Thanks for your tutorial from scratch. It helps me a lot.
In article part 3, there are some codes you wrote. I copy that codes but some error for me.

model = Darknet("cfg/yolov3.cfg")
inp = get_test_input()
pred = model(inp)
print (pred)

TypeError: forward() missing 1 required positional argument: 'CUDA'

and I want to ask you that training module is the only left work

I add a function(according the code in the tutorial4) to calculate the bleu score, but i get the vey low score(0.09), could you tell me why?
This is code to calculate bleu:

def translate_sentence(sentence, src_field, trg_field, model, device, max_len = 50):
    
    model.eval()
        
    if isinstance(sentence, str):
        nlp = spacy.load('de')
        tokens = [token.te

Need help for retraining and cross validation and see if the ROUGE score matches exactly (or better) with the numbers reported in the paper.
I just train for 500k iteration (with batch size 8) with pointer generation enabled + coverage loss disabled and next 100k iteration (with batch size 8) with pointer generation enabled + coverage loss enabled.

It would be great if someone can help re-r

I have trained almost 80thousand examples within 2000 labels，valid acc almost 92%，but test result all example prob is blew 0.01.
I have tried tranning examples to predict.

@huangzh13,

At the outset, this is a great implementation of StyleGAN in PyTorch. I really like the way the modules are structured.
This is more of a suggestion from my side:
Seems like you are not sanitizing your gradients in the code. Please check this from the official StyleGAN implementation.
I am currently