def predict(self, image):
image = image*0.5 + 0.5 #rescale from [-1,1]-->[0,1]
image = F.interpolate(image,size=(224,224),mode='bilinear')
with torch.no_grad():
prob = self.model(image).data.cpu().numpy()[0]
mean_score = get_mean_score(prob)
std_score = get_std_score(prob)
return mean_score+std_score
def predict(self, image):
image = self.transform(image)
image = image.unsqueeze_(0)
image = torch.autograd.Variable(image, volatile=True)
prob = self.model(image).data.numpy()[0]
mean_score = get_mean_score(prob)
std_score = get_std_score(prob)
return format_output(mean_score, std_score, prob)
def predict(self, image):
image = self.transform(image)
image = image.unsqueeze_(0)
image = image.to(device)
prob = self.model(image).data.cpu().numpy()[0]
mean_score = get_mean_score(prob)
std_score = get_std_score(prob)
return format_output(mean_score, std_score, prob)