ClassFile.py and imported it to FirstProgram and it worked.
In Ubuntu 14.04, I have installed Graphlab based on https://dato.com/download/install-graphlab-create-command-line.html and it seems to be working fine.
However, I receive this error when trying to use a recommender module:
import graphlab
from graphlab.recommender import ranking_factorization_recommender
In the first line, graphlab is imported without any error. However, the second line causes this error:
---------------------------------------------------------------------------
ImportError Traceback (most recent call last)
<ipython-input-5-34df81ffb957> in <module>()
----> 1 from graphlab.recommender import ranking_factorization_recommender
ImportError: No module named recommender
How can the problem be solved? Thanks
It's just a namespace issue. recommender actually lives in the `toolkits module, so this should work:
import graphlab
from graphlab.toolkits.recommender import ranking_factorization_recommender
Graphlab has already imported everything for you in their __init__.py file.
Just do:
from graphlab import ranking_factorization_recommender
from graphlab import <any_other_recommender>
Here is a snippet of graphlab.__init__.py file:
from graphlab.util import get_runtime_config
from graphlab.util import set_runtime_config
import graphlab.connect as _mt
import graphlab.connect.aws as aws
from . import visualization
import os as _os
import sys as _sys
if _sys.platform != 'win32' or \
(_os.path.exists(_os.path.join(_os.path.dirname(__file__), 'cython', 'libstdc++-6.dll')) and \
_os.path.exists(_os.path.join(_os.path.dirname(__file__), 'cython', 'libgcc_s_seh-1.dll'))):
from graphlab.data_structures.sgraph import Vertex, Edge
from graphlab.data_structures.sgraph import SGraph
from graphlab.data_structures.sarray import SArray
from graphlab.data_structures.sframe import SFrame
from graphlab.data_structures.sketch import Sketch
from graphlab.data_structures.image import Image
from graphlab.data_structures.sgraph import load_sgraph, load_graph
from graphlab.toolkits._model import Model, CustomModel
import graphlab.aggregate
import graphlab.toolkits
import graphlab.toolkits.clustering as clustering
import graphlab.toolkits.distances as distances
...