How to show images after fit?

[turgut090]

Hi. The learner has a method learn.progress.show_cycle_gan_imgs. However, how to plot it with matplotlib's plt.show() if I use python repl.
There is an argument event_name in learn.progress.show_cycle_gan_imgs.
I would like to do it after fit:

>>> learn.fit_flat_lin(1,1,2e-4)
epoch     train_loss  id_loss_A  id_loss_B  gen_loss_A  gen_loss_B  cyc_loss_A  cyc_loss_B  D_A_loss  D_B_loss  time    
/home/turgut/.local/share/r-miniconda/envs/r-reticulate/lib/python3.6/site-packages/fastprogress/fastprogress.py:74: UserWarning: Your generator is empty.
  warn("Your generator is empty.")
0         10.809340   1.621755   1.690260   0.420364    0.452442    3.359353    3.504819    0.370692  0.370692  00:08     
1         9.847495    1.283465   1.510985   0.353303    0.349454    2.682495    3.135504    0.253919  0.253919  00:07 

UPIT/upit/train/cyclegan.py

Lines 167 to 176 in 020f8e2

	def after_epoch(self):
	"Update images"
	if (self.learn.epoch+1) % self.show_img_interval == 0:
	if self.imgA: self.imgA_result = torch.cat((self.learn.xb[0][1].detach(),self.learn.pred[0].detach()),dim=-1); self.last_gen=self.imgA_result
	if self.imgB: self.imgB_result = torch.cat((self.learn.xb[0][0].detach(),self.learn.pred[1].detach()),dim=-1); self.last_gen=self.imgB_result
	if self.imgA and self.imgB : self.last_gen = torch.cat((self.imgA_result,self.imgB_result),dim=-2)
	img = TensorImage(self.learn.dls.after_batch.decode(TensorImage(self.last_gen[0]))[0])
	self.imgs.append(img)
	self.titles.append(f'Epoch {self.learn.epoch}')
	self.progress.mbar.show_imgs(self.imgs, self.titles,imgsize=10)

[tmabraham]

Typically I get preds on a batch and show like this:

b = dls.train.one_batch()
_,_,preds = learn.get_preds(dl=[b], with_decoded=True)

dls.show_batch((b[0], b[1]), max_n=2, show=True)
dls.show_batch((preds[0],preds[1]), max_n=2, show=True)

Check this kernel for another example (kernel on training and testing).

[turgut090]

That's great! Thanks, again!