train_latent error
Opened this issue · 4 comments
oscarwooberry commented
oscarwooberry commented
I guess my question is "To train latent model, we directly derive semantic embedding layers from model without additional inference." I am a bit confused about how we load these semantic codes as input
naturalDNA commented
I have the same question. Did you solve it?
WeiyunJiang commented
I have the same question. Did you solve it?
On line 432, experiment.py, I just skip log_samples when imgs = None
It would suppress the error
if self.conf.train_mode.require_dataset_infer():
imgs = None
idx = None
else:
imgs = batch['img']
idx = batch["index"]
self.log_sample(x_start = imgs, step = self.global_step, idx = idx)
naturalDNA commented
我有同样的问题。你解决了吗?
在第 432 行,experiment.py,我只是跳过 log_samples 当 imgs = None 它会抑制错误
if self.conf.train_mode.require_dataset_infer(): imgs = None idx = None else: imgs = batch['img'] idx = batch["index"] self.log_sample(x_start = imgs, step = self.global_step, idx = idx)
Thank you for your help!