def on_epoch_end(self, pbar, epoch, last_metrics, *args, **kwargs):
"Put the various losses in the recorder and show a sample image."
"""
data = self.learn.data
inputBPre = torch.unbind(self.last_input[1], dim=0)
aToA = im.Image(self.last_gen[0]/2+0.5)
bToB = im.Image(self.last_gen[1]/2+0.5)
aToB = im.Image(self.last_gen[2]/2+0.5)
bToA = im.Image(self.last_gen[3]/2+0.5)
self.imgs.append(aToA)
self.imgs.append(aToB)
self.imgs.append(bToB)
self.imgs.append(bToA)
self.titles.append(f'Epoch {epoch}-A to A')
self.titles.append(f'Epoch {epoch}-A to B')
self.titles.append(f'Epoch {epoch}-B to B')
self.titles.append(f'Epoch {epoch}-B to A')
pbar.show_imgs(self.imgs, self.titles)
"""
# pdb.set_trace()
orig, orig2 = self.last_input
orig.detach()
orig2.detach()
orig_cont, orig_hooks = self.content_encoder(orig)
orig_style = self.style_encoder(orig)
orig2_cont, orig2_hooks = self.content_encoder(orig2)
orig2_style = self.style_encoder(orig2)
one2two = self.decoder(orig_cont, orig2_style, orig_hooks).detach()
two2one = self.decoder(orig2_cont, orig_style, orig2_hooks).detach()
o1 = im.Image(orig[0] / 2 + 0.5)
o2 = im.Image(orig2[0] / 2 + 0.5)
o2t = im.Image(one2two[0] / 2 + 0.5)
t2o = im.Image(two2one[0] / 2 + 0.5)
self.imgs.append(o1)
self.imgs.append(o2)
self.imgs.append(o2t)
self.imgs.append(t2o)
self.titles.append(f'Epoch {epoch}-o1')
self.titles.append(f'Epoch {epoch}-o2')
self.titles.append(f'Epoch {epoch}-onetrans')
self.titles.append(f'Epoch {epoch}-twotrans')
pbar.show_imgs(self.imgs, self.titles)
return
def load_image(image_file):
pil_img = PIL.Image.open(image_file)
img = pil_img.convert('RGB')
img = image.pil2tensor(img, np.float32).div_(255)
img = image.Image(img)
return img, np.asarray(pil_img)
def process_image(self, img_stream):
img = (PIL.Image.open(img_stream)).convert(mode='RGB')
img = self.transforms(img)
img = image_func.Image(img)
img = img.set_sample()
return img
def to_one(self):
tensor = 0.5 + torch.cat(self.data, 2) / 2
return im.Image(tensor)
st.write('## This is ',str(pred_class).capitalize(),' with probablity of ',pred_prob,'%')
options = st.radio('',('Select Image','Enter Image URL','Upload an image'))
#Select image
if options =='Select Image':
static = os.listdir('static')
test_image = st.selectbox('Please select a image:',static)
file_path = 'static/' + test_image
pil_img =Image.open(file_path)
#read the image
img = pil_img.convert('RGB')
img = image.pil2tensor(img,np.float32).div_(224)
img = image.Image(img)
#display image
display_img = mpimg.imread(file_path)
st.image(pil_img,use_column_width=True)
predict(img)
#Image URL
if options == 'Enter Image URL':
url = st.text_input('Enter Image URL')
if url != '':