Following this Tutorial
to visualize images in tensorboard using torch.utils.tensorboard got error
from torch.utils.tensorboard import SummaryWriter
writer = SummaryWriter('runs/fashion_mnist_experiment_1')
writer.add_embedding(features,
metadata=class_labels,
label_img=images.unsqueeze(1))
writer.close()
Error:
AttributeError: module 'tensorflow_core._api.v2.io.gfile' has no attribute 'get_filesystem'
try adding
writer = SummaryWriter(path)
img_grid = torchvision.utils.make_grid(torch.FloatTensor(imgs)) #imgs represent a batch of images with shape BxCxHxW
writer_add_image('images', img_grid)
source : https://pytorch.org/tutorials/intermediate/tensorboard_tutorial.html#writing-to-tensorboard
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When I try to run my code for Donut for DocVQA model, I got the following error
"""Test"""
from donut import DonutModel
from PIL import Image
import torch
model = DonutModel.from_pretrained(
"naver-clova-ix/donut-base-finetuned-cord-v2")
if torch.cuda.is_available():
model.half()
device = torch.device("cuda")
model.to(device)
else:
model.encoder.to(torch.bfloat16)
model.eval()
image = Image.open(
"./src/png-report-page_capone_v1_August_2017_GMS_Balance_Sheet_0.png").convert("RGB")
output = model.inference(image=image, prompt="<s_cord-v2>")
print(output)
The error`
ValueError: The following `model_kwargs` are not used by the model: ['encoder_outputs'] (note: typos in the generate arguments will also show up in this list)
Check the transformers library version.
For me, I reinstalled the 4.21.3 version and it worked.
I have trained transformer using simpletransformers model on colab ,downloaded the searialized model and i have little issues on using it to make inferences.
Loading the model on model on jupyter works but while using it with fastapi gives an error
This is how I,m using it on jupyter:
from scipy.special import softmax
label_cols = ['art', 'politics', 'health', 'tourism']
model = torch.load("model.bin")
pred = model.predict(['i love politics'])[1]
preds = softmax(pred,axis=1)
preds
It gives the following result:array([[0.00230123, 0.97465035, 0.00475409, 0.01829433]])
I have tried using fastapi as follows but keeps on getting an error:
from pydantic import BaseModel
class Message(BaseModel):
text : str
model = torch.load("model.bin")
#app.post("/predict")
def predict_health(data: Message):
prediction = model.predict(data.text)[1]
preds = softmax(prediction, axis=1)
return {"results": preds}
Could you please specify the error you get, otherwise its quite hard to see debug
Also it seems that the model.predict function in the jupyter code gets an array as input, while in your fastapi code you are passing a string directly to that function.
So maybe try
...
prediction = model.predict([data.text])[1]
...
It's hard to say without the error.
In case it helps you could have a look at this article that shows how to build classification with Hugging Face transformers (Bart Large MNLI model) and FastAPI: https://nlpcloud.io/nlp-machine-learning-classification-api-production-fastapi-transformers-nlpcloud.html
I am trying to use this model https://github.com/aninda052/Disasters-on-social-media-NLP/blob/master/Disasters%20on%20social%20media.ipynb
, I searched for a way to save this model and use it with new dataset in other application an I find out use pickle, and I add this to code like this
import pickle
model_tfidf=LogisticRegression( C=30.0,class_weight='balanced', solver='newton-cg',
multi_class='multinomial', n_jobs=-1, random_state=5)
model_tfidf.fit(x_train_tfidf, y_train)
predicted_tfidf=model_tfidf.predict(x_test_tfidf)
Pkl_Filename = "Pickle_RL_Model.pkl"
with open(Pkl_Filename, 'wb') as file:
pickle.dump(model_tfidf, file)
after that I tried to create new project to load and use this model and the code is:
from sklearn.feature_extraction.text import CountVectorizer, TfidfVectorizer
import pandas as pd
import pickle
with open('Pickle_RL_Model.pkl', 'rb') as file:
Pickled_LR_Model = pickle.load(file)
x=["hi disaster","flood disaster","cry sad bad ","srong storm"]
tfd=TfidfVectorizer()
new_data_vec=tfd.fit_transform(x)
Ypredict = Pickled_LR_Model.predict(new_data_vec)
but I got error said:
X has 8 features per sample; expecting 16988
I don't know what I did wrong, any help please.
i am trying to train a pytorch model on colab then save the model parameters and load it on my local computer.
After training, the model parameters are stored as below:
torch.save(Model.state_dict(),PATH)
loaded as below:
device = torch.device('cpu')
Model.load_state_dict(torch.load(PATH, map_location=device))
error:
AttributeError: 'Sequential' object has no attribute 'copy'
Does anyone know how to solve this issue?
Your question does not provide sufficient details to be answered correctly. If you are trying to save and load your own model and have a class definition for it see this well known answer and clarify why that's not sufficient for your use.
If you are loading a torch.nn.Sequential model then as far as I know simply loading the model directly and just using it should be sufficient. If it's not post on the pytorch forum what error you get.
For now look at my example show casing loading a sequential model and then using it without error:
# test for saving everything with torch.save
import torch
import torch.nn as nn
from pathlib import Path
from collections import OrderedDict
import numpy as np
import pickle
path = Path('~/data/tmp/').expanduser()
path.mkdir(parents=True, exist_ok=True)
num_samples = 3
Din, Dout = 1, 1
lb, ub = -1, 1
x = torch.torch.distributions.Uniform(low=lb, high=ub).sample((num_samples, Din))
f = nn.Sequential(OrderedDict([
('f1', nn.Linear(Din,Dout)),
('out', nn.SELU())
]))
y = f(x)
# save data torch to numpy
x_np, y_np = x.detach().cpu().numpy(), y.detach().cpu().numpy()
db2 = {'f': f, 'x': x_np, 'y': y_np}
torch.save(db2, path / 'db_f_x_y')
db3 = torch.load(path / 'db_f_x_y')
f3 = db3['f']
x3 = db3['x']
y3 = db3['y']
xx = torch.tensor(x3)
yy3 = f3(xx)
print(yy3)
there should be an official answer how to save and load nn.Sequential models How does one save torch.nn.Sequential models in pytorch properly? but for now torch.save and torch.load seem to work just fine.
I am trying to load a LightGBM.Booster from a JSON file pointer, and can't find an example online.
import json ,lightgbm
import numpy as np
X_train = np.arange(0, 200).reshape((100, 2))
y_train = np.tile([0, 1], 50)
tr_dataset = lightgbm.Dataset(X_train, label=y_train)
booster = lightgbm.train({}, train_set=tr_dataset)
model_json = booster.dump_model()
with open('model.json', 'w+') as f:
json.dump(model_json, f, indent=4)
with open('model.json') as f2:
model_json = json.load(f2)
How can I create a lightGBM booster from f2 or model_json? This snippet only shows dumping to JSON. model_from_string might help but seems to require an instance of the booster, which I won't have before loading.
There's no such method for creation of Booster directly from json. No such method in the source code or documentation, also there's no github issue.
Because of it, I just load models from a text file via
gbm.save_model('model.txt') # gbm is trained Booster instance
# ...
bst = lgb.Booster(model_file='model.txt')
or use pickle to dump and load models:
import pickle
pickle.dump(gbm, open('model.pkl', 'wb'))
# ...
gbm = pickle.load(open('model.pkl', 'rb'))
Unforunately, pickle files are unreadable (or, at least, this files are not so clear). But it's better than nothing.