hologerry / attr2font Goto Github PK

Hello. I am very interested in your experiment, and I have trained in the data set provided, and the effect is very good, so I want to further train in the Chinese data set. Is it convenient for attrfont CN and attrfont eng data set over there?

Hi, I can not run "Sampling " part code .

it occur in this part code .
And I confused L242 and L243 are the both set variable val_attr_A_intensity but with different value, why ?

It looks like the generator model includes a character classifier output, which classifies the character to one of 52 one-hot values. The number 52 is hardcoded: https://github.com/hologerry/Attr2Font/blob/master/model.py#L231. What is this for? The discriminator takes in a character class param, but then ignores it and never references it https://github.com/hologerry/Attr2Font/blob/master/model.py#L367. Is the char class being in this algorithm a mistake, or is it necessary for the font generation to function? If so, why?

I have done Model Training and Model Testing. When I start to do Model Interpolation, the error occurs as follows:

$ python main.py --phase test_interp --test_epoch EPOCH
usage: main.py [-h] [--init_epoch INIT_EPOCH] [--n_epochs N_EPOCHS]
               [--decay_epoch DECAY_EPOCH] [--multi_gpu MULTI_GPU]
               [--data_root DATA_ROOT] [--dataset_name {explor_all}]
               [--img_size IMG_SIZE] [--batch_size BATCH_SIZE]
               [--n_threads N_THREADS] [--n_style N_STYLE] [--channel CHANNEL]
               [--attr_channel ATTR_CHANNEL] [--attr_embed ATTR_EMBED]
               [--style_out_channel STYLE_OUT_CHANNEL]
               [--n_res_blocks N_RES_BLOCKS] [--attention ATTENTION]
               [--dis_pred DIS_PRED] [--lr LR] [--b1 B1] [--b2 B2]
               [--experiment_name EXPERIMENT_NAME] [--check_freq CHECK_FREQ]
               [--sample_freq SAMPLE_FREQ] [--log_freq LOG_FREQ]
               [--phase {train,test,test_interp}] [--test_epoch TEST_EPOCH]
               [--interp_cnt INTERP_CNT] [--lambda_l1 LAMBDA_L1]
               [--lambda_char LAMBDA_CHAR] [--lambda_GAN LAMBDA_GAN]
               [--lambda_cx LAMBDA_CX] [--lambda_attr LAMBDA_ATTR]
main.py: error: argument --test_epoch: invalid int value: 'EPOCH'

There is some issue with the downloading of the dataset. The downloading is too slow and stops after some time. I think there is some issue on the server side.

The StyleEncoder class takes in a boolean attention param, defaulting to true, but this param is ignored and no attention is ever used: https://github.com/hologerry/Attr2Font/blob/master/model.py#L173

@hologerry

Could you explain each options meaning? (--channel, --attr_channel, --attr_embed, etc)
Actually, I want to know how to input attributes value to model.
If I want to make font which is similar with Serif font, how can I do ?

Thanks : )

Is there some small modification or setup I can do to run this on windows?

I try three times and got the same bug report

when input image size is 64 in style encoder，it will be transformed to a tensor which size is (1, 165, 1 ,1)
and it will be fed into residual black , which has a conv(kernel = 3) and generate an error.

The dataset download link is invalidate， can you upload the dataset again?

is it possible to get the software to explore the results?

used opt.selected_attrs but opt no this ！！

Hi!
Found that the generator always produces random glyphs with the same attribute parameters passed (intensity). The font as source for generation is always the same.
Is this normal behavior?
Can the generation be constant with constant input values ​​to the generator?

ValueError: Expected more than 1 spatial element when training, got input size torch.Size([32, 165, 1, 1])

I want to ask how to solve this problem,thanks

I have a trained model ，I want test this model like your Demostration， But， it seems no code here

想请教一下您项目中的Model Architecture的图片是用什么软件绘制的呢？感觉非常好看，而且结构非常清晰！

感谢您出色的工作，我自己创建了一个中文数据集，运行时出现了以下错误
Traceback (most recent call last):
File "main.py", line 593, in
main()
File "main.py", line 581, in main
train(opts)
File "main.py", line 135, in train
attr_A_intensity = label_A * attr_A_data + (1 - label_A) * attr_A_embd
RuntimeError: The size of tensor a (17) must match the size of tensor b (37) at non-singleton dimension 1
出错的位置如图所示，图中两个print的输出分别为 torch.Size([64, 17])，torch.Size([64, 37])